explaining goodwill write-off decisions under ias 36 for ...file/dis4517.pdf · explaining goodwill...

Explaining goodwill write-off decisions under IAS 36

for capital market-implied triggering events

D I S S E R T A T I O N

of the University of St. Gallen,

School of Management,

Economics, Law, Social Sciences

and International Affairs

to obtain the title of

Doctor of Philosophy in Management

submitted by

Korbinian Eichner

from

Germany

Approved on the application of

Prof. Dr. Peter Leibfried

and

Prof. Dr. Tami Dinh

Dissertation no. 4517

Bookstation Gmbh, Anzing 2016

The University of St. Gallen, School of Management, Economics, Law, Social

Sciences and International Affairs hereby consents to the printing of the present

dissertation, without hereby expressing any opinion on the views herein expressed.

St. Gallen, May 30, 2016

The President

Prof. Dr. Thomas Bieger

I

Vorwort

Goodwill ist der wahrscheinlich komplexeste und zugleich risikoreichste Ver-

mögenswert von Unternehmen. Er spiegelt eine Vielzahl unternehmens-indivi-

dueller, zukunftsgerichteter Annahmen wider, die von verschiedenen Motivationen

geleitet werden können. Womöglich sind dies auch die Hauptgründe, warum die

Bilanzposition Goodwill während der letzten Jahre immer wieder stark in den Fokus

von Wissenschaft und Praxis gelangte. Unzählige wissenschaftliche Artikel und

Kommentare wurden weltweit bereits zum Thema Goodwill verfasst. Warum also

noch eine wissenschaftliche Untersuchung?

Trotz der Vielzahl von veröffentlichten akademischen Studien wurden bestimmte

Bereiche des Impairment-Only Approaches bisher nur unzureichend untersucht.

Hierzu zählt insbesondere ein möglicher Einfluss der zunehmend kritisierten Flexi-

bilität des Impairment-Only Approaches auf das Ergebnis der Werthaltigkeits-

überprüfung, welcher diese Dissertation unter anderem umfassend analysiert.

Die vorliegende Doktorarbeit wäre ohne die grossartige Unterstützung mehrerer

Personen nicht möglich gewesen. Zunächst möchte ich Prof. Dr. Peter Leibfried

danken, der mir die Möglichkeit gegeben hat an seinem Lehrstuhl zu promovieren.

Insbesondere sein Expertenwissen zu immateriellen Vermögenswerten erlaubten mir

das Thema Goodwill ganzheitlich und aus unterschiedlichen Blickwinkeln zu

betrachten. Des Weiteren möchte ich mich bei Prof. Dr. Tami Dinh in aller Form für

die Übernahme des Korreferates bedanken.

Meinen Kommilitonen Marc Sager und Christoph Lutz gebührt ebenso großer Dank

für ihren wertvollen Input während des Doktorats. Gerade deren Expertenwissen

bzgl. Forschungsmethoden und des externen Rechnungswesens waren für mich sehr

hilfreich.

Während meiner beruflichen Laufbahn habe ich viele Kollegen kennengelernt, die

mich inspiriert und gefördert haben. Hier ist insbesondere Ralf Weimer zu nennen.

Bis heute habe ich keinen fachkundigeren Bewertungsexperten kennengelernt. Auch

Prof. Dr. Vera Elter und Christian Klingbeil sei herzlich gedankt.

II

Besonderer Dank gilt jedoch meinen Eltern Monika und Heribert sowie meinen

Geschwistern Benjamin und Louisa, die mit ihrer Zusprache und Unterstützung

diese Doktorarbeit erst ermöglichten.

Zürich, Juli 2016

Korbinian Eichner

III

“(…) in practice, entities might be hesitant to impair

goodwill, so as to avoid giving the impression that

they made a bad investment decision. Newly

appointed CEO’s, on the other hand, have a strong

incentive to recognize hefty impairments on their

predecessor’s acquisitions. Starting with a clean

slate, they can more or less ensure a steady flow of

earnings in the future. The question is if our current

rules provide sufficient rigor to these decisions.” 1

Hans Hoogervorst, Chairman of the IASB

FEE Conference on Corporate Reporting of the Future,

Brussels, Belgium, Tuesday, 18 September 2012

1 Hoogervorst (2012), p. 5.

Table of contents

IV

Table of contents

1 Introduction ...................................................................................................... 1

1.1 Research motivation ............................................................................... 1

1.2 Research questions.................................................................................. 4

1.3 Research structure ................................................................................. 11

2 The concept of goodwill in economic theory ................................................ 14

2.1 Context-dependent definition of goodwill ............................................ 14

2.2 Going concern (or internally generated goodwill) ................................ 16

2.3 Acquired goodwill ................................................................................ 46

3 Goodwill treatment and impairment-only approach under IFRS ............. 80

3.1 Relevance of IFRS 3 for generating acquired goodwill ........................ 83

3.2 Recognition possibilities of other intangible assets apart from

goodwill in business combinations according to IAS 38 ...................... 95

3.3 Procedure for goodwill impairment testing under IAS 36 .................. 102

4 Implications of reporting flexibility in the impairment-only approach .. 118

4.1 Possible relationships between reporting flexibility and managing

goodwill write-offs ............................................................................. 119

4.2 Timely goodwill impairment recognition and its impact on the

quality of financial reporting .............................................................. 120

4.3 Reporting flexibility and its impact on the enforcability of the

impairment-only approach .................................................................. 122

4.4 Areas of reporting flexibility in the impairment-only approach ......... 125

5 Theoretical concepts helping to understand goodwill write-off

decisions ........................................................................................................ 142

5.1 Private information on changes of a firm’s future financial

performance ........................................................................................ 143

5.2 Agency theory ..................................................................................... 146

Table of contents

V

6 Literature review on research findings that can be linked to goodwill

write-off decision making ............................................................................ 153

6.1 Timing of goodwill write-offs ............................................................ 154

6.2 Economic consequences of goodwill write-offs ................................. 160

6.3 Personal incentives of managers influencing goodwill write-off

decisions ............................................................................................. 179

6.4 Summary of literature review and observable research gaps .............. 201

7 Research design and research methodology .............................................. 205

7.1 Hypotheses formulation ...................................................................... 205

7.2 Model design ...................................................................................... 235

7.3 Sample selection ................................................................................. 257

8 Results ........................................................................................................... 265

8.1 Sample description.............................................................................. 265

8.2 Univariate analysis of goodwill write-off and non-write-off firms ..... 273

8.3 Pearson correlations between explanatory variables........................... 307

8.4 Multivariate regression results ............................................................ 310

8.5 Summary of research findings and their implications ........................ 363

Appendix ............................................................................................................... 372

Table of contents

VI

Table of contents

Preface ........................................................................................................................ I

Table of contents ..................................................................................................... IV

List of figures ....................................................................................................... XIII

List of tables ......................................................................................................... XVI

List of abbreviations ......................................................................................... XVIII

List of symbols .................................................................................................. XXIII

Executive summary ............................................................................................. XVI

Zusammenfassung ............................................................................................ XVIII

1 Introduction ...................................................................................................... 1

1.1 Research motivation ............................................................................... 1

1.2 Research questions.................................................................................. 4

1.2.1 Disclosure of private information on changes in a firm’s

future financial performance ..................................................... 5

1.2.2 Incentives predicted by agency theory ...................................... 7

1.2.3 Reporting flexibility of IAS 36 ................................................. 9

1.3 Research structure ................................................................................. 11

2 The concept of goodwill in economic theory ................................................ 14

2.1 Context-dependent definition of goodwill ............................................ 14

2.2 Going concern (or internally generated goodwill) ................................ 16

2.2.1 Goodwill as the result of a firm’s excess earnings.................. 17

2.2.2 Goodwill as the result of investment and strategic

management decisions ............................................................ 22

2.2.3 Goodwill as the result of intellectual capital ........................... 39

2.3 Acquired goodwill ................................................................................ 46

2.3.1 Top-down and bottom-up approaches in understanding

goodwill .................................................................................. 46

Table of contents

VII

2.3.2 Components of goodwill from an acquirer’s point of view .... 47

2.3.2.1 Goodwill components according to

Wöhe (1980) .......................................................... 47


Johnson and Petrone (1998) ................................... 50


Sellhorn (2000) ...................................................... 57

2.3.2.4 Value drivers of acquired goodwill ........................ 62

2.3.2.4.1 Value of synergies .............................. 62

2.3.2.4.2 Value of control ................................. 69

2.3.2.4.3 Value of restructuring possibilities .... 73

3 Goodwill treatment and impairment-only approach under IFRS ............. 80

3.1 Relevance of IFRS 3 for generating acquired goodwill ........................ 83

3.1.1 Definition of a business combination ..................................... 84

3.1.2 Acquisition method ................................................................. 84

3.1.2.1 Identifying the acquirer and determining the

acquisition date ...................................................... 85

3.1.2.2 Recognizing assets acquired and liabilities

assumed ................................................................. 86

3.1.2.3 Measuring assets acquired and liabilities

assumed ................................................................. 87

3.1.2.4 Recognizing and measuring goodwill .................... 88

3.1.3 Disclosures ............................................................................. 89

3.1.4 Typical impacts on financial statements in a purchase

price allocation ....................................................................... 91

3.2 Recognition possibilities of other intangible assets apart from

goodwill in business combinations according to IAS 38 ...................... 95

3.2.1 Definition of an intangible asset ............................................. 98

3.2.2 Recognition and measurement .............................................. 100

Table of contents

VIII

3.3 Procedure for goodwill impairment testing under IAS 36 .................. 102

3.3.1 Frequency of impairment testing and indicators of

impairment ............................................................................ 104

3.3.2 Cash generating units as valuation objects in goodwill

impairment testing ................................................................ 107

3.3.3 Measuring the carrying amount of a CGU ............................ 108

3.3.4 Measuring the recoverable amount of a CGU ...................... 110

3.3.4.1 Fair value less costs of disposal ........................... 110

3.3.4.2 Value in use ......................................................... 112

3.3.5 Disclosures ........................................................................... 116

4 Implications of reporting flexibility in the impairment-only approach .. 118

4.1 Possible relationships between reporting flexibility and managing

goodwill write-offs ............................................................................. 119

4.2 Timely goodwill impairment recognition and its impact on the

quality of financial reporting .............................................................. 120

4.3 Reporting flexibility and its impact on the enforcability of the

impairment-only approach .................................................................. 122

4.4 Areas of reporting flexibility in the impairment-only approach ......... 125

4.4.1 Cash generating units’ construction and goodwill

allocation .............................................................................. 126

4.4.2 Valuation parameters to determine recoverable amount ....... 132

4.4.2.1 Cash flow forecasts .............................................. 134

4.4.2.2 Discount rates ...................................................... 138

5 Theoretical concepts helping to understand goodwill write-off

decisions ........................................................................................................ 142

5.1 Private information on changes of a firm’s future

financial performance ......................................................................... 143

5.2 Agency theory ..................................................................................... 146

5.2.1 Contract incentives ............................................................... 147

5.2.2 Reputational concerns ........................................................... 149

Table of contents

IX

5.2.3 Valuation concerns ............................................................... 151

6 Literature review on research findings that can be linked to

goodwill write-off decision making ............................................................. 153

6.1 Timing of goodwill write-offs ............................................................ 154

6.2 Economic consequences of goodwill write-offs ................................. 160

6.2.1 Stock market reactions to goodwill write-offs ...................... 168

6.2.2 Debt contract consequences from goodwill write-offs ......... 173

6.3 Personal incentives of managers influencing goodwill write-off

decisions ............................................................................................. 179

6.3.1 Top management changes and goodwill write-off

decisions ............................................................................... 180

6.3.2 Goodwill write-off decisions over the regular tenure of

senior executives ................................................................... 185

6.3.3 Insider trading of senior management teams prior to

goodwill write-offs ............................................................... 199

6.4 Summary of literature review and observable research gaps .............. 201

7 Research design and research methodology .............................................. 205

7.1 Hypotheses formulation ...................................................................... 205

7.1.1 Relationship between private information on the firm’s

future financial performance and goodwill write-off

decisions ............................................................................... 206

7.1.2 Impact of incentives predicted by agency theory on

goodwill write-off decisions ................................................. 215

7.1.3 Impact of goodwill reporting flexibility on goodwill

write-off decisions ................................................................ 225

7.2 Model design ...................................................................................... 235

7.2.1 Regression model ................................................................. 235

7.2.2 Variables definition .............................................................. 237

7.2.2.1 Dependent variable: Goodwill write-off and

non-write-off decision .......................................... 237

Table of contents

X

7.2.2.2 Independent variables .......................................... 237

7.2.2.2.1 Private information on a firm’s

future financial performance ............ 237

7.2.2.2.2 Incentives predicted by agency

theory ............................................... 240

7.2.2.2.3 Goodwill reporting flexibility .......... 243

7.2.2.2.4 Other, control variables .................... 247

7.2.3 Expected signs of explanatory variables in the regressions .. 253

7.3 Sample selection ................................................................................. 257

7.3.1 Sample selection based on capital market-implied

triggering events ................................................................... 257

7.3.1.1 Observable market valuation gaps as a strong

sign for an economically impaired goodwill ........ 258

7.3.2 Sample selection methodology ............................................. 262

8 Results ........................................................................................................... 265

8.1 Sample description.............................................................................. 265

8.1.1 General information .............................................................. 265

8.1.2 Financial information............................................................ 267

8.1.3 Goodwill write-off and non-write-off decisions ................... 271

8.2 Univariate analysis of goodwill write-off and non-write-off firms ..... 273

8.2.1 Financial characteristics........................................................ 274

8.2.1.1 Goodwill .............................................................. 274

8.2.1.2 Size and valuation ................................................ 275

8.2.1.3 Historical financial performance .......................... 276

8.2.2 Private information on changes of the firms’ future

financial performance ........................................................... 282

8.2.2.1 Private information proxied by future stock

returns and changes in accounting earnings ......... 282

8.2.2.2 Private information proxied by share buybacks

and changes in CEO share ownership .................. 284

Table of contents

XI

8.2.3 Incentives predicted by agency theory .................................. 288

8.2.3.1 Capital structure and the cost of borrowing ......... 288

8.2.3.2 CEO tenure .......................................................... 289

8.2.3.3 CEO compensation and share ownership ............. 290

8.2.4 Goodwill reporting flexibility ............................................... 295

8.2.4.1 Goodwill allocation to reporting segments .......... 295

8.2.4.2 CGU or reporting segments changes ................... 302

8.2.4.3 Subsequent valuation ........................................... 303

8.3 Pearson correlations between explanatory variables........................... 307

8.4 Multivariate regression results ............................................................ 310

8.4.1 Multivariate results on the basis of the baseline

regressions ............................................................................ 312

8.4.1.1 Private information variables ............................... 312

8.4.1.2 Incentives predicted by agency theory ................. 323

8.4.1.3 Goodwill reporting flexibility variables ............... 333

8.4.2 Sensitivities of the baseline regressions regarding

goodwill reporting flexibility ................................................ 335

8.4.2.1 Sensitivities on goodwill reporting flexibility ..... 335

8.4.3 Explanatory power of baseline regressions........................... 344

8.4.4 Multivariate results on the basis of subsamples .................... 344

8.4.4.1 Private information variables ............................... 346

8.4.4.2 Incentives predicted by agency theory ................. 351

8.4.5 Explanatory power of the regressions based on

subsamples ............................................................................ 360

8.4.6 Limitations of research findings ........................................... 361

8.5 Summary of research findings and their implications ........................ 363

8.5.1 Goodwill write-off decisions and private information on

firms’ future financial performance ...................................... 365

Table of contents

XII

8.5.2 Goodwill write-off decisions and agency theory-based

incentives .............................................................................. 367

8.5.3 Goodwill write-off decisions and goodwill reporting

flexibility .............................................................................. 369

Appendix ............................................................................................................... 372

Appendix I: List of sample firms......................................................................... 373

Appendix II: Multicollinearity diagnostics in baseline regressions

and regressions on the basis of subsamples ............................................... 378

Appendix III: Bibliography ................................................................................. 380

List of figures

XIII

List of figures

Fig. 1: Derivation of research questions ............................................................... 5

Fig. 2: Research structure ................................................................................... 13

Fig. 3: Context-dependent definitions of goodwill ............................................. 15

Fig. 4: Sources of going concern goodwill ......................................................... 17

Fig. 5: Mathematical derivation of going concern

goodwill ................................................................................................... 22

Fig. 6: Strategic management view of goodwill ................................................. 24

Fig. 7: Strategic management concepts related to going concern goodwill ........ 25

Fig. 8: Firm resources and value creation ........................................................... 31

Fig. 9: Availability of resources and value creation............................................ 34

Fig. 10: Core competences and value creation ...................................................... 36

Fig. 11: Firm resources, competitive advantage and value creation ..................... 38

Fig. 12: Intellectual properties and intellectual capital ......................................... 41

Fig. 13: The components of intellectual capital .................................................... 42

Fig. 14: Constituents of human capital, customer capital,

and organizational capital ........................................................................ 45

Fig. 15: Goodwill components according to Johnson and Petrone (1998) ............ 52

Fig. 16: Goodwill components according to Sellhorn (2000) ............................... 58

Fig. 17: Sources of operating synergies ................................................................ 65

Fig. 18: Sources of financial synergies ................................................................. 67

Fig. 19: Differentiation between corporate and financial restructuring ................ 76

Fig. 20: Objectives of IFRS 3, IAS 36, and IAS 38 .............................................. 81

Fig. 21: Disclosure requirements under IFRS 3 .................................................... 90

Fig. 22: Procedure of a purchase price allocation under IFRS 3 ........................... 92

Fig. 23: Consolidated balance sheet impacts from a

purchase price allocation under IFRS 3 ................................................... 93

Fig. 24: Possible intangible assets apart from goodwill in business

combinations ........................................................................................... 97

Fig. 25: Properties of an intangible asset under IFRS ........................................... 98

Fig. 26: Impairment test methodology according to IAS 36 ............................... 104

List of figures

XIV

Fig. 27: Carrying amount components of a cash generating unit ........................ 109

Fig. 28: Measuring the recoverable amount of a CGU ....................................... 110

Fig. 29: Valuation approaches to determine recoverable amounts...................... 115

Fig. 30: Implications of reporting flexibility in the IOA on quality of

financial statements ............................................................................... 119

Fig. 31: Reporting flexibily, enforceability and managing goodwill write-offs . 123

Fig. 32: Areas of reporting flexibility in the impairment-only approach ............ 126

Fig. 33: Number of CGUs in European firms ..................................................... 128

Fig. 34: Number of CGUs with significant goodwill in European firms ............ 129

Fig. 35: CGUs classification in Germany and Europe ........................................ 130

Fig. 36: Levels of CGUs in German firms .......................................................... 132

Fig. 37: Over-estimation of projected cash flows ............................................... 135

Fig. 38: Maximum long-term growth rates in European firms ........................... 137

Fig. 39: Distribution of discount rates in European firms ................................... 140

Fig. 40: Theoretical concepts explaining managerial motivations for writing

or not writing off goodwill in the impairment-only approach ............... 143

Fig. 41: Research areas that could be linked to goodwill write-off decision

making ................................................................................................... 154

Fig. 42: Classification of research findings on stock market reactions ............... 161

Fig. 43: Overview on research findings regarding stock market reactions to

goodwill write-offs ................................................................................ 171

Fig. 44: Classification of research findings on debt contract consequences ....... 174

Fig. 45: Overview on research findings regarding senior management

changes and goodwill write-offs ............................................................ 184

Fig. 46: Hypotheses and research areas .............................................................. 206

Fig. 47: Methodology of sample selection .......................................................... 263

Fig. 48: Distribution of observations by country ................................................ 265

Fig. 49: Cumulative stock market performance of sample firms (index) ............ 269

Fig. 50: Market to book value ratios of sample firms ......................................... 270

Fig. 51: Market to book value ratios of goodwill write-off and

non-write-off firms ................................................................................ 276

List of figures

XV

Fig. 52: Cumulative stock market performance of goodwill write-off and

non-write-off sample firms (index) ....................................................... 277

Fig. 53: Herfindahl-Hirschman Index by write-off and non-write-off

subsample .............................................................................................. 297

Fig. 54: Goodwill allocation to reporting segments with goodwill (mean) ........ 298

Fig. 55: Goodwill allocation to available reporting segments (mean) ................ 298

Fig. 56: Goodwill allocation to least risky reporting segments (mean) .............. 301

Fig. 57: Preferred valuation approach to test the recoverability of goodwill

in year t .................................................................................................. 304

Fig. 58: Relationship between CEO tenure and goodwill write-off probability

in year t .................................................................................................. 328

Fig. 59: Goodwill concentration and corresponding goodwill write-off

probabilities in year t ............................................................................. 343

List of tables

XVI

List of tables

Table 1: Sources of control value ....................................................................... 71

Table 2: Application of discount rates below expectations in Australia

and New Zealand ................................................................................ 141

Table 3: Summary of hypotheses ...................................................................... 234

Table 4: Summary of independent variables ..................................................... 252

Table 5: Expected signs of independent variables ............................................ 256

Table 6: Derivation of final sample .................................................................. 264

Table 7: Distribution of observations by industry............................................. 266

Table 8: Distribution of observations by financial year .................................... 267

Table 9: Financial characteristics of sample firms ............................................ 268

Table 10: Goodwill write-off probability by financial year ................................ 271

Table 11: Goodwill write-off amounts by financial year .................................... 272

Table 12: Overview on financials of goodwill write-off and

non-write-off firms ............................................................................. 281

Table 13: Overview on variables related to private information on future

financial performance ......................................................................... 287

Table 14: Overview on variables related to contract-based incentives ............... 294

Table 15: Overview on variables related to goodwill reporting flexibility ......... 306

Table 16: Pearson correlations of explanatory variables .................................... 309

Table 17: Explanatory variables in baseline regressions and subsamples .......... 311

Table 18: Results of baseline regressions ........................................................... 315

Table 19: Results of baseline regressions with accounting earnings .................. 321

Table 20: Results of baseline regressions with sensitivities on CEO tenure ....... 332

Table 21: Results of baseline regressions with sensitivities on goodwill

reporting flexibility ............................................................................. 338

Table 22: Observable goodwill concentration in reporting segments and

corresponding goodwill write-off probabilities .................................. 341

Table 23: Results of regressions with additional variables for private

information on future financial performance ...................................... 350

List of tables

XVII

Table 24: Results of regressions with additional variables for contract-based

incentives ............................................................................................ 354

Table 25: Results of regressions with variables for valuation motives ............... 358

Table 26: Overview on confirmations of hypotheses ......................................... 364

Table 27: List of sample firms ............................................................................ 377

Table 28: Multicollinearity diagnostics .............................................................. 379

List of abbreviations

XVIII


AF Acquiring firm

AG Aktiengesellschaft (German for: Public limited company)

Approx. Approximately

AT Austria

AUS Australia

AUS$ Australian Dollar

B Book value of equity

B/S Balance sheet

BC Basis for conclusion

BE Belgium

bn Billion(s)

Bps Basis points

C Combined entity

CA Carrying amount

CAPEX Capital expenditures

CAPM Capital asset pricing model

CEO Chief Executive Officer

CF Cash flow

Cf. confer

CFO Chief Financial Officer

CGU Cash generating unit

CH Switzerland

CM Current management

D Dividend

d.f. Degrees of freedom

DAX Deutscher Aktienindex (German for: German stock index)

DCF Discounted cash flow

DDM Dividend discount model

DE Germany

DK Denmark


XIX

DPR Deutsche Prüfstelle für Rechnungslegung e.V. (German for:

Financial Reporting Enforcement Panel)

E Earnings

e.g. Exempli gratia (Latin for: for example)

e.V. Eingetragener Verein (German)

Ea Abnormal earnings

EBIT Earnings before interest and taxes

EBITDA Earnings before interest, taxes, depreciation and amortization

EMH Efficient Market Hypothesis

EPS Earnings per share

ES Spain

et al. et alii (Latin for: and others)

etc. Et cetera (Latin)

EU European Union

EUR Euro

excl. Excluding

FASB Financial Accounting Standards Board

FFE Fédération des Experts-comptables Européens (French for:

Federation of European Accountants)

FI Finland

Fig. Figure

FR France

FREP Financial Reporting Enforcement Panel

FRS Financial Reporting Standards

FVLCD Fair value less costs of disposal

GAAP Generally Accepted Accounting Principles

GB Great Britain

GW Goodwill

H Hypothesis

HHI Herfindahl–Hirschman Index

i.e. Id est (Latin for: that is)

IAS International Accounting Standard


XX

IASB International Accounting Standards Board

IASC International Accounting Standards Committee

IE Ireland

iff If and only if

IFRS International Financial Reporting Standard(s)

Incl. Including

IOA Impairment-only approach

IPO Initial public offering

IRR Internal rate of return

IT Italy

LU Luxembourg

M Market value of equity

m Million(s)

MD Managing director

MDAX Mid-Cap DAX

MTB Market to book value of equity ratio

MV Market value

n Number (of observations)

n/a Not applicable

n/k Not known

NAV Net asset value

NL Netherlands

NM New management

NO Norway

No. Number

NPV Net present value

OLS Ordinary least squares

P (Share) price of a firm

p. Page

Plc Public limited company

PP&E Property, plant and equipment

pp. Pages


XXI

Pr Probability

PT Portugal

QC Qualitative characteristics

r Discount rate

R&D Research and development

RA Recoverable amount

re Cost of equity

rf Risk-free rate

ROA Return on assets

ROE Return on equity

ROIC Return on invested capital

RR Redovisningsrådet Rekommendationer (Swedish for: Swedish

Financial Accounting Council)

rv Cost of capital

S Synergies

S.A. Société anonyme (French for: Public limited company)

S.E. Standard error

SA Società Anonima (Italian for: Public limited company)

SDAX Small-Cap DAX

SE Sweden

SFAS Statements of Financial Accounting Standards

sig. Significance

SMAC Society of Management Accountants in Canada

SpA Società per azioni (Italian for: Joint-stock company)

T Period of time

t Point of time

TecDAX Technology sector DAX

TF Target firm

TMI Total Market Index

TV Terminal value

UK United Kingdom

US GAAP United States Generally Accepted Accounting Principles


XXII

V Enterprise value

VIF Variance inflation factor

VIU Value in Use

Vol. Volume

WACC Weighted average cost of capital

WO Write-off

yr Year

yrs Years

List of symbols

XXIII

List of symbols

˄ And

& And

$ Dollar

ε Error term (regression model)

∞ Infinity

> Larger than

% per cent

® Registered trademark

< Smaller than

∑ Sum

Executive summary

XVI

Executive summary

This PhD thesis studies determinants of goodwill write-off and non-write-off

decisions in firms for which very strong indicators of an economically impaired

goodwill are observable. By using a hand-collected sample of listed European firms

reporting under IFRS, various sets of variables related to (i) incentives predicted by

agency theory, (ii) potential private information on future changes of the firms’

financial performance held by their management teams, as well as (iii) goodwill

reporting flexibility under IAS 36 are studied for their influence on goodwill write-

off and non-write-off decisions.

In only 58% of all observations, goodwill was actually written off; a percentage

expected to be much higher and thereby implying that goodwill write-offs are often

not recorded when they become due and that factors might be present in the sample

firms that hinder timely goodwill write-off recognition. Whilst one finds some

evidence that private information on an improving financial performance held by

senior management exists and can hinder management teams from taking a write-

off, the effect from agency theory-based incentives is at least equally strong. More

specifically, goodwill write-off probabilities are lower when a write-off would

reduce CEO compensation, damage the reputation of the CEO, or cause violations of

existing debt covenants. However, the strongest and most stable effects across

different subsamples emerge from variables that proxy for reporting flexibility under

the impairment-only approach. Highly statistically significant relationships between

goodwill allocations to reporting segments and corresponding goodwill write-off

probabilities in the sample firms are detected. The more concentrated goodwill is in

reporting segments, the lower the observable write-off probabilities. The effects are

strongest for larger and more profitable reporting segments, suggesting that such

allocations allow covering up an economically impaired goodwill through the

substitution of acquired goodwill with internally generated goodwill. Additionally,

changing reporting structures like cash generating units or reporting segments with

goodwill statistically significantly reduces goodwill write-off probability in the

sample firms.

Executive summary

XVII

The findings on agency theory-based incentives and goodwill reporting flexibility

stand in contrast to the IASB’s original intention when introducing the impairment-

only approach. The results derived in this PhD thesis suggest that accounting

standard setters need to revisit the impairment-only approach as it allows for too

much managerial discretion and flexibility in its application that can serve personal

motivations of key executives and thereby not being in the best interest of the firms’

shareholders.

Zusammenfassung

XVIII

Zusammenfassung

Seit 2004 sind Unternehmen, welche nach den sog. International Financial

Reporting Standards (IFRS) Rechnung legen, aufgefordert mindestens jährlich die

Werthaltigkeit der aus Unternehmenstransaktionen hervorgegangenen Geschäfts-

oder Firmenwerte (sog. Goodwill) zu überprüfen. Diese neue Rechnungslegungs-

vorschrift ersetzte die bis dahin geltende planmässige Abschreibung von Goodwill.

Bei der Einführung dieses neuen Rechnungslegungsstandards erhoffte sich das

International Accounting Standards Board (IASB), dass Unternehmen durch den

neuen Rechnungslegungsstandard Informationsasymmetrien bzgl. der Werthaltigkeit

und der zukünftigen Ertragskraft des Geschäfts- oder Firmenwerts und der

erworbenen Geschäftsbereiche zwischen den Unternehmen und Investoren abbauen

könnten.

Seit Einführung dieser mindestens jährlich stattfindenden Werthaltigkeitsprüfung,

dem sog. Impairment-Only Approach, kritisierten Fachvertreter und Wissenschaftler

den Rechnungslegungsstandard als zu komplex in der Ausführung und zu

willkürlich von den Unternehmen anwendbar. Die vorliegende Dissertation

untersucht deshalb, ob eine Willkürlichkeit in der Anwendung des Impairment-Only

Approachs in der Praxis zu beobachten ist oder Unternehmen tatsächlich im Sinne

des IASB den Rechnungslegungsstandard anwenden. Des Weiteren überprüft die

vorliegende Arbeit die Auswirkungen der inhärenten Flexibilität des Impairment-

Only Approach auf das Ergebnis der Werthaltigkeitsüberprüfung. Die Untersuchung

basiert auf einem eigens erstellten Datensatz von Unternehmen für welche starke

Anzeichen beobachtbar sind, die für eine nicht gegebene Werthaltigkeit des

Geschäfts- oder Firmenwerts sprechen.

Grundsätzlich ist zu beobachten, dass Unternehmen in der Stichprobe mit ihren

Abschreibungsentscheidungen zum Teil Informationen über die zukünftige Wirt-

schaftlichkeit des Goodwills übermitteln. Jedoch ist auch zu beobachten, dass die

Abschreibungswahrscheinlichkeit mit Faktoren zusammenhängt, die grundsätzlich

unabhängig von der Wirtschaftlichkeit der Bilanzposition sind. Dies sind insbe-

sondere Reputations- und Kompensationsrisiken von Vorständen sowie Risiken,

Zusammenfassung

XIX

welche aus bestehenden Kreditvereinbarungen mit Gläubigern hervorgehen. Des

Weiteren beeinflusst die Aufteilung des Goodwills auf Reporting Segmente sowie

deren finanzielle Charakteristika stark die Abschreibungswahrscheinlichkeit. Die

Ergebnisse der Doktorarbeit sprechen stark für die Notwendigkeit einer zeitnahen

Überarbeitung des Impairment-Only Konzepts durch den IASB, da sich die Willkür

und Flexibilität stark gegen die Interessen von Investoren und Gläubigern richtet.

1 Introduction

1

1 Introduction

1.1 Research motivation

With the introduction of the revised accounting standard IAS 36 Impairment of

Assets the International Accounting Standards Board (IASB) changed fundamentally

the accounting for goodwill for financial years starting 31.03.2004.2 The accounting

standard replaced the pre-existing periodic amortization of goodwill by the so-called

impairment-only approach (IOA), in which firms test at least annually the

recoverability of goodwill.3 In case it is found out that the recoverable amount of

goodwill has fallen below its book value (also termed carrying amount), firms are

required to book a one-off goodwill write-off.4 By introducing the impairment-only

approach for firms reporting under IFRS, the IASB conceptually aligned goodwill

accounting to that of firms reporting under US GAAP, which had already replaced

periodic amortizations by the impairment-only approach in 2001 through the

accounting standard SFAS 142 Goodwill and Other Intangible Assets.5

The theoretical argumentation of the IASB for its rather drastic change in goodwill

accounting was based on an anticipated improved decision usefulness of accounting

information from business combinations under the application of the revised

accounting standard.6 As according to the IASB, “the useful life of acquired

goodwill and the pattern in which it diminishes generally are not possible to

predict”7, periodic amortisation would not properly reflect the economic reality8, and

therefore failing to provide useful information9. Consequently, advocating for the

impairment-only approach, the IASB reasoned that through “a rigorous and

2 Cf. Meyer and Halberkann (2012), p. 312, Leibfried (2010), p. 478. 3 Cf. Leibfried (2010), p. 478, Amiraslani et al. (2013), p. 12. 4 Cf. Amiraslani et al. (2013), pp. 12-14, Zülch and Siggelkow (2011), p. 2, Detzen and Zülch (2012), p 107, Küting (2010), p. 1857. 5 Cf. Meyer and Halberkann (2012), p. 312, Leibfried (2010), p. 478, Liberatore and Mazzi (2010), p. 333. 6 Cf. Liberatore and Mazzi (2010), p. 334, Meyer and Halberkann (2012), p. 312, Amiraslani et al. (2013), p. 12. 7 IAS 36.BC131E. 8 Cf. Meyer and Halberkann (2012), p. 312. 9 IAS 36.BC131E.

1 Introduction

2

operational impairment test (…) more useful information would be provided to users

of an entity’s financial statements under an approach in which goodwill is not

amortised, but instead tested for impairment annually or more frequently if events or

changes in circumstances indicate that the goodwill might be impaired”10. As

conceptually acquired goodwill is primarily made up of expected synergies from a

business combination and methodologically derived from future cash flows, through

goodwill write-off decisions firms are able to provide private information11 to

investors about the future prospects of a firm.12

Therefore the IASB’s conceptual shift to apply a fair value accounting approach also

on goodwill grounds in the assumption that the management teams of firms would

actually make willingly use of the impairment-only approach to provide information

on the firm’s future prospects to investors (also referred to as the private information

disclosure hypothesis) and thereby reducing potential information asymmetries.13

This rationale however stands in contrast to agency theory predicting that a firm’s

management could also use goodwill write-offs to strive for incentives emerging

from write-off or non-write-off decisions and thereby potentially working against

the proclaimed private information disclosure assertion by the IASB.14

Consequently, (i) potential private information on a firm’s future financial

performance held by the management team15 as well as (ii) agency theory offer

different motives for a firm’s management to engage in goodwill write-off

activities.16 Whilst the confirmation of the private information hypothesis would be

desirable from the perspective of the IASB’s original intention, confirmation of

motives suggested by agency theory would certainly be not. The reporting flexibility

provided by IAS 36 as well as the partially unverifiable assumptions underlying the

10 IAS 36.BC131G. 11 Cf. Glaum et al. (2015), p. 2, who argue “(i)f applied neutrally, discretion allows management to convey private information and thus make financial statements more informative” (Glaum et al. (2015), p. 2). 12 Cf. Ramanna and Watts (2012), p. 757, Vanza et al. (2011), p. 3, AbuGhazaleh et al. (2011), p. 166. 13 Cf. Amiraslani et al. (2013), pp. 18-19, Chen et al. (2013), p. 4, Gordon and Hsu (2014), p. 13, Vanza et al. (2011), pp. 2-3, AbuGhazaleh et al. (2011), p. 166, Lapointe-Antunes et al. (2009), pp. 62-63. 14 Cf. Vanza et al. (2011), p. 3, AbuGhazaleh et al. (2011), p. 170, Amiraslani et al. (2013), pp. 18-19. 15 Cf. Li et al. (2011), p. 746, Ramanna and Watts (2012), p. 749, Ramanna (2008), p. 255. 16 Cf. AbuGhazaleh et al. (2011), p. 170, Amiraslani et al. (2013), pp. 18-19.

1 Introduction

3

fair value estimation of goodwill could potentially provide means to achieve the

preferred outcome of the firm’s management.17 This assertion is backed by concerns

regarding the managerial discretion in the derivation of the recoverable amount of

goodwill raised over the last years by practitioners, auditors and academics alike.18

As following the adoption of IAS 36 firms with strong capital market implied

goodwill impairment indicators can be identified19 as well as their actual write-off

decisions, this information provides a rich testing ground whether firms

implemented the impairment-only approach according to the IASB’s original

intention. Therefore, this PhD thesis focuses on whether goodwill write-off

decisions can be explained by a firm’s management intention to disclose private

information on the firm’s future performance or whether motives predicted by

agency theory, which theoretically could work against a firm’s private information

disclosure motivation, better explain write-off and non-write-off decisions.

Furthermore, it should be tested in what way reporting flexibility and managerial

discretion allowed by IAS 36 impact the management of goodwill write-offs to

achieve the desired impairment outcome by a firm’s management.

17 Cf. Liberatore and Mazzi (2010), p. 333, Ramanna and Watts (2012), p. 760, Amiraslani et al. (2013), p. 19. 18 Cf. Kasperzak (2011), pp. 12-13, Teitler-Feinberg (2006), p. 18, Brösel and Klassen (2006), pp. 463-464, Müller and Reinke (2010), p. 29, Kirchner (2006), p. 66, Zülch and Siggelkow (2012), p. 383, Meyer and Halberkann (2012), p. 312, Leibfried (2010), p. 478. 19 i.e. that indicators can be observed for firms that suggest that goodwill is economically impaired and would require a write-off.

1 Introduction

4

1.2 Research questions

Since the adoption of the impairment-only approach according to IAS 36, academic

studies on goodwill write-off decisions of firms have been numerous and some of

them have derived results that stand in contrast to the expectations of accounting

standard setters and academic alike.20 It has been observable that aggregated

goodwill write-offs substantially declined compared to the fiscal years in which

goodwill had been amortized on a straight line basis.21 At first glance, a finding that

was reasonably expected through the adoption of IAS 36 as the average economic

useful lifes of acquired goodwill are most likely longer than implied under the

straight-line amortization scheme during pre-IOA periods. However these findings

on the average write-off rates22 hold also true for the recent financial crisis, i.e. 2008

and 2009, during which one could have expected that goodwill write-offs should

have peaked due to the downturn in global economic activities.23 Equally surprising

and unexpected are the findings of research studies that looked into the goodwill

write-off decisions of firms for which strong capital market implications are

observable that indicate that goodwill could be impaired, however also documenting

relatively low write-off rates.24

These findings on the relatively low write-off rates raise the question of the

underlying motives of firms to write off or not write off goodwill, especially in those

situations when firm outsiders would argue for an economically impaired goodwill.

Consequently, this PhD thesis focusses on various motives potentially explaining

goodwill write-off decisions of senior management teams under the impairment-

only approach. In particular, the focus should be placed on the principal motivations

underlying write-off and non-write-off decisions and thereby concentrating on (i) a

potentially intended disclosure of private information on the firm’s future financial

performance and (ii) motives derived on the basis of agency theory-considerations.

20 Cf. IASB (2014), pp. 13-14. 21 Cf. Leibfried (2010), p. 478. 22 Defined as the ratio of goodwill write-off amounts per year to book value of goodwill. 23 Cf. Leibfried (2010), p. 478. 24 Cf. Hayn and Hughes (2006), p. 223, Amiraslani et al. (2013), p. 19, Ramanna and Watts (2012), p. 751, Chen et al. (2013), pp. 2, 8, Beatty and Weber (2006), pp. 258, 284, Muller et al. (2009), p. 2, Chen et al. (2008), pp. 72-73.

1 Introduction

5

Furthermore, adding to this analysis it should be tested in what way managerial

discretion and reporting flexibility of the impairment-only approach under IAS 36

helps managers to achieve their perceived goals.

Fig. 1: Derivation of research questions Source: Own illustration.

1.2.1 Disclosure of private information on changes in a

firm’s future financial performance

Consistent with the IASB’s opinion of the private information disclosure hypothesis

in the IOA, the write-off decision of management teams could potentially be

influenced by their intention to disclose private information to investors about the

firm’s future financial performance.25 As the recoverable amount of goodwill relies

on unverifiable estimates which are usually not fully known by investors26, write-off

decisions could potentially convey private information on the firm’s future

performance.27 Under this assumption, non-write off decisions would imply a stable

25 Cf. Vanza et al. (2011), pp. 2-3, Chen et al. (2013), p. 4, AbuGhazaleh et al. (2011), p. 166, Lapointe-Antunes et al. (2009), pp. 62-63, Lhaopadchan (2010), p. 125, Riedl (2002), pp. 2-3, Zang (2008), p. 40. 26 Cf. Wilson (1996), p. 173, Hilton and O’Brian (2009), p. 179. 27 Cf. Ramanna and Watts (2012), p. 749, Ramanna (2008), p. 255, Li et al. (2011), p. 746.

(A) Managerial

motivations

(B) Ability

Private information disclosure

Agency theory-based incentives

IAS 36 Reporting flexibility

Goodwill write-off or non-write-off

(C) Observable

outcome

1 Introduction

6

or positive performance outlook of the firm, whilst a write-off would imply the

opposite.28

Ramanna and Watts (2012) provide support for the private information disclosure

hypothesis by arguing that “managers’ failure to impair goodwill can be attributed to

information asymmetries between managers and shareholders, in particular,

situations in which managers are likely to have favourable private information on

future cash flows”29. Such information transfer strategies are based on the

assumption that “managers have superior information on their firms’ current and

future performance than outside investors”30 and that such information transfers

through goodwill write-off decisions “provide a potentially important means for

corporate managers to impart their knowledge to outside investors, even if capital

markets are efficient”31, as suggested by Healy and Palepu (1993). On the basis of

these considerations, a firm’s management that possesses better and more positive

information of the future financial performance of the firm might be less willing to

write off goodwill. As a consequence, write-off rates would be expected to be lower

for such firms.32

To test whether private information on future performance is related to goodwill

write-off decisions, it should be analysed if variables that are understood in

academia as proxies for positive private information about a firm’s future

performance held by the management team are related to observable goodwill write-

off decisions. This includes the ex-post analysis of changes in financial performance

(future stock returns and changes in accounting earnings)33, the utilization and

extend of share repurchase programs to make use of an apparent undervaluation of

the firm34, as well as changes in a CEO’s share ownership35.

28 Cf. Gordon and Hsu (2014), p. 32. 29 Ramanna and Watts (2012), p. 757. 30 Healy and Palepu (1993), p. 1. 31 Healy and Palepu (1993), p. 1. 32 Cf. Gordon and Hsu (2014), p. 32. 33 Cf. AbuGhazaleh et al. (2011), p. 172. 34 Cf. Ramanna and Watts (2012), p. 757. 35 Cf. Muller et al. (2009), pp. 3-4.

1 Introduction

7

Research question 1:

Do goodwill write-off decisions convey private information held by a company’s

senior management team on changes of a firm’s future financial performance?

1.2.2 Incentives predicted by agency theory

According to Watts (2003) and Ramanna (2008), agency theory presumes that senior

managers will on average make use of unverifiable discretion in accounting

decisions, like in the impairment-only approach, to manage financial reporting

opportunistically and thereby striving for private incentives.36 Incentives to manage

goodwill write-offs under agency theory considerations could emerge from

earnings-based bonus plans of CEOs37, share ownership of CEOs38, debt

covenants39, cost of borrowing40, as well as CEO tenure41, i.e. reputational concerns

impacting future payoffs from employment contracts.

Frequently, managerial compensation is closely linked to the financial performance

of the firm.42 By doing so, from an agency theory perspective, the utility function of

the manager should be aligned to that of the firm’s owner.43 Then, when the

manager tries to maximize his/her individual utility, she/he does so in the best

interest of the owner by maximizing the firm’s value.44 This compensation strategy

however could pose the risk of delaying or abstaining from booking goodwill write-

36 Cf. Ramanna (2008), p. 254, Watts (2003), p. 209, Ramanna and Watts (2012), p. 758. 37 Cf. Beatty and Weber (2006), p. 264, Fields et al. (2001), p. 257, Watts and Zimmermann (1990), p. 133, Darrough et al. (2013), p. 10. 38 Cf. AbuGhazaleh et al. (2011), p. 182, Darrough et al. (2013), p. 19. 39 Cf. Beatty and Weber (2006), pp. 264-265, Fields et al. (2001), p. 257, Watts and Zimmermann (1990), p. 133. 40 Cf. Mazzi et al. (2014), p. 4. 41 Cf. Francis et al. (1996), p. 123, Beatty and Weber (2006), p. 266, Darrough et al. (2013), p. 12. 42 Cf. Healy (1985), p. 95, Bowen (2009), p. 200, Gibbs et al. (2004), p. 411, Reda & Associates (2012), p. 11. 43 Cf. Baker et al. (1988), p. 594, Jensen and Meckling (1976), p. 5, Healy (1985), p. 89, Oberholzer-Gee and Wulf (2012), p. 2. 44 Cf. Jensen and Meckling (1976), p. 10, Coughlan and Schmidt (1985), p. 43, Berle and Means (1932), p. 68.

1 Introduction

8

offs.45 Depending on what fraction of the senior management team’s compensation

is linked to a financial measure that would be directly impacted by a goodwill write-

off46, the senior management might want to avoid such a loss as their private wealth

would be directly affected.47

On the basis of agency theory considerations, a similar managerial behaviour could

emerge from a senior management’s equity ownership. Frequently, members of the

senior management are substantial shareholders in the firm. Due to the frequently

observable negative share price movement after the announcement of a goodwill

write-off, the management of a firm might be motivated to abstain from booking a

write-off.48 One might argue that the higher the individual’s wealth which is tied to

the performance of the firm, the harder the individual might want to abstain from

writing off goodwill.49

Besides compensation and wealth concerns, potential violations of debt covenants

based on accounting numbers can have an impact on accounting choice and in

particular on goodwill write-off decisions of senior executives.50 Breaching debt

covenants often trigger the immediate repayment of outstanding debt, a limitation of

managerial decision space due to a potential board representation of the lender, as

well as an increase in funding costs.51 Similar to compensation and wealth concerns,

concerns regarding existing debt covenants fall also under the category of agency

theory-based incentives.

Consequently, incentives predicted by agency theory might play a role regarding

managing goodwill write-offs as the senior management team might want to wait as

45 Cf Jensen et al. (2004), p. 77, giving the example of bonus plans based on accounting income that could lead to increase short-term profits at the expense of future value creation. Cf. Gibbs (2012), p. 35, who even talks about the risk of manipulating performance measures on which the remuneration is determined. 46 Cf. Bowen (2009), pp. 201, 203. 47 Cf. Jensen et al. (2004), pp. 4, 40. 48 Cf. Li and Sloan (2012), p. 49, Muller et al. (2009), p. 5, Hirschey and Richardson (2002), p. 181, Francis et al. (1996), p. 128, Bens et al. (2011), p. 537, Lhaopadchan (2010), p. 125, Li et al. (2010), p. 26. 49 Cf. Oberholzer-Gee and Wulf (2012), p. 1, Healy (1985), p. 95, who analyse the impact of bonus structures and other CEO incentives on the likelihood of earnings management or overstated earnings. 50 Cf. Watts and Zimmerman (1990), pp. 133-134, 139, Beatty and Weber (2006), p. 259, Zang (2008), p. 39, Lapointe-Antunes et al. (2009), p. 63, Riedl (2004), p. 833, Fields et al. (2001), p. 275. 51 Cf. Beneish and Press (1993), p. 234, Chen and Wei (1993), p. 218.

1 Introduction

9

long as possible to book such a loss in order to maximize the current payoffs of their

compensation contracts, to influence the market value of their shareholdings, or to

avoid breaching existing debt covenants.52


Do incentives predicted by agency theory (i) linked to a CEO’s tenure,

compensation, share ownership as well as (ii) linked to a firm’s debt contracts have

an influence on goodwill write-off or non-write-off decisions?

1.2.3 Reporting flexibility of IAS 36

According to Holthausen and Watts (2001), fair values which are not derived from

market prices in actively traded markets, and therefore predominately unverifiable

for outsiders, potentially allow for opportunistic accounting disclosures, depending

on the motivation of the management team.53 This reasoning also applies to the fair

value measurement of acquired goodwill due to its predominant reliance on

unverifiable estimates by a firm’s senior management.54

For firms applying International Financial Reporting Standards (IFRS), IAS 36

Impairment of Assets represents the accounting standard on which basis the

recoverable amount of goodwill has to be determined. This accounting standard is

considered to allow for substantial reporting flexibility in its application55, adding to

the unverifiable nature of goodwill as an asset.56 This reporting flexibility is argued

in academia and accounting practice to be used possibly to the manage goodwill

impairments.57 Areas of managerial discretion in its application include (i) the

52 Cf. Bergstresser and Philppon (2006), p. 511. Cf. also Cheng and Warfield (2005), who argue that “managers with high equity incentives are more likely to sell shares in the future and this motivates these managers to engage in earnings management to increase the value of the shares to be sold” (Cheng and Warfield (2005), p. 441). 53 Cf. Holthausen and Watts (2001), pp. 21, 28, 30, Ramanna (2008), p. 253. 54 Cf. AbuGhazaleh et al. (2011), p. 166, Beatty and Weber (2006), p. 284, Vanza et al. (2011), p. 4, Carlin and Finch (2008), p. 1. 55 Cf. Vettiger and Hirzel (2010), p. 387, Engel-Ciric (2012), p. 421, Mandl (2005), p. 476, Budde (2005), p. 2567, Kasperzak (2011), p. 3, Küting (2005), p. 2759. 56 Cf. Ramanna (2008), p. 260. 57 Cf. Ernst (2012), p. 640, Kirchner (2006), p. 73, Pellens et al. (2005), p. 24, Müller and Reinke (2010), p. 29.

1 Introduction

10

allocation of goodwill to those reporting segments/CGUs58 that management

assumes to benefit the most from the transaction that created goodwill in the first

place and (ii) the selection of the most appropriate valuation methodology to

determine the recoverable amount of goodwill.59 In particular, the CGU construction

and subsequent goodwill allocation procedures are argued to be problematic in the

IOA as they allow for substituting acquired goodwill with internally generated

goodwill60 and thereby impacting valuation results.61 Additionally, findings in

research and corporate studies imply that the number of CGUs is highly company-

specific62 and can change over time.63

On the basis of these considerations, one can argue that CGU structures and

goodwill allocations to reporting segments could be used for managing goodwill

write-offs in subsequent reporting periods.64 In this respect, it should be analysed

whether firms make use of this reporting flexibility to accomplish the desired

impairment outcome by a firm’s management.


Does reporting flexibility related to goodwill allocation to reporting

segments/CGUs influence goodwill write-off and non-write-off decisions in later

years?

58 i.e. CGUs which are part of reporting segments. 59 Cf. Brösel and Klassen (2006), p. 466, Brösel and Zwirner (2009), p. 196, Zülch and Siggelkow (2012), p. 385, Kasperzak (2011), p. 4, Engel-Ciric (2012), p. 421, Ruhnke (2008), p. 43. 60 also termed going concern goodwill. 61 Cf. Brösel and Klassen (2006), p. 463, Teitler-Feinberg (2006), p. 18, Brösel and Zwirner (2009), p. 196, Pawelzik (2009), p. 60. 62 Cf. Glaum and Wyrwa (2011), p. 62, Duff and Phelps (2013), pp. 26-27, Amiraslani et al. (2013), p. 42, Meyer and Halberkann (2012), p. 313. 63 Cf. Amiraslani et al. (2013), pp. 42, 51, Duff and Phelps (2013), p. 27. 64 Cf. Ramanna and Watts (2012), p. 760, Duff and Phelps (2013), p. 27, Müller and Reinke (2010), pp. 29, 31, Glaum and Wyrwa (2011), p. 64.

1 Introduction

11

1.3 Research structure

Chapter 1 of this PhD thesis outlines the research motivation, the three central

research questions and the structure regarding the analysis of goodwill write-off and

non-write-off decisions for capital market-implied triggering events.

As a starting point, chapter 2 introduces the concept of goodwill in economic

theory, thereby laying the foundation for the subsequent chapters. In particular, this

section provides a classification of goodwill on the basis of the context in which it

occurs. Additional information on the so-called going concern goodwill (also termed

internally generated goodwill) and acquired goodwill is given, focusing on their

individual components which ultimately represent the basis of the goodwill

recognized on an acquirer’s balance sheet from an accounting point of view.

Subsequently, chapter 3 focusses on the treatment of goodwill in financial

accounting according to IFRS. Here, a distinction between internally generated and

acquired goodwill is made. The relevant international accounting standards which

relate to the treatment of goodwill are presented. In particular, those include IFRS 3

Business combinations and IAS 36 Impairment of assets. Information provided in

this chapter also lays the foundation for the subsequent discussions of reporting

flexibility in the impairment-only approach.

Chapter 4 emphasizes the implication of reporting flexibility in the application of

IAS 36 on the quality of a firm’s financial statements. In particular, the granted

reporting flexibility is discussed from the perspective of the enforceability of the

impairment-only approach. The chapter also provides information on the areas of

reporting flexibility according to IAS 36.

Thereafter, chapter 5 outlines the two principal theoretical concepts that could be

used to understand managerial motivation to write off or not to write off goodwill.

Particular emphasis is placed on the (i) private information disclosure theory which

assumes that a senior management team’s goodwill write-off decision is based

primarily on the private information they hold on the company’s future financial

performance. This stands in contrast to (ii) agency theory which foresees that the

unverifiability in accounting choices in the impairment-only approach could be used

1 Introduction

12

opportunistically to manage a company’s financial reporting and thereby striving for

personal incentives. Chapter 6 contains a literature review on research findings that

can be linked to goodwill write-off decisions. Particular emphasis is placed on

evidence regarding managerial opportunistic behavior in the impairment-only

approach.

Chapter 7 then lays the ground for the empirical analysis. It contains the sample

selection and construction. Furthermore, the research model is introduced by

outlining the explanatory variables and dependent variable. Additionally, the section

contains the hypotheses to be tested. Chapter 8 highlights the research findings by

presenting evidence which motives, approximated through various variables, are

primarily related to write-off and non-write-off decisions of senior management

teams in the sample. The chapter concludes by summarizing and discussing the

research findings and elaborates on the implications of these findings.

1 Introduction

13

Fig. 2: Research structure Source: Own illustration.

Cha

pter

1 1. Introduction 1.1 Research motivation

1.2 Research questions

1.3 Research structure

Cha

pter

2 2.1 Context-dependent definition of goodwill

2.2 Going concern goodwill

2. Concept of goodwill in economic theory

2.2.1-2.2.3 “Drivers”

Part of: 2.3 Acquired goodwill

2.3.1-2.3.2 Components of GW

Cha

pter

3

3. Goodwill treatment in accounting

3.1 IFRS 3 Business combination 3.2 IAS 38 Intangible assets

3.3 IAS 36 Impairment of assets

Cha

pter

4 4.1 Implications of flexibility

4.2 Timeliness of recognition

4. Reporting flexibility in the IOA

4.3 Enforceability of IOA 4.4 Areas of reporting flexibility

Cha

pter

5 5.1 Private information on future financial performance 5. Theoretical

concepts explaining write-off decisions

Contract incentives Reputation concerns

5.2 Agency theory

Valuation concerns

Cha

pter

6 6.1 Timing of write-offs 6. Literature

review on goodwill write-off decision

6.2 Economic consequences of write-offs 6.3 Managerial opportunistic behaviour

Stock prices Debt contracts CEO tenure Earnings mgmt

Cha

pter

8

8.1 Sample description 8. Results 8.2 Univariate analysis 8.3 Correlation analysis

Write-off firms

Non-write-off firms

8.5 Summary of research findings and their implications

8.4 Multivariate analysis

Sensitivities Subsamples

Cha

pter

7

7. Research design

7.1 Hypotheses formulation 7.2 Model design

Regression model

Variables definition

Private information

Agency theory

Reporting flexibility

7.3 Sample selection

Capital market-implied triggering events

Expected signs

2 The concept of goodwill in economic theory

14


The following chapter introduces goodwill accounting and impairment testing by

providing background information on the concept of goodwill in economic theory.

The section emphasizes the nature and inherent risks of goodwill, both from a stand-

alone and an acquirer’s point of view by discussing (i) sources, (ii) various

theoretical concepts as well as (iii) fundamental components of goodwill.

In the chapter, a classification of goodwill according to the concept in which it

occurs is chosen. In a first step, information on the concept of going concern

goodwill is provided, building the foundation for the concept of acquired goodwill

and therefore also the treatment of goodwill in financial accounting.

2.1 Context-dependent definition of goodwill

Generally, the definition of goodwill depends on the context in which it occurs. For

the purpose of this PhD thesis, goodwill should be defined from an economic point

of view. This means that goodwill can be defined in a stand-alone context of a firm,

in the context of an acquisition and in the context of financial accounting. The

following table summarizes these key definitions of goodwill depending on the

context in which it occurs and outlines the principal reasons for its occurrence and

components. The content of this chapter is based on the classification outlined in the

figure below.


15

Fig. 3: Context-dependent definitions of goodwill

Source: Own illustration. Information from: Brief (1969), Ohlson (1995), Peasnell (1981), Higson (1998), Johnson and Petrone (1998), Henning et al. (2000), Sellhorn (2000), and Zanoni (2009).

The key characteristics summarized in the table above show that differences in the

definition of goodwill can be observed. However the results of the context

dependent analysis indicate that the goodwill definitions build upon one another.65

65 Cf. Haaker (2008), p. 135, Engel-Ciric (2012), p. 421, Hoogervorst (2012), p. 5.

• Difference between

consideration transferred

and the fair value of the

identifiable net assets

acquired.

• Intangible asset.

• Indefinite useful life.

• Difference between price

paid for the sum of

subjectively expected future

monetary benefits from the

acquisition over the fair

value of the identifiable net

assets acquired.

• Difference between a

firm’s going concern

value and its net asset

value (Going concern

goodwill).

• For listed firms,

difference between

market cap and NAV.

• Top-down approach • Bottom-up approach • Top-down approach

• Bottom-up approach

Stand-alone point of

view of a company

Goodwill

Acquirer’s

point of view

Financial accounting

point of view Context:

Definition:

Reason for

occurrence:

Component(s): • Intangible resources and

assets

• Intellectual capital

(human and structural

capital)

• Going concern goodwill

• Synergy goodwill

• Strategy goodwill

• Restructuring goodwill

• Flexibility component

• Overpayment for target

• Overvaluation of target

• Future economic benefits

arising from acquisition that

are not capable of being

individually identified and

separately recognised.

• Pure “residual”: no focus on

individual components.

• (Future) excess return

over cost of capital.

• Result of managerial

decisions on capital

allocation and strategy.

• Consideration transferred

(price paid) for net asset

values assumed exceeds

fair value of the

identifiable net assets

acquired.

• Acquirer assumes to make

better use of available

resources which lead to

higher (future) excess

returns for the combined

entity.

Analytical

Approach:


16

The going concern goodwill which arises from a stand-alone view of a firm on the

basis of excess return considerations66 can be considered a component of the

acquired goodwill.67 The analysis of acquired goodwill in academia follows rather a

bottom-up approach, which means that research builds upon the question why a

potential buyer is willing to pay a premium over the net asset value or the going

concern value of a firm.68 To the contrary, financial accounting standard setters are

more concerned about the nature of goodwill, i.e. whether goodwill is internally

generated or acquired.69 If acquired, it is simply defined as a residual between the

consideration transferred and the fair value of the identifiable net assets assumed.70

Consequently, financial accounting defines goodwill primarily through a top-down

approach.71

In the following sections, the context dependent definitions of goodwill, reasons for

its occurrence and possible components should be explained in detail.

2.2 Going concern (or internally generated goodwill)

A substantial stream of research has dedicated itself to understanding the nature of

going concern goodwill (or frequently termed internally generated goodwill72) by

mathematically formulating hypotheses that explain why the going concern value of

a company diverges from its net asset value.73 The going concern value of a

company can be defined as the result of a company’s operational use of its available

collection of assets,74 leading to future economic benefits in the form of a cash flow

metric, whilst a company’s net asset value represents the sum of its book value of

66 Cf. Hail and Meyer (2006), pp. 102-103, Wöhe (1980), p. 92, Zanoni (2009), p. 2. 67 Cf. Zanoni (2009), p. 2, Haaker (2008), p. 135. 68 Cf. Brösel and Klaasen (2006), pp. 448-449, Johnson and Petrone (1998), p. 295, Kühnberger (2005), pp. 677-678. 69 Cf. IAS 38.48, Teitler-Feinberg (2006), p. 15, Brösel and Klaasen (2006), p. 450. 70 Cf. IFRS 3.52, Brösel and Zwirner (2009), p. 191, Castedello (2009), p. 56. 71 Cf. IFRS 3.52, Pratt (2010), p. 503, Brösel and Zwirner (2009), p. 191. 72 Or also termed economic goodwill. 73 Cf. IFRS3.BC313, Henning et al. (2000), p. 376, Hail and Meyer (2006), pp. 102-103, Zanoni (2009), p. 2, Haaker (2008), pp. 127, 129. 74 Cf. Johnson and Petrone (1998), p. 295.


17

assets currently in use.75 Reasons for the phenomenon of going concern goodwill

can be found in firms’ capital allocation decisions that enabled them to develop

either company resources or products that allow them to generate excess return, and

therefore linked to strategic management research.76

Fig. 4: Sources of going concern goodwill Source: Own illustration.

2.2.1 Goodwill as the result of a firm’s excess earnings

One of the most prominent explanations for the occurrence of going concern

goodwill lies in a firm’s ability “to earn a higher rate of return on an organized

collection of net assets than would be expected if those net assets had to be acquired

separately (…).”77 Or put differently, the going concern value represents the

outcome of a difference between the firm’s cost of capital (i.e. cost of equity or net

assets) and the returns the firm can generate by using its available resources.78 Going

75 Cf. IFRS3.BC313, Zanoni (2009), p. 2, Haaker (2008), p. 135, Kuster (2007), pp. 11-12. 76 Cf. Chan et al. (1995), p. 81, Stratman and Sepe (1989), p. 80, McConnell and Muscarella (1985), p. 399, Blackwell et al. (1990), p. 287, Chan et al. (1990), p. 255, Cochrane (1991), p. 210. 77 Johnson and Petrone (1998), p. 295. 78 Cf. Hail and Meyer (2006), pp. 102-103, Vincent et al. (2001), pp. 164-165.

Company resources (resource-

based view of a company) Company strategy

Earnings Cost of

invested capital

Going concern value

of a company

Going concern Goodwill

Managerial decisions

on capital allocation

Financial view on

value generation

Net asset value


18

concern goodwill can therefore be interpreted as the result of future cash flows that

are available to the shareholders of a firm after subtracting a cost component related

to the capital usage of the firm.79

Ohlson (1995) and Peasnell (1981) were among the first researchers that

mathematically proved that internally generated goodwill, defined as the valuation

difference between a firm’s going concern value and its net asset value,80 represents

the outcome of accounting profits that exceed the firm’s cost of capital.81 Ohlson’s

proof (1995) builds upon the cash flow-based dividend discount model for valuing a

firm’s market value of equity, and subsequently replaces the expected future

dividends with a combination of accounting profits and book values of equity.82

Ohlson’s model represents a powerful valuation tool as it holds true for the certainty

and uncertainty case, i.e. whether future profits and dividends are known (certainty

case) or unknown (uncertainty case) at time point t.83

By using the certainty case as a starting point, the applied logic assures that the basic

equilibrium requirements are satisfied.84 Returns to a shareholder between t and t+1

can only be the results of capital gains, i.e. an increase in the share price (Pt+1 - Pt)

and dividends (Dt). Under the no-arbitrage condition in the certainty case, it follows

intuitively that at any point of time (t = 0, 1, 2, 3, … n), the value of a share at t (Pt)

equals its discounted value and any dividends distributed at t+1 (i.e. Pt+1; Dt+1). In

the certainty case, the discount rate equals the rate of return of a risk-free asset (rf).

For the uncertainty case, the discount rate would get adjusted to reflect the inherent

uncertainty of future earnings (re).

79 Cf. Barker (2001), p. 168, Peasnell (1981), pp. 53-54. 80 Cf. Lundholm (1995), p. 749. 81 Cf. Bernard (1995), p. 733, Ohlson (1995), pp. 666-667, Peasnell (1981), pp. 53-54, Lundholm (1995), p. 749, Kohlbeck and Warfield (2007), p. 25. 82 Cf. Feltham and Ohlson (1995), p. 690, Penman and Sougiannis (1998), p. 348, Francis et al. (2000), p. 48, Farrell (1985), p. 17, Vincent et al. (2001), p. 162. 83 Cf. Ohlson (1991), p. 1. 84 Cf. Lundholm (1995), p. 750. Equilibrium requirements imply risk neutrality and homogeneous believes among the market participants, and that interest rates are non-stochastic, acting as the prerequisites for Ohlson’s valuation model.


19

Non-Arbitrage Condition in certainty case:

= + 1 + (2.1)

with

Pt = the market value of a firm’s equity (or its price)85 at time point t.

Dt = dividends paid to shareholders at time point t.

rf = the risk-free rate.

Non-Arbitrage Condition in certainty case: with substituting

Pt+1 = (Pt+2+Dt+2)(1+rf)-1

= 1 + + + 1 + (2.2)

By substituting indefinitely with Pt+n = (Pt+n+1 + Dt+n+1)(1+rf)-(t+n+1)

, it leads in the

limit (n →∞ to the equilibrium value represented as a function of the present

values of the expected future dividends (also known as the dividend discount

formula86):

= ∑ 1 + (2.3)

In the following steps, the variable D which represents an actual stream of cash

should be substituted through accounting profits and book values to prove that

goodwill can be defined as the difference between a firm’s market value of equity

(P) and its book value (B), which can be explained by abnormal earnings ( ).87

The so-called clean surplus restriction argues that the change of a firm’s book value

of equity (B) between two dates (t and t+n) can only be the result of earnings

generated between these two dates (E) and respective dividends (D) (if applicable).88

Additionally, paid out dividends reduce only the book value of the current equity

85 Price equals value in certainty case as profits and dividends are known to market participants. 86 Cf. Ohlson (1995), p. 666, Penman and Sougiannis (1998), p. 348, Francis et al. (2000), p. 48, Farrell (1985), p. 17. 87 Cf. Peasnell (1982), p. 362, Bernard (1995), p. 736, Ohlson (1995), p. 666, Feltham and Ohlson (1995), p. 694. 88 Cf. Lundholm (1995), p. 751, Bernard (1995), p. 736.


20

position whilst having no impact on current earnings.89 This leads to the following

mathematical equation, also known as clean surplus restriction.90

= + − (2.4)

with

Bt = the book value of a firm’s equity at time point t.

Et = earnings generated between time points t and t-1.

By rearranging formula (2.4), the paid out dividend can be expressed as a function

of accounting book values of equity and future (expected) earnings:

= − + (2.5)

Abnormal earnings (Ea) can be defined as the monetary surplus a firm generates (E)

above the earnings expected to be generated by its equity capital usage, in terms of

the book value of a firm’s equity capital at the end of the prior accounting period.91

The concepts of normal (E) and abnormal earnings (Ea) are interrelated.92 Whilst

normal earnings refer to the expected return on the capital used at the beginning of a

period, and therefore the outcome of multiplying the net asset value of a firm by an

appropriate interest rate, abnormal earnings can be interpreted as the earnings

differential between the total generated earnings and a related charge for the u se of a

firm’s invested capital:93

= − (2.6)

with

Eta = abnormal earnings generated between time points t and t-1.

Formula (2.6) can be rearranged for Et so that:

= + (2.7)

89 Cf. Peasnell (1982), p. 362, Feltham and Ohlson (1995), p. 694. 90 Cf. Ohlson (1991), p. 14, Begley et al. (2010), p. 36. 91 Cf. Ohlson (1995), p. 666. 92 Cf. Bernard (1995), p. 741. 93 Cf. Ohlson (1995), p. 667, Bernard (1995), p. 741.


21

The clean surplus restriction (2.5) can therefore be rewritten using the rearranged

abnormal earnings formula (2.7) as:

= + − + (2.8)

Or: = + 1 + − (2.9)

By using expression (2.9) as input for the to be discounted dividends (i.e. D1, D2,

…Dn) in the dividend discount formula (2.3), one finds the present value of a firm’s

equity capital as:

= + 1 + + 1 + +. . . + 1 + (2.10)

Or in a more abstract way, the dividend discount formula can be rewritten as a

function of the expected abnormal earning and the current equity book value of a

firm:

= +∑ 1 + (2.11)

given that 1 + → 0, for → ∞

If the going concern goodwill is defined as the value gap between a firm’s net asset

value and its current market value,94 formula (2.11) can be rearranged so that:

= = − = ∑ 1 + (2.12)

Ohlson (1995) argues for this explanatory concept as being intuitive and

straightforward: “a firm’s value equals its book value adjusted for the present value

of anticipated abnormal earnings. In other words, the future profitability as

measured by the present value of the anticipated abnormal earnings sequence

reconciles the difference between market and book values.”95 Consequently,

profitability and the related use of a firm’s invested capital can be considered as the

principal explanatory variables of internally generated goodwill.96

94 Cf. Ohlson (1995), p. 662, Feltham and Ohlson (1995), p. 690, Begley et al. (2010), p. 34. 95 Ohlson (1995), p. 667. 96 Cf. Zanoni (2009), p. 24, Zhang (2005), p. 69, Ohlson (1995), p. 667, Cho and Pucik (2005), p. 555.


22

Fig. 5: Mathematical derivation of going concern goodwill Source: Own illustration.

2.2.2 Goodwill as the result of investment and strategic

management decisions

After the mathematical foundations of the explanatory variables of internally

generated goodwill have been laid out in the previous section, the sources of

abnormal earnings and therefore superior profitability need to be addressed and

analyzed as well. As outlined above, goodwill from an economic perspective

represents the outcome of future economic benefits, measured by a cash flow metric,

with which a firm needs to cover its cost of capital.97

97 Cf. Hail and Meyer (2006), pp. 102-103, Vincent et al. (2001), pp. 164-165, Barker (2001), p. 168, Peasnell (1981), pp. 53-54.

0%

17%

33%

50%

67%

83%

100%

(1)

Book Value of Acquiree's Net Assets

(2)

Cost of Acquired Company

(2)-(1)=

Acquistion Premium

(3)

Going Concern Goodwill

(4)

Restructuring Goodwill

(5)

Synergy Goodwill

(5)

Strategic Goodwill

(6)

Flexibility Component

(1)

Net asset

value of

stand-alone

firm

(2)

Market

value of

equity of

stand-

alone firm

(3) =

(2) – (1)

Going

concern

goodwill of

stand-alone

firm

(3) > 0 ˄ (2) > (1), iff:

Goodwill=GWn=Pn-Bn=∑ En+ta (1+rf)

-(n+t)∞t=1

with:

∑ En+ta 1+rf

- n+t∞t=1 >∑ En+t

⬚ 1+rf- n+t∞

t=1


23

Explanatory approaches, directing at a firm’s abnormal earnings, profitability and

value generation and thereby influencing the creation of internally generated

goodwill target at prior, current and future capital budgeting and investment

decisions of a firm.98 Capital budgeting and investments can be defined as the intra-

firm allocation of capital and monetary funds to build up resources, capabilities or

assets.99 Among the strategic management theories and approaches that offer

valuable insights on the sources of abnormal earnings and value creation are:

(a) The resource-based view of a firm by Penrose (1959) and Barney (1991),

(b) The theory of a firm’s competitive advantage by Porter (1985), and

(c) The theory of a firm’s core competencies by Prahalad and Hamel (1990).

Any of those three theories aims at explaining the value generation of a firm from a

strategic management point of view and can be considered as interlinked with one

another.100 However, all of them ground in a firm’s investment decisions that allow

it to have superior resources, a competitive advantage and/or a core competence

compared to its competitors.101 The stronger the uniqueness of those three attributes,

the stronger the impact on a firm’s value generation and therefore internally

generated goodwill.102

98 Cf. Kohlbeck and Warfield (2007), p. 24, Cheng (2005), p. 85, Asthana and Zhang (2006), p. 124, Coelho et al. (2011), p. 49. 99 Cf. Miller and O’Leary (1997), p. 258, Baldenius et al. (2007), pp. 837-838, Dhaene et al. (2012), pp. 1-2, Graham and Harvey (2002), pp. 10-11. 100 Cf. Peteraf (1993), pp. 179, 186, Barney (1991a), pp. 203, 211-213, Dierickx and Cool (1989), p. 1504. 101 Cf. Maritan (2001), p. 513, Amit and Schoemaker (1993), p. 40, Collins and Montgomery (2008), p. 145, Dierickx and Cool (1989), p. 1504. 102 Cf. Conner (1991), p. 121, Barney (1991a), p. 211.


24

Fig. 6: Strategic management view of goodwill Source: Own illustration.

The three strategic concepts of value generation can be defined not only by the

source which ultimately drives the abnormal earnings but also by the analytical view

which they apply. Whilst the resource-based view applies primarily an internal view

on a firm, i.e. of which resources and assets a firm consists, the theory of a firm’s

competitive advantage looks rather on the outside of a firm and its environment in

which it operates.103 The theory of a firm’s core competencies can be considered as a

combination between an internal and external view of a firm as it argues that the

principal driver of value generation is whether a firm is able to put its available

resources efficiently in use to successfully compete in the market place.104

103 Cf. Barney (1991a), p. 204, Barney (1991b), p. 100. 104 Cf. Galunic and Rodaqn (1998), p. 1193, Leonard-Barton (1992), p. 112, Prahalad (1993), p. 42.

Tangible resources Intangible resources (a) Resource-based view of a firm

Investment decisions

(b) Competitive advantage (c) Core competence

Value creation

Goodwill


25

Fig. 7: Strategic management concepts related to going concern goodwill

Source: Own illustration.

Investment decisions as a prerequisite for a firm’s resources, core competences,

and competitive advantage and therefore internally generated goodwill:

Capital investments represent a necessary prerequisite for building up tangible and

intangible resources that might translate in a higher company value and therefore

internally generated goodwill after the investment decision was made.105 At a first

stage, a firm needs to know in which resources or projects it wants to invest or

provide funding for.106 Thereby it can be differentiated between resources for which

the future economic benefits can be rather easily and reliably measured before funds

are provided for, and others for which the future economic benefits are difficult to

quantify.107

For tangible resources, like machinery or equipment, investment proposals are

usually less difficult to evaluate as future economic benefits can be quantified

beforehand in case costs and future supply and demand are known.108 For

investments in intangible resources like a firm’s personnel, brands or customer

relationships however the evaluation can become complex given the difficult

105 Cf. Sirmon et al. (2007), pp. 277, 279, Rugman and Verbeke (2002), p. 770, Penrose (1959), p. 22, Peteraf (1993), p. 183, Maritan (2001), p. 513, Crook et al. (2008), p. 1141. 106 Cf. Maritan (2001), p. 513, Amit and Schoemaker (1993), p. 42. 107 Cf. Damodaran (2006), p. 3, Gu and Lev (2003), p. 17. 108 Cf. Beyer (2005), pp. 178-182, Stewart (1991).

Resource-based view

of a firm

Internal view of a firm

Source of value generation:

Existing corporate resources,

assets and capabilities


Capacities of identifying,

allocating and leveraging

resources


Products, strategies, markets

and competitors

Core

competences

External view of a firm

Competitive

advantage


26

measurement of their future economic benefits.109 What makes it also problematic in

this respect is that some intangible resources can be considered as “by-products”

from tangible resources and therefore difficult to evaluate in isolation as their future

economic benefits arise from the combined deployment of other resources.110 An

example for that interconnectivity could be stable customer relationships due to

highly advanced and state-of-the-art production processes that improve the quality

of a firm’s outputs and therefore strengthen customer loyalty.

In case future economic benefits are reliably quantifiable, traditional investment

appraisal methods can be used to understand the investments impact on value

creation.111 In corporate financial theory, a firm can increase its company value by

allocating capital to and investing in current and future projects that yield larger

present values of future cash inflows than their corresponding initial monetary

outlays.112 Depending on the applied investment appraisal method, investment

decisions can be guided by analyzing relative or absolute return measures. Among

the most prominent appraisal techniques are the net present value (NPV) and the

internal rate of return method (IRR).113 Furthermore, other methods for deciding on

the usefulness of investment projects exist.114 Being confronted with several

potential investment projects, rational investors will chose the one from which they

can expect the largest increase in company value.

Consequently, investment decisions lay the ground for the resources a firm can use

in its production process and therefore directly the value generation of a firm.

However, the nature of certain resources can make the evaluation of investment

decisions difficult as their future economic benefits might not be reliably measurable

and therefore risky.115

109 Cf. Beyer (2005), pp. 150-153. 110 Cf. Damodaran (2006), p. 9. 111 Cf. Graham and Harvey (2002), p. 11. 112 Cf. Ross et al. (2005), pp. 60-62, Brealy et al. (2011), pp. 260-261. 113 Cf. Graham and Harvey (2002), p. 11, Ross et al. (2005), p. 152. 114 Cf. Graham and Harvey (2002), p. 11. 115 Cf. Damodaran (2006), p. 9.


27

(1) Resource-based view of a firm:

Under the resource-based view of a firm, a firm can be thought of as a bundle

of various resources, thereby arguing for firms as being fundamentally

heterogeneous to their competitors.116 The pioneering research contributions

of Penrose (1959) and Barney (1991a) together with those of Wernerfelt

(1984), Mahoney and Pandian (1992) and Peteraf (1993) laid the ground for

linking company resources to a firm’s profitability and returns, and thereby

relating company resources also to value creation and internally generated

goodwill.117

Firm resources:

Firm-specific resources are essential in a firm’s production processes as they

represent necessary input factors for generating products or services.118

Resources can be employed either for individual or several products.119 By

their use, resources impact a firm’s profitability as they are directly related to

a firm’s output.120 Building up and owning valuable resources with which a

firm can compete in the marketplace is found to be a determining factor in its

long-term survival.121 Furthermore, resources allow firms to grow and

depending on their uniqueness permit them to take on different strategic

paths.122 This rational builds upon the traditional concept of strategy which

argues for firm-individual resources and competencies as being the principal

driver of financial success.123

Penrose (1959) argues that firms represent collections of productive

resources which are used to produce and sell goods and services. Those

116 Cf. Penrose (2009), p. 66, Barney (1991a), p. 203, Crook et al. (2008), p. 1143, Kor and Mahoney (2004), p. 184. 117 Cf. Crook et al. (2008), p. 1141, Hansen and Wernerfelt (1989), p. 400, Kor and Mahoney (2004), p. 184. 118 Cf. Penrose (2009), p. 67, Wernerfelt (1984), p. 171, Mahoney and Pandian (1992), p. 371, Peteraf (1993), p. 180, Amit and Schoemaker (1993), p. 35. 119 Cf. Wernerfelt (1984), p. 171, Rubin (1973), p. 936. 120 Cf. Amit and Schoemaker (1993), p. 44, Crook et al. (2008), p. 1153. 121 Cf. Barney (1991a), p. 212. 122 Cf. Rubin (1973), p. 941, Wernerfelt (1984), p. 178, Mahoney and Pandian (1992), p. 366. 123 Cf. Barney (1991a), p. 204.


28

productive resources consist of physical resources and human resources that

are available to a firm and “include the physical things a firm buys, leases, or

produces for its own use, and the people hired on terms that make them

effectively part of the firm.”124 Physical resources comprise of “tangible

things – plant, equipment, land and natural resources, raw materials, semi-

finished goods, waste products and by-products, and even unsold stocks of

finished goods.”125 Physical resources can differ in their (i) durability in the

production process (how long the resource can be used in production), (ii)

applicability in the production process (transformability in a single or more

products or services), (iii) origin of the resource (either self-generated by the

firm or acquired), and (iv) mobility (whether or not those resources can be

transferred to other firms or sold).126 Human resources contain the skills,

knowledge and experience of the firm’s workforce that is used in a firm.

These human resources are not limited to the actual production process.

Human resources are also critical in support functions like administrative,

financial, and legal roles, as well as managerial functions.127 The application

of both physical and human resources together allows a firm to generate

economic rent.128

In the early definition of a fiirm’s resources by Penrose (1959), intangible

assets apart from human resources are not explicitly mentioned by the author.

This could be potentially explained by the fact that when Penrose (1959)

defined economic resource, the role of intangible assets was not as important

as in today’s economy. However, given that intangible assets played a rapidly

increasing role in the production and selling processes of firms in the decades

to come129, academia expanded the definition of economic resources to

124 Penrose (1959), p. 67. 125 Penrose (1959), p. 24. 126 Cf. Penrose (2009), pp. 21-22, Amit and Schoemaker (1993), p. 38. 127 Cf. Penrose (2009), p. 22. 128 Cf. Peteraf (1993), p. 180, Amit and Schoemaker (1993), p. 36, Sirmon et al. (2007), p. 274, Rugman and Verbeke (2002), p. 772. 129 Cf. Damodaran (2009), p. 2, Damodaran (2006), p. 3, Gu and Lev (2003), p. 3.


29

include explicitly also other intangible assets in the resource-based view of a

firm.130

Necessary conditions of firm resources to create value:

Barney (1991a) builds upon the research of Penrose (1959) and argues that

the existence of resources in a firm represents a necessary however not

sufficient condition to generate abnormal returns, value and therefore

internally generated goodwill.131 Barney (1991a) reasons that firms having

resources that are not evenly distributed across firms in the same industry

(firm resource heterogeneity) and not perfectly mobile across firms (firm

resource immobility) can outperform competitors and thereby enhance the

value of the firm.132 In order to be value enhancing, Barney (1991a) reasons

that resources must fulfill the following conditions:

• Value: Resources are considered to be valuable, “when they enable a

firm to conceive of or implement strategies that improve its efficiency

and effectiveness.”133 Through Barney’s argumentation (1991a), the

characterization of being valuable is expressed by a firm’s resources to

enhance performance.134 This implies that from an economic

perspective, a valuable resource refers to the future economic benefits

that arise from it by employing it in the current or planned operations of

a firm. • Rareness: The attribute of rareness is defined by the number of

competitors that have the same or highly similar resource.135 To be

value enhancing, the resource needs to be as rare as possible so that

competing firms do not have the possibility to exploit the same or a

130 Cf. Wernerfelt (1984), p. 172, Mahoney and Pandian (1992), p. 364, Amit and Schoemaker (1993), p. 36, Guillen (2000), p. 365. 131 Cf. Barney (1991a), p. 203. 132 Cf. Barney (1991a), pp. 208-210, Mahoney and Pandian (1992), p. 364, Peteraf (1993), p. 180, Crook et al. (2008), p. 1141. 133 Barney (1991a), p. 211. 134 Cf. Barney (2001), p. 648, Barney (1991a), p. 211. 135 Cf. Barney (1991a), pp. 211-212, Barney (2001a), p. 41, Barney (2001b), p. 43, Priem and Butler (2001), p. 23, Peteraf (1993), p. 188.


30

highly similar resource.136 This reasoning implies that the more unique a

resource is, the higher the possibility of having a positive effect on the

value creation of a firm. The rareness criterion, however, is not only

applicable to individual resources, but also to rare and unique

combinations or bundle of resources.137

• Imperfect imitability: The criterion of imperfect imitability refers to the

situation in which competitors that do not own the same or highly

similar resource are unable to obtain it, irrespective of their efforts.138 A

resource is considered to be imperfect imitability if its existence is the

result of unique historical conditions, if the resource is socially

complex, and/or if its application or role in a firm is causal ambiguous

to competitors.139 Unique historical conditions might have created a

particular resource or under which a resource was obtained, and

therefore make it almost impossible to imitate the resource.140 Social

complexity refers primarily to resources which require a high degree of

human intervention in its application and which are extremely difficult

to manage or influence.141 Examples for such resources are a firm’s

reputation with suppliers or customers, organizational culture, or

interpersonal relationships between managers. Causal ambiguity is

present if it is difficult for competitors to understand the role of a

particular resource in the production process and therefore in a firm’s

value generation strategy.142

136 Cf. Barney (1991a), p. 212. Cf. also Collins and Montgomery (2008), p. 144, who also speak of scarcity in this context. 137 Cf. Barney (1991a), pp. 211-212. 138 Cf. Priem and Butler (2001), p. 23, Barney (1991a), p. 212, Barney (2001a), p. 641, Barney (2001b), p. 43, Peteraf (1993), p. 188. Cf. also Lippman and Rumelt (1982), p. 418, who speak of uncertain

imitability. 139 Cf. Barney (1991a), pp. 212-213, Barney (2001b), p. 45, Priem and Butler (2001), p. 31, Peteraf (1993), p. 183, Amit and Schoemaker (1993), p. 38. 140 Cf. Amit and Schoemaker (1993), p. 38, Barney (1991a), pp. 213-214, Barney (2001b), p. 45. 141 Cf. Barney (1991a), pp. 214-215, Barney (2001a), p. 645. 142 Cf. Barney (1991a), pp. 215-216.


31

• Non-substitutability: This requirement relates to the situation in which

substitutes for a firm’s resource exist.143 In order to be value enhancing,

the resource should not have substitutes that would allow competitors to

come up with an equivalent output (good or service) by the use of other

resources.144 In case competitors have the ability to use different

resources than the assumed unique resource held by a firm in a similar

production process, the value enhancing ability of a resource declines

substantially.145 According to Barney (1991a), substitutes can either be

similar or different resources that allow to copy a production process,

both of which having the same negative result for the firm that assumes

to hold a unique resource.146

Fig. 8: Firm resources and value creation

Source: According to Barney (1991a), p. 218.

In case those conditions are met, a firm can use a single or set of resources to

build a so-called resource position barrier that allows it to extract economic

rents from their clients and/or competitors positively influencing the value of

a firm.147 Consequently, resources contain strategic implications given that

they allow firms to take on different growth paths.148 In case a resource

position barrier can be established by a firm due to its unique resources, the

143 Cf. Priem and Butler (2001), pp. 24, 31, Barney (1991a), p. 216, Barney (2001b), p. 45. 144 Cf. Barney (1991a), p. 216, Barney (2001b), p. 45, Amit and Schoemaker (1993), p. 38. Cf. also Dierickx and Cool (1989), p. 1507, who refer to this characteristic also as imitability. 145 Cf. Amit and Schoemaker (1993), p. 38, Barney (1991a), p. 217, Peteraf (1993), p. 183. 146 Cf. Barney (1991a), p. 217. 147 Cf. Wernerfelt (1984), p. 173, Hoffman et al. (2005), p. 95, Hallwood (1997), p. 540, Makadol (1998), p. 693. 148 Cf. Hallwood (1997), p. 540, Wernerfelt (1984), p. 172.

Firm resource

heterogeneity • Value

• Rare

• Imperfect imitability

• Non-substitutability

Value creation

Firm resource

immobility

Conditions:


32

success of a firm’s strategy depends on how successful it can exploit its

existing resources and whether the firm is able to develop new resources that

defends their market position.149 In case, unique resources cannot be

identified in a firm, corporations need to invest and build them up internally.

This however might require substantial monetary funds as well as time.150

Resource identification, building and deployment to create economic

rents:

The pure existence of a potentially value-enhancing resource in a firm does

not necessarily imply that value is created in the firm.151 Besides having

resources that fulfill the criteria of being valuable, rare, imperfectly imitable,

and non-substitutable, their deployment in the production processes

represents an equally important prerequisite in the value generation

process.152 Consequently, the identification and building (so-called resource

picking mechanism) and the subsequent deployment of a resource (resource

deployment) together enable firms to create value.153 The resource picking

mechanism builds upon the view that the existence of the majority of

resources available to a firm represents the outcome of prior firm-internal

decision making processes and the subsequent investments by the firm to

build up resources.154 Identifying and selecting specific resources that are

thought to enhance the value of a firm in the long-run build upon information

available to decision makers of a firm.155 Resource deployment refers to a

firm’s capabilities of integrating valuable resources in the production process

according to the firm’s business strategy.156

149 Cf. Fahy (1996), p. 27, Wernerfelt (1984), p. 178, Jacoby (1990), p. 59. 150 Cf. Jacoby (1990), p. 59. 151 Cf. Barney (1991a), p. 203, Makadok (2001), p. 388. 152 Cf. Makadok (2001), p. 387, Fang and Zou (2009), pp. 745-746, Lounsbury and Glynn (2001), p. 553. 153 Cf. Fang and Zou (2009), pp. 745-746, Kim and Mahoney (2008), p. 11, Makadok (2001), p. 396, Lounsbury and Glynn (2001), p. 553, Makadok (2002), p. 1051, Newbert (2008), p. 748. 154 Cf. Makadok (2001), p. 388, Fang and Zou (2009), pp. 745-746, Makadok (2002), p. 1052, Adegbesan (2007), p. 17. 155 Cf. Kim and Mahoney (2008), p. 11, Makadok (2001), p. 387, Fang and Zou (2009), p. 745, Makadok (2002), p. 1052. 156 Cf. Amit and Schoemaker (1993), p. 35, Makadok (2001), p. 387, Fang and Zou (2009), pp. 745-746.


33

Due to the apparent reasoning that resource picking and resource deployment

together are driver of value creation, academics extended the definition of

resources by also incorporating resource deployment capabilities in the

resource-based view of a firm.157 Resource deployment capacities as defined

by Makadok (2001) refer to “a special type of resource – specifically, an

organizationally embedded nontransferable firm-specific resource whose

purpose is to improve the productivity of the other resources possessed by a

firm.”158 This means that the term resources also considers the capacity of a

firm to use its resources in the most productive and efficient way.159

It is argued that resource picking together with resource deployment are

drivers of economic rent and economic profit.160 Economic rent in the

resource-based view of a firm is created when firms are able to create a

resource (cost view) and implement it in the production process for less than

its marginal productivity.161

According to Makadok (2001), resource picking and resource deployment

can be differentiated in the way they generate economic profit for a firm. The

value contribution of resource picking is assumed to start already before a

resource is actually available to a firm.162 “Firms with superior resource-

picking skill apply that skill to discern which resources are winners and

which are losers, so that they can bid on the former while avoiding the latter.

Under the resource-picking mechanism, all this takes place before the firm

actually comes into possession of the resource. A corollary of this

observation is that resource-picking skills can affect a firm’s economic profit

even if the firm does not acquire any resources. This is true because resource-

picking skills not only help a firm to acquire good resources, but also help a

157 Cf. Moliterno and Wiersema (2007), p. 1067, Amit and Schoemaker (1993), p. 35, Newbert (2008), p. 748. 158 Makadok (2001), p. 388. 159 Cf. Newbert (2008), p. 748, Kim and Mahoney (2008), p. 11, Amit and Schoemaker (1993), p. 35, Makadok (2001), p. 389. 160 Cf. Kim and Mahoney (2008), p. 11, Makadok (2001), p. 396, Newbert (2008), p. 748. 161 Cf. Lounsbury and Glynn (2001), p. 553, Makadok (2001), p. 387, Moliterno and Wiersema (2007), p. 1067. 162 Cf. Makadok (2001), p. 387.


34

firm to avoid acquiring bad resources. Indeed, this avoidance of bad

resources may have an even greater impact on a firm’s economic profit than

the selection of good resources.”163

This stands in contrast to the impact on economic profit of a firm’s

capabilities of deploying resources which takes place after a resource is

available.164 This suggests that deployment capabilities only create economic

profit if the firm succeeds in having a resource available in the first place.

“No matter how great a firm’s capabilities might be, they do not generate

economic profit if the firm fails to acquire the resources whose productivity

would be enhanced by its capabilities.”165 This reasoning builds upon the

argument that resource picking abilities target at the decision phase of a firm

(i.e. in which resources it should or should not invest), whereas a firm’s

capacity building mechanisms impact the deployment and implementation

phase.166

Fig. 9: Availability of resources and value creation Source: Own illustration.

(2) Core competence:

Linked but not congruent to the resources deployment concept of Mahadok

(2001) in explaining internally generated goodwill is the strategic

management concept of core competencies. The idea of a firm’s core

competencies as a driver of its value generation extends the rather static view

of resource availability and deployment and adds a dynamic, forward looking

163 Makadok (2001), p. 388. 164 Cf. Makadok (2001), p. 389. 165 Makadok (2001), p. 389. 166 Cf. Fang and Zou (2009), p. 745, Kim and Mahoney (2008), p. 11.

Result of:

Result of:

Resources

available to a firm

Resources picking skills

Resources deployment capabilities

Impact on economic profit before

firm possesses resource

Impact on economic profit

after firm possesses resource

Value generation


35

strategic component to it.167 The idea of core competencies first expressed by

Prahalad and Hamel (1990) and Prahalad (1993) and its role in value

generation argues that value creation to a firm can be explained not by

exclusively focusing on resource positions, i.e. the resource available to a

firm, but rather by the ability of a firm to leverage its resources for future

success.168 The authors’ argument is based on the rational that value creation

depends on company growth and new business development.169 The internal

capacity to grow is therefore identified as the main value driver: resources

allow firms to grow only if managers find a way of applying resources

according to the strategy that adds the most value to a firm and its customers

(i.e. leveraging resources).170

Leveraging resources is argued to be achieved by “the development of a

strategic architecture (a way to capture the pattern of likely industry

evolution), identifying core competencies and core products. Reusability of

invisible assets, as well as core products, in new and imaginative

configurations to create new market opportunities is at the heart of the

process of leverage.”171 This implies that firms should use resources that are

unique in an industry and only available to the firm in current and future

products that have the potential to add substantially to their financial success,

while having a business strategy that is aligned with expected industry

developments.172

167 Cf. Prahalad (1993), p. 42, Prahalad and Hamel (1990), p. 84. 168 Cf. Wilcox King and Zeithaml (2001), p. 76, Prahalad and Hamel (1990), p. 81, Prahalad (1993), pp. 42-43, Mascarenhas et al. (1998), p. 117. 169 Cf. Danneels (2002), p. 1108, Prahalad (1993), p. 42,Galunic and Rodan (1998), p. 1197. 170 Cf. Danneels (2002), p. 1108, Lei et al. (1996), p. 549. 171 Prahalad (1993), p. 42. 172 Cf. Mascarenhas et al. (1998), p. 117, Prahalad (1993), pp. 42-47, Prahalad and Hamel (1990), p. 87.


36

Fig. 10: Core competences and value creation Source: Own illustration.

Core competencies therefore translate given resources into products that add

value to customers and consequently also to the firm.173 A core competence

can be defined through the following four attributes: (i) it adds significantly

to the differentiation from competitors, (ii) unlike a physical asset, a core

competence does not diminish with usage, (iii) it is shared within a range of

business units and not bound to boundaries of discrete business units, and (iv)

it is difficult if not impossible for competitors to imitate.174 The concept of

core competencies in adding value to a firm therefore focusses not only on an

internal view of a firm; it also considers how resources translate into

products.175 Prahalad and Hamel (1990) use the metaphor of a tree to explain

the relationship between end products and core competencies: “The trunk and

major limbs are core products, (…). The root system that provides

nourishment, sustenance, and stability is the core competence.”176

Core competences are the result of internal learning, continuous

enhancement, constant improvement and substantial investment processes

that can span over a decade or longer.177 Losing core competencies through

divestment or outsourcing strategies can come at a high cost to the firm as

173 Cf. Chen and Wu (2007), p. 160, Danneels (2002), p. 1108, Agha et al. (2012), p. 194. 174 Cf. Prahalad (1993), p. 45, Mascarenhas et al. (1998), p. 117, Leonard–Barton (1992), p. 112. 175 Cf. Leonard-Barton (1992), pp. 111, 117, Prahalad (1993), p. 42, Chen and Wu (2007), p. 160, Danneels (2002), p. 1108. 176 Prahalad and Hamel (1990), p. 81. 177 Cf. Prahalad and Hamel (1990), p. 84, Oliver (1997), p. 702.

Strategic

architecture

Resources Core products Core competencies

Leveraging resources

Value

creation


37

they have to build up again in case a change in the business strategy is

undertaken in the future.178

(3) Competitive advantage:

The strategic management model of competitive advantage also picks up the

idea for firms being different due to their resources availability.179 The

resources under the discretion of firms allow them to take on different

strategic initiatives in order to become more competitive in the market

place.180 The term competitive advantage was first established by Porter

(1985) and argues that value creation depends primarily on the competitive

environment and how firms position themselves by using their available

resources.181

The concept of competitive advantage looks at the resource utilization from

the perspective of the so-called value chain.182 Basically, the value chain

spans over the entire value adding process of a firm to its outputs and allows

disaggregating the value adding process into separate activities the firm

performs.183 In detail, those are the designing, the production, the marketing

and the distribution of its products.184 Additionally, the value chain allows

understanding which firm activities add most of a value to its output. The

value chain therefore breaks up the difference between price received and its

corresponding costs by corporate functions.

178 Cf. Blaich et al. (2003), p. 23. 179 Cf. Barney (1991a), pp. 221-222, Barney (2001), pp. 644, 647, Barney and Zajac (1994), p. 6, Cockburn et al. (2000), p. 1124, Rouse and Daellenbach (1999), p. 487. 180 Cf. Black and Boal (1994), pp. 132, 139, Yeoh and Roth (1999), p. 637, Grant (2001), p. 115, Peteraf (1993), p. 186. 181 Cf. Porter (1985a), pp. 1, 26, Porter (1987), p. 46, Porter (1979), p. 141, Porter (1985b), p. 61, Barney (1991a), p. 204. 182 Cf. Barney (1991a), p. 210, Porter (1985a), pp. 33-35, Conner and Prahalad (1996), p. 495, Shapiro et al. (1993), p. 103. 183 Cf. Shapiro et al. (1993), pp. 103-104, Porter (1985a), pp. 26, 36, Magretta (2012), p. 73. 184 Cf. Porter (1985a), pp. 26, 37, Magretta (2012), p. 73.


38

Fig. 11: Firm resources, competitive advantage and value creation


Porter (1985) argues that firms should try to maximize the value add in each

stage of the value chain.185 Strategies to do so can either focus on costs or

prices. The so-called cost strategy aims at minimizing the cost base of an

output, whilst the differentiation strategy targets at increasing the price

charged for a firm’s output.186 Whether the cost or the differentiation strategy

is the most appropriate one for a firm depends primarily on the resources

available to a firm and the competitive environment. In case several

competitors can be identified that apply a similar strategy, e.g. competing on

costs, then it would be meaningful to adopt an opposing strategy, i.e. through

differentiation.187 In case a firm is able to build up and defend a competitive

advantage, it can achieve superior financial performance compared to its

competitors due to its applied strategy and market position.188

Consequently, internally generated goodwill can be explained by abnormal earnings

which represent the outcome of resource availability, resource deployment and the

ability of resources in supporting a firm’s strategy to achieve a market position of

competitive advantage.

185 Cf. Pitkethly (2003), p. 257. 186 Cf. Kreitner and Cassidy (2011), pp. 189-190, Schermerhorn (2011), pp. 222-223, Griffin (2012), p. 72, Brennan and Connell (2000), p. 231. 187 Cf. Pitkethly (2003), pp. 251, 256-257. 188 Cf. Schermerhorn (2011), pp. 222-223, Griffin (2012), p. 72, Kreitner and Cassidy (2011), pp. 189-190.

Technology

Resources

available in the

value chain

Competitive

advantage

Value

creation

Cost strategy

Differentiation strategy

Competition


39

2.2.3 Goodwill as the result of intellectual capital

The concept of intellectual capital aims at explaining the valuation gap between a

firm’s book value of net assets and its market value.189 Although the traditional

concept of intellectual capital offers insights on the existence and components of

internally generated goodwill, its original idea was more centered on understanding

and controlling the individual components of a valuation gap between a firm’s

capital and market value.190 This means that the original concept of intellectual

capital focused rather on identifying the components of intellectual capital, than on

quantifying the future economic benefits that might arise from the individual

components, implying the explanatory nature of this approach.191

The origin of the terminology intellectual capital can be traced back to a

conversation between the economists John Kenneth Galbraith and Michael Kalecki,

in which Galbraith refers to intellectual capital as a factor of competence,

differentiation and value generation.192 The context in which Galbraith uses

intellectual capital however initially refers more to human characteristics than to

firm attributes. Stewart (1991) was among the first to introduce the term intellectual

capital to a wider audience and to put it into a managerial context.193 Intellectual

capital was his response to an economy that had changed considerably in the

deployment of production resources over the last decades. Stewart (1991) points out

the ever increasing dependence of firms on intangible assets and their application in

production processed to create value for shareholders.194 “Intellectual capital is

intellectual material – knowledge, information, intellectual property, experience –

that can be put to use to create wealth. (…) It’s hard to quantify and harder still to

deploy effectively. But once you find it and exploit it, you win. You win because

189 Cf. Edvinsson and Malone (1997), p. 13, Edvinsson (1997), p. 367, Brennan and Connell (2000), p. 206. 190 Cf. Brennan and Connell (2000), p. 206, Bontis (1998), p. 65, Edvinsson (1997), p. 367, Andriessen (2004), pp. 232-233. 191 Cf. Bontis (1998), p. 65, Brennan and Connell (2000), p. 226. 192 Cf. Back et al. (2000), p. 6; “I wonder if you realize how much those of us in the world around have owed to intellectual capital you have provided over the past decades.” Cited in: Back et al. (2000), p. 6. 193 Cf. Stewart (1991), Stewart (1994). 194 Cf. Petty and Guthrie (2000), p. 156, Stewart (1991), Stewart (1994), Barsky and Marchant (2000), p. 60, Dzinkowski (2000), p. 32.


40

today’s economy is fundamentally different from yesterday’s. (…) In this new era,

wealth is the product of knowledge.”195

Almost at the same time, the actual concept of intellectual capital in an

organizational sense was pioneered in an organizational research setting by

Edvinsson and Malone (1997) thereby focusing on explaining the valuation

difference from a financial market perspective as well as managing and measuring

the performance of firms’ available resources that allow them to be competitive on

the marketplace.196 Edvinsson and Malone (1997) understand intellectual capital as

“the possession of the knowledge, applied experience, organizational technology,

customer relationships and professional skills that provide (…) a competitive edge

in the market.”197 The authors follow that the value of intellectual capital depends

primarily “on the extent to which these intangible assets could be converted into

financial returns for the company.” 198 Additionally, intellectual capital can be

understood as being borrowed from stakeholders like employees and customers.199

Pfeil (2004) argues that on the basis of Edvinsson and Malone’s definition (1997),

intellectual capital is considered to include a static as well as a dynamic

component.200 In the static view of intellectual capital, it can be understood as a pool

of resources, comparable to a firm’s inventory that is put to work.201 This static view

is also implied by the characterization of Klein and Prusak (1994) who proclaim that

intellectual capital is “intellectual material that has been formalized, captured and

leveraged to produce a higher valued asset”202 and by the description of Edvinsson

and Sullivan (1994) stating that intellectual capital represents in their view

“knowledge that can be converted into value.”203 A dynamic concept is added to the

definition of intellectual capital when it is understood “as a company’s ability to

195 Stewart (1997), p. X (Foreword). 196 Cf. Edvinsson (1997), p. 367. 197 Edvinsson and Malone (1997), p. 44. 198 Edvinsson and Malone (1997), p. 44. 199 Cf. Edvinsson and Malone (1997), p. 43, Edvinsson (1997), p. 368. 200 Cf. Pfeil (2004), p. 26. 201 Cf. Back et al. (2000), p. 6, Stewart (1991). 202 Klein and Prusak (1994), p. 2 203 Edvinsson and Sullivan (1996), p. 358.


41

employ its intangibles inventory for the creation of shareholder value”204, as Pfeil

(2004) argues.

Fig. 12: Intellectual properties and intellectual capital

Source: Edvinsson and Malone (1997), p. 43.

In order to show that intellectual capital can be understood as an extension of the

book value of a firm’s capital and thereby explaining a potentially observable

market to book value gap, Edvinsson and Malone (1997) display intellectual capital

in the form of accounts in an extended balance sheet.205 The authors argue that the

reason for the existence of intellectual capital can be found on the firm’s asset

side.206 Given that not all assets fulfill the recognition criteria under accounting

standards, unrecognized assets like technology, competence and goodwill, termed

intellectual properties, a difference between a firm’s book values of assets and its

market values exits.207 The concept of intellectual capital consequently tries to

explain the valuation gap by splitting up that difference, i.e. the intellectual capital,

in the respective components that make up for it.208

204 Pfeil (2004), p. 26. 205 Cf. Edvinsson and Malone (1997), p. 43, Edvinsson (1997), p. 367. However Edvinsson and Malone (1997) were not the only researchers creating a balance sheet model including intellectual capital components. Please refer to a very similar model developed by Sveiby (1997), p. 11. 206 Cf. Edvinsson (1997), p. 368. 207 Cf. Barsky and Marchant (2000), p. 60, Brennan and Connell (2000), pp. 206, 222, Edvinsson and Kivikas (2007), p. 379, Marr et al. (2004), p. 553, Stewart (1991). 208 Cf. Guthrie (2001), p. 34, Edvinsson and Kivikas (2007), p. 380, Edvinsson (2013), pp. 164-165, Edvinsson (1997), pp. 369, 372.

Assets Debt

Equity

Goodwill

Technology

Competence

Intellectual

capital

Intellectual

properties

Hidden

values

Official balance

sheet


42

Fig. 13: The components of intellectual capital

Source: Edvinsson and Malone (1997), p. 52.

The proposed classification of intellectual capital by Edvinsson and Malone (1997)

depends primarily on the availability of intangible resources that have a value

adding impact on the firm’s value and the possibility to separate them, thereby being

firm specific.209 This means that not all of the components displayed in the figure

above are equally important in every organization. In their widely-accepted

classification, Edvinsson and Malone (1997) separate intellectual capital in human

capital and structural capital which comprises of customer and organizational

capital.210

• Human capital:

Human capital represents the value component resulting from the

capabilities, skills, knowledge and experience of a firm’s managers and

employees that are applied in the organization throughout their daily work.211

Edvinsson and Malone (1997) also add a forward looking learning attribute

209 Cf. Pawlowsky and Edvinsson (2012), pp. 27-28, Edvinsson (1997), pp. 365, 368. 210 Cf. Tan et al. (2007), p. 77, Petty and Guthrie (2000), p. 156, Guthrie (2001), p. 34, Brennan and Connell (2000), p. 219, Pawlowsky and Edvinsson (2012), p. 28, Edvinsson (1997), p. 368, Marr et a. (2004), p. 555. 211 Cf. Edvinsson and Malone (1997), p. 34, Dzinkowski (2000), p. 33, Society of Management Accountants of Canada (SMAC) (1998), p. 14. Guthrie (2001), pp. 29, 36, Bontis (1998), p. 65.

Financial capital

Human capital

Customer capital Organizational capital

Innovation capital Process capital

Structural capital

Intellectual capital

Market value


43

to it by stating that human capital also captures “the dynamics of an

intelligent organization in a changing competitive environment.”212 Although

human capital adds to the value of a firm, it is not owned by the firm; it can

only be rented given that it is due to the firm’s employees.213

• Structural capital:

Structural capital can be described “as the embodiment, empowerment, and

supportive infrastructure of human capital.”214 It consequently represents

those assets that provide “the necessary infrastructure for human capital to be

productive.”215 More specifically, it embodies the organizational capabilities

which comprise of physical systems used for storing and transmitting

intellectual material.216 It can therefore be understood as the remainder that is

left when hypothetically all employees would depart from the firm or be

exchanged by others.217 Structural capital is built by human capital, however

once built it fulfills a supporting mechanism for a firm’s human capital to

become better.218 This dependency is also referred to as double arrow

dynamic.219

Factors that could fall under the category of structural capital include IT

systems, databases, or concepts applied in an organization.220 Due to diversity

of those factors that fall under the term structural capital, Edvinsson and

Malone (1997) further sub-categorize it into customer capital and

organizational capital, which consists of innovation and process capital.

Customer capital represents the value generated from existing relationships

with customers.221 Customers include, under the definition of Edvinsson and

Malone (1997), clients (buying goods and services), investors (providing

212 Edvinsson and Malone (1997), p. 34. 213 Cf. Edvinsson and Malone (1997), p. 46, Edvinsson (1997), p. 369. 214 Edvinsson and Malone (1997), p. 35. 215 Pfeil (2004), p. 28. 216 Cf. Labra and Sánchez (2013), p. 584, Bontis (1998), p. 66, Brennan and Connell (2000), pp. 222, 230. 217 Cf. Ross and Ross (1997), p. 415. 218 Cf. Brennan and Connell (2000), pp. 222, 230, Bontis (1998), p. 66. 219 Cf. Stam (2010), p. 530, Edvinsson and Malone (1997), p. 35. 220 Cf. Edvinsson (1997), p. 368, Dzinkowski (2000), p. 33, Guthrie (2001), p. 29. 221 Cf. Bontis (1998), p. 67.


44

capital) or suppliers (providing production inputs from the outside of the

firm).222 The existence and value contribution of customer capital is primarily

dependent on the loyalty of those groups. Indications for the strength of

customer capital include price sensitivity of clients, longevity of the business

relationships with clients, suppliers, and investors, or their satisfaction with

goods and services.223

Organizational capital turns the focus to inter-company processes which are

considered to be of value to a firm. Those include investments in operational

systems as well as applied technologies and tools that improve the flow of

expertise and knowledge through the firm, as well as out to the distribution

and supply channels.224

222 Cf. Brennan and Connell (2000), p. 230. 223 Cf. Dzinkowski (2000), p. 33. 224 Cf. Brennan and Connell (2000), p. 222, Dzinkowski (2000), p. 33.


45

Concrete examples of intellectual capital components according to the classification

proposed by Edvinsson and Malone can be found in the table below:

Fig. 14: Constituents of human capital, customer capital, and organizational capital

Source: Dzinkowski (2000), p. 33, Society of Management Accountants of Canada

(SMAC) (1998), p. 14. Guthrie (2001), p. 36.

The assessment of the value attribution of the individual components of intellectual

capital to the firm however is considered to be challenging and difficult due to the

identification and the separation of the individual cash flows that arise from them.225

Therefore the concept of intellectual capital represents rather an explanatory

approach to the market to book value gap leaving the question of the individual

value attribution of the distinct components open.226

225 Cf. Andriessen (2004), p. 231, Stewart (1991), Bontis (1998), p. 65, Brennan and Connell (2000), p. 231, Evaggelia (2010), pp. 7, 11. 226 Cf. Brennan and Connell (2000), pp. 226, 231, 234, Kneisel at al. (2012), pp. 43, 51-52, Alwert (2012), p. 115, Stewart (1994).

Human capital Customer capital Organizational capital

• Know-how

• Education

• Vocational qualifications

• Work-related knowledge

• Work-related

competencies

• Psychometric

assessments

• Models and frameworks

• Occupational

assessments

• Cultural diversity

• Brands

• Customers (names,

purchase history)

• Customer loyalty

• Customer penetration

and breadth

• Company names

• Distribution channels

• Business collaborations

• Licensing agreements

• Favorable contracts

• Copyrights, patents and

trademarks

• Corporate strategies and

methods

• Trade secrets

• Management

philosophy

• Design rights

• Management processes

• Information systems

• Financial relations


46

2.3 Acquired goodwill

2.3.1 Top-down and bottom-up approaches in

understanding goodwill

As soon as a firm with a positive market to book value of equity ratio is acquired,

the internally generated (or going concern) goodwill becomes part of the acquired

goodwill.227 Acquired goodwill can be understood and analysed from both a top-

down perspective and bottom-up perspective.228 Top-down refers to the ex-post view

that goodwill simply represents “a component of the acquirer’s investment in the

acquiree, which is based on the acquirer’s expectations about future earnings from

the acquiree and the combination”229. This view understands goodwill as the

remainder of whatever could not get recognized on the balance sheet apart from pre-

existing assets and liabilities as well as newly identified assets and liabilities.230

Johnson and Petrone (1998) also use the term left over when referring to goodwill

under the top-down perspective.231

The bottom-up perspective is more concerned about understanding of which

components goodwill constitutes.232 It places its focus on what goodwill is made of

and whether it is possible to break goodwill down into subsets of value

components.233 The bottom-up perspective is based on the argumentation that

acquirers must have had a rational that made them pay an amount in excess of the

227 Cf. Henning et al. (2000), p. 376, Sellhorn (2000), p. 888, Wöhe (1980), p. 92, KPMG (2010), p. 12, IFRS 3.BC313, SFAS 141.b102. 228 Cf. Brösel and Zwirner (2009), p. 191, Brösel and Klassen (2006), p. 449, Haaker (2008), p. 78, Johnson and Petrone (1998), p. 294. 229 Johnson and Petrone (1998), p. 294. 230 Cf. Pellens and Sellhorn (2001), p. 1685, Higson (1998), p. 141, Sellhorn (2000), p. 888, Haaker (2008), pp. 59, 119, 121. Cf. also Epstein and Jermakowicz (2008), p. 293, who state that “(g)oodwill (…) is a residual which incorporates all the intangibles that cannot be reliably measured separately, and is often analysed as containing both these and benefits that the purchasing company expected to gain from the synergies or other efficiencies arising from a business combination and cannot normally be transferred to a new owner without also selling the other assets and/or the operations of the business” (Epstein and Jermakowicz (2008), p. 293). 231 Cf. Johnson and Petrone (1998), p. 294. 232 Cf. Brösel and Klassen (2006), p. 449. 233 Cf. Haaker (2008), p. 78, Brösel and Klassen (2006), p. 449, Henning et al. (2000), p. 376, Sellhorn (2000), p. 889.


47

value of the acquiree’s net asset value. The bottom-up approach represents therefore

a more thorough analytical process than the top-down analysis as it allows to

understand what other resources of the acquiree were considered as valuable to the

acquirer. In the bottom-up approach, the so-called purchase premium represents the

starting point for the analysis. Johnson and Petrone (1998) define purchase premium

as “the premium paid by the acquirer over the book value of the acquiree’s net

assets”234.

Among the researchers that provided valuable insights on the individual goodwill

components are Brief (1969)235, Wöhe (1980)236, Johnson and Petrone (1998)237,

Henning et al. (2000)238, Sellhorn (2000)239, and Zanoni (2009)240. Some of their

concepts even provide insights on how to value the individual components of

goodwill. However due to the mostly private nature of the information that is

required to value the individual components, those insights might be only gained on

an ex-post basis. Other researchers, like Moxter (1979), are convinced however that

breaking down goodwill in its constituents has little information content about the

acquiree and acquirer, as goodwill as such represents rather a residual amount.241

2.3.2 Components of goodwill from an acquirer’s point

of view

2.3.2.1 Goodwill components according to Wöhe (1980)

The explanatory concept of the German researcher Wöhe represents one of the

earlier attempts to understand what acquired goodwill is made of.242 Wöhe (1980)

234 Johnson and Petrone (1998), p. 294 235 Cf. Brief (1969), pp. 22-23. 236 Cf. Wöhe (1980), p. 99. 237 Cf. Johnson and Petrone (1998), p. 295. 238 Cf. Henning et al. (2000), pp. 375-376. 239 Cf. Sellhorn (2000), pp. 889-891. 240 Cf. Zanoni (2009), pp. 50-55. 241 Cf. Moxter (1979), p. 746, Moxter (1996), p. 478. 242 Cf. Sellhorn (2000), p. 888, Wöhe (1980), p. 99.


48

argues for a three component concept in the analysis of acquired goodwill.243

Considerations about expected economic benefits from the deployment of acquired

assets are assumed to be the most important reason why a potential buyer is willing

to pay a premium over the net asset value of the acquiree.

In detail, acquired goodwill, according to Wöhe (1980), can be broken down in the

following components:

(1) The value of intangible assets that do not meet the recognition criteria

according to applicable accounting standards (unrecognized assets).

(2) The value that results from the deployment of the firm’s combined assets

compared to the sum of their combined, individual book values (combination

effect).

(3) And a possible overpayment component, which however is only observable

after the transaction (overpayment).244

Irrespective of an apparent business combination, not all intangible assets that are of

value to a firm are allowed to be recognized on a firm’s balance sheet from an

accounting perspective.245 Applicable accounting standards limit the recognition

possibility to those intangible assets that are controllable (IAS 38.13-16), reliably

measurable (IAS 38.21), identifiable (IAS 38.11-12), and proven to provide a firm

with future economic benefits (IAS 38.17 and 21-23).246 Whilst the future economic

benefit-criterion is assumed to be fulfilled for most of a firm’s intangible assets,

certainly not all are controllable or reliably measurable. A typical example for such

an asset would be the workforce of a firm (i.e. its employees).247 While employees,

especially in certain industries like technology or services, are of high relevance to

the firm as they might provide future economic benefits given their knowledge and

skills, they certainly cannot be capitalized separately. Wöhe (1980) argues, without

specifically stating example of assets being of value to a firm however not being

243 Cf. Wöhe (1980), p. 99. 244 Cf. Wöhe (1980), p. 99. 245 Cf. Beyer (2005), p. 145, Behr and Leibfried (2010), pp. 403-407. 246 Cf. Behr and Leibfried (2010), p. 411, Beyer (2005), p. 145. 247 Cf. KPMG (2010), p. 12, PricewaterhouseCoopers (2006), p. 2.


49

able to be recognized, that those unrecognizable assets are attributable to

goodwill.248

This argument however needs to be treated with caution, especially in circumstances

when accounting standards allow capitalizing assets after a business combination

that would not have been allowed to be recognized prior to a business

combination.249 Examples of those include internally generated brands or

trademarks, technologies or customer relationships.250 In those cases, their values are

certainly not part of the acquired goodwill. Consequently, Wöhe’s argument only

holds true for the value of those assets that could not be recognized prior to an

acquisition and cannot be recognized after an acquisition. Only the value of those

assets that are not allowed to be capitalized ultimately will become part of acquired

goodwill.251

The combination effect can be understood as the going concern goodwill of the

acquiree that results from the deployment of the combined asset base.252 It therefore

represents the value that is generated from combining the assets available to a

firm.253 This combination and deployment results in future economic benefits,

measured through cash flows that explain an apparent valuation gap between the

sum of a firm’s book value of net assets and the going concern value of a firm.

Wöhe (1980) argues that the overpayment component is not of value to the acquirer

despite having paid for it as no economic benefit is expected to result from that part

of acquired goodwill.254 Conceptually, this overpayment or overvaluation

component does not represent a part of goodwill,255 as it is highly likely that this part

of goodwill will be written off in subsequent periods after the acquisition, in case the

acquirer does not find ways to increase the going concern value of an acquiree that

248 Cf. Wöhe (1980), p. 99. 249 Cf. Heyd and Lutz-Ingold (2005), p. 51. 250 Cf. Beyer (2005), p. 145, KPMG (2010), p. 12. 251 Cf. Higson (1998), p. 141, Sellhorn (2000), p. 888, Haaker (2008), pp. 59, 119, 121, Epstein and Jermakowicz (2008), p. 293. 252 Cf. Wöhe (1980), p. 99, Sellhorn (2000), p. 888, Haaker (2008), pp. 126-127. 253 Cf. Johnson and Petrone (1998), p. 295, Wöhe (1980), p. 99. 254 Cf. Wöhe (1980), p. 99. 255 Cf. IFRS3.BC315.


50

might have an offsetting valuation effect.256 The argumentation of Wöhe (1980)

however leaves it open why an acquiree should pay a purchase price that exceeds the

going concern value of a potential acquisition candidate. On that topic, one might

think of synergy or reorganization considerations that have a positive impact on the

bidding behaviour of a potential acquirer and ultimately the purchase price if the

acquisition is successfully completed.257

Despite the highly limited differentiation of the unrecognized assets component and

the exclusion of any considerations on synergy and reorganization effects, Wöhe’s

explanatory approach is certainly of value in the context of explaining the

components of goodwill given, that it was one of the first concepts published in

German-speaking countries.258 In particular, Wöhe’s explanatory approach points

out that not the entire purchased goodwill might be of value to an acquirer

(overpayment component).259

2.3.2.2 Goodwill components according to Johnson and

Petrone (1998)

The concept of Johnson and Petrone (1998) represents on the most detailed and most

cited concepts in the analysis of acquired goodwill.260 The authors argue that

goodwill can be fragmented theoretically in a going concern, synergy, overvaluation

and overpayment component.261. The purchase premium, measured as the difference

between the purchase price and the acquiree’s book value of net assets can be

broken down into 6 components, according to Johnson and Petrone (1998), while

only four represent constituents of the goodwill that ultimately get recognized on the

256 Cf. Henning et al. (2000), p. 376. 257 Cf. Johnson and Petrone (1998), p. 295, Sellhorn (2000), p. 891. 258 Cf. Sellhorn (2000), p. 888. 259 Cf. Johnson and Petrone (1998), p. 295. 260 Cf., for example, Fülbier (2009), p. 54, Teitler-Feinberg (2006), p. 20, Sellhorn (2000), p. 889, Haaker (2008), p. 130, Henning et al. (2000), p. 378, Giuliani and Brännström (2011), p. 163, Bugeja and Gallery (2006), p. 522, Hachmeister und Kunath (2005), pp. 64-65. 261 Cf. Johnson and Petrone (1998), p. 295. Please note that Henning et al. (2000) follow a similar pattern in their analysis to the one used by Johnson and Petrone (2000), additionally introducing the term core

goodwill in an academic setting which is solely made up by a going concern and synergy component (Cf. Henning et al. (2000), p. 376).


51

acquirer’s balance sheet.262 The capitalized goodwill is made up of the fair value of

the going concern element of acquiree’s existing business, the fair value of the

expected synergies, and any overpayment and overvaluation of the acquiree which is

part of the consideration paid.263 Fair value step-ups on pre-existing capitalized net

assets on the acquiree’s balance sheet as well as newly identified net assets that meet

the recognition criteria after the business combination are all part of the purchase

premium, however do not represent components of the goodwill that get capitalized

in the end.

262 Cf. Johnson and Petrone (1998), p. 295, Teitler-Feinberg (2006), p. 20, Fülbier (2009), p. 54, Sellhorn (2000), p. 889, KPMG (2010), p. 12. 263 Cf. Haaker (2008), pp. 130-131, Bugeja and Gallery (2006), p. 522, Hachmeister und Kunath (2005), pp. 64-65.


52

Fig. 15: Goodwill components according to Johnson and Petrone (1998)


(1) Fair value step-ups of the acquiree’s recognized net assets represent a part of

the purchase premium, however not of goodwill. Prior to the acquisition, it is

likely that not all assets and liabilities are carried at their fair value on the

acquisition candidate’s balance sheet.264 This stems from the fact that not all

firms opt for a fair value measurement on their net assets (prior to an

264 Cf. Christensen and Nikolaev (2013), p. 734.

Fair value of going concern

Fair value of

synergies

Over-valuation

Over-payment

0%

20%

40%

60%

80%

100%

(1)


(2)


(2)-(1)=

Acquistion Premium

(3)

Fair Value Step-up of Acquiree's Recognized Net Assets

(4)

Fair Value of Other Net Assets not Recognized by Acquiree

(5)

Fair Value of Going Concern Element of Acquiree's Existing Business

(6)

Fair Value of Synergies

(7)

Overvaluation of Acquiree by Acquirer

(8)

Overpayment

(5)+(6)+(7)+(8)=

Capitalized Goodwill

Amount

Book

value of

acquiree’s

net assets

Cost of

acquired

company

Purchase

premium

(1)

Fair value

step-ups

of

acquiree’s

recognized

net assets

(2)

Fair value

of other

net assets

not

recognized

by acquiree

(3)

Fair value

of going

concern

element of

acquiree’s

existing

business

(4)

Fair

value of

synergies

(5)

Over-

valuation

of acquiree

by acquirer

and con-

sideration

paid

(6)

Over-

payment

by

acquirer

(3)+(4)+

(5)+(6)=

Capitalized

goodwill


53

acquisition) in case they have a right to choose.265 This means that certain

assets might exist which contain fair value reserves as they are capitalized at

historical cost rather than at their fair values.266 Well-known examples for such

assets are property, plant and equipment (PP&E) and inventories.267 During the

purchase price allocation, however, a fair value revaluation of all assets and

liabilities needs to take place, which might lead to substantial fair value step-

ups.268 Those step-ups might already be considered in the purchase premium as

the acquirer might have spotted indications for such undervalued assets during

the due diligence process which lead him to consider such effects in the

purchase premium. After the revaluation of those assets and liabilities in

question, those assets and liabilities will be carried at their respective fair

values on the balance sheet of the acquirer after the business combination. This

means that this corresponding part of the purchase price will be allocated to the

pre-existing assets and liabilities and not to goodwill. This argumentation

however is based on the rational that the fair value of those pre-existing assets

and liabilities in question can be reliably measured. Johnson and Petrone

(1998) also come to a similar conclusion as they state that fair value step-ups of

pre-existing assets and liabilities might sometimes be included in goodwill

“because of difficulties in ascertaining the fair values of the net assets or

perhaps a desire to minimize the annual charge to earnings (because the

amortization period might be shorter if recognized separately from

goodwill).”269 However under the assumption that firms do not act

opportunistically and that they are able to reliably measure the fair value of the

net assets acquired, this component of the purchase premium is not part of

capitalized goodwill.

265 Cf. Christensen and Nikolaev (2013), p. 734. 266 Cf. Zelger (2005), p. 106. 267 Cf. KPMG (2010), p. 7, Zelger (2005), p. 107, Heyd and Lutz-Ingold (2005), pp. 158-159. 268 Cf. IFRS 3.18, Zelger (2005), p. 106. 269 Johnson and Petrone (1998), p. 294.


54

(2) Fair values of other net assets not yet recognized by the acquiree represent

another part of the purchase premium.270 However this component is

capitalized individually after the acquisition through the purchase price

allocation.271 This component primarily refers to separately identifiable and

recognizable intangible assets like technology related, contract related,

customer related or marketing related intangible assets, that by itself have not

meet the recognition criteria prior to the acquisition.272 Prominent examples of

newly recognized intangible assets during a purchase price allocation are

brands and trade names, customer lists, licences, patents, or franchise

agreements.273 Those newly identified assets get amortized according to their

estimated useful lifes and therefore impact earnings in the subsequent years

after the acquisition. Examples for intangible assets with are assumed to have

an indefinite useful live are copyrights and patents for which the possibility

exists to prolong exclusive rights of their use on a continuing basis.274

(3) Fair value of the going concern element of the acquiree’s existing business

represents the first constituent of the to-be-capitalized goodwill after a business

combination.275 Johnson and Petrone (1998) define the going concern element

of the acquiree’s existing business as the “ability of the acquiree as a stand-

alone business to earn a higher rate of return on an organized collection of net

assets than would be expected if those net assets had to be acquired separately

(…).”276 Several explanations have been brought forward to explain this

valuation gap between a firm’s going concern value and corresponding book

value of net assets. Two of the most prominent are (i) the capabilities of firms

to generate synergies from using their existing bundle of resources277 and (ii)

270 Cf. Henning et al. (2000), p. 376, Johnson and Petrone (1998), p. 295, Sellhorn (2000), p. 889, Haaker (2008), p. 130. 271 Cf. Heyd and Lutz-Ingold (2005), pp. 155-156, Leibfried and Fassnacht (2007), p. 51-52. 272 Cf. Heyd and Lutz-Ingold (2005), pp. 155-156, Zelger (2005), pp. 106-111. 273 Cf. Altmann and Schilling (2011), p. 3, Schilling et al. (2012), p. 6, KPMG (2011), p. 4. 274 Cf. Glaum and Wyrwa (2011), p. 26. 275 Cf. Giuliani and Brännström (2011), p. 163, Henning et al. (2000), p. 376, Johnson and Petrone (1998), p. 295, Sellhorn (2000), p. 889, Haaker (2008), p. 130. 276 Johnson and Petrone (1998), p. 295. 277 Cf. Teitler-Feinberg (2006), p. 20, Bugeja and Gallery (2006), p. 522, IFRS3.BC315.


55

market imperfections278. Market imperfections allow firms to generate

monopoly profits or to earn higher economic rents given existing market entry

barriers which are hard to overcome by competitors.279

(4) Fair value of synergies represent the second component of acquired goodwill

to be capitalized.280 Synergies are the expected financial benefits from

combining the acquiree’s and acquirer’s existing operations.281 Synergies

denote a highly subjective valuation component from the acquirer’s point of

view.282 It reflects the assumed strategic combination, reorganisation,

expansion as well as restructuring possibilities for the newly created entity

through the acquisition. Research and literature on synergies are extensive

however predominately ground on the notion of operating and financial

synergies.283 Operating synergies include the possibility of increasing revenues

(so-called revenue synergies) or lowering the current cost base (so-called cost

synergies).284 Revenue synergies, which refer to the prospects of generating

more revenues together than the two stand-alone entities would individually,

might result from the possibility of cross selling through the acquired business

units and the expansion of the global reach in distribution through the acquired

distribution network.285 Cost synergies primarily refer to reducing overhead

costs in supporting business units that are obsolete after combining the

operations. This usually reduces the cost base of the two combined entities

with either stable or increasing output levels, termed as economies of scale, i.e.

declining marginal costs of production whilst increasing a firm’s output

level.286 These strategic options, expressed as synergies for the combined new

entity, possess a value from the perspective of the acquirer, however are also

278 Cf. Johnson and Petrone (1998), p. 295, IFRS3.BC315. 279 Cf. Hachmeister and Kunath (2005), p. 65, Johnson and Petrone (1998), p. 295, 280 Cf. Teitler-Feinberg (2006), p. 20, Fülbier (2009), p. 54, Johnson and Petrone (1998), p. 295, Sellhorn (2000), p. 889, Haaker (2008), p. 130. 281 Cf. Damodaran (2005b), p. 3, Johnson and Petrone (1998), p. 295, Teitler-Feinberg (2006), p. 20. 282 Cf. Rosenbaum and Pearl (2013), p. 333, Damodaran (2005b), p. 6. 283 Cf. Bruner (2004), pp. 327-329, Damodaran (2005b), pp. 4-5, Rosenbaum and Pearl (2013), pp. 333-334. 284 Cf. Rosenbaum and Pearl (2013), p. 334, Bruner (2004), p. 328. 285 Cf. Bruner (2004), p. 328. 286 Cf. Damodaran (2005b), p. 4.


56

difficult to quantify in monetary units which sometimes leads to overvaluation

of the assumed synergies.287

(5) Overvaluation of the acquiree and acquirer’s consideration transferred for the

acquiree can also be part of the goodwill that gets capitalized after the business

combination.288 Overvaluation of the acquiree refers to incorrect assumptions

made by the acquirer that underlie the estimation of the firm’s going concern

value.289 Valuation models are sensible to expected growth rates, profitability

margins and discount rates. Any too optimistic assumption result in a company

value that might diverge from its intrinsic value.290 As the acquirer is usually

confronted with information asymmetries prior to the acquisition, forecasting

expected company performance can be difficult. Incorrect and too optimistic

valuation assumptions and parameters ultimately increase the subjective

company value from the acquirer’s point of view and therefore also the

acquired goodwill.291

Overvaluation of the acquirer’s consideration transferred for the acquiree

primarily refers to those cases when the acquirer’s consideration is paid in

stock and not in cash.292 Empirical studies show that stock as an acquisition

consideration is usually used by the acquirer when the acquirer’s firm is

overvalued.293 As any consideration transferred to the acquiree represents the

basis for the subsequent purchase price allocation, any over- and

287 Cf. Damodaran (2005b), p. 4, Bruner (2004), p. 347. 288 Cf. Johnson and Petrone (1998), p. 295, Henning et al. (2000), p. 379, Teitler-Feinberg (2006), p. 20, Haaker (2008), p. 130, Sellhorn (2000), p. 889. 289 Cf. Sellhorn (2000), p. 889, Teitler-Feinberg (2006), p. 20, Johnson and Petrone (1998), p. 295, IFRS3.BC315, SFAS 141.b102. 290 Cf. Damodaran (2014b), pp. 2-4. 291 Cf. Giuliani and Brännström (2011), p. 163, Johnson and Petrone (1998), p. 295, Teitler-Feinberg (2006), p. 20, IFRS3.BC315, SFAS 141.b102. 292 Cf. Johnson and Petrone (1998), p. 295. 293 Cf. Vermaelen and Xu (2014), p. 73, Rhodes–Kropf et al. (2005), pp. 561-562, Fua et al. (2013), p. 24, Ang and Cheng (2006), p. 199, Rhodes–Kropf and Viswanathan (2004), p. 2685, Dong et al. (2006), p. 729.


57

undervaluation of the consideration transferred impacts the amount of

recognized goodwill.294

(6) Overpayment by the acquirer is usually the result of bidding processes where

two or more bidders bid for the same acquisition target.295 The existing of

another bidder increases the likelihood of a higher price and therefore a higher

purchase premium. In a bidding process, the proposals submitted for the

potential target move closer to the subjective valuation of the target where

marginal benefits from the acquisition still exceed the marginal costs (price).296

From an accounting perspective, components (4) to (6) will ultimately end up

recognized as goodwill on the acquirer’s balance sheet.297 It is highly likely that

those parts of goodwill that are attributable to an overvaluation and overpayment

effect will be written off in subsequent periods if the acquirer does not find ways to

offset them by either increasing the going concern value of the acquiree or the value

component that is assumed to result from expected synergies.298

2.3.2.3 Goodwill components according to Sellhorn (2000)

Sellhorn (2000) builds upon the work of Johnson and Petrone (1998) and presents a

more granular view of the components of goodwill.299 Sellhorn (2000) however

focusses exclusively on the so-called economic goodwill in a transaction, i.e. on

those components of goodwill that are assumed to be of value to the acquirer.300 He

therefore leaves out considerations regarding overvaluations and overpayments of

the target firm.

294 Cf. Heyd and Lutz-Ingold (2005), pp. 141-144, Leibfried and Fassnacht (2007), pp. 48-49, 56, Zelger (2005), pp. 102-103. 295 Cf. Haaker (2008), p. 130, Johnson and Petrone (1998), p. 295, Henning et al. (2000), p. 376, Sellhorn (2000), p. 889, IFRS3.BC315, SFAS 141.b102. 296 Cf. Brösel and Zwirner (2009), p. 192, Brösel and Klassen (2006), p. 450. 297 Cf. Teitler-Feinberg (2006), p. 20. 298 Cf. Henning et al. (2000), pp. 377, 385. 299 Cf. Haaker (2008), p. 131. 300 Cf. Sellhorn (2000), p. 889, Haaker (2008), p. 131.


58

In his analysis, the breakdown of the acquired goodwill follows a more strategic

approach. The results infer that most of the expected value coming from an

acquisition, interpreted as goodwill, is based on the acquirer’s involvement after the

takeover.

Fig. 16: Goodwill components according to Sellhorn (2000)


Sellhorn (2000) argues that acquired goodwill can be broken down in a going

concern component, restructuring component, strategic component and a flexibility

component.301 While Sellhorn’s explanations on the sources of the going concern

element of the acquired goodwill as well as the synergy component in the purchase

301 Cf. Sellhorn (2000), p. 889, Haaker (2008), p. 131.

Going concern goodwill

Re-structuring goodwill

Synergy goodwill

Strategy goodwill

Flexibility component

0%

20%

40%

60%

80%

100%

(1)


(2)


(2)-(1)=

Acquistion Premium

(3)


(4)


(5)

Synergy Goodwill

(5)

Strategic Goodwill

(6)


(3)+(4)+(5)+(6)=


Amount

Book

value of

acquiree’s

net assets

Cost of

acquired

company

Purchase

premium

(1)

Going

concern

goodwill

(2)

Restruct-

uring

goodwill

(4)

Strategic

goodwill

(5)

Flexibility

component

(1)+(2)+(3)

+(4)+(5)=

Capitalized

goodwill

(3)

Synergy

goodwill


59

price are similar to those of Johnson and Petrone (1998), the components that relate

to restructuring, flexibility and strategic options deserve additional explanations.

• Restructuring goodwill:

The expected value that is derived from planned restructuring activities

basically emerges from the acquirer’s right to control the target firm.302 These

controlling rights allow the acquirer to alter the target firm’s organizational

structure according to the favoured strategy or if no change in the

organizational structure is planned to keep it unchanged as a stand-alone

business in the portfolio of the parent firm.303 Restructuring activities usually

aim at increasing the competitiveness of the firm in the industry in which it

operates.304

The most prominent forms of corporate restructurings focus on a more efficient

resources deployment and/or the disposal of non-operating assets as well as

non-core business activities.305 The value gains from planned corporate

restructuring activities need to be differentiated from those derived from cost

synergies, as in the corporate restructuring case the value gain results from

increasing the competitiveness of the firm and not from eliminating functional

duplications. Value gains from corporate restructuring activities are expected to

be positive, because otherwise the acquiring firm would not have any

incentives to become active in corporate reorganizations. Empirical evidence

supports this notion that restructuring activities are on average value enhancing

for the firm’s owners.306

302 Cf. Damodaran (2005a), p. 2, Ross et al. (2005), p. 804. 303 Cf. Fülbier (2009), p. 54, Haaker (2008), p. 132, Sellhorn (2000), p. 890, Damodaran (2005a), p. 3, KPMG (2010), p. 12. 304 Cf. Lorson and Heiden (2002), p. 388, Sellhorn (2000), p. 890. 305 Cf. Ross et al. (2005), p. 804, Lorson and Heiden (2002), p. 388, Sellhorn (2000), p. 890, Bruner (2004), p. 123. 306 Cf. Eckbo and Thorburn (2013), pp. 103-105, Bruner (2004), pp. 54,154-157 who provide an extensive overview on stock market returns to shareholders from corporate restructuring activities by respective research papers.


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• Strategy goodwill:

According to Sellhorn (2000), strategy goodwill reflects the added value of an

acquisition to the planned corporate strategy of the acquiring firm.307 This

reasoning implies that without an acquisition, the intended strategy of the

acquirer could not have been implemented, implemented with a delay, or only

implemented at a higher cost. Typical cases for such strategies would be a

firm’s ambition to enter new markets with substantial entry barriers that on its

own could not be crossed.308 In those situations, acquiring a firm which has

already successfully entered such a market might be the only way to gain

access immediately.309 By acquiring a target firm that has already successfully

entered such a market, e.g. an emerging market, “the acquiring company is

buying an option to expand in the emerging market rather than a set of

expected cash flows”.310 Another business case which according to Sellhorn

(2000) also falls under the notion of strategy goodwill represents the situation

when through a business acquisition a threatening competitor can be

eliminated.311

Conceptually, the valuation of such a strategy goodwill component prior to an

acquisition can be considered as challenging as in such situations real option

valuation approaches are primarily used.312 Real option valuation approaches

do not rely exclusively on expected cash flows but also on the estimated

probabilities that one or another event is likely to occur in the future,313

reflecting in the prior emerging market example that the implemented strategy

could also potentially fail. Methodological wise, “real options proponents are

proposing (…) that a premium be added on the discounted cash flow value of

307 Cf. Sellhorn (2000), p. 890. 308 Cf. Smith and Triantis (2001), pp. 405, 407, Haaker (2008), p. 132, Damodaran (2005b), pp. 19-20, Lorson and Heiden (2002), p. 388. 309 Cf. Damodaran (2005b), p. 19. 310 Cf. Damodaran (2005b), p. 19. 311 Cf. Sellhorn (2000), p. 890. 312 Cf. Smith and Triantis (2001), pp. 408-409. 313 Cf. Ross et al. (2005), pp. 223-224.


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the combined firm.”314 It can however be argued in how far a (thematic)

overlap between strategy goodwill and synergy goodwill exists.315

• Flexibility goodwill:

An acquiring firm might also be willing to pay a premium that accounts for

alternative courses of action of the acquirer after the acquisition is made.316

Sellhorn (2000) argues that such alternatives can be considered also a real

option with an intrinsic value to the option holder, i.e. the buyer of a firm.317

Initiatives that would fall under the terminology of flexibility goodwill would

be an acquiring firm’s option to increase or reduce its equity stake in the target

firm after gaining control or even the possibility to sell the firm shortly after

the acquisition or at another date in the future.318 Theoretically, the intrinsic

value of a flexibility option to sell a firm at a future date would increase during

times of increasing mergers & acquisition activities. As discounted cash flow

models cannot incorporate such flexibilities, such hypothetical courses of

actions would represent a premium over the value of the combined entities.319

Although Sellhorn’s reasoning (2000) makes sense from a theoretical

perspective, it can certainly be argued in how far a buyer can quantify

monetarily this flexibility and incorporate these considerations in the

determination of the purchase price.

314 Cf. Damodaran (2005b), p. 20. 315 Cf. Haaker (2008), p. 132, Sellhorn (2000), p. 890, Damodaran (2005b), p. 20. 316 Cf. Sellhorn (2000), p. 890, Haaker (2008), p. 132, Lorson and Heiden (2002), p. 388. 317 Cf. Sellhorn (2000), p. 890. 318 Cf. Smith and Triantis (2001), p. 414, Lorson and Heiden (2002), p. 388. 319 Cf. Damodaran (2005b), p. 20.


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2.3.2.4 Value drivers of acquired goodwill

Whilst the individual components of goodwill have been outlined in the previous

section, a holistic overview should also be provided on the value drivers of those

acquired goodwill components. The aim is to describe the complexity of their

determination and valuation from an acquirer’s point of view prior to the

transaction. Complexity can be considered a source of risk in finding the right

purchase price and of overpaying.320 This risk then certainly also translates in the

recoverability of goodwill that ultimately gets capitalized on an acquirer’s balance

sheet and tested for impairment in the subsequent periods.321 Through the

information provided in the following section, it should be argued that the sources of

goodwill are multi-fold and complex to determine, and therefore also impact

impairment risk in subsequent periods after the transaction.322 Misjudging the value

drivers of acquired goodwill can be a fundamental source of impairment risk of

acquired goodwill and therefore goodwill that ultimately gets recognized on an

acquirer’s balance sheet.

In particular, the focus should be drawn to the components of acquired goodwill that

relate to synergies, restructuring possibilities, and to having control over a firm’s

assets deployment through a management change (value of control). Those

components, as found out by Johnson and Petrone (1998), Sellhorn (2000), and

Wöhe (1980), are additive to the going concern goodwill which can be explained by

expected excess earnings, i.e. a positive difference between a firm’s actual earnings

and absolute cost of capital.323

2.3.2.4.1 Value of synergies

Synergies represent “the most widely used and misused rationale in mergers and

acquisitions”324 and also make up a considerable part of the purchase price paid325,

320 Cf. Bruner (2004), p. 247. 321 Cf. Duff and Phelps (2009), p. 4, Henning et al. (2000), p. 385. 322 Cf. Li et al (2011), p. 745, Gu and Lev (2011), pp. 1998, 2016, Hayn and Hughes (2006), p. 241. 323 Cf. Schultze (2005), p. 282. 324 Damodaran (2005b), p. 3.


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and therefore also acquired goodwill. Accounting research on synergies comes to the

conclusion that quantifying and valuing expected synergies is challenging and also

frequently incorrectly carried out in practice.326 This implies that the value

component of acquired goodwill that is attributed to expected synergies and carried

on the acquirer’s balance sheet after the acquisition might not be completely of

value to the acquirer going forward, as the acquirer might realise on an ex-post basis

that synergies have been overestimated.327 Also, due to the fact that the concept of

valuing synergies is rather complex and relies on numerous assumptions, it can be

questioned whether the present value of expected synergies that is reflected in the

purchase price fully materializes during future periods.328 This reasoning implies

that assumed synergies that on an ex-post basis cannot be realized are a driver of

goodwill impairments.329

Sources of synergies:

Synergy can be defined as “the additional value that is generated by combining two

firms, creating opportunities that would not been available to these firms operating

independently.”330 Generally, synergies can be classified in operating synergies and

financial synergies.331

(1) Operating synergies:

Operating synergies arise from the combination of two firms’ operations.332 Among

the most popular sources of operating synergies are economies of scale, a stronger

pricing power of the combined entity, and an accelerated growth potential.333 Those

325 Cf. Pratt and Niculita (2008), pp. 384, 393, Sirower and Sahni (2006), p. 86, Ernst & Young (2014a), p. 1, Rosenbaum and Pearl (2013), p. 333. 326 Cf. Fidrmuc et al. (2012), pp. 837, 841, Bruner (2004), pp. 332-334, Damodaran (2005b), p. 3. 327 Cf. Li et al (2011), p. 745, Gu and Lev (2011), pp. 1998, 2016, Hayn and Hughes (2006), p. 241. 328 Cf. PricewaterhouseCoopers (2010), p. 9, Teitler-Feinberg (2006), p. 20. 329 Cf. Gu and Lev (2011), pp. 1998, 2016, Hayn and Hughes (2006), p. 241, Li et al (2011), p. 745. 330 Damodaran (2005b), p. 3. 331 Cf. Devos et al. (2008), p. 1180, Kapil (2011), p. 654, Chatterjee (1992), p. 269, Damodaran (2005b), p. 3. 332 Cf. Rosenbaum and Pearl (2013), p. 333, Damodaran (2005b), p. 3. 333 Cf. Bruner (2004), p. 328, Damodaran (2005b), p. 4.


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factors usually have a positive impact on expected cash flows, as they can increase

revenues or lower the cost base of the combined entity.334

Economies of scale allow the combined entity to become more cost-efficient as

economies of scale marginal costs on an unit basis. Economies of scale are

especially an important consideration in business acquisitions that involve firms in

the same industry (i.e. horizontal integration or horizontal merger).335 An increased

pricing power results from the reduction of competition in an industry and a larger

market share through the completed acquisition.336 Similar to economies of scale, an

increase in pricing power is more likely to occur in horizontal mergers than in

acquisition that combine firms operating in different industries (i.e. vertical

integration or vertical merger). Accelerated growth potential focusses rather on

increasing revenues than reducing costs.337 Two of the most popular ways of

accelerating revenues growth are expanding into new geographical markets and

broadening a firm’s product portfolio offered to clients.338 By acquiring a firm that

has already a distribution network in geographical markets in place through which

the acquiring firm is able to sell its products immediately after the acquisition, an

acquirer can avoid building up its own presence in new markets.339 This might come

at a higher cost however can be considered as less risky than investing on its own in

new countries.340 Cross-selling, i.e. broadening the product offering to existing and

new clients, represents another important way of increasing revenues.341 Especially,

when the products of the acquired firm can be considered as complements to the

existing product portfolio of the acquirer.342

334 Cf. Rosenbaum and Pearl (2013), p. 334, Bruner (2004), p. 328. 335 Cf. Bernile and Bauguess (2011), p. 12, Devos et al. (2008), p. 1181, Madura and Ngo (2008), p. 333, Rosenbaum and Pearl (2013), p. 334. 336 Cf. Healy at al. (1992), p. 161, Madura and Ngo (2008), p. 333, Bradley et al. (1988), p. 4, Nguyen et al. (2012), p. 1359. However from a legal point of view, this source of synergy needs to be viewed with caution as anti-trust laws and authorities allow an increased pricing power only to a certain extend. 337 Cf. Devos et al. (2008), p. 1181, Ismail (2011), p. 881, Madura and Ngo (2008), p. 1360. 338 Cf. Nguyen et al. (2012), p. 1360, Rosenbaum and Pearl (2013), p. 334. 339 Cf. Kaplan (2000), p. 4, Rosenbaum and Pearl (2013), p. 335. 340 Cf. KPMG (2011), p. 10. 341 Cf. Bruner (2004), p. 328, Bradley et al. (1988), p. 4. 342 Cf. Bradley et al. (1988), p. 4.


65

Operating synergies impact the generation of absolute cash flows by reducing costs

or increasing revenues.343 The quantification and valuation of operating synergies

requires substantial information, preparation and judgement that might lead to an

overestimation of synergies, an unjustified purchase price and therefore a higher risk

of goodwill impairment in subsequent periods, as any overpayment leads to a higher

acquired goodwill (and vice versa).344

Fig. 17: Sources of operating synergies

Source: Damodaran (2005b), p. 32.

(2) Financial synergies:

Financial synergies comprise of the use of the acquiree’s access cash holdings, the

capacity of increasing the leverage of the combined firm, and tax benefits.345

343 Cf. Healy at al. (1992), p. 161, Rosenbaum and Pearl (2013), p. 334, Bruner (2004), p. 328. 344 Cf. Damodaran (2005b), pp. 6-7, Bruner (2004), pp. 332-334. 345 Cf. Devos et al. (2008), p. 1181, Bruner (2004), pp. 329-330, Nguyen et al. (2012), p. 1359, Healy at al. (1992), p. 136, Leland (2007), p. 766, Damodaran (2005b), p. 5, Ghosh and Jain (2000), p. 377, Hayn (1989), p. 121, Lewellen (1971), pp. 533-534.

Economies of scale

Operating synergies to the combined firm

Synergies can result when two firms are combined and can be both financial or operating in nature

Financial synergies

Potential for higher returns on new investments

More new investments opportunities

Potential for more sustainable excess returns

Lower cost base due to cost savings

Strategic advantages and new opportunities

Higher return on capital Higher growth rates

Higher reinvestment Higher growth rates

Longer growth period

Higher profitability margin Higher operating income


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Compared to operating synergies, financial synergies can have an impact on both

cash flows and discount rates which are applied to value future cash flows.346

Firstly, excess cash holdings in combinations with project opportunities might be of

interest for the acquirer and therefore a driver on the purchase price.347 “A

combination of a firm with excess cash, or cash slack, (and limited project

opportunities) and a firm with high-return projects (and limited cash) can yield a

payoff in terms of higher value for the combined firm.”348 In times when cash is

expensive in terms of borrowing rates, it might be cheaper to use cash holdings of a

potential acquisition candidate that currently makes no particular use of it. In those

cases, both firms would benefit as the returns of new investment projects exceed the

interest gains of the cash holdings whilst still being lower than the cost of borrowing

money elsewhere.349

Secondly, synergies arising from tax benefits include the possibility of taking

advantage of existing tax losses carried forward from the acquiree.350 In some tax

jurisdictions, tax losses carried forward enable the combined entity to reduce its tax

burdens in future periods, as carried forward tax losses from earlier periods might be

used to offset taxes on current and future profits.351 Consequently, those tax losses of

prior periods might be of value and be of interest for the acquirer who is willing to

pay for them as they have a positive impact on expected after tax cash flows.352

And finally, the optimal debt capacity of the combined entity could be increased in

case the business combination has a positive impact on earnings’ and cash flow

volatility, which could be of value for a potential bidding firm.353 This reasoning

builds on the assertion that the acquisition improves the stability and predictability

of the combined earnings and cash flows, positively impacting the after-tax cost of

346 Cf. Damodaran (2005b), p. 4, Bruner (2004), pp. 328-329. 347 Cf. Faleye (2004), p. 2059. 348 Damodaran (2005b), p. 3. 349 Cf. Jensen (1986), pp. 323-324, 328, Faleye (2004), p. 2059. 350 Cf. Devos et al. (2008), p. 1181, Nguyen et al. (2012), p. 1359, Damodaran (2005b), p. 4, Hayn (1989), p. 121, Healy at al. (1992), p. 136. 351 Cf. Hayn (1989), p. 122. 352 Cf. Bruner (2004), p. 328. 353 Cf. Ghosh and Jain (2000), p. 377, Leland (2007), p. 766, Lewellen (1971), pp. 533-534, Bruner (2004), p. 328.


67

capital of the combined firm. This reduction in firm risk could be of value, however

only if the stand-alone firms prior to the business combination had already an

optimal debt to equity capital structure in place.354

Fig. 18: Sources of financial synergies

Source: Damodaran (2005b), p. 32.

Valuation of synergies:

In order to determine the value impact of operating and financial synergies, it is

necessary to gather relevant information that allow to estimate what form of

synergies are expected to be achieved and when they will start after the business

combination.355

The valuation of synergies starts with determining the stand-alone values of the

target firm (VTF) and its potential acquirer (VAF).356 To do so, the relevant cash flows

(CFTF and CF

AF) need to be discounted with the firms’ individual discount rates (rTF

and rAF).357

= ∑ 1 + (2.13)

354 Cf. Damodaran (2005b), pp. 27-28, Bruner (2004), p. 328. 355 Cf. Gaughan (2007), p. 132, Bruner (2004), pp. 332-334. 356 Cf. Damodaran (2005b), pp. 6-7, Gaughan (2007), p. 132. 357 Cf. Damodaran (2005b), pp. 6-7.

Financial synergies to the combined firm

Synergies can result when two firms are combined and can be both financial or operating in nature

Operating synergies

Excess cash Tax benefits Increased debt capacity

• Positive NPV of projects add to firm value that would have not been carried out without the acquisition

• Lower tax expenses in future periods due to usage of existing tax losses carried forward

• Higher leverage ratio • Lower cost of capital


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= ∑ 1 + (2.14)

In a second step, the value of the combined entity (VC) is determined by adding the

stand-alone values excluding any synergy effects together.358

V = V + V = ∑ CF 1 + + ∑ CF 1 + ; (2.15)

This allows comparing the stand-alone values with the value of the newly merged

entity (VN) including the impacts of synergies on the expected cash flows (CFS) as

well as discount rates (rvN). Thereof the value of the synergy component can be

derived by subtracting the value of the combined entity (VC) excluding any synergy

effects:

Incl. synergies (newly merged company):

= ∑ 1 + = ∑ + + 1 + ;(2.16)

Value of synergies (VS), i.e. (16) – (15):

= − = (2.17)

= ∑ 1 + − ∑ 1 + + ∑ 1 + ; The above outlined sources of synergies are multi-fold and certainly can have an

impact on the acquisition price.359 However given the complexity of their

determination and the uncertainty when synergies will occur, an inherent risk can be

identified that those synergies might come not as expected.360 Consequently, those

risks are attached to the value of acquired goodwill that might be subject to

impairment if synergies have been overestimated.361

358 Cf. Gaughan (2007), p. 132, Damodaran (2005b), pp. 6-7. 359 Cf. Pratt and Niculita (2008), pp. 384, 393, Sirower and Sahni (2006), p. 86, Ernst & Young (2014a), p. 1, Rosenbaum and Pearl (2013), p. 333. 360 Cf. PricewaterhouseCoopers (2010), p. 9, Bain & Company (2014), p. 10. 361 Cf. Li et al (2011), p. 745, Gu and Lev (2011), pp. 1998, 2016, Hayn and Hughes (2006), p. 241.


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2.3.2.4.2 Value of control

The value of control element represents a subjective valuation component from a

potential acquirer’s point of view.362 It derives from the assertion that in some cases

a firm’s financial performance is considered as non-optimal by a potential acquirer,

and could be improved by exchanging members of the management team who are

more experienced.363 The value of control concept can be understood as a

restructuring measure in the wider sense, as it focusses on how the business is

currently run and whether replacing key decision makers, i.e. the management team,

would result in a higher company value.364 The concept is not built on the traditional

corporate restructuring concept of exchanging or altering assets currently used;365 it

rather targets at the current asset utilization by the management team and aims at

making the usage of existing assets more efficient.366

Damodaran (2005a) argues on that topic that “when we value a business, we make

implicit or explicit assumptions about both who will run that business and how they

will run it. In other words, the value of a business will be much lower if we assume

that it is run by incompetent managers rather than by competent ones.”367 He further

reasons that “we can value the company run by the incumbent managers and derive

what we can call a status quo value. We can also revalue the company with a

hypothetical “optimal” management team and estimate an optimal value. The

difference between the optimal and the status quo values can be considered the value

of controlling the business.”368

362 Cf. Pratt and Niculita (2008), p. 385, Cornell (2013), p. 1, Damodaran (2005a), p. 3. 363 Cf. Damodaran (2005a), p. 3, Cornell (2013), p. 3, Booth (2001), p. 150, Pratt and Niculita (2008), p. 385. 364 Cf. Furtado and Rozeff (1987), p. 147, Worrell et al. (1993), p. 387, Kang and Shivdasani (1996), p. 1061, Adams and Mansi (2009), p. 522, who generally all document a positive stock market reaction to a forced CEO turnover. 365 Cf. Bruner (2004), pp. 149-151. 366 Cf. Mackey (2008), p. 1364, Wasserman et al. (2001), p. 22, Crossland and Hambrick (2007), p. 782, Cornell (2013), p. 15. 367 Damodaran (2005a), p. 3. 368 Damodaran (2005a), p. 3.


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Sources of control value:

The table below outlines some of the most prominent factors that impact the value of

control.369 By fixing those problems from a firm’s value perspective, the status quo

value converges towards the value derived from an optimal resource deployment. Or

put differently, a potential acquirer might be willing to pay for an apparent gap

between the status quo value and the optimal value, if the acquiring company is

confident to close this gap by implementing a more capable management team.370

Potential Problem: Possible fixes: Value Consequence:

1. Management team is

poorly managing

existing assets

Management team should

focus on managing existing

assets more efficient

Profitability margins and

return on invested capital

increase

2. Management team is

under investing in

growth opportunities

and/or over investing in

new investments which

destroy value

• Management team

should invest in

investment opportunites

with growth prospects,

even if this means that

return on capital

measures will be lower

in the short-term

• Management team

should invest only in

those investment

opportunities which

cover their costs of

capital (marginal return

on capital should be

higher than cost of

capital)

In the long run, investments

will result in higher

earnings growth rates

369 Cf. Pratt and Niculita (2008), p. 385. 370 Cf. Booth (2001), p. 130.


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3. Management team does

not exploit possible

strategic advantages


be aware of the firm’s

competitive advantages and

build strategy upon them

Period of high earnings

growth will be longer (than

otherwise), resulting in

larger excess returns

thereby increasing the

firm’s value

4. Management team

keeps too much cash

which hinders

investments


reduce excess cash through

dividends or stock buybacks

Firm value drops by the

cash amount distributed, but

shareholders gain as they

can invest at a higher rate of

return (than firm can)

Table 1: Sources of control value Source: Damodaran (2005a), pp. 17-18.

Valuation of control:

The value of control is directly linked to how much the firm’s financial performance

could be improved by changing its management.371 “The value of changing

management will be zero in a firm that is already optimally managed and substantial

for a firm that is badly managed.”372

In a situation of a business combination373, to derive the value of control component

in the purchase price and therefore in acquired goodwill, the acquirer firstly

determines the value of the target firm on a stand-alone basis with the current

management (VCM) by considering the cash flows that the current management are

most likely to extract from the current asset base (CFCM).374

= ∑ 1 + (2.18)

371 Cf. Cornell (2013), p. 3, Pratt and Niculita (2008), p. 385, Damodaran (2005a), p. 16. 372 Damodaran (2005a), p. 16. 373 Cf. Damodaran (2014a), p. 25 who actually speaks of a hostile acquisition in his derivation of the value of control. 374 Cf. Cornell (2013), p. 15, Damodaran (2005a), p. 3.


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In a second step, the potential acquirer estimates what cash flow impact a new

management team would have.375 This includes to what extend a new management

team could improve expected cash flows (CFNM) and when after an acquisition those

changes are most likely to occur, i.e. a time lag is assumed between the exchange of

the existing management team and the subsequent cash flow improvement. On the

basis of this assessment, the stand-alone value of the firm with a new management

team can be determined (VNM).

= ∑ 1 + (2.19)

The value of the control component is then derived from comparing the cash flow

impacts of a new management team. To do so, the stand-alone value of the firm with

the to-be-exchanged management team (status quo value) is deducted from the

stand-alone value of the firm with the new management team (optimal value):

= − = ∑ 1 + − ∑ 1 + ; (2.20)

In this context, it needs to be added that having a more experienced management

might come at a greater cost. In the workplace, the demand for highly skilled and

experienced managers is greater than for their less skilled counterparts. Knowledge

is of value to a firm, and as long as employees are aware of that, they will ask

theoretically for a higher compensation (salary) than their less experiences

colleagues. These potentially higher personnel costs might work partly against the

value generated from the implementation of a new management team. Additionally,

it needs to be considered that an optimal management team might not be available at

the time when a potential acquirer makes a move towards an acquisition candidate.

This implies that cash flow improvements would occur at a later date in the future,

making their net present value smaller.376 Cornell (2013) also points out that

financial performance projections from a management change are difficult to assess

and to forecast, implying the risky nature of this component of acquired goodwill.377

375 Cf. Cornell (2013), p. 15. 376 Cf. Damodaran (2014a), p. 26. 377 Cf. Cornell (2013), p. 15.


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The value of control component can be considered as being part of restructuring

goodwill in Sellhorn’s approach (2000), and part of the synergy component under

Johnson and Petrone’s (1998) definition of synergy as the authors comprise

expected value effects that result from reorganisation, expansion as well as

restructuring possibilities under the term synergies.378

On the basis of how the value of control is derived, it becomes obvious that this

component of acquired goodwill can be considered as firm specific and highly

subjective in nature.379 Especially, the immediate availability of an optimal

management team and the uncertainty whether the existing management takes sole

responsible for the valuation gap between status quo and optimal value might hinder

the potential of unlocking extra value coming from a sole replacement of top

executives shortly after a transaction. Problematic might also be the fact that a

potential acquirer pays for this highly subjective future value gain already upfront

prior to any management replacements or performance improvements. Additionally,

it might take considerable time for a new management team to become familiar with

the company that could delay significant changes in how a firm is run after a take-

over. All these factors add to the risky nature of acquired goodwill in case an

acquirer was willing to pay upfront for the value of control.

2.3.2.4.3 Value of restructuring possibilities

Planned corporate financial restructuring activities might also be considered as a

source of value by a potential buyer and therefore could translate in a higher

purchase price and ultimately become part of acquired goodwill.380 The availability

of restructuring possibilities and the related value gains depend however to a large

extend on characteristics of the target firm and the expertise of the buyer.381 The

expected value adding impacts of such activities can be considered as risky and

378 Cf. Cornell (2013), p. 14. 379 Cf. Booth (2001), p. 132, Damodaran (2014a), p. 27, Pratt and Niculita (2008), p. 385. 380 Cf. Sellhorn (2000), pp. 889-890, Fülbier (2009), p. 54, Gaughan (2007), p. 401, KPMG (2010), p. 12. 381 Cf. Gaughan (2007), p. 401, Crum and Goldberg (1998), p. 340.


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uncertain, given that the buyer pays for them upfront before any restructuring

activities have been implemented.

Corporate financial restructurings:

Corporate financial restructuring activities focus on reshaping the structure of an

organization with the objective of increasing a firm’s value which otherwise would

have been blocked.382 Theoretically, a firm can be thought of as a nexus of contracts

which it entered with various stakeholders, including customers, suppliers,

employees, creditors and shareholders.383 Restructuring can be understood as “the

process by which these contracts are changed - to increase the value of all

claims”384. Those activities that aim at increasing a firm’s value usually target at

improving the performance of its assets, i.e. business activities, or at lowering a

firm’s cost of capital, i.e. the way the firm is financed.385 By doing so, the returns of

a firm’s invested capital should be increased, whilst reducing its funding costs.

Activities that address the performance of a firm’s assets are usually termed as

corporate restructuring activities.386 Corporate restructuring activities typically start

with analysing existing assets regarding their contribution to a firm’s strategy and

their performance.387 Assets that are deemed to be necessary for implementing and

pursuing a defined strategy are also frequently referred to as core assets or core

business. Given that a firm has a competitive advantage in its core business

compared to its competitors, the returns and performance of those activities should

be higher than those of non-core business activities. In case, a competitive advantage

in the market has not been achieved yet or the performance of the core business is

considered as unsatisfactory by the management or owners, further investments in

those activities might become necessary, frequently with the help of specialized

consulting firms.

382 Cf. Eckbo and Thorburn (2013), p. 1. 383 Cf. Eisenberg (1998), p. 819, Jensen and Meckling (1976), p. 311, Giddy (2004), p. 4. 384 Cf. Giddy (2004), p. 4. 385 Cf. Eckbo and Thorburn (2013), p. 1. 386 Cf. Crum and Goldberg (1998), p. 340, Gaughan (2007), p. 401, Eckbo and Thorburn (2013), p. 1. 387 Cf. Gaughan (2007), p. 401.


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Non-core business activities become regularly the target of corporate break-ups, as

they are considered to contribute to corporate inefficiencies within the firm.388 The

performance of non-core business activities is usually also below that of core

assets.389 In most of these corporate break-up forms in which certain groups of assets

are separated and their legal ownership transferred, the disposing company accepts

exchanging streams of uncertain, future cash flows that might arise from that group

of assets for an immediate payoff, i.e. the consideration transferred in exchange for

the ownership.390 The proceeds generated through such a disposal of non-core assets

are then typically used to finance further investments in core business activities, to

pay down debt, or to distribute to shareholders in form of dividends.391

Financial restructuring activities target at how a firm is financed.392 Theoretically, a

firm should aim at finding the equity-debt ratio that allows for the lowest cost of

capital and thereby increasing its firm value.393 Financing decisions and the

composition of the capital structure impact the value of a firm as the cost of capital

is used to discount future cash flows. However increasing a firm’s leverage might

come at a higher opportunity cost in times of economic crises, when credit and

default spreads widen and a firm might find it difficult to repay its liabilities from its

operating cash flows.

388 Cf. Evans et al. (2013), p. 118, Gaughan (2007), p. 407, Edmans and Mann (2014), pp. 2-3. 389 Cf. Schlingemann et al. (2002), p. 131, Edmans and Mann (2014), pp. 2-3, Gaughan (2007), p. 410. 390 Cf. Edmans and Mann (2014), pp. 2-3, Gaughan (2007), p. 426. 391 Cf. Eckbo and Thorburn (2013), p. 7. 392 Cf. Evans et al. (2013), p. 119, Gaughan (2007), p. 19, Bowman et al. (1999), p. 35. 393 Cf. Giddy (2004), p. 4.


76

Fig. 19: Differentiation between corporate and financial restructuring

Source: Own illustration, according to Giddy (2004), p. 13.

Corporate break-ups and asset disposals as a means to reduce organizational

inefficiencies:

Organizational inefficiencies, also referred to as negative synergies, between a

firm’s assets, divisions or even subsidiaries are considered to be negatively

correlated with firm value and a frequent target of corporate restructuring activities

shortly after business acquisitions.394 The so-called cross-subsidization of business

units is considered to be value destroying, as in such cases investments in poorly

performing business divisions are financed by better performing ones.395 Such

inefficiencies might have historically grown over time and not removed as managers

have not had the right incentives to correct them or not been controlled properly

through adequate corporate governance systems.396 In business transactions,

potential buyers might be willing to pay for such inefficiencies, as some of them are

394 Cf. Bowman et al. (1999), p. 46, Gatti and Spotorno (2014), p. 3, Gaughan (2007), pp. 403, 409, John and Ofek (1995), pp. 105-106, 108-109, Scharfstein and Stein (2000), p. 2537. 395 Cf. Scharfstein and Stein (2000), p. 2538, Eckbo and Thorburn (2013), p. 11. 396 Cf. Jensen (1986), p. 323, Scharfstein and Stein (2000), p. 2538.

“Corporate break-up”

(→ dispose assets)

B/S Equity/LiabilitiesAssets

Equity

Debt

Core business

Non-core business

“Fix the business”

(→ improve performance)

“Fix the financing”

(→ reduce cost of capital)

Corporate Restructuring Financial Restructuring


77

fairly easy to correct. Such value increasing impacts can already be part of the

acquisition price and would later translate into acquired goodwill.

Through corporate break ups, such inefficiencies can be corrected by the acquirer.397

Corporate break ups include divestitures398, spinoffs

399, and equity carve-outs400.401

All of them provide means to improve the operating efficiency of the overall

(parent) firm, the generation of future cash flows, and the (parent) firm’s

profitability. In most of these corporate break up forms in which certain groups of

assets are separated and their legal ownership transferred, the disposing company

accepts exchanging streams of uncertain, future cash flows that might arise from that

group of assets for an immediate payoff, i.e. the consideration transferred in

exchange for the ownership.402

Sources of value from restructuring possibilities:

The value gains from corporate break ups can be explained by expected

improvements in the firms’ future performance. Amongst the most prominent

explanations that argue for an increase in the equity value of the restructured firm

are expectations regarding the firm’s corporate focus, elimination of inefficiencies,

and a higher information transparency after the break-up.

397 Cf. Scharfstein and Stein (2000), p. 2537. 398 In a divesture, parts of a firm’s assets are sold to a third party – usually to another company – structured as a private transaction. These assets might include a firm’s division, business unit, segment, subsidiary or product line (Cf. Eckbo and Thorburn (2013), p. 8). 399 Through a spinoff, the equity ownership in a subsidiary by a listed, parent firm is completely separated, transferred and distributed to the existing shareholders of the parent firm. The allocation mechanism to the existing parent firm’s shareholders follows a pro-rata basis, i.e. depending on the overall shareholders’ ownership in the parent firm. By means of a spinoff, the subsidiary itself becomes a listed firm. After the spinoff, existing stockholders hold now shares in two separate entities, instead of one compared to the situation prior to the spinoff. This form of corporate break up does not generate any cash proceeds for the parent company; instead it simply separates parts of the parent company and converts indirect ownership of shareholders in a subsidiary into direct ownership (Cf. Eckbo and Thorburn (2013), pp. 15-16). 400 In an equity carveout, parts of an equity ownership in a subsidiary are offered by the parent company to the public in an initial public offering (IPO). Compared to a spinoff, ownership is not only offered to existing but also to new shareholders. Depending on the offering structure in the IPO, the cash proceeds which are generated from the disposal can either remain with the subsidiary or transferred to the parent firm (Cf. Eckbo and Thorburn (2013), pp. 25-26). 401 Cf. Bowman et al. (1999), p. 34, Boone et al. (2001), p. 22. 402 Cf. Edmans and Mann (2014), pp. 2-3.


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(1) Increased resource focus of the disposing firm:

Under the assumption that the management team acts in the best interest of

the shareholders, such a reorganization implies that the firm keeps those

assets or business units that allow for the highest returns, profitability or

future growth; all of which have a direct impact on the cash flow generation

of the firm and therefore its value. By disposing assets or business units, the

firm signals to become more focused on the remaining operations, as it is

highly likely that it exits those assets that it does not consider to be part of its

core business.403 The argument of the increased focus builds on the rational

that value can get created if the management team concentrates its efforts on

those assets that allow for the highest returns and thereby operating the firm

in a more efficient way.404

(2) Elimination of negative synergies:

The cross-subsidization of investment projects between business units is

considered costly and value destructive for the firm.405 By disposing business

units that are not part of the core business or underperforming due to a lack

of managerial expertise in those areas, the extend of cross-subsidization can

be reduced.406 This is also documented in various research studies that show

that sellers’ gains from disposals are higher if the assets disposed represent

business units that are unrelated to the remaining business areas of a firm,

thereby reducing the degree of diversification of the parent firm.407

(3) Higher transparency and reduction of information asymmetries:

A reduction of the complexity of a firm ultimately leads to lower information

asymmetries with which (potential) investors are confronted.408 The negative

impact of information asymmetries on a firm’s cost of capital has been

403 Cf. John and Ofek (1995), p. 105, Edmans and Mann (2014), p. 5, Evans et al. (2013), p. 121, Hambick and Schecter (1983), p. 233. 404 Cf. John and Ofek (1995), pp. 105-106, 108. 405 Cf. Chevalier (2004), p. 19, Scharfstein and Stein (2000), p. 2538, Eckbo and Thorburn (2013), p. 11. 406 Cf. Eckbo and Thorburn (2013), p. 4, Scharfstein and Stein (2000), p. 2538. 407 Cf. Burch and Nanda (2003), p. 69, Daley et al. (1997), p. 257, Jain et al. (2011), p. 596, Eckbo and Thorburn (2013), pp. 18-19, Gaughan (2007), p. 431. 408 Cf. Krishnaswami and Subramaniam (1999), p. 73, Burch and Nanda (2003), p. 86.


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documented in academia.409 These findings imply that reducing firm

complexity could translate in lower information asymmetries and lower cost

of capital,410 and thereby leading to a higher firm value.

Research studies on corporate restructurings document the value adding impact of

such activities.411 Therefore restructuring activities represent a source of future value

gains, for which a potential buyer might be willing to pay if he/she plans to

implement them after a transaction.412 The extend of the value added of such

activities depends however on the pursued restructuring path and the scale of the

restructuring activities. Consequently, expected value gains from restructuring

activities can become part of acquired goodwill if inefficiencies in the target firm

can be identified prior to the transaction and the acquirer is willing to pay for them.

409 Cf. Eckbo and Thorburn (2013), p. 28. 410 Cf. D’Mello et al. (2005), p. 5, Gilson et al. (2001), p. 566, Krishnaswami and Subramaniam (1999), p. 73, Nanda and Narayanan (1999), p. 176. 411 Cf. Eckbo and Thorburn (2013), pp. 103-104, Habib and Johnson (1997), p. 154. 412 Cf. Haaker (2008), p. 132, Sellhorn (2000), p. 890, KPMG (2010), p. 12.

3 Goodwill treatment and impairment-only approach under IFRS

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3 Goodwill treatment and impairment-only approach

under IFRS

All of the most widely internationally used financial accounting standards (i.e. IFRS

and US-GAAP) distinguish goodwill on its creational level; this means whether

goodwill has been generated internally or was acquired.413 Both IFRS and US-

GAAP prohibit the capitalization of internally generated goodwill, whilst require to

capitalize acquired goodwill on the parent firm’s consolidated balance sheet.414

Acquired goodwill is seen from a top-down approach in financial accounting and

considered as a residual stemming from a completed business combinations.

Goodwill from an accounting perspective represents the difference between the costs

of purchasing a firm and the corresponding fair value of net assets acquired that

fulfill the conditions of being separately recognizable on the consolidated balance

sheet.415

Due to the followed research set-up of this thesis, the focus should be placed on the

International Financial Reporting Standards (IFRS) that deal with goodwill

regarding its treatment in financial accounting. This emphasis was chosen given the

to-be-analyzed research sample and the apparent domination of IFRS application in

Europe. Since 2005, the International Financial Reporting Standards represent the

most widely used accounting standards in the world.416

Relevant accounting standards:

The International Accounting Standards Board (IASB) represents the independent,

accounting standard-setting body of the IFRS Foundation and is responsible for

developing and approving International Financial Reporting Standards (IFRS).417

The most relevant accounting standards developed by the IASB that outline how

413 Cf., for example, IASB (2012), p. 2, SFAS 142.10, Ernst & Young (2014b), p. 6. 414 Cf. BDO International (2014a), p. 3, Kieso et al. (2011), p. 674. 415 Cf. Kieso et al. (2011), p. 674. 416 For academic research on dominance of IFRS, cf., for example, DeFond et al. (2012), pp. 27-28, for industry information on the same matter, cf., for example, Grant Thornton (2012), p. 2, KPMG (2006), p. 5. 417 Cf. IFRS Foundation (2014), p. 1.


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goodwill and other intangible assets should be recorded and dealt with on an

acquirer’s consolidated balance sheet over their respective useful lifes are IFRS 3

Business Combinations, IAS 38 Intangible Assets, and IAS 36 Impairment of

Assets.418

While the accounting standards IFRS 3, IAS 36 and IAS 38 will be explained in

more detail further below, in general they focus on the following main areas:

Fig. 20: Objectives of IFRS 3, IAS 36, and IAS 38 Source: Own illustration.

Under IFRS the recognition of internally generated goodwill on a firm’s

consolidated balance sheet is not allowed, due to the fact that goodwill does not

fulfill the recognition criteria of an intangible asset according to IAS 38 Intangible

418 Cf. Vettiger and Hirzel (2010), pp. 387-388.

Objectives:

• Recognition and measurement of net assets acquired and non-controlling interests

• Recognition and measurement of acquired goodwill and gain from bargain purchase

• Disclosure of information allowing users of financial statements to understand the related financial effects of business combinations

Objectives:

• Accounting treatment of intangible assets (incl. goodwill) that fall under the definition of IAS 38

• Recognition and measurement of intangible assets in financial statements

• Relevant disclosures about intangible assets in notes to the financial statements

Objectives:

• Describes procedures that should be followed to make sure that assets are not carried at more their recoverable amount

• Asset impairment required in case this is not the case (incl. goodwill)

• Impairment review necessary annually or on exception basis

• Disclosure requirements on impaired assets

IFRS 3 Business Combinations

IAS 38 Intangible Assets

IAS 36 Impairment of Assets


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Assets (IAS 38.48).419 IAS 38 requires an entity to recognize an intangible asset,

whether purchased or self-created (at cost) if, and only if:

(1) it is probable that the future economic benefits that are attributable to the

asset will flow to the entity; and

(2) the cost of the asset can be measured reliably (IAS 38.21).420

Whilst one can argue for the future economic benefits that arise from internally

generated goodwill, the criteria of the reliable measurement of the historical cost of

goodwill can certainly be refuted. Consequently, internally generated goodwill

cannot be capitalized (IAS 38.48).

The situation however changes when goodwill is acquired as part of a business

acquisition. This means that the accounting for and recognition of goodwill (and

other intangible assets) then does not depend on their respective type, but rather on

the acquisition situation.421 For goodwill acquired in business combinations, IFRS 3

Business Combinations represents the accounting standard to be applied. Acquired

goodwill under the definition of IFRS 3.32 represents a residual amount, measured

as the difference between the fair value of the consideration transferred (i.e. price

paid) and the net amounts of the identifiable assets acquired and the liabilities

assumed (measured in accordance with IFRS 3).422

The standard setter argues that the reliable measurement exception for goodwill as

outlined in IAS 38 can be refuted due to the assumed at arm’s length transaction of

the acquired assets.423 Acquired goodwill does not get amortized as an indefinite

useful life is implied.424 In subsequent periods, the recoverability of the recognized

goodwill needs to be tested at least annually as outlined in IAS 36 Impairment of

Assets.425

419 Cf. BDO International (2014a), p. 3. 420 Cf. Deloitte (2014a). 421 Cf. Hunter et al. (2012), p. 111. 422 Cf. Deloitte (2014b), Ernst & Young (2013), p. 50. 423 Cf. Kieso et al. (2011), p. 674. 424 Cf. IFRS 3.B69, Ernst & Young (2011), p. 3. 425 Cf. IFRS 3.B69, Ernst & Young (2011), p. 3.


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3.1 Relevance of IFRS 3 for generating acquired

goodwill

The first version of IFRS 3 Business Combinations came into effect on April 1, 2004

and resulted from a joint project between the accounting standard setters IASB and

Financial Accounting Standards Board (FASB).426 Similar to the IASB, the FASB

assumes responsibility in developing generally accepted accounting principles

(GAAP) within the United States of America.427 These joint projects, to which also

IFRS 3 belongs, emerged from the Norwalk Agreement signed by the IASB and

FASB in 2002 which aims at converging IFRS and US GAAP into one set of high

quality and compatible standards.428

The principal goal of IFRS 3 is to improve the financial reporting of business

combinations, so that the financial statements of acquirers reflect the business

combinations’ underlying economics better.429 It intends to convey the

management’s information to the users of financial statements by allocating the

purchase price to the assets acquired and the liabilities assumed, and consequently to

goodwill.

In 2008, the IASB issued a revised version of IFRS 3 to further improve the

accounting for business combinations and to achieve a higher degree of convergence

between IFRS 3 and SFAS 141 which represents FASB’s counterpart to that of the

IASB. The revised version of IFRS 3 is applied prospectively to business

combinations occurring in the first accounting period beginning on or after 1 July

2009. It can be applied early but only to an accounting period beginning on or after

30 June 2007.430

426 Cf. Küting et al. (2008), p. 139, Deloitte (2014b). 427 Cf. FASB (2014c). 428 Cf. Epstein and Jermakowicz (2010), p. 3, FASB (2014b). 429 Cf. IFRS 3.1, Zelger (2005), p. 91, Küting et al. (2008), p. 139. 430 Cf. IFRS 3.64.


84

3.1.1 Definition of a business combination

Business combinations defined by IFRS 3 are transactions or events that result in an

acquirer obtaining control over one or more businesses (IFRS 3.B5).431 According to

the standard, control can be obtained, for example, through a cash-, cash equivalent-

or an asset transfer, or through incurring liabilities of the target firm (IFRS 3.B5).432

A business is defined as ”an integrated set of activities and assets that is capable of

being conducted and managed for the purpose of providing a return in the form of

dividends, lower costs or other economic benefits directly to investors or other

owners, members or participants”433. The standard stresses that in order to qualify as

a business, inputs and process that are applied to those inputs for the purpose of

generating returns have to be observable (IFRS 3.B5). Inputs can be understood as

economic resources in the form of non-current assets (like property, plant and

equipment, machinery, or intangible assets) as well as intellectual property (IFRS

3.B5) that have the ability to create outputs.

Excluded from the scope of IFRS 3 are joint ventures and combinations of

businesses or entities under common control of two or more parties.434 Furthermore,

the accounting standard is not applicable to sole acquisitions of (a group of) assets

that do not constitute a business (IFRS 3.2).

3.1.2 Acquisition method

Business combinations within the scope of IFRS 3 are accounted for under the so-

called acquisition method (IFRS 3.4).435 The application of the acquisition method

follows a four-step approach (IFRS 3.5)436:

431 Cf. Zelger (2005), p. 93. 432 Cf. Schüppen and Walz (2005), pp. 40, 42. 433 IFRS 3 Appendix A. 434 Cf. Zelger (2005), p. 94. 435 Cf. Sommer et al. (2010), pp. 447-448, Deloitte (2014b), BDO International (2014b), p. 3. 436 Cf. Sommer et al. (2010), p. 448, Deloitte (2014b), BDO International (2014b), p. 3.


85

(1) Identification of the acquirer: the combining entity that obtains control of the

acquiree.

(2) Determination of the acquisition date: the date on which the acquirer obtains

control of the acquiree.

(3) Recognition and measurement of the identifiable assets acquired, the

liabilities assumed and any non-controlling interest in the acquired firm.

(4) Recognition and measurement of goodwill or a gain from a bargain

purchase.

3.1.2.1 Identifying the acquirer and determining the acquisition

date

Since the business combination is accounted for from an acquirer firm’s perspective,

the acquirer has to be identified (IFRS 3.6).437 The acquirer in a business

combination is defined as the entity that gains control over another entity (IFRS

3.7). IFRS 3 refers to IFRS 10 Consolidated Financial Statements as well as IAS 27

Consolidated and Separate Financial Statements (IAS 27.4 and IAS 27.13) on how

a controlling influence over an entity in a business combination should be

understood.438 According to IFRS 10.6, “an investor controls an investee when it is

exposed, or has the rights, to variable returns from its involvement with the investee

and has the ability to affect those returns through its power over the investee”439. Put

differently, (i) power to affect returns and (ii) exposure to those returns represent

necessary conditions to affirm the control property. Power refers to an investor’s

ability in directing relevant activities that ultimately translate into the investee’s

returns (IFRS 10.10) and can, for example, be exercised through voting rights or

other contractual arrangements (IFRS 10.11). Exposure usually results from having

437 Cf. Küting et al. (2008), p. 140, Zelger (2005), p. 97, Deloitte (2014b). 438 Cf. Zwirner et al. (2012), p. 425. 439 IFRS 10.6.


86

a claim on surpluses generated by the acquired firm, either through equity ownership

or other legal contracts.440

In case the acquirer cannot be clearly identified by applying the factors outlined in

IFRS 10, IFRS 3 provides additional guidance on the determination of the acquiring

party. Analyzing the deal structure and thereby understanding which party

transferred cash or other assets as well as incurred the liabilities can provide

valuable insights on the question who the acquirer is (IFRS 3.B14). Additionally,

evaluating the relative sizes of the involved parties might be useful. According to

IFRS 3.B16, the acquirer is usually the entity whose size in terms of revenues, assets

or profit exceeds that of the other party involved in a business combination. The

governance structure of the combined entity after a business combination can

provide additional guidance on who the acquirer is in case all the other checks fail to

clearly identify the acquirer. The acquirer is typically the party that holds the largest

proportion of voting rights in the combined entity or is able to elect, appoint or

remove most of the members of the combined entity’s governing body. The

composition of the management team of the combined entity might also provide

insights on the acquiring party (IFRS 3.B15).441

In the following step, IFRS 3 orders to determine the correct acquisition date (IFRS

3.8).442 This is of particular importance as from that day onwards the risks and

rewards of the assets acquired and liabilities assumed will impact the acquirer. The

acquisition date represents the date when control over an acquiree is obtained. This

usually occurs when the consideration is legally transferred, assets acquired and

liabilities of the acquiree assumed (IFRS 3.9).443

3.1.2.2 Recognizing assets acquired and liabilities assumed

As per the acquisition date, the acquirer has to recognize, apart from goodwill, any

identifiable assets acquired, liabilities assumed and non-controlling interest that the

440 Cf. Zelger (2005), p. 101. 441 Cf. PricewaterhouseCoopers (2014). 442 Cf. Küting et al. (2008), p. 141, Sommer et al. (2010), p. 448, Deloitte (2014b). 443 Cf. Sommer et al. (2010), p. 448.


87

acquirer holds in the acquiree (IFRS 3.10).444 Recognition in that context represents

the process of incorporating an item in the acquirer’s balance sheet (IASB

Framework 82). In order to be recognized on the balance sheet, the acquired assets

and assumed liabilities have to fulfill the definition of assets and liabilities according

to the IASB Framework for the preparation and presentation of financial statements

(also referred to as IASB Framework).

As stated in the IASB framework, “an asset is recognized in the balance sheet when

it is probable that the future economic benefits will flow to the enterprise and the

asset has a cost that can be measured reliably”445. Similar to the definition of assets,

the definition of liabilities is also based on the economic benefit and measurement

criteria by stating that “a liability is recognized in the balance sheet when it is

probable that an outflow of resources embodying economic benefits will result from

the settlement of a present obligation and the amount at which the settlement will

take place can be measured reliably”446.

The recognition principle includes also identifiable assets and liabilities that have

not been recognised in the acquiree’s financial statements prior to the business

combination (IFRS 3.13).447 This comprises first and foremost intangible assets for

which a separate accounting standard exists, i.e. IAS 38 Intangible Assets.

3.1.2.3 Measuring assets acquired and liabilities assumed

According to IFRS 3.18, the assets acquired and liabilities assumed in a business

combination should be measured at their respective fair values as per the acquisition

date.448 IFRS understand fair values as “the price that would be received to sell an

asset or paid to transfer a liability in an orderly transaction between market

participants at the measurement date”449. However, for some balance sheet items,

444 Cf. Küting et al. (2008), p. 141, Vettiger and Hirzel (2009), p. 836. 445 IASB Framework 89. 446 IASB Framework 90. 447 Cf. Vettiger and Hirzel (2009), p. 836, Sommer et al. (2010), p. 448, Zelger (2005), p. 107, Zwirner et al. (2012), p. 426. 448 Cf. Sommer et al. (2010), p. 448. 449 IFRS 13.9, IFRS 3 Appendix A.


88

IFRS 3 provides limited recognition or measurement exceptions,450 including

deferred tax assets and liabilities (IFRS 3.24), employee benefits (IFRS 3.26),

indemnification assets (IFRS 3.27), reacquired rights (IFRS 3.29), share-based

payment transactions (IFRS 3.30), and assets held for sale (IFRS 3.31).451

Regarding the measurement of any non-controlling interest in the acquiree, IFRS

3.19 allows the acquiring firm recognizing it at either the non-controlling interest’s

proportionate share of the acquiree’s identifiable net assets or at its fair value.452

This measurement option of the non-controlling interest can be exercised by the

acquiring firm in every transaction in which a non-controlling interest is observable,

meaning that the acquiring firm can decide which option it prefers. This option

however has an impact on the amount of goodwill that ultimately gets capitalized on

the acquirer’s balance sheet.

3.1.2.4 Recognizing and measuring goodwill

The value of recognized goodwill is determined by the difference between the

amount of (i) any transferred consideration (i.e. purchase price), (ii) non-controlling

interest in the acquired firm, and (iii) any previously held equity interest in the target

firm prior to the business combination, over (iv) the acquisition-date fair values of

the acquired net assets (IFRS 3.32).453 In case the acquirer opts for measuring a non-

controlling interest in the acquiree at its fair value, the capitalized goodwill in the

consolidated balance sheet contains amounts that are attributable to the non-

controlling interest. However, if the acquirer chooses to measure the non-controlling

interest by its proportionate fraction of identifiable net assets, then the capitalized

goodwill comprises only of those amounts which are attributable to the controlling

interest (IFRS 3.19). Consequently, the selection of the measurement option of any

450 Cf. IFRS 3.21, Deloitte (2014b). 451 For most of these balance sheet items, IFRS 3 refers back to the principal accounting standards that deal with their individual measurement procedures, i.e. IAS 12 Income Taxes, IAS 19 Employee Benefits, IFRS 2 Share-based Payment, and IFRS 5 Non-current Assets Held for Sale and Discontinued

Operations. 452 Cf. Sommer et al. (2010), p. 449. 453 Cf. Deloitte (2014b), Sommer et al. (2010), p. 448.


89

non-controlling interest in the acquiree influences the amount of goodwill that

ultimately gets recognized on the acquirer’s balance sheet.454

In certain situations, when the value of goodwill in a business combination as per

the acquisition date is negative, a so-called bargain purchase occurs (IFRS 3.34).455

This can happen if the seller of a firm is forced to sell at a price below the fair value

of the firm’s net assets, for example in a forced or distressed sale (IFRS 3.35). In a

bargain purchase, then the acquirer has to recognize a profit of this amount as per

the acquisition date (IFRS 3.35).456

3.1.3 Disclosures

IFRS 3 requires the acquirer to make substantial information disclosure about the

business combination that “enables users of its financial statements to evaluate the

nature and financial effect of a business combination (…)”457. Disclosure

requirements not only include information about acquisitions during the current

accounting period. They also include information on the effects of business

combinations of previous reporting periods that are observable in the current

reporting period (IFRS 3.61).458

Some of the disclosure requirements for the acquirer can be found in the table

below:

454 Cf. Gianini and Riniker (2009), pp. 69-70, Küting et al. (2008), p. 141, Zelger (2005), pp. 132-133, Zwirner et al. (2012), p. 426. 455 Cf. Sommer et al. (2010), p. 448. 456 Cf. Heyd and Lutz-Ingold (2005), p. 161. 457 IFRS 3.59. 458 Cf. Zelger (2005), pp. 131-132.


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Fig. 21: Disclosure requirements under IFRS 3 Source: IFRS 3.B64 and B67.

Disclosure requirements in the current reporting period

Relating to business combinations during the

current reporting period

Relating to the effects of business

combinations during the previous reporting

periods

Disclosure requirements include:

• Goodwill reconciliation for the beginning and end of the reporting period, including:

- Gross amount of goodwill at the beginning of the reporting period

- Any accumulated impairment

charges as per the beginning of the reporting period

- Any new, additional amounts of

goodwill recognized during the reporting period

- Any new impairment charges incurred during the reporting period

- Gross amount of goodwill and accumulated impairment charges as per the end of the reporting period

• Amounts and reasons for any gains or

losses that resulted from assets acquired

or liabilities assumed in a business combination during the reporting period

• Fraction of goodwill that is allocated to business units classified as held for sale

• Any adjustments and reasons for adjustments to previously published amounts in a business combination

Disclosure requirements include:

• Name of acquiree and its business

description

• Acquisition date when control was obtained

• Percentage of acquired voting equity

interest

• Reasons for acquisition and information on how control was obtained

• Qualitative description of goodwill

components like synergies or unrecognizable intangible assets

• Amounts recognized of each major class of acquired assets and liabilities

assumed

• Information on acquired receivables, contingent consideration arrangements and indemnification assets

• For bargain purchases, the amount of the recognized gain (if applicable)

• For transaction in which less than 100 per cent of equity interest is acquired, the amount of any non-controlling

interest in the acquiree (if applicable)

• Amount of revenues and profit/loss in the consolidated financial statements that have resulted from the business

combination


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3.1.4 Typical impacts on financial statements in a

purchase price allocation

A typical business combination impacts not only the balance sheet of the combined

entity at the acquisition date but also the profit and loss statement in the subsequent

reporting periods.459 Whilst every acquisition and target firm is unique, some of the

most frequent occurrences in business combinations should be analyzed from the

acquirer’s point of view.

The effects of a business combination according to IFRS 3 become primarily

observable in the consolidated financial statements of the combined entity after the

acquisition. According to IFRS 10 Consolidated Financial Statements, consolidated

financial statements have to be prepared when an entity, i.e. the parent firm, controls

one or more other entities, i.e. subsidiaries (IFRS 10.2). In a consolidated financial

statement, the financial positions and operational results are presented in a combined

format as if the individual firms were a single entity by eliminating any intra-entity,

also referred to as intra-group, activities.460

The reason for goodwill and other newly recognized assets of the acquiree, primarily

intangible assets, emerging in the consolidated balance sheet grounds on the

required consolidation technique. Whilst the parent firm’s investment in a

subsidiary’s equity capital is usually recorded in the individual balance sheet as an

investment, in the consolidated balance sheet this investment (in itself) gets

eliminated due to the single-entity-principle.461

Methodological-wise, the difference between the historical cost, i.e. purchase price,

and the book value of the acquired proportion of the acquiree’s equity capital, then

gets translated on the consolidated balance sheet in individual balance sheet

459 Cf. Vettiger and Hirzel (2009), pp. 836-837. 460 Cf. Stickney et al. (2010), p. 776. The separate financial statements of the parent firm and subsidiaries act as the starting point for the consolidation. During an accretion process, the balances of the firms are added together and any intra-entity transactions eliminated. First and foremost the elimination and adjustment process includes (i) investments in subsidiaries, (ii) intercompany revenues, i.e. transfer pricing, and (iii) intra-entities financing schemes. 461 Cf. Stickney et al. (2010), p. 776.


92

positions. This process is also called purchase price allocation (PPA).462 The

purpose of a PPA is primarily to break down the purchase price in individually

recognizable balance sheet positions, i.e. identification, and to value them at their

correct fair value as per the acquisition date, i.e. valuation.463

Fig. 22: Procedure of a purchase price allocation under IFRS 3

Source: Zelger (2005), p. 97.

Newly recognized assets, remeasurements of existing assets and goodwill:

After the business combination, the consolidated balance sheet is impacted by

several new accounting entries. Amongst the most typical and common ones are

goodwill, newly recognized intangible assets, fair-value step ups on exiting assets

and liabilities, and deferred taxes.464

462 Cf. Leibfried and Fassnacht (2007), p. 48. 463 Cf. Sommer et al. (2010), p. 447, Vettiger and Hirzel (2009), p. 836. 464 Cf. Zelger (2005), p. 118, Heyd and Lutz-Ingold (2005), p. 171, KPMG (2010), p. 7, Altmann and Schilling (2011), p. 2, Leibfried and Fassnacht (2007), p. 56.

Identification of acquirer

Determination of purchase

price

Assets acquired and liabilities assumed

Identification Valuation

Goodwill allocation

Disclosure in notes to financial

statements

Determination of materiality

threshold

Determination of useful lifes


93

Fig. 23: Consolidated balance sheet impacts from a purchase price allocation

under IFRS 3

Source: Own illustration, according to Zelger (2005), p. 118.

(1) Goodwill:

In the purchase price allocation, the derivation of goodwill represents a pure

residual.465 It emerges if the fair value of the consideration transferred, i.e.

purchase price, exceeds the book value of acquired net assets capitalized on the

acquiree’s balance sheet prior to the acquisition, any impacts from fair value

remeasurements of existing assets and liabilities, the fair value of newly

recognized assets or liabilities, and deferred taxes that might arise due to

different treatment of assets from a tax accounting versus a financial

accounting point of view. Goodwill is therefore interconnected with the assets’

and liabilities’ characteristics of the target firm and the subjective assumptions

by the acquirer expresses in the purchase price.466

465 Cf. IFRS 3.52, Brösel and Zwirner (2009), p. 191, Castedello (2009), p. 56. 466 Cf. Heyd and Lutz-Ingold (2005), p. 160, Shalev (2007), p. 2.

Goodwill (1)

Equity increase

Debt

Equity/LiabilitiesAssets

Other intangible assets (2)

Existing assets

Consolidated balance sheet of parent company after a business combination

Typical balance sheet effects from a business combination:

(1) Recognition of goodwill (residual) from the acquisition

(2) Recognition of new intangible assets of acquiree that meet recognition criteria

(3) Fair value step-ups of existing net assets of acquiree

(4) Creation of deferred tax liabilities (or assets)

Fair value step-ups (3)

Deferred tax liab. (4)

►

Net balance sheet effect of (1) – (4) booked against equity reserves account in the consolidated balance sheet

Equity (before business combination)

B/S


94

(2) Newly recognized intangible assets:

The acquisition method required an acquiring firm to recognize not only those

assets that have been previously recognised on the balance sheet of the

acquiree, but also previously unrecognised intangible assets acquired in the

business combination.467 Those typically include company and product brands,

technologies, patents, or research and development projects, which according

to IFRS 3 have to be fair valued at their initial recognition.468 Basically, those

newly recognized intangible assets can be categorized into469:

• Marketing-related intangible assets, like trademarks, internet domain

names or non-competition agreements;

• Customer-related intangible assets, like customer lists, order or production

backlogs, customer contracts and related customer relationships;

• Artistic-related intangible assets, like pictures and photographs, video or

audiovisual material including films and TV programs;

• Contract-based intangible assets, like licensing or royalty agreements,

franchise agreements, service or supply contracts, mining or drilling rights;

• Technology-based intangible assets, like patented and unpatented

technologies, computer software, databases or trade secrets.

(3) Fair-value step ups on exiting assets:

According to the acquisition method, assets acquired and liabilities assumed

that fulfil the recognition criteria have to be fair valued on the consolidated

balance sheet (IFRS 3.18). For assets and liabilities that have already be

recognized on the acquiree’s balance sheet prior to the business combinations,

so-called fair value step ups might be required.470 If their book value lies below

their fair value, fair value step-ups are recorded in the consolidated balance

sheet. Certain asset classes tend to be prone for such step-ups like inventory,

machinery and equipment, real estate, or buildings, as those might have been

467 Cf. IFRS3.11, Zwirner et al. (2012), p. 425, Küting et al. (2008), p. 141. 468 Cf. IAS 3.18, Küting et al. (2008), p. 141, Sommer et al. (2010), p. 448. 469 Cf. IFRS 3 Illustrative Examples A-E, Zelger (2005), p. 111, KPMG (2011), p. 21, Austin (2007), p. 69. 470 Cf. Sommer et al. (2010), p. 451, Vettiger and Hirzel (2009), p. 836, Zwirner et al. (2012), p. 425.


95

recorded at their historical costs on the acquiree’s balance sheet prior to the

acquisition.471

(4) Deferred taxes:

Deferred tax assets or liabilities might also occur as a consequence of a

business combination.472 In a business combination, deferred tax assets or

liabilities get primarily triggered due to different treatments of fair value step-

ups of pre-exiting net assets from a financial accounting and taxation point of

view as well as new assets that have not been recognized prior to the

acquisition on the acquiree’s balance sheet.473 Whilst the book values of pre-

exiting assets can increase due to the fair value re-measurement requirement of

IFRS 3, local tax laws might not allow such step-ups in the tax balance sheet of

the firm, resulting in differences in depreciation charges over future periods.474

Additionally, deferred tax assets and liabilities could emerge due to differences

in useful lifes of newly recognized intangible assets under local GAAP and

local tax laws. In case allowed useful lifes of newly recognized intangible

assets differ under national tax laws and local GAAP, temporary differences in

the income deductibility of an asset’s depreciation charges could emerge in the

balance sheet prepared under local GAAP and tax laws.

3.2 Recognition possibilities of other intangible assets

apart from goodwill in business combinations

according to IAS 38

To fully understand the nature of acquired goodwill from an accounting perspective,

it is important to understand which intangible assets apart from goodwill can be

recognized in a business combination and which, even more importantly, cannot and

471 Cf. KPMG (2010), p. 7, Zelger (2005), p. 107, Heyd and Lutz-Ingold (2005), pp. 158-159. 472 Cf. Glaum and Wyrwa (2011), p. 24, Heyd and Lutz-Ingold (2005), p. 160, Zwirner et al. (2012), p. 431, Vettiger and Hirzel (2006), p. 8. 473 Cf. Glaum and Wyrwa (2011), p. 24, Zelger (2005), pp. 126-127, Heyd and Lutz-Ingold (2005), pp. 161-164. 474 Cf. Zelger (2005), p. 126.


96

therefore subsumed in the acquired goodwill.475 As outlined above, accounting

standards use a top down approach in their definition of acquired goodwill, thereby

stripping out any recognizable assets from the purchase premium, leaving the

acquired goodwill as a pure residual of non-separable assets.476

The recognition of intangible assets in purchase price allocations determines the

amount of goodwill that ultimately gets capitalized on the acquirer’s balance

sheet.477 The higher the amount of intangible assets recognized on the acquirer’s

balance sheet as a consequence of the purchase price allocation, the lower the to-be-

capitalized amount of goodwill, and vice versa.478

475 Cf. Wendlandt and Vogler (2003), p. 68. 476 Cf. Brösel and Zwirner (2009), p. 191, Castedello (2009), p. 56, Kieso et al. (2011), p. 674. 477 Cf. Shalev et al. (2013), p. 820, Shalev (2007), p. 33, Detzen and Zülch (2012), p. 108. 478 Cf. Leibfried and Fassnacht (2007), p. 56.


97

Fig. 24: Possible intangible assets apart from goodwill in business combinations


Which intangible assets, apart from goodwill, can be recognized on an acquirer’s

consolidated balance sheet in a business combination gets specified in IAS 38

Intangible Assets. However, IAS 38 not only deals with acquired intangible assets in

0%

20%

40%

60%

80%

100%

(1)


(2)


(2)-(1)=

Acquistion Premium

(3)


(4)


(6)


(3)+(4)+(5)+(6)=


Amount

(1)

Book

value of

acquiree’s

net assets

(2)

Cost of

acquired

company

(3)

= (2)-(1)

Excess

purchase

premium

(4)

Fair value

step-up

of acquiree’s

already

recognized

net assets

(5)

Fair value of

intangible

assets from

first time

consolidation

(6)

Deferred

taxes (on fair

value step-

ups and

intangible

assets)

(7) = (3)-(4)

-(5)+/-(6)

Goodwill

(as a

residual)

Technology

based

Contract

related

Artistic

related

Customer

related

Marketing

related Good-

will


98

a business combination.479 It also clarifies the recognition possibilities of internally

generated intangible assets as well as separately acquired intangible assets.480 IAS

38’s overall objective is the accounting treatment of intangible assets and requires a

firm to recognize an intangible asset in the sense of this standard if both the

intangibility as well as asset criteria are fulfilled (IAS 38.1).481

3.2.1 Definition of an intangible asset

IAS 38.8 defines an intangible asset as an asset that is (i) identifiable, (ii) non-

monetary and (iii) without physical substance.482 The accounting standard

understands an asset as a resource that is controlled by the entity and expected to

provide the entity with future economic benefits (IAS 38.8). According to the

definitions made by IAS 38, an intangible asset has to fulfill both the properties of

being an asset as well as being intangible.

Fig. 25: Properties of an intangible asset under IFRS

Source: Own illustration, according to IAS 38.8-17.

479 Cf. Tsalavoutas et al. (2014), p. 25. 480 Cf. Austin (2007), p. 67, Powell (2003), p. 800. 481 IAS 38 specifically excludes (i) intangible assets that are dealt with in other IAS/IFRS standards, (ii) financial assets (IAS 32), (iii) exploration and evaluation assets, as well as (iv) expenditures that relate to the extraction and development of non-generative resources (Cf. IAS 38.2). Among the intangible assets covered in other standards are leases (IAS 17), deferred tax assets (IAS 12), employee benefits (IAS 19), or acquired goodwill (IFRS 3) (Cf. IAS 38.2). 482 Cf. Austin (2007), p. 65, Bucher and Wildberger (2004), p. 610, Tsalavoutas et al. (2014), p. 25.

Intangible assset

(1) Identifiability (3) Future

economic benefits (2) Control

Properties (IAS 38.8-17)


99

In its definition, IAS 38 focuses primarily on the criteria identifiability, control, and

future economic benefits when describing the properties of an intangible asset.483

This observation can imply that the IASB as the standard setter seems less

concerned with the non-monetary and without physical substance criteria given their

characteristics of being rather intuitive and easier to be tested for the firm as well as

for auditors than the other defining principles. The primary criteria of an intangible

asset set out in IAS 38.8-17, i.e. identifiability, resource control, and future

economic benefits arising from it to the entity, represent necessary prerequisites for

an intangible asset to be recognized on an entity’s balance sheet.484

(1) Identifiability: An asset is considered to be identifiable if it is hypothetically

possible for a firm to separate or divide it from the entity for the purposes of

selling, transferring, licensing, or exchanging it (so-called separability

criterion).485 The identifiability criterion is also fulfilled if the asset represents

the outcome of a contractual or other legal right that the entity holds.486 In this

case, these rights do not have to be necessarily transferable or separable (IAS

38.11-12).

(2) Control: Control emerges from a firm’s power to exclusively make use of the

future economic benefits that arise from an asset.487 Additionally, having the

power to restrain other firms from using the asset qualifies also for the

definition of control.488 A strong indication for control represents a legal right

which is enforceable. Legal enforceability however does not represent a

necessary prerequisite for control, in case exclusivity of future economic

benefits can be controlled or restricted in other ways (IAS 38.13-16).

(3) Future economic benefits: In most cases, future economic benefits from an

intangible asset represent factors which have the ability to positively impact a

firm’s cash flow generation, for example, through higher revenues, accelerated

483 Cf. Wendlandt and Vogler (2003), p. 67. 484 Cf. Epstein and Jermakowicz (2008), pp. 296-297. 485 Cf. Bucher and Wildberger (2004), p. 610, Austin (2007), p. 65. 486 Cf. Wendlandt and Vogler (2003), p. 67. 487 Cf. Christian and Lüdenbach (2013), p. 338. 488 Cf. Hayd and Lutz-Ingold (2005), p. 25, Epstein and Jermakowicz (2008), p. 297.


100

growth rates, lower costs or other monetary quantifiable benefits.489 The future

economic benefits of, for example, intellectual property in use in a firm’s

production process could increase the firm’s current and future revenues or

reduce future production costs (IAS 38.17).

3.2.2 Recognition and measurement

In order to be eligible for recognition on a firm’s balance sheet, an intangible asset

needs also to fulfill the specific recognition criteria outlined in the standard, besides

meeting the definition criteria of an intangible asset of identifiability, control, and

future economic benefits (IAS 38.18).490 The underlying principles of the

recognition criteria lie in a firm’s ability to (i) reliably measure the cost of the

intangible asset in question as well as in (ii) the probability of the intangible asset’s

future economic benefits flowing to the entity (IAS 38.21).491 The probability of

future economic benefits arising from the asset and flowing to the firm in future

periods needs to be demonstrated by the firm that aims to capitalize an intangible

asset. On that topic, IAS 38 requires a firm to “assess the probability of expected

future economic benefits using reasonable and supportable assumptions that

represent management’s best estimate of the set of economic conditions that will

exist of the useful life of the asset”492. In case an intangible asset in question fulfills

the recognition criteria, it is measured initially at cost (IAS 38.24).493

On the topic of acquired intangible assets, IAS 38 additionally provides specific

guidance for and details about their treatment in business combinations.494 When an

intangible asset gets acquired in a business combination, the fair value of the

intangible asset as per the acquisition date represents its cost and therefore the basis

for its capitalization on the acquirer’s consolidated balance sheet (if the recognition

489 Cf. Christian and Lüdenbach (2013), p. 338. 490 Cf. Austin (2007), p. 65, Epstein and Jermakowicz (2008), p. 297, Christian and Lüdenbach (2013), p. 338. 491 Cf. Powell (2003), p. 800, Christian and Lüdenbach (2013), p. 338. 492 IAS 38.22. 493 Cf. Christian and Lüdenbach (2013), p. 338. 494 Cf. Tsalavoutas et al. (2014), p. 25, Hayd and Lutz-Ingold (2005), p. 37, Wendlandt and Vogler (2003), p. 67.


101

criteria are met).495 According to the accounting standard, for intangible assets

acquired in business combinations, the probability criterion of future economic

benefits as well as the measurement criterion can always be affirmed (IAS 38.33).496

This assertion on the probability criterion is based on the rational that intangible

assets acquired in business transactions reflect the positive expectation of the

acquirer of the thereof resulting future economic benefits.497 This means that an

intangible asset that fulfills the above stated criteria will always be recognized on

the acquirer’s consolidated balance sheet irrespective of whether it has been

previously capitalized on the acquiree’s balance sheet.

IAS 38.35 outlines that under normal circumstances the fair value of in business

combinations acquired intangible assets can be measured with sufficient reliability,

in order to recognize it separately from goodwill in the consolidated balance sheet of

the acquirer. This is supposed to hold true for intangible assets with an indefinite

useful life. An asset’s useful life represents the time period that it is expected to be

available for its use to an entity (IAS 38.8). The assessment whether an asset is

deemed to be finite or infinite must be made by the entity by considering all

reasonable and supportable assumptions.498 In the periods after the initial

recognition, the entity can choose to apply either a cost or revaluation model in case

the intangible asset is or could be traded in an active market (IAS 38.72).499

However for most intangible assets in business combinations the availability of

active markets must be refuted due to their high individuality, requiring them to be

carried at cost.500 Similar to tangible assets, under the historical cost accounting

policy, intangible assets are carried at their historical costs less any accumulated

amortizations or impairments (IAS 38.74). In the revaluation case, the asset is

capitalized on the acquirer’s balance sheet with its revalued amount, representing its

495 Cf. Zelger (2005), p. 112, Hayd and Lutz-Ingold (2005), p. 37. 496 Cf. Wendlandt and Vogler (2003), p. 67, Tsalavoutas et al. (2014), p. 25, Zelger (2005), p. 112, Hayd and Lutz-Ingold (2005), p. 37. 497 Cf. Tsalavoutas et al. (2014), p. 25. 498 Cf. Wendlandt and Vogler (2003), p. 67, 499 Cf. Epstein and Jermakowicz (2008), p. 306. 500 Cf. Christian and Lüdenbach (2013), p. 342.


102

fair value, i.e. its current market price, less any subsequent impairments or

amortizations (IAS 38.75).

In each reporting period, the firm is required to analyze whether the initially

assumed useful lifes can still be reasonably supported (IAS 38.104). This

requirement holds true for intangible assets with both finite and infinite useful

lifes.501 A shortening of the applicable useful lifes would not only require changing

the annual amortization charges in periods to come, but also in severe cases an

impairment of the intangible asset in question (IAS 38.109-110).502

3.3 Procedure for goodwill impairment testing under

IAS 36

The IASB requires firms to make sure that assets recognized on firms’ balance

sheets are generally not overstated (IAS 36.1).503 Specifically, IAS 36 Impairment of

Assets focusses exclusively on detailing procedures to follow and methods to apply

in order to ensure that assets, including acquired goodwill, are not carried at more

than the amount that could be recovered by either using them in the firm’s

operations or selling them to a third party (IAS 36.1).504 The value that builds upon

those considerations is called recoverable amount and after being determined,

compared to the asset’s book value, referred to as carrying amount (IAS 36.6).505

If assets like goodwill are identified to be recorded at a higher value than their

recoverable amount, they must to be impaired and an impairment loss has to be

501 Cf. IAS 38.104, Bucher and Wildberger (2004), p. 610. 502 The assessment and the methodology of an asset impairment is specified in detail in IAS 36 Impairment of Assets and outlined in the chapter 3.3. 503 Cf. Kolitz et al. (2009), p. 285, Wendlandt and Vogler (2003), p. 70. The assets scoped out are inventories, assets arising from construction contracts, deferred tax assets, assets arising from employee benefits, financial assets within the scope of IAS 39 Financial instruments: Recognition and

measurement, investment properties measured at fair value, biological assets, assets falling within the scope of IFRS 4 Insurance contracts, and non-current assets held for disposal in accordance with IFRS 5 Non-current assets held for sale and discontinued operations (IAS 36.2) (Cf. Hachmeister (2005), p. 194). 504 Cf. Amiraslani et al. (2013), p. 12. 505 Cf. Glaum and Wyrwa (2011), p. 25, Amiraslani et al. (2013), p. 12.


103

recognized in the firm’s profit and loss statement (IAS 36.8).506 In contrast to other

assets that fall under the regulations of IAS 36, impairment losses on goodwill

cannot be reversed at a later date even it is found out that its value has recovered

since the impairment loss had been booked (IAS 36.1 and 124).507 This means that

write-offs on goodwill are definitive, even if the recoverable amount increases in

subsequent periods.

The following figure provides an overview about the impairment test methodology

according to IAS 36 Impairment of Assets. Highlighted in green are the relevant

steps to follow as required by the standard and explained in detail further below:

506 Cf. Zülch and Siggelkow (2012), p. 384. 507 Cf. Tsalavoutas et al. (2014), p. 35, Glaum and Wyrwa (2011), p. 25, Meyer and Halberkann (2012), p. 314.


104

Fig. 26: Impairment test methodology according to IAS 36

Source: Amiraslani et al. (2013), p. 16.

3.3.1 Frequency of impairment testing and indicators

of impairment

Due to the risky economic nature of acquired goodwill and its recognition on an

acquirer’s balance sheet, IAS 36 requires firms to test goodwill at least annually for

Reduce carrying amount of goodwill

End

Carrying amount >

Recoverable amount?

Reduce carrying amount to recoverable

amount

Yes No

Identify CGU to which asset belongs

Allocate goodwill and indefinite-life intangibles

to CGU(s)

Determine recoverable amount

Carrying amount >

Recoverable amount?

Any indications of impairment?

Recoverable amount can be estimated individually?

Start

Goodwill or other assets with indefinite useful life?

Yes No

Yes No No Yes

No Yes

If also: CA - RA > goodwill,

then reduce also other assets pro rata based on their

carrying amounts


105

impairment (IAS 36.8 and 10).508 Besides the annual review, firms are obligated to

test acquired goodwill on an ad-hoc basis if indications have been identified that

could argue for an impairment loss (IAS 36.9).509 IAS 36 provides a non-exhaustive

list of various indicators that a firm should consider in assessing the likelihood of

goodwill impairment (IAS 36.12).510 In general, both internal and external sources

of information should be used in the firm’s analysis (IAS 36.12). These indicators

are also frequently referred to as triggering events.511

It is important to understand that assets apart from goodwill can have a determining

impact on the value of goodwill and impairment risk, as in business combinations

the acquirer normally pays a premium to gain control over an acquiree’s assets in

order to deploy them according to his/her desired strategy. In some cases, certain

assets deemed necessary for the implementation of a strategy fail to deliver the

expected benefits or become damaged. Then these assets have a significant impact in

a firm’s goodwill impairment risk. Amongst those triggering events that are

explicitly highlighted in IAS 36.12 are:

(1) External information sources512

:

(a) Declining market value of an asset:

Indicators can be observed that the market value of an asset has declined

significantly due to other reasons than its normal usage in the firm’s

production process.

(b) Adverse effects from changes in the firm’s operational environment:

Changes in the economic, market, technological or legal environment to

which an asset is prone could have resulted in adverse effects on the asset,

thereby negatively impacting the asset’s future economic benefits.

508 Cf. Zülch and Siggelkow (2011), p. 4, Tsalavoutas et al. (2014), p. 43. 509 Cf. Wendlandt and Vogler (2003), p. 71, Zülch and Siggelkow (2011), pp. 3-4. 510 The indications outlined in IAS 36.12 are of general nature, i.e. they deal not only with goodwill to be impaired. The indications are applicable to any asset covered by this accounting standard. 511 Cf. Amiraslani et al. (2013), p. 12, Glaum and Wyrwa (2011), p. 26. 512 Cf. Glaum and Wyrwa (2011), p. 65, Zülch and Siggelkow (2012), p. 384, Comiskey and Mulford (2010), pp. 751-752. Cf. also Duff and Phelps (2009) and Duff and Phelps (2011) who surveyed and analysed the frequency of both internal and external triggering events in firms.


106

(c) Increasing discount rates used for valuation purposes:

Market interest rates and market returns that are linked to the determination

of the discount rates used in the calculation of the recoverable amounts

could have increased and thereby negatively impacting its value.

(d) Observable market vs. book value gap of assets:

A widening gap between a firm’s net asset value and its market

capitalization could point into the direction that some of the firm’s assets

might be impaired and have to be written off in order to converge the

opinions of key stakeholders. This however is only observable for firms

that are partially or fully quoted on a stock exchange.

(2) Internal information sources513

:

(a) Damage or obsolescence of an important asset:

The firm observes that a key asset or resource is damaged and therefore has

limited usability in a firm’s operations. An asset’s obsolescence is also

considered as a triggering event according to IAS 36.

(b) Strategic considerations impacting the future usage of key assets:

The firm could consider strategic changes or reorganizations that would

result in a different usage of certain assets. In case these considerations

would negatively impact the expected future economic benefits of certain

assets or asset classes, the firm is required to test their recoverable

amount(s).

(c) Declining economic performance of an asset:

The firm obtains information from internal reporting, for example during

the business planning process, that lead to the conclusion that an asset’s

current or future performance is worse than expected.

The sophistication of internal reporting systems plays an important role in a firm’s

ability to spot or argue against potential triggering event, especially then, when a

firm is not listed on a stock exchange.514 Many of the above outlined indicators rely

513 Cf. Zülch and Siggelkow (2012), p. 384, Comiskey and Mulford (2010), p. 752. 514 Cf. Haaker (2005), Haaker (2006a), Olbrich (2006), Haaker (2006b), and Klingelhöfer (2006), who discuss in how far internal reporting systems can support the impairment-only approach in firms and whether the impairment-only approach can also be used for performance measurement purposes.


107

on forecasted business plans and the thereof resulting variance analyses in which

expected cash flows are compared to actual cash flows generated during the

reporting period (IAS 36.14). In case one or more indicators can be identified that

could argue for an asset to be impaired, the firm is required to calculate its

recoverable amount.515

3.3.2 Cash generating units as valuation objects in

goodwill impairment testing

In general, IAS 36 requires a firm to test for impairment on an individual asset basis

(IAS 36.22, 66 and 68).516 However, as goodwill does not generate largely

independent cash flows from other assets by itself, the recoverable amount of

goodwill must be determined in combination with other tangible and intangible

assets in a so-called cash-generating unit (IAS 36.67 and 81).517 The accounting

standard defines a cash-generating unit (CGU) as “the smallest identifiable group of

assets that generates cash inflows that are largely independent of the cash inflows

from other assets or groups of assets”518.

For the purpose of goodwill impairment testing, the acquirer allocates portions of

acquired goodwill to CGUs or groups of CGUs that are assumed to benefit from the

effects subsumed in acquired goodwill, i.e. primarily synergies (IAS 36.80).519

Basically, the acquirer has the freedom of defining CGUs according to his subjective

believes which business units will benefit to what extend from the expected

synergies of the business combination.520 Restrictions however apply from the firm’s

internal monitoring systems and the firm’s operating segments point of view (IAS

36.80). The standard outlines that CGUs should be large enough to efficiently

515 Cf. Comiskey and Mulford (2010), p. 746, Amiraslani et al. (2013), p. 12, Zülch and Siggelkow (2011), p. 2, Meyer and Halberkann (2012), p. 313. 516 Cf. Kasperzak (2011), p. 3, Budde (2005), p. 2567. 517 Cf. Siggelkow and Zülch (2013b), p. 70, Amiraslani et al. (2013), p. 13, Meyer and Halberkann (2012), p. 313. 518 IAS 36.6. 519 Cf. Glaum and Wyrwa (2011), p. 25, Pottgießer et al. (2005), p. 1748, Amiraslani et al. (2013), p. 14. 520 Cf. Müller and Reinke (2010), p. 234, Amiraslani et al. (2013), p. 14.


108

monitor its performance from an internal management perspective, however not be

larger than the firm’s operating segments in accordance to IFRS 8 Operating

segment (IAS 36.80).521

Between those two limits, the acquirer can choose any number or combination of

CGUs to which the acquirer allocates acquired goodwill. The firm’s autonomy in

CGU construction is also underscored by the its freedom to allocate goodwill to

CGUs that do not even contain any assets or liabilities of the acquiree (IAS 36.80).

Consequently, a separation between acquired goodwill and acquired net assets can

take place in the goodwill allocation and CGU construction process. The CGU or

group of CGUs have to be tested whenever indications are present that could lead to

the conclusion of goodwill being impaired. Besides the ad-hoc impairment testing,

the firm is required to test the recoverable amount of CGUs to which acquired

goodwill has been allocated at any point of time during the reporting period as long

as it is carried out at the same time each year (IAS 36.96).

3.3.3 Measuring the carrying amount of a CGU

The carrying amount of a cash generating unit consists of the book values of those

assets that are directly and exclusively attributed to the CGU and expected to

generate the CGU’s cash inflows used to determine its recoverable amount (IAS

36.76).522 It is important that the allocation of assets by the firm follows a reasonable

and consistent methodology, as the CGU’s recoverable amount builds only on those

cash inflows that those attributed assets generate and excludes those of assets that

are not part of the CGU’s carrying amount (IAS 36.76).523 This congruence and

matching principle between a CGU’s assets and their cash inflows is not only

important from the perspective of measuring reliably the recoverable amount. As

IAS 36’s impairment testing procedures follow a comparative measurement

methodology between the recoverable and carrying amount, it is even more

521 Cf. Kasperzak (2011), pp. 3-4, Zülch and Siggelkow (2012), pp. 384-385, Pottgießer et al. (2005), p. 1748. 522 Cf. Meyer-Wegelin (2009), p. 96, Brösel and Klassen (2006), p. 454, Kasperzak (2011), p. 12. 523 Cf. Kasperzak (2011), p. 12.


109

important that the CGU contains all assets that underlie the CGU’s cash inflow

forecast. Leaving assets aside in determining the carrying amount and including

them in the calculation of the recoverable amount could make the CGU appear to be

fully recoverable when actually an impairment loss has evolved (IAS 36.77).524

Consequently, the allocation mechanism poses substantial risks during the

impairment testing process as assets can easily be left aside to the benefit of the firm

that conducts the impairment test.525

In case a hypothetical buyer of the CGU in question would assume also directly

related liabilities, for example for site restorations linked to a particular business

unit, a CGU can also contain recognized liabilities (IAS 36.76). Those would then

be subtracted from the allocated assets to come up with the carrying amount of the

CGU (IAS 36.76).526 Typical assets allocated to a CGU include net working capital

positions, tangible and intangible assets incl. corporate assets, and goodwill.527

Fig. 27: Carrying amount components of a cash generating unit

Source: PricewaterhouseCoopers (2009), p. 9.

524 Cf. Müller and Reinke (2010), p. 234. 525 Cf. Pottgießer et al. (2005), p. 1748, Müller and Reinke (2010), p. 234. 526 Cf. Brösel and Klassen (2006), p. 455. 527 Cf. PricewaterhouseCoopers (2009), p. 9, Hachmeister (2005), pp. 201-202.

Net working capital

Directly attributable assets (tangible and intangible assets)

Allocated goodwill

Allocated share of corporate assets

Attributable liabilities (if applicable)

Carrying amount of cash generating unit (CGU)

plus:

plus: plus:

less:

=


110

3.3.4 Measuring the recoverable amount of a CGU

After having derived the carrying amount of a CGU, the firm is required to

determine its recoverable amount to understand whether goodwill allocated to a

CGU is impaired or not. IAS 36 allows to choose between two valuation

approaches, i.e. fair value less costs of disposal (FVLCD) and value in use (VIU),

and to select the one that provides the highest value for a CGU (IAS 36.19).528 CGU

characteristics however can limit the applicability of the FVLCD measurement

concept (IAS 36.20). Only if both valuation methodologies come up with a value for

a CGU that is below the carrying amount, an impairment loss must be recognized

(IAS 36.59).

Fig. 28: Measuring the recoverable amount of a CGU

Source: Amiraslani et al. (2013), p. 13.

3.3.4.1 Fair value less costs of disposal

The measurement concept fair value less costs of disposal represents one of the two

valuation methodologies to determine a CGU’s recoverable amount (IAS 36.18).529

FVLCD is defined as the “amount obtained from the sale of (…) a cash generating

unit in an arm’s length transaction between knowledgeable, willing parties, less the

528 Cf. Pottgießer et al. (2005), p. 1748, Hachmeister (2005), p. 194, Amiraslani et al. (2013), p. 13. 529 Cf. Brösel and Klassen (2006), pp. 454-455, Meyer and Halberkann (2012), p. 314, Tsalavoutas et al. (2014), p. 36.

The value of an asset or CGU

Carrying amount

Recoverable amount

Fair value less costs of disposal

Value in use

lower of:

higher of:


111

costs of disposal”530. The costs of disposal represent incremental costs that would

incur to the firm due to the sale of the CGU (IAS 36.6). Typical example of such

costs include legal fees, costs of removing the CGU, or incremental costs that occur

in order to bring the CGU into condition for its sale (IAS 36.28).

The underlying principle of the FVLCD is to base the value of a CGU on a third

party evaluation.531 The best indicators for the FVLCD denotes a binding sale

agreement at arm’s length between the firm and a willing third party in which the

acquisition price for the CGU in question is specified or the availability of quoted

prices on a market with a sufficiently large number of transactions for identical

assets (IAS 36.20).532 Additionally, information on transactions of similar assets (or

bundles of assets) that occurred in a sufficiently timely manner to the impairment

test point of time might be used to derive the fair value of a CGU.533 The timeliness

criterion is of particular importance as the underlying economic conditions in which

the transaction occurred can substantially impact a buyer’s willingness and ability to

pay.534 As the standard setter is aware of the often encountered unique

characteristics of CGUs and the inexistence of active markets for the assets in

question, IAS 36 also offers the possibility to apply a discounted cash flow (DCF)

valuation methodology, in case no active market for the assets exists (IAS 36.20).535

The application of a DCF approach as a substitute for active markets however needs

to be treated with caution.536 The application of a DCF based valuation model in the

context of the FVLCD however adds substantially to the complexity of the

impairment test, as the methodology should incorporate the views of market

participants in their determination of the CGU’s fair value.537

The applicable DCF methodology therefore allows circumventing some tight

restrictions on the cash flow determination that the VIU concept imposes on the

530 IAS 36.6. 531 Cf. Glaum and Wyrwa (2011), pp. 68-69, Brösel and Klassen (2006), p. 459, Kasperzak (2011), p. 11. 532 Cf. Ernst & Young (2011), p. 8, PricewaterhouseCoopers (2013), pp. 4-5. 533 Cf. Kasperzak (2011), p. 8, Zülch and Siggelkow (2012), p. 384. 534 Cf. Ernst & Young (2011), p. 8. 535 Cf. Kasperzak (2011), p. 8, Pottgießer et al. (2005), p. 1751. 536 Cf. Ernst & Young (2011), p. 9, Kasperzak (2011), pp. 8-9. 537 Cf. Glaum and Wyrwa (2011), p. 5, PricewaterhouseCoopers (2013), pp. 4-6.


112

firm. It can therefore be argued that the DCF based model in the FVLCD approach

might lead to a higher value than the one derived by the VIU concept.538 In

particular, the DCF method in the FVLCD approach allows for:

• Adjusting cash flow projections so that they are in line with assumptions of

market participants;

• Incorporating restructurings and enhancement investments in the cash flow

forecast of a CGU to an extend as market participants would do them;

• Including cash flows from financing activities and taxes (excluded in the VIU

methodology);

• Using an asset beta in the calculation of the discount rate that is based on a peer

group of other market participants (even if this beta is lower than the firm’s

asset beta).539

Also different to the VIU concept, the FVLCD should be based on a post-tax

discount rate, applied to determine the present value of future cash flows, to be in

line with the cash flows derived on an after taxes basis.540

3.3.4.2 Value in use

The VIU concept is based on the individual assessment of the value of a CGU from

the firm’s point of view, as opposed to the FVLCD which uses a third party’s point

of view (IAS 36.33).541 It differs not only substantially from the FVLCD concept

regarding the applicable methodology, but also in the cash inflow and outflow

components that it considers in its derivation (IAS 36.53A).542

According to IAS 36.6, value in use is defined as “the present value of the future

cash flows expected to be derived from an asset or cash generating unit”543. The VIU

538 Cf. PricewaterhouseCoopers (2013), p. 6. 539 Cf. PricewaterhouseCoopers (2009), p. 14. 540 Cf. PricewaterhouseCoopers (2009), p. 13. 541 Cf. Brösel and Klassen (2013), p. 455, Müller and Reinke (2010), p. 230, Kasperzak (2011), p. 8, Lienau and Zülch (2006), p. 319. 542 Cf. Kasperzak (2011), p. 8, Zülch and Siggelkow (2012), p. 384. 543 IAS 36.6.


113

concept however focusses exclusively on those CGU cash flows that can be

generated from a CGU’s continuing use and, if applicable, through its disposal at the

end of its economic useful life (IAS 36.31).544 It therefore reflects company specific

valuation parameters that are derived from the characteristics of the CGU and its

current usage in a firm.545 The forecast of cash in- and outflow should build on

reasonable and supportable assumptions that denote the best estimate of the firm’s

management of current and upcoming economic conditions over the CGU’s

remaining useful life (IAS 36.33(a)).546 The most recent approved financial budget

or forecast should act as the principal basis for the derivation of the CGU’s expected

cash flows (IAS 36.33(b)). The detailed planning horizon for the expected cash

flows should not exceed five years, unless valid reasons can be brought forward that

would argue for a longer period. Thereafter cash flows should be extrapolated by

applying a steady or declining growth rate (IAS 36.33(c)).547 Applied long-term

growth rates can be approximated by and should not exceed those of products,

industries, markets, or countries in which the CGU operates (IAS 36.33(d)).548

The accounting standard applies restrictions on future capital expenditures that

underlie the operating cash flow forecast.549 As the current condition of the CGU

represents the sole basis for the cash flow forecast, any future cash in- and outflows

arising from planned restructurings, reorganizations and CGU improvement or

enhancements must be excluded from the valuation.550 Only capital expenditures

that preserve the continuing usage of the CGU are allowed to enter the computation

(IAS 36.39 and 44). It can however be question whether firms are able to segregate

total capital expenditures in detail in components deemed to preserve the CGU’s

current condition and others that focus more on CGU improvement or

enhancements.551 Specifically excluded in the derivation of the value in use are cash

flows resulting from financing activities and income tax receipts or payments (IAS

544 Cf. Zülch and Siggelkow (2011), pp. 5-6. 545 Cf. Lienau and Zülch (2006), p. 321, PricewaterhouseCoopers (2009), p. 12. 546 Cf. Amiraslani et al. (2013), p. 13, Holt (2013), p. 8. 547 Cf. Zülch and Siggelkow (2011), p. 6, Amiraslani et al. (2013), p. 13. 548 Cf. Amiraslani et al. (2013), p. 13. 549 Cf. Wendlandt and Vogler (2003), p. 71, Kasperzak (2011), p. 9, Holt (2013), p. 8. 550 Cf. Zülch and Siggelkow (2011), p. 6, Holt (2013), p. 8. 551 Cf. Kasperzak (2011), p. 9.


114

36.50).552 In the context of acquired goodwill in a CGU, the standard explicitly

states that expected cash flows from synergies are to be considered in the cash flow

forecast. In the FCLCD those synergies would have been excluded (IAS 36.53A(b)).

The derived, expected cash flows are then discounted with a CGU specific pre-tax

discount rate (to be congruent with the pre-tax cash flows). CGU-specificity stems

from the applied asset beta which should reflect the assets’ risk and return profile

(IAS 36.55).

552 Cf. Lienau and Zülch (2006), p. 321, Kasperzak (2011), p. 11, Zülch and Siggelkow (2011), p. 6.


115

Fig. 29: Valuation approaches to determine recoverable amounts

Source: PricewaterhouseCoopers (2009), p. 14, Kasperzak (2011), p. 11.

• Internal value:

Company perspective

• Generally, IAS 36 para 55 requires

applying a pre-tax discount rate;

regularly in practice, post-tax rate is

used to determine an appropriate

pre-tax rate

• Discount rate to be determined

using the WACC of the CGU or

company as a starting point

• Some CGUs may require the use of

WACC derived from a peer group


Valuation approaches to determine recoverable amounts

Value in use Methodology:

Perspective:

Valuation

approach:

Cash flow

projections:

• Perspective of a hypothetical buyer:

Market participants’ perspective

Cost of

capital:

(1) Market approach, or

(2) Income approach

• Income approach only

• Exclude all owner-specific synergies

• Adjust all projections so that

assumptions are in line with those of

market participants

• Consider restructurings and growth/

enhancing capex and investments if

usual in the market

• Consider cash flows related to

financing and taxes.

(in line with IFRS 13 Fair Value

Measurement)

• Recognise all synergies

• Eliminate all effects from

restructurings, if no provision in

accordance with IAS 37 has been

made

• Eliminate all effects from enhancing

investments; only maintenance

investments should be incorporated

• Exclude cash inflows or outflows

from financing activities

• Exclude income tax receipts or

payment

• Post-tax weighted average cost of

capital (WACC) considering market

participants’ view

• A peer group should reflect the

market participants (hypothetical

buyer)


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3.3.5 Disclosures

IAS 36 requires extensive disclosures related to the applied impairment testing

methodology and, if applicable, the thereof resulting impacts on the firm’s financial

statements.553 Impairment test disclosures are usually presented in the notes of the

firm’s financial statements.554 Although required in detail by the IASB, the current

disclosure practice of firms is considered often to be too intransparent and not

detailed enough, and therefore improvable as being not in line with the requirements

outlined in IAS 36.555

For CGUs with allocated goodwill disclosure requirements include stating the

carrying amount of goodwill, the applied valuation methodology (FVLCD or VIU),

underlying key valuation assumptions, as well as applied growth and discount rates

(IAS 36.134).556 Additional disclosure requirements become necessary in case an

impairment loss gets recognized during the reporting period (IAS 36.130).

For each CGU with allocated goodwill, the firm is required to disclose the carrying

amount of allocated goodwill (IAS 36.134(a)). Additionally, information should be

provided on which basis the recoverable amount was determined, i.e. FCLCD or

VIU. In case the VIU valuation approach was chosen, the firm should disclose the

key assumptions on which the cash flow forecast was built upon during the detailed

planning phase.557 Key assumptions are those to which the result of the VIU concept

is most sensitive. Furthermore, the firm should state whether those key assumptions

are in line with historical observations and market estimates, and if not its reasons

for their divergence (IAS 36.134(d)). The firm should also inform over what period

of time the management has projected cash flows in their assessment of the CGU’s

recoverable amount (IAS 36.134(d)). Regarding the extrapolation of cash flows

beyond the detailed planning phase, it should be disclosed which terminal value

553 Cf. Glaum and Wyrwa (2011), p. 26. 554 Cf. Tsalavoutas et al. (2014), p. 36, Glaum and Wyrwa (2011), p. 13, Meyer and Halberkann (2012), p. 313. 555 Cf. ASBJ et al. (2014), p. 41, Financial Reporting Council (2012), p. 24, FREP (2013), p. 15, European Securities and Markets Authority (2013), p. 3. 556 Cf. Amiraslani et al. (2013), p. 15. 557 Cf. ASBJ et al. (2014), p. 40, Tsalavoutas et al. (2014), p. 37.


117

growth rates have been applied, and in case they diverge from long-term average

growth of products, markets, countries or industries in which the CGU primarily

operates, any justifications to do so (IAS 36.134(d)). Furthermore, the firm should

provide information on which discount rates it applies in the assessment of the

recoverable amount by using the VIU methodology (IAS 36.134(d)).558

Disclosure requirements related to the usage of the FVLCD in determining the

recoverable amount are similar to those of the VIU valuation concept. In case the

FVLCD is not determined by using quoted prices on an active market, the firm

should present every key assumption that underlies its determination of the

recoverable amount (IAS 36.134(e)).559 Furthermore, management should describe

whether these key assumptions are in line with past observations, and if applicable

with external information sources. In case, key assumptions deviate from internal

and external information sources, the firm is required to provide reasonable and

conclusive arguments for not using them (IAS 36.134(e)). Changes in valuation

techniques between reporting periods have to be commented on by the firm, too

(IAS 36.134(e)). In case the firm bases its determination of the FVLCD on a

discounted cash flow model, information should be provided, similar to the VIU

case, on how long the period is over which management has forecasted cash flows in

detail, which long-term growth rates it applied to extrapolate the projections of

future cash flows, and the applied discount rates (IAS 36.134(e)). In case an

impairment loss in one of the CGUs is recognized, additional information

disclosures are required by the firm. Most importantly in this context are the

circumstances and events that ultimately lead to the loss (IAS 36.130(a)).

Furthermore, the firm should provide information on the nature of the CGU in which

the impairment loss occurred. This includes whether the assets of the CGU’s

carrying amount were grouped, for example, on a product line, operational, segment,

geographical or any other basis (IAS 36.130(d)).

558 Cf. Amiraslani et al. (2013), p. 15. 559 Cf. Tsalavoutas et al. (2014), p. 37, Amiraslani et al. (2013), p. 15.

4 Implications of reporting flexibility in the impairment-only approach

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4 Implications of reporting flexibility in the

impairment-only approach

As described in the previous chapter, for an economically impaired goodwill, the

impairment-only approach requires firms to write off the difference between a

CGU’s carrying amount and recoverable amount when such a difference is

observable.560 However, potentially working against this requirement is the reporting

flexibility that the impairment-only approach allows in its application, meaning that

the write-off behaviour of a firm’s management could diverge from what is required

on the basis of goodwill accounting under IAS 36.561 Motives for such a

management of goodwill write-offs could lie in a management team’s individual

incentives, predicted, for example, by agency theory.562 This means that

management teams might write off goodwill although not economically impaired, or

do not write off goodwill although economically impaired, motivated by their

individual incentives of the senior management teams’ members. The issue of

reporting flexibility in the impairment-only approach has been raised by several

academics as well as practitions and accounting standards enforcement panels alike,

potentially impacting both the (i) timing of goodwill impairment charges and (ii) the

write-off amounts in financial statements.563

The following chapter focusses therefore on reporting flexibility in the impairment-

only approach. It should be analysed to what exend reporting flexibility exists and in

how far this flexibility could be used opportunistically and therefore could

potentially have impacts on the quality of financial statements.

560 Cf. IAS 36.59, Zülch and Siggelkow (2012), p. 384. 561 Cf. Castedello (2009), p. 57, Fülbier (2009), p. 55, Heintges (2009), p. 58, Pawelzik (2009), p. 60, Lieck (2009), p. 61, Kasperzak (2011), p. 9. 562 Cf. Ramanna and Watts (2012), pp. 758-759, and chapter 5 of this dissertation for a discussion on potential motivational factors influencing the management of goodwill write-offs. 563 Cf. Ernst (2012), p. 640, Müller und Reinke (2010), p. 29, Engel-Ciric (2012), p. 421, Kirchner (2006), p. 66, Zülch and Siggelkow (2012), p. 383, Teilter-Feinberg (2006), p. 18, Kasperzak (2011), pp. 12-13, Brösel and Klassen (2006), pp. 463-464, Kirchner (2006), p. 66.


119

4.1 Possible relationships between reporting flexibility

and managing goodwill write-offs

The assertion of managing goodwill write-offs builds on the assumption that a firm’s

management is aware of an economically impaired goodwill however its financial

reporting does not capture the required write-off accordingly. Managing goodwill

write-offs can therefore result in not writing off an economically impaired goodwill

(i) at the required point of time (i.e. when the firm becomes aware of the economic

impairment) or (ii) that the reported write-off amout is smaller or larger than

actually required.

Fig. 30: Implications of reporting flexibility in the impairment-only approach on

quality of financial statements


The management of goodwill write-offs stands in strong contrast to the IASB’s

original objective of improving the decision usefulness of information in financial

statements through the introduction of the impairment-only approach in financial

Reporting flexibility in the IOA

Timing

Managing goodwill write-offs

Write-off amount

Quality of financial statements

► Goodwill write-offs are recognized too late or too early in a firm’s financial statements compared to what would have been economically required

► Goodwill write-off amounts are too high or too low compared to what would have been economically required

Impact on:


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accounting.564 By using the impairment-only approach to manage goodwill write-

offs which serves a firm’s financial reporting strategy and does not reflect economic

reality, the quality of goodwill accounting and the quality of information in financial

statement actually decreases.565

4.2 Timely goodwill impairment recognition and its

impact on the quality of financial reporting

Generally, timeliness represents one measure of financial reporting quality.566

According to the IASB, qualitative characteristics of financial statements are the

attributes that make the information provided in financial statements useful to users

(IASB framework 24). Timeliness implies having information available to decision-

makers in time to be capable of influencing their decisions (IASB framework

QC.29).567 Consequently, from the viewpoint of the IASB, timeliness relates to the

speed with which changes in the economic values of assets are recognized and any

impairment losses are reflected in earnings, as Amiraslani et al. (2013) point out.568

From a general accounting perspective, Basu (1997) argues that “more timeliness

means that more current value relevant news is recognized contemporaneously in

earnings, leaving less current value relevant news to be recognized in future

earnings.”569 Or put differently, timeliness represents a measure in how far

information that is currently available on the value of assets and liabilities is

reflected in current accounting earnings.570 Basu’s (1997) view gets substantiated by

Chen et al. (2008) who define timeliness from a time interval perspective as a

“recognition lag - i.e., whether accounting is contemporaneous with recognition in

564 Cf. Amiraslani et al. (2013), pp. 18-19, Chen et al. (2013), p. 4, Gordon and Hsu (2014), p. 13, Vanza et al. (2011), pp. 2-3, AbuGhazaleh et al. (2011), p. 166, Lapointe-Antunes et al. (2009), pp. 62-63. 565 Cf. Vanza et al. (2011), p. 3, AbuGhazaleh et al. (2011), p. 170, Amiraslani et al. (2013), pp. 18-19. 566 Cf. Ball and Shivakumar (2004), p. 2, Amiraslani et al. (2013), p. 19, Chen et al. (2008), p. 73. 567 Cf. Amiraslani et al. (2013), p. 19. 568 Cf. Amiraslani et al. (2013), p. 19. 569 Basu (1997), p. 19. 570 Cf. Vyas (2009), pp. 1-2, Ball et al. (2013), p. 13, Basu (1997), pp. 11, 19, Bens and Heltzer (2005), p. 28.


121

returns.”571 Their view on timeliness implies that a lack of timeliness is apparent if a

firm has information on whether certain assets are economically impaired, however

without having recognized this revaluation in accounting earnings on a timely

basis.572

Caplan and Harris (2002) add to the topic of timeliness by arguing that timeliness

carries as much weight as the actual amount recognized in accounting earnings to

the users of financial statements.573 This means that not only the actual write-off

amounts are important (due to their effects on the firm’s earnings), but also whether

they are reflected in a timely manner due to the information content a write-off

has.574 Consequently, the financial reporting quality is higher if timeliness of

impairment recognition (of goodwill or other assets) is higher.575

On the topic of timeliness, Ball and Shivakumar (2004) also address the link

between timely loss recognition and corporate governance. They also agree that

timeliness in the recognition of economic losses in accounting earnings represents

an important attribute of financial reporting quality.576 “Timely loss recognition

increases financial statement usefulness generally, particularly in corporate

governance (…) agreements. Governance is affected because timely loss recognition

makes managers less likely to make investments they expect ex ante to be negative-

NPV, and less likely to continue operating investments with ex post negative cash

flows. (…) We therefore argue that timely recognition of economic losses is an

important attribute of financial reporting quality.”577 Consequently, Ball and

Shivakumar (2004) present a link between timeliness and corporate governance

activities that aim at uncovering managerial activities that are value destructive to

571 Chen et al. (2008), p. 72. 572 Cf. Ramanna and Watts (2012), p. 751, Ball et al. (2013), p. 13, Basu (1997), pp. 11, 19. 573 Cf. Caplan and Harris (2002), p. 53. 574 Cf. Ball and Shivakumar (2004), p. 6. 575 Cf. Chen et al. (2008), p. 73, Ball and Shivakumar (2004), p. 2. Please note in this respect that “reporting quality and usefulness differ from economic efficiency because they do not address optimality. Lower quality does not imply sub-optimality because it can arise from either lower demand for or higher cost of supplying quality” (Ball and Shivakumar (2004), p. 4). 576 Cf. Ball and Shivakumar (2004), p. 2. 577 Cf. Ball and Shivakumar (2004), p. 2.


122

strengthen their argumentation regarding the importance of timeliness in financial

reporting quality.

Summing up, an untimely recognition of impairment charges reduces the quality and

usefulness of financial statements to its users, as the book values of goodwill would

be overstated until the impairment charge would ultimately get recognized. This

would then also have implications on the comparability of goodwill values across

firms if timeliness of loss recognition also differs across firms.

4.3 Reporting flexibility and its impact on the

enforcability of the impairment-only approach

On the topic of the impairment-only approach, the reporting flexibility that the IOA

allows firms in testing the recoverability of goodwill is identified as the most

frequently cited area of concern regarding goodwill in both academia and

practice.578 This managerial discretion is argued not only to potentially influence the

timeliness and the amounts written off but also to reduce the enforceability of the

impairment-only approach by auditors and enforcement panels.579 Difficulties in the

enforceability of the IOA could be linked to managing goodwill write-offs,

especially then when it is difficult to disprove that certain valuation assumptions are

not in line with IAS 36.580 Additionally, a lack of enforceability could negatively

impact the reliability of financial statements and comparability across firms.581

578 Cf. Castedello (2009), p. 57, Fülbier (2009), p. 55, Heintges (2009), p. 58, Pawelzik (2009), p. 60, Lieck (2009), p. 61, Kasperzak (2011), p. 9, Müller und Reinke (2010), p. 29, Engel-Ciric (2012), p. 421, Kirchner (2006), p. 66, Zülch and Siggelkow (2012), p. 383, Teilter-Feinberg (2006), p. 18. 579 Cf. Ernst (2012), p. 640. 580 Cf., for example on the range of applied discount rates in practice, Zwirner and Mugler (2011), p. 447, Zwirner and Zimny (2013), p. 24. 581 Cf. Ernst (2012), p. 640.


123

Fig. 31: Reporting flexibily, enforceability and managing goodwill write-offs


In general, concerns related to reporting flexibility in the impairment-only approach

focus on optimistic assumptions in business plans (i.e. cash flow forecasts) which

underlie the determination of the recoverability of goodwill, the determination of the

cost of capital (i.e. applied discount rates) and the firm’s freedom in constructing

CGUs that potentially could allow combining acquired goodwill from a business

combination with internally generated goodwill of the acquirer.582 All of those

factors have a direct impact on the determination of the recoverable amount of a

CGU to which goodwill has been allocated.

This reporting flexibility is argued to translate into a lack of enforceability of the

impairment-only approach of auditing firms, as Ernst (2012), President of the

German Financial Reporting Enforcement Panel (FREP) points out.583 The FREP

represents a government-appointed privately organised institution that enforces

financial reporting of firms quoted on one of the German stock exchanges.584 Ernst

(2012) and Kirchner (2006) reason that discretion of firms in fair value

determination adds to the complexity of audit procedures and therefore also adds

difficulties of financial reporting standards’ enforcement.585 According to the FREP,

enforceability tends to be higher and auditability more difficult for accounting

standards that rely on valuation models that build on subjectively evaluated

parameters and not on objectively observable market variables.586 This poses the risk

that values that are derived on the basis of such valuation models often lack the

582 Cf. Zülch and Siggelkow (2012), p. 385, Kasperzak (2011), p. 4, Engel-Ciric (2012), p. 421. 583 Cf. Ernst (2012), p. 640. 584 Cf. FREP (2013), p. 1. 585 Cf. Ernst (2012), p. 640, Kirchner (2006), p. 73. 586 Cf. Ernst (2012), p. 640, Brösel and Zwirner (2009), p. 198.

influences Reporting flexibility Managing goodwill write-offs

Enforcability reduces influences


124

aimed qualitative characteristics of financial statements of reliability and

comparability, as Ernst (2012) also points out.587 Since its founding, the FREP

considers the application of IAS 36 Impairment of Assets in practice as one of its

focus areas, being aware of the reporting flexibility the impairment-only approach

allows.588

Additionally, Mayer-Wegelin (2009), also from the FREP, remarks that IAS 36

lacks guidance and precision by being too broad in its descriptions of the applicable

valuation methodologies by leaving out important details.589 This, as she follows,

allows for opportunistic behaviour from a firm’s management.590 Pellens et al.

(2005) as well as Müller and Reinke (2010) also remark the substantial reporting

flexibility and limited guidance that the impairment-only approach offers to firms

that have to follow the valuation concepts described in the accounting standard IAS

36.591

On the basis of the views of the German FREP and its members as well as other

practitioners and academics alike, it can be argued that the apparent reporting

flexibility of the IOA offers possibilities to manage goodwill write-offs. This

reporting flexibility also reduces the enforceability of the accounting standard, as

academics infer.592 Research studies that document relatively low goodwill write-

offs during the financial crisis (2008 and 2009)593 seem to support this assumption of

an apparent reporting flexibility in the impairment-only approach.594

587 Cf. Ernst (2012), p. 640, Brösel and Zwirner (2009), p. 202, Kirchner (2006), p. 71. 588 Cf. FREP (2007), p. 1, FREP (2008), p. 1, FREP (2009), p. 1, FREP (2010), p. 1, FREP (2011), p. 1, FREP (2012), p. 1, FREP (2013), p. 1, Heintges (2009), p. 59, Lieck (2009), p. 61. 589 Cf. Mayer-Wegelin (2009), p. 94. 590 Cf. Mayer-Wegelin (2009), p. 94. 591 Cf. Pellens et al. (2005), p. 24, Müller and Reinke (2010), p. 29. 592 Cf. Pellens et al. (2005), p. 24. 593 Cf. Leibfried (2010), p. 479, Meyer and Halberkann (2012), p. 314, Zülch and Siggelkow (2012), p. 387. 594 Cf. Ernst (2012), p. 640, Ruhnke et al. (2010), p. 415. As a result the FREP has been arguing since 2012 to amortize goodwill again on a straight line basis instead of testing for impairment (Cf. Ernst (2012), p. 641).


125

4.4 Areas of reporting flexibility in the impairment-

only approach

On the basis of IAS 36, reporting flexibility primarily falls in the areas of (i)

defining and structuring cash generating units, (ii) allocating goodwill and net asset

to these CGUs, as well as (iii) determining the recoverable amount of a CGU to

which goodwill has been allocated.

According to the respective accounting standard, before the recoverability of

goodwill can be tested, firstly firms need to define and structure the company

specific cash generating units to which goodwill should be allocated. This becomes

necessary as goodwill by itself does not generate individual cash flows which can be

valued separately and therefore the valuation approach requires other tangible and/or

intangible assets that profit from the economic benefits reflected in the allocated

goodwill component. Consequently, the firm needs to decide which other assets

apart from goodwill should be allocated to a CGU. Once decided which net assets

should form a CGU, a firm’s management has to allocate goodwill to those CGUs

on the basis of assumed future economic benefits to the CGU from the business

combination. On the basis of this asset allocation, the firm determines the

recoverable amount of the CGU.


126

Fig. 32: Areas of reporting flexibility in the impairment-only approach


By reviewing the required valuation concept in determining the recoverable amount

of a CGU (and therefore goodwill) presented above, it becomes visible that the

recoverable amount of a CGU is highly dependent on the net assets (apart from

goodwill) which form the CGU.595 This dependency derives from the future cash

flows that the assets will generate, forming the basis of a CGU’s valuation.596

4.4.1 Cash generating units’ construction and goodwill

allocation

As the cash flows which are used as the basis for testing the recoverability derive

from their underlying net assets, the actual net assets components of a CGU can be

595 Cf. Zhang and Zhang (2006), pp. 31-32. 596 Cf. Carlin et al. (2010), p. 3.

Goodwill A

Other net assets A

Goodwill B

Other net assets B

Goodwill n

Other net assets n

(i) Definition and

structuring of

CGUs

(ii) Allocation of

net assets and

acquired goodwill

(carrying

amount)

CGU A CGU B CGU n

Expected cash flows

and discount rates, or

fair value of CGU A

(iii) Determination

of recoverable

amount of CGU

Expected cash flows


fair value of CGU B

Expected cash flows


fair value of CGU n

Recoverable amount A Recoverable amount B Recoverable amount n


127

considered an influencing factor during the impairment testing process.597 Academia

has raised concern about reporting flexibility related to a potential opportunistic

behaviour of managers during the CGU construction and subsequent goodwill

allocation process.598

Analyses of actual CGU structures of firms by Zhang and Zhang (2006), Carlin et al.

(2010), Glaum and Wyrwa (2011), and Meyer and Halberkann (2012) show that

CGU structures are highly company specific, can change over time in firms, and can

vary substantially across firms in the same industry.599 One of the most frequently

raised concerns about CGU construction practices is that the freedom in constructing

company specific CGUs could be used, for example, to substitute acquired goodwill

with internally generated goodwill.600 This would then certainly pose the risk that an

obvious economic impairment of acquired goodwill, for example from unrealizable

synergies, could be covered by other, non-acquired net assets in a CGU that perform

strongly and on a net basis move the recoverable amount of a CGU above the

carrying amount.601

Glaum and Wyrwa (2011) study CGU structures in European firms. They find that

approx. half of the sample firms that disclose this information work with a relatively

small number of CGUs. The authors show that every second firm in their sample

(51,7%) have 5 or less CGUs. Surprisingly, every fifth firm in the sample (20,3%)

has 10 or more CGUs.602 Glaum and Wyrwa’s analysis (2011) documents the large

variations between firms regarding CGU structures, however does not link them

unfortunately to company characteristics like firm size, industry, or the number of

reporting segments the firms have, and consequently not to potential goodwill

597 One might argue that goodwill impairment risk is higher for CGUs with net assets that are more risky, less profitable or have lower future growth rates. Vice versa, impairment risk would be lower for highly profitable, high growth or low risk CGUs. 598 Cf. Hayn and Hughes (2006), p. 266. 599 Cf. Zhang and Zhang (2006), pp. 31-32, Carlin et al. (2010), p. 3, Meyer and Halberkann (2012), p. 313. 600 Cf. Brösel and Klassen (2006), p. 463, Teitler-Feinberg (2006), p. 18, Brösel and Zwirner (2009), p. 196, Pawelzik (2009), p. 60. 601 Cf. Brösel and Zwirner (2009), p. 196, Pawelzik (2009), p. 60. 602 Cf. Glaum and Wyrwa (2011), p. 62.


128

substitution effects. Also, the authors do not link the members of CGUs to actually

observable goodwill write-off probabilities.

Fig. 33: Number of CGUs in European firms

Source: Glaum and Wyrwa (2011), p. 62.

In a further analysis, Glaum and Wyrwa (2011) study concentration effects of

goodwill in CGUs by analysing the number of CGUs with significant goodwill

proportions.603 They come to the conclusion that “goodwill is often concentrated in

only few CGUs. (…) Managers may have an interest in allocating goodwill from

acquisitions in only few, relatively high aggregated CGUs. Firstly, this means that

fewer goodwill impairment tests will have to be conducted in subsequent periods.

Secondly, allocating goodwill in few large CGUs reduces the probability of future

impairment losses.”604

603 Cf. Glaum and Wyrwa (2011), p. 63. 604 Glaum and Wyrwa (2011), p. 63.

5,9%

9,6%

11,5%

7,1%

9,3%

7,5% 7,5%5,9%

2,8% 3,1%

7,1%

2,8%4,0%

15,8%

0%

6%

12%

18%

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Fig. 34: Number of CGUs with significant goodwill in European firms


Corporate and academic studies show that the majority of firms use a product or

legal entity CGU classification, which could point into the direction of anticipated

potential goodwill substitution effects by a firm’s management. Functional and

geographical classifications are not as common.605

605 Cf. KPMG (2011), p. 16, KPMG (2010), p. 14.

18,6%

25,8% 25,2%

11,5%

3,1%1,6%

0,0% 0,3% 0,0% 0,0% 0,3%

13,7%

0%

10%

20%

30%

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Fig. 35: CGUs classification in Germany and Europe

Source: KPMG (2011), p. 16, KPMG (2010), p. 14.

Hayn and Hughes (2006) were among the first researchers that addressed the

problem of CGU structures in the IOA by stating that “identifying reporting units

and assigning goodwill to them has proven to be one of the most difficult

implementation issues (…), raising concerns by both preparers and users of financial

statements regarding the complexity, cost, and inconsistency of this process (…).”606

Zhang and Zhang (2006) picked up that concern and provide empirical evidence that

more goodwill is allocated to more profitable CGUs by arguing that “(i)f managers

prefer greater flexibility in future goodwill assessments and/or impairment

decisions, more profitable reporting units are likely to receive more goodwill

allocation.”607

Carlin and Finch (2010) add to that discussion by showing that in Australia and New

Zealand, CGUs with surprisingly low discount rates which are used to determine the

recoverable amount on the basis of the value in use concept get more allocated

goodwill than vice versa.608 Additionally, CGU structures can change over time as

Carlin et al. (2010) show. They introduce the term “CGU portfolio inter temporal

606 Hayn and Hughes (2006), p. 266. 607 Zhang and Zhang (2006), p. 5. 608 Cf. Carlin and Finch (2010), p. 17.

11%

18%

17%

39%

5%

41%

9%

22%

21%

37%

5%

39%

0% 10% 20% 30% 40% 50%

Others

Geographic

Sales markets/customer groups

Product groups

Functions

Legal entities/subgroups

Europe Germany


131

structural stability”609 and document that the average number of CGUs per firm

increased between 2006 and 2008,610 with firms in industries where the ratio of

goodwill to total assets is larger changing CGU structures more often in subsequent

years (i.e. industries like media, transportation, telecommunications, or commercial

services).611 By changing CGU structures more often, enforcement of the IOA can

be lower as auditors require more time to understand a potential impairment as

comparisons to previous years could be lacking. However, one needs to keep in

mind that firms are probably more willing to pay a premium for more profitable

assets which would then also lead to more goodwill in more profitable CGUs.

Therefore the observation that more goodwill is allocated to more profitable CGUs

can also be explained partly by transaction characteristics and transaction

motivations of the firm’s management and not purely by its opportunistic behaviour;

whereas the assumption of opportunistic behaviour is immediately suggested.

What adds to the complexity and difficulties of goodwill impairment enforcement is

that goodwill impairment tests are carried out at a relatively high level.612 This

reasoning is supported by the findings of corporate studies. KPMG (2011) shows,

for example, that the majority of German listed firms prefers to test the

recoverability of goodwill either at segment level or one level below segment level.

These findings could imply that firms are aware of the possibilities that IAS 36

allows them in constructing CGUs that potentially would allow them to combine

acquired goodwill with internally generated goodwill.

609 Carlin et al. (2010), p. 18. 610 Cf. Carlin et al. (2010), p. 20. 611 Cf. Carlin et al. (2010), p. 23, KPMG (2010), p. 11. 612 Cf. KPMG (2011), p. 16.


132

Fig. 36: Levels of CGUs in German firms

Source: KPMG (2011), p. 16.

4.4.2 Valuation parameters to determine recoverable

amount

Various research studies, including those of Glaum and Wyrwa (2011)613, Meyer

and Halberkann (2012)614, Amiraslani et al. (2013)615, as well as Carlin and Finch

(2008)616 document that the value in use concept is by far the most widely applied

valuation method to determine a CGU’s recoverable amount.617 Accounting

researchers and practitioners suppose that one of the principal reasons why the value

613 Cf. Glaum and Wyrwa (2011), p. 78. Sample consists of 303 European firms; 82% ViU, 8% FVLCD, 10% both. 614 Cf. Meyer and Halberkann (2012), p. 313. Sample consists of SPI listed firms in Switzerland. Sample size not specified; 89% ViU, 6% FCLCD, 5% both. 615 Cf. Amiraslani et al. (2013), p. 41. Sample consists of 160 European firms; 88% ViU, 12% FVLCD. 616 Cf. Carlin and Finch (2008), p. 18. Sample consists of 200 Australian firms, with 19 firms (9.5% of sample size) not disclosing the applied valuation concept; 87% ViU, 9% FVLCD, 4% both. 617 Cf. Glaum and Wyrwa (2011), p. 78, Meyer and Halberkann (2012), p. 313, Amiraslani et al. (2013), pp. 40-41, Carlin and Finch (2008), p. 18.

39% 40% 39%

50%37% 45%

11%16% 7%

1,0% 7,0% 9,0%

0%

25%

50%

75%

100%

2007 2008 2009

Others

Two levels belowreporting segment level

One level belowreporting segment level

Reporting segmentlevelS

hare

of

sam

ple

firm

s


133

in use concept is so popular in impairment testing might be that it allows for the

highest flexibility and managerial discretion in its application.618

Reporting flexibility in the value in use concept primarily refers to the derivation of

a CGU’s future cash flows as well as in the computation of the to-be-applied

discount rates. That those two value drivers predominately determine a CGU’s

recoverable amount becomes obvious when looking at the specific valuation

guidelines outlined in IAS 36. According to IAS 36.6, value in use is defined as “the

present value of the future cash flows expected to be derived from an asset or cash

generating unit”619. As the VIU concept focusses on those cash flows that a CGU

generates over its economic useful life (IAS 36.31)620, the VIU can be expressed

mathematically in the following way:

with

CFCGU = expected cash flow(s) generated by a CGU

t = point of time

r = discount rate

g = sustainable, long-term growth rate

618 Cf. Schildbach (2005), p. 558, Castedello (2006), p. 17. 619 IAS 36.6. 620 Cf. Zülch and Siggelkow (2011), pp. 5-6, Lienau and Zülch (2006), p. 321, PricewaterhouseCoopers (2009), p. 12, Amiraslani et al. (2013), p. 13, Holt (2013), p. 8.

CFCGU,t(1+r)-t

∞

t=1

CFCGU,1(1+rCGU)-1+...+CFCGU,5(1+rCGU)-5

+ CFCGU,5(1+gCGU

)(r-gCGU

)-1(1+rCGU)-5

Value in Use CGU

=

= (4.1)


134

On the basis of these considerations, one can clearly identify the two principal

drivers of the value in use of a CGU. These are:

• The cash flows of a CGU that are forecasted by the firm which carries out the

valuation. This includes revenues growth rates, profitability margins and

investments (in for example fixed assets or working capital).

• The applied discount rate (cost of capital) that is used to discount future cash

flows to the valuation date.

4.4.2.1 Cash flow forecasts

Reporting flexibility in forecasting cash flows of a CGU primarily refers to the

conception of IAS 36 that a firm’s management is solely responsible for determining

the cash flows on which basis the valuation of a CGU is carried out. Being too

aggressive or too conservative on the estimation of future cash flows of a CGU can

impact both the timing and actual write-off amounts.

Although auditors can certainly challenge certain valuation assumptions that seem to

be too aggressive or too conservative to them, given the better informed

management and forward looking nature of those cash flows, auditors can find it

difficult proving the opposite.621 Consequently, due to the managerial discretion in

deriving the most likely cash flows forecast on the basis of the management team’s

best estimates, reporting flexibility in forecasting cash flows might offer possibilities

to manage goodwill write-offs.622

621 Cf. Ernst (2012), p. 640, Kirchner (2006), p. 73, Brösel and Zwirner (2009), p. 198. 622 Cf. Mayer-Wegelin (2009), p. 94, Pellens et al. (2005), p. 24, Müller and Reinke (2010), p. 29.


135

Fig. 37: Over-estimation of projected cash flows Source: Own illustration.

Consequently, as future cash flows are uncertain, forward looking and partly based

on unverifiable private information held by the firm’s management, their potential

over- or underestimation can become problematic in the impairment-only approach

as auditors might find it difficult to verify them and thereby allowing possibilities to

manage goodwill write-offs. This holds especially true when no historical reference

points exist that could be used for testing the planning accuracy of the firm’s

management. Therefore, the possibility to manage goodwill write-offs is higher and

the enforceability lower during the first years after a business combination.

However, the more time evolves between a business combination and the actual date

of the audit, the better the visibility of audit teams becomes to understand whether

cash flow forecasts have been too aggressive (or too conservative) if actual cash

flows diverge from previously forecasted cash flows. As the observation period

becomes larger during which the actual and forecasted cash flows can be compared

against each other, the forecasting errors (or over-/underestimation) become more

transparent and the possibilities to manage write-offs thereafter be reduced.

In particular, reporting flexibility in the estimation of future cash flows can lie in the

approximation of short- and long-term revenues growth rates, profitability margins

(1) Projected cash flows

(3) = (1) - (2) Over-estimation of cash flows

(2) Actual cash flows

Projected cash flows of a CGU

Observable forecasting errors

Projected time period (years after business combination)


136

and/or investments that can lead to higher or lower projected cash flows and

therefore to lower or higher goodwill write-off amounts.

Maybe the most relevant valuation parameter, which needs to be mentioned in the

context of flexibility in forecasting future cash flows, represents the long-term

growth rate. After the detailed planning phase during which the expected cash flows

are comprehensively forecasted (covering usually a time period of five years)623, the

cash flows of the last planning year get extrapolated to reflect a state of sustainable,

long-term growth.624 As goodwill is assumed to have an indefinite useful life, also

the cash flows that are expected to emerge from it (and those of the other net assets

of a CGU to which it has been allocated to) have to be forecasted indefinitely.

As it is simply not feasible to forecast an indefinitely number of annual cash flows,

valuation models apply a (growing) perpetuity after the detailed planning period of

cash flows, also referred to as terminal value year (TV). The advantage of the

perpetuity is that one can handle an indefinite number of constant cash flows

reflected in one value. This allows working with a lower number of cash flows in

valuation models. However as the effect of this TV year on the overall value can be

considered as substantial, potentially ranging between 60-80%, reasonable

assumptions need to be applied for this TV year. This holds particularly true for the

long-term growth rate which is assumed to be achievable until infinity.625

Consequently, in case a firm’s management wants to manage goodwill write-offs in

order to, for example, cover a potential economic goodwill impairment, the

adjustment of the long-term growth rate would offer a possibility to do so. However

one needs to keep in mind that large changes from the previous year’s level are

hardly to argue for from a firm’s management perspective, in particular when

revenue growth rates and profitability margins have been declining. However, one

might think of applying a relatively high terminal value growth rate already from the

start when an impairment test is carried out for the first time (as long as one can

623 Cf. IAS 36.33(b). 624 Cf. IAS 36.33(c). 625 Please note that the term “infinity” could be misleading in this respect as the discounted cash flows after a period of 40 to 50 years become so small from a present value perspective that most of the present value of a CGU is derived from cash flows generated over a period of 40 years.


137

argue for such a high growth rate). This reasoning can be substantiated by the vague

descriptions of IAS 36.33(c), stating “this growth rate shall not exceed the long-term

average growth rate for the products, industries, or country or countries in which the

entity operates, or for the market in which the asset is used, unless a higher growth

rate can be justified.”626 The question certainly arises here about how this forward

looking growth rate should reasonably be determined.627

That high long-term growth rates are fairly common has been documented by Glaum

and Wyrwa (2011) who studied the long-term growth rates in 217 European firms

applying the Value in Use-valuation concept in the impairment-only approach.

Fig. 38: Maximum long-term growth rates in European firms


Glaum and Wyrwa (2011) show that long-term growth rates over 3,00% are

surprisingly common (25,9% of all firms in their sample that disclose their growth

626 IAS 36.33(c). 627 Cf. KPMG (2013), p. 36. Some practitioners and academics argue that the long-term growth rate should be closely tied to the long-term rate of inflation, as otherwise a firm would be growing faster than the market and therefore would dominate the market in the long-run. As any market is supposed to have only a particular size and capacity (due to demands of customers, their available spending power, and a market’s life-cycle), the assumption of growing faster than the market can be questioned in certain cases (unless a firm can be considered as a market leader, for example).

6,8%

0,7%

27,3%

17,3%

9,0%

5,4%2,5% 1,8%

7,2%

21,9%

0%

10%

20%

30%

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rates).628 7,2 % of the sample firms even use a terminal value growth rate that

exceeds 7%. The higher the long-term growth rate, the higher the impact on the

terminal value cash flow of a CGU. The authors come to the conclusion that

“growth rates vary significantly between companies (…).”629

On the basis of these considerations it becomes apparent that forecasting cash flows

in the impairment-only approach offers substantial reporting flexibility and

represents one way of managing goodwill write-offs if a management team has the

motivation to do so.

4.4.2.2 Discount rates

Another frequently discussed subject on the topic of reporting flexibility represents

the applicable discount rates when using the value in use concept in the impairment

only-approach. Discount rate can be considered a key valuation parameter as “small

changes can have substantial effects on the estimated recoverable amount of a

CGU”630.

When relying on expected future cash flows in valuation models, discount rates need

to be applied that reflect the inherent risk of the respective cash flow streams to

determine their present values. Consequently, the to be applied discount rate can be

defined as the opportunity cost of financing the CGU in question,631 or according to

IAS 36.56 as the rate of return that “investors require if they were to choose an

investment that would generate cash flows of amounts, timing and risk profile

equivalent to those that the entity expects to derive from the asset.”632

The individual assessment of the risk profile of a CGU relies ultimately with the

firm, whilst auditors can assess the accuracy of the applied methodology during the

annual audit procedures. Especially then, when the risk profile of a CGU deviates

628 Cf. Glaum and Wyrwa (2011), p. 74. 629 Glaum and Wyrwa (2011), p. 74. 630 Glaum and Wyrwa (2011), p. 75. 631 Cf. Glaum and Wyrwa (2011), p. 75. 632 IAS 36.56.


139

from that of the overall firm, additional risk premia or discounts are required.633 In

general, the lower the cost of capital (with all other valuation parameters being

equal), the higher the present value of the forecasted cash flows and therefore the

lower the probability of future impairment losses.634 Consequently, a firm’s

management might be willing to apply a low discount rate if it has the motivation to

reduce the firm’s goodwill impairment risk.

In case the asset or CGU is listed on a capital market, the risk profile can be assessed

through the application of the capital asset pricing model (CAPM),635 which is the

most widely applied concept in practice.636 When a listing is not given, a comparable

approach via the CAPM can still be followed in which the risk profile of a CGU is

matched to those of listed assets that share a similar risk profile.637 This indirect risk

assessment of a non-listed asset or CGU can be frequently observed in practice.638

However, also the selection of peer assets or firms falls under the discretion of the

firm that performs the valuation. Here again, a firm’s management might be willing

to select comparable assets or CGUs that generally have a lower risk profile than the

valuation object (i.e. CGU) in order to reduce the asset beta which is part of the cost

of capital calculation of a CGU. However, once the firm has decided on a respective

peer group of assets, changes in the composition of the peer group in the subsequent

years are difficult to argue due to comparability reasons. Consequently, a firm’s

management might carefully and mindfully choose those comparable assets in the

beginning that ultimately end up in the peer group.

Glaum and Wyrwa (2011) show that European firms apply a wide range of different

discount rates in the IOA. Large differences are not only found between firms in

different industries, but also within firms having multiple CGUs. The authors come

to the conclusion that “(d)iscount rates do not appear to be associated with industry

633 From a conceptual point of view, a firm’s overall cost of capital “can be interpreted as a weighted averae of the costs of capital of all its (…) CGUs” (Glaum and Wyrwa (2011), p. 75). 634 Cf. Glaum and Wyrwa (2011), p. 63. 635 Cf. Bollmann and Joest (2010), p. 210. 636 Cf. Damodaran (2015), p. 14, KPMG (2010), p. 29. 637 Cf. Castedello (2006), p. 20, KPMG (2013), p. 25. 638 Cf. Castedello (2006), p. 20.


140

or country of domicile in any obvious systematic way.”639 However when looking at

the entire sample, discount rates tend to be normally distributed, and not too skewed

to the lower end. The authors state that the majority of firms apply a discount rate

between 6% and 11%,640 as the figure below displays.

Fig. 39: Distribution of discount rates in European firms


Carlin and Finch (2010) add to the discussion of reporting flexibility in the

impairment-only approach regarding the applied discount rates by documenting a

potential opportunistic behavior by a firm’s management in selecting relatively low

discount rates.641 In their study, the authors compare actually applied discount rates

in the impairment-only approach with expected discount rates through an individual,

external appraisal of the sample firms. They find out that for approx. 50% of total

goodwill (and therefore also the CGUs to which goodwill has been allocated) a

639 Glaum and Wyrwa (2011), p. 76. 640 Cf. Glaum and Wyrwa (2011), p. 76. Zwirner and Zimny (2013) and Zwirner and Mugler (2011) studied the discount rates in German firms between 2009 and 2011 and show that the range of discount rates can vary substantially over time. 641 Cf. Carlin and Finch (2010), p. 14.

3,6%5,0%

11,2%

15,8%15,5%14,7%

11,9%

6,5%

2,2%2,9%

0,4%1,1%

9,4%

0%

6%

12%

18%

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discount rate is used that is between 1,5 and 2,5% too low from their perspective.642

In only 3% of the cases which the authors analyze a discount rate is applied which is

obviously too high. The large amount of goodwill that apparently is tested with a

discount rate which is found out to be too low raises additional concern about the

reporting flexibility in the impairment-only approach and could be used to support

the assumption of potential opportunistic behaviors of managers in testing the

recoverability of goodwill.

Table 2: Application of discount rates below expectations in Australia and New

Zealand

Source: Carlin and Finch (2010), p. 14.

642 Cf. Carlin and Finch (2010), p. 14.

Sector

Number

of firms

>250 Bps

below

expectation

150-250 Bps

below

expectation

+/-150 Bps

i.e. within

expectable range

150-250 Bps

above

expectation

>250 Bps

above

expectation

Total

goodwill

(in AUS$)

Capital goods 11 75% 7% 10% 7% 808

Commercial services & supplies 17 63% 9% 11% 17% 1.438

Consumer services 5 45% 32% 12% 11% 381

Diversified financials 12 74% 16% 2% 8% 406

Energy 3 97% 3% 1.406

Food, beverage & staples 10 40% 57% 3% 5.951

Health care 12 86% 6% 7% 2% 3.263

Materials 16 80% 9% 9% 2% 3.311

Media 3 91% 9% 1.383

Real estate 5 96% 4% <1% 398

Retailing 11 71% 2% 21% 4% 2% 1.363

Software & services 10 8% 9% 83% 1.910

Technology & telecommunications 3 8% 89% 3% 262

Utilities & transportation 6 1% 99% 8.120

Total 124 38% 12% 46% 1% 2% 30.400

Comparison of actual and expected discount rates and goodwill impairment testing

Percentage of goodwill tested

for impairment with a discount

rate which is too low

Percentage of goodwill tested

for impairment with a discount

rate which is too high

5 Theoretical concepts helping to understand goodwill write-off decisions

142

5 Theoretical concepts helping to understand goodwill

write-off decisions

The previous chapter described the reporting flexibility that the impairment-only

approach allows firms in its application from a technical and methodological point

of view. Generally, this reporting flexibility can have its benefits but also costs for

addressees of financial statements.643 Benefits emerge when goodwill write-offs

reflect economic impairments on a timely basis and thereby revealing information

on future changes in the firm’s financial performance.644 This reasoning stems from

the fact that the recoverability of goodwill is based on future cash flows which the

CGU, to which goodwill has been allocated, will generate in the future. A write-off

would signal that the performance of a CGU will be deteriorating in the future and

that the book values of a CGU (incl. Goodwill) are not fully supported by a CGU’s

future financial performance.

Costs to shareholders and other stakeholders result if goodwill write-offs mislead

addressees of financial statements, for example then, when write-offs are unrelated

to the underlying economics of goodwill.645 As write-off decisions are influenced by

the individual motivations of the management team,646 the following chapter

elaborates on two of the most prominent theoretical concepts explaining managerial

motivations in the impairment-only approach. Specifically, the theoretical concepts

described in this chapter relate to the (i) management team’s information on the

future performance of the business units to which goodwill has been allocated,

termed private information,647 and (ii) personal incentives of the management team

643 Cf. Ramanna and Watts (2012), p. 753, Zang (2008), p. 40. 644 Cf. Lhaopadchan (2010), p. 125, Zang (2008), p. 40, Knauer and Wöhrmann (2013), p. 1. 645 Cf. AbuGhazaleh et al. (2011), p. 166, Ramanna and Watts (2012), p. 753, Lapointe-Antunes et al. (2009), p. 63. 646 Cf. Wilson (1996), pp. 172-173, Healy and Palepu (1993), p. 1, Mazzi et al. (2013), p. 1. 647 Cf. AbuGhazaleh et al. (2011), p. 165, Brochet and Welch (2011), p. 7, Siggelkow and Zülch (2013a), p. 29, Riedl (2004), p. 823, Ramanna and Watts (2012), p. 751, Lapointe (2005), p. 5, Lapointe-Antunes et al. (2009), pp. 62-63, Li et al. (2011), p. 746, Knauer and Wöhrmann (2013), p. 1.


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predicted by agency theory, which would influence their goodwill write-off or non-

write-off decisions.648

The theoretical concepts outlined in this chapter should act as the foundation to

derive meaningful hypotheses which will be tested in the empirical analysis of this

PhD thesis.

Fig. 40: Theoretical concepts explaining managerial motivations for writing or not

writing off goodwill in the impairment-only approach


5.1 Private information on changes of a firm’s future

financial performance

The first theoretical concept presented in this chapter represents the ideal case to

which accounting standard setters usually refer to when arguing for the superiority

of the impairment-only approach to the continuous amortization of goodwill.649

Accounting standard setters justify the fair value measurement which finds its

application also in goodwill accounting “on the grounds of being more relevant for

the decisions by users of financial statements”650. The higher decision usefulness of

648 Cf. Ramanna and Watts (2012), p. 753, Mazzi et al. (2013), p. 1, Amiraslani et al. (2013), p. 20, Knauer and Wöhrmann (2013), p. 1. 649 Cf. IAS 36.BC131G, FASB (2014a), AbuGhazaleh et al. (2011), p. 196, Liberatore and Mazzi (2010), p. 334, Meyer and Halberkann (2012), p. 312, Amiraslani et al. (2013), pp. 18-19, Chen et al. (2013), p. 4, Gordon and Hsu (2014), p. 13, Vanza et al. (2011), pp. 2-3, Lapointe-Antunes et al. (2009), pp. 62-63, Li et al. (2011), p. 746, Ramanna and Watts (2012), p. 749, Ramanna (2008), p. 255. 650 Christensen and Nikolaev (2013), p. 741.

Incentives predicted by agency theory

Private information on changes in the firm’s future financial

performance

Goodwill write-off decision in the impairment-only approach


144

fair values is based on their higher transparency regarding true economic values of

assets and liabilities as well as timeliness of accounting information, besides being

more comparable across firms.651

When introducing the impairment-only approach under US GAAP, the standard

setter FASB (2014a) argued that the fair value measurement of goodwill “will

provide users with a better understanding of the expectations about and changes in

(goodwill) over time, thereby improving their ability to assess future profitability

and cash flows.”652 This assessment on the impairment-only approach is based on

the valuation methodologies with which the recoverability of goodwill has to be

tested. Testing the recoverability of goodwill generally requires applying forward

looking valuation methodologies which make the application of private information

held by a firm’s management necessary.653

Through the application of the impairment-only approach this private information

becomes public as the write-off or non-write-off decision discloses information on

the expected firm’s performance or business segments to which goodwill has been

allocated, which most likely has been private before.654 Siggelkow and Zülch (2011)

support this view by arguing that goodwill’s “expected future benefit (to a firm) is a

private information available to the management only”655.

Therefore theoretically, in an ideal setting every write-off decision, being either

positive (i.e. no write-off) or negative (i.e. write-off), reveals private information

held by management to capital market participants and other stakeholders about the

expected performance of the firm or the reporting units to which goodwill has been

allocated (i.e. CGUs).656 Lapointe-Antunes et al. (2009) confirm this reasoning by

arguing that through the impairment-only approach, managers can reduce

651 Cf. Christensen and Nikolaev (2013), p. 741, Schipper (2005), pp. 117-118. 652 FASB (2014a). The original statement reads as: “The enhanced disclosures about goodwill and intangible assets subsequent to their acquisition also will provide users with a better understanding of the expectations about and changes in those assets over time, thereby improving their ability to assess future profitability and cash flows” (FASB (2014a)). 653 Cf. Mazzi et al. (2013), p. 1. 654 Cf. Fields et al. (2001), p. 257, AbuGhazaleh et al. (2011), p. 195, Siggelkow and Zülch (2013a), p. 29, Lapointe-Antunes et al. (2009), p. 62, Mazzi et al. (2013), p. 1. 655 Siggelkow and Zülch (2011), p. 10. 656 Cf. Riedl (2002), p. iii, AbuGhazaleh et al. (2011), p. 195, Lapointe-Antunes et al. (2009), p. 63.


145

information asymmetries through the disclosure of private information on the

expected financial performance, as usually “no financial information is publicly

available at the reporting-unit level unless every reporting unit is a public firm itself

(which happens very rarely).”657 And disclosures on goodwill write-offs help to

reduce information asymmetries, as “it is virtually impossible for outsiders to collect

the information necessary to make an external appraisal of the fair value of goodwill

at the reporting-unit level.”658 Consequently, in an ideal setting, goodwill write-off

decisions, being either positive or negative, have an information value to market

participants,659 as this information allows then to value the firm more accurately.660

This holds true not only when information asymmetries are present.661

A general theoretical foundation for this private information hypothesis in

accounting choices has been provided by Holthausen and Leftwich (1983) and

Holthausen (1990). In their papers, the authors offer insights on the economic

consequences of mandatory and voluntary accounting techniques and standards.662

Those consequences allow drawing conclusions on why managers potentially prefer

certain accounting techniques over others. In their so-called information perspective-

theory the authors argue that managers could have a preference for those accounting

techniques that allow them to reveal their private information on the firm’s future

cash flows as “managers have a comparative advantage in providing information

about their firms”663, thereby helping market participants to price the firm more

accurately or reducing uncertainty which market participants might have abount the

intrinsic value of the firm.664 The theory offered by Holthausen and Leftwich (1983)

that the “accounting technique choice reflects management’s expectation of future

cash flows”665 could also be linked to explaining goodwill write-off decisions, as

657 Lapointe-Antunes et al. (2009), p. 63. 658 Lapointe-Antunes et al. (2009), p. 63. 659 Cf. Amiraslani et al. (2013), p. 20, Mazzi et al. (2013), p. 1. 660 Cf. Watts and Zimmerman (1990), p. 132, Vanza et al. (2011), p. 6. 661 Cf. Fields et al. (2001), p. 259. 662 Cf. Holthausen and Leftwich (1983), p. 77, Holthausen (1990), pp. 208-209. 663 Holthausen and Leftwich (1983), p. 112. 664 Cf. Vanza et al. (2011), p. 2. 665 Holthausen and Leftwich (1983), p. 112.


146

those decisions allow the senior management team to reveal its expectations about

future cash flows (either on a firm or business segment level).

Empirical results documenting that goodwill write-offs negatively correlate with

future cash flows would provide support the private information disclosure

hypothesis.666 No or small correlations with future cash flows would certainly raise

questions on the argued for private information hypothesis of accounting standard

setters.667 Consequently, on the basis of the information disclosure hypothesis and

the reasoning of accounting standard setters, one would expect that goodwill write-

offs correlate with future firm performance. Future firm performance can be

measured through various proxies, for example accounting earnings, cash flows or

capital market returns.

5.2 Agency theory

To the contrary of the above described private information theory outlined above,

agency theory foresees that the reporting flexibility and the unverifiability in

accounting choices668 in the impairment-only approach can be used opportunistically

by a firm’s senior executives to manage a firm’s financial reporting.669 Incentives to

manage goodwill write-offs predicted by agency theory can be subcategorized into

(i) contracting motives like existing compensation agreements or debt covenants, (ii)

reputational concerns, and (iii) valuation concerns.670 Consequently, agency theory

provides a strong theoretical argumentation for goodwill write-offs not being

necessarily a pure reflection of economic fundamentals.671

666 Cf. Li et al. (2011), pp. 750-751, Lhaopadchan (2010), p. 124, Lapointe (2005), p. 5, Gordon and Hsu (2014), p. 2. 667 Cf. Gordon and Hsu (2014), p. 2. 668 Cf. Fields et al. (2001), p. 256, who define accounting choice as “any decision whose primary purpose is to influence (either in form or substance) the output of the accounting system in a particular way, including not only financial statements published in accordance with GAAP, but also tax returns and regulatory filings” (Fields et al. (2001), p. 256). 669 Cf. Ramanna and Watts (2012), p. 758, Riedl (2004), p. 833, Li and Sloan (2009), p. 12. 670 Cf. Fields et al. (2001), p. 257, Ramanna and Watts (2012), p. 758, Beatty and Weber (2006), p. 264. 671 Cf. AbuGhazaleh et al. (2011), p. 170, Lapointe-Antunes et al. (2009), p. 63, Knauer and Wöhrmann (2013), p. 1.


147

In general, agency theory applies to specific situations and the thereof resulting

consequences when duties and tasks are delegated from a principal to an agent.672

According to its definition, principal agency theory deals with contractual problems

that result when one or more persons, i.e. the principal(s), assign another person, i.e.

the agent, to fulfill specific services on their behalf, which involves the delegation of

individual decision making to the agent.673 Due to the issue of incomplete contracts,

meaning that for the principal it would be impossible to contractually specify any

desired and undesired actions of the agent and the thereof resulting legal

consequences to the agent, some individual decision freedom, so-called control

rights, remain with the agent.674 Principal agency theory assumes that managers

(agents) will use these control rights, i.e. discretionary power, to rather maximize

their individual utility than to act in the very best interest of the owner (principal).

This reasoning also stems from the fact that managers usually do not face the major

share of the financial consequences which result from their decisions.675

Theoretically, the principal possesses several possibilities to counteract and limit

undesired behavior of the agent. This includes the alignment of the management’s

interest to that of the owners through compensation agreements which are linked to

the financial success of the firm or the reduction of the agent’s discretionary

decision space through the implementation of control mechanisms, thereby focusing

on reducing agency costs.676

5.2.1 Contract incentives

Agency costs can be reduced by introducing compensation structures that align the

utility functions of the management with those of the owners, so that when the

management team tries to maximize their individual utilities they do so in the best

interest of the owners.677 To do so, the owner’s short-, medium-, and long-term goals

672 Cf. Jensen and Meckling (1976), pp. 308-309, 323. 673 Cf. Shleifer and Vishny (1997), p. 740, Jensen and Meckling (1976), p. 308. 674 Cf. Fama and Jensen (1983), p. 303, Shleifer and Vishny (1997), pp. 766-767. 675 Cf. Hill and Phan (1995), p. 705. 676 Cf. Kaplan and Strömberg (2004), pp. 2183-2185. 677 Cf. Watts and Zimmerman (1990), p. 133.


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have to be converted into measurable performance indicators that reflect the owner’s

goals, according to which the management’s compensation is determined. Generally,

such performance indicators can be based either on financial or non-financial

information.678 Financial performance indicators usually target at the firm’s current

profit and loss statement, balance sheet or share price.679 In case, the management’s

compensation is linked to a financial ratio that would include amortization effects of

intangible assets, like goodwill write-offs, the management team would be

motivated to avoid booking an impairment charge as they would be directly

impacted by its consequences.680 Consequently, compensation schemes can have an

impact on the managers’ motivation to be unwilling to book impairment losses.681

On that topic, Guler (2006) reasons that “(b)onus plans (which are directly linked to

earnings) provide executives with incentives to reduce goodwill impairment

charges”682. One might argue that the larger the individual impact of a goodwill

write-off on personal wealth, the harder the individual might want to abstain from

booking an impairment loss.683

Besides remuneration concerns, potential violations of debt covenants written on

accounting numbers can have an impact on accounting choice and in particular on

goodwill write-off decisions of senior executives.684 Concerns of existing debt

covenants whose breaches would potentially reduce the senior managers’ decision

space fall therefore under the category of contract incentives. Fields et al. (2001)

refer to this phenomenon also as the “debt hypothesis”685, meaning that debt

covenant violation concerns could influence the accounting choice of a firm’s senior

management.686 On that topic, Watts and Zimmerman (1990) argue that managers of

678 Cf. Fields et al. (2001), p. 266. 679 Cf. Fields et al. (2001), p. 266. 680 Cf. Zülch and Siggelkow (2012), p. 385. 681 Cf. Healy (1985), p. 86, Zülch and Siggelkow (2012), p. 385, Fields et al. (2001), p. 266. 682 Guler (2006), p. 10. 683 Cf. Oberholzer-Gee and Wulf (2012), pp. 1, 37, Siggelkow and Zülch (2013a), p. 37, Healy (1985), p. 95, who analyze the influence of bonus structures and other CEO incentive on the likelihood of earnings management or overstated earnings. 684 Cf. Watts and Zimmerman (1990), pp. 133-134, 139, Beatty and Weber (2006), p. 259, Zang (2008), p. 39, Lapointe-Antunes et al. (2009), p. 63, Riedl (2004), p. 833. 685 Fields et al. (2001), p. 272. 686 Cf. Fields et al. (2001), p. 275.


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highly leverage firms have an incentive to use income increasing accounting

methods in order to reduce the risk of technical default and the thereof resulting

costs.687

5.2.2 Reputational concerns

The information content of goodwill write-offs conveyed to investors poses the risk

of individual reputational damages to the firm’s management,688 as impairments

imply that expected cash flows that were assumed to get realized through an

acquisition in the past are unable to get realized in the future given the information

the firm’s management currently has.689

This poses the risk that shareholders might question the meaningfulness of recent

acquisitions and paid purchase premiums as those transactions obviously destroyed

value rather than to created value.690 Depending on their power, shareholders might

use their influence and exchange members of the senior management team who were

in charge of the transactions that now seem to have failed.691 Research studies

support this reasoning as it has been documented that the risk of being replaced as a

CEO is higher in underperforming firms (from a return and accounting profit

perspective) and in firms with earnings that substantially negatively deviate from

market expectations, than vice versa.692 This reasoning implies that CEOs have an

incentive not to write-off goodwill although being economically impaired during

their regular tenure in order to reduce individual reputational damages from the

687 Cf. Watts and Zimmerman (1990), p. 139. 688 Cf. Li et al. (2011), p. 745, Gu and Lev (2011), p. 1995. 689 Cf. Hirschey and Richardson (2002), p. 173, Chen et al. (2013), p. 1, Hirschey and Richardson (2003), p. 75, Lee (2011), p. 236, Francis et al. (1996), p. 134. As the fair value of a firm represents a function of expected cash flows, investors will adjust the cash flow expectations downward as soon as the news becomes public, leading to a lower fair value of the firm. 690 Cf. Gu and Lev (2011), p. 1995. 691 Cf. Coughlan and Schmidt (1985), p. 50, Farrell and Whidbee (2003), pp. 165-167. 692 Cf. Coughlan and Schmidt (1985), p. 50, Farrell and Whidbee (2003), pp. 165-167, Mobbs (2009), p. 1, also citing the research studies of Warner et al. (1988), Weisbach (1988) and Parrino (1997).


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write-off693, especially then when the CEO who had made the acquisition is now

ultimately responsible for deciding on a potential goodwill write-off.

Theoretical approaches around how reputation is built imply that reputation

increases over a CEO’s tenure and is therefore time dependent.694 These

considerations imply that individual reputation, in a theoretical context, is higher for

CEOs with longer tenures than for their peers with shorter tenures in a firm; most

likely due to their longer track record in the respective position and senior

management experience that would make them theoretically more effective in

managing a firm, as Cornett et al. (2008) argue.695

According to Francis et al. (2004), the rent extraction theory predicts that managers

with a higher reputation (CEOs with longer tenure) and who are concerned with

securing their reputation would take actions to protect their reputation,696 by for

example meeting earnings forecasts or market expectations, as these factors are seen

as reasons for building up reputation, as Graham et al. (2005) point out.697 By doing

so, CEOs might try to secure their positions due to career concerns.698 From the

viewpoint of managing goodwill write-offs, the rational of the rent extraction theory

would therefore suggest that the willingness to write off an economically impaired

goodwill is lower for CEOs with a longer tenure in their senior management position

and therefore having a higher reputation at risk than for CEOs with shorter

tenures.699

693 Cf. Graham et al. (2005), p. 13, Francis et al. (2004), p. 3. The rent extraction theory predicts that managers with a higher reputation (i.e. CEOs with longer tenures) and who are concerned with securing their reputation will take actions to protect their reputation (Cf. Graham et al. (2005), p. 13, Francis et al. (2004), p. 3). Cf., for example, Demers and Wang (2010) and Francis et al. (2004), who find that earnings management and lower financial reporting quality is more present in firms with CEOs with longer tenure. 694 Cf. Francis et al. (2004), p. 3. Factors that can further enhance one’s reputation building are (i) superior knowledge (i.e. industry or functional expertise) and (ii) a track record of managing firms with above average or stable performance, whilst both cannot be viewed most likely as mutually exclusive as they are probably interrelated (Cf. Graham et al. (2005), p. 13, Francis et al. (2004), p. 3). 695 Cf. Cornett et al. (2008), p. 360. 696 Cf. Francis et al. (2004), p. 3. 697 Cf. Graham et al. (2005), p. 13. 698 Cf. Demers and Wang (2010), p. 1, Francis et al. (2004), p. 1, who find that earnings management and lower financial reporting quality is more present in firms with older CEOs than in frms with younger CEOs. 699 Cf. Francis et al. (2004), p. 1, Cornett et al. (2008), p. 360.


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Beatty and Weber (2006) provide support for this reasoning by hypothesising that

“CEOs that made the acquisition decision may be more likely to delay goodwill

impairment charges.”700 Vice versa, new CEOs have an incentive to write-off

goodwill early during their tenure in order to limit individual reputational damages

from potential future goodwill write-off which might become necessary during later

years, and thereby allowing to put the blame for an apparent unsuccessful

acquisition on their predecessor.701 Francis et al. (1996) further reason that “new

management has incentives to “clear the deck” of impaired assets to improve

investors’ perceptions of the future financial performance of the firm”702, thereby

providing additional support for the agency based reputational concern theory.703

5.2.3 Valuation concerns

It has been well documented in academia that accounting earnings are considered to

be an important source of information regarding current and future financial

performance of a firm and therefore also its stock price.704 This reasoning builds on

the argumentation that current accounting earnings are frequently used to forecast

future accounting earnings, future dividends, and cash flows in general which

primarily serve as the basis for the valuation of a security or firm.705 This association

grounds in the residual income valuation model pioneered by Ohlson (1995) which

explains the theoretical relationship between future earnings and the current market

value of a firm or its share price.706 In his widely accepted model, the equity value of

a firm can be expressed as a function of its current book value of equity and any

700 Beatty and Weber (2006), p. 266. 701 Cf. Beatty and Weber (2006), p. 266, Francis et al. (2004), p. 1, Cornett et al. (2008), p. 360, Francis et al. (1996), p. 125, Masters-Stout et al. (2008), p. 1379, Hamberg et al. (2011), p. 273, Zang (2008), p. 38. 702 Francis et al. (1996), p. 123. 703 Cf. Ramanna and Watts (2012), p. 759. 704 Cf. Eisele (2012), p. 8. 705 Cf. Beaver (1998), p. 86, Nichols and Wahlen (2004), p 266, Eisele (2012), p. 9. 706 Cf. Feltham and Ohlson (1995), p. 690, Penman and Sougiannis (1998), p. 348, Francis et al. (2000), p. 48, Farrell (1985), p. 17, Vincent et al. (2001), p. 162, Ohlson (1995), p. 667.


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future abnormal earnings, defined as total future earnings less the firm’s cost of

capital.707

Frequently, members of the senior management are substantial shareholders in the

firm. In case the senior management team owns shares of its own firm, senior

executives have an incentive not to reduce earnings through goodwill write-offs as

the firm’s share price could react negatively to this information, thereby having

direct wealth effects on the executives.708 “Recording a goodwill impairment loss

should be associated with a decline in the value of expected future cash flows of the

firms and a decrease in stock price”709, as Guler (2006) reasons.

On this topic Guler (2006) argues that “(i)f managers have concerns regarding the

negative valuation consequences of goodwill impairment losses on firms’ stock

prices, (…), managers could use the accounting discretion (…) to mislead financial

statement users regarding the underlying value of reported goodwill.”710 One might

argue that the larger the individual’s wealth which is tied to the performance of the

firm, the harder the individual might want to abstain from impairing goodwill.711

This reasoning however certainly only holds true if the market participants have not

already priced in the impaired goodwill in their estimation of the firm’s share price.

707 Under Ohlson’s model (1995), the components of future earnings represent one of the primary sources of the current market value of a firm. The link between accounting earnings and security values has also been confirmed empirically by, for example, Dechow (1994) and Penman and Sougiannis (1998), who found out that stock returns significantly correlate with current earnings and that valuation errors are on average smaller when applying an earning based valuation model compared to a traditional cash flow based model. 708 Cf. Ramanna and Watts (2012), p. 760, Beatty and Weber (2006), p. 265, Jarva (2009), p. 1079, Li and Sloan (2012), p. 49, Muller et al. (2009), p. 5, Hirschey and Richardson (2002), p. 181, Francis et al. (1996), p. 128, Bens et al. (2011), p. 537, Lhaopadchan (2010), p. 125, Li et al. (2010), p. 26. This reasoning however certainly only holds true if the market participants have not already priced in the economically impaired goodwill in their estimation of the firm’s share price. 709 Guler (2006), p. 10. 710 Guler (2006), p. 2. 711 Cf. Oberholzer-Gee and Wulf (2012), p. 1, Healy (1985), p. 95, who analyse the relationship between bonus structures as well as other CEO incentives and the likelihood of earnings management and overstated earnings.

6 Literature review on goodwill write-off decision making

153

6 Literature review on research findings that can be

linked to goodwill write-off decision making

After having described theoretical concepts that help understanding the motives of a

firm’s management for writing or not writing off goodwill, the following section of

this PhD thesis focusses on selected research findings on the topic of goodwill write-

off decision making in firms.

Research findings that can be linked to the willingness of senior executives to write

or not write off goodwill can be grouped in two broad research streams, being (i) the

economic consequences to the firm when booking an impairment loss (like

subsequent stock market reactions and impacts on debt contracts) and (ii) personal

incentives of senior executives of a firm influencing their write-off or non-write-off

decisions.

The research stream that has dedicated itself to personal incentives can be further

broken down in the analysis of a possible opportunistic behavior of managers to use

goodwill write-offs for earnings management purposes, the effects of top

management turnover on goodwill write-off probabilities and the opportunistic use

of private information by a firm’s management prior to goodwill write-offs.


154

Fig. 41: Research areas that could be linked to goodwill write-off decision making


6.1 Timing of goodwill write-offs

So far, research studies come to mixed conclusions whether firms recognize

goodwill write-offs on average in a timely or an untimely manner, although the

majority of these findings imply an untimely recognition.712 Findings documenting

an untimely recognition can be used to support the assumption that company-

specific factors might exist that impact goodwill write-off decision making as

otherwise firms would recognize write-offs in a more timely manner.

Research studies related to the timeliness of goodwill write-offs are primarily based

on samples of US firms, reporting under US GAAP and therefore applying Financial

Accounting Standards No. 142, Goodwill and Other Intangible Assets, (SFAS 142).

SFAS 142, issued by the Financial Accounting Standards Board (FASB), represents

the equivalent to IAS 36 developed and issued by the IASB. As the applicable

methodology for testing the recoverability of goodwill according to SFAS 142 is

712 Cf. Chen et al. (2008), p. 72, Iatridis et al. (2006), p. 2, Beatty and Weber (2006), pp. 275-276, Bens and Heltzer (2005), p. 4, Li and Sloan (2009), p. 2, Li et al. (2011), p. 745, Hayn and Hughes (2006), p. 226.

Influence

1. Link between top management

turn-over and goodwill write-offs

Economic consequences from

recognizing goodwill write-offs

2. Goodwill write-offs used for

earnings management purposes

over regular tenure of a CEO

2. Potential negative impacts on debt

contracts (cost of debt)

1. Potential negative stock market

reactions (firm’s equity value)

Personal incentives of managers influencing

goodwill write-off decisions

Goodwill write-off decisions by a firm’s management team

Influence

Influence


155

comparable to that of IAS 36, empirical findings on the basis of both accounting

standards can be considered as equally relevant for analyzing the impairment-only

approach in practice.

As a starting point, the analysis of timeliness of goodwill write-offs requires not

only the definition of timeliness but also its measurement. For the measurement of

timeliness, researchers need to have sufficient evidence that goodwill is actually

economically impaired. Building on these considerations, timeliness, according to

Amiraslani et al. (2013), implies having information available to decision-makers in

time to be capable of influencing their decisions.713 Consequently, timeliness as

Amiraslani et al. (2013) define it relates to the speed with which changes in the

economic values of assets are recognized and any impairment losses are reflected in

earnings.714 Chen et al. (2008) define timeliness from a time interval perspective as a

“recognition lag - i.e., whether accounting is contemporaneous with recognition in

returns.”715

Existing research studies have used capital market data and changes in the earnings

situation of a firm as strong indicators of an apparent economically impaired

goodwill. This has included primarily the analysis of changes in a firm’s market to

book value of equity ratio and backward looking stock market returns. Additionally,

earnings based measures include significant negative changes in sales, operating

income, net income, or return on capital measures like return on assets (ROA).716

However on the basis of published research studies, one can observe that the

argumentation for an impaired goodwill follows a rather subjective, researcher

individual approach, as different researchers use different measures for their

argumentation for a required goodwill write-off.

713 Cf. Amiraslani et al. (2013), p. 19, IASB/FASB (2008), p. 21. 714 Cf. Amiraslani et al. (2013), p. 19. 715 Chen et al. (2008), p. 72. 716 Cf. Hayn and Hughes (2006), p. 255.


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Research findings on timeliness of goodwill write-off in financial accounting:

Bens and Heltzer (2005) study whether after the adoption of SFAS 142 firms record

goodwill write-offs on a timely basis by applying a long window stock return test.

This stock return test is applied to firms that wrote off goodwill and analyses the

influence of historical stock returns during the previous year (year t-1) and the year

in which the write-off was recognized (year t=0). The authors also split up their

observation period in a pre- and post-SFAS 142 adoption period. They come to the

conclusion “that goodwill write-offs (…) after the adoption of SFAS 142 are fairly

timely, in that they are negatively associated with stock returns in year 0.”717 This

effect is found to be stronger for larger firms, as for smaller firms this effect

disappears in their regression model.718 Firm complexity, measured by the number

of a firm’s business segments, is found to have no influence on timeliness.719

However it needs to mentioned that their sample only included firms that actually

recorded a write-off leaving the question open whether all firms that should have

booked an impairment charge actually did so. Additionally, Bens and Heltzer (2005)

only study firms that took a write-off of more than 5% of the total book value of

assets, thereby excluding firms that recorded smaller write-offs.

Similar to Bens and Heltzer (2005), Chen et al. (2008), who focus on firms adopting

SFAS 142 for the first time, use also a backward-looking return based model for

their analysis of timely goodwill write-offs. The authors argue that “(a)n association

with prior returns is consistent with lack of timeliness (…).”720 This reasoning

implies that capital market participants are able to assess correctly available

information in their firm valuation and therefore (partly) know about goodwill

impairments before firms actually recognize them in their accounting earnings.721

Whether this assessment is actually true depends then on the stock market reaction

at the time when the write-off is booked. The authors draw their conclusion about

the timeliness of goodwill write-offs on the basis of their findings that stock returns

717 Bens and Heltzer (2005), p. 4. 718 Cf. Bens and Heltzer (2005), p. 4. 719 Cf. Bens and Heltzer (2005), pp. 4, 27. 720 Chen et al. (2008), p. 74. 721 Cf. Van Hulzen et al. (2011), p. 99.


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lead goodwill write-offs, however on average by only one year. Consequently, Chen

et al. (2008) argue that goodwill write-offs recognized in accounting earnings is

fairly timely on average for firms applying SFAS 142 as only a time lag of one year

is observable in their sample.722

Hayn and Hughes (2006) provide strong evidence for untimely goodwill write-offs

in accounting earnings. By using a variety of different post-acquisition performance

measures of a firm’s operating business segments as well as acquisition

characteristics, the authors find that “goodwill write-offs lag behind the economic

impairment of goodwill by an average of three to four years. For one third of the

companies examined, the delay can extend up to ten years.”723 They further

elaborate that “(t)his substantial delay may reflect the exercise of managerial

discretion on timing goodwill write-offs to meet certain reporting objectives.” 724

Hayn and Hughes (2006) base their argumentation on findings derived by the

application of a write-off prediction model. In their model, the authors regress

actually observed goodwill write-offs on accounting measures like return on assets

(ROA), changes in ROA, operating losses, and changes in sales and find that

goodwill write-offs clearly lag negative changes in operating performance.725

Beatty and Weber (2006) also study the timing of goodwill write-offs and provide

evidence for an untimely recognition of goodwill write-offs in practice. The authors

focus in their study on various managerial incentives to either accelerate or delay the

recognition of goodwill write-offs. The authors use a sample of firms during the

adoption period of SFAS 142. Prior to that point of time, firms had to amortize

goodwill on an annual, straight line basis. Once adopted, goodwill would be tested

for impairment at least annually. In the financial year when SFAS 142 would be

adopted, firms were allowed to record a one-time impairment charge (cumulative

write-off) to bring the book value of goodwill in line with its economic value and

record this charge below the line, i.e. excluded from a firm’s income from

722 Cf. Chen et al. (2008), p. 80. 723 Hayn and Hughes (2006), p. 223. 724 Hayn and Hughes (2006), p. 226. 725 When reviewing these findings, one needs to bear in mind that the sample of Hayn and Hughes (2006) predominantly consists of observations gained prior to the introduction of SFAS 142.


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operations.726 Alternatively, firms could delay a pending impairment charges to

future periods, thereby hoping for a potential recovery of the economic situation of

the firm.727 However, if optioned to do so, future goodwill impairments would then

be recorded above the line, i.e. included in a firm’s income from operations.728 This

trade-off by firms gets analysed by Beatty and Weber (2006) by studying various

sets of variables capturing personal incentives of managers, firms’ debt contracting

incentives, capital market considerations, as well as firm risk. The findings of Beatty

and Weber (2006) suggest that certain firms willingly delay goodwill write-offs

although strong indicators exist that goodwill is economically impaired. In

particular, by using a probit regression the authors find that firms with debt

covenants and accounting earnings-linked bonus plans as well as firms listed on a

stock exchange that has requirements regarding balance sheet or profit and loss

statement information delay goodwill impairments to future periods. Additionally by

applying a censored regression, the authors find that the expected write-off amounts

statistically significantly deviate from the predicted impairment amounts.729

In the timeliness analysis of Li and Sloan (2009), the authors include both historical

stock returns as well as return on assets as an operating performance variables. Their

findings also provide strong evidence for an untimely goodwill write-off

recognition. The authors come to the conclusion that “(g)oodwill impairments lag

deteriorating operating performance and stock returns by at least two years.”730 They

further elaborate on their findings that the impairment-only approach under SFAS

142 “produces accounting numbers that deviate from economic reality and results in

the delayed recognition and pricing of declines in the fair value of goodwill.”731 The

authors also highlight that their results suggest that managers try to postpone

pending goodwill write-offs as long as possible to hide that substantial expected

benefits from goodwill and therefore from a business combination have expired.732

726 Cf. Chen et al. (2008), p. 72. 727 Cf. Beatty and Weber (2006), p. 262. 728 Cf. Beatty and Weber (2006), p. 262. 729 Cf. Beatty and Weber (2006), p. 280. 730 Li and Sloan (2009), p. 2. 731 Li and Sloan (2009), p. 4. 732 Cf. Li and Sloan (2009), p. 38.


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To derive their findings, the authors apply a logistic regression model including a

combination of historic stock return data and return on asset data on actual goodwill

write-off.733 The analysis of Li and Sloan (2009) shows that impairment risk is

statistically significantly negatively correlated with current and historical operating

margins, an observation violating the rational that goodwill encompasses future

economic benefits. Additionally, the “fact that a great portion of firms write off the

majority of their goodwill balance in a single year suggests that goodwill

impairments are delayed until goodwill is largely exhausted.”734 These findings also

suggest that goodwill write-offs in reality rather act as a lagging indicator of

unachievable benefits from a business combination and not as a leading one.

Li et al. (2011) study the causes and consequences of goodwill write-offs in US

firms, however primarily from a market reaction and overpayment point of view in

prior business combinations.735 Although the question of timeliness of goodwill

write-offs does not represent the focus of their research study, they provide indirect

evidence that goodwill write-offs are not recorded on a timely basis by arguing “that

firms with potentially impaired goodwill (…) may have used their managerial

discretion to avoid taking the loss.”736 The basis for their conclusion rests in the

identification of firms in their sample for which the authors have sufficient evidence

that they have an economically impaired goodwill. However surprisingly, not all of

those firms report a write-off during the SFAS 142 adoption period. By studying

these non-impairing firms more closely, the authors find that capital market

participants do not revise their share price expectations upward for these firms,

given that they record no impairment loss. The authors infer from these findings that

these firms potentially have used their managerial discretion to postpone pending

impairment losses to future periods, however investors have already priced in the

effect of an impaired goodwill.737

733 Cf. Li and Sloan (2009), p. 4. 734 Li and Sloan (2009), p. 18. 735 Cf. Li et al. (2011), p. 745. 736 Li et al. (2011), p. 745. 737 Cf. Li et al. (2011), p. 748.


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Summing up, the findings regarding goodwill write-offs suggest that frequently

goodwill write-offs are carried out in an untimely manner, as the majority of studies

show that on average they lag negative stock market returns or changes in

accounting earnings for a considerable period of time. Although various studies

analyzing the timeliness of goodwill write-offs during pre- and post SFAS 142 time

periods show that timeliness seems to have increased through the introduction of the

impairment-only approach, timeliness as such still can be further improved as

empirical findings reveal.738

6.2 Economic consequences of goodwill write-offs

In the following section, research findings on the observable economic

consequences of goodwill write-offs are presented. In particular, the focus is placed

on stock market reactions to write-offs and impacts on debt contracts from

recognized impairments. Most of the presented studies come to the conclusion that

goodwill write-offs have negative economic consequences to a firm after their

announcements, predominantly in form of subsequent negative stock market

reations. In case a senior management team is concerned about the firm’s share price

stability, it might incorporate these potential negative consequences in their financial

reporting strategy and thereby trying to delay or abstain from booking a goodwill

write-off.

Research studies that analyze stock market reactions to goodwill write-off

announcements are also termed value relevance studies. Generally, these studies

focus on two broad interrelated research areas. Firstly, whether goodwill as an asset

actually represents a source of value to shareholders and, secondly, how stock

markets react to the announcement of a write-off. If the hypothesis holds that

goodwill represents value to a firm’s shareholders, then the announcement of an

impairment would be expected to cause a negative stock market reaction. Here,

however, also related to the issue of timeliness, two separate cases need to be

differentiated: (i) whether investors have already inferred from a firm’s prior

738 Cf. Chen et al. (2008), p. 72.


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earnings and other information that goodwill is most likely impaired and therefore a

firm’s share price does not react to the announcement of a write-off as this

information is already priced into their estimations, or (ii) whether the information

of a goodwill write-off actually represents new information and therefore causes a

drop of the firm’s stock price.739

Fig. 42: Classification of research findings on stock market reactions


That goodwill represents actually a source of value to shareholders has been well-

documented in various studies.740 Recent empirical evidence however implies that

this value relevance diminishes over time, meaning that recently recognized

goodwill contains more explanatory power in value relevance studies than older

goodwill.741 Furthermore value relevance seems to be stronger in certain industries

as various studies show.742 It needs to be added, however, that the majority of these

studies uses samples which were derived at a time when the impairment-only

739 Cf. Fama (1970), p. 383, Kothari (2001), p. 110. On the basis of these considerations, one can link value relevance studies on goodwill write-offs also to the subject of efficient capital markets, a concept pioneered by Fama (1970). According to Fama (1970), capital market can be categorized with respect to what sets of information are included in current security prices. Capital markets are defined to be efficient if “security prices at any time “fully reflect” all available information” (Fama (1970), p. 383). 740 Cf. Bugeja and Gallery (2006), pp. 519, 524. 741 Cf. Bugeja and Gallery (2006), p. 533, Churyk (2005), p. 1353. 742 Cf. Chauvin and Hirschey (1994), p. 178, Dahmash et al. (2009), p. 132.

Stock market reactions to goodwill write-offs

Negative impact on share price observable

Information is already captured in a firm’s share price through a firm’s deteriorating

financial performance in the past as investors have already adjusted the firm’s

earnings forecast downward

No impact on share price observable

Represents new information to investors. Information on impaired goodwill not fully

incorporated in share price from prior information leading to a downward

adjustment of future earnings

expectations in the current period, causing the share price to drop


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approach in goodwill accounting has not yet been implemented and therefore deals

with goodwill amortized on a straight line basis. Nevertheless, one can certainly

argue that these findings from a pre-IOA period are likewise applicable to firms

using the impairment-only approach in goodwill accounting, due to the fact that the

sources of capitalized goodwill, i.e. the assumed future economic benefits, are

independent of its accounting treatment in subsequent periods.

Value relevance studies build on an “association between a security price-based

dependent variable and a set of accounting variables. An accounting number is

termed “value relevant” if it is significantly related to the dependent variable”743.

This reasoning grounds in the widely agreed and well-proven hypothesis that the

market value of a firm can be expressed as a function of its book value of equity and

earnings as formulated by Ohlson (1991, 1995)744 and several years later further

refined by Barth et al. (2001)745. The reason to include accounting earnings in value

relevance models is not only justified through their obvious value driving character.

Earnings, as Barth (2000) points out, capture information about a firm’s resources

that are currently not recognized or not recognizable in a firm’s accounting balance

sheet.746 Consequently, earnings as an independent variable can add explanatory

power to value relevance models.747

The operationalization of testing the value relevance hypothesis of goodwill as being

a source of value usually involves the application of a regression model that

regresses a firm’s market value of equity on various accounting variables capturing

information of the firm’s balance sheet as well as income statement.748 To reduce

heteroskedasticity, frequently all variables in the regression model are scaled by the

total number of outstanding ordinary shares749 or by total assets750. Until today,

empirical analyses have addressed the relevance of goodwill in the market

743 Beaver (2002), p. 459. Cf. also Godfrey and Koh (2001), p. 40, Barth et al. (2001), p. 79. 744 Cf. Ohlson (1991), p. 3, Ohlson (1995), p. 663, Barth et al. (2001), p. 91, Bugeja and Gallery (2006), p. 524, Kothari (2001), p. 142. 745 Cf. Barth et al. (2001), pp. 91-95, Bugeja and Gallery (2006), p. 524. 746 Cf. Barth (2000), p. 13. 747 Cf. Barth and Landsman (1995), p. 100, Bugeja and Gallery (2006), p. 525. 748 Cf. Bugeja and Gallery (2006), pp. 522, 524. 749 Cf. Godfrey and Koh (2001), p. 41, Kothari (2001), p. 154. 750 Cf. Jennings et al. (1996), p. 517, Kothari (2001), p. 143.


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participants’ valuation of a firm’s equity across countries and across time.751

Amongst the researchers that provided evidence for the value relevance of goodwill

are Chauvin and Hirschey (1994), McCarthy and Schneider (1995), Barth and

Clinch (1996), Jennings et al. (1996), Johnson and Petrone (1998), Henning et al.

(2000), Godfrey and Koh (2001), and Bugeja and Gallery (2006).

Chauvin and Hirschey (1994) were among the first researchers testing the value

relevance of recognized goodwill. By applying an OLS regression in their value

relevance study, the authors test the impact of various assets, including goodwill,

other intangible assets and tangible assets, as well as earnings components, like

advertising expenditures and research and development (R&D) expenditures.752 By

studying the influence of those independent variables on 2’693 US firm’s market

value of equity between 1988-1991, they find a highly statistically significant impact

of goodwill on the firms’ current market values.753 Interestingly, however, is that

when the authors subdivide their sample in firms operating in the manufacturing

sector and nonmanufacturing sector, the effect for firms in the manufacturing sector

disappears, thereby implying that goodwill as an asset is only of value relevance for

firms in the nonmanufacturing sector of the analyzed sample.754 Dahmash et al.

(2009) extend the work of Chauvin and Hirschey (1994) by looking into the value

relevance of goodwill in Australian firms operating in various industries (total of

2’611 firm year observations).755 Their analysis included industries like health care,

consumer goods, information technology, industrials, energy, telecommunications,

and utilities. While the authors find a statistically significantly value relevance of

goodwill for firms operating in consumer goods, information technology, and

utilities, only a weak or no effect is found for firms operating in industrials,

healthcare, energy and telecommunications industries.756 Surprisingly, for firms

operating in the health care and energy industries, the effect of goodwill on firm

751 Cf. Bugeja and Gallery (2006), p. 520. 752 Cf. Chauvin and Hirschey (1994), p. 169. 753 Cf. Chauvin and Hirschey (1994), p. 175. 754 Cf. Chauvin and Hirschey (1994), p. 178. 755 Cf. Dahmash et al. (2009), p. 132. 756 Cf. Dahmash et al. (2009), p. 132.


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value is actually found out to be negative, meaning that investors do not consider

goodwill as value adding but apparently view it as value destructive for a firm.

By using a pooled sample of 172 Australian firms from 1999, a value-adding impact

of goodwill as an asset on a firm’s value was also found by Godfrey and Koh

(2001).757 The authors include not only goodwill in their multiple linear regression

analysis, but also other intangible assets like research & development. The authors

find that “(c)onsitent with our expectations, the results (…) show that the estimated

coefficients for GW (…) are positive and statistically significant at the 1% level

(…).”758 The author also infer from their results that capital market participants view

goodwill to add value to the firm above its book value, as the estimated coefficient

in their model was statistically significantly greater than one.759

A similar, however more simplified, analysis is carried out by McCarthy and

Schneider (1995). The authors study the impact of recognized goodwill on a firm’s

market value of equity by regressing the firm’s market value on the book values of

goodwill, assets less goodwill and liabilities as well as net income.760 To be included

in the sample, firms must have been incorporated in the US and carry goodwill on

their balance sheet between 1988 and 1992.761 They authors also find a highly

statistically significant impact of goodwill as an asset in every year by studying

6’216 observation points in their regression model.762

Barth and Clinch (1996) study not only the impact of recognized goodwill on firm

value but also on stock market returns for UK and Australian firms listed on an US

stock exchange. Their research questions are predominately built on the different

accounting treatments of various assets, including goodwill, in different

jurisdictions. In total, the authors study a sample of 400 firm year observations for

their returns analysis and 189 for their valuation analysis between 1985 and 1991.

The findings of Barth and Clinch (1996) show that net income including the effects

757 Cf. Godfrey and Koh (2001), p. 42. 758 Godfrey and Koh (2001), p. 45. 759 Cf. Godfrey and Koh (2001), pp. 45-46. 760 Cf. McCarthy and Schneider (1995), p. 74. 761 Cf. McCarthy and Schneider (1995), p. 74. 762 Cf. McCarthy and Schneider (1995), p. 78.


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of goodwill amortizations statistically significantly explain stock market returns of

UK and Australian firms.763 Regarding the value relevance of goodwill on share

prices, the author come to the conclusion that goodwill as an asset has “a positive

relationship to price, although only marginally significant for Australian firms.”764

For UK firms, no statistically significant effect was found, however this was

predominantly due to the fact that the majority of firms immediately wrote-off

goodwill against the firm’s equity which was allowed at the time when the study

was carried out.765

Jennings et al. (1996) also want to understand whether goodwill adds value to

shareholders by studying a sample of US firms between 1982 and 1988. By applying

a cross-sectional regression with the independent variables total assets excl.

goodwill, goodwill, property, plant and equipment, and liabilities, the authors find

that “the year-by-year results indicate a strong cross-sectional relation between

equity values and accounting assets and liabilities. In particular, the estimated

coefficients for goodwill are positive and highly significant in each of the seven

years. This suggests that, in the view of investors, purchased goodwill represents an

economic source.”766 The explanatory power of goodwill in the regression model is

found to be higher than for any other balance sheet variable.767 An interesting

additional analysis is also carried out by the authors by studying whether their

findings also hold true for older goodwill. To do so, Jennings et al. (1996) subdivide

capitalized goodwill by its age. Although the t-statistics for newer goodwill are

higher than those of older goodwill, both are found to be statistically significant in

their model. Consequently, the authors argue that their results “suggest that the

positive association between recorded goodwill and equity values persist over longer

periods of time (…).”768

In the study of Henning et al. (2000), the authors also subdivide capitalized goodwill

on an acquirer’s balance sheet by focusing on goodwill components that relate to the

763 Cf. Barth and Clinch (1996), p. 154. 764 Barth and Clinch (1996), p. 157. 765 Cf. Barth and Clinch (1996), p. 158. 766 Jennings et al. (1996), p. 519. 767 Cf. Jennings et al. (1996), p. 521. 768 Jennings et al. (1996), p. 522.


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(i) going concern element of the acquiree, (ii) paid synergies and (iii) overpayments

for the target firms.769 To do so, the study is based on 1’576 purchase acquisitions

(3’097 firm year observations) between 1990 and 1994 in US firms. In a preliminary

step, the authors define the going concern element of goodwill as the pre-acquisition

market value of the target firm less the acquired net assets measured on a fair value

basis (i.e. including newly recognized assets as well as fair value step ups on

excising assets). The synergy component of goodwill is measured by the authors as

the increase of the combined market value of both the acquirer and target firm

shortly after the initial announcement date (time window of 11 days). The

overpayment component according to Henning et al. (2000) represents the difference

(i.e. residual) between the purchase price and the sum of the target’s market value

before the acquisition and the synergy goodwill.770 By applying a regression of the

acquirer’s market value of equity after the business combination on assets (excl.

goodwill), liabilities and the defined goodwill components, a statistically significant

firm value relevant impact is found for the goodwill components going concern and

synergies, whilst overpayments are statistically significant value-destructive.771 The

findings show that investors place different weightings on various goodwill

components in their valuation analysis and price expectations, and not all

components of goodwill can be considered as an asset from an accounting

perspective given the negative weightings capital market participants apply on some

of them.772

Churyk (2005) aims at analyzing whether the impairment-only approach or a

straight line amortization better reflects the value relevance of goodwill viewed by

market participants subsequent to an acquisition.773 To do so, she studies a sample of

162 US firms over a two year period after an acquisition between 1996 and 1998.

Her principal motivation is to understand whether the proposed impairment-only

approach by the FASB is in line with market expectations about the value of a firm

and therefore superior to a periodic amortization. To do so, she runs two separate

769 Cf. Henning et al. (2000), p. 382. 770 Cf. Henning et al. (2000), p. 378. 771 Cf. Henning et al. (2000), p. 385. 772 Cf. Churyk (2005), p. 1354. 773 Cf. Churyk (2005), p. 1353.


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OLS regressions for each year after the acquisition. She finds that on average

acquired goodwill is viewed as an asset by investors in both years due to the positive

and highly statistically significant regression coefficients. In those cases, amortizing

goodwill would not be justified from a value relevance perspective. However, she

provides strong evidence that in case a MTB ratio of equity below 1 or significantly

negative stock market returns of the acquirer are observable, goodwill is most likely

impaired and a write-off would be required. Consequently, the author comes to the

conclusion that “(o)verall, the results provide evidence that goodwill is not typically

overvalued when initially recorded, supporting the contention that systematic

amortization is not warranted.”774

Bugeja and Gallery (2006) add to the existing literature by studying whether older

goodwill is equally value relevant as recently recognized goodwill. To do so, the

authors use a sample of 136 firms (475 firm years) listed on one of the Australian

stock exchanges.775 This allows the authors tracking the value relevance of goodwill

between 1995 and 2001. For each year under analysis, the authors divide reported

goodwill in those components acquired (i) over the most recent year, (ii) over each

of the last two years thereafter, and (iii) the remaining balance. In a subsequent step,

Bugeja and Gallery (2006) regress the firms’ market value of equity on various

balance sheet asset classes and net income measures besides the individual goodwill

subcategories.776 The authors provide evidence that “goodwill acquired in the

observation year and each of the previous 2 years is positively associated with firm

value, but there is no significant association with goodwill acquired more than 2

years previously. (…) The absence of a significant relationship between the market

value of equity and goodwill acquired more than 2 years previously suggests that

older goodwill is not considered to be an asset by investors. One possible

explanation for this result is that the purchase price paid in corporate acquisitions

does not represent unidentified future economic benefits, or that any benefits

purchased are quickly consumed.”777

774 Churyk (2005), p. 1360. 775 Cf. Bugeja and Gallery (2006), p. 524. 776 Cf. Bugeja and Gallery (2006), p. 530. 777 Bugeja and Gallery (2006), p. 533.


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The majority of the research studies outlined above which aimed at understanding

the firm value relevance of recognized goodwill comes to the conclusion that

goodwill is actually a source of value to a firm.

6.2.1 Stock market reactions to goodwill write-offs

Thematically closely linked to the analysis of value relevance of goodwill to a firm

is the analysis of stock market reactions to write-offs announcements, as both

research streams assume a value adding impact from the recognized intangible asset

to a firm.778 Research studies aiming at analyzing these effects usually apply time-

windows analyses that focus on abnormal stock market returns surrounding the date

when goodwill write-offs are announced.779 However, challenges can arise in

isolating the separate effect of goodwill write-offs on stock prices as they are usually

announced together with other earnings’ components (as part of quarterly or annual

financial results).780

Due to the generally positive empirical findings on the value relevance of goodwill

outlined above, one would assume that theoretically investors revise their price

expectations downward due to the information content of such a write-off.781 This

assumption however only holds true if write-offs are recognized on a timely basis

and that the information content of such a write-off has not been fully reflected in

current stock prices at the time of the announcement.782 Consequently, those studies

that find no statistically significant stock market reactions at the time of the

announcement might simply be dominated by sample firms that do not record write-

offs in earnings in a timely manner and therefore have been already reflected in a

firm’s share price. This would indirectly confirm the assumption that goodwill

write-offs are not recorded in a timely manner by a firm’s management.

778 Cf. Lhaopadchan (2010), p. 125, Muller et al. (2009), p. 5. 779 Cf. Lhaopadchan (2010), p. 125. 780 Cf. Hirschey and Richardson (2002), p. 182, Francis et al. (1996), pp. 119, 129. 781 Cf. Hirschey and Richardson (2002), p. 175, Francis et al. (1996), p. 128. 782 Cf. Muller et al. (2009), p. 5, Li and Sloan (2012), p. 12.


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Jennings et al. (1996) were among the first researchers studying the effects of

discretionary goodwill write-offs on stock prices. To do so, they analyze 507 write-

off announcements of US firms between 1989 and 1992. The authors first

subcategorize write-offs according to the corresponding asset classes (goodwill,

inventory, and property, plant and equipment). In a subsequent step, they apply OLS

regressions to the stock price change (excess returns) observable over a two day

period. Excess returns as defined by Jennings et al. (1996) represent the difference

between the stock returns of the firm that wrote off parts of their assets and those of

the industry as a whole.783 The time period under investigation starts one day before

the write-off announcement and ends at the end of the trading day the write-off was

made public. Although a highly statistically significant effect was found by the

authors on the pooled write-off amounts of all assets, the isolated impact of goodwill

write-offs had no impact.784 Although the authors do not provide the actual average

drop in security prices at the time of the write-off announcements in their research

paper, they provide evidence that asset write-offs as a whole are considered

negatively by market participants.

To the contrary of the findings by Jennings et al. (1996), Hirschey and Richardson

(2002) find that discretionary goodwill write-off announcements are actually viewed

highly negatively by investors.785 In their paper, Hirschey and Richardson (2002)

study a sample of 80 write-off announcements by US firms between 1992 and 1996.

Analogous to Jennings et al. (1996), the authors analyze excess returns (cumulative

average abnormal returns) during a two day-time window. Hirschey and Richardson

(2002) come to the conclusion that their findings suggest “a 2-3% adverse stock-

price reaction to goodwill write-off announcement irrespective of contemporaneous

announcements or industry grouping.”786 The effects are found to be largest for

industrial and commercial machinery firms (-6.60%) and firms with negative

earnings (-7.45%).787

783 Cf. Francis et al. (1996), p. 130. 784 Cf. Francis et al. (1996), p. 133. 785 Cf. Hirschey and Richardson (2002), p. 181. 786 Hirschey and Richardson (2002), p. 181. 787 Cf. Hirschey and Richardson (2002), p. 181.


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Bens and Heltzer (2005), Bens et al. (2007), and Bens et al. (2011) include in their

analysis also goodwill write-off announcements that occurred after the introduction

of the impairment-only approach in US firms. By studying size adjusted abnormal

stock returns between the announcement day and the day thereafter, the authors find

highly statistically significant share price drops of -2.75% (median) in their pre-

SFAS 142 sample and of -3.95% (median) in their post-SFAS 142 sample.788

Building on these findings, the authors also apply a regression model to confirm

their hypothesis that goodwill write-offs actually triggered these negative stock

market returns.789

Li and Sloan (2012) also find in their post-SFAS 142 sample a negative and highly

statistically significant stock price change of -1,8% between one day before and one

day after the goodwill write-off announcement was made.790 Li and Sloan (2012)

therefore confirm the findings by Li, Shroff, Venkataraman and Zhang (2011) who

also analyze US sample firms applying SFAS 142.791 What is interesting however

that Li, Shroff, Venkataraman and Zhang (2011) find that abnormal returns are

larger in the pre-SFAS 142 sample than in their post-SFAS 142 sample, as Bens et

al. (2011) came to the opposite conclusion with their data samples. Knauer and

Wöhrmann (2013) also look into the topic of stock market reactions by focusing on

firms that apply either IAS 36 or SFAS 142.792 For both groups of firms, however,

the authors do not find a statistically significant stock market reaction surrounding

the announcement date.793

788 Cf. Bens et al. (2011), p. 537. 789 Cf. Bens et al. (2011), p. 539. 790 Cf. Li and Sloan (2012), p. 49. 791 Cf. Li et al. (2011), p. 757. 792 Cf. Knauer and Wöhrmann (2013), p. 16. 793 Cf. Knauer and Wöhrmann (2013), p. 45.

6 Literature review

on goodwill w

rite-off decision making

171

Fig. 43: Overview on research findings regarding stock market reactions to goodwill write-offs . Source: Own illustration.

Overview on findings of research studies regarding the stock return impact of goodwill write-off announcements

Research study Country Observations

Time period

Pre-/Post IOA

Observation period in days (0 = date of

announcement) Measure Statistic

Hirschey and Richardson (2002) US (US GAAP) 80 1992-1996 pre IOA [-1;0] -3,31% *** Cumulative abnormal returns n/k

Bens, Heltzer and Segal (2011) US (US GAAP) 116 1996-2001 pre IOA [0;+1] -3,40% * Abnormal stock returns Mean

Bens, Heltzer and Segal (2011) US (US GAAP) 116 1996-2001 pre IOA [0;+1] -2,75% *** Abnormal stock returns Median

Li and Sloan (2012) US (US GAAP) n/k 1996-2000 pre IOA [-1;+1] -1,40% *** Cumulative abnormal returns n/k

Li, Amel-Zadeh and Meeks (2010) UK (FRS) 87 1997-2002 pre IOA [-5;0] -4,40% *** Cumulative abnormal returns n/k

Li, Amel-Zadeh and Meeks (2010) UK (FRS) 87 1997-2002 pre IOA [-1;+1] -1,90% *** Cumulative abnormal returns n/k

Li, Amel-Zadeh and Meeks (2010) UK (FRS) 87 1997-2002 pre IOA [0;+10] 2,00% *** Cumulative abnormal returns n/k

Li, Shroff, Venkataraman and Zhang (2011) US (US GAAP) 477 1996-2001 pre IOA [-1;+1] -2,70% *** Abnormal stock returns Mean

Li, Shroff, Venkataraman and Zhang (2011) US (US GAAP) 477 1996-2001 pre IOA [-1;+1] -1,70% *** Abnormal stock returns Median

Bens, Heltzer and Segal (2011) US (US GAAP) 141 2002-2003 post IOA [0;+1] -4,20% *** Abnormal stock returns Mean

Bens, Heltzer and Segal (2011) US (US GAAP) 141 2002-2003 post IOA [0;+1] -3,95% *** Abnormal stock returns Median

Li and Sloan (2012) US (US GAAP) n/k 2004-2011 post IOA [-1;+1] -1,80% *** Cumulative abnormal returns n/k

Li, Shroff, Venkataraman and Zhang (2011) US (US GAAP) 854 2002-2006 post IOA [-1;+1] -1,39% *** Abnormal stock returns Mean

Li, Shroff, Venkataraman and Zhang (2011) US (US GAAP) 854 2002-2006 post IOA [-1;+1] -0,87% *** Abnormal stock returns Median

Knauer and Wöhrmann (2013) US (US GAAP) 417 2005-2009 post IOA [-1;+1] -2,10% *** Cumulative abnormal returns Median

Knauer and Wöhrmann (2013) US (US GAAP) 417 2005-2009 post IOA [-2;+2] -2,10% ** Cumulative abnormal returns Median

Knauer and Wöhrmann (2013) US (US GAAP) 417 2005-2009 post IOA [-3;+3] -1,30% * Cumulative abnormal returns Median

Knauer and Wöhrmann (2013) EU (IFRS) 374 2005-2009 post IOA [-1;+1] 0,10% † Cumulative abnormal returns Median

Knauer and Wöhrmann (2013) EU (IFRS) 374 2005-2009 post IOA [-2;+2] -0,10% † Cumulative abnormal returns Median

Knauer and Wöhrmann (2013) EU (IFRS) 374 2005-2009 post IOA [-3;+3] 0,10% † Cumulative abnormal returns Median

*, **, *** denote statistical significance at the 90, 95 and 99% confidence level, respectively. † not significant, n/k not disclosed by authors in research study.

Sample

Share price change due to write-off announcement


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Li et al. (2010) further add to the research topic by looking not only into the

profitability of firms that wrote off goodwill but also by extending the time periods

under analysis. On the basis of their sample containing observations from UK firms

between 1997 and 2002, the authors find highly statistically significant stock market

reactions prior to the announcement (-4.4% between 5 days prior to and the actual

announcement date) and shortly before and after the announcement (-1.9% between

one day before and one day after the announcement).794 Thereafter, the authors find

however a stock price increase of 2.0% (between announcement and 10 days after)

and further 13.2% over the subsequent two months (between 10 days after and 65

days after the announcement).795 These findings could suggest that investors

potentially overreact to impairment information and find it hard to reflect them

correctly in their price expectations.796 The authors’ findings however stand in

contrast to those of Hirschey and Richardson (2002) who show that in their write-off

sample share prices drop further by almost -15% between 10 days and 250 days after

the announcement.797 That stock markets tend to over-react to goodwill write-off

announcements has also been documented by Chen et al. (2013). They study

goodwill write-offs of firms with and without poor economic fundamentals. They

find that firms can be driven to record a goodwill write-off despite sound financial

performance measures due to a firm’s undervaluation on capital markets. This

phenomenon is termed by the authors as “market-driven non-fundamental

impairments”798. Chen et al. (2013) argue that “the market reacts negatively to the

announcement of market-driven impairment, suggesting that investors do not

understand the nature of such impairment, leading to further undervaluation of the

reporting firms.”799 In those cases however the authors find that the undervaluation

reverses in the subsequent period, observable through positive stock returns.800

794 Cf. Li et al. (2010), p. 26. 795 Cf. Li et al. (2010), p. 26. 796 Cf. Chen et al. (2013), p. 4. 797 Cf. Hirschey and Richardson (2002), pp. 185-186. 798 Chen et al. (2013), p. 2. 799 Chen et al. (2013), p. 31. 800 Cf. Chen et al. (2013), p. 22.


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Li et al. (2010) further subdivide their sample and find that “(t)he negative impact is

greater for firms with a higher proportion of assets carried as goodwill (…)”801 with

a -6.1% share price drop compared to a surprising positive 1.4% for low goodwill to

assets-ratio firms over the period of one day prior to and one day after the

announcement.802 The authors further show that firms with a negative EBITDA

experience larger negative returns than firms with a positive EBITDA, however this

effect is found only to be marginally statistically significant. The author therefore

confirm the findings of Hirschey and Richardson (2002) who also report that firms

with negative earnings get penalized more by investors after the announcement is

made.803

6.2.2 Debt contract consequences from goodwill write-

offs

The relationship between goodwill, goodwill write-offs and debt contracts has not

been as well researched as the analysis of stock market reactions to write-off

announcements. However, here as well a link between the potential effects on debt

contracts and goodwill write-off decision making can be drawn in case a firm’s

management is concerned about the potentially resulting effects. So far, most

research papers have focused on the effect of total leverage and leverage ratios on

observable goodwill write-offs. Only few studied the relationship between debt

covenants and actual write-off decisions. One reason for this could lie in the

accessability of information. Information on debt covenants is usually not available

through financial data providers and needs to be collected by hand in annual reports.

Conceptually, the impairment-only approach in goodwill accounting requires the

recognition of a goodwill write-off as an amortization charge in a firm’s profit and

loss statement and thereby reducing the firm’s accounting earnings in the financial

801 Li et al. (2010), p. 26. 802 Cf. Li et al. (2010), p. 26. 803 Cf. Hirschey and Richardson (2002), pp. 178, 185.


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year the impairment is recognized.804 Technically, the asset account “goodwill” is

credited and the expense account “goodwill amortization charge” debited. The

amortization charge subsequently reduces the firm’s book value of equity when the

net income of the financial period is booked against the firm’s equity account.805

The recognition of a goodwill write-off consequently leads to the following two

effects in the firm’s balance sheet:

(1) The overall asset base of the firm decreases due to the amortization charge

(book value of goodwill ↓ and therefore total assets ↓).

(2) The book value of equity decreases (B ↓) and thereby increasing the leverage

ratio of a firm (Debt/Equity ↑).

Fig. 44: Classification of research findings on debt contract consequences


Given that equity and debt investors, i.e. lenders, view an increase of the leverage

ratio as a sign of higher default risk, one could argue that higher leverage increases a

firm’s costs of financing (as the probability of default increases).806 As managers

might be aware of the effects of a goodwill write-off on the firm’s leverage ratio and

the subsequent impact on funding possibilities, managers of firms with a higher

leverage ratio might try harder to avoid goodwill write-offs (in order to keep funding

804 Cf. IAS 36.60, Holt (2013), p. 8, Amiraslani et al. (2013), p. 19, Hamberg et al. (2011), p. 269. 805 Cf. Hamberg et al. (2011), p. 269, Holt (2013), p. 8, Amiraslani et al. (2013), p. 19. 806 Cf. Siggelkow and Zülch (2013a), p. 36, Givoly et al. (2013), p. 32.

Impact:

Debt contract consequences from goodwill write-offs

Negative impact on shareholders’ equity

Accounting earnings’ volatility increases

Negative impact on accounting earnings

Leverage ratio increases

• Potential impact on future funding costs • Potential violation against debt

covenant restrictions


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costs lower or not to breach potentially existing debt covenants which would have

material effects on the firm’s economic situation).

Several research studies have analyzed the relationship between a firm’s leverage

ratio and goodwill write-off decisions.807 However, the findings are mixed. While

some researchers provide empirical evidence that the likelihood of firms recognizing

asset and goodwill write-offs increases with lower leverage ratios, other studies do

not find a relationship. Indirectly related to the topic of leverage and goodwill write-

off decisions are studies that found out that higher leverage leads to more earnings

increasing accounting procedures applied by a firm’s management.808 These findings

build on the theory of “positive accounting”809 which argues that accounting choice

represents a function of company specific variables like leverage, size, or personal

incentives,810 pioneered by Watts and Zimmerman (1978). In particular, Watts and

Zimmerman (1986) argue that “(…) the larger a firm’s debt/equity ratio the more

likely the firm’s manager is to select accounting procedures that shift reported

earnings from future periods to the current period.”811

Sweeney (1994) shows empirically that “managers of firms approaching default

respond with income-increasing accounting changes and that the default costs

imposed by lenders and the accounting flexibility available to managers are

important determinants of managers’ accounting responses.”812 By studying a

sample of 130 US firms between 1980 and 1989, the author applies a time-series

analysis and finds that firms approaching default use income-increasing accounting

discretion like inventory revaluation or changes in pension assumptions to a

statistically significantly greater extent to delay default than firms in her control

sample.813 Income-increasing accounting changes are also found by DeFond and

807 Cf. Riedl (2004), p. 828, Beatty and Weber (2006), p. 273, Hamberg et. al. (2011), p. 272, Ramanna and Watts (2012), p. 774, Siggelkow and Zülch (2013a), p. 54, Kuster (2007), p. 106, Chen et al. (2008), p. 79, Li et al. (2011), p. 756, Henning et al. (2004), p. 113, Zang (2008), p. 38. 808 Cf. Siggelkow and Zülch (2013b), p. 83. 809 Watts and Zimmerman (1990), p. 132. 810 Cf. Watts and Zimmerman (1978), p. 112, Watts and Zimmerman (1990), p. 132, Beatty and Weber (2006), p. 264. 811 DeFond and Jiambalvo (1994), p. 147, citing Watts and Zimmerman (1986). 812 Sweeney (1994), p. 281. 813 Cf. Sweeney (1994), p. 296.


176

Jiambalvo (1994) who show in their sample of 94 US firms that firms respond to

potential violations of debt covenants with abnormal accruals which positively

influence the firm’s accounting earnings.814 The findings by Sweeney (1994) and

DeFond and Jiambalvo (1994) therefore confirm the earlier results by Press and

Weintrop (1990) who argue that “the presence of a leverage constraint is associated

with income-increasing accounting choices (…)”815. That a firm’s debt covenants

restrictions and potential violations can be approximated by a firm’s actual debt to

equity ratio (i.e. leverage) has been documented by both Duke and Hunt (1990) and

Press and Weintrop (1990).816

Cotter et al. (1998) study the relationship between leverage and the magnitude of

asset write-offs in Australian firms. Their analysis contains 82 asset write-offs from

1983.817 Whilst the authors include goodwill write-offs in their sample, in their

further analyses unfortunately not disaggregation between goodwill and other assets

is made. In their OLS regression, Cotter et al. (1998) include independent variables

like leverage (total debt plus preferred stock divided by total assets), firm size, cash

reserves, and management changes. Although they find a negative impact of

leverage on asset write-offs, the results are not highly statistically significant (p-

value of 0.069), however allow room for discussions.818 Riedl (2004) performs a

similar analysis and further subdivides leverage in private and public debt. In his

study, Riedl (2004) focusses on US firms’ write-off decisions before and after the

adoption of SFAS 121 Accounting for the Impairment of Long-Lived Assets.819

Unfortunately, he also does not test goodwill individually but as part of his entire

write-off sample. Of his 455 write-off observations, 27% (124) came from goodwill

or other intangible asset write-offs.820 By performing a tobit regression, he also finds

that debt components contain explanatory power for write-off decisions. In a

preliminary step, he subdivides debt in those components that are publicly traded

814 Cf. DeFond and Jiambalvo (1994), pp. 174-175. 815 Press and Weintrop (1990), p. 67. 816 Cf. Duke and Hunt (1990), p. 61, Press and Weintrop (1990), p. 93. 817 Cf. Cotter et al. (1998), p. 165. 818 Cf. Cotter et al. (1998), p. 171. 819 Cf. Riedl (2004), p. 823. 820 Cf. Riedl (2004), p. 838.


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and held privately, as he argues that “private debt is more likely to have financial

covenants than publicly issued debt, due to the relatively greater cost of coordinating

monitoring efforts among the group of lenders that are typical of public debt

issuances (…).”821 The descriptive statistics of his write-off and non-write-off

samples show that write-off firms have statistically significantly lower levels of

debt.822 Furthermore the results of the author’s regression provide evidence that

private debt has a higher, statistically significantly negative impact on the magnitude

of asset write-offs than public debt.823

Beatty and Weber (2006) find in their study of US firms for the financial year 2001

that leverage, defined as the ratio of debt to total assets, has a negative, however no

strong statistically significant impact on both goodwill write-off decisions and the

magnitude of write-offs. The effect of actual debt covenants however is found to be

highly statistically significant in both cases.824 The authors use a probit as well as a

censored regression for testing their hypothesis and come to the conclusion that their

findings suggest “firms’ debt contracting (…) incentives affect their decisions to

accelerate or delay expense recognition”825.

Chen et al. (2008) also look into the topic of leverage in their study on timeliness of

goodwill write-off recognition under the initial adoption of SFAS 142 by US firms.

The authors cannot detect an apparent relationship between timeliness of goodwill

write-offs and leverage.826 Although not showing the results separately in their

research paper, the authors state that their backward-looking return based model

would not have yielded different results when including leverage as an independent

variable.827 Li et al. (2011) come to a similar conclusion in their analysis of factors

influencing goodwill write-offs. By using a tobit regression on US firms before and

after the adoption of SFAS 142, the authors find that “impairment losses are

significantly associated with prior returns, extreme earnings news, and the

821 Riedl (2004), p. 833. 822 Cf. Riedl (2004), p. 837. 823 Cf. Riedl (2004), p. 843. 824 Cf. Beatty and Weber (2006), p. 280. 825 Beatty and Weber (2006), p. 257. 826 Cf. Chen et al. (2008), p. 72. 827 Cf. Chen et al. (2008), p. 79.


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stringency of delisting requirements but exhibit insignificant association with (…)

leverage.”828

Hamberg et al. (2011) study goodwill write-off decisions of Swedish firms in the

adoption year of the impairment-only appraoch under IAS 36. Prior to its adoption,

Swedish GAAP (RR 1:96 and 1:100) required straight line amortization of goodwill

over its economic useful life of 5-20 years.829 By using a sample of 180 Swedish

public firms and applying a probit regression similar to the one used by Beatty and

Weber (2006) on the firm’s write-off decisions, Hamberg et al. (2011) find a

negative however statistically insignificant relationship between leverage (ratio of

interest bearing debt and equity) and the firms’ goodwill write-off decisions (p-value

of 0.417).830

Ramanna and Watts (2012) also study the effect of leverage on goodwill write-off

decisions in 124 firms for which the authors have sufficient evidence that goodwill

is economically impaired. The descriptive statistics of their US based (SFAS 142)

sample shows that 78% of the non-write-off firms had accounting based debt

covenants in place, whereas in the write-off firms only 63% had.831 These

differences however are found not to be highly statistically significantly different (p-

value of 0.087). By using multivariate (OLS) regressions, regressing the magnitude

of the observable goodwill write-offs on independent variables capturing

characteristics of the firms’ capital structure, the authors “find some evidence of

association between goodwill non-impairment and debt covenant violation

concerns.”832 In particular, the effect of leverage defined as debt divided by assets on

goodwill write-off is found out to be negative and statistically significant, however

not highly significant and also not in all of the authors’ analyses.833 Consequently,

the findings of Ramanna and Watts (2012) do not allow for a clear assessment of the

relevance of leverage in goodwill write-off decisions.

828 Li et al. (2011), p. 756. 829 Cf. Hamberg et al. (2011), p. 266. 830 Cf. Hamberg et al. (2011), p. 280. 831 Cf. Ramanna and Watts (2012), p. 770. 832 Ramanna and Watts (2012), p. 777. 833 Cf. Ramanna and Watts (2012), p. 775.


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The overall findings of research studies suggest that leverage can be, however does

not necessarily have to be, a driver of avoiding or delaying goodwill write-offs.

Basically the analysis of existing accounting based debt covenants offers more

insights on the topic of goodwill write-off decision making, as if breached the

negative economic consequences to a firm would be more severe and more

immediate than in the case of a sole increase in the firm’s leverage ratio. Breaching

debt covenants often trigger the immediate repayment of outstanding debt, a

limitation of managerial decision space due to a potential board representation of the

lender, as well as an increase in funding costs.834 Consequently, due to their

immediate and more severe effects to a firm, senior management teams would have

stronger incentives to avoid breaching them. However the fairly small number of

studies on the impact of existing debt covenants does not provide a conclusive

answer on this topic and further research on this topic would certainly be

advantageous.

6.3 Personal incentives of managers influencing


Another well researched area in goodwill accounting research focuses on a potential

opportunistic behaviour of firms’ senior executives in the impairment-only

approach. In particular, academics have studied the (i) effects of senior management

changes on goodwill write-off decisions as well as the (ii) use of write-offs for

earnings smoothing and so-called big bath accounting purposes. A relatively new

research stream has dedicated itself to the analysis of (iii) senior executives’

behaviour ahead of observable goodwill write-offs, for example by looking into

insider share tradings.

Positive findings in each of these areas would imply that write-off decisions are not

solely the result of economic considerations regarding the recoverability of

goodwill, but could also be partly driven by personal incentives influencing write-

834 Cf. Beneish and Press (1993), p. 234, Chen and Wei (1993), p. 218.


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off or non-write-off decisions. Given their relevance to the overall research topic of

goodwill write-off decision making of this PhD thesis, the main findings of those

three research streams are presented below.

6.3.1 Top management changes and goodwill write-off

decisions

The effect of management changes on goodwill write-offs has been well

documented in academia and allows for a rather clear assessment. The majority of

these research studies find that the likelihood of new management teams writing off

goodwill during the first years of their tenure is statistically significantly higher than

at a later stage. Basically, one can infer from these findings that either prior

management teams did not perform write-offs in a timely manner or that new

management teams want to reduce goodwill write-off risks during their upcoming

tenure which would negatively reflect on their individual performance.835

Strategic management research studies come to the conclusion that frequently with

the replacement of a CEO adjustments are made to a firm’s corporate strategy.836

This can be partly explained by the reason for the actual management change and

the revised expectations by stakeholders with the appointment of a new CEO.837

Strategy adjustments often include subsequent organizational changes like

modifications to current and planned product or services offerings or rather drastic

changes like corporate restructurings including asset divestments.838

Accounting research on senior management changes and discretionary accounting

choices has found out that during the year of the replacement, new CEOs tend to

835 Cf. Masters-Stout et al. (2008), p. 1370, Hayn and Hughes (2006), p. 254, Francis et al. (1996), p. 125, Zang (2008), p. 39, Watts and Zimmerman (1986), p. 209, Walsh et al. (1991), p. 174, Elliott and Shaw (1988), p. 92. 836 Cf. Hambrick and Fukutomi (1991), p. 724, Gabarro (2007), pp. 109-110, Francis et al. (1996), pp. 118, 123. 837 Cf. Farrell and Whidbee (2003), p. 166. 838 Cf. Murphy and Zimmerman (1993), p. 280.


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manage accounting earnings downward and in the following year upward.839

Pourciau (1993) finds in her sample of US firms that downward earnings

management in the year of the CEO change is accomplished through unexpected

high accruals, discretionary asset write-offs and the recognition of special items in a

firm’s profit and loss statement.840 Using a sample of Australian firms, Godfrey at

al. (2003) come to a similar conclusion by examining the explanatory power of

unexpected accruals which negatively impact the firm’s accounting earnings in the

year of the management change.841 The authors state that “(t)here is some evidence

of income reducing earnings management in period t (negative unexpected accruals

overall, and more strongly for CEO resignations). (…) As predicted, we find strong

evidence of income increasing earnings management in period t+1.”842

These findings strongly imply that on average new senior executives are motivated

to actively manage earnings downward in the year they take office. Francis et al.

(1996) state that personal incentives could be one of the dominating reasons to do

so.843 The authors argue that a “new management has incentives to “clear the deck”

(…) to improve investors’ perceptions of the future financial performance of the

firm.”844 This behavious allows to partly or fully blame the former CEO for the poor

performance in the year a new CEO takes over, whilst the new CEO will be credited

with the relatively better performance in the following years.

Asset write-offs, and in particular goodwill write-offs, surrounding CEO

replacements are one of the principal measures negatively impacting accounting

earnings when a new CEO takes office.845 The relationship between goodwill write-

offs and CEO turnover has been studied and documented with various country data

sets.846 In general, research findings have looked into the topic of goodwill write-

839 Cf. Godfrey at al. (2003), p. 95, Pourciau (1993), p. 317. 840 Cf. Pourciau (1993), p. 317. 841 Cf. Godfrey at al. (2003), p. 95. 842 Godfrey at al. (2003), p. 119. 843 Cf. Francis et al. (1996), p. 123. 844 Francis et al. (1996), p. 123. 845 Cf. Pourciau (1993), p. 317. 846 Cf. Beatty and Weber (2006), p. 280, Cotter et al. (1998), pp. 157, 171, Francis et al. (1996), p. 125, Hamberg et al. (2011), p. 263, Masters-Stout et al. (2008), p. 1370, Ramanna and Watts (2012), pp. 774-775, Riedl (2004), p. 843, Zang (2008), p. 53.


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offs on a separate asset basis as well as on a pooled asset write-off sample basis,

including goodwill and others. The majority of these research studies comes to the

conclusion that the likelihood of new management teams writing off goodwill

during the first years of their tenure is statistically significantly higher than at a later

stage. A similarly strong impact is found for the magnitude of goodwill write-offs,

i.e. write-off amount divided by total assets or total goodwill. These impacts are also

found to be independent of the application of the impairment-only approach in

goodwill accounting as samples from pre- and post-IOA periods return similar

results.

Francis et al. (1996) study US firms between 1988 and 1992 and find in their tobit

model that the replacement of a CEO one year prior to a goodwill write-off has a

highly statistically significant impact on the write-off magnitude, measured by the

write-off amount divided by total assets.847 Masters-Stout et al. (2008) come to a

similar conclusion working with a sample of US firms having adopted SFAS 142.

The authors apply a CEO tenure cut-off point of 3 years prior to the write-off. By

running various multivariate regressions in order to explain both the absolute

magnitude of goodwill write-offs and the relative magnitude (write-off amount

divided by total assets and total sales), the authors also find a statistically significant

impact of their dummy variables on the relative amount of goodwill write-offs.848

Their findings show that write-off amounts are higher on average in firms with

CEOs that are new in their role.849 Hamberg et al. (2011) use a sample of Swedish

firms and apply a longer observation period of up to 5 years (senior management

change before goodwill write-off). Their study not only focusses on the replacement

of a firm’s CEO but also on the replacement of a firm’s chairman of the board.850

Although they still find evidence for a relationship between tenure and write-off

decisions, the effect is not as strong as in the studies of Francis et al. (1996) and

Masters-Stout et al. (2008).851

847 Cf. Francis et al. (1996), p. 125. 848 Cf. Masters-Stout et al. (2008), p. 1379. 849 Cf. Masters-Stout et al. (2008), p. 1376. 850 Cf. Hamberg et al. (2011), p. 273. 851 Cf. Hamberg et al. (2011), p. 280.


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Zang (2008) studies goodwill write-off decisions in US firms in the SFAS 142

adoption year. Amongst other variables, the author tests the impact of a recent

replacement of a key member of a firm’s management team in the year prior to the

adoption of the impairment-only approach.852 By using a multivariate tobit

regression, the author finds a highly statistically significant impact of the recent

management change on the relative magnitude of the goodwill write-off in the

transition year (i.e. amount of goodwill write-off divided by total assets).853

Ramanna and Watts (2012) also include a variable measuring the tenure of a CEO in

years in their goodwill write-off decision model. The authors study the impact of a

CEO’s tenure on the absolute amount of a write-off scalled by total assets in their

regression model, using a sample of US firms that have already adopted SFAS

142.854 Ramanna and Watts (2012) confirm the statistically significant findings of

Zang (2008), which however are not as strong as in Zang’s (2008) transition period

sample.855

The overall results imply that the effect of a new CEO is statistically more

significant on goodwill write-off probability if the measurement period is smaller.

Francis et al. (1996) and Zang (2008) select an observation period of one year before

the goodwill write-off decision was made (during which a replacement of a CEO or

another member of a firm’s top management might have occurred) and find a highly

statistical impact of a top managerment change on the write-off probability with p-

values below 1% in their regressions. Masters-Stout et al. (2008) and Hamberg et al.

(2011) use a longer back-ward looking observation period of 3 and 5 years,

respectively, and find that the impact on goodwill write-off probability is still

significant however weaker than the effects documented by Francis et al. (1996) and

Zang (2008). Nevertheless, the findings document a clearly negative relationship

between CEO tenure and goodwill write-offs.

852 Cf. Zang (2008), p. 38. 853 Cf. Zang (2008), p. 53. 854 Cf. Ramanna and Watts (2012), p. 759. 855 Cf. Ramanna and Watts (2012), pp. 774-775.


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Fig. 45: Overview on research findings regarding senior management changes and

goodwill write-offs


However, the overall findings on this relationship allow room for interpretation. The

reason for being careful when interpreting these findings lies in the frequent

adjustment of the firm’s corporate strategy that could go hand in hand with the CEO

replacement.856 Prior business acquisitions, and therefore also acquired goodwill,

usually have served the purpose of a chosen business strategy. As in practice

goodwill represents to a large extend expected synergies from business

856 Cf. Hambrick and Fukutomi (1991), p. 724, Gabarro (2007), pp. 109-110, Francis et al. (1996), pp. 118, 123.

Overview on findings of research studies regarding the impact of management changes on goodwill write-offs

Observations

Research study Time period Independent variable (measurement) IMP WO%

Goodwill write-offs (separately):

Francis, Hanna and Vincent

(1996)

93 (US firms)

1988-1992

CEO change one year prior to or in the year of the write-off (dummy). < 1%

Beatty and Weber

(2006)

176 (US firms)

2001

CEO tenure in years for actual and potential write-off firms (period). < 5% < 5%

Masters-Staut, Costigan and Lovata (2008)

990 (US firms)2003-2005

CEO tenure is less than 3 years and CEO has been with the firm less than 3 years prior to the appointment (dummy).

< 5%

Masters-Staut, Costigan and

Lovata (2008)

990 (US firms)

2003-2005

CEO tenure is less than 3 years and CEO has been with the firm more than 2 years

prior to the appointment (dummy).

< 5%

Masters-Staut, Costigan and

Lovata (2008)

990 (US firms)

2003-2005

CEO tenure is less than 3 years (dummy). < 5%

Zang (2008)

870 (US firms)2001-2003

Key management change in the year of the actual or potential write-off (dummy). < 1%

Hamberg, Paananen and

Novak (2011)

180 (SE firms)

2004

CEO in year t-5 is either the CEO or chairman of the board in year t for write-off and

non-write-off firms (dummy).

< 10%

Ramanna and Watts

(2012)

124 (US firms)

2003-2006

CEO tenure in years for firms with most likely impaired goodwill (period). < 5%

Saastamoinen and Pajunen (2012)

487 (FI firms)2005-2009

CEO change during observation period (dummy). < 1% < 1%

Goodwill write-offs as part of a pooled sample with other asset write-offs:

Cotter, Stokes and Wyatt (1998)

82 (AUS firms)1993

Number of director, CEO and MD changes during observation period, scaled by the number of directors on the board at the end of the observation period.

< 10%

Riedl

(2004)

2754 (US firms)

1992-1998

Senior management change (top three compensated positions within the firm) one year

prior to or in the year of the write-off (dummy).

< 10%

IMP represents a dichotomous variable equal to one if the firm recorded a goodwill write-off. WO% represents the currency value of the goodwill write-off recognized divided by (i) the amount of goodwill at the beginning of the year or (ii) total assets. MD Managing director. CEO Chief

Executive Officer.

level of sig.


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combinations, an adjustment of a firm’s future strategy by a new CEO could make

parts of acquired goodwill (i.e. parts of the synergy component) simply worthless,

and therefore actually necessitate a write-off. However, in case a new CEO or other

new member of the senior management team would follow the business strategy of

his/her predecessor, a goodwill write-off immediately after taking office would

strongly imply that either goodwill impairments have not been recorded in a timely

manner in the past (and therefore require a catch-up write-off to bring its book value

in line with its recoverable amout) or the new CEO wants to reduce future goodwill

impairment risks which would negatively impact his or her individual

performance.857

6.3.2 Goodwill write-off decisions over the regular

tenure of senior executives

Besides the analysis of CEO turnover and goodwill write-off decisions, the use of

goodwill write-offs over a CEO’s regular tenure to manage earnings has been the

focus area of various research studies.858 Here again, positive findings would imply

that write-off decisions are not solely driven by economic considerations regarding

the recoverability of goodwill but can also be influenced by other, unrelated factors.

In general, “earnings management occurs when managers use judgment in financial

reporting and in structuring transactions to alter financial reports to either mislead

some stakeholders about the underlying economic performance of the company or to

influence contractual outcomes that depend on reported accounting numbers.”859 On

the basis of this definition, earnings management includes both the upward and

downward management of earnings depending on the perceived goals of the

involved individuals.

857 Cf. Masters-Stout et al. (2008), p. 1370, Hayn and Hughes (2006), p. 254, Francis et al. (1996), p. 125, Zang (2008), p. 39. 858 Cf. AbuGhazaleh et al. (2011), p. 165, Francis et al. (1996), p. 127, Riedl (2004), p. 828, Siggelkow and Zülch (2013a), p. 23, Sevin and Schroeder (2005), p. 48. 859 Healy and Wahlen (1999), p. 368.


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Conceptually, managerial motivation to manage earnings can emerge from

contracting, regulatory, and capital market incentives, as suggested by Healy and

Wahlen (1999).860 Contracting incentives, encouraging senior executives to

opportunistically exercise discretion in financial accounting and reporting, could

potentially arise from compensation agreements that build on earnings based

performance measures861 or covenants embedded in debt contracts862. By doing so,

executives are assumed to actively influence the payoffs of their remuneration

contracts or to withhold the economic costs that would occur if accounting based

debt covenants would be breached. Earnings management can also be triggered by

anti-trust and other regulations imposed by governments and other regulatory

authorities using accounting related data in their decision making process.863 The

role of regulatory incentives has been analysed by various research studies focussing

on firms operating in particularly strict regulatory environments like banking864 or

insurance865. The majority of those research studies finds a link between regulatory

monitoring activities and the likelihood of engaging in earnings management

activities, especially then when the economic costs of violating against regulations

like capital adequacy and liquidity ratios or of new, tighter regulations is perceived

to be high for a firm.866 Another extensively researched area of incentives to manage

earnings relates to influencing the decision making process of capital market

participants. The rational to do so rests in the importance of accounting information

in security and firm valuations by financial analysts and investors.867 Due to their

higher predictive power than current cash flows, current earnings are frequently used

to forecast future cash flows which represent a principal driver of a firm’s value.868

860 Cf. Healy and Wahlen (1999), pp. 370-379. 861 Cf. Healy (1985), p. 85, Holthausen et al. (1995), p. 29, Fields et al. (2001), p. 257. 862 Cf. DeAngelo et al. (1994), p. 113, DeFond and Jiambalvo (1994), p. 145, Fields et al. (2001), p. 257. 863 Cf. Healy and Wahlen (1999), p. 370, Cahan (1992), p. 77. 864 Cf. Moyer (1990), p. 123, Scholes et al. (1990), p. 626, Beatty et al. (1995), p. 231, Collins et al. (1995), p. 289, Ramesh and Revsine (2001), p. 159. 865 Cf. Petroni (1992), p. 485, Adiel (1996), p. 207, Gaver and Paterson (2004), p. 393. 866 Cf. Key (1997), p. 309, Jones (1991), p. 193, Gaver and Paterson (2004), p. 393. 867 Cf. Healy and Wahlen (1999), pp. 370-371. 868 Cf. Dechow et al. (1998), p. 133, Dechow (1994), p. 3.


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By actively influencing accounting earnings, executives are assumed to try

influencing short-term share price movements and subsequent investors’ capital

allocation decisions.869 Research studies that analysed earnings management

motivated by capital market incentives found out that a higher degree is observable

when firms would miss analyst expectations (without the effect of managed

earnings)870 and prior to capital market transactions (IPOs, secondary equity

offerings, transactions financed via shares, and share buybacks)871. In general,

findings imply a motivation to manage earnings due to capital market incentives.872

The relationship between goodwill write-offs and earnings smoothing or big

bath accounting:

Existing research studies on goodwill write-offs over the regular tenure of a CEO

have tried to find confirmation for the influence of capital market incentives on

write-off decisions. Here it has been analyzed whether write-offs are used

strategically in years when firms perform particularly good or bad.873 The rational to

use goodwill write-offs strategically builds on the perceived motivation of senior

executives to either meet analyst earnings forecasts874 or to reduce earnings volatility

over several financial periods875.

Several research studies have analyzed the general benefits from meeting analyst

earnings forecasts.876 Capital markets tend to reward firms that meet analyst

869 Cf. Healy and Wahlen (1999), p. 371. 870 Cf. Burgstahler and Eames (2003), p. 253, Abarbanell and Lehavy (2003), p. 1, Keung et al. (2010), p. 105. 871 Cf. Erickson and Wang (1999), p. 149, Ball and Shivakumar (2008), p. 324, Gong et al. (2008), p. 947, Rangan (1998), p. 101, Shivakumar (2000), p. 339, Teoh et al. (1998a), p. 63, Teoh et al. (1998b), p. 1935, Teoh et al. (1998c), p. 175. 872 Cf. Beyer (2008), p. 334. 873 Cf. Brochet and Welch (2011), p. 4, Walsh et al. (1991), p. 173, AbuGhazaleh et al. (2011), pp. 174-175, Sevin and Schroeder (2005), p. 47. 874 Cf. Athanasakou et al. (2009), p. 3, Abarbanell and Lehavy (2003), p. 1, Beyer (2008), p. 334. 875 Cf. Albrecht and Richardson (1990), p. 713, Barefield and Comiskey (1972), p. 291, Barnea et al. (1976), p. 110, Beidleman (1973), pp. 653-654, Copeland (1968), p. 101, DeFond and Park (1997), p. 115, Dichev et al. (2013), pp. 15, 26, Eckel (1981), p. 28. 876 Cf. Burgstahler and Eames (2006), p. 633, Keung et al. (2010), p. 105, Kasznik and McNichols (2002), p. 727.


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expectations consistently with higher abnormal returns and higher valuations.877 The

findings imply that investors place either a positive value on firms with low earnings

surprise potentials or a negative value to those that do.878 In either case, meeting

earnings expectations of analysts can have a positive effect on a firm’s market

value.879 This can be explained by the importance of current accounting earnings in

forecasting future earnings and cash flows.880 The more difficult it is to forecast

future earnings, the higher the implied risk of the underlying firms, i.e. uncertainties

in predicting its future performance.881 Risk and uncertainty about future cash flows

usually translate into higher discount rates in equity valuation models, and thereby

negatively impacting the value of a firm.882

On this basis, it would make sense for managers to use earnings consensus by

financial analysts as a benchmark for their earnings management measures in order

to signal capital market participants that the firm’s future performance is persistently

predicable. In case the earnings of a firm, excluding any discretionary earnings

management measures would exceed the earnings consensus by analysts, the

management team could benefit from taking a goodwill write-off to bring the firm’s

earnings closer to the estimate. On the contrary, in case the firm would perform

unexpectedly poor and would not be able to meet the analyst consensus even by

managing its earnings aggressively, then the management team could benefit from

taking an extra goodwill write-off as there is no possibility to meet the forecast for

the current year.883 By doing so, the management team can reduce the risk of

possible goodwill write-offs in future periods that could potentially hinder meeting

877 Cf. Bartov et al. (2002), p. 173, Kasznik and McNichols (2002), p. 727, Lopez and Rees (2002), p. 155. 878 Cf. Skinner and Sloan (2002), p. 289, Kinney et al. (2002), p. 1297, Burgstahler and Eames (2006), p. 634. 879 Cf. Bartov et al. (2002), p. 173, Kasznik and McNichols (2002), p. 727, Lopez and Rees (2002), p. 155. 880 Cf. Nichols and Wahlen (2004), p. 266, Beaver (1998), p. 86, Beaver (1968), p. 84, Dechow et al. (1998), p. 133. 881 Cf. Burgstahler and Eames (2006), p. 634. 882 Cf. Damodaran (2003), p. 2, Palepu et al. (2013), p. 311, Brealey and Myers (1996), p. 226, Ross et al. (2005), p. 255, Beidleman (1973), p. 654. 883 Cf. DeFond and Park (1997), p. 115.


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analyst earnings forecasts in the future.884 This earnings management technique is

also referred to as big bath accounting, when future expenses are recognized in

current accounting earnings to improve the prospects of future earnings at the

expense of current earnings.885

Although linked however not directly related to meeting analyst expectations is the

earnings management technique of income smoothing (also referred to as earnings

smoothing), which has a long history in accounting research.886 Beidleman (1973)

understands income smoothing as “the intentional dampening of fluctuations about

some level of earnings that is currently considered to be normal for the firm.”887 This

definition implies that senior executives envision a normal level of accounting

earnings and try not only to achieve this normal level but also to reduce intra-

periodic earnings volatilities.888 Theoretical models, trying to explain the widespread

earnings management practice of income smoothing, are multifold, however ground

primarily in (i) personal, contractual and (ii) capital market incentives.889

Fudenberg and Tirole (1995) developed a theoretical model focusing on personal,

contractual incentives.890 The authors argue that job security concerns of senior

managers trigger earnings smoothing activities as executives are motivated to

maximize their tenure with a firm.891 It is hypothesized that “when future

performance is expected to be poor, managers wish to shift current period earnings

into the future in order to reduce the likelihood of future dismissal.”892 Brochet and

884 Cf. AbuGhazaleh et al. (2011), p. 174, Brochet and Welch (2011), p. 3, Jordan and Clark (2004), p. 63. Sevin and Schroeder (2005), p. 49. 885 Cf. Zucca and Campbell (1992), p. 35, Elliott and Shaw (1988), p. 92. 886 Cf., for example, Gordon (1964), p. 262, Copeland and Licastro (1968), p. 540, Barefield and Comiskey (1972), p. 291, Albrecht and Richardson (1990), p. 713, Barnea et al. (1976), p. 110, Beidleman (1973), p. 653, Copeland (1968), p. 101, DeFond and Park (1997), p. 115, Dichev at al. (2013), p. 15, Dechow (1994), p. 19, Dichev and Tang (2008), p. 1432, Dichev and Tang (2009), p. 162, Eckel (1981), p. 28, Graham et al. (2005), p. 4. 887 Beidleman (1973), p. 653. 888 Cf. Barnea et al. (1976), p. 111, DeFond and Park (1997), p. 115. 889 Cf. Albrecht and Richardson (1990), p. 713, Brochet and Gao (2004), p. 3, Fudenberg and Tirole (1995), pp. 75-76. 890 Cf. Fudenberg and Tirole (1995), p. 75, Kirschenheiter and Melumad (2002), p. 764, Tucker and Zarowin (2006), p. 253. 891 Cf. Fudenberg and Tirole (1995), pp. 77-78, Brochet and Gao (2004), p. 6, DeFond and Park (1997), p. 115. 892 DeFond and Park (1997), p. 116.


190

Gao (2004) test Fudenberg and Tirole’s theoretical model (1995) empirically and

find confirmation for their predictions.893 In the context of goodwill write-offs, these

considerations imply that managers might recognize write-offs in years when the

firm’s performance is remarkably positive as they have room to absorb the negative

earnings impact from the write-off. This makes particular sense when managers

have private information suggesting that the firm’s future financial performance will

worsen compared to the current year. By doing so, earnings volatility can be

reduced. This reasoning is indirectly confirmed by Barefield and Comiskey (1972)

who argue that “managers perceive their performance measure to be a decreasing

function of earnings variability. Based upon this assumption, managers could be

expected to make accounting policy decisions which tend to smooth reported

earnings”894.

Another research stream has dedicated itself to the analysis of earnings smoothing

and capital market incentives. Empirical findings provide evidence that firms with

smoother earnings have lower market-derived risk measures. One of the first studies

on the relationship between accounting earnings volatility and various firm specific

risk measures was conducted by Lev and Kunitzky (1974).895 In their regression

model, the authors test for an association between the variability of earnings and (i)

the overall riskiness of a stock, expressed as stock returns’ standard deviation and

(ii) a stock’s systematic risk, expressed as Sharpe’s β value.896 Lev and Kunitzky

(1974) come to the conclusion that “(t)he smoothing measures (…) for sales,

dividends, capital expenditures, and earnings are significantly associated (at the 0.01

level or higher) with both risk measures. (…) i.e., the larger the smoothing measure

(indicating high volatility, or lack of smoothing), the higher the risk.”897 These

findings hold true for both their pooled sample as well as for their intra-industry

samples.898 Cao and Narayanamoorthy (2012) focus on abnormal returns and

earnings volatility in their study. They provide evidence on a positive effect between

893 Cf. Brochet and Gao (2004), p. 20. 894 Barefield and Comiskey (1972), p. 291. 895 Cf. Lev and Kunitzky (1974), p. 259. 896 Cf. Lev and Kunitzky (1974), p. 262. 897 Lev and Kunitzky (1974), p. 265. 898 Cf. Lev and Kunitzky (1974), p. 267.


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earnings smoothing measures and abnormal returns.899 These results hold true for

both short- and long-term time periods (3 days and one quarter after the earnings

announcement, respectively).900 Graham et al. (2005) apply an interviews based

approach to analyze the motivation of senior executives to engage in earnings

smoothing activities by talking to 401 senior executives.901 The authors find that

“(a)n overwhelming majority of CFOs prefer smooth earnings (versus volatile

earnings). Holding cash flows constant, volatile earnings are thought to be riskier

than smooth earnings. Moreover, smooth earnings ease the analyst’s task of

predicting future earnings. Predictability of earnings is an over-arching concern

among CFOs. The executives believe that less predictable earnings - as reflected in a

missed earnings target or volatile earnings - command a risk premium in the

market.”902 The authors’ assertion builds on almost 97% of the respondents who

state a clear preference for smoothed earnings over consecutive financial periods.903

Reasons for such a dominating preference include perceived lower cost of capital

(both equity and debt), better credit ratings, and higher stock prices, highlighting the

opinion of many CFOs that smooth earnings are perceived to be less risky by capital

market participants.904 Empirical confirmation on greater difficulties by analysts to

predict future earnings for firms with more volatile historical earnings streams was

also provided by Dichev and Tang (2009).905

Given the potential motivation of senior executives to engage in earnings

management activities to obtain the above described benefits, several authors

analyzed whether and how goodwill write-offs are used to manage earnings over the

regular tenure of a CEO. As goodwill write-offs negatively impact accounting

earnings, the cases apply in which (i) the firm would have exceeded a normal level

of earnings, i.e. earnings smoothing,906 or (ii) the firm would performed particularly

poor financially over the last year, potentially motivating senior management to

899 Cf. Cao and Narayanamoorthy (2012), p. 41. 900 Cf. Cao and Narayanamoorthy (2012), p. 57. 901 Cf. Graham et al. (2005), p. 4. 902 Graham et al. (2005), p. 5. 903 Cf. Graham et al. (2005), p. 5. 904 Cf. Graham et al. (2005), pp. 44-45. 905 Cf. Dichev and Tang (2009), p. 160. 906 Cf. Barnea et al. (1976), p. 111.


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engage in big bath accounting activities.907 In general, empirical findings tend to

support the view that goodwill write-offs are used strategically for earnings

management purposes.908

One of the earlier studies was conducted by Elliott and Shaw (1988),909 who look

into the issue of asset write-offs (incl. goodwill and other intangible assets) for

earnings management purposes; however the authors use a pooled sample of write-

offs and do not focus on goodwill write-offs exclusively. In study of Elliott and

Shaw (1988), the authors analyse 240 discretionary write-offs in US firms and find

that the mean write-off (incl. intangible assets like goodwill) amounts to 8.2% of

total assets, with the majority (62.9%) being booked in the fourth financial

quarter.910 Additionally, the authors document that “firms disclosing large

discretionary write-offs are larger than other firms in their industries (revenues and

assets) and are more highly leveraged”911. Furthermore, their descriptive statistics

imply that discretionary write-offs correlate strongly with particularily poor firm

performance during the year when the write-off was booked,912 which could be

interpreted as a finding supporting the big bath accounting hypothesis. Zucca and

Campbell (1992) also study the discretionary write-off behavior of impaired assets

in firms. For their analysis, a sample of 77 US firms was used, which had booked

write-offs between 1978 and 1983.913 Similar to the findings of Elliott and Shaw

(1988), Zucca and Campbell (1992) show that the vast majority of firms that

disclose the timing of write-offs (78,7%) perform write-offs late in the financial year

(fourth financial quarter) when senior management teams have good visibility over

the overall financial performance of the full reporting period.914 Also in their study,

average write-offs are considerable in size, amounting on average to 4.05% of total

907 Cf. Jordan et al. (2007), p. 23, AbuGhazaleh et al. (2011), p. 174. 908 Cf. AbuGhazaleh et al. (2011), p. 170, Brochet and Welch (2011), p. 21, Saastamoinen and Pajunen (2012), p. 2, Walsh et al. (1991), p. 173. 909 Cf. AbuGhazaleh et al. (2011), p. 170. 910 Cf. Elliott and Shaw (1988), p. 95. 911 Elliott and Shaw (1988), p. 93. 912 Cf. Elliott and Shaw (1988), pp. 93, 95. 913 Cf. Zucca and Campbell (1992), pp. 32, 36. 914 Cf. Zucca and Campbell (1992), p. 34.


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assets (mean).915 In their descriptive analysis, the authors provide compelling

evidence for the earnings management hypothesis as they find that “the majority of

the firms wrote down their assets in a period of already below normal earnings (a

“big bath”), but 25 percent offset the writedown with other gains or unusually high

earnings (income smoothing). These results provide support for the contention that

writedowns are being used to manage earnings.”916

The study by Francis et al. (1996) separates discretionary write-offs by asset class

and analyzes the relationship between unusually good or bad operating returns on

assets (excluding any effects from goodwill write-offs) and observable goodwill

write-off amounts in the same year.917 Francis et al. (1996) argue that the big bath

accounting hypothesis “suggests a negative correlation between pre-write-off

earnings performance and write-offs”918 while the income smoothing hypothesis

would imply “a positive correlation between pre-write-off earnings performance and

write-offs”919. On the basis of 356 US write-off firms, Francis et al. (1996) apply a

tobit regression to understand whether a relationship between unusually good or bad

operating performance and goodwill write-offs exists. Although they find some

support for their big bath accounting hypothesis for goodwill write-offs (due to a

negative correlation), the effect is not statistically significant in their regression

model (p-value of 0.13). Surprisingly, the authors find a negative correlation

between operating return on assets and goodwill write-offs, standing in sharp

contract to the expectations derived from the earnings smoothing hypothesis.920

They follow that “(t)hese results contrast sharply with Zucca and Campbell’s (1992)

conclusion that write-off firms are engaging in either big bath or smoothing

behavior.”921

Jordan and Clark (2004) analyse the goodwill write-off decisions of Fortune 100

firms one year prior to and in the year of the adoption of the impairment-only

915 Cf. Zucca and Campbell (1992), p. 34. 916 Zucca and Campbell (1992), pp. 40-41. 917 Cf. Francis et al. (1996), p. 125. 918 Francis et al. (1996), p. 123. 919 Francis et al. (1996), p. 123. 920 Cf. Francis et al. (1996), p. 125. 921 Francis et al. (1996), p. 127.


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approach according to SFAS 142, i.e. 2001 and 2002.922 The authors apply a series

of t-tests on their sample of 80 firms in order to find evidence for the big bath

accounting hypothesis in the impairment-only approach. Jordan and Clark (2004)

observe that earnings measures like return on assets and return on sales statistically

significantly decline in the goodwill write-off group between 2001 and 2002, whilst

in 2001 before the adoption of SFAS 142 no statistically significant difference

between the write-off and non-write-off groups was observable. The authors

document a strong correlation between goodwill write-offs and a substantial change

in financial performance during this time periode of two years and argue for “the

presence of big bath earnings management in 2002.”923 However one shortcoming of

their analysis is that one could argue that the write-off was a simple consequence of

the deteriorating performance between 2001 and 2002. On that topic, Jordan and

Clark (2004) argue that “it is unlikely that depressed earnings in one period alone

would cause management to doubt the value of its goodwill. (…) The results show

that the companies taking the goodwill write downs in 2002 experienced earnings

problems in 2002 but not in the prior year. This suggests that the impairment losses

were likely recorded because managers for these companies viewed 2002 as an

opportune time to take big baths and further reduce their already depressed

earnings.”924 Consequently, Jordan and Clark (2004) argue strongly for an apparent

big bath accounting behavior for firms with depressed earnings, although their line

of reasoning could be questioned.925

Riedl (2004) focusses in his study on both big bath accounting and earnings

smoothing write-off decisions.926 By using a pooled sample of asset write-offs

including those on goodwill and other intangible assets, Riedl (2004) applies a tobit

regression on 1’306 write-off and non-write-off observations in US firms, applying

SFAS 121 Accounting for the Impairment of Long-Lived Assets and for Long-Lived

Assets to be Disposed Of, which had set out the accounting rules for purchased

922 Cf. Jordan and Clark (2004), p. 65. 923 Jordan and Clark (2004), p. 67. 924 Jordan and Clark (2004), p. 68. 925 Cf., also, Sevin and Schroeder (2005) and Jordan et al. (2007) who come up with similar findings. 926 Cf. Riedl (2004), p. 829.


195

goodwill before SFAS 142 superseded it.927 The computation of the proxies applied

by Riedl (2004) for big bath accounting and earnings smoothing are rather similar.

They both build on an observable change in pre-write-off earnings between period t-

1 and t, i.e. the year the write-off is observable.928 Whilst he finds a highly

statistically significant impact of his big bath accounting variable (p-value < 0.01)

on write-off decisions, he finds no conclusive evidence for the earnings smoothing

hypothesis.929 The findings of Riedl (2004) therefore also strongly support the big

bath accounting hypothesis and not the assumption that write-offs are influenced by

a management team’s motivation to smooth accounting earnings over several

periods.

Brochet and Welch (2011) further add to the existing literature on the potential

relationship between the impairment-only approach and big bath accounting as well

as earnings smoothing by studying the backgrounds of CEOs and their impact on

goodwill write-off decisions. To do so, the authors collect the employment history of

CEOs of 2’168 listed US firms applying the impairment-only approach under SFAS

142.930 The authors’ observation period stretches from 2002 to 2009. For testing

their big bath accounting hypothesis, Brochet and Welch (2011) apply an indicator

variable which equals to one if earnings exclusive of any goodwill write-offs are

negative and below the earnings of the previous year. Their analysis of the earnings

smoothing hypothesis is based on a rather complex dummy variable which is set to

“one for year t if (i) post-impairment earnings at t-1, t and t+1 are such that the

change from t to t+1 is greater than the change from t-1 to t, whereas in the absence

of goodwill impairment, the change from t-1 to t would have been higher than the

change from t to t+1, and (ii) if the change from t-1 to t be strictly positive, and zero

otherwise.”931 By applying a censored regression model on 6’236 firm year

observations, the authors find that CEOs with prior so-called transaction experience,

meaning having worked either in investment banking, management consulting or

927 Cf. Bens and Heltzer (2005), pp. 5-6. 928 Cf. Riedl (2004), p. 829. 929 Cf. Riedl (2004), p. 849. 930 Cf. Brochet and Welch (2011), p. 2. 931 Brochet and Welch (2011), p. 31.


196

private equity before becoming CEO932, use goodwill write-offs as a means to

smooth earnings to a larger extend than CEOs with no transaction experience (p-

value < 0.01).933 The authors also document that CEOs with prior transaction

experience are more likely to write-off goodwill when “pre-impairment income is

already lower than zero and lower than last year’s income, consistent with a “big

bath” behaviour”934, than executives with no prior transaction experience (p-value <

0.10). Consequently, Brochet and Welch (2011) provide evidence for both of their

earnings management hypotheses and document further that earnings management

by CEOs through goodwill write-offs can be partly explained by their professional

backgrounds.

Until today, research studies on European firms and firms located in German-

speaking countries that potentially engage in earnings management practices

through goodwill write-offs are limited in number. The research studies by

AbuGhazaleh et al. (2011) and Siggelkow and Zülch (2013a and 2013b) however

represent positive exceptions.

The study of AbuGhazaleh et al. (2011) builds on a sample of the largest 500 listed

UK firms (by market capitalization) which apply the impairment-only approach in

accordance with IAS 36. The authors analyze earnings management on the basis of

528 goodwill write-offs between 2005 and 2006.935 Similar to the studies of Francis

et al. (1996) and Riedl (2004), AbuGhazaleh et al. (2011) include in their

multivariate pooled tobit regression explanatory variables capturing a possible

relationship between earnings management and goodwill write-off decisions.936 The

definitions of their big bath accounting and earnings smoothing variables are

identical to the ones used by Riedl (2004), meaning that they build on a change in

pre-write-off earnings between two consecutive financial periods. Similar to Riedl

(2004), the authors find a highly statistically significant impact of their big bath

accounting variable (p-value < 0.001) on the observable write-off decisions under

932 Cf. Brochet and Welch (2011), p. 2. 933 Cf. Brochet and Welch (2011), p. 22. 934 Brochet and Welch (2011), p. 3. 935 Cf. AbuGhazaleh et al. (2011), p. 184. 936 Cf. AbuGhazaleh et al. (2011), p. 178.


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the impairment-only approach. However remarkably, AbuGhazaleh et al. (2011)

find also compelling evidence supporting their earnings smoothing hypothesis.

Although the effect is found out not to be as strong as in the big bath accounting

case, the relationship is still statistically significant with a p-value of 0.03.

Consequently, the study of AbuGhazaleh et al. (2011) provides evidence for both

earnings management practices through the application of the impairment-only

approach.937

The research papers by Siggelkow and Zülch (2013a, 2013b) provide interesting

insights into earnings management practices of European and German firms through

discretionary write-offs on various assets including goodwill. Firstly, Siggelkow and

Zülch (2013a) study a sample of 165 listed German firms that were listed on either

the German DAX, MDAX, TecDax or SDAX between 2004 and 2010.938 The

sample of the authors comprises of 805 firm year observation including write-offs

on goodwill, other intangible assets, as well as property, plant and equipment.939

Unfortunately, the authors do not provide additional information on how many of

their 805 write-off observations represent goodwill write-offs, as the authors work

with a pooled write-off sample. Methodologically, in the probit regression of

Siggelkow and Zülch (2013a) in which the write-off decision represents a

dichotomous variable, the authors use similar proxies for earnings smoothing and

big bath accounting as Francis et al. (1996). In a first step, an earnings management

indicator is calculated, measuring the change in pre-tax earnings adjusted for the

respective write-offs (EBIT before impairment) scaled by total assets, between the

write-off and the previous year.940 Depending on whether this difference is found out

to be positive or negative, the authors argue for unexpectedly high or low earnings

in the write-off year.941 In their regression analysis, Siggelkow and Zülch (2013a)

find support for only one of their earnings management hypotheses as only the

proxy for income smoothing is found out to be related to the write-off decision.

937 Cf. AbuGhazaleh et al. (2011), p. 165. 938 Cf. Siggelkow and Zülch (2013a), p. 50. 939 Cf. Siggelkow and Zülch (2013a), p. 39. 940 Cf. Siggelkow and Zülch (2013a), p. 39. 941 Cf. Siggelkow and Zülch (2013a), p. 51.


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However the effect is highly statistically significant (p-value < 0.01).942 This leads

the authors to conclude that “(t)he results of our analysis show that the write-off

decisions of German listed companies are materially influenced by firm

performance. In contrast with different studies on the US-American market, we do

not find big bath accounting to be significant, but we do find that the write-off

decision is materially influenced by income smoothing.”943 The authors offer

interesting explanations for their observations by mentioning that firms might be

motivated to write off assets in order to reduce their overall tax payments in a given

year or that lending banks might view the firm as less risky as their earnings are less

volatile.944

In another study by Siggelkow and Zülch (2013b), a European wide sample of firms

applying the impairment-only approach according to IAS 36 is used by the authors

in order to understand the potential impact of earnings management on goodwill

write-off decisions. To do so, Siggelkow and Zülch (2013b) base their analysis on a

sample of 558 firms headquartered in one of the EU15 member countries. Their

sampling process results in 1’183 firm year observations (write-off decisions)

between 2004 and 2011.945 In the descriptive analysis of their sample, the authors

find that the mean absolute and relative goodwill write-offs are relatively low and

amount to EUR 18 Mio. or 0.29% of total assets respectively.946 To understand

whether goodwill write-offs are used for earnings management purposes in

European firms, the authors apply a Cragg model which represents an extension of a

tobit regression.947 Similar to the methodology which the authors applied in their

previous study (2013a), the observable write-off decision is operationalized through

a dichotomous variable in their regression.948 Additionally, Siggelkow and Zülch

(2013b) run a similar regression on the goodwill write-off magnitude, measured as

the natural logarithm of the write-off relative to total assets (with total assets

942 Cf. Siggelkow and Zülch (2013a), p. 54. 943 Siggelkow and Zülch (2013a), p. 26. 944 Cf. Siggelkow and Zülch (2013a), p. 23. 945 Cf. Siggelkow and Zülch (2013b), p. 109. 946 Cf. Siggelkow and Zülch (2013b), p. 90. 947 Cf. Siggelkow and Zülch (2013b), pp. 84-85. 948 Cf. Siggelkow and Zülch (2013b), pp. 84-85.


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measured at the end of the previous financial period). For the analysis of the overall

goodwill write-off decision (dummy variable), the findings are contrary to the

authors’ expectations meaning that they find no evidence that the write-off

probability is higher in years with unexpectedly high or low earnings. The findings

are actually imply the opposite, suggesting that write-off probability is lower in

years with unexpectedly high earnings and higher in years with unexpectedly low

earnings.949 For the case of goodwill write-off magnitudes (continuous variable), the

signs of the explanatory variables for earnings management through goodwill write-

offs are in line with the authors’ expectations, however the effects of the explanatory

variables are not found out to be statistically significant in the tobit regression.950

Consequently, the results of Siggelkow and Zülch (2013b) imply that earnings

management considerations do not play a strong influencing role on goodwill write-

offs in European firms and stand in contrast to authors like AbuGhazaleh et al.

(2011). For studies based on samples of US firms applying the impairment-only

approach according to SFAS 142 the findings imply a relationship between earnings

management considerations and goodwill write-off decisions while for European

firms the evidence is rather mixed.

6.3.3 Insider trading of senior management teams

prior to goodwill write-offs

A relatively new research stream has dedicated itself to the analysis of senior

executives’ behaviour ahead of observable goodwill write-offs. The most prominent

paper in this research area was published by Muller et al. (2009) who have looked

into strategic insider share trades by senior executives prior to goodwill write-offs.

When the impairment-only approach in financial accounting was introduced,

proponents argued that one of its benefits would be that the new accounting

technique could be used to disclose private information on the firms’ future

prospects, thereby reducing information asymmetries between the firm and its

949 Cf. Siggelkow and Zülch (2013b), p. 104. 950 Cf. Siggelkow and Zülch (2013b), p. 127.


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stakeholders.951 However the research results of Muller et al. (2009) imply that

actually the opposite could hold true in practice, meaning that managers use their

private information for their individual benefits and not necessarily to reduce

information asymmetries.952

By analyzing a sample of 612 US firms applying SFAS 142 between 2002 and 2007,

the univariate analyses of Muller et al. (2009) show that the likelihood of net insider

selling953 of senior executives 24 months prior to recognizing a goodwill write-off is

statistically significantly higher than for a control group of firms that do not report a

goodwill write-off (p-values < 0.01).954 The findings of Muller et al. (2009)

regarding the strategic use of private information are robust for sample firms

operating in high quality or low quality information environments, i.e. whether

many or only a few financial analysts cover a firm’s stock.955 In a second analysis,

Muller et al. (2009) try to explain the occurrence of strategic insider trading ahead of

a write-off by means of multivariate logistic regressions. The authors find that

especially firm size measured as total assets is positively related to insider trading

(p-value < 0.01) and market valuations of a firm measured as the ratio between a

firm’s book value and market value are negatively related. Overall, one could use

the findings of Muller et al. (2009) to argue that executives might potentially

influence write-off decisions by willingly delaying write-offs in order to sell their

holdings of the firm before the write-off negatively impacts the share price and

therefore their personal wealth.956

951 Cf. Ramanna and Watts (2012), p. 750, Riedl (2004), p. 845. 952 Cf. Ramanna and Watts (2012), p. 753, Li and Sloan (2012), p. 8. 953 i.e. more shares are sold than bought over a certain time period 954 Muller et al. (2009), p. 4. 955 Cf. Muller et al. (2009), pp. 3-4. 956 Cf. Muller et al. (2009), pp. 5, 43.


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6.4 Summary of literature review and observable

research gaps

The overall findings of the presented research papers on the topic of goodwill write-

offs under the impairment-only approach imply that executives might use goodwill

write-offs to meet their favoured financial reporting strategy. Evidence on the

management of goodwill write-offs tends to be more profound for samples of US

than for European firms. Given that for many of the goodwill write-off management

hypotheses evidence is mixed, further research in this area is vital. This holds

particularly true for analyses on the basis of European firms as the majority of

research papers is based on US firms applying the impairment-only approach under

SFAS 142, and not IAS 36. Consequently one can observe a limited research

coverage of European firms until today.957

Although some research studies present sound evidence on the overall hypothesis

that executives manage goodwill write-offs during their tenure, their motivations to

do so has not been fully captured in academia yet. Whilst various authors argue for

capital market incentives stemming from income smoothing or big bath accounting

or for contractual incentives resulting from not breaching debt covenants (primarily

approximated through observable leverage ratios and not actual covenants), personal

incentives of executives emerging from their compensation agreements and personal

shareholdings in the firm have not been studied in great detail yet and deserve

special attention. With respect to goodwill write-off decisions the area of personal

incentives that has received most attention in academia so far represents the widely

observable, higher probability of goodwill write-offs early in the career of a new

CEO, offering the possibility to reduce impairment risks in the future without

actually being responsible for the write-off as the transaction from which recognized

goodwill emerged most likely occurred under the tenure of the previous CEO. Other

areas which fall under the category of personal incentives still remain questionable

due to the mixed results that research studies offer so far. However, it would

certainly be beneficial to understand which other personal benefits could motivate

957 Cf. Knauer and Wöhrmann (2013), p. 4.


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executives to manage goodwill write-offs and whether one can actually measure and

observe them in economic models.

Strongly related to the hypothesis that senior executives might manage goodwill

write-offs due to personal incentives and thereby potentially acting opportunistically

is the question on what ground one can argue for a managerial misconduct in the

impairment-only approach. The argumentation for or against a potential managerial

misconduct rests on the notion of whether goodwill is actually economically

impaired or not, as one could argue for an opportunistic behavior in case goodwill is

not written off although being impaired or vice versa. Consequently the

argumentation for or against an opportunistic behavior in the impairment-only

approach depends on how strong the indicators are that goodwill is actually

economically impaired or not. This means that the selection of the sample on the

basis of the right sample characteristics is essential in the research set-up. So far

research studies have focusses on negative changes in historical firm performance

for arguing for an impaired goodwill. This has included mainly return measures

based on accounting information like return on assets, absolute performance

measures like operating cash flows or capital market return measures like stock

returns. Certainly these variables of firm performance can provide valuable insights

into the economic performance of a firm however do not provide absolute evidence

on an economic impairment of goodwill. More recent studies argue that an

observable, negative difference between a firm’s market and book value potentially

provides the strongest indicator for an economically impaired goodwill. This view is

substantiated by the views of the IASB and FASB who consider such an observable

difference as a triggering event for performing an ad-hoc impairment test.

Nevertheless, this indicator has not received as much attention in academia as others

although its explanatory power can be considered to be greatest as this measure

directly links the historical and expected performance of a firm to its current market

value of assets.

Falling also into the research area of goodwill write-off management is the question

whether observable write-offs, irrespectively of whether managed or not, provide

information on the future financial performance of the firms, as originally intended


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by the IASB through the introduction of the impairment-only approach. Although

one could assume that the explanatory power of write-offs on future firm

performance declines the longer executives wait until booking a write-off, the

argument for a managerial misconduct would be stronger if this assumption on the

declining explanatory power of write-offs is found to be backed by empirical

evidence. Because only then, the IASB’s objective would have been clearly missed

in practice and the introduction of the impairment-only approach could be

questioned due to their limited information content.

Another fairly untapped research area related to potential motivations of senior

executives to manage goodwill write-offs represents the reporting flexibility that

IAS 36 allows in carrying out the impairment test. Even if managers are motivated

to manage goodwill write-offs, the question remains whether they have the

conceptual tools and methods to do so. Although reporting flexibility has been often

criticized by academics and practitioners alike, its links to write-off decision making

have not been researched comprehensively so far. Reporting flexibility in the

impairment-only approach could actually represent one of the main tools for

accomplishing a favored reporting strategy. Consequently understanding the

mediating effect of reporting flexibility in goodwill write-off decision making could

offer important insights into whether the criticism on the reporting flexibility in the

impairment-only approach is actually founded as it maybe opens the door for the

management of goodwill write-offs.

The analysis of the presented research papers shows that several research areas exist

which would provide additional insights into the write-off decision making of senior

executives when testing the recoverability of goodwill, which however have

received only limited attention in academia so far. Furthermore, existing research

studies focusing on some of those areas come to mixed conclusions. In particular,

these research areas include the relevance of personal incentives during the regular

tenure of senior executives, the information content of goodwill write-offs regarding

a firm’s future performance and the issue of reporting flexibility as a mediator for

accomplishing a preferred reporting strategy. Interestingly, it can be argued that all


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of those three areas tend to be interrelated which makes the analysis of them even

more relevant and necessary.

7 Research design and research methodology

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On the basis of the outlined research questions in chapter 1.2, this PhD thesis aims at

making a contribution to the prevailing research gap to which extends (i) potential

private information held my management on changes of a firm’s future financial

performance and (ii) incentives predicted by agency theory influence the goodwill

write-off decisions of senior management teams in the impairment-only approach.

Additionally, the scope of this PhD thesis is extended to the role of goodwill

reporting flexibility in the goodwill write-off decision making process as goodwill

reporting flexibility could potentially provide means to achieve the preferred

outcome of the firm’s management in the impairment-only approach.958

7.1 Hypotheses formulation

In the following, the to-be-tested hypotheses are described which target at

understanding factors influencing goodwill write-off decisions in the impairment-

only approach for firms for which strong capital market-implied triggering events

are observable. The hypotheses are based on the research questions, described in

chapter 1.2, and the presented theoretical concepts which potentially explain

goodwill write-off decision making (chapter 5).

958 Cf. Liberatore and Mazzi (2010), p. 333, Ramanna and Watts (2012), p. 760, Amiraslani et al. (2013), p. 19, Vettiger and Hirzel (2010), p. 387, Engel-Ciric (2012), p. 421, Mandl (2005), p. 476, Budde (2005), p. 2567, Kasperzak (2011), p. 3, Küting (2005), p. 2759.


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Fig. 46: Hypotheses and research areas Source: Own illustration.

7.1.1 Relationship between private information on the

firm’s future financial performance and goodwill

write-off decisions

Accounting standard setters argued for the superiority of the impairment-only

approach over the periodic amortization of goodwill on the basis of the possibility to

disclose information on expected cash flows and therefore future financial

performance of firms.959 This reasoning stems from the requirement of the

impairment-only approach to apply forward looking valuation methodologies which

usually make the evaluation and application of private information necessary.960 In

this context, Siggelkow and Zülch (2011) are of the opinion that goodwill’s

959 Cf. IAS 36.BC131G, FASB (2014a), AbuGhazaleh et al. (2011), p. 196, Liberatore and Mazzi (2010), p. 334, Meyer and Halberkann (2012), p. 312, Amiraslani et al. (2013), pp. 18-19, Chen et al. (2013), p. 4, Gordon and Hsu (2014), p. 13, Vanza et al. (2011), pp. 2-3, Lapointe-Antunes et al. (2009), pp. 62-63, Li et al. (2011), p. 746, Ramanna and Watts (2012), p. 749, Ramanna (2008), p. 255. 960 Cf. Mazzi et al. (2013), p. 1.

Managerial

motivations

Ability

Private information on firms’

future financial performance

Agency theory-based

incentives

IAS 36

Goodwill reporting flexibility

H1: Stock returns H2: Share buybacks H3: CEO insider trading

H4: CEO tenure H5: CEO compensation H6: Accounting covenants H7: Financial leverage H8: CEO equity ownership

H9: Goodwill concentration H10: Segments’ size H11: Segments’ profitability H12: CGU/reporting changes


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“expected future benefit is a private information available to the management

only”961.

From a methodological perspective, the impairment-only approach allows not only

to disclose information on future cash flows on a firm level, but also on a business

segment level to which goodwill has been allocated.962 Therefore theoretically, in an

ideal setting every write-off decision, being either positive (i.e. no write-off) or

negative (i.e. write-off), reveals private information held by management about the

expected performance of the firm or the reporting segments (to which CGUs with

goodwill have been allocated) to capital market participants and other

stakeholders.963 Lapointe-Antunes et al. (2009) confirm this reasoning by arguing

that through the impairment-only approach, managers can reduce information

asymmetries through the disclosure of private information on the expected financial

performance, as usually “no financial information is publicly available at the

reporting-unit level unless every reporting unit is a public firm itself (which happens

very rarely)”964. Furthermore, disclosures on goodwill write-offs help to reduce

information asymmetries, as “it is virtually impossible for outsiders to collect the

information necessary to make an external appraisal of the fair value of goodwill at

the reporting-unit level”965. Consequently, in an ideal setting, goodwill write-off

decisions, being either positive or negative, have an information value to market

participants,966 as this information allows for valuing the firm more accurately.967

This holds particularly true when information asymmetries are present.968

A general theoretical foundation for this private information hypothesis in

accounting choices by a firm’s management has been provided by Holthausen and

Leftwich (1983) and Holthausen (1990) in their so-called information perspective-

961 Siggelkow and Zülch (2011), p. 10. 962 Cf. Fields et al. (2001), p. 257, AbuGhazaleh et al. (2011), p. 195, Siggelkow and Zülch (2013a), p. 29, Lapointe-Antunes et al. (2009), p. 62, Mazzi et al. (2013), p. 1. 963 Cf. Riedl (2002), p. iii, AbuGhazaleh et al. (2011), p. 195, Lapointe-Antunes et al. (2009), p. 63. 964 Lapointe-Antunes et al. (2009), p. 63. Please note that the term “reporting unit” is the SFAS 142 terminology for CGU according to IAS 36. 965 Lapointe-Antunes et al. (2009), p. 63. 966 Cf. Amiraslani et al. (2013), p. 20, Mazzi et al. (2013), p. 1. 967 Cf. Watts and Zimmerman (1990), p. 132, Vanza et al. (2011), p. 6. 968 Cf. Fields et al. (2001), p. 259.


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theory.969 The authors argue that managers might be willing to choose those

accounting techniques that allow them to reveal their private information on the

firm’s future cash flows as “managers have a comparative advantage in providing

information about their firms”970 over other market participants and thereby helping

market participants to price the firm more accurately or to reduce the uncertainty of

market participants on the fair value of the firm.971 The theory offered by

Holthausen and Leftwich (1983) that the “accounting technique choice reflects

management’s expectation of future cash flows”972 could also be linked to

explaining goodwill write-off decisions, as those decisions allow the senior

management team to reveal its expectations about future cash flows (either on a firm

or business segment level).973

That goodwill balances under the impairment-only approach have generally the

ability to predict future cash flows and accounting earnings was analysed and

confirmed, for example, in the research studies of Lee (2011)974, Lys et al. (2012)975,

and Chalmers et al. (2012)976. Lee (2011) studied the relationship between firms’

goodwill balances and future cash flows from operations before and after the

introduction of the impairment-only approach under SFAS 142.977 He finds that

recognized goodwill under the IOA better predicts one-year and two-year ahead cash

flows from operations than pre-SFAS 142 goodwill balances which were amortized

periodically.978 He reasons that “goodwill’s ability to predict future cash flows has

improved since the FASB adopted SFAS 142”979. The findings of Chalmers et al.

969 Cf. Holthausen and Leftwich (1983), p. 77, Holthausen (1990), pp. 208-209. 970 Holthausen and Leftwich (1983), p. 112. 971 Cf. Vanza et al. (2011), p. 2. 972 Holthausen and Leftwich (1983), p. 112. 973 Cf. Lee (2011), p. 244. 974 Cf. Lee (2011), p. 253. 975 Cf. Lys et al. (2012), p. 2. 976 Cf. Chalmers et al. (2012), p. 691. 977 Lee (2011) regresses in separate regressions one-year and two-year ahead cash flows (dependent variables) on goodwill balances under the pre- and the post-SFAS 142 regimes. To do so, Lee (2011) studies a sample of 4’825 firms (13’848 firm-year observations), which were derived from testing periods before (1996-1998) and after (2004-2006) the introduction of the impairment-only accounting standard under US-GAAP (SFAS 142). 978 Cf. Lee (2011), pp. 248-250. 979 Lee (2011), p. 253.


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(2012) also add to the topic of the ability of goodwill balances to predict future

earnings under the impairment-only approach. The authors provide evidence that

after the introduction of IAS 36 goodwill balances lead to greater errors of one-year

ahead analysts’ earnings forecasts and dispersions than before its adoption. By

analysing goodwill and other intangible assets balances of Australian firms under

Australian GAAP and IFRS980, the authors find that dispersion of earnings forecasts

by analysts “is largely attributable to reported goodwill, rather than other intangible

assets, suggesting that the impairment approach to goodwill valuation required by

IFRS conveys more useful information than does the former straight-line

amortization approach.”981

Lys et al. (2012) also study the ability of goodwill balances under SFAS 141 and

142 of US firms and related economic profits or losses from merger transactions982

to predict one-year and two-year ahead operating performance (EBITDA).983

Working with separate samples of transactions for which market participants

presumed economic profits or economic losses984, the authors find that the ability of

recorded goodwill balances, which emerged from transactions that created value for

shareholders (i.e. economic profits), to predict one-year and two-year ahead

EBITDA is highly statistically significant.985 For goodwill balances which emerged

from transactions that generated economic losses this is not the case. The results for

the latter case however change if the goodwill balances were manually adjusted by

the economic losses by the authors to reflect a necessary write-off.986 The results of

Lys et al. (2012) suggest that goodwill balances under the IOA (if generated by

transactions which created value for shareholders or when written off timely when

980 The sample of Chalmers et al. (2012) consists of 458 Australian firms (1’885 firm year observations) between 1993 and 2007. 981 Chalmers et al. (2012), p. 691. 982 Lys et al. (2012) define economic profits (losses) as the difference between the fair values of the net assets acquired (target) plus expected synergies by capital market participants less the purchase price (Cf. Lys et al. (2012), p. 7). In case this difference is negative, an economic loss to the acquirer occurred as the acquiring firm overpaid for the target and related synergies. 983 Cf. Lys et al. (2012), p. 39. 984 The sample of Lys et al. (2012) includes 2’123 mergers completed between 2002 and 2006. 59% (1’252) of them resulted in an economic profit, with the remaining 41% (871) in an economic loss. 985 Cf. Lys et al. (2012), p. 39. 986 Cf. Lys et al. (2012), p. 39.


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transactions destroyed shareholder value) are a predictor of future operating

performance.

Empirical results documenting that goodwill write-offs negatively correlate with

future cash flows would provide support the private information disclosure

hypothesis.987 No or small correlations with future cash flows would certainly raise

questions on the argued private information disclosure hypothesis of accounting

standard setters.988 Consequently, on the basis of the information disclosure

hypothesis and the reasoning of accounting standard setters, one would expect that

goodwill write-offs correlate with future firm performance. Future firm performance

can be measured through various proxies, for example accounting earnings, cash

flows or capital market returns. In the analysis of this PhD thesis firm-wide capital

market returns should be used as a proxy for private information as accounting

measures can be managed to a certain extent, whilst stock returns are based on third

party assessments. In case the private information hypothesis holds true, non-write-

off firms should have higher 1-year- and/or 2-year-ahead stock returns than write-off

firms. Consequently, on the basis of the private information disclosure theory the

first hypothesis to be tested reads as:

H1: Stock returns:

Goodwill write-off probability in year t is lower for firms with higher stock

returns in years t+1 and/or t+2, than for firms with lower stock returns in

years t+1 and/or t+2.

Furthermore, private information held by management on their firm’s future

performance potentially influencing goodwill write-off decisions should be

measured by the extent to which a firm’s management team engages in share

buybacks during the observation period, i.e. when one can observe MTB <1 over

two consecutive financial year-ends.989 Share buybacks can signal market

participants positive information on the firm’s expected financial performance and

987 Cf. Li et al. (2011), pp. 750-751, Lhaopadchan (2010), p. 124, Lapointe (2005), p. 5, Gordon and Hsu (2014), p. 2. 988 Cf. Gordon and Hsu (2014), p. 2. 989 Cf. Ramanna and Watts (2012), p. 757.


211

therefore a potential undervaluation of the firm from the management’s point of

view,990 as argued by Vermaelen (1981)991, Dann (1981)992, and Ramanna and Watts

(2012)993. On this topic, Comment and Jarell (1991) argue that this so-called

signalling explanation theory represents a widely accepted theory in academia,

which has been frequently proven empirically.994 Nevertheless it needs to be added

that besides the signalling explanation theory other theoretical concepts have been

brought forward in academic aiming at explaining share buybacks.995

One of the strongest empirical evidences on the private information content of share

buybacks on future operating performance has been provided by Lie (2005). He

studied 4,729 announcements of open market share repurchase programs between

1981 and 2000 and finds that operating performance996 improves statistically

significantly over the next 8 quarters after the announcement.997 On the basis of his

findings, Lie (2005) reasons that “a side effect of share repurchases is that they

convey favorable information to the market about future performance”998. The

findings of Lie (2005) are in line with those of earlier research studies of Hertzel and

Prem (1991)999, Bartov (1991)1000, Nohel and Tarhan (1998)1001 who find upward

990 Cf. Brav et al. (2005), pp. 514, 518. 991 Cf. Vermaelen (1981), p. 166. 992 Cf. Dann (1981), p. 113. 993 Cf. Ramanna and Watts (2012), p. 757. 994 Cf. Comment and Jarell (1991), pp. 1243-1244, Nohel and Tarhan (1998), p. 188. 995 Besides the signalling theory, the so-called free cash flow hypothesis represents another widely accepted theory why firms engage in share buybacks. The free cash flow hypothesis presumes that a firm’s management returns excess cash to shareholders in form of share buybacks due to limited investment possibilities within the firm thereby reducing agency costs of free cash flows (Cf. Jensen (1986), pp. 323-324, Nohel and Tarhan (1998), p. 188). Amongst others, the research studies by Lang et al. (1991), Nohel and Tarhan (1998), Fenn and Liang (1998), Stephens and Weisbach (1998), Barth and Kasznik (1999), Chan et al. (2004) provide evidence for the free cash flow hypothesis. 996 Measured as operating income divided by cash-adjusted assets. 997 Cf. Lie (2005), p. 421. 998 Lie (2005), p. 412. 999 Cf. Hertzel and Prem (1991), p. 253, who find that financial analysts revise a firm’s EPS forecasts upward after the announcement of stock repurchase tender offers. The analysis is based on tender offer announcements of 127 firms between 1970 and 1984. 1000 Cf. Bartov (1991), p. 293, who also find that financial analysts revise EPS forecasts upward. Additionally, the authors find that repurchasing firms have more often positive unexpected annual earnings in the announcement year than non-repurchasing firms. The study of Bartov (1991) is based on 160 firms (185 observations) that announced open-market stock repurchases between 1978 and 1986. 1001 Cf. Nohel and Tarhan (1998), p. 220. The authors find that the cumulative industry-adjusted operating performance (EBITDA/total assets) of firms that buy back shares statistically significantly improves over


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revisions of EPS forecasts by financial analysts and improvements in actual EPS,

both in the year of the announcement, as well as improvements in the firms’

EBITDA margins over a 3 years period.

Consequently, if the private information disclosure hypothesis proclaimed by

accounting standard setters holds true, one would expect that write-off probability is

lower for firms which engage in substantial share buyback activities during the

observation period than for those which do not.1002 This reasoning is due to the fact

that a firm’s management most likely holds favorable private information on an

improving financial performance when engaging in share buyback activities and

therefore this favorable information on future performance most likely negatively

impacts goodwill write-off probability.1003 These considerations lead to the

following hypothesis:

H2: Share buybacks:

Goodwill write-off probability in year t is lower for firms with larger share

repurchase programs in year t, than for firms with smaller share repurchase

programs in year t.

That firms’ senior executives frequently trade on private information has been well

documented empirically.1004 The analysis of strategic insider trading prior to public

announcements has a long standing history in academia, including the analysis of

purchases and selling of shares prior to earnings announcements1005, dividend

announcements1006, share repurchases1007, acquisitions1008, and new equity

offerings1009. The majority of these studies come to the conclusion that firms’

a three year period. The findings however hold only true for low growth firms (Tobin’s q < 1). Their analysis is based on 242 tender offer announcements (Dutch actions and fixed-price type) between 1978 and 1991. 1002 Cf. Ramanna and Watts (2012), p. 757. 1003 Cf. Ramanna and Watts (2012), p. 757. Please note that this hypothesis builds on the assumption that firms hold enough liquidity to buy back shares or have access to liquidity. 1004 Cf. Muller et al. (2009), p. 1. 1005 Cf. Piotroski and Roulstone (2005), p. 78, Jagolinzer (2009), pp. 235-237, Beneish and Vargus (2002), p. 788. 1006 Cf. John and Lang (1991), p. 1385. 1007 Cf. Lee et al. (1992), p. 1960. 1008 Cf. Seyhun (1990), p. 460. 1009 Cf. Karpoff and Lee (1991), p. 25.


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insiders frequently profit from their private information through strategic buying or

selling ahead of corporate announcements which can influence the share price of the

firm.1010 Consequently strategic insider trading of senior executives can act as a

proxy for the positive or negative private information on the future financial

performance of a firm held by senior executives.1011

Ke et al. (2003) provide strong evidence that members of senior management teams

trade on private information about “specific and economically significant

forthcoming accounting disclosures as long as 2 years prior to the disclosure”1012.

Piotroski and Roulstone (2005) add to the discussion by documenting empirically

that senior executives trade strategically the shares in their own firm “on the basis of

superior knowledge about future cashflow news”1013, measured as changes in

accounting earnings. The authors find that in particular insider purchases carry

information and explanatory power about future earnings and argue that stock

market participants should consider these trades as reliable indications when

developing earnings forecasts.1014 Similar findings are obtained by Roulstone

(2008), who documents that insider trading decisions can be explained by future

earnings announcement returns and that the explanatory power is more significant

for insider purchases of shares by senior executives than for insider sales.1015

Consequently empirical evidence suggests that insider purchases represent a better

proxy for private information on positive future earnings changes than share sales by

insiders for negative earnings changes in the future. This reasoning is also shared by

Lakonishok and Lee (2001).1016

That corporate insiders potentially trade ahead of goodwill write-offs under SFAS

142 and thereby implying that corporate insiders can hold private information was

documented in an empirical study of Muller et al. (2009). The authors show that the

1010 Cf. Muller et al. (2009), p. I. 1011 Cf. Ramanna and Watts (2012), p. 757, Jagolinzer (2009), p. 224, Roulstone (2008), p. 28. 1012 Ke et al. (2003), p. 315. 1013 Piotroski and Roulstone (2005), p. 55. 1014 Cf. Piotroski and Roulstone (2005), pp. 50, 55. 1015 Cf. Roulstone (2008), p. 2. 1016 Cf. Lakonishok and Lee (2001), p. 79.


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likelihood of net insider selling1017 of senior executives 24 months prior to

recognizing a goodwill write-off is statistically significantly higher than for a control

group of firms that do not report a goodwill write-off.1018 If the information on

required goodwill write-offs would have been public already before the insider

trades, there would have been no personal benefit from selling the shares for the

firm’s executives.1019

These considerations and empirical findings lead to the proposition that insider

trading can act as a viable proxy for private information on future cash flows held by

insiders. Thereby net insider buying1020 would imply having positive information on

future cash flows and net insider selling1021 would imply the opposite. Empirical

studies document that insider trading can convey private information on improving

future firm performance.1022 Consequently, under consideration of the private

information disclosure theory, goodwill write-off probabilities for firms whose

CEOs engage to a higher degree in insider trading (i.e. net insider buying) over the

observation period should be lower than for firms whose CEOs trade less shares.

This reasoning leads to the following hypothesis:

H3: CEO Insider trading:

Goodwill write-off probability in year t is lower for firms whose CEOs

increase their stockholdings in year t, than for firms whose CEOs do not

increase their stockholdings in year t.

1017 i.e. more shares are sold than bought over a certain time period. 1018 Cf. Muller et al. (2009), p. 4. 1019 Please refer also to chapter 6.3.3 of the literature review section of this PhD thesis regarding insider trading of senior management teams prior to goodwill write-offs. 1020 i.e. difference between shares bought minus shares sold is positive. 1021 i.e. difference between shares bought minus shares sold is negative. 1022 Cf. Piotroski and Roulstone (2005), p. 55.


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7.1.2 Impact of incentives predicted by agency theory

on goodwill write-off decisions

According to Watts (2003) and Ramanna (2008), agency theory presumes that senior

managers will on average make use of unverifiable discretion in accounting

decisions, like in the impairment-only approach, to manage financial reporting

opportunistically and thereby striving for personal incentives.1023 On the specific

topic of the discretion in the impairment-only approach, Guler (2006) argues that

“(a)gency theory predicts that by using this discretion afforded by the accounting

standard, executives will transfer wealth from bondholders and shareholders to

themselves”1024 through delaying or accelerating goodwill write-offs. Incentives to

manage goodwill impairments under agency theory considerations could potentially

emerge from earnings-based bonus plans1025, share ownership1026, debt

covenants1027, and CEO tenure1028, i.e. CEOs’ reputational concerns that possible

future write-offs will impact future payoffs from their employment contracts.1029

The information content of goodwill write-offs to investors poses the risk of

substantial individual reputational damages to the firm’s management,1030 as write-

offs imply that expected cash flows that were assumed to get realized through an

acquisition in the past are unable to get realized in the future given the information

the firm’s management currently has.1031 This poses the risk that shareholders might

question the meaningfulness of recent acquisitions and paid purchase premiums as

1023 Cf. Ramanna (2008), p. 254, Watts (2003), p. 209, Ramanna and Watts (2012), p. 758. 1024 Guler (2006), pp. 9-10. 1025 Cf. Beatty and Weber (2006), p. 264, Fields et al. (2001), p. 257, Watts and Zimmermann (1990), p. 133, Darrough et al. (2014), p. 10. 1026 Cf. AbuGhazaleh et al. (2011), p. 182, Darrough et al. (2014), p. 19. 1027 Cf. Beatty and Weber (2006), pp. 264-265, Fields et al. (2001), p. 257, Watts and Zimmermann (1990), p. 133. 1028 Cf. Francis et al. (1996), p. 123, Beatty and Weber (2006), p. 266, Darrough et al. (2014), p. 12. 1029 Please refer also to chapter 5.2 of this PhD thesis for further information regarding agency theory considerations. 1030 Cf. Darrough et al. (2014), p. 1, Li et al. (2011), p. 745, Gu and Lev (2011), p. 1995. 1031 Cf. Hirschey and Richardson (2002), p. 173, Chen et al. (2013), p. 1, Hirschey and Richardson (2003), p. 75, Lee (2011), p. 236, Francis et al. (1996), p. 134. As the fair value of a firm represents a function of expected cash flows, investors will adjust the cash flow expectations downward as soon as the news becomes public, leading to a lower fair value of the firm.


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those transactions obviously destroyed value rather than created value.1032

Depending on their power, shareholders might use their influence and exchange

members of the senior management team who were in charge of the transactions that

now seem to have failed.1033 Research studies support this reasoning as it has been

documented that the risk of being replaced as a CEO is higher in underperforming

firms (from a return and accounting earnings perspective) and in firms with earnings

that substantially negatively deviate from market expectations, than vice versa.1034

This reasoning implies that CEOs have an incentive not to write off goodwill

although being economically impaired during their regular tenure in order to reduce

reputations damages from the write-off1035, especially then when the CEO who made

the acquisition is now ultimately responsible for deciding on a potential goodwill

write-off.1036 Furthermore, Francis et al. (1996) reason that “new management has

incentives to “clear the deck” of impaired assets to improve investors’ perceptions of

the future financial performance of the firm”1037, thereby providing additional

support for the agency theory-based reputational concern hypothesis.1038

Theoretical approaches around how reputation is built imply that reputation

increases over a CEO’s tenure and is therefore time dependent.1039 These

considerations imply that individual reputation, in a theoretical context, is higher for

CEOs with longer tenures than for their peers with shorter tenures in a firm; most

1032 Cf. Gu and Lev (2011), p. 1995, Darrough et al. (2014), p. 1. 1033 Cf. Coughlan and Schmidt (1985), p. 50, Farrell and Whidbee (2003), pp. 165-167. 1034 Cf. Coughlan and Schmidt (1985), p. 50, Farrell and Whidbee (2003), pp. 165-167, Mobbs (2009), p. 1, also citing the studies of Warner et al. (1988), Weisbach (1988) and Parrino (1997). 1035 Cf. Graham et al. (2005), p. 13, Francis et al. (2004), p. 3. The rent extraction theory predicts that managers with a higher reputation (i.e. CEOs with longer tenures) and who are concerned with securing their reputation would take actions to protect their reputation (Cf. Graham et al. (2005), p. 13, Francis et al. (2004), p. 3). Cf. also, for example, Demers and Wang (2010) and Francis et al. (2004), who find that earnings management and lower financial reporting quality is more present in firms with CEOs with longer tenure. 1036 Cf. Beatty and Weber (2006), p. 266, Francis et al. (2004), p. 1, Cornett et al. (2008), p. 360, Francis et al. (1996), p. 125, Masters-Stout et al. (2008), p. 1379, Hamberg et al. (2011), p. 273, Zang (2008), p. 38. 1037 Francis et al. (1996), p. 123. 1038 Cf. Ramanna and Watts (2012), p. 759. 1039 Cf. Francis et al. (2004), p. 3. Factors that can further enhance one’s reputation are (i) superior knowledge (i.e. industry or functional expertise) and (ii) a track record of managing firms with above average or stable performance, whilst both cannot be viewed most likely as mutually exclusive as they are probably interrelated (Cf. Graham et al. (2005), p. 13, Francis et al. (2004), p. 3).


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likely due to their longer track record in the respective position and senior

management experience that would make them theoretically more effective in

managing a firm, as Cornett et al. (2008) argue.1040

According to Francis et al. (2004), the rent extraction theory predicts that managers

with a higher reputation (CEOs with longer tenure) and who are concerned with

securing their reputation would take actions to protect their reputation.1041 This

includes, for example, meeting earnings forecasts or market expectations regarding

accounting earnings, as these factors are seen as reasons for building up reputation,

as Graham et al. (2005) point out.1042 By doing so, CEOs might try to secure their

positions due to career concerns.1043 From the viewpoint of managing goodwill

write-offs, the rational of the rent extraction theory would therefore suggest that the

willingness to write off an economically impaired goodwill is lower for CEOs with a

longer tenure in their senior management position and therefore having a higher

reputation at risk than for CEOs with shorter tenures.1044 Empirical studies,

predominately on US firms applying the impairment-only approach under SFAS

142, document a relationship between top management changes and goodwill write-

off decisions thereby supporting the rent extraction theory.1045

Therefore, under agency theory considerations, concerns about reputational damages

proxied by CEO tenure can have an impact on the goodwill write-off decision,

leading to the following hypothesis:

H4: CEO tenure:

Goodwill write-off probability in year t is lower for firms whose CEOs have

been longer in office in year t, than for firms whose CEOs have been shorter

in office in year t.

1040 Cf. Cornett et al. (2008), p. 360. 1041 Cf. Francis et al. (2004), p. 3. 1042 Cf. Graham et al. (2005), p. 13. 1043 Cf. Demers and Wang (2010), p. 1, Francis et al. (2004), p. 1, who find that earnings management and lower financial reporting quality is more present in firms with older CEOs than younger CEOs. 1044 Cf. Francis et al. (2004), p. 1, Cornett et al. (2008), p. 360. 1045 Please refer also to chapter 6.3.1 of the literature review section of this PhD thesis regarding additional empirical findings on top management turnover and goodwill write-off decisions.


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Frequently in practice, the compensation structures and amounts of the senior

management team are closely linked to the performance of the firm.1046 By doing so,

from an economics point of view, the utility function of the manager should be

aligned to that of the firm’s owner(s).1047 Then, when the manager tries to maximize

his/her individual utility, she/he does so in the best interest of the owner by

maximizing the firm’s value.1048 This compensation strategy however could pose a

risk from the perspective of goodwill write-off decision making.1049 Depending on

what fraction of the senior management team’s compensation is linked to a financial

measure that would be directly impacted by a goodwill write-off (for example share

price, EPS, EBIT, net income, ROE, ROIC, ROA),1050 the senior management might

want to try everything they can to avoid such a loss as their private wealth would be

directly affected.1051 Guler (2006) reasons that “(b)onus plans (which are directly

linked to earnings) provide executives with incentives to reduce goodwill

impairment charges”1052. One might argue that the larger the individual impact of a

goodwill write-off on personal wealth, the harder the individual might want to

abstain from booking an impairment loss.1053

Research on the compensation effects from goodwill write-offs is very limited in

number and has been based primarily on firms applying SFAS 142.1054 Beatty and

Weber (2006) studied the effects of earnings-based bonus plans on transitional

goodwill write-offs in the year of the adoption of SFAS 142. The results of the

1046 Cf. Healy (1985), p. 95, Bowen (2009), p. 200, Gibbs et al. (2004), p. 411, Reda & Associates (2012), p. 11, Siggelkow and Zülch (2013a), p. 36. 1047 Cf. Baker et al. (1988), p. 594, Jensen and Meckling (1976), p. 5, Healy (1985), p. 89, Oberholzer-Gee and Wulf (2012), p. 2. 1048 Cf. Jensen and Meckling (1976), p. 10, Coughlan and Schmidt (1985), p. 43, Berle and Means (1932), p. 68. 1049 Cf. Jensen et al. (2004), p. 77, giving the example of bonus plans based on accounting income that could lead to increase short-term profits at the expense of future value creation. Gibbs (2012), p. 35, talks even about the risk that managers manipulate performance measures on which basis the remuneration is determined. 1050 Cf. Bowen (2009), pp. 201, 203. 1051 Cf. Jensen et al. (2004), pp. 4, 40. 1052 Guler (2006), p. 10. 1053 Cf. Oberholzer-Gee and Wulf (2012), pp. 1, 37, Siggelkow and Zülch (2013a), p. 37, Healy (1985), p. 95, who analyze bonus structures and other CEO incentive regarding their influence on earnings management or overstated earnings. 1054 Cf. Darrough et al. (2014), p. 4, Ramanna and Watts (2012), p. 764.


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authors document that “having a bonus-based compensation plan that does not

explicitly exclude special items reduces the probability of taking an SFAS 142

write-off by 22%.”1055 Guler (2006) extends the analysis of Beatty and Weber

(2006) and studies goodwill write-off decisions in the subsequent year after the

adoption of SFAS 142. He finds that bonus grants in the prior year to the goodwill

write-off decision are strongly negatively associated with goodwill write-offs in the

following year,1056 implying that compensation concerns could reduce the

observable goodwill write-off probability. Ramanna and Watts (2012) who also

study a sample of firms applying SFAS 142 document that the probability for

receiving a cash bonus for CEOs is higher when not writing off goodwill in a given

year.1057 This probability is found to be statistically significant.1058 The authors

however do not differentiate between the levels of this variable compensation

component “cash bonus” in their study. This means that no differentiation is made

between small or large variable cash bonuses, as the authors work with a dummy

variable. A more granular analysis is provided by Darrough et al. (2014). The

authors find that audit committees reduce the total compensation of CEOs in the

year goodwill is written off.1059 This includes primarily cash- and option-based

compensation in their sample of firms applying SFAS 142.1060

On the basis of existing research findings from firms applying SFAS 142, individual

incentives from compensation contracts might play a role regarding the decision not

to write off goodwill. Due to existing compensation contracts the senior

management team might want to delay a write-off in order to maximize the payoff

of their compensation contracts in the current year, additionally offering the

1055 Beatty and Weber (2006), p. 279. 1056 Cf. Guler (2006), p. 4. 1057 Cf. Ramanna and Watts (2012), p. 764. 1058 Cf. Ramanna and Watts (2012), p. 770. 1059 Cf. Darrough et al. (2014), p. 4. 1060 Cf. Darrough et al. (2014), p. 4. However Darrough et al. (2014) also reason that goodwill write-offs do not necessarily have to lead to reductions in compensation of CEOs; especially because (i) impairments might be the result of factors beyond the control of the senior management team for which they are not responsible, (ii) that pay cuts could potentially lead to an underinvestment of the firm (i.e. that risky however value enhancing projects might not be realized in the future as CEOs fear to get penalized ex-post), and (iii) that CEOs could have a bargaining power due to their key position in the firm.


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possibility to improve the prospects of the firm in the next period.1061 Consequently,

on the basis of agency theory, these considerations lead to the following hypothesis:

H5: CEO performance based compensation:

Goodwill write-off probability in year t is lower for firms with CEOs who

receive a larger variable cash bonus in year t, than for firms with CEOs who

receive a smaller variable cash bonus in year t.

Besides remuneration considerations, potential violations of debt covenants written

on accounting numbers can have an impact on accounting choices and in particular

on goodwill write-off decisions of senior executives.1062 Fields et al. (2001) refer to

this phenomenon also as the “debt hypothesis”1063, meaning that debt covenant

violation concerns could influence accounting choice.1064 On that topic, Watts and

Zimmerman (1990) argue that managers of highly leverage firms and firms with

debt covenants have an incentive to use income increasing accounting methods in

order to reduce the risk of technical default and the thereof resulting costs.1065

Despite this rather intuitive theoretical association, empirical evidence on the effect

of debt covenants on accounting choices has not been conclusive so far.1066 This

holds also true for a possible relationship between debt covenant violation

considerations and goodwill write-off decisions; primarily due to the very limited

number of research studies that tried to analyze this effect.

In the study of Beatty and Weber (2006), the authors hypothesize that potential debt

covenant violation considerations could have an impact on goodwill write-offs; and

in particular the current available headroom firms have in their covenant calculations

1061 Cf. Bergstresser and Philppon (2006), p. 511. Cf. also Cheng and Warfield (2005), who argue that “managers with high equity incentives are more likely to sell shares in the future and this motivates these managers to engage in earnings management to increase the value of the shares to be sold” (Cheng and Warfield (2005), p. 441). 1062 Cf. Watts and Zimmerman (1990), pp. 133-134, 139, Begley (1990), p. 125, Beatty and Weber (2006), p. 259, Zang (2008), p. 39, Lapointe (2005), p. 37, Riedl (2004), p. 833, DeAngelo et al. (1994), p. 113, Sweeney (1994), p. 281, DeFond and Jiambalvo (1994), p. 145, Press and Weintrop (1990), p. 65. 1063 Fields et al. (2001), p. 272. 1064 Cf. Fields et al. (2001), p. 275, Begley (1990), p. 125, Sweeney (1994), p. 281. 1065 Cf. Watts and Zimmerman (1990), p. 139, Begley (1990), p. 126, DeAngelo et al. (1994), p. 113, Sweeney (1994), p. 281, and Riedl (2004), p. 833, who also refers to this as the “debt-covenant hypothesis” (Riedl (2004), p. 833). 1066 Cf. Fields et al. (2001), p. 275, Sweeney (1994), p. 281, Loh and Tan (2002), p. 138.


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is assumed to play a decisive role in the recognition of write-offs.1067 Apart from the

descriptive analysis of Ramanna and Watts (2012), the analysis of actual debt

covenants in firms performing goodwill write-offs has not received much attention

in academia so far; possibly due to the time consuming analysis of annual reports in

which information on debt covenants could be found.1068 Although the difference in

the analysis of Ramanna and Watts (2012) is not highly statistically significant, the

authors’ findings in their univariate analysis suggest that write-off probability for

firms applying SFAS 142 could be lower when debt covenants are in place.1069 The

authors however do not include this information in their multivariate analysis later

on.

Siggelkow and Zülch (2013a) point out that the majority of credit agreements

include debt covenants.1070 They further reason that the “breach of a given covenant

can lead to an immediate re-payment claim from the creditor, which would result in

extensive liquidity problems for most companies”1071. On the basis of agency theory

and in particular the resulting costs from debt covenant violations to the firm, one

could expect that debt covenants offer an incentive to withhold write-offs, especially

then when covenants are calculated including the effects from goodwill write-offs,

so-called goodwill inclusive covenants.1072

The above described contracting motive related to agency theory considerations on

the topic of debt covenant violations lead to the following hypothesis:

1067 Cf. Beatty and Weber (2006), p. 265. 1068 Cf. Duke and Hunt (1990), p. 46, Ramanna and Watts (2012), p. 777, Beatty and Weber (2006), p. 269. 1069 Cf. Ramanna and Watts (2012), p. 770. The authors apply a χ2 statistic to test the differences in write-off probabilities, resulting in a p-value of 0.0867 for their sample. 1070 Cf. Siggelkow and Zülch (2013a), p. 36. 1071 Siggelkow and Zülch (2013a), p. 36. 1072 Cf. Ramanna and Watts (2012), p. 758, Beatty et al. (2002), p. 205, Sweeney (1994), p. 281, Loh and Tan (2002), p. 138, Riedl (2004), p. 833.


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H6: Accounting covenants:

Goodwill write-off probability in year t is lower for firms with existing

financial covenants in year t which would be affected by a goodwill write-

off, than for firms with no such financial covenants in year t.

Besides information on the firms’ existing covenants, the firms’ current leverage

ratios should also be included in the analysis of goodwill write-off probability in this

PhD thesis.1073 According to Duke and Hunt (1990), a firm’s leverage ratio can be

used as an approximation for the tightness to debt covenant restrictions (i.e. how

close firms are to breach debt covenants).1074 Beatty and Weber (2006)1075, Ramanna

and Watts (2012)1076, Chen et al. (2008)1077, Li et al. (2011)1078, and Hamberg et al.

(2011)1079 follow similar approaches is their goodwill write-off studies by

considering the firms’ actual leverage ratios to approximate for the tightness of

breaching existing covenants.

As goodwill write-offs are booked through a firm’s profit and loss statement1080, the

impairment of goodwill reduces the firms’ book value of equity. This leads to a

change in a firm’s capital structure and increases its leverage ratio.1081 Siggelkow

and Zülch (2013a) argue that “the level of borrowing costs is based on the

assessment of financial risk for which leverage is an important determinant, meaning

that higher leverage can result in higher borrowing costs”1082. Given that equity and

debt investors, i.e. lenders, view an increase in a firm’s leverage ratio as a sign of

higher default risk, one can argue that a higher leverage ratio increases a firm’s costs

of financing (as the probability of default increases).1083 As managers potentially are

aware of the effects of a goodwill write-off on the firm’s leverage ratio and the

1073 Cf. Ramanna and Watts (2012), p. 759. 1074 Cf. Duke and Hunt (1990), p. 61. 1075 Cf. Beatty and Weber (2006), p. 280. 1076 Cf. Ramanna and Watts (2012), p. 775. 1077 Cf. Chen et al. (2008), p. 79. 1078 Li et al. (2011), p. 756. 1079 Cf. Hamberg et al. (2011), p. 280. 1080 Cf. IAS 36.60, Holt (2013), p. 8, Amiraslani et al. (2013), p. 19. 1081 Cf. Hamberg et al. (2011), p. 269. 1082 Siggelkow and Zülch (2013a), p. 36. 1083 Cf. Siggelkow and Zülch (2013a), p. 36, Givoly et al. (2013), p. 32.


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subsequent impact on funding possibilities, managers of firms with a higher leverage

ratio might be motivated to avoid goodwill impairments (so that funding costs do

not increase).1084

Amiraslani et al. (2013)1085 and AbuGhazaleh et al. (2011)1086 indirectly confirm this

theoretical relationship; however argue that the occurrence in practice can be

mitigated through the assumed greater monitoring activities by external stakeholders

like lenders. Amiraslani et al. (2013) reason that highly leveraged firms have higher

agency costs and therefore are subject to greater monitoring.1087 AbuGhazaleh et al.

(2011) confirm this view by arguing that “highly levered firms are likely to have the

value of their assets under scrutiny from debt holders which may act as a

disciplining device against opportunism and force the recognition of existing

impairments that reflect the underlying performance of the firm”1088. The authors

however agree that the motivation for opportunistic behaviour of the management

team is still given, however the quality and intensity of corporate governance

mechanism might limit the managerial discretion of the impairment-only approach

in practice to a certain extent.

Consequently, the above stated agency theory considerations related to the

relationship of leverage and write-off probability lead to the following hypothesis:

H7: Financial leverage:

Goodwill write-off probability in year t is lower for firms with higher levels

of financial leverage in year t, than for firms with lower levels of financial

leverage in year t.

On the basis of agency theory considerations, hesitance to write off goodwill could

also emerge from a senior management’s equity ownership.1089 Frequently, members

of the senior management are substantial shareholders in the firm. In case the senior

1084 Cf. DeAngelo et al. (1994), p. 113, DeFond and Jiambalvo (1994), p. 145, Fields et al. (2001), p. 257, Hamberg et al. (2011), p. 269. 1085 Cf. Amiraslani et al. (2013), p. 45. 1086 Cf. AbuGhazaleh et al. (2011), p. 174. 1087 Cf. Amiraslani et al. (2013), p. 45. 1088 AbuGhazaleh et al. (2011), p. 174. 1089 Cf. Guler (2006), p. 10.


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management team owns shares of its own firm, senior executives have an incentive

not to reduce earnings through goodwill write-offs as the firm’s share price could

react negatively to this information, thereby having direct wealth effects on the

executives.1090 Empirical evidence on negative stock price reactions from goodwill

write-offs has been provided by, for example, Hirschey and Richardson (2002)1091,

Li et al. (2010)1092, Bens et al. (2011)1093, Li, Shroff, Venkataraman and Zhang

(2011)1094, or Li and Sloan (2012)1095.

One might argue that the larger the individual’s wealth which is tied to the

performance of the firm, the harder the individual might want to abstain from

booking a goodwill write-off.1096 Research on equity ownership and goodwill write-

off decision making is extremely limited in number.1097 Guler (2006) analysed

whether in-the-money stock options have an impact on goodwill write-off

probability and goodwill write-off amounts under SFAS 142.1098 He finds that the

market values of CEOs’ holdings of in-the-money options in the year prior to the

write-off decision have a negative association with goodwill write-offs in the

subsequent year.1099 AbuGhazaleh et al. (2011) study goodwill write-off decisions in

the top 500 UK firms by market capitalization.1100 To the contrary to Guler (2006),

the authors find a statistically significant positive relationship between the number

of common shares held by executive directors and goodwill write-offs. This means

that on the basis of AbuGhazaleh et al.’s (2011) sample a larger equity ownership of

1090 Cf. Ramanna and Watts (2012), p. 760, Beatty and Weber (2006), p. 265, Jarva (2009), p. 1079, Li and Sloan (2012), p. 49, Muller et al. (2009), p. 5, Hirschey and Richardson (2002), p. 181, Francis et al. (1996), p. 128, Bens et al. (2011), p. 537, Lhaopadchan (2010), p. 125, Li et al. (2010), p. 26. This reasoning however certainly only holds true if market participants have not already priced in the economically impaired goodwill in their estimation of the firm’s share price. 1091 Cf. Hirschey and Richardson (2002), p. 181. 1092 Cf. Li et al. (2010), p. 26. 1093 Cf. Bens et al. (2011), p. 537. 1094 Cf. Li, Shroff, Venkataraman and Zhang (2011), p. 757. 1095 Cf. Li and Sloan (2012), p. 49. Please refer also to chapter 6.2.1 of the literature review section of this PhD thesis regarding empirical findings on stock market reactions to goodwill write-off 1096 Cf. Oberholzer-Gee and Wulf (2012), p. 1, Healy (1985), p. 95. 1097 Cf. Guler (2006), p. 2. 1098 Cf. Guler (2006), p. 2. 1099 Cf. Guler (2006), p. 30. 1100 Cf. AbuGhazaleh et al. (2011), p. 183.


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senior executives would result in larger goodwill write-offs.1101 The authors reason

on the basis of agency cost considerations that equity ownership actually restricts

managerial opportunism.1102 It can however be discussed whether the variable used

by the authors can fully capture managerial motivation to delay or abstain from

booking goodwill write-offs as the variable is calculated on the basis of the number

of shares only and omits therefore their market values and consequently potential

wealth effects.1103

In this thesis, Guler’s market value based approach (2006) should be extended by

analysing whether the market values of the actual shares held by the senior

management, and not only in-the-money stock options or the number of shares, have

an impact on goodwill write-off probability. Agency theory considerations would

therefore lead to the following hypothesis:

H8: CEO equity ownership:

Goodwill write-off probability in year t is lower for firms with CEOs whose

market value of shares is larger in year t, than for firms with CEOs whose

market value of shares is smaller in year t.

7.1.3 Impact of goodwill reporting flexibility on


As outlined above, on the basis of the private information disclosure theory one

would reason that firms write off goodwill whenever the senior management

becomes aware of an impaired goodwill and by doing so they willingly disclose

private information on the future performance of the firm to addressees of financial

statements.1104 Opposite to this view, agency theory foresees that the goodwill

reporting flexibility in the impairment-only approach will be used opportunistically

1101 Cf. AbuGhazaleh et al. (2011), p. 183. 1102 Cf. AbuGhazaleh et al. (2011), p. 182. 1103 Additionally it can be questioned whether the share holdings of all executive directors have to be considered in the analysis as not all executive directors are in the position to influence goodwill write-off decisions. 1104 Cf. Li et al. (2011), p. 746, Ramanna and Watts (2012), p. 749, Ramanna (2008), p. 255.


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by a firm’s senior executives to manage a firm’s financial reporting.1105 No matter

which motivation is stronger for a CEO, hypothetically the goodwill reporting

flexibility under IAS 36 provides a means to achieve either of the senior

management’s preferred outcome.1106 Whilst not every aspect of reporting flexibility

under IAS 36 can be tested empirically for their influence on goodwill write-off

decisions due to data unavailability, reporting flexibility with regards to goodwill

allocation to reporting segments particularly is, as this information is frequently

disclosed in the notes of the financial statements.

As outlined in chapter 3.3.2, the impairment-only approach under IAS 36 requires

firms to allocate acquired goodwill to so-called cash-generating units (CGU).1107 A

CGU is defined as “the smallest identifiable group of assets that generates cash

inflows that are largely independent of the cash inflows from other assets or groups

of assets”1108. In the subsequent years after the transaction, the firm is required to

test the recoverability of goodwill on the level of these cash-generating units

(CGU).1109

CGUs are part of a firm’s reporting segments. IAS 36 clarifies that each CGU or

group of CGUs to which goodwill has been allocated should not be larger than the

individual operating segments of a firm according to IFRS 8 Operating Segments,

which usually also represent a firm’s reporting segments.1110 In the strict sense of the

accounting standard, acquired goodwill should be allocated to those reporting

segments (i.e. CGUs or groups of CGUs) that are assumed to profit from the

assumed benefits of the transaction, i.e. primarily synergies.1111 Consequently the

allocation of goodwill to reporting segments requires substantial managerial

1105 Cf. Ramanna and Watts (2012), p. 758, Riedl (2004), p. 833, Li and Sloan (2009), p. 12, AbuGhazaleh et al. (2011), p. 170, Amiraslani et al. (2013), pp. 18-19. 1106 Cf. Liberatore and Mazzi (2010), p. 333, Ramanna and Watts (2012), p. 760, Amiraslani et al. (2013), p. 19. 1107 Cf. IAS 36.80 and 88, Glaum and Wyrwa (2011), p. 25, Pottgießer et al. (2005), p. 1748, Amiraslani et al. (2013), p. 14, AbuGhazaleh et al. (2011), p. 183. 1108 IAS 36.6. 1109 Cf. IAS 36.88. 1110 Cf. IAS 36.80, IFRS 8.5 and 8.11, AbuGhazaleh et al. (2011), p. 169. 1111 Cf. IAS 36.68 and 36.80, Glaum and Wyrwa (2011), p. 25, Pottgießer et al. (2005), p. 1748, Amiraslani et al. (2013), p. 14, AbuGhazaleh et al. (2011), p. 183.


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discretion as often the assumed benefits of a transaction are difficult to allocate to

reporting segments.1112 Additionally identifying the most appropriate cash

generating unit structures can be a difficult task in the impairment-only approach.1113

Despite the difficulties in the allocation process, guidelines in the relevant

accounting standard are very limited.1114 Consequently substantial managerial

discretion on the goodwill allocation to reporting segments rests with the executives

performing the impairment test.1115

Wines et al. (2007) argue that regarding identifying reporting segments and CGUs,

“there is the potential for considerable subjectivity. Major uncertainties can be

involved in initially identifying the level or levels at which cash-generating units are

to be recognised”1116. By identifying too few CGUs or allocating goodwill to very

profitable reporting segments, “impairment testing manipulation”1117 can result as

firms can substitute acquired goodwill with internally generated goodwill of the

CGUs in question, as reasoned by Lonergan (2007).1118 ASBJ et al. (2014) come to a

similar assessment by hypothesizing that CGUs which are too broad may allow for

not recognizing impairment losses as gains of certain units may offset losses of other

units within the same CGU.1119 On that topic, Wines et al. (2007) assume that IAS

36 might even incentivise senior executives to “recognise cash-generating units at as

high a level of aggregation as possible”1120, as “impairment losses could potentially

1112 Cf. Wines et al. (2007), pp. 862, 870, Petersen and Plenborg (2010), p. 420, ASBJ et al. (2014), p. 35. Cf. also Brösel and Klassen (2006), p. 466, Brösel and Zwirner (2009), p. 196, Zülch and Siggelkow (2012), p. 385, Kasperzak (2011), p. 4, Engel-Ciric (2012), p. 421, Ruhnke (2008), p. 43. 1113 Cf. KPMG (2011), p. 16. Generally, the IASB was aware of the substantial managerial discretion in the CGU construction process; given that the standard specifically states that the identification of the most suitable structure of cash-generating units involves judgement (IAS 36.68). 1114 Cf. ASBJ et al. (2014), p. 37. Cf. also Engel-Ciric (2012), p. 421, Kasperzak (2011), p. 3, Küting (2005), p. 2759 on the issue of managerial discretion in the impairment-only approach. 1115 Cf. Wines et al. (2007), pp. 862, 870, Petersen and Plenborg (2010), p. 420, ASBJ et al. (2014), p. 35. Cf. also Brösel and Klassen (2006), p. 466, Brösel and Zwirner (2009), p. 196, Zülch and Siggelkow (2012), p. 385, Kasperzak (2011), p. 4, Engel-Ciric (2012), p. 421, Ruhnke (2008), p. 43. 1116 Wines et al. (2007), p. 870. 1117 Mazzi et al. (2013), p. 2. 1118 Cf. Lonergan (2007), p. 15. Cf. also Brösel and Klassen (2006), p. 463, Teitler-Feinberg (2006), p. 18, Brösel and Zwirner (2009), p. 196, Pawelzik (2009), p. 60, who argue similarly. 1119 Cf. ASBJ et al. (2014), p. 36. 1120 Wines et al. (2007), p. 868.


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be avoided by aggregating units at too high a level”1121. This view is also shared by

Carlin and Finch (2008)1122 as well as Finch (2010)1123.

Despite the assertions of several researchers1124, empirical analyses on the allocation

of goodwill to reporting segments and CGUs are found to be highly limited in

number. The studies by Carlin and Finch (2010), Glaum and Wyrwa (2011), as well

as Zhang and Zhang (2006) represent rare exceptions. Carlin and Finch (2010)

analyse in their sample of listed Australian firms the number of CGUs with allocated

goodwill. They find that between 2006 and 2008, 60% of the analysed firms had

goodwill allocated to up to 3 CGUs, with the remainder (40%) having more than 3

CGUs.1125 The study of Glaum and Wyrwa (2011) on the basis of European firms

allows for a clearer assessment. The authors evaluate concentration effects of

goodwill in CGUs by analysing the number of CGUs with significant goodwill

proportions.1126 They come to the conclusion that goodwill is frequently

concentrated in few CGUs of a firm (70% of the firms have 3 or less CGUs with

significant proportions of goodwill).1127 The results by Zhang and Zhang (2006)

most likely provide the strongest indications on managerial exploiting of discretion

in accounting choices in the impairment-only approach, as they find that “acquirers

with multiple segments tend to allocate more goodwill to the relatively more

profitable segment(s)”1128.

Whether goodwill reporting flexibility, in terms of having multiple reporting

segments to which hypothetically goodwill could be allocated, has an impact on

goodwill write-off decisions and therefore goodwill write-off probability was

analysed in a very limited number of empirical studies, and primarily based on

samples of firms applying SFAS 142. Beatty and Weber (2006) were among the first

researchers that looked into the topic of goodwill reporting flexibility. The authors

1121 Wines et al. (2007), p. 870. 1122 Cf. Carlin and Finch (2010), p. 5. 1123 Cf. Finch (2010), p. 16. 1124 Cf. Carlin and Finch (2010), p. 5, Finch (2010), p. 6, Lonergan (2007), p. 15, Wines et al. (2007), pp. 862, 870, Petersen and Plenborg (2010), p. 420. 1125 Cf. Carlin and Finch (2010), p. 22. 1126 Cf. Glaum and Wyrwa (2011), p. 63. 1127 Cf. Glaum and Wyrwa (2011), p. 63. 1128 Zhang and Zhang (2006), p. I.


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study whether the number of existing business segments to which goodwill

hypothetically could be allocated is related to transitional goodwill write-offs in the

year of the adoption of SFAS 142.1129 By applying a dummy variable measuring

whether the analysed firms have either one or multiple business segments, the

authors find in three of their 8 regressions a highly statistically significant negative

relationship to goodwill write-offs,1130 suggesting that firms with more than one

business segments tend to have lower goodwill write-off risks. However due to the

mixed results of Beatty and Weber (2006) no conclusive evidence on the hypothesis

is obtained. Ramanna and Watts (2012) used a similar methodology as Beatty and

Weber (2006) in their analysis. For a sample of firms applying SFAS 142, the

authors use the number of existing business segments to which goodwill

hypothetically could be allocated.1131 The authors find in two of their 4 regressions a

very limited statistically significant negative relationship between the number of

business segments and goodwill write-offs.1132 Similar to Beatty and Weber (2006),

Ramanna and Watts’ (2012) results weakly imply that having a larger number of

business segments to which goodwill could be allocated decreases the portion of

goodwill written off.1133 The authors’ approach however only analyses the

hypothetical goodwill reporting flexibility, irrespective of whether firms make

actually use of their allocation flexibility or not.1134

On the basis of the very limited number of existing research studies which have

looked into a potential relationship between reporting flexibility in the impairment-

only approach and write-offs, as well as the descriptive statistics from Carlin and

1129 Cf. Beatty and Weber (2006), p. 257. 1130 Cf. Beatty and Weber (2006), pp. 280, 283. 1131 Cf. Ramanna and Watts (2012), p. 761. Information on business segments has to be disclosed according to SFAS 131 “Disclosures about Segments of an Enterprise and Related Information” under US-GAAP. 1132 Cf. Ramanna and Watts (2012), pp. 774-775. 1133 Cf. Ramanna and Watts (2012), p. 776. 1134 Cf. AbuGhazaleh et al. (2011), p. 183, for example, who base their analysis on the number of existing CGUs and not segments. In their sample of the largest UK firms measured by market capitalization the authors find no statistically significant relationship between goodwill write-offs and having either one or multiple CGUs. AbuGhazaleh et al.’s (2011) results imply that the number of CGUs is unrelated to impairment risk.


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Finch (2010)1135, Glaum and Wyrwa (2011)1136, and Zhang and Zhang (2006)1137, the

question remains whether goodwill allocation strategies correlate also with goodwill

write-off decisions.1138 Consequently, this PhD thesis aims at studying (i) how

goodwill gets allocated to reporting segments, (ii) which financial characteristics

these reporting segments have, and (iii) whether this has an influence on goodwill

write-off or non-write-off decisions.1139

In a first step, it should be analysed whether goodwill concentration to particular

reporting segments is observable in the sample and whether this concentration

impacts goodwill write-off probabilities. Under the assertion that CEOs would make

willingly use of their discretion to reduce write-off risk in the future, concentration

of goodwill to certain reporting segments could potentially allow to substitute

internally generated goodwill with acquired goodwill.1140 This assertion leads to the

following hypothesis:

H9: Goodwill concentration:

Goodwill write-off probability in year t is lower for firms that have a higher

concentration of goodwill in their reporting segments in year t, than for firms

that have a lower concentration of goodwill in their reporting segments in

year t.

Besides the pure concentration of goodwill in certain reporting segments, the

financial characteristics of these reporting segments with goodwill should be

analysed. In particular the size of the reporting segments to which goodwill was

1135 Cf. Carlin and Finch (2010), p. 22. 1136 Cf. Glaum and Wyrwa (2011), p. 63. 1137 Zhang and Zhang (2006), p. I. 1138 In several frequently cited research studies, it is observable that instead of working with the actual number of CGUs, researchers work with the overall number of reporting segments, i.e. number of available reporting segments to which goodwill could be allocated, or the number of reporting segments to which goodwill has been actually allocated (Cf., for example, Ramanna and Watts (2012), p. 761, Beatty and Weber (2006), pp. 271-272, 278, Hayn and Hughes (2006), p. 238). This is done as the number of CGUs is frequently not fully disclosed in annual reports. Otherwise studying CGU structures would represent another interesting area of study. 1139 Cf. Ramanna and Watts (2012), p. 761, Beatty and Weber (2006), pp. 280, 283. In the empirical analyses by Ramanna and Watts (2012) as well as Beatty and Weber (2006), the authors work with the number of available reporting segments, leaving it open whether firms make actually use of this flexibility and how. 1140 Cf. Zhang and Zhang (2006), p. I, Glaum and Wyrwa (2011), p. 63.


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allocated as well as their profitability should be investigated, as both are principal

drivers of future cash flows on which basis the recoverability of goodwill is tested.

Similar to Lonergan (2007)1141 and Zhang and Zhang (2006)1142, this PhD thesis uses

the reasoning that the larger the fractions of goodwill allocated to larger and/or more

profitable reporting segments are, the lower the write-off risk in future periods.

H10: Size of reporting segments with goodwill:

Goodwill write-off probability in year t is lower for firms that have allocated

larger fractions of goodwill to larger reporting segments in year t, than for

firms that have allocated smaller fractions of goodwill to larger reporting

segments in year t.

H11: Profitability of reporting segments with goodwill:

Goodwill write-off probability in year t is lower for firms that have allocated

larger fractions of goodwill to more profitable reporting segments in year t,

than for firms that have allocated smaller fractions of goodwill to more

profitable reporting segments in year t.

Whilst frequently observable in practice, the issue of structural changes of CGUs or

reporting segments between periods in the impairment-only approach has not

received much attention by academia so far, too.1143 Descriptive analyses of actual

CGU structures of firms by Zhang and Zhang (2006)1144, Carlin et al. (2010)1145,

Finch (2010)1146, Petersen and Plenborg (2010)1147, Glaum and Wyrwa (2011)1148,

and Meyer and Halberkann (2012)1149 show that CGU structures are highly firm

specific, can vary substantially across firms in an industry, and most importantly can

change over time in firms. The justification for CGU changes in the impairment-

1141 Cf. Lonergan (2007), p. 15. Cf. also Brösel and Klassen (2006), p. 463, Teitler-Feinberg (2006), p. 18, Brösel and Zwirner (2009), p. 196, Pawelzik (2009), p. 60, who argue similarly. 1142 Cf. Zhang and Zhang (2006), p. I. 1143 Cf. Amiraslani et al. (2013), p. 42, Meyer and Halberkann (2012), p. 313. 1144 Cf. Zhang and Zhang (2006), pp. 31-32. 1145 Cf. Carlin et al. (2010), p. 3. 1146 Cf. Finch (2010), p. 31. 1147 Cf. Petersen and Plenborg (2010), p. 420. 1148 Cf. Glaum and Wyrwa (2011), p. 63. 1149 Cf. Meyer and Halberkann (2012), p. 313.


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only approach is provided by IAS 36.IN12 and 36.72.1150 According to the

accounting standard, “(c)ash-generating units shall be identified consistently from

period to period for the same asset or types of assets, unless a change is

justified”1151. Structural CGU changes primarily represent the outcome of changes of

internal or external reporting structures, triggered, for example, by acquisitions,

divestitures or internal restructurings or reorganizations.1152 According to IAS

36.IN12, “when an entity reorganises its reporting structure in a manner that changes

the composition of cash-generating units (groups of units) to which goodwill has

been allocated, the goodwill should be reallocated to the units (groups of units)

affected”1153. Given that CGUs are part of a firm’s reporting segments, changes in a

firm’s reporting structure most likely also effects the CGUs.

Structural changes of CGUs in the impairment-only approach can become

problematic if changes are based on opportunistic considerations.1154 Finch (2010)

points out that “(s)tructural changes of CGU portfolios may manifest in various

ways, such as the addition to or deletion of CGUs from a prior year, the splitting of a

CGU into several CGUs, the combination of several CGUs into one, purely

changing the label of a CGU without any substantial change in its nature”1155. In

case senior executives observe a pending impairment in one of their previously

established CGUs, the management team might be motivated to change the CGU

structure by, for example, combining the underperforming CGU with an over-

performing one so that the pending write-off disappears on an aggregated level,

thereby arguing for internal reorganizations. As guidance on structural changes of

CGUs is limited in the accounting standard, this behaviour is not unlikely.

1150 Cf. also Ramanna and Watts (2012), p. 17, on the possibilities under SFAS 142. 1151 IAS 36.72. 1152 Cf. Finch (2010), p. 31. 1153 IAS 36.IN12. 1154 Cf. Duff and Phelps (2013), p. 27, who quote one of their survey respondents mentioning that if there is the opportunity to do so, firms will try to combine CGUs “given the fact that having more CGUs can make it harder to shield impairments” (Duff and Phelps (2013), p. 27). Cf. also Ramanna and Watts (2012), p. 760. 1155 Finch (2010), p. 31.


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To understand the influence of structural changes of CGUs and/or reporting

segments, it should be tested whether reorganizations of CGUs and/or reporting

structures are related to goodwill write-off decisions in the sample.

H12: CGU/Reporting structures changes:

Goodwill write-off probability in year t is lower for firms that changed their

CGU and/or reporting structures between year t-1 and t, than for firms that

did not change their CGU and/or reporting structures between year t-1 and t.

The following table summarizes the hypotheses to be analysed in this PhD thesis.

Explaining goodwill write-off decisions under IAS 36 for capital market-implied triggering events

Hypotheses overview

Research Area Hypothesis

Private

information on

the firm’s future

financial

performance

H1 Stock returns: Goodwill write-off probability in year t is lower for firms with higher stock returns in year t+1 and/or t+2, than for firms with lower stock returns in year t+1

and/or t+2.

H2 Share

buybacks:

Goodwill write-off probability in year t is lower for firms with larger share repurchase programs in year t, than for firms with smaller share repurchase programs in year t.

H3 CEO insider

trading:

Goodwill write-off probability in year t is lower for firms whose CEOs increase their stockholdings in year t, than for firms whose CEOs do not increase their

stockholdings in year t.

Incentives

predicted

by agency theory

H4 CEO tenure: Goodwill write-off probability in year t is lower for firms whose CEOs have been longer in office in year t, than for firms whose CEOs have been shorter in office in year t.

H5 CEO

performance

based

compensation:

Goodwill write-off probability in year t is lower for

firms with CEOs who receive a larger variable cash bonus in year t, than for firms with CEOs who receive a smaller variable cash bonus in year t.


234

H6 Accounting

covenants:


firms with existing financial covenants in year t which would be effected by a goodwill write-off, than for firms with no financial covenants in year t.

H7 Financial

leverage:

Goodwill write-off probability in year t is lower for firms with higher levels of financial leverage in year t, than for firms with lower levels of financial leverage

in year t.

H8 CEO equity

ownership:

Goodwill write-off probability in year t is lower for firms with CEOs whose market value of shares owned is larger in year t, than for firms with CEOs whose market value of shares owned is smaller in year t.

Reporting

flexibility

H9 Goodwill

concentration:


firms that have a higher concentration of goodwill in their reporting segments in year t, than for firms that have a lower concentration of goodwill in their reporting segments in year t.

H10 Size of

reporting

segments with

goodwill:

Goodwill write-off probability in year t is lower for firms that have allocated larger fractions of goodwill

to larger reporting segments in year t, than for firms that have allocated smaller fractions of goodwill to larger reporting segments in year t.

H11 Profitability of

reporting

segments with

goodwill:

Goodwill write-off probability in year t is lower for firms that have allocated larger fractions of goodwill to more profitable reporting segments in year t, than for firms that have allocated smaller fractions of goodwill

to more profitable reporting segments in year t.

H12 CGU/Reporting

structures

changes:

Goodwill write-off probability in year t is lower for firms that changed their CGU and/or reporting structures between year t-1 and t, than for firms that did not change their CGU and/or reporting structures between year t-1 and t.

Table 3: Summary of hypotheses Source: Own illustration.


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7.2 Model design

To understand which factors influence the goodwill write-off decisions in firms that

have adopted IAS 36 Impairment of Assets and for which strong capital market-

implied triggering events are observable, a binary logit regression model using

various explanatory variables is applied. The application of a binary logit regression

model follows the approaches used in earlier empirical studies on goodwill

accounting and asset write-offs, in particular those of Henning et al. (2004)1156,

Hayn and Hughes (2006)1157, and Gu and Lev (2011)1158.

The considered variables in the regressions are based on the above described

hypotheses focussing on the research areas (i) private information on changes of the

future financial performance of a firm, (ii) agency theory-based incentives, and (iii)

reporting flexibility under the impairment-only approach. Data on goodwill write-off

decisions and reporting flexibility are hand-collected from the annual reports of the

sample firms. This is also the case for most data on agency theory-based incentives.

Firm specific financial data and capital markets data relevant for the variables on

private information on the firm’s future performance are sourced from the

professional financial data providers COMPUSTAT, Thomson Reuters

(Worldscope), as well as CapitalIQ, supplemented and validated with information

disclosed in the firms’ annual reports. Financial statements of firms reporting in a

currency different to EUR are converted into the respective EUR amounts with

exchange rates (spot rates) as at the balance sheet date.

7.2.1 Regression model

The standard regression model applied in this thesis is a binary logit regression

model.1159 As the goodwill write-off decision represents a dichotomous variable, a

binary logit regression is used to analyse the influence of the research areas (i)

1156 Cf. Henning et al. (2004), p. 114. 1157 Cf. Hayn and Hughes (2006), p. 237. 1158 Cf. Gu and Lev (2011), p. 2017. 1159 Cf. Hamberg et al. (2011), p. 272, Gu and Lev (2011), p. 2017, Hayn and Hughes (2006), p. 237, Henning et al. (2004), p. 114, Li and Sloan (2012), p. 20.


236

private information, (ii) agency theory-based incentives, and (iii) reporting

flexibility. Information of goodwill write-off decisions is pooled across multiples

years (since introduction of IAS 36).

In the analysis of this PhD thesis, the following regression is used:

Short form:

Pr(GW_WOi,t) = αi,t + β1,i,t x (PRIVATE INFORMATION)

+ β2,i,t x (AGENCY THEORY-BASED INCENTIVES)

+ β3,i,t x (GOODWILL REPORTING FLEXIBITY) + ε i,t

With:

PRIVATE INFORMATION:

= set of independent variables which proxy for private information on the firm’s

future financial performance held by senior management teams;

AGENCY THEORY-BASED INCENTIVES

= set of independent variables which proxy for agency theory-based incentives incl.

compensation, private wealth and reputation concerns of CEOs as well as debt

covenants violation concerns;

GOODWILL REPORTING FLEXIBITY

= set of independent variables which proxy for managerial discretion in the

impairment-only approach under IAS 36.

The number of independent variables in the regression model should be defined as a

function of observations in the sample. According to Peduzzi et al. (1996), the

number of observations per independent variable should not be smaller than

approximately 10, in order to reduce the risk that regression coefficients are biased

in either direction, being either positive or negative.1160

1160 Cf. Peduzzi et al. (1996), p. 1373. Nevertheless see also Vittinghoff and McCulloch (2007), p. 710, who argue that under certain conditions this rule can be relaxed.


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7.2.2 Variables definition

7.2.2.1 Dependent variable: Goodwill write-off and non-write-off

decision

The endogenous variable GW_WOi,t represents a dichotomous variable and is set to

1 if firm i performs a goodwill write-off in year t, and equals to 0 otherwise. The

coding is in line with earlier empirical studies.1161 For the analysis it is not relevant

in which financial quarter the goodwill write-off was recognized. The application of

a binary dependent variable in the regression model for analysing goodwill write-

offs follows the approaches by Henning et al. (2004)1162, Hayn and Hughes

(2006)1163, Gu and Lev (2011)1164, Hamberg et al. (2011)1165, Beatty and Weber

(2006)1166, and Li and Sloan (2012)1167.

7.2.2.2 Independent variables

7.2.2.2.1 Private information on a firm’s future financial performance

For testing whether a senior management team’s private information on the firm’s

future financial performance influences its goodwill write-off or non-write-off

decision in year t, three sets of principal explanatory variables should be computed

and analysed.1168 The computations follow earlier research studies on the topic of

private information held by companies’ executives.1169

The first two variables STOCK_RETURNi,t+1 and STOCK_RETURNi,t+2 measure the

annualized stock market return in % of the firm in the subsequent two years after the

1161 Cf. Henning et al. (2004), p. 114, Hayn and Hughes (2006), p. 237, Gu and Lev (2011), p. 2017, Hamberg et al. (2011), p. 272, Beatty and Weber (2006), p. 273, Li and Sloan (2012), p. 20. 1162 Cf. Henning et al. (2004), p. 114. 1163 Cf. Hayn and Hughes (2006), p. 237. 1164 Cf. Gu and Lev (2011), p. 2017. 1165 Cf. Hamberg et al. (2011), p. 272. 1166 Cf. Beatty and Weber (2006), p. 273. 1167 Cf. Li and Sloan (2012), p. 20. 1168 These variables are used in the model to proxy for the private information that the senior management team might have on changes in the firm’s future performance, if any. 1169 Cf. Ramanna and Watts (2012), p. 779.


238

goodwill write-off or non-write-off decision.1170 In case the management team has

private information on a performance improvement in the future, one would expect

that non-write-off decisions correlate with future stock performance in the

subsequent year, meaning that a firm’s market valuation improves in the short-

term.1171 This relationship would be expected on the basis of the IASB’s motivation

for introducing the impairment-only approach, i.e. the information content of write-

offs about future performance of a firm.1172 Information for calculating the dividend-

adjusted STOCK_RETURNi,t+1 and STOCK_RETURNi,t+2 is sourced from the

CapitalIQ database, validated with information from COMPUSTAT.

Besides the capital markets’ derived future stock return variables (STOCK_

RETURNi,t+n), changes in future accounting earnings are computed and included in

the regressions to further test potential private information on future financial

performance changes held by a firm’s senior management influencing their goodwill

write-off decision. The inclusion of an accounting based performance metric follows

the approaches applied in earlier research studies on goodwill accounting.1173 In this

PhD thesis, the continuous variables EBITDA_MARGIN_CHANGEi,t+1 and

EBITDA_MARGIN_CHANGEi,t+2 are selected which should act as a proxy for

changes in future operating cash flows. A relative performance metric is better

suited than an absolute performance metric as it is less affected by changes in the

size of a firm or the size of its operations (in the subsequent years after the goodwill

write-off decision).1174 The variables measure the relative change in a firm’s

EBITDA margin (EBITDA divided by total revenues) in the first and second year

after the goodwill write-off decision. The changes are calculated on the basis of the

EBITDA margin from the previous year. Information to calculate the variable

1170 Cf. Ramanna and Watts (2012), pp. 758, 775, Li and Sloan (2012), pp. 44, 45, Hayn and Hughes (2006), p. 237, Henning et al. (2004), p. 114, Chen et al. (2014), pp. 17, 42. 1171 Cf. Li et al. (2011), pp. 750-751, Lhaopadchan (2010), p. 124, Lapointe (2005), p. 5, Gordon and Hsu (2014), p. 2. 1172 Cf. IAS 36.BC131G, FASB (2014a), AbuGhazaleh et al. (2011), p. 196, Liberatore and Mazzi (2010), p. 334, Meyer and Halberkann (2012), p. 312, Amiraslani et al. (2013), pp. 18-19, Chen et al. (2013), p. 4, Gordon and Hsu (2014), p. 13, Vanza et al. (2011), pp. 2-3, Lapointe-Antunes et al. (2009), pp. 62-63, Li et al. (2011), p. 746, Ramanna and Watts (2012), p. 749, Ramanna (2008), p. 255. 1173 Cf. Jarva (2009), p. 1084, Lee (2011), p. 251, Li et al. (2011), p. 769, Li and Sloan (2012), p. 50. 1174 Often when firms are in difficult financial situations, restructuring measures are initiated which affect absolute financial performance metrics more than relative financial performance metrics.


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EBITDA_MARGIN_CHANGEi,t+n is sourced from the CapitalIQ database and

supplemented by corporate disclosures.

The variable SHARE_BUYBACKSi,t measures whether an analysed firm engaged in

buybacks of common stock in the year of the write-off decision (year t).1175 Share

buybacks can signal market participants positive information on the firm’s expected

financial performance and therefore a potential undervaluation of the firm from the

management’s point of view.1176 Share buybacks should act as a proxy for positive,

private information on the firm’s future financial performance held by senior

management in the empirical analysis of this PhD thesis.1177 To calculate the

variable the currency amount spent on share buybacks in year t is used, scaled by

total revenues as of year-end t. To limit possible size effects in the sample, the

amount spent on share buybacks is scaled by total revenues. Basically it is

reasonable to assume that larger firms spend more cash on share buybacks (in

absolute terms) than smaller firms. Therefore scaling the amounts spent with total

revenues appears reasonable to derive more meaningful results. Information on the

amounts spent for buying back shares and total revenues in year t is sourced from

CapitalIQ and hand collected from corporate disclosures of the firms in the sample.

CEO_INSIDER_TRADINGi,t and CEO_INSIDER_TRADING_DUMMYi,t represent

the third set of variables which proxy for private information held by senior

managers.1178 In case the senior management team holds favourable information on

the firm’s future performance, one could expect the occurrence of larger, positive

changes of shares held, meaning that on aggregate more shares are bought than sold

by the senior management team.1179 The observations are based on the year of the

goodwill write-off decision t. Information on the number of shares held by a firm’s

CEO at the end of year t-1 and t is sourced from CapitalIQ and hand collected from

corporate disclosures of the firms in the sample. The explanatory variable

1175 Cf. Chen et al. (2014), p. 27, Ramanna and Watts (2012), p. 775. 1176 Cf. Brav et al. (2005), pp. 514, 518, Vermaelen (1981), p. 166, Dann (1981), p. 113, Comment and Jarell (1991), pp. 1243-1244. 1177 Cf. Lie (2005), p. 412, Ramanna and Watts (2012), p. 757. 1178 Cf. Jagolinzer (2009), p. 224, Roulstone (2008), p. 28, Ke et al. (2003), p. 315, Ramanna and Watts (2012), p. 757, Piotroski and Roulstone (2005), p. 55, Griffin et al. (2014), p. 145. 1179 Cf. Roulstone (2008), p. 2, Lakonishok and Lee (2001), p. 79, Piotroski and Roulstone (2005), p. 50.


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CEO_INSIDER_TRADINGi,t is defined as a continuous variable and calculated as

the percentage change in shares held by a CEO between the end of the years t-1 and

t. 1180 For validation purposes of the results derived from the variable

CEO_INSIDER_TRADINGi,t, this effect is also studied through a dummy variable

which is set to 1 if CEO_INSIDER_TRADINGi,t is positive, and 0 otherwise.

7.2.2.2.2 Incentives predicted by agency theory

The regression model analyses also the impact of a firm’s leverage ratio on goodwill

write-off decisions with the variable LEV_RATIOi,t.1181 For the purpose of this PhD

thesis, the leverage ratio is defined as the ratio of total debt1182 to total assets1183,

adjusted for any goodwill write-offs which occurred during the year under analysis.

The amount of total debt and total assets both represent the balance sheet amounts as

at the financial year-end of the goodwill write-off decision (year t). Information on

the amounts is sourced from the CapitalIQ financial database, as well as

supplemented with information from the firms’ annual reports.

Besides the overall leverage ratio, a more granular view on the firms’ debt

components and their influence on goodwill write-off decisions should be applied in

the analysis. To do so, the variable LEV_BANK_DEBT_RATIOi,t is computed and

included in the multivariate analysis of the PhD thesis. The variable is calculated as

the ratio of total bank debt1184 to total assets as at financial year-end t. Similar to the

variable LEV_RATIOi,t total assets are manually adjusted for goodwill write-off

1180 Cf. Bonaime and Ryngaert (2013), p. 35, Agrawal and Nasser (2012), p. 601, Kraft et al. (2014), p. 14, Li and Zhang (2006), p. 15. 1181 Cf. Cotter et al. (1998), pp. 170, 173, AbuGhazaleh et al. (2011), p. 174, Ramanna and Watts (2012), p. 775, Beatty and Weber (2006), p. 274, Hamberg et al. (2011), p. 273, Chen et al. (2014), p. 27. Increasing a relatively high leverage ratio through a goodwill write-off can further limit the decision space of senior executives, as lenders might become aware of the higher default risk of the borrowers and therefore follow more closely the strategic directions of the firm and might want to influence the senior management team. 1182 Total debt is the sum of all interest bearing and capitalized lease obligations. It is the sum of long- and short-term debt (i.e. of short-term debt, current portion of long-term debt and long-term debt). 1183 Total assets is the sum of total current assets, long-term receivables, investment in unconsolidated subsidiaries, other investments, net property plant and equipment and other assets (incl. goodwill and other intangible assets). 1184 Total bank debt is the sum of all interest bearing obligations, where a bank or other financial institution is the creditor.


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amounts which might have occurred during the financial year t. Information to

calculate the variable is derived from the CapitalIQ financial database, and

supplemented with information from the firms’ annual reports.

The dichotomous variable COVENANTS_GWi,t analyses the existence of debt

covenants which would be directly affected by a goodwill write-off. The variable

therefore proxies for the potential negative consequences from breaching debt

covenants through the decision of writing off goodwill.1185 The variable is set to 1 if

a firm has debt covenants in the year of the write-off decision t, which would be

directly affected by a goodwill write-off.1186 If no information on covenants is

disclosed in the firm’s annual report or other corporate disclosures the variable is set

to 0. Information on the specific debt covenants is hand-collected. To do so, the

disclosures regarding debt covenants in the annual reports were studies by hand.

Besides, the variable COVENANTS_GWi,t the more general independent variable

COVENANTSi,t is computed and analysed. It reflects the overall existence of

covenants in a firm. It does not differentiate between covenants which would be

affected or not affected by a goodwill write-off. It simply measures whether a firm

has covenants in place or not. Similar to COVENANTS_GWi,t, the independent

variable COVENANTSi,t is a dummy variable set to 1 if a firm has covenants in place

and 0 otherwise.1187

The variable PERFORMANCE_BONUSi,t analyses the performance-based, cash

awards granted to the CEO in % of fixed salary in the year of the goodwill write-off

decision t and therefore proxies for incentives predicted by agency theory.1188

Variable compensation components include cash-based performance bonuses and

excludes option-based bonuses1189 or restricted share awards. Managers might have

1185 Cf. Ramanna and Watts (2012), p. 770. 1186 Cf. Beatty and Weber (2006), p. 273, Ramanna and Watts (2012), p. 770. 1187 If no information on covenants is disclosed in the firm’s annual report or other corporate disclosures it was assumed that the firm is covenant-free and the variable set to 0. 1188 Cf. Ramanna and Watts (2012), p. 775, Beatty and Weber (2006), p. 274, Hamberg et al. (2011), p. 273, Darrough et al. (2014), p. 15, Guler (2006), p. 15. 1189 Options granted to the CEO in the year of the analysis have been excluded as they are usually vested until a date in the future, meaning that the CEO can exercise them at a future date and not at grant date. Additionally, options are usually out-of-the-money at grant date, incentivising the CEO to increase the share price in the future, meaning that the fair value of those share is purely made up of their time values.


242

an incentive to delay write-offs, especially then when their overall amount of their

salary is linked to the financial performance of the firm. Information on the

compensation structure and components is hand-collected from the firms’ annual

reports and compensation reports published by the firms. For validation purposes of

the results derived from the variable PERFORMANCE_BONUSi,t, this effect is also

studied through the dummy variable PERFORMANCE_BONUS_DUMMYi,t which is

set to 1, if PERFORMANCE_BONUSi,t is larger than 0, and 0 otherwise. In other

words the dummy variable amounts to 1 if the respective CEO received a cash bonus

in year t, and 0 otherwise.

The variable CEO_TENUREi,t measures a CEO’s number of years in office as at

financial year-end of the goodwill write-off decision (year t).1190 For the analysis,

CEO tenure is always rounded up to the full year. Information on the names of the

CEOs and dates of the key executive’s appointments are hand collected from the

firms’ annual reports or other corporate disclosures. Further, the trimmed tenure

variable CEO_TENURE_TRIMi,t is calculated which replaces observable tenure

outliers by the respective subsample means (incl. outliers).

CEO_EQUITY_OWNERSHIPi,t represents the last variable used as a proxy for

agency theory-based incentives.1191 It measures the market value of a CEO’s equity

ownership as at financial year-end in the year of the goodwill write-off decision

(year t). The market value equals the end of day share price multiplied by the

number of shares owned by the CEO, both at the end of year t. Information on share

ownership are hand-collected from the firms’ annual reports and/or compensation

reports, supplemented by information from CapitalIQ. Information on the year-end

share prices are sourced from CapitalIQ. For validation purposes of the results

derived from the variable CEO_EQUITY_OWNERSHIPi,t, variables considering a

CEO’s share ownership in % of common shares outstanding as at year-end t

(CEO_EQUITY_OWNERSHIP_CSO%i,t), as well as the market value of the CEO’s

share ownership as a % of his/her fix compensation, both as of year-end t

1190 Cf. Beatty and Weber (2006), p. 274, Ramanna and Watts (2012), p. 775, Hamberg et al. (2011), p. 273. 1191 Cf. AbuGhazaleh et al. (2011), p. 178, Darrough et al. (2014), p. 39.


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(CEO_EQUITY_OWNERSHIP_FIX_COMP%i,t), are calculated and studied in the

regressions.

7.2.2.2.3 Goodwill reporting flexibility

The variables which proxy goodwill reporting flexibility under IAS 36 are derived

exclusively on the basis of the firms’ corporate disclosures, i.e. notes of the

respective financial statements in the annual reports.

Similar to Ramanna and Watts (2012), a variable that proxies goodwill

concentration across reporting segments, however on a more detailed and granular

level, is applied.1192 To do so, the variable GOODWILL_HHIi,t is calculated which

processes the number of reporting segments a firm has and the amounts of goodwill

separately allocated to them.

The so-called Herfindahl–Hirschman index (HHI) is calculated for each firm in the

sample as at the end of year t as:

GOODWILL_HHIi,t= GWj2

j=1

,

with n being the number of reporting segments and GWj the ratio of allocated

goodwill to the jth reporting segment to the book value of total goodwill as per

financial year-end t (adjusted for any goodwill write-off in year t). This variable

allows analysing whether firms actually prefer concentrating acquired goodwill to a

lower number of reporting segments over a higher number. The closer the

GOODWILL_HHIi,t variable is to 1, the more concentrated goodwill is over all

reporting segments. If the variable is low, the level implies that goodwill is allocated

to multiple segments in lower ratios. Information on the reporting segments, incl.

total goodwill, number of reporting segments with goodwill, amounts of allocated

goodwill, goodwill write-offs in the reporting segments, are hand collected.

1192 Cf. Ramanna and Watts (2012), p. 761.


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Illustrative examples of how the independent variable GOODWILL_HHIi,t is calculated:

Firm A has 3 reporting segments and has a consolidated total goodwill of EUR 100m as at financial

year-end t. The firm’s management allocated goodwill to the reporting segments in the following way:

Reporting segment 1

Reporting segment 2

Reporting segment 3

GOODWILL_HHIA,t

Goodwill allocated

to individual

reporting segments

(in year t)

EUR

95m

EUR

5m

EUR

0m = (0,95)2+(0,05)2+(0,00)2

= 0,905

Ratio of total

goodwill (in year t) 0,95 0,05 0,00

The independent variable GOODWILL_HHIA,t for firm A would be 0,905 (i.e. high concentration of

goodwill in reporting segments).

Firm B has also 3 reporting segments and has also a consolidated total goodwill of EUR 100m as at

financial year-end t. The firm’s management allocated goodwill to the reporting segments in the

following way:

Reporting segment 1

Reporting segment 2

Reporting segment 3

GOODWILL_HHIB,t

Goodwill allocated

to individual

reporting segments

(in year t)

EUR 35m EUR 35m EUR 30m = (0,35)2+(0,35)2+(0,30)2

= 0,335

Ratio of total

goodwill (in year t) 0,35 0,35 0,30

The independent variable GOODWILL_HHIB,t for firm B would be 0,335 (i.e. low concentration of

goodwill in reporting segments).

The variable SEGMENT_SIZEi,t analyses the percentage of total goodwill allocated

to the largest reporting segment with goodwill. Here, firstly the reporting segments

is ranked on the basis of their sizes. Size is defined as the reporting segments’ total

revenues in year t. Thereafter it is analysed what fraction of total goodwill (adjusted


245

for any goodwill write-off in year t) is allocated to them. The variable

SEGMENT_SIZEi,t can range between 0 and 1. Information to derive the variable is

sourced exclusively from firm disclosures (notes of financial statements).

Illustrative example of how the independent variable SEGMENT_SIZEA,t is calculated:



Reporting segment 1

Reporting segment 2

Reporting segment 3

SEGMENT_SIZEA,t

Revenues of

individual

reporting

segments

(in year t)

EUR 200m

(largest

reporting

segment)

EUR 50m

(2nd largest

reporting

segment)

EUR 10m

(3rd largest

reporting

segment)

= 0,80

Goodwill

allocated to

individual

reporting

segments

(in year t)

EUR 80m EUR 15m EUR 5m

Ratio of total

goodwill

(in year t)

0,80 0,15 0,05

The independent variable SEGMENT_SIZEA,t for firm A would be 0,80 (i.e. high concentration of

goodwill in largest reporting segment).

The variable SEGMENT_PROFITABILITYi,t analyses the percentage of total

goodwill allocated to the most profitable reporting segment with goodwill. To do so,

in a first step, the reporting segments are ranked on the basis of their profitability.

Profitability is defined as EBITDA margin in year t.1193 In a second step it is

analysed what percentage of existing goodwill as of year-end t (adjusted for any

1193 Depending on the availability of the metric EBITDA margin in certain industries, alternatively the operating profit margin (adjusted for depreciation and amortization) was used.


246

goodwill write-offs in year t) is allocated to the individual reporting segments. The

variable SEGMENT_PROFITABILITYi,t can range between 0 and 1. Information to

derive the variable SEGMENT_PROFITABILITYi,t is derived entirely from firm

disclosures (notes of financial statements).

Illustrative example of how the independent variable SEGMENT_PROFITABILITYA,t is calculated:



Reporting segment 1

Reporting segment 2

Reporting segment 3

SEGMENT_

PROFITABILITYA,t

EBITDA margin

of individual

reporting

segments

(in year t)

20%

(most

profitable

reporting

segment)

10%

(2nd most

profitable

reporting

segment)

5%

(3rd most

profitable

reporting

segment)

= 0,90

Goodwill

allocated to

individual

reporting

segments

(in year t)

EUR 95m EUR 5m EUR 5m

Ratio of total

goodwill

(in year t)

0,90 0,05 0,05

The independent variable SEGMENT_PROFITABILITYA,t for firm A would be 0,90 (i.e. high

concentration of goodwill in most profitable reporting segment).

The dichotomous variable CGU_REPORTING_CHANGEi,t is set to 1 if a firm

changes its CGUs or reporting segments in the year of the goodwill write-off

decision (year t). Information on such changes is analyzed on a year by year basis

and hand collected from the firms’ annual reports. For each observation in the

sample, the annual report of year t as well as year t-1 are analysed, to understand

whether CGUs or reporting segments changed between year t-1 and year t.


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7.2.2.2.4 Other, control variables

Various researchers argue that a firm’s MTB is related to goodwill write-off

decisions.1194 To test the assertion whether the absolute difference of the MTB gap

contains also explanatory power for goodwill write-off decisions in case of capital-

market implied triggering events, the variable MTBi,t should also be included in the

model.1195 MTBi,t measures the ratio between a firm’s market capitalization as per

financial year-end t and last publicly available book value of equity. Market

capitalization equals to the last closing price of a firm’s shares as of financial year-

end t multiplied by the number of shares outstanding. Information on closing prices

of shares, shares outstanding, and book values of equity are sourced through

Thomson Reuters (Worldscope), CapitalIQ, as well as supplemented by the firms’

annual reports.

The variable GOODWILL_INTENSITYi,t represents the ratio of a firm’s book value

of goodwill to total assets1196 as at the financial year-end t.1197 Both amounts are

adjusted for any potential goodwill write-offs during the last financial year t and

taken as at the financial year-end of the goodwill write-off decision (year t).

Information on the book values are sourced from Thomson Reuters (Worldscope),

CapitalIQ, as well as supplemented by the firms’ annual reports.

FIRM_SIZEi,t measures the firm’s market capitalization1198 as per financial year-end

t.1199 Information to calculate FIRM_SIZEi,t is also obtained from Thomson Reuters

(Worldscope), as well as CapitalIQ, and supplemented by the firms’ annual reports.

1194 Cf. Beatty and Weber (2006), pp. 272, 274, Francis et al. (1996), p. 122, Ramanna and Watts (2012), p. 751, Chen et al. (2013), p. 2, Moser (2011), pp. 232-235, Li and Sloan (2012), pp. 38, 44, Bens et al. (2011), p. 527. 1195 Cf. Li et al. (2011), p. 754, AbuGhazaleh et al. (2011), p. 178, Francis et al. (1996), p. 126, Li and Sloan (2012), pp. 44, 45, Ramanna and Watts (2012), p. 775, Beatty and Weber (2006), p. 274, Gu and Lev (2011), p. 2004, Hamberg et al. (2011), p. 272, Chen et al. (2014), p. 18. 1196 Total assets is the sum of total current assets, long-term receivables, investment in unconsolidated subsidiaries, other investments, net property plant and equipment and other assets (incl. goodwill and other intangible assets). 1197 Cf. AbuGhazaleh et al. (2011), p. 178, Li and Sloan (2012), pp. 44, 45, Ramanna and Watts (2012), p. 775, Hamberg et al. (2011), p. 273. 1198 Market capitalization equals to the last closing price of a firm’s share as of financial year-end t multiplied by the number of shares outstanding.


248

The following table summarizes the variables to be used in the regression models.

Variable Description Source of data

Dependent variable

GW_WOi,t

Dichotomous (dummy) variable set to 1 if firm i reports a goodwill write-off in year t, and 0 otherwise.

(i) CapitalIQ (ii) Annual reports (hand collected)

Private information on changes of a firm’s future financial performance

STOCK_

RETURNi,t+2

Annualized stock return of firm i in % in the second year (t+2) after the goodwill write-off or non-write-off decision.

(i) COMPUSTAT (ii) CapitalIQ

STOCK_

RETURNi,t+1

Annualized stock return of firm i in % in the first year (t+1) after the goodwill write-off decision.

(i) COMPUSTAT (ii) CapitalIQ

EBITDA_MARGIN_

CHANGEi,t+2

Relative change of firm i’s EBITDA margin in the second year (t+2) after the goodwill write-off or non-write-off decision. Change calculated on the basis of the EBITDA margin from year t+1.

(i) Annual reports (hand collected) (ii) CapitalIQ

EBITDA_

MARGIN_

CHANGEi,t+1

Relative change of firm i’s EBITDA margin in the first year (t+1) after the goodwill write-off decision. Change calculated on the basis of the EBITDA margin from year t.


SHARE_

BUYBACKSi,t

Amount spent for share buybacks1200 by firm i in year t, scaled by firm i’s total revenues in financial year t.


1199 Cf. AbuGhazaleh et al. (2011), p. 178, Ramanna and Watts (2012), p. 775, Hayn and Hughes (2006), p. 247, Francis et al. (1996), p. 126, Cotter et al. (1998), p. 173, Beatty and Weber (2006), p. 274, Gu and Lev (2011), p. 2004, Hamberg et al. (2011), p. 273, Henning et al. (2004), p. 114, Chen et al. (2014), p. 37. 1200 Annual cash amount spend to decrease the outstanding shares of common and preferred stock. Position includes (i) purchase of treasury shares, (ii) repurchase of stock, (iii) retirement of preferred stock, and (iv) exchange of common stock for debentures.


249

SHARE_

BUYBACKS_

DUMMYi,t

Dichotomous (dummy) variable set to 1 if SHARE_BUYBACKSi,t >1.

(i) Annual reports (hand collected)

(ii) CapitalIQ

CEO_INSIDER_

TRADINGi,t

Percentage change of common shares held by firm i’s CEO between financial year-end t-1 and year-

end t.

(i) Annual and compensation reports

(hand collected) (ii) CapitalIQ

CEO_INSIDER_

TRADING_

DUMMYi,t

Dichotomous variable set to 1 if

CEO_INSIDER_TRADINGit > 0, and 0 otherwise.

(i) Annual and

compensation reports (hand collected) (ii) CapitalIQ

Incentives predicted by agency theory

LEV_RATIOi,t Ratio of firm i’s total debt1201 to total assets1202 as at financial year-end t. Total assets manually adjusted for goodwill write-off amounts which might have

occurred during financial year t.


LEV_BANK_

DEBT_RATIOi,t

Ratio of firm i’s total bank debt1203 to total assets1204 as at financial year-end t. Total assets

manually adjusted for goodwill write-off amounts which might have occurred during financial year t.

(i) CapitalIQ (ii) Annual reports

(hand collected)

COVENANTS_

GWi,t

Dummy variable set to 1 if firm i has debt

covenants at financial year-end t, that would be affected by a goodwill write-off.

(i) Annual reports

(hand collected) (ii) Other firm filings (hand collected)

COVENANTSi,t Dummy variable set to 1 if firm i has debt covenants at financial year-end t.

(i) Annual reports (hand collected) (ii) Other firm filings (hand collected)

1201 Total debt is the sum of all interest bearing and capitalized lease obligations. It is the sum of long- and short- term debt (i.e. of short-term debt, current portion of long-term debt and long-term debt). 1202 Total assets is the sum of total current assets, long-term receivables, investment in unconsolidated subsidiaries, other investments, net property plant and equipment and other assets (incl. goodwill and other intangible assets). 1203 Total bank debt is the sum of all interest bearing obligations, where a bank or other financial institution is the creditor. 1204 Total assets is the sum of total current assets, long-term receivables, investment in unconsolidated subsidiaries, other investments, net property plant and equipment and other assets (incl. goodwill and other intangible assets).


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PERFORMANCE_

BONUSi,t

Variable, cash-based compensation granted to firm i’s CEO in % of his/her fixed salary for the

financial year of the goodwill write-off or non-write-off decision (financial year t). Variable compensation components include cash-based performance bonuses and exclude option-based bonuses1205 or restricted share awards.



PERFORMANCE_

BONUS_DUMMYi,t

Dichotomous variable set to 1, if firm i’s CEO received a cash bonus for the financial year of the goodwill write-off or non-write-off decision (financial year t).

(i) Annual and compensation reports (hand collected) (ii) CapitalIQ

PERFORMANCE_

BONUS_CHANGEi,t

Percentage change of variable, cash-based compensation granted to firm i’s CEO in % of his/her fixed salary between year t-1 and t, i.e. (PERFORMANCE_BONUSi,t minus

PERFORMANCE_BONUSi,t-1).


CEO_TENUREi,t Number of full years in office of firm i’s CEO in the year of the goodwill write-off or non-write-off

decision (financial year t). Years in office always rounded up.

Annual reports (hand collected)

CEO_TENURE_

TRIMi,t

Number of full years in office of firm i’s CEO in

the year of the goodwill write-off or non-write-off decision (financial year t). Years in office always rounded up. Outliers (ranked, above 67% of observations) replaced by subsample means (before replacing outliers).

Annual reports

(hand collected)

CEO_EQUITY_

OWNERSHIPi,t

Market value of shares owned by firm i’s CEO as at financial year-end in the year of the goodwill write-off or non-write-off decision (financial year t). Market value equals end of year share price

multiplied by the number of common shares owned by the CEO.


1205 Options granted to the CEO in the year of the analysis have been excluded as they are usually vested until a date in the future, meaning that the CEO can exercise them at a future date and not at grant date. Additionally, options are usually out-of-the-money at grant date, incentivising the CEO to increase the share price in the future, meaning that the fair value of those shares is purely made up of their time values.


251

CEO_EQUITY_

OWNERSHIP_

CSO%i,t

Market value of shares owned by firm i’s CEO as at financial year-end in the year of the goodwill

write-off or non-write-off decision (financial year t), as % of total common shares outstanding.



CEO_EQUITY_

OWNERSHIP_

FIX_COMP%i,t

Market value of shares owned by firm i’s CEO as

at financial year-end in the year of the goodwill write-off or non-write-off decision (financial year t), scaled by the CEO’s fixed compensation for financial year t.

(i) Annual and

compensation reports (hand collected) (ii) CapitalIQ

Goodwill reporting flexibility

GOODWILL_

HHIi,t

Herfindahl Hirschman index (HHI) of firm i, calculating the goodwill concentration over all

reporting segments as at financial year-end in the year of the goodwill write-off or non-write-off decision (financial year t). Book value of goodwill adjusted for any goodwill write-offs during the year of the analysis.


SEGMENT_

PROFITABILITYi,t

Percentage of firm i’s total book value of goodwill allocated to the most profitable reporting segment with goodwill as at financial year-end t. Profitability defined as firm i’s EBITDA margin as

at financial year-end t. Book value of goodwill adjusted for any goodwill write-offs during the year of the analysis.


SEGMENT_

SIZEi,t Percentage of firm i’s total book value of goodwill allocated to the largest reporting segment with goodwill as at financial year-end t. Size defined as total revenues as at financial year-end t. Book value of goodwill adjusted for any goodwill write-offs during the year of the analysis.


SEGMENT_

RISKi,t

Percentage of firm i’s total book value of goodwill allocated to the reporting segment with the lowest risk profile as at financial year t. Risk defined as firm i’s minimum discount rate used to test the

recoverability of goodwill allocated to that reporting segment as at financial year t. Book value of goodwill adjusted for any goodwill write-offs during the year of the analysis.



252

CGU_

REPORTING_

CHANGEi,t

Dummy variable set to 1 if firm i’s CGUs or reporting segments were changed in the year of the

goodwill write-off or non-write-off decision (financial year t).


Others (control variables)

MTBi,t Ratio of firm i’s market capitalization as per financial year-end t and last publicly available book value of equity. Market capitalization equals to

closing price of firm i’s shares as of financial year-end t times common shares outstanding.


(iii) Thomson Reuters (Worldscope)

GOODWILL_

INTENSITYi,t

Ratio of firm i’s book values of goodwill and total assets as per financial year-end t (both adjusted for

goodwill write-offs during the financial year t).

(i) CapitalIQ (ii) Annual reports

(hand collected) (iii) Thomson Reuters (Worldscope)

FIRM_SIZEi,t Firm i’s market capitalization as at financial year-end t. Market capitalization equals to closing price of firm i’s shares as of financial year-end t times common shares outstanding.

(i) CapitalIQ (ii) Annual reports (hand collected) (iii) Thomson Reuters (Worldscope)

Table 4: Summary of independent variables Source: Own illustration.


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7.2.3 Expected signs of explanatory variables in the

regressions

On the basis of the respective theoretical concepts, described in chapter 5, and the

outlined hypotheses, described in chapter 7.1, I expect that the coefficients of the

applied independent variables carry the signs stated in the tables below. Positive

coefficients of the independent variables imply that a positive change (i.e. increase)

of the independent variable in year t increases on average the observable goodwill

write-off probability in the sample firms in year t. Vice versa, negative factors

would imply that a positive change (i.e. increase) of the independent variable

decreases the goodwill write-off probability.

Under the private information disclosure hypothesis, I expect that goodwill write-off

probability in year t is lower for firms with more positive future stock returns.

Therefore I assume that the coefficients of the independent variables

STOCK_RETURNi,t+1 and STOCK_RETURNi,t+2 will carry a negative sign in the

regressions. In some regressions, I substitute the independent variables STOCK_

RETURNi,t+1 and STOCK_RETURNi,t+2 with variables measuring future changes in

financial performance on the basis of accounting earnings. In particular those are

EBITDA_MARGIN_CHANGEi,t+1 and EBITDA_MARGIN_CHANGEi,t+2. Similar to

my expectations on the coefficients of the independent variables measuring future

stock returns, I assume that the coefficients of both the independent variables

EBITDA_MARGIN_CHANGEi,t+1 and EBITDA_MARGIN_CHANGEi,t+2 will be

negative, meaning that an improvement (i.e. increase) of a firm’s future EBITDA

margin in year t+1 or t+2 (compared to year t) has a goodwill write-off probability

reducing effect in the regressions.

I assume the same relationships for the independent variables SHARE_BUYBACKSi,t

and SHARE_BUYBACKS_DUMMYi,t. The private information disclosure hypothesis

would foresee that engaging in (larger) share buyback activities in year t reduces the

observable goodwill write-off probability in the same year. Similarly, I would apply

the same rationale for CEO insider trading in year t and its relationship to goodwill

write-off probability. Similarily, I assume that the coefficients of the explanatory


254

variables CEO_INSIDER_TRADINGi,t. and CEO_INSIDER_TRADING_DUMMYi,t

will be negative in the regressions. This means that observing CEO net insider

buying in year t reduces the observable goodwill write-off probability in the same

year.

On the basis of agency theory considerations, I presume that the coefficients of the

independent variables related to the sample firms’ leverage ratios (LEV_RATIOi,t and

LEV_BANK_DEBT_RATIOi,t) have negative signs in the regressions, meaning that

the higher a firm’s leverage ratios in year t, the lower its observable goodwill write-

off probability in the same year. From the dummy variables COVENANTS_GWi,t

and COVENANTSi,t I also expect a write-off probability reducing effect in the

regressions. This would translate into negative signs of the coefficients.

Reputational concerns in combination with agency theory considerations would

foresee that CEOs with shorter tenures can personally benefit from writing off

goodwill shortly after being installed as CEO. This would translate into higher

goodwill write-off probabilities for firms with shorter tenured CEOs in year t than

vice versa. I follow this reasoning and expect therefore that the coefficient of the

continuous variable CEO_TENUREi,t carries a negative sign. I argue similarly for the

independent variable variable CEO_TENURE_TRIMi,t.

Potential private wealth effects arising from write-offs can motivate CEOs to

withhold goodwill write-offs, although being economically impaired. Under agency

theory considerations and the assumptions that write-offs potentially lead to lower

bonuses for year t and potentially let share prices drop shortly after the

announcement of the write-off, I expect that the independent variable

CEO_EQUITY_OWNERSHIPi,t has a write-off probability reducing effect in year t

and therefore a negative coefficient in the regressions. I presume the same

relationship for the two additional continuous independent variables

CEO_EQUITY_OWNERSHIP_FIX_COMP%i,t and CEO_EQUITY_OWNERSHIP_

CSO%i,t. The variables measure a CEO’s stock ownership in terms of his/her fix

compensation received for his/her services in year t and in terms of common shares

outstanding in year t. For both I expect a negative relationship with goodwill write-

off probability, i.e. the higher the respective ratios, the lower the observable write-


255

off probability. Consequently, the coefficients of these independent variables are

assumed to be negative in the regressions.

The coefficients of the variables PERFORMANCE_BONUSi,t, PERFORMANCE_

BONUS_CHANGEi,t and PERFORMANCE_BONUS_DUMMYi,t are expected to be

negative as bonus amounts for year t could substantially be related to recognizing a

write-off in year t. This would imply that receiving a cash bonus or other variable

compensation payments related to performance for year t would reduce the

observable goodwill write-off probability in the same year, as CEOs do not want to

reduce their overall compensation by lowering their bonus amounts through a write-

off.

For all goodwill concentration variables related to goodwill reporting flexibility in

year t ((i) GOODWILL_HHIi,t, (ii) SEGMENT_PROFITABILITYi,t, (iii) SEGMENT_

SIZEi,t, (iv) SEGMENT_RISKi,t), I expect their coefficients to have negative signs in

the regressions. I believe that high concentrations of goodwill in reporting segments

have a write-off probability reducing effect due to the possibility to substitute

acquired goodwill with internally generated goodwill. The coefficient of the

dichotomous independent variable CGU_REPORTING_CHANGEi,t is also expected

to be negative in the regressions. This means that a change of a firm’s CGU

structures or reporting segments in year t reduces a firm’s goodwill write-off

probability.


256

Table 5: Expected signs of independent variables Source: Own illustration.

Explaining goodwill write-off decisions under IAS 36 for capital market-implied triggering events

Expected signs of coefficients of independent variables

Change in Expected change Resulting

Research Area Independent variable independent variable in write-off probability expected sign

Private information STOCK_RETURN i,t+2 ↑ Pr (GW_WO i,t ) ↓ (-)

on future cash flows STOCK_RETURN i,t+1 ↑ Pr (GW_WO i,t ) ↓ (-)

EBITDA_MARGIN_CHANGE i,t+2 ↑ Pr (GW_WO i,t ) ↓ (-)

EBITDA_MARGIN_CHANGE i,t+1 ↑ Pr (GW_WO i,t ) ↓ (-)

SHARE_BUYBACKS_DUMMY i,t Dummy (1=yes/0=no) Pr (GW_WO i,t ) ↓ (-)

SHARE_BUYBACKS i,t ↑ Pr (GW_WO i,t ) ↓ (-)

CEO_INSIDER_TRADING_DUMMY i,t Dummy (1=yes/0=no) Pr (GW_WO i,t ) ↓ (-)

CEO_INSIDER_TRADING i,t ↑ Pr (GW_WO i,t ) ↓ (-)

Agency theory- LEV_RATIO i,t ↑ Pr (GW_WO i,t ) ↓ (-)

based incentives LEV_BANK_DEBT_RATIO i,t ↑ Pr (GW_WO i,t ) ↓ (-)

COVENANTS_GW i,t Dummy (1=yes/0=no) Pr (GW_WO i,t ) ↓ (-)

COVENANTS i,t Dummy (1=yes/0=no) Pr (GW_WO i,t ) ↓ (-)

CEO_TENURE i,t ↑ Pr (GW_WO i,t ) ↓ (-)

CEO_TENURE_TRIM i,t ↑ Pr (GW_WO i,t ) ↓ (-)

PERFORMANCE_BONUS_DUMMY i,t Dummy (1=yes/0=no) Pr (GW_WO i,t ) ↓ (-)

PERFORMANCE_BONUS i,t ↑ Pr (GW_WO i,t ) ↓ (-)

PERFORMANCE_BONUS_CHANGE i,t ↑ Pr (GW_WO i,t ) ↓ (-)

CEO_EQUITY_OWNERSHIP i,t ↑ Pr (GW_WO i,t ) ↓ (-)

CEO_EQUITY_OWNERSHIP_FIX_COMP% i,t ↑ Pr (GW_WO i,t ) ↓ (-)

CEO_EQUITY_OWNERSHIP_CSO% i,t ↑ Pr (GW_WO i,t ) ↓ (-)

Goodwill reporting GOODWILL_HHI i,t ↑ Pr (GW_WO i,t ) ↓ (-)

flexibility SEGMENTS_PROFITABILITY i,t ↑ Pr (GW_WO i,t ) ↓ (-)

SEGMENTS_SIZE i,t ↑ Pr (GW_WO i,t ) ↓ (-)

SEGMENTS_RISK i,t ↑ Pr (GW_WO i,t ) ↓ (-)

CGU_REPORTING_CHANGE i,t Dummy (1=yes/0=no) Pr (GW_WO i,t ) ↓ (-)


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7.3 Sample selection

To test the hypotheses with the outlined research model, as a starting point research

objects (i.e. firms) need to be identified with a high implied impairment risk. As

outlined in chapter 3.3.1, IAS 36 defines several internally and externally observable

goodwill impairment indicators, however not all of them are equally observable for

a firm’s outsider.1206 Due to that, firms need to be selected on the basis of strong

externally observable triggering events. One can argue that the longer triggering

events are apparent and observable, the higher the probability that goodwill is

actually economically impaired, and therefore require a write-off.1207

7.3.1 Sample selection based on capital market-implied

triggering events

Especially an apparent negative valuation gap between a firm’s market value and

book value of equity is considered in academia and practice as an indicator of an

economically impaired goodwill.1208 This indicator is particularly strong, when

information asymmetries between the firm (i.e. the management team) and outsiders

(e.g. investors, financial analysts etc.) are low and when such a valuation gap is

observable for a considerable period of time.1209 Some researchers like Beatty and

Weber (2006) go even one step further in their argumentation by stating that the

observable valuation difference acts as an approximation of the implied, required

goodwill write-off charge.1210 Additionally, corporate studies show that strong

correlations between such a negative valuation gap and goodwill impairment

charges exist.1211 However as the name implies, goodwill impairment indicators do

1206 Cf. Amiraslani et al. (2013), p. 12, Glaum and Wyrwa (2011), p. 26. 1207 Cf. Ramanna and Watts (2012), p. 751, Chen et al. (2013), p. 2. 1208 Cf. Beatty and Weber (2006), p. 284, Ramanna and Watts (2012), pp. 751, 756, Chen et al. (2013), p. 2. Cf. also the studies of Amiraslani et al. (2013), p. 16, Duff and Phelps (2013), pp. 18-19, Duff and Phelps (2011), pp. 5-6, Li and Sloan (2012), pp. 38, 44, who argue for a relationship between the MTB ratio and goodwill impairment risk. 1209 Cf. Chen et al. (2013), p. 2, Ramanna and Watts (2012), pp. 751, 756. 1210 Cf. Beatty and Weber (2006), p. 274. 1211 Cf. Duff and Phelps (2013), pp. 18-19, Duff and Phelps (2011), pp. 5-6.


258

not provide a definitive answer whether goodwill is actually economically impaired,

however can act as a valuable information source regarding an apparent impairment

risk.1212

7.3.1.1 Observable market valuation gaps as a strong sign for an

economically impaired goodwill

As the empirical analysis builds on the reasoning that a negative difference between

a firm’s market value and book value of equity (i.e. capital market-implied

triggering event) represents a strong implication for an economically impaired

goodwill, in the following various arguments should be presented that support this

view.

(1) Interpretation of a negative market to book value difference:

• A negative market to book value difference implies that a firm’s market value

(M) which arises from the firm’s collective usage of available assets falls

short of their sum of individual book values (B).1213

• Depending on the duration of this valuation gap (T) and in case capital

market participants possess the relevant information to price the firm

accurately, it can be argued that the reported book values are perceived to be

too high by investors, relative to their underlying values (M < B).1214

• In case the capital market would be wrong in pricing the security (i.e. fair

value > market value), principal agency theory predicts managers to either try

to correct this mispricing or to profit personally from such a situation.1215 If

the firm’s management would possess information that would argue for an

apparent mispricing, it could signal capital market participants that the

current market valuation is incorrect by, for example, introducing share

buyback programs, buying shares for their individual accounts and/or

1212 Cf. Ramanna and Watts (2012), p. 751. 1213 Cf. Johnson and Petrone (1998), p. 295. 1214 Cf. Duff and Phelps (2011), p. 5. 1215 Cf. Muller et al. (2009), p. 2.


259

communicating to the capital market that the value deviates substantially

from the firm’s underlying economic fundamentals.1216

• In general, capital market participants are assumed to price firms more or less

accurately as long as low information asymmetries between the firm and the

capital market exist. In particular, this holds true for larger firms, less

complex firms, and firms which are covered by a relatively large number of

financial analyst, as research studies have documented.1217

(2) Implications of a negative MTB difference for recognized goodwill:

• An observable negative difference between the market value of a firm’s

assets and the book value of equity over several financial quarters signals that

certain assets on the firm’s balance sheet (including goodwill) are most likely

economically impaired, i.e. overstated in the balance sheet.1218

• Given that goodwill carries the highest risk on a firm’s balance sheet, it is

more likely that goodwill is impaired than any other asset when a valuation

gap is observed.1219 This view is also substantiated by the procedure that the

IASB outlines in case a negative difference between the carrying amount and

the recoverable amount of a CGU is identified. IAS 36 states that firstly

goodwill needs to be written off and then other assets on a pro rata basis (in

case this valuation gap is larger than the actual book value of goodwill).1220

This view implies that in case a valuation gap is observable, most likely

goodwill is impaired than any other asset in a CGU.

• Ramanna and Watts (2012) argue that a MTB ratio of below one “suggests

that the market expects goodwill impairments”1221. Furthermore, it can be

1216 Cf. Ramanna and Watts (2012), pp. 757-759, Muller et al. (2009), p. 2. 1217 For the relationship of analyst coverage, size and complexity regarding information asymmetries see for example: Bens et al. (2011), pp. 532-534, citing the works of Roulstone (2003), Engel et al. (2007), Doyle et al. (2007), Collins et al. (1987), Freeman (1987), Gilson et al. (2001), Berger and Hann (2003). 1218 Cf. Ramanna and Watts (2012), p. 751, 756, Churyk (2005), p. 1360. 1219 Cf. Moser (2011), pp. 232-235, Kalantary (2011), pp. 267, 274. 1220 Cf. Amiraslani et al. (2013), p. 16. 1221 Ramanna and Watts (2012), p. 751.


260

claimed that the longer such a market to book ratio below one is observable,

the stronger the implication that goodwill is economically impaired.1222

Beatty and Weber (2006) document empirically that the level of a firm’s

MTB ratio is statistically significantly related to observable impairment

risk.1223 Corporate studies identify strong correlations between goodwill

write-offs and a MTB ratio below 1 during the financial crisis when 65-80%

of firms with a negative market to book value difference recorded a goodwill

write-off.1224 Li and Sloan (2012) show empirically that the write-off risk is

statistically significantly higher for firms with a lower MTB ratio.1225

• Watts (2003) supports this view by stating that for listed firms the observable

market value represents an “objective measure”1226 to understand whether

goodwill is most likely economically impaired or not.1227

• The German Financial Reporting Enforcement Panel has a similar view by

arguing that firms need to validate the reasonableness of their valuation

assumptions of the impairment test if they differ from market data and market

views.1228 Furthermore, the FREP selects firms during their annual

impairment test review on the basis of a MTB ratio below one, besides other

factors.

• The arguments by Beatty and Weber (2006), Ramanna and Watts (2012),

Moser (2011), Chen et al. (2013), Watts (2003), and Amiraslani et al. (2013)

suggest that it is very likely that goodwill is economically impaired in case a

sustainable, negative market to book value difference is observable.

To derive meaningful results in the empirical analysis of this PhD thesis, one has to

make sure that for the sample firms the MTB ratio below one1229 represents a

1222 Cf. Chen et al. (2013), p. 2. 1223 Cf. Beatty and Weber (2006), p. 280. 1224 Cf. Duff and Phelps (2013), pp. 18-19, Duff and Phelps (2011), pp. 5-6. 1225 Cf. Li and Sloan (2012), pp. 38, 44. 1226 Watts (2003), p. 24. 1227 Cf. Watts (2003), p. 24. 1228 Cf. FREP (2012), p. 1. 1229 Cf. Ramanna and Watts (2012), pp. 751, 756, Beatty and Weber (2006), p. 284, Chen et al. (2013), p. 2.


261

sufficiently powerful indicator of an economically impaired goodwill.1230 As the

sample should not contain firms where the market is not fully correct in pricing the

security accurately and thereby limiting the explanatory power of a MTB < 1

regarding an economically impaired goodwill,1231 one should predominantly focus

on firms that combine the following attributes:

(1) Longer duration: MTB < 1 is observable for a considerable period of time.

The longer the duration of a negative market to book value gap, the more

likely it is that goodwill is actually economically impaired.1232

(2) Large size: Firm size (measured either by market capitalization or total

assets) represents a proxy for analyst coverage. The higher the analyst

coverage of a firm is (i.e. the more analyst cover a certain firm), the lower the

assumed information asymmetries between the firm and capital market

participants.1233

(3) High stock liquidity: the more liquid shares are (i.e. the higher the

observable trading volume is), the lower the assumed information

asymmetries between the firm and capital market participants.1234

According to Ramanna and Watts (2012), one can argue that observing a MTB < 1

over two consecutive financial year-ends is a strong indicator for an economically

impaired goodwill.1235 If the firm would actually be mispriced on the capital market

from the point of view of the firm’s management, it is more than likely that the

senior management team would be able to correct such a mispricing through

efficient capital market communication measures within this period of time.1236

1230 Cf. Chen et al. (2013), p. 2. 1231 Cf. Chen et al. (2013), p. 2. 1232 Cf. Ramanna and Watts (2012), p. 751. 1233 Cf. Bens et al. (2011), pp. 532-534. 1234 Cf. Bens et al. (2011), pp. 532-534. 1235 Cf. Ramanna and Watts (2012), pp. 757-759. 1236 Cf. Ramanna and Watts (2012), pp. 757-759, Muller et al. (2009), p. 2.


262

7.3.2 Sample selection methodology

Given that the explanatory power of the MTB < 1 criterion for an economically

impaired goodwill is higher for larger, public firms with a higher stock liquidity, the

analysis of this PhD thesis is based on firms which are constituents of the broad

benchmark index STOXX® Europe 600 as of 31 December 2014.1237

The STOXX® Europe 600 Index contains the 600 largest company stocks measured

on the basis of free-float market capitalization from the STOXX® Europe Total

Market Index (TMI),1238 which acts as a benchmark index for the domestic market

capitalizations of listed European firms.1239 The TMI, from which the STOXX®

Europe 600 Index is sourced, covers approximately 90 per cent of the free float

market capitalisation of Europe.1240

With a fixed number of 600 components, the STOXX® Europe 600 Index has a

representative character of the individual sizes of national capital markets in Europe

and contains public firms, headquartered in the European countries Austria,

Belgium, Czech Republic, Denmark, Finland, France, Germany, Greece, Ireland,

Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, Sweden, Switzerland

and the United Kingdom.1241 The market capitalization of the index constituents

amounts to EUR 8’819.3 bn as at 31 December 2014.

By already selecting large, liquid firms of a broad and representative benchmark

index, information asymmetries are argued to be lower than for less liquid firms (i.e.

outside of the index), as information asymmetries are found to be negatively

correlated with stock liquidity.1242 However differences among the firms in the

STOXX® Europe 600 Index regarding firm size are certainly given and have to be

controlled for in the analysis.

1237 Cf. STOXX (2015a), p. 1. The STOXX® Europe 600 Index has been built by the index provider STOXX Ltd. which is owned by the German Deutsche Börse AG and the Swiss SIX Group AG. 1238 Cf. STOXX (2015c), p. 40. 1239 Cf. STOXX (2015b), p. 11. 1240 Cf. STOXX (2015b), p. 12. 1241 Cf. STOXX (2015a), p. 1, STOXX (2015b), p. 1. 1242 Cf. Tang (2006), p. 2.


263

In order to be included in the sample used in this PhD thesis, several capital market-

driven triggering events, defined as a market to book value of equity ratio of below

1, must be observable. In particular, the analysis is based on firms for which a MTB

< 1 over two consecutive financial year-ends is apparent. Firms which do not fulfil

this selection criterion do not enter the sample to be used. The same holds true for

firms with no goodwill from prior transactions. The following figure displays the

sampling methodology.

Fig. 47: Methodology of sample selection Source: Own illustration.

Derivation of sample:

The firms which are part of the STOXX® Europe 600 Index as of 31 December

2014 (n = 600) represent the basis for the sampling process. Since the introduction

of IAS 36 in 2004, one can observe a MTB ratio <1 over at least one financial year-

end for 246 of the total 600 firms. For 161 of those 246 firms, a sustainably longer

period of at least two financial year-ends with MTB <1 is observable.

As outlined above, this duration of two consecutive financial year-ends with MTB

<1 is observable represents the principal selection criteria for firms in the STOXX®

Constituents of STOXX® Europe 600 as of 31 December 2014 • with goodwill • reporting under IFRS

No goodwill write-off in year t

Goodwill write-off in year t

Capital market-implied triggering events (i.e. MTB < 1) observable at two

consecutive financial year-ends (at t and t-1)

No goodwill write-off in year t

Goodwill write-off in year t

Capital market-implied triggering events (i.e. MTB < 1) not observable at two

consecutive financial year-ends (at t and t-1)

Include in sample Exclude Exclude


264

Europe 600 Index to be of relevance for the analysis of this PhD thesis.1243 19 of

these 161 firms do not have goodwill on the consolidated balance sheet and are

therefore excluded. Further 5 firms are excluded as they did not apply IFRS. This

leaves a final sample of 136 unique firms.

18 of these 136 (13,2%) unique firms enter the pool of observation twice, meaning

that a MTB <1 over at least two consecutive financial year-ends is observed twice

during the observation period 2004-2014.1244 This sampling process results in 154

observations of 136 firms between 2004 and 2014.

Table 6: Derivation of final sample Source: Own illustration.

1243 Cf. Ramanna and Watts (2012), p. 751, Chen et al. (2013), p. 2. 1244 In the univariate and multivariate analysis of this PhD thesis it was tested whether including the multiple observations of these 17 firms have a statistically significant influence on the results. This was analysed by performing (a) t-tests and (b) regressions with and without those multiple observations. However it was found out that this is not the case. Additionally it was tested whether the write-off probability is significantly different in the firms that enter the pool of observations twice. This was also not the case. Therefore it was decided to include them in the final sample of observations.

Selection criteria Firms Observations

Firms in STOXX® Europe 600 as of 31 December 2014 600 n/a

thereof: firms with MTB < 1 during observation period 2004-2014, 246 886

over at least one financial year end


over at least two consecutive financial year ends

of which firms without goodwill 19 29


over at least two consecutive financial year ends, with goodwill

of which firms not applying IFRS 5 5

Final sample:

Firms in STOXX® Europe 600 as of 31 December 2014, with MTB < 1 least two consecutive 136 154

financial year ends during observation period 2004-2014, with goodwill and applying IFRS

Overview on process of selecting firms being part of STOXX® Europe 600 as of 31 December 2014 with

MTB < 1 over two consecutive financial year ends, having goodwill and applying IAS 36

8 Description of results

265

8 Results

8.1 Sample description

The following sections provide financial and non-financial information on the

sample firms which entered the pool of observations. The final sample of

observations is derived from 136 firms located in 17 different European countries,

operating in 18 different industries.1245

8.1.1 General information

The firms which enter the pool of observations are located throughout Europe.

Observations from British firms (32 observations; 20,8%) and French firms (31;

20,1%) lead the sample, followed by Italian (14), German (10), Spanish (9) and

Swiss (8) firms.1246 The relatively large fraction of British and French firms can be

partly explained by the composition methodology of the STOXX® Europe 600

Index as it builds on the size of the respective country equity capital markets.1247

Fig. 48: Distribution of observations by country Source: Own illustration.

1245 Industry classification based on STOXX Supersector. 1246 Cf. Siggelkow and Zülch (2013b), p. 109. 1247 Cf. STOXX (2015a), p. 2, STOXX (2015b), p. 1.

Overview on observations by country (i.e. MTB<1 over two consecutive financial year-ends)

Country

Country

Code

Number of

observations

in % of

full sample Country

Country

Code

Number of

observations

in % of

full sample

Austria AT 7 4,5% Italy IT 14 9,1%

Belgium BE 4 2,6% Luxembourg LU 2 1,3%

Denmark DK 6 3,9% Netherlands NL 5 3,2%Finland FI 7 4,5% Norway NO 6 3,9%

France FR 31 20,1% Portugal PT 1 0,6%Germany DE 10 6,5% Spain ES 9 5,8%

Great Britain GB 32 20,8% Sweden SE 4 2,6%

Greece GR 5 3,2% Switzerland CH 8 5,2%Ireland IE 3 1,9% Total 154 100,0%


266

The analysis of this PhD thesis includes observations from both non-financial and

financial services firms1248. 55% of the observations (84) are derived from non-

financial services firms, whereas 45% of the observations come from financial

services firms (70). Of the non-financial services firms, constituents of the Industrial

Goods & Services (17), Real Estate (12) and Basic Resources (11) sectors make up

26% of the sample. The majority of the observations from the financial services

firms are derived from firms in the banking industry (43), followed by insurance

companies (21).1249

Table 7: Distribution of observations by industry Source: Own illustration.

Although observations are identified in each year since the introduction of the

impairment-only approach (IAS 36), they are found out to follow a cyclical pattern,

1248 Defined as banks, insurance companies and other financial services companies. 1249 Cf. Siggelkow and Zülch (2013b), p. 109.

Overview on observations by industry

(i.e. MTB<1 over two consequtive financial year-ends)

Industry

(STOXX Supersector)

Number of

observations

in % of

full sample

Industry

(STOXX Supersector)

Number of

observations

in % of

full sample

Automobiles & Parts 4 2,6% Media 1 0,6%

Banks 43 27,9% Oil & Gas 6 3,9%Basic Resources 11 7,1% Personal & Household Goods 3 1,9%

Chemicals 1 0,6% Real Estate 12 7,8%Construction & Materials 4 2,6% Retail 3 1,9%

Financial Services 6 3,9% Technology 2 1,3%Food & Beverages 1 0,6% Telecommunications 4 2,6%

Industrial Goods & Services 17 11,0% Travel & Leisure 8 5,2%Insurance 21 13,6% Utilities 7 4,5%

Total 154 100,0%

thereof:

Financial Services [1] 70 45%Non-Financial Services 84 55%

[1] Includes banks, insurance firms an other financial services firms.


267

reaching the climax in 2009 (53).1250 Also approximately one fifth of the

observations is identified in 2012, as the table below shows.

Table 8: Distribution of observations by financial year Source: Own illustration.

8.1.2 Financial information

The firms represented in the sample are rather large, measured by both market

capitalization and total assets (adjusted for goodwill write-offs in year t). The mean

market capitalization amounts to EUR 9’745m (median: EUR 4’017m), with

Vodafone Group Plc (EUR 111’857m), Banco Santander S.A. (EUR 49’092m) and

GDF Suez SA (EUR 47’574m) leading the sample.

The largest firms in the sample measured on the basis of total assets are BNP

Paribas S.A. (EUR 1’965bn), Credit Agricole S.A. (EUR 1’653bn) and Deutsche

Bank AG (EUR 1’501bn). The mean is found out to amount to EUR 172’381m

(median: EUR 41’699m). Approximately 8,4% or EUR 4’615m (both mean values)

of total assets (adjusted for goodwill write-offs in year t) is goodwill on the balance

sheet of the sample firms. The median values of these variables are both smaller

(2,0% of total assets or EUR 1’036m). Firms with the largest goodwill amounts in

terms of total assets are Vodafone Group Plc (50,7% of total assets), Telekom Italia

1250 Cf. Chen et al. (2014), p. 37.

Overview on observations by financial year (i.e. MTB<1 over two consequtive financial year-ends)

Financial year

Number of observations

in % of full sample

Financial year

Number of observations

in % of full sample

2014 4 2,6% 2009 53 34,4%2013 7 4,5% 2008 19 12,3%2012 32 20,8% 2007 1 0,6%2011 19 12,3% 2006 4 2,6%2010 9 5,8% 2005 6 3,9%

Total 154 100,0%


268

S.p.A. (50,6%) and Thomas Cook Group Plc (46,6%), which operate in the services

industry.

Table 9: Financial characteristics of sample firms Source: Own illustration.

When constructing an equally weighted equities index based on the share price

movements of the firms in the sample from two years prior (t-730 days) to two years

after the goodwill write-off or non-write-off decision (t+730 days), it becomes

obvious that these firms experienced substantial reductions in market capitalizations

prior to their write-off or non-write-off decisions.1251 The largest reduction is

observable between t-730 days and t-365 days, when share prices dropped on

average by -36,8%. A further decline in the share price of -2,9% is detected in the

year of the goodwill write-off or non-write-off decision (in total -39,7% between t-

730 days and t). Between t and t+365 days, the share prices recover by +13,8%

1251 Cf. Li and Sloan (2012), p. 42.

Overview on financial characteristics of sample firmsin EURm

Statistic

Total assets [1]

Market capitalization [2]

Total revenues [3]

Total Goodwill [1]

Goodwill

Intensity (%)[4]

Mean 172'381 9'745 17'731 4'615 8,4%

Standard deviation 350'525 14'790 24'782 11'491 12,0%

Min 683 121 213 1 0,0%25%-Quartile 11'610 1'856 2'013 243 0,5%

Median 41'699 4'017 7'271 1'036 2,0%

75%-Quartile 138'551 10'310 22'542 3'371 11,4%Max 1'965'389 111'857 132'474 109'440 50,7%

Total 26'546'622 1'500'659 2'730'534 710'691 n/m

n 154 154 154 154 154

[1] In EURm, adjusted for goodwill write-offs in year t .

[2] Market capitalization in EURm, as of financial year-end t . [3] Total revenues in EURm, as of financial year-end t .

[4] Defined as total goodwill divided by total assets, as of financial year-end t , both adjusted for

goodwill write-offs in year t .


269

before declining further by -2,7% between t+365 to t+730. Overall, the lowest index

values are observable 6 months prior to the financial year-end t.

Fig. 49: Cumulative stock market performance of sample firms (index)


The mean and median daily MTB ratios of the sample firms over the same

observation period follow a similar pattern as the equities share price index outlined

above.1252 This is due to the fact that the MTB ratio builds on the market

capitalizations of the firms besides the book values of equity. Two years prior to the

goodwill write-off or non-write-off decision (t-730 days) both the median and mean

MTB ratios are above 1,0. Approximately 18 months prior to t, the mean and

median MTB ratios of the sample firms fall below one, indicating that investors

view the market values of the firms below their book values of assets. Between

t-365 days and t, the average MTB ratio of the sample is surprisingly stable, moving

around 0,7. Between t and t+730 days, the ratio is found out to recover slowly,

1252 Cf. Li and Sloan (2012), p. 40.

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1

t-730 days t-365 days t t+365 days t+730 days

Cum

mul

ativ

e st

ock

perf

orm

ance

(eq

uall

y w

eigh

ted

inde

x)

All companies in the sample

Year t-1 Year t Year t+1 Year t+2


270

implying that the sample firms continue to have problems to generate (current and

expected) earnings that exceed their cost of capital.

Fig. 50: Market to book value ratios of sample firms Source: Own illustration.

0,4

0,6

0,8

1,0

1,2

1,4


Mar

ket t

o B

ook

Val

ue R

atio

All companies in the sample (mean) All companies in the sample (median)



271

8.1.3 Goodwill write-off and non-write-off decisions

After having looked into selected descriptive financial characteristics of all sample

firms related to (i) size, (ii) stock market performance and (iii) market valuations,

the following section analyses the actual goodwill write-off and non-write-off

decisions in year t.

Over the observation period 2005-2014, out of the 154 observations where firms

with strong capital market-implied triggering events have been identified, in 90 of

the cases goodwill is actually written off. This represents a write-off probability of

58,4%. In 64 of the cases, goodwill is not written off. Surprisingly, one could have

expected that this write-off probability would be much closer to 100%, as a MTB

ratio <1 is a very strong indicator for an economically impaired goodwill.1253

Consequently, strong implications exist that factors might be present in the sample

firms that hinder goodwill write-off decisions.

Table 10: Goodwill write-off probability by financial year Source: Own illustration.

1253 Cf. Ramanna and Watts (2012), p. 751.

Overview on goodwill write-off probability by financial year

Financial year

Number of observations [1]

Number of goodwill write-off observations

Number of no goodwill write-off observations

Goodwill write-offprobability

2014 4 2 2 50,0%2013 7 1 6 14,3%2012 32 24 8 75,0%2011 19 13 6 68,4%2010 9 7 2 77,8%2009 53 23 30 43,4%2008 19 13 6 68,4%2007 1 0 1 0,0%2006 4 3 1 75,0%2005 6 4 2 66,7%

Total 154 90 64 58,4%

[1] Firms with two consecutive financial year-ends where MTB <1.


272

The majority of the goodwill write-off decisions (132) is identified in the years

2008-2012.1254 In those years, goodwill is written off 80 times. However the

goodwill write-off probability over those years (60,6%)1255 is not statistically

significantly different to the full observation period. Given that in some years the

number of observations are relatively small, a comparison of goodwill write-off

probabilities between single years where n is smaller than 10 might not provide

meaningful results.

Table 11: Goodwill write-off amounts by financial year Source: Own illustration.

The mean goodwill write-off amount is EUR 678m in the sample. The median

amount is found out to be smaller (EUR 108m). The reason why the median amount

is substantially smaller is that several very large write-offs are observed in the

sample. Amongst those are the ones recorded by Vodafone Group Plc in 2006 (EUR

1254 Cf. Li and Sloan (2012), p. 43, Chen et al. (2014), p. 37. 1255 Calculated as 80 / 132 = 0,606.

Overview on goodwill write-offs by financial year

Financial year

Number of goodwill

write-off observations

Mean goodwill

write-off amount

in EURm [1]

Median goodwill

write-off amount in EURm [1]

Mean goodwill write-off

amount in % of goodwill [1] [2]

Median goodwill write-off

amount in % of goodwill [1] [2]

2014 2 -93 -93 -43,5% -43,5%2013 1 -512 -512 -2,0% -2,0%

2012 24 -243 -45 -16,0% -2,6%2011 13 -391 -224 -7,6% -5,7%

2010 7 -379 -35 -6,1% -3,1%2009 23 -236 -140 -11,5% -8,9%

2008 13 -510 -72 -33,8% -11,3%2007 0 n/a n/a n/a n/a

2006 3 -11'547 -710 -18,9% -18,5%2005 4 -28 -15 -18,2% -4,0%

Total 90 -678 -108 -16,1% -5,9%

[1] Mean/median values for write-off firms only.[2] Goodwill amounts adjusted for write-off in year t .


273

33’808m), Alcatel Lucent S.A. in 2008 (EUR 4’725m) and Enel SpA in 2012 (EUR

2’584m).

When looking at the write-off amounts in year t in relation to total goodwill in year

t1256, mean and median values amount to 16,1% and 5,9%, respectively. These

numbers mean that in the write-off subsample on average 16,1% (mean) or 5,9%

(median) of total goodwill was written off in year t. The goodwill write-off amount

in % of total assets1257 amounts to 1,4% (mean) and 0,1% (median). The mean

goodwill write-off amount in % of total assets amounts for non-financial firms in the

sample to 2,5%, whilst for financial firms to 0,1%. This difference can be explained

by the relatively larger asset base of banks and insurance companies compared to

industrial companies.

8.2 Univariate analysis of goodwill write-off and non-

write-off firms

In the univariate analysis various variables which capture the sample firms’ financial

and non-financial characteristics are compared between the firms of the write-off

and non-write-off subsamples. The univariate analysis explores each defined

variable independently1258 by comparing the distributions of the variables in the two

subsamples.1259 Differences in means were analysed by applying a Student’s t-test

and/or a Mann-Whitney U-test.

In the context of this PhD thesis and the outlined research questions stated above,

the univariate analyses focus on the following research areas:

(i) financial characteristics of the subsamples related to goodwill, firm size,

market valuations, and historical financial performance,

1256 Total goodwill in year t adjusted for the goodwill write-off amounts in year t. 1257 Total assets in year t adjusted for the goodwill write-off amounts in year t. 1258 Cf. Fielding and Gilbert (2006), p. 50. 1259 Cf. Saint-Germain (2015). Differences in means were analysed by applying a Student’s t-test and/or a Mann-Whitney U-test.


274

(ii) potential private information on future changes in financial performance held

by the firms’ management teams,

(iii) Agency theory-based incentives related to existing debt contracts,

compensation and reputation concerns of a CEO, and valuation motives, as

well as

(iv) Goodwill allocation decisions, CGU or reporting segment changes as well as

subsequent goodwill valuation methodologies.

8.2.1 Financial characteristics

8.2.1.1 Goodwill

Total goodwill in year t, adjusted for any write-off amount in year t, amounts on

average to EUR 4’615m (mean) across the entire sample. In the write-off subsample,

this amount is found out to be statistically significantly larger than in the non-write-

off sample (p-value < 0,05). Before the write-off is recorded in year t, the write-off

firms carry on average EUR 6’278m of goodwill on their balance sheets, whilst for

non-write-off firms this amount is substantially lower with EUR 2’277m in the

respective year.

In the write-off subsample the mean goodwill write-off amount is found out to be

EUR 678m, representing 16,1% of total goodwill.1260 The earnings impact of the

write-off in terms of total revenues is equal to 5,3% in the year when the write-off is

recognized, indicating that write-offs usually are large in scale and substantially

influence accounting earnings in the year of the write-off.

The goodwill intensity1261 between the two subsamples in year t is found out to be

marginally statistically significantly different from one another (p-value < 0,1). The

mean goodwill intensity over the full sample amounts to 8,4% (mean) and 2,0%

(median). The mean ratio in the write-off subsample is found out to be 9,5%,

compared to the non-write-off subsample of 6,8%.

1260 Ranging over the entire write-off sample between 0,01% and 100% 1261 Defined as total goodwill divided by total assets, as of financial year-end t, adjusted for goodwill write-offs in year t.


275

8.2.1.2 Size and valuation

Similar to the findings on the total goodwill amounts in year t, also total assets

(adjusted for any goodwill write-off amounts in year t) of the goodwill write-off

firms are found out to be statistically significantly larger than their non-write-off

counterparts in the sample (p-value < 0,05). With EUR 223’233m of total assets, the

goodwill write-off firms are on average more than twice as large as the non-write-off

firms (EUR 100’870m). Together with the findings on the total goodwill amounts

and that goodwill intensity is not highly statistically significantly different between

the subsamples, these findings could imply that larger firms are under greater

scrutiny from auditors and that they review impairment tests more closely due to the

existence of higher reputational risks larger audit clients could have to them.

Also from a market capitalization point of view, goodwill write-off firms are found

out to be larger on average (p-value < 0,05). With a mean market capitalization of

EUR 11’650m, non-write-off firms are on average 39% smaller with a mean value

of EUR 7’066m.

Also statistically significant differences are found when comparing the MTB

valuations of up to one year prior to the write-off observation. 365 days before the

goodwill write-off or non-write-off decision, firms in both subsamples trade with a

substantial discount on their book values of equity. For write-off firms this valuation

ratio (MTBt-1) is found out to be 0,724 (mean), meaning that write-off firms trade on

average approx. 7% higher than their non-write-off counterparts (MTBt-1 = 0,654).

This suggests that two years before the write-off observation non-write-off firms had

lower market valuations than those who actually recorded a write-off one year later.

However over the course of the year (t-365 days to t), the market value of the non-

write-off firms recovers slightly until t (MTBt = 0,717) whilst that of the write-off

subsample further deteriorates to 0,641. The difference in MTBt is found out to be

statistically significantly different (p-value < 0,05).


276

Fig. 51: Market to book value ratios of goodwill write-off and non-write-off firms


8.2.1.3 Historical financial performance

When looking at various performance measures, one comes to the conclusion that

the firms in the full sample had difficulties in terms of financial performance

between two years prior to the goodwill write-off or non-write-off decision (t-730

days) and t. This holds true for both stock market returns as well as financial

performance measures based on accounting earnings. However whilst a statistically

significant difference in terms of stock returns between the two subsamples is

identified for year t (p-value < 0,05), changes in financial performance measured in

terms of accounting earnings are not. The findings on changes in accounting

earnings over the preceding two financial years are stable across various variables

like changes in ROA, EBITDA, EBITDA margin and revenues.

Stock returns of the firms in the full sample declined by -35,3% (mean) between t-

730 days and t-365 days, suggesting that the market capitalizations of those firms

0,4

0,6

0,8

1,0

1,2

1,4

1,6


Mar

ket t

o B

ook

Val

ue R

atio

Goodwill write-off firms (mean) Goodwill non-write-off firms (mean)



277

dropped by more than one third in one year on average. Thereafter from t-365 days

up to the t, average stock returns for all sample firms remain at a very low level.

When considering the median values, the stock returns decline even more over the

full two years period. No statistically significant difference is observable between

the two subsamples in terms of stock returns between t-730 days and t-365 days,

which both show a similar stock market performance.1262 Analogous to the

development of MTB over the time period t-365 days to t, stock returns improve for

the non-write-off sample (mean: +25,5%) whilst stock returns of the firms in the

write-off subsample are again negative (-1,7%). This difference in terms of stock

returns in year t is found out to be statistically significant (p-value < 0,05).

Fig. 52: Cumulative stock market performance of goodwill write-off and non-write-

off sample firms (index)


1262 Stock returnst-1 (between t-730 days and t-365 days): -35,9% for the subsample of write-off firms and -34,6% for the subsample of non-write-off firms.

0,4

0,5

0,6

0,7

0,8

0,9

1,0

1,1


Cum

mul

ativ

e st

ock

perf

orm

ance

(eq

uall

y w

eigh

ted

inde

x)

Goodwill write-off firms (mean) Goodwill non-write-off firms (mean)



278

When looking at the absolute levels as well as changes in financial performance,

measured exclusively on the basis of accounting earnings, it becomes obvious that

over the years prior to the goodwill write-off or non-write-off decision, firms in the

full sample experience substantial deteriorations in financial performance, including

growth, profitability, and return on capital measures. Nevertheless the level of these

performance changes is similar across the two subsamples, given that no statistically

significant difference in means is observable.

In the write-off subsample, the average return on assets (ROA1263) amounts to 1,5%

(mean) in year t-1. This measure is marginally higher for the non-write-off firms

with 2,5% (mean). In the subsequent year t, a further deterioration of this measure is

observable for firms in both subsamples (0,9% for the write-off firms and 1,7% for

the non-write-off firms).

Besides the absolute level of the sample firms’ financial performance before the

goodwill write-off or non-write-off decision, the relative changes in financial

performance in the two subsamples are analysed. This includes revenues growth

rates, changes in ROA, and EBITDA growth. Relatively low revenues growth levels

are observable across the full sample as well as across the subsamples. Over a three

year period prior to the goodwill write-off or non-write-off decision, revenues

remained almost flat with an observable growth of 2,8% for the write-off firms and

2,3% for the non-write-off firms (both median values). Across the full sample, the

absolute and relative profitability measures EBITDA and EBITDA margin decline

during the prior two years before the goodwill write-off or non-write-off decision

(i.e. in years t-1 and t), and therefore follow a similar pattern as the performance

measure ROA. Absolute EBITDA decreases on average by -5,4% in year t-1 and by

a further -1,5% in year t. Similar findings are observable when looking at the change

in the sample firms’ EBITDA margins which decline by -8,6% in year t-1 and by

another -2,9% in year t. Firms in both subsamples show very low growth in total

revenues and experience substantial declines in ROA and profitability. These

findings imply that firms in both subsamples are confronted with strong

1263 Defined as net income divided by total assets.


279

deteriorations in financial performance during a time period of 2 years prior to the

write-off or non-write-off decisions.


280

Overview on financials of goodwill write-off and non-write-off firms

Note: Mean Median

Standard

deviation n Mean Median

Standard


Standard

deviation n

Goodwill Total Goodwillt [1] [4] 6'278 2'496 13'893 90 2'277 513 6'078 64 4'615 1'036 11'491 154

Goodwill intensityt [2] 0,09 0,02 0,13 90 0,07 0,02 0,11 64 0,08 0,02 0,12 154

Goodwill write-off amountt [4] -678 -108 3'569 90 0 0 0 64 -396 -4 2'749 154

Goodwill write-offt in % of goodwillt [3] -0,16 -0,06 0,25 90 0,00 0,00 0,00 64 -0,09 -0,01 0,21 154

Goodwill write-offt in % of revenuest -0,05 -0,01 0,12 90 0,00 0,00 0,00 64 -0,03 0,00 0,10 154

Size Total Assetst [1] [4] 223'233 57'872 402'751 90 100'870 27'474 242'479 64 172'381 41'699 350'525 154

Market Capitalizationt [4] 11'650 4'535 17'643 90 7'066 3'940 8'737 64 9'745 4'017 14'790 154

Valuation MTBt 0,64 0,68 0,24 90 0,72 0,77 0,21 64 0,67 0,72 0,23 154

MTBt -1 0,72 0,79 0,22 90 0,65 0,71 0,23 64 0,69 0,75 0,23 154

[1] Book value adjusted for goodwill write-offs in year t.

[2] Defined as total goodwill divided by total assets, as of financial year end t, adjusted for goodwill write-offs in year t.

[3] Total goodwill as of financial year-end t, adjusted for goodwill write-offs in year t.

[4] In EURm.

Write-off firms Full sampleNon-write-off firms


281

Table 12: Overview on financials of goodwill write-off and non-write-off firms Source: Own illustration.

Overview on financials of goodwill write-off and non-write-off firms (continued)

Note: Mean Median

Standard


Standard


Standard

deviation n

Historical Stock returnst -0,017 -0,108 0,679 90 0,255 0,148 0,604 64 0,096 0,000 0,663 154

performance

Stock returnst-1 -0,359 -0,313 0,312 90 -0,346 -0,388 0,354 64 -0,353 -0,370 0,331 154

ROAt [5] 0,009 0,006 0,025 90 0,017 0,010 0,029 64 0,012 0,008 0,027 154

ROAt-1 [5] 0,015 0,011 0,023 90 0,025 0,020 0,030 64 0,019 0,014 0,026 154

EBITDA margint [6] 0,163 0,138 0,467 90 0,111 0,123 0,782 64 0,141 0,123 0,618 154

EBITDA margint-1 [6] 0,145 0,128 0,400 90 0,207 0,124 0,322 64 0,171 0,124 0,371 154

Change in Revenues growtht-1 to t 3,918 0,021 36,819 90 0,671 0,011 3,729 64 2,569 0,021 28,295 154

historical

performance Revenues growtht-3 to t 0,063 0,028 0,285 90 0,119 0,023 0,615 64 0,087 0,028 0,453 154

ROA changet-1 to t [5] -0,006 -0,002 0,017 90 -0,008 -0,002 0,022 64 -0,007 -0,002 0,019 154

ROA changet-2 to t-1 [5] -0,005 -0,003 0,014 90 -0,005 -0,003 0,015 64 -0,005 -0,003 0,014 154

EBITDA growtht-1 to t 0,000 -0,034 2,340 90 -0,036 -0,058 0,623 64 -0,015 -0,041 1,833 154

EBITDA growtht-2 to t -0,149 -0,149 0,979 90 0,044 -0,142 1,835 64 -0,069 -0,149 1,403 154

[5] Defined as net income divided by total assets.

[6] Defined as EBITDA divided by total revenues.

Write-off firms Non-write-off firms Full sample


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8.2.2 Private information on changes of the firms’


A possible reason why senior management teams might be reluctant to write off

goodwill is that they are convinced that financial performance will improve in the

short-term future, leading again to a full recoverability of goodwill. Even when

current financial performance is low, senior management teams might be able to

convince auditors on the basis of prepared business plans that financial performance

will strengthen again and that the current state of negative financial performance is

not expected to sustain. This information would most likely not be known to

outsiders of the firms like shareholders in year t, and consequently considered to be

private.

Therefore it is analysed whether future financial performance over the subsequent

two years (year t+1 and t+2) after the goodwill write-off or non-write-off decision

(year t) differs between the subsamples of write-off and non-write-off firms, and

whether indicators are observable that would imply that senior management teams

hold information on a positive financial performance outlook.

8.2.2.1 Private information proxied by future stock returns and

changes in accounting earnings

To analyse whether future performance is statistically different for the firms in the

goodwill write-off and non-write-off sample, various financial performance

measures are studied at hind sight. This means that financial performance measures

based on both capital markets information and accounting earnings are analysed up

to two years after the actual goodwill write-off or non-write-off decision in year t to

understand whether they could have had an influence on the decisions of senior

management teams not to write off goodwill despite strong current capital market

indications that goodwill is economically impaired.

For the firms in both subsamples increasing stock returns are observable one year

after the goodwill write-off and non-write-off decision (Stock returnst+1). The mean


283

stock returns over the full sample amounts to 29,1% (mean). Surprisingly, firms in

the write-off subsample tend to perform better in terms of stock returns than non-

write-off firms, an observation also described by Chen et al. (2014).1264 This

difference in stock returns in the first year after the write-off decision (year t+1) is

however not found out to be statistically significant. One reason for the observation

of increasing stock returns could be, that goodwill write-offs are frequently

announced by firms’ managements together with restructuring programs which

should improve the financial performance of the underperforming business divisions

in which goodwill was written off.1265 Another reason might be that equity investors

honour that the management team is aware of the underperformance of certain

business units in which the write-off was recognized and acts transparent towards

their shareholders on the issue.

A similar analysis is performed for the stock returns in the second year after the

write-off or non-write-off decision (Stock returnst+2). Over the full sample, returns

are found out to be negative, amounting to -0,1% (mean). When looking at the

subsamples individually, write-off firms perform worse (-6,8%; mean) than their

non-write-off counterparts (6,6%; mean). This difference in means is found out to be

statistically significant at a 95% confidence level.

Future revenues growth and changes in ROA in the subsequent years after the

goodwill write-off or non-write-off decision are not found out to be statistically

significantly different between the subsamples. For firms in both subsamples an

improvement in financial performance in terms of revenues growth and changes in

ROA is observable in yeat t+1. The mean revenues growth across the entire sample

amounts to 20,5% in t+1 (Revenues growtht+1). Here as one could have expected,

the non-write-off firms grow stronger in terms of revenues in the year subsequent to

the goodwill non-write-off decision, however not at a statistically significantly

different level. Also ROA in year t+1 is found out to recover, however at a low

level, compared to the previous year t with a mean ROA changet+1 of 0,5%. Total

EBITDA in year t+1 grows by 2,0% across the full sample, however with large

1264 Cf. Chen et al. (2014), p. 3. 1265 Cf. Hirschey and Richardson (2002), p. 183.


284

variations between the sample firms. Non-write-off firms tend to perform better than

their write-off counterparts in terms of EBITDA growth and EBITDA margin

growth in year t+1, however without being statistically significantly different.

Nevertheless in year t+2, the EBITDA margin growth rates of the non-write-off

firms are found out to be statistically significantly higher than those of the write-off

firms. On average, the EBITDA margins of the non-write-off firms grow by 24,3%

(mean), compared to a growth level of 7,4% (mean) for the goodwill write-off firms

in year t+2. This difference in means is found out to be statistically significant at a

95% confidence level.

8.2.2.2 Private information proxied by share buybacks and

changes in CEO share ownership

The results of the share buyback analysis imply that senior managers could hold

positive information on the firms’ future financial performance improvements,1266

which could explain why goodwill write-offs are observable for some firms and not

in others despite equally strong capital market indications that goodwill is

economically impaired. On the basis of earlier research findings on the correlation

between share buybacks and future financial performance improvements, one could

expect that in case senior management teams hold private information on future

performance improvements that share buybacks are observable to a greater extend in

goodwill non-write-off firms than in write-off firms.1267 This reasoning was firstly

introduced by Ramanna and Watts (2012) in the context of goodwill accounting.1268

To test this reasoning, three variables are analysed. The first looks into the total

amount of cash spent on repurchasing common stock on the open market

(Repurchase of common stockt) in year t, i.e. the year of the write-off or non-write-

1266 Cf. Muller et al. (2009), p. 2, Brav et al. (2005), pp. 514, 518, Vermaelen (1981), p. 166, Dann (1981), p. 113. 1267 In case senior management teams think that the current stock price does not properly reflect the underlying fundamentals of the firm and the expected positive performance, they might introduce share buyback programs. By doing to senior management teams could signal market participants the positive information on the firm’s expected financial performance and therefore a potential undervaluation (Cf. Comment and Jarell (1991), pp. 1243-1244). 1268 Cf. Ramanna and Watts (2012), pp. 757-759.


285

off decision. Over the entire sample, firms spent on average EUR 413,4m (mean) to

repurchase common stock in year t. For the write-off firms this amount was lower

(EUR 304,7m) than for the non-write-off firms (EUR 566,1m). This difference

however was found out not to be statistically significantly different. In 82 of the

total 154 cases (53,2%), firms engaged in share buyback activities in year t. This

ratio is higher for the non-write-off firms (65,6%; mean1269) than for the write-off

firms (44,4%1270; mean). This difference in means is found out to be statistically

significant at a 95% confidence level (p-value < 0,05).

As described above, write-off firms are on average larger in terms of total assets and

market capitalization. To control for this size difference, the repurchase amount

divided by total revenues (Repurchase of common stock quotat) is also calculated.

This helps to understand how much the write-off and non-write-off firms spent on

average to repurchase shares in terms of total revenues. Across both subsamples the

level of this quota is found out to be 1,8%. Here also a statistically significant

difference is means is observable (p-value < 0,05). Whilst write-off firms spent on

average 0,9% of total revenues on share buybacks, this ratio is approx. three times

higher for non-write-off firms (3,2%; mean)

When looking at the changes of the individual share ownerships of the respective

CEOs of the write-off and non-write-off firms in year t, no statistically significant

difference is observable. As described above, the change in individual share

ownership could be used as a proxy for the private information a CEO might possess

on the future performance of a firm that might not already be fully reflected in the

share price.1271 Earlier research findings suggest that managers frequently profit

from private information, which can move the share price of the firm in the future,

through strategic buying or selling ahead of corporate announcements.1272 These

findings would imply that CEOs with positive private information on future

performance improvements, which keeps them from booking a goodwill write-off

1269 In 40 out of total 64 cases, share buybacks were observable. 1270 In 42 out of total 90 cases, share buybacks were observable. 1271 Cf. Piotroski and Roulstone (2005), p. 78, Jagolinzer (2009), pp. 235-237, Beneish and Vargus (2002), p. 788, John and Lang (1991), p. 1385. 1272 Cf. Muller et al. (2009), p. I.


286

despite strong capital market implications that goodwill is economically impaired,

would acquire more shares in year t than their CEO counterparts in the write-off

firms who do not have such information.1273

Over the entire sample, CEO share ownership increased on average by 87’008

(mean) in the year of the write-off or non-write-off decision, compared to the prior

year-end. This amount is found out to be greater for the write-off subsample with

128’879, compared to the non-write-off subsample with 32’394. Additionally, the

percentage change in the number of shares held by the CEOs of the firms in the

sample is analysed. During the time period t-365 days and t, CEO share ownership

increased on average by 52,8% (mean). The changes in share ownerships of CEOs

of non-write-off firms are found out to be higher on average, amounting to 65,3%

(mean), compared to 43,2% for the CEOs of the write-off firms, however without

being statistically significantly different.

1273 Cf. Ramanna and Watts (2012), p. 757, Jagolinzer (2009), p. 224, Roulstone (2008), p. 28.


287

Table 13: Overview on variables related to private information on future financial performance


Overview on variables related to private information on changes of the firms’ financial performance

Note: Mean Median

Standard


Standard


Standard

deviation n

Future company Stock returnst to t+1 0,362 0,219 0,717 90 0,191 0,134 0,574 64 0,291 0,189 0,667 154

performance

Stock returnst+1 to t+2 -0,068 -0,058 0,419 90 0,066 0,121 0,407 64 -0,013 0,002 0,420 154

EBITDA margin growtht to t+1 [1] -0,030 0,000 0,469 90 0,117 0,011 0,789 64 0,031 0,003 0,627 154

EBITDA margin growtht+1 to t+2 [1] 0,074 0,054 0,574 90 0,243 0,147 0,622 64 0,144 0,074 0,601 154

Revenues growtht to t+1 0,109 0,010 0,452 90 0,339 0,014 2,745 64 0,205 0,012 1,807 154

ROA changet to t+1 [2] 0,006 0,001 0,022 90 0,003 0,001 0,022 64 0,005 0,001 0,022 154

EBITDA growtht to t+1 -0,004 -0,012 0,657 90 0,055 0,059 0,919 64 0,020 -0,004 0,777 154

Share buybacks Repurchase of common stock_DUMMYt [3] 0,444 0,000 0,497 90 0,656 1,000 0,475 64 0,532 1,000 0,499 154

Repurchase of common stock quotat [4] 0,009 0,000 0,017 90 0,032 0,002 0,103 64 0,018 0,000 0,068 154

Repurchase of common stockt [5] 304,7 0,0 1'190 90 566,1 5,5 2'944 64 413,4 0,4 2'109 154

Change in Change in number of sharest-1 to t 128'879 6'232 449'882 64 32'394 1'707 131'530 51 87'008 3'691 352'643 115shares ownership

Change in shares_DUMMYt-1 to t [6] 0,67 1,00 0,47 64 0,63 1,00 0,48 51 0,65 1,00 0,48 115

Change in sharest-1 to t [7] 0,43 0,06 1,28 64 0,65 0,11 1,60 51 0,53 0,07 1,43 115

[1] Defined as EBITDA divided by total revenues.

[2] Defined as net income divided by total assets.

[3] Dummy variable coded 1 if share buybacks in year t, and 0 otherwise.[4] in % of total revenues as of year t.

[5] in EUR million.

[6] Dummy variable coded 1 if change in shares is positive in year t, and 0 otherwise.

[7] Change in % of share ownership in year t as of year-end t-1.



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8.2.3 Incentives predicted by agency theory

Regarding incentives predicted by agency-theory, variables related to (i) capital

structure and debt covenants, (ii) CEO tenure, and (iii) CEO compensation and share

ownership are calculated and compared between the two subsamples.

8.2.3.1 Capital structure and the cost of borrowing

Three sets of variables linked to debt contracts are analysed in the full sample and

compared in a subsequent step between the two subsamples of write-off and non-

write-off firms.

The first set analyses the capital structure of the write-off and non-write-off firms.

The first leverage ratio compares the book value of debt to total assets in year t.

Over the full sample, this quota amounts to 0,40 (mean). On average, write-off firms

have a higher leverage ratio (0,41; mean), compared to the leverage ratio of non-

write-off firms (0,38; mean). However, this difference in means is not found to be

statistically significant. Besides the overall leverage ratio, the ratio of bank debt to

total assets is calculated and compared between the two subsamples. In write-off

firms, this ratio amounts to 0,21 (mean), whereas in the non-write-off firms the bank

debt to total assets ratio is lower with 0,12 (mean). This ratio is found out to be

statistically significantly higher in the write-off subsample at a 90% confidence

level.

The second set of variables looks into the existence of debt covenants in the two

subsamples. In a first step, an analysis of accounting based covenants, i.e. covenants

that consider financial ratios derived from financial statements, is made on a general

basis. In a subsequent step, a refinement of the covenants variable is made by

analysing the existence of covenants that would be directly impacted by a goodwill

write-off.

Generally, accounting based covenants have been identified in 46,1% of the write-

off and non-write-off firms. The percentage is higher for the non-write-off

subsample (57,8%; mean), than for the write-off subsample (37,8%; mean). This


289

difference is found out to be statistically significant at a 95% confidence level (p-

value < 0,05). When looking exclusively at the existence of accounting-based

covenants that would be impacted by a goodwill write-off, one also finds that the

probability of observing such covenants is higher in the subsample of non-write-off

firms than in the subsample of write-off firms. In 26,6% of the non-write-off

observations, covenants that would be impacted by a goodwill write-off are found,

whereas this ratio amounts to 13,3% in the write-off subsample. Here, as well, a

statistically significant difference in means at a confidence level of 95% is observed

(p-value < 0,05).

The third set of variables studies the actual cost of borrowing of non-write-off and

write-off firms.1274 Whilst write-off firms can borrow debt cheaper in the year of the

write-off or non-write-off decision at a mean interest rate of 3,7%, financing

conditions for non-write-off firms are less favourable with 5,1%; also being

statistically significant different at a 90% confidence level (p-value < 0,1).

8.2.3.2 CEO tenure

CEO tenure at the time of the goodwill write-off or non-write-off decision in year t

is found out to amount to 6,0 years. This means that on average CEOs have been 6

years in office when they decided to write off or not to write off goodwill. CEO

tenure in the write-off subsample with 5,5 years is lower than in the non-write-off

subsample with 6,6 years; a result that that has been frequently observed in other

research studies on goodwill write-off decisions.

Due to several outliers, the trimmed CEO tenure for the two subsamples is

calculated. Here, outliers are replaced by the respective subsample means (before

having replaced outliers). This results in a trimmed CEO tenure of 3,7 years for the

write-off subsample and 4,4 years. This difference in means between the two

subsamples is found out to be statistically significant at a 95% confidence level (p-

value < 0,05).

1274 Calculated as interest expense (from income statement) in year t over interest-bearing total debt.


290

8.2.3.3 CEO compensation and share ownership

Various variables related to the CEO’s existing compensation contracts as well as

their share ownership are calculated and compared between the two subsamples. In

particular, those variables refer to a CEO’s (i) fix and variable payoff structures

from his/her salary compensation contract and (ii) existing share ownership in the

firm measured in terms of number of shares owned as well as their market value.

The average number of shares owned by a CEO for whose firm a goodwill write-off

was observable in year t is not statistically different to the average CEO share

ownership in the non-write-off subsample. Mean CEO share ownership as a

percentage of common shares outstanding (CSO) is found out to be higher in the

non-write-off subsample with 0,17% than in the write-off subsample with 0,06%.

This observation holds also true for the year prior to the goodwill write-off decision

(year t-1). Nevertheless these differences in means are also not statistically

significant.

The market value of the shares owned by a CEO is computed as of the end of the

financial year t as well as of the end of the financial year t-1 in order to understand

whether share ownership is different between the two subsamples, and therefore

could potentially impact the goodwill write-off decision. In the year of the write-off

decision (year t), over the entire sample the mean market value of the shares owned

by the CEOs amounts to EUR 2,95m. Whilst the mean market value of the shares

owned by the CEOs who decided to write off goodwill is found out to be EUR

3,32m, this amount is lower for a CEO who decided not to write-off goodwill with

EUR 2,47m. However, this variable is also not found out to be statistically different

between the two subsamples. The mean market value of shares owned by a CEO in

the non-write-off subsample at the end of the prior financial year-end (t-1) is also

found out to be lower with EUR 2,50m than in the write-off subsample with EUR

3,37m. However here also no statistically significant differences in means is

detected.

For 106 of the 154 CEOs represented in the sample, information on their salaries in

terms of awarded compensation components was available. The mean CEO total


291

cash compensation (fix and variable) in year t over the full sample amounts to EUR

1,52m. Whilst the mean total cash compensation for CEOs of the non-write-off

firms slightly increases between year t-1 and year t, total cash compensation of

CEOs of the write-off firms decreases over the same time period. To understand the

reasons for this change in total cash compensation, the individual cash compensation

components were studied in the annual reports and/or compensation reports of the

firms in the sample. In particular, emphasis in the analysis was placed on fix and

variable cash compensation structures in the year of the goodwill write-off or non-

write-off decision (year t) as well as in the prior year (year t-1).

To do so, the variable Cash bonust/Cash fix compensationt is calculated which

analyses the ratio of the total cash bonus amount to the total cash fix salary of a

CEO in year t. This ratio allows understanding whether CEOs of write-off firms

received on average a lower cash bonus than their non-write-off counterparts in the

year of the observable write-off or non-write-off decision. This ratio amounts to

0,513 (mean) for the write-off subsample and 0,725 (mean) for the non-write-off

subsample. This finding suggests that CEOs of non-write-off firms received on

average a higher cash bonus in year t compared to the CEOs who decided to write

off goodwill. However, when applying a t-test on the two subsample means, only a

limited statistically significant difference in means is observable.

This difference in means is primarily driven by a downward adjustment of the

CEOs’ variable cash bonus compared to their bonus levels of the previous year.

Overall, the bonus fraction, i.e. the ratio of the cash bonus to fix compensation,

decreased on average by -28,4% (mean) for the CEOs who wrote off goodwill. This

change implies that CEOs of write-off firms received in the prior year a higher

bonus in terms of their fix compensation, whereas CEOs of non-write-off firms saw

their cash bonus amount relative to their fix salary increasing by +16,3% (mean),

compared to the previous year. This difference in means between the two

subsamples is found out to be statistically significant at a 95% confidence level (p-

value < 0,05). Also it is found out that for CEOs of non-write-off firms, the

likelihood of receiving a cash bonus is higher than for CEOs of write-off firms.

Whilst in 73% of the observations a cash bonus is awarded to the CEOs of non-


292

write-off firms, this is the case only in 62% of the write-off observations. This

difference in means between the two subsamples is however not found out to be

statistically significant.


293

Overview on variables related to agency theory-based incentives

Note: Mean Median

Standard


Standard


Standard

deviation n

Company Cost of borrowingt 0,037 0,042 0,032 90 0,051 0,044 0,053 64 0,043 0,043 0,043 154

financing

Leverage (Debt / Assets)t 0,414 0,373 0,437 90 0,376 0,410 0,222 64 0,398 0,389 0,364 154

Leverage (Bank debt / Assets)t 0,209 0,072 0,439 90 0,123 0,062 0,168 64 0,173 0,067 0,355 154

Covenant (financial accounting based)t [1] 0,378 0,000 0,485 90 0,578 1,000 0,494 64 0,461 0,000 0,498 154

Covenant (effected by goodwill write-off)t [1] 0,133 0,000 0,340 90 0,266 0,000 0,442 64 0,188 0,000 0,391 154

CEO tenure CEO tenure (Trimmed)t [2] [3] 3,73 4,00 1,78 90 4,42 5,00 2,08 64 4,02 4,00 1,94 154

CEO tenuret [3] 5,54 4,00 4,76 90 6,64 5,00 6,42 64 6,00 4,00 5,54 154

New CEO (first year in office)t [1] 0,18 0,00 0,38 90 0,11 0,00 0,31 64 0,15 0,00 0,36 154

New CEO (up to two years in office)t [1] 0,32 0,00 0,47 90 0,23 0,00 0,42 64 0,29 0,00 0,45 154

New CEO (up to three years in office)t [1] 0,42 0,00 0,49 90 0,41 0,00 0,49 64 0,42 0,00 0,49 154

Compensation Number of share owned by CEOt 682'568 96'085 1'261'014 72 449'578 105'245 1'125'041 54 581'460 98'234 1'209'420 126

and

shares ownership Number of share owned by CEOt-1 561'092 80'456 1'155'116 64 403'309 62'831 1'100'218 51 491'968 62'847 1'134'098 115

Shares owned by CEO as % CSOt [4] 0,06 0,01 0,12 72 0,17 0,02 0,62 54 0,10 0,02 0,42 126

Shares owned by CEO as % CSOt-1 [4] 0,05 0,01 0,11 64 0,15 0,02 0,58 51 0,09 0,02 0,39 115

[1] Dummy variable coded 1 if observable and 0 otherwise.

[2] Trimmed (66,7%) to exclude outliers.

[3] in years.

[4] in %.



294

Table 14: Overview on variables related to contract-based incentives Source: Own illustration.

Overview on variables related to agency theory-based incentives (continued)

Note: Mean Median

Standard


Standard


Standard

deviation n

Compensation CEO market value of sharest (MV CEOt) [4] 3,32 0,60 5,38 72 2,47 0,80 4,27 54 2,95 0,65 4,95 126

and

shares ownership CEO market value of sharest-1 (MV CEOt-1) [4] 3,37 0,60 5,63 64 2,50 0,40 6,05 51 2,99 0,60 5,83 115

(continued)

MV CEOt / fix cash compensationt-1 4,22 0,89 8,23 60 4,19 1,09 9,94 46 4,21 0,93 9,00 106

MV CEOt / total cash compensationt-1 2,11 0,49 5,15 60 2,34 0,54 6,23 46 2,21 0,53 5,64 106

Total cash compensationt [6] 1'557 1'226 1'139 60 1'464 1'046 1'092 46 1'517 1'200 1'120 106

Fix compensationt [6] 1'013 900 543 60 903 713 581 46 966 803 562 106

Cash bonust [6] 537 314 717 60 560 220 839 46 547 262 771 106

Cash bonus_DUMMYt [1] 0,617 1,000 0,486 60 0,733 1,000 0,442 46 0,667 1,000 0,471 106

Cash bonust / Cash fix compensationt 0,513 0,430 0,644 60 0,725 0,360 1,319 46 0,604 0,405 0,997 106

Bonus fraction increaset-1 to t -0,284 -0,104 0,718 60 0,163 0,000 0,655 46 -0,092 0,000 0,726 106

[1] Dummy variable coded 1 if CEO received a cash bonus in year t, and 0 otherwise.

[2] Trimmed (66,7%) to exclude outliers.

[3] in years.

[4] in %.

[5] in EUR million.

[6] in thousand EUR.



295

8.2.4 Goodwill reporting flexibility

Various variables related to managerial discretion and goodwill reporting flexibility

under the impairment-only approach are studied as part of this PhD thesis. These

variables focus in particular on how goodwill has been allocated to reporting

segments and which financial characteristics these reporting segments possess.

Financial characteristics captured in the variables include the reporting segments’

size, profitability and risk.

Additionally, it is analysed in how far changes of reporting segments or CGUs from

one financial period to the next could potentially be related to goodwill write-off

probabilities. Furthermore, it is studied whether the valuation methodology, i.e.

value in use or fair value less costs of disposal could potentially be linked to

goodwill write-off or non-write-off decisions.

8.2.4.1 Goodwill allocation to reporting segments

As outlined above, IAS 36 allows certain flexibility on how goodwill is allocated to

reporting segments.1275 Potentially this reporting flexibility could have an impact on

write-off probability, in particular if those reporting segments possess financial

characteristics that could substantially influence the recoverability of acquired

goodwill.1276 These characteristics are size of the reporting segment, profitability and

risk. Each of these characteristics has an influence on the cash flows on which basis

the recoverability of goodwill is tested.

The first variable studies the number of available reporting segments to which

hypothetically goodwill could be allocated (Available segmentst). Over the entire

sample, the number of available segments amounts on average to 4,8 per firm. For

the non-write-off subsample, this number is found to be lower on average (mean

1275 Cf. Grant Thornton (2014), p. 18. 1276 This reasoning builds on the notion that a firm’s management can apply discretion on choosing between various reporting segments and therefore has sound arguments for its goodwill allocation strategy so that it is approved by the firm’s auditors. Generally, goodwill allocation to reporting segments should be based on the perceived benefits of the transaction for the reporting segment. Such benefits however are difficult to measure in practice and difficult to argue against from the auditor’s side.


296

segment number of 4,2) compared to the write-off subsample (mean segment

number of 5,3). Consequently, non-write-off firms have on average fewer reporting

segments than firms for which a write-off was observable in the sample. This

difference in means was found out to be highly statistically significant at a 99%

confidence level. Similar findings are observable for the average number of

reporting segments to which goodwill had been actually allocated (No. of segments

with goodwillt). Whilst non-write-off firms allocate goodwill to fewer reporting

segments (mean segment number with goodwill of 2,7), write-off firms allocated

goodwill on average to more segments (mean segment number with goodwill of

3,8). Here again a very low p-value < 0,01 is observable. When looking at the

percentage of available reporting segments to which goodwill has been allocated

(Segments usedt), one can observe that non-write-off firms appear to be more

selective when it comes to allocating goodwill.

The advantage of the concentration variable Herfindahl-Hirschman Indext (HHIt) is

that it considers not only the number of reporting segments with goodwill but also

the relative amount of goodwill allocated to each of them. It analyses therefore the

concentration of goodwill to individual reporting segments. Observing a relatively

low HHIt implies that goodwill has been fairly evenly allocated across a large

number of reporting segments. The higher HHIt, the higher the concentration of

goodwill in certain reporting segments. To calculate HHIt for each goodwill write-

off and non-write-off observation in the sample, the individual goodwill amounts1277

allocated over all reporting segments have been collected from the sample firms’

annual reports.

Over the entire sample of observations, mean HHIt amounts to 0,598. For the non-

write-off subsample a mean HHIt of 0,720 is observable, whilst for the non-write-off

subsample the mean HHIt amounts to 0,512. Due to the relatively low standard

deviations, the difference in means is highly statistically significant with a p-value <

0,01. The results on HHIt strongly suggest that firms in the non-write-off subsample

1277 Goodwill amounts allocated to the individual reporting segments were adjusted for any write-off amounts in year t.


297

concentrate goodwill to a greater extent in individual reporting segments than write-

off firms do.

Fig. 53: Herfindahl-Hirschman Index by write-off and non-write-off subsample


In the following it is analysed whether the goodwill allocation and concentration

mechanisms by senior management teams follow certain observable patterns. To do

so, the underlying fundamentals of the reporting segments to which goodwill was

allocated were researched. This includes (i) size (proxied by the individual reporting

segments’ total revenues and total assets1278), (ii) profitability (proxied by the

individual reporting segments’ EBITDA or operating profit1279 margins) as well as

(iii) risk (proxied by the discount rates applied to test the recoverability of goodwill

allocated to that reporting segment).

The figures below display the goodwill allocation and goodwill concentration in

reporting segments with goodwill (upper figure) and over all available segments

(irrespective of whether goodwill was allocated to that segment or not) (lower

figure):

1278 Total assets of the individual reporting segments were adjusted for any write-off amounts in year t in that particular reporting segment. 1279 Operating profit adjusted for any depreciations and amortization in year t.

0,720

0,512

0,751

0,469

0,00

0,20

0,40

0,60

0,80

Non-write-off firms (n = 64) Write-off firms (n = 90)

Mean Median

HH

I


298

Fig. 54: Goodwill allocation to reporting segments with goodwill (mean)


Fig. 55: Goodwill allocation to available reporting segments (mean)


Size:

When studying the size of the individual reporting segments to which acquired

goodwill was allocated, it becomes obvious that non-write-off firms allocated a

0,6570,584

0,697

0,479

0,360

0,462

0,00

0,20

0,40

0,60

0,80

% of goodwill allocated to thelargest segment with goodwill

(total revenues)

% of goodwill allocated to themost profitable segment with

goodwill

% of goodwill allocated to theleast risky segment


0,5060,454

0,697

0,409

0,227

0,462

0,00

0,20

0,40

0,60

0,80

% of goodwill allocated to thelargest segment available

(total revenues)

% of goodwill allocated to themost profitable segment

available

% of goodwill allocated to theleast risky segment available


Goo

dwil

l con

cent

rati

on

Goo

dwil

l con

cent

rati

on


299

larger portion of goodwill to larger reporting segments than write-off firms. Non-

write-off firms allocated on average 47,5% (mean) of existing goodwill to the

largest reporting segment available1280, whilst this number is substantially lower for

the write-off firms in the sample (34,5%). This difference in means again is

statistically significant (p-value < 0,05). The same holds true when studying the size

of the individual reporting segments on the basis of total revenues.1281

When looking only at those reporting segments to which goodwill was allocated, the

differences become even more statistically significant. Here, non-write-off firms

allocated on average 62,4% (mean) of existing goodwill to the largest reporting

segment (with goodwill), whilst this number is considerably lower for the write-off

firms in the sample (38,9%). With a p-value < 0,01, the difference in means is

highly statistically significant. This finding is irrespective of whether using total

revenues or total assets as a proxy for the size of the reporting segment with

goodwill.

Profitability:

In the following step, it is analysed whether write-off and non-write-off firms differ

in the way they allocate goodwill to more or less profitable reporting segments. To

do so, initially a firm’s available reporting segments were ranked according to their

profitability. To derive this ranking, profitability was defined as either (i) EBITDA

relative to total revenues (EBITDA margin) or (ii) operating profit excluding

depreciations and amortizations relative to total revenues (operating profit margin),

as of the end of the financial year t.1282 Both profitability ratios are unaffected by

amortization charges as they are either not included (EBITDA) or manually reversed

(operating profit1283 excluding depreciations and amortizations). Whether EBITDA

1280 With segment size measured on the basis of total assets as of year-end t, adjusted for any goodwill write-offs in year t. 1281 On average write-off firms allocated 50,6% (mean) of goodwill to the largest reporting segment in terms of total revenues, whilst write-off firms allocated only 40,9% (mean) of goodwill to their largest reporting segment. 1282 In case EBITDA or operating profit in financial year t was heavily distorted due to exceptional, non-recurring effects, then profitability margins as of financial year t-1 were used for the ranking. 1283 Operating profit is frequently also referred to as EBIT.


300

or operating profit was used to perform the ranking was primarily due to the

principal reporting format of the individual firms in the sample.

For the firms represented in the sample, on average 32,2% (mean) of existing

goodwill1284 is allocated to the most profitable reporting segment available as of year

t. For non-write-off firms this percentage is found out to be substantially higher with

45,4% (mean) of existing goodwill than for the write-off firms, which have 22,7% of

existing goodwill (mean) allocated to the most profitable reporting segment

available. With a p-value < 0,01, this difference is found out to be highly statistically

significant.

Further analyses are performed on the cumulative portion of existing goodwill

allocated to the most and second most profitable reporting segments, yielding very

similar results to the ones outlined above. Whilst non-write-off firms allocated

63,3% of existing goodwill (mean) to the two most profitable reporting segment

available, this percentage of goodwill is substantially lower for the write-off firms

with 45,3% (mean). Here again, the difference is found out to be a highly

statistically significant with a p-value < 0,01.

Risk (Discount rates):

The last variable regarding financial characteristics of the reporting segments to

which goodwill was allocated is risk. For the purpose of this PhD thesis, risk is

defined as the lowest discount rate with which the recoverability of goodwill in this

reporting segment is tested.1285 To do so, in a first step (i) the CGUs, (ii) the

allocated goodwill proportions of those CGUs, and (iii) the respective discount rates

with which the recoverability of goodwill in these CGUs had been tested were

studied. For the majority of the firms in the sample (128 out of 154 possible

observations), this information was available in the notes of the annual reports. In a

subsequent step, these CGUs with the corresponding discount rates were mapped to

the reporting segments to which these CGUs belonged. By doing so, it was possible

1284 Book value of goodwill as of financial year-end t was adjusted for the write-off amount in year t. 1285 If several discount rates were used to test the recoverability of goodwill allocated to a specific reporting segment, the lowest disclosed discount rate per reporting segment was selected for the analysis of this PhD thesis.


301

to observe the discount rates by reporting segments to which goodwill had been

allocated. On the basis of these discount rates, the reporting segments were ranked

according to their inherent risk.

It can be argued that discount rates are good proxies for the inherent risks of the

CGUs and therefore also the reporting segments, as in valuation theory, discount

rates should reflect the risk of the expected cash flows being discounted.1286

Consequently, higher discount rates imply a higher risk of a CGU’s or reporting

segment’s underlying cash flows.1287 When studying the goodwill allocation on the

basis of the inherent risk of the reporting segment, one can observe that non-write-

off firms allocated greater portions of goodwill to reporting segment with lower risk

than write-off firms.

Fig. 56: Goodwill allocation to least risky reporting segments (mean)


Over the entire sample, firms allocated 56,3% (mean) of total goodwill as of

financial year-end t to the least risky reporting segment, proxied by the discount rate

applied in year t. This amount is found out to be substantially higher for non-write-

off firms (mean value of 69,7%) than for write-off firms (mean value of 46,2%).

1286 Cf. Damodaran (2015), p. 2. 1287 Cf. Damodaran (2015), p. 3.

0,697

0,891

0,462

0,710

0,00

0,20

0,40

0,60

0,80

1,00

% of goodwill allocated to least riskysegment with goodwill (discount rate)

% of goodwill allocated to least andsecond least risky segment with

goodwill (discount rate)


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These results suggest that non-write-off firms allocated approx. 24% more goodwill

to the least risky reporting segment than their write-off counterparts. The p-value <

0,01 shows that the allocation is indeed statistically significantly different. By

extending the analysis to the least and second least risky reporting segment,

similarly strong results are observable (p-value < 0,01).

The above stated results on financial characteristics of reporting segments and

goodwill allocation strongly imply that write-off and non-write-off firms differ

substantially in the way they allocated goodwill. In particular, statistically

significant differences are identified for the financial characteristics (i) size, (ii)

profitability and (iii) risk of the reporting segments to which goodwill have been

allocated.

8.2.4.2 CGU or reporting segments changes

Besides the goodwill allocation to reporting segments and the respective financial

characteristics of these segments, it is further evaluated whether changes in the

structure of CGUs or reporting segments in year t are more often observable for non-

write-off firms than for write-off firms. For this dummy variable, the annual reports

of the firms in the sample were studied. To perform the analysis, both the annual

reports of financial year t as well as year t-1 were studied so that conclusions on

possible changes in the reporting format can be drawn.

Overall, in 53 cases (out of the 154 goodwill write-off and non-write-off

observation) a modification of either the firms’ CGU or reporting structures is

observable in year t. In 101 cases no adjustments to the reporting format are made

by the firms. These 53 changes in the reporting format account for 34% of the entire

sample.

The 53 reporting format changes are made up of 11 observations in the subsample of

the write-off firms and 42 observations in the subsample of non-write-off firms.

Correspondingly, the probability of observing adjustments to the existing reporting

format in year t amounts to 12,2% for the write-off firms and 65,6% for the non-


303

write-off firms. This difference is also found out to be highly statistically significant

(p-value < 0,01).

8.2.4.3 Subsequent valuation

According to IAS 36.134, firms applying the impairment-only approach are required

to provide information on how they tested the recoverability of existing goodwill. 1288 In the full data set, the valuation methodology value in use (ViU) is found out to

be the dominating valuation approach to test the recoverability of goodwill.1289 Over

both subsamples, the ViU concept is applied in more than 90% of the goodwill

write-off or non-write-off observations (mean value of 94,8%). With 93,8% for the

non-write-off firms and 95,6% for the write-off firms, no statistically significant

difference is observable. Compared to the ViU concept, the valuation concept fair

value less costs of disposal (FVLCD) is comparatively rarely used in the sample.

The application of the FVLCD is observable in only 20,1% of the cases. Here again

no significant difference between the two subsamples is detected.

Whereas the ViU concept builds exclusively on projected cash flows arising from

the assets allocated to a CGU, the FVLCD of a CGU could be based, for example,

on trading multiples, transaction multiples, recent purchase agreements, or also on

discounted cash flows similar to the ViU concept.1290 In the analysis of the applied

valuation methodologies, no clear industry pattern is observable, i.e. whether firms

in certain industries prefer the ViU or FCLCD concept.

1288 Cf. Glaum and Wyrwa (2011), p. 68. 1289 Similar findings were obtained by Glaum and Wyrwa (2011), p. 68. 1290 Cf. PricewaterhouseCoopers (2009), p. 14, Glaum and Wyrwa (2011), p. 69.


304

Fig. 57: Preferred valuation approach to test the recoverability of goodwill in year t


0,222

0,956

0,172

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0,00 0,20 0,40 0,60 0,80 1,00


Value in Use



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Overview on variables related to goodwill reporting flexibility

Goodwill allocation Note: Mean Median

Standard


Standard


Standard

deviation n

Segment No. of segments with GWt [1] 3,81 4,00 1,67 90 2,69 2,00 1,49 64 3,34 3,00 1,69 154

choice

Available segmentst [2] 5,28 5,00 1,98 90 4,20 4,00 1,99 64 4,83 5,00 2,05 154

Segments usedt [1] 0,73 0,75 0,22 90 0,69 0,67 0,27 64 0,71 0,75 0,24 154

Goodwill

concentration Herfindahl-Hirschman Index (HHI)t [3] 0,51 0,47 0,23 90 0,72 0,75 0,24 64 0,60 0,55 0,26 154

Segment % of goodwill allocated to:size

largest segment available (total assets)t [4] 0,34 0,25 0,34 90 0,47 0,42 0,39 64 0,40 0,32 0,37 154

largest segment with GW (total assets)t [4] 0,39 0,32 0,33 90 0,62 0,75 0,35 64 0,49 0,43 0,36 154

largest segment available (total revenues)t [5] 0,41 0,37 0,33 90 0,51 0,50 0,39 64 0,45 0,39 0,36 154

largest segment with GW (total revenues)t [5] 0,48 0,42 0,31 90 0,66 0,77 0,33 64 0,55 0,54 0,33 154

Segment % of goodwill allocated to:profitability

most profitable segment availablet [6] 0,23 0,16 0,27 90 0,454 0,34 0,41 64 0,32 0,20 0,36 154

most profitable segment with GWt [6] 0,36 0,26 0,30 90 0,58 0,70 0,38 64 0,45 0,36 0,36 154

Segment % of goodwill allocated to:risk

least risky segment with GWt [7] 0,46 0,44 0,28 73 0,697 0,78 0,30 55 0,563 0,60 0,31 128

[1] According to information disclosed in annual reports as of financial year-end t.

[2] According to information disclosed in annual reports as of financial year-end t. Generally, in accordance to IFRS 8 - Operating Segments or principal reporting structure by the respective firm.

[3] Herfindahl-Hirschman Index (HHI) computed on the basis of goodwill allocation as of the end of financial year t. Book values of goodwill adjusted for goodwill write-offs in year t.

[4] Ranking of largest reporting segments based on total assets as of financial year-end t adjusted for goodwill write-offs in year t.

[5] Ranking of largest reporting segments based on total revenues as of financial year-end t.

[7] Risk approximated by the discount rates used to determine the recoverability of goodwill in the respective reporting segment.

[6] Profitability defined as either (i) EBITDA relative to total revenues (EBITDA margin) or (ii) operating profit excluding depreciations and amortizations relative to total revenues, as of the end of the

financial year t.



306

Table 15: Overview on variables related to goodwill reporting flexibility Source: Own illustration.

Overview on variables related to goodwill reporting flexibility (continued)

Goodwill allocation (continued) Note: Mean Median

Standard


Standard


Standard

deviation n

Segment % of goodwill allocated to:size

largest + second largest segment available (total assets)t [4] 0,55 0,60 0,34 90 0,68 0,88 0,37 64 0,61 0,68 0,36 154

largest + second largest segment with GW (total assets)t [4] 0,67 0,73 0,30 90 0,86 1,00 0,23 64 0,75 0,87 0,29 154

largest + second largest segment available (total revenues)t [5] 0,65 0,68 0,29 90 0,71 0,90 0,36 64 0,67 0,76 0,32 154

largest + second largest segment with GW (total revenues)t [5] 0,70 0,77 0,27 90 0,86 1,00 0,24 64 0,77 0,86 0,27 154

Segment % of goodwill allocated to:profitability

most + second most profitable segment availablet [6] 0,45 0,38 0,32 90 0,63 0,87 0,41 64 0,53 0,49 0,37 154

most + second most profitable segment with GWt [6] 0,62 0,63 0,31 90 0,78 1,00 0,33 64 0,69 0,81 0,33 154

Segment % of goodwill allocated to:risk

least + second least risky segment with GWt [7] 0,71 0,78 0,26 73 0,89 0,99 0,19 55 0,79 0,86 0,25 128

Segment

change Change of reporting segments or CGUst 0,12 0,00 0,33 90 0,66 1,00 0,47 64 0,34 0,00 0,48 154

Valuation Value in uset 0,96 1,00 0,21 90 0,94 1,00 0,24 64 0,95 1,00 0,22 154

method

Fair value less costs of disposalt 0,22 0,00 0,42 90 0,17 0,00 0,38 64 0,20 0,00 0,40 154

[4] Ranking of largest reporting segments based on total assets as of financial year-end t adjusted for goodwill write-offs in year t.

[5] Ranking of largest reporting segments based on total revenues as of financial year-end t.

[7] Risk approximated by the discount rates used to determine the recoverability of goodwill in the respective reporting segment.

[6] Profitability defined as either (i) EBITDA relative to total revenues (EBITDA margin) or (ii) operating profit excluding depreciations and amortizations relative to total revenues, as of the end of the

financial year t.



307

8.3 Pearson correlations between explanatory variables

In subsequent tests, the determinants of goodwill write-off and non-write-off

decisions under IAS 36 should be tested in a multivariate setting. Before describing

the multivariate results, in this sub-section the Pearson correlations between the

explanatory variables to be tested in the regressions are reported (Table 16).

Generally, in multivariate regression models multicollinearity can be an issue.

Multicollinearity can create unstable coefficient estimates emerging from inflated

standard errors.1291 Given that the principal source of multicollinearity is high

correlations between the explanatory variables, the levels of the correlations need to

be analysed carefully.1292

For this PhD thesis and the models applied, a correlation cut-off point of 0,35 is

defined, meaning that no explanatory variables are included in the models which

have a correlation in access of 0,35. This cut-off point lies below the ones suggested

by other authors which according to their view could indicate a concern for

multicollinearity.1293

Besides the definition of a correlation cut-off point, multicollinearity in the

multivariate regression models is tested through the analysis of the variance inflation

factor (VIF)1294 of each independent variable in the individual regressions to be

presented in this PhD thesis. The results of the VIF analyses are displayed in

Appendix II of this PhD thesis.

1291 Cf. Greene (2003), pp. 56-59. 1292 See also Appendix II for multicollinearity diagnostics for all regressions analysed in this PhD thesis. 1293 Cf. Schendera (2008), p. 136. 1294 Cf. O’Brien, R. (2007), p. 688, Menard (1995), p. 66, Neter et al. (1989), p. 409, Kennedy (1992), p. 183.


308

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STOCK_ Correlation 1,000

RETURNi,t+2 Sig. (2-tailed)

STOCK_ Correlation 0,336 1,000

RETURNi,t+1 Sig. (2-tailed) (0,000)

EBITDA_MARGIN_ Correlation -0,072 -0,102 1,000

CHANGEi,t+2 Sig. (2-tailed) (0,374) (0,208)

EBITDA_MARGIN_ Correlation 0,112 0,048 0,031 1,000

CHANGEi,t+1 Sig. (2-tailed) (0,167) (0,555) (0,705)

SHARE_BUYBACKS_ Correlation -0,077 -0,156 0,180 -0,084 1,000

DUMMYi,t Sig. (2-tailed) (0,343) (0,053) (0,025) (0,299)

SHARE_ Correlation -0,076 -0,089 0,057 -0,041 0,263 1,000

BUYBACKSi,t Sig. (2-tailed) (0,349) (0,271) (0,479) (0,615) (0,001)

CEO_INSIDER_ Correlation 0,140 -0,050 0,155 -0,058 0,221 0,105 1,000

TRADING_DUMMYi,t Sig. (2-tailed) (0,135) (0,593) (0,099) (0,537) (0,018) (0,262)

CEO_INSIDER_ Correlation -0,033 -0,035 0,045 0,024 -0,080 0,002 0,347 1,000

TRADINGi,t Sig. (2-tailed) (0,726) (0,708) (0,634) (0,798) (0,396) (0,984) (0,000)

CEO_INSIDER_ Correlation -0,022 0,098 -0,009 0,035 -0,157 -0,031 0,009 0,040 1,000

TRADINGi,t-1 Sig. (2-tailed) (0,821) (0,309) (0,925) (0,716) (0,103) (0,750) (0,928) (0,695)

LEV_RATIOi,t Correlation 0,217 0,341 -0,063 0,024 -0,134 -0,061 -0,049 -0,205 0,002 1,000

Sig. (2-tailed) (0,007) (0,000) (0,439) (0,770) (0,098) (0,449) (0,606) (0,028) (0,986)

LEV_BANK_ Correlation 0,150 0,348 -0,007 0,030 -0,121 0,018 0,063 -0,083 -0,077 0,864 1,000

DEBT_RATIOi,t Sig. (2-tailed) (0,064) (0,000) (0,929) (0,708) (0,136) (0,825) (0,501) (0,380) (0,427) (0,000)

COVENANTS_GWi,t Correlation 0,061 0,090 -0,032 0,000 0,118 -0,062 0,117 -0,015 -0,167 0,248 0,093 1,000

Sig. (2-tailed) (0,453) (0,268) (0,691) (0,999) (0,143) (0,443) (0,214) (0,875) (0,082) (0,002) (0,250)

COVENANTSi,t Correlation 0,142 0,143 -0,117 0,068 0,023 -0,010 0,043 -0,118 0,047 0,242 0,073 0,523 1,000

Sig. (2-tailed) (0,080) (0,078) (0,149) (0,403) (0,781) (0,899) (0,649) (0,209) (0,629) (0,002) (0,370) (0,000)

PERFORMANCE_ Correlation 0,063 0,048 -0,165 0,049 0,026 0,013 0,061 -0,132 -0,070 0,024 -0,058 0,049 0,152 1,000

BONUS_DUMMYi,t Sig. (2-tailed) (0,464) (0,581) (0,054) (0,569) (0,766) (0,885) (0,536) (0,178) (0,488) (0,785) (0,503) (0,572) (0,078)

PERFORMANCE_ Correlation 0,121 0,083 -0,094 -0,018 0,108 0,073 0,005 -0,124 -0,025 -0,114 -0,132 -0,061 -0,083 0,457 1,000

BONUSi,t Sig. (2-tailed) (0,159) (0,339) (0,276) (0,836) (0,211) (0,395) (0,957) (0,205) (0,803) (0,188) (0,125) (0,482) (0,339) (0,000)

PERFORMANCE_ Correlation -0,060 -0,038 -0,008 -0,039 -0,035 0,125 0,011 -0,028 0,031 0,004 -0,008 0,075 -0,083 0,299 0,480

BONUS_CHANGEi,t Sig. (2-tailed) (0,490) (0,666) (0,926) (0,651) (0,691) (0,150) (0,913) (0,774) (0,757) (0,964) (0,928) (0,387) (0,339) (0,000) (0,000)

CEO_TENURE_TRIMi,t Correlation 0,050 0,013 -0,057 0,113 0,137 0,037 0,005 -0,092 0,159 -0,016 -0,014 0,048 0,125 0,138 0,196

Sig. (2-tailed) (0,534) (0,872) (0,481) (0,163) (0,090) (0,653) (0,957) (0,328) (0,098) (0,845) (0,867) (0,558) (0,124) (0,109) (0,022)

CEO_TENUREi,t Correlation -0,008 -0,021 -0,045 0,021 0,115 0,024 -0,107 -0,131 0,398 -0,055 -0,033 -0,006 0,017 -0,015 0,123

Sig. (2-tailed) (0,924) (0,798) (0,578) (0,797) (0,155) (0,771) (0,257) (0,164) (0,000) (0,499) (0,684) (0,941) (0,838) (0,861) (0,155)

CEO_EQUITY_ Correlation -0,076 -0,140 -0,037 0,018 0,216 0,178 -0,142 -0,124 -0,112 -0,171 -0,084 -0,098 0,007 0,059 0,347

OWNERSHIPi,t Sig. (2-tailed) (0,398) (0,119) (0,681) (0,845) (0,015) (0,046) (0,131) (0,186) (0,259) (0,056) (0,352) (0,276) (0,936) (0,525) (0,000)

CEO_EQUITY_ Correlation 0,083 -0,006 0,004 0,016 0,130 0,095 -0,120 -0,044 -0,045 0,009 0,045 -0,064 0,087 -0,038 0,380

OWNERSHIP_FIX_COMP%i,t Sig. (2-tailed) (0,376) (0,949) (0,970) (0,860) (0,163) (0,311) (0,220) (0,655) (0,667) (0,927) (0,633) (0,492) (0,353) (0,690) (0,000)

CEO_EQUITY_ Correlation 0,179 0,292 -0,012 -0,001 0,152 0,011 -0,095 -0,049 -0,032 -0,074 -0,027 -0,074 0,023 0,081 0,534

OWNERSHIP_CSO%i,t Sig. (2-tailed) (0,045) (0,001) (0,896) (0,995) (0,090) (0,903) (0,314) (0,606) (0,750) (0,412) (0,761) (0,408) (0,800) (0,386) (0,000)

GOODWILL_HHIi,t Correlation -0,028 -0,098 0,172 0,150 0,093 0,021 -0,108 0,103 -0,124 0,013 0,047 0,005 0,040 -0,032 -0,059

Sig. (2-tailed) (0,730) (0,225) (0,033) (0,063) (0,254) (0,793) (0,252) (0,271) (0,198) (0,873) (0,560) (0,948) (0,625) (0,714) (0,495)

SEGMENT_ Correlation 0,084 0,017 0,156 0,230 -0,003 -0,007 0,062 0,131 -0,047 0,084 0,102 0,048 -0,038 -0,036 -0,005

PROFITABILITYi,t Sig. (2-tailed) (0,297) (0,834) (0,053) (0,004) (0,974) (0,933) (0,511) (0,163) (0,628) (0,303) (0,209) (0,557) (0,639) (0,680) (0,957)

SEGMENT_ Correlation 0,033 -0,033 0,115 0,190 0,108 0,067 -0,059 0,117 -0,055 -0,058 -0,041 0,095 0,144 -0,038 -0,034

SIZEi,t Sig. (2-tailed) (0,688) (0,688) (0,155) (0,019) (0,182) (0,408) (0,534) (0,213) (0,568) (0,473) (0,613) (0,242) (0,074) (0,659) (0,697)

SEGMENT_RISKi,t Correlation 0,073 0,044 0,132 0,214 0,215 -0,002 0,015 0,027 -0,017 0,035 0,046 0,075 -0,022 0,122 0,024

Sig. (2-tailed) (0,414) (0,620) (0,137) (0,015) (0,015) (0,981) (0,887) (0,796) (0,872) (0,694) (0,605) (0,401) (0,807) (0,192) (0,798)

CGU_REPORTING_ Correlation 0,205 -0,056 -0,030 0,094 0,049 -0,037 -0,068 0,096 0,182 -0,029 -0,076 0,001 0,008 0,117 0,123

CHANGEi,t Sig. (2-tailed) (0,011) (0,493) (0,715) (0,248) (0,548) (0,646) (0,467) (0,306) (0,058) (0,721) (0,346) (0,993) (0,923) (0,176) (0,155)

MTBi,t Correlation -0,117 -0,127 -0,119 -0,031 0,177 0,114 -0,003 -0,302 -0,032 -0,039 -0,090 -0,023 0,005 0,331 0,304

Sig. (2-tailed) (0,147) (0,115) (0,140) (0,699) (0,028) (0,161) (0,973) (0,001) (0,738) (0,632) (0,269) (0,779) (0,948) (0,000) (0,000)

FIRM_SIZEi,t Correlation -0,082 -0,152 -0,074 0,031 0,059 0,109 0,124 0,036 0,148 -0,142 -0,121 -0,167 -0,185 0,114 0,204

Sig. (2-tailed) (0,311) (0,060) (0,363) (0,707) (0,466) (0,179) (0,186) (0,699) (0,124) (0,078) (0,134) (0,038) (0,022) (0,186) (0,017)

GOODWILL_ Correlation 0,198 0,176 -0,083 0,034 -0,017 -0,110 -0,023 -0,111 0,148 0,111 -0,035 0,046 0,310 -0,008 -0,056

INTENSITYi,t Sig. (2-tailed) (0,014) (0,029) (0,304) (0,676) (0,834) (0,173) (0,810) (0,239) (0,126) (0,172) (0,670) (0,570) (0,000) (0,929) (0,518)

Pearson correlations of independent variables


309

Table 16: Pearson correlations of explanatory variables Source: Own illustration.

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STOCK_ Correlation


STOCK_ Correlation


EBITDA_MARGIN_ Correlation

CHANGEi,t+2 Sig. (2-tailed)

EBITDA_MARGIN_ Correlation

CHANGEi,t+1 Sig. (2-tailed)

SHARE_BUYBACKS_ Correlation

DUMMYi,t Sig. (2-tailed)

SHARE_ Correlation

BUYBACKSi,t Sig. (2-tailed)

CEO_INSIDER_ Correlation

TRADING_DUMMYi,t Sig. (2-tailed)


TRADINGi,t Sig. (2-tailed)


TRADINGi,t-1 Sig. (2-tailed)

LEV_RATIOi,t Correlation

Sig. (2-tailed)

LEV_BANK_ Correlation

DEBT_RATIOi,t Sig. (2-tailed)

COVENANTS_GWi,t Correlation

Sig. (2-tailed)

COVENANTSi,t Correlation

Sig. (2-tailed)

PERFORMANCE_ Correlation

BONUS_DUMMYi,t Sig. (2-tailed)

PERFORMANCE_ Correlation

BONUSi,t Sig. (2-tailed)

PERFORMANCE_ Correlation 1,000

BONUS_CHANGEi,t Sig. (2-tailed)

CEO_TENURE_TRIMi,t Correlation -0,160 1,000

Sig. (2-tailed) (0,065)

CEO_TENUREi,t Correlation -0,148 0,732 1,000

Sig. (2-tailed) (0,088) (0,000)

CEO_EQUITY_ Correlation -0,310 0,331 0,335 1,000

OWNERSHIPi,t Sig. (2-tailed) (0,001) (0,000) (0,000)

CEO_EQUITY_ Correlation -0,604 0,194 0,257 0,635 1,000

OWNERSHIP_FIX_COMP%i,t Sig. (2-tailed) (0,000) (0,037) (0,005) (0,000)

CEO_EQUITY_ Correlation 0,105 0,207 0,174 0,251 0,224 1,000

OWNERSHIP_CSO%i,t Sig. (2-tailed) (0,259) (0,020) (0,051) (0,005) (0,015)

GOODWILL_HHIi,t Correlation -0,001 0,000 -0,016 -0,008 0,015 0,111 1,000

Sig. (2-tailed) (0,988) (0,998) (0,846) (0,929) (0,876) (0,215)

SEGMENT_ Correlation -0,001 -0,074 -0,122 -0,166 0,080 0,107 0,602 1,000

PROFITABILITYi,t Sig. (2-tailed) (0,990) (0,362) (0,131) (0,062) (0,392) (0,235) (0,000)

SEGMENT_ Correlation 0,026 -0,041 -0,017 0,077 0,084 0,118 0,668 0,392 1,000

SIZEi,t Sig. (2-tailed) (0,767) (0,613) (0,838) (0,393) (0,368) (0,187) (0,000) (0,000)

SEGMENT_RISKi,t Correlation -0,033 0,067 0,069 0,025 0,062 0,163 0,716 0,408 0,454 1,000

Sig. (2-tailed) (0,731) (0,451) (0,436) (0,802) (0,546) (0,099) (0,000) (0,000) (0,000)

CGU_REPORTING_ Correlation 0,070 0,130 0,104 -0,019 0,120 0,149 0,080 0,110 -0,011 0,169 1,000

CHANGEi,t Sig. (2-tailed) (0,420) (0,108) (0,201) (0,837) (0,196) (0,096) (0,323) (0,174) (0,896) (0,057)

MTBi,t Correlation 0,173 0,328 0,214 0,217 0,082 0,084 -0,016 -0,006 -0,104 0,086 0,195 1,000

Sig. (2-tailed) (0,045) (0,000) (0,008) (0,015) (0,382) (0,350) (0,845) (0,940) (0,199) (0,336) (0,015)

FIRM_SIZEi,t Correlation 0,029 0,091 0,063 0,181 -0,012 -0,112 -0,275 -0,136 -0,220 -0,197 -0,064 0,112 1,000

Sig. (2-tailed) (0,743) (0,260) (0,435) (0,042) (0,900) (0,211) (0,001) (0,093) (0,006) (0,026) (0,433) (0,166)

GOODWILL_ Correlation -0,171 0,055 -0,004 0,045 0,134 0,117 -0,173 -0,132 0,025 -0,060 -0,011 0,021 0,148 1,000

INTENSITYi,t Sig. (2-tailed) (0,048) (0,498) (0,957) (0,614) (0,149) (0,191) (0,032) (0,104) (0,762) (0,498) (0,890) (0,799) (0,067)

Pearson correlations of independent variables


310

8.4 Multivariate regression results

The following section reports on multivariate tests on the determinants on goodwill

write-off and non-write-off decisions in the IAS 36 sample. The specification of the

multivariate regression is provided in section 7.2.

To begin with, the multivariate analysis is based on all 154 observations and

explores the explanatory power of variables related to (i) private information on

changes in future financial performance, (ii) incentives predicted by agency theory

and (iii) goodwill reporting flexibility on goodwill write-off and non-write-off

decisions (so-called baseline regressions incl. variations on certain explanatory

variables, i.e. sensitivities). At a later stage, various subsamples of the original 154

observations are created to expand the analysis by considering additional variables

(so-called subsamples). The principal reason to do so is that not for all firms

represented in the full sample of 154 observations the information to calculate

certain explanatory variables was publicly available.1295

1295 In particular, this information relates to the compensation and bonus structures for the firms’ CEOs, their equity ownership, as well as their change in shares over year t, i.e. CEO insider trading.


311

Table 17: Explanatory variables in baseline regressions and subsamples


Determinants of goodwill write-off and non-write-off decisions - Binary logistic regressions

Binary logistic regressions of goodwill write-off and non-write-off decisions on the hypothesized determinants

Overview on variables included in the baseline regressions (A.1-A.18) as well as in the subsamples regressions (S.1-S.10)

Baseline Sub- Baseline Sub-

regressions samples regressions samples

Independent variable (A.1-A.18) (S.1-S.10) Independent variable (A.1-A.18) (S.1-S.10)

STOCK_RETURN i,t+2 ✓ ✓ Contract. LEV_RATIO i,t ✓

motivesSTOCK_RETURN i,t+1 ✓ ✓ LEV_RATIO_BANK_DEBT i,t ✓

EBITDA_MARGIN_CHANGE i,t+2 ✓ COVENANTS_GW i,t ✓ ✓

EBITDA_MARGIN_CHANGE i,t+1 ✓ COVENANTS i,t ✓

SHARE_BUYBACKS_DUMMY i,t ✓ PERFORMANCE_BONUS_DUMMY i,t ✓

SHARE_BUYBACKS i,t ✓ ✓ PERFORMANCE_BONUS i,t ✓

CEO_INSIDER_TRADING_DUMMY i,t ✓ PERFORMANCE_BONUS_CHANGE i,t ✓

CEO_INSIDER_TRADING i,t ✓ Reputation CEO_TENURE i,t ✓ ✓

motives

GOODWILL_HHI i,t ✓ ✓ CEO_TENURE_TRIM i,t ✓

SEGMENT_SIZE i,t ✓ CEO_TENURE_1st_YEAR_DUMMY i,t ✓

SEGMENT_SIZE_1st+2nd i,t ✓ CEO_TENURE_1st or 2nd_YEAR_DUMMY i,t ✓

SEGMENT_PROFITABILITY i,t ✓ CEO_TENURE_1st, 2nd or 3rd_YEAR_DUMMY i,t ✓

SEGMENT_PROFITABILITY_1st+2nd i,t ✓ Valuation CEO_EQUITY_OWNERSHIP i,t ✓

motives

SEGMENT_RISK i,t ✓ CEO_EQUITY_OWNERSHIP_FIX_COMP% i,t ✓

CGU_REPORTING_CHANGE i,t ✓ ✓ CEO_EQUITY_OWNERSHIP_CSO% i,t ✓

Key: ✓Included in regressions

Priv

ate

info

rmat

ion

on f

utu

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inan

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per

form

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Go

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ill

rep

orti

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lex

ibil

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Ag

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the

ory

-bas

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ncen

tives


312

8.4.1 Multivariate results on the basis of the baseline

regressions

8.4.1.1 Private information variables

Concerning possible private information about the future financial performance of

the firms held by their senior management teams hindering or accelerating goodwill

write-offs, the baseline regressions (A.1-A.18) test the influence of forward looking

stock returns (STOCK_RETURNi,t+1 and/or STOCK_RETURNi,t+2) as well as share

buybacks (SHARE_BUYBACKSi,t or SHARE_BUYBACKS_DUMMYi,t) on goodwill

write-off and non-write-off decisions. The baseline regressions (A.7-A.9)

supplement forward looking stock returns with proxies for changes in future

operating cash flows (EBITDA_MARGIN_CHANGEi,t+1 and/or EBITDA_MARGIN_

CHANGEi,t+2), and therefore accounting earnings.

Empirical findings on future stock returns (STOCK_RETURNi,t+1 and

STOCK_RETURNi,t+2):

In line with the private information hypothesis, evidence is found that goodwill

write-off and non-write-off decisions in year t reveal information on the firms’

future financial performance. In all baseline regressions (A.1-A.6, A.8, A.10-A.18),

containing all 154 observations, the independent variable STOCK_RETURNi,t+2

which measures the annualized stock returns in the second year after the goodwill

write-off or non-write-off decision is found out to be statistically significantly

related to the write-off decision in year t. With p-values < 0,01 in the baseline

regressions (A.1-A.7, A.9-A.17), the relationship with the actual write-off

probability in year t is found out to be strong.

In all the baseline regressions, the signs of the coefficient of STOCK_RETURNi,t+2

are found to be in line according to the original expectations.1296 The negative signs

of the factors imply that the higher the two-years-ahead stock returns (year t+2), the

lower the goodwill write-off probability in year t. Considering only the two-years-

1296 Please refer to chapter 7.2.3 of the expected signs of explanatory variables in the regressions.


313

ahead stock returns, one comes to the conclusion that goodwill write-off and non-

write-off decisions have predictive value for future firm performance.1297 This

finding would be in line with the IASB’s original argumentation when introducing

the impairment-only approach that goodwill write-off decisions reveal information

on the firms’ future financial performance.1298

1297 Cf. Boennen and Glaum (2014), p. 41. 1298 Cf. IAS 36.BC131G.


314



Baseline regressions (A.1-A.6)

(A.1) (A.2) (A.3)

Dependent variable: Predicted Est. Est. Est.

GW_WO i,t Independent variable sign (S.E.) sig. (S.E.) sig. (S.E.) sig.

Private information on STOCK_RETURN i,t+2 (-) -3,272 *** -3,087 *** -2,926 ***

future financial performance (1,032) (0,987) (0,925)

STOCK_RETURN i,t+1 (-) 0,616 0,767 0,709

(0,689) (0,672) (0,635)

SHARE_BUYBACKS i,t (-) -0,344 ** -0,341 **

(0,136) (0,132)

SHARE_BUYBACKS_DUMMY i,t (-) -0,687

(0,562)

Agency theory- Contract. LEV_RATIO i,t (-) 1,975 1,242 1,554

based incentives motives (1,313) (1,109) (1,246)

COVENANTS_GW i,t (-) -2,132 *** -1,853 **

(0,782) (0,724)

COVENANTS i,t (-) -1,229 *

(0,705)

Reputation CEO_TENURE i,t (-) -0,101 ** -0,109 ** -0,105 **

motives (0,049) (0,046) (0,048)

Goodwill reporting GOODWILL_HHI i,t (-) -6,493 *** -5,614 *** -5,482 ***

flexibility (1,606) (1,394) (1,381)

CGU_REPORTING_CHANGE i,t (-) -3,988 *** -3,579 *** -3,660 ***

(0,788) (0,701) (0,713)

Control MTB i,t -2,310 -2,115 -2,276 *

variables (1,464) (1,336) (1,362)

FIRM_SIZE i,t 0,000 0,000 0,000

(0,000) (0,000) (0,000)

GOODWILL_INTENSITY i,t 5,458 * 5,841 * 6,180 **

(3,275) (3,212) (3,093)

(Intercept) 8,388 *** 7,472 *** 7,380 ***(2,167) (1,946) (1,950)

Observations 154 154 154

[1] Chi-square (d.f.) 122,96 (14) 113,26 (14) 117,64 (14)

-2 Log likelihood 86,12 95,82 91,44

Percent correctly predicted (%) 88,96 84,42 87,66

Cox & Snell R Square 0,550 0,521 0,534

Industry dummy included YES YES YES

Year dummies included YES YES YES

*, **, *** denote statistical significance at the 90, 95 and 99% confidence level, respectively.

[1] All exceed 99th percentile of Chi-square distribution.


315

Table 18: Results of baseline regressions Source: Own illustration.




(A.4) (A.5) (A.6)





STOCK_RETURN i,t+1 (-) 0,493 0,469 0,774

(0,702) (0,772) (0,654)

SHARE_BUYBACKS i,t (-) -0,371 *** -0,513 *** -0,509 ***

(0,143) (0,187) (0,176)

Agency theory- Contract. LEV_RATIO i,t (-) 2,310

based incentives motives (1,480)

LEV_BANK_DEBT_RATIO i,t (-) 8,659 *** 7,833 ***

(2,632) (2,508)

COVENANTS_GW i,t (-) -2,159 *** -2,368 ***

(0,796) (0,912)

COVENANTS i,t (-) -1,378 *

(0,772)

Reputation CEO_TENURE i,t (-) -0,115 ** -0,169 *** -0,110 **

motives (0,051) (0,060) (0,053)


flexibility (1,577) (2,131) (1,789)


(0,826) (1,046) (0,885)

Control MTB i,t -2,454 -2,463

variables (1,773) (1,581)

FIRM_SIZE i,t 0,000 0,001 * 0,001 *

(0,000) (0,000) (0,000)

GOODWILL_INTENSITY i,t 5,081 9,103 ** 8,403 **

(3,214) (3,807) (3,267)

STOCK_RETURN i,t -0,877 *

(0,477)

STOCK_RETURN i,t-1 -1,243

(1,287)

(Intercept) 6,559 *** 10,872 *** 10,299 ***(1,759) (2,888) (2,551)

Observations 154 154 154[1] Chi-square (d.f.) 124,52 (15) 138,90 (14) 128,35 (14)

-2 Log likelihood 84,56 70,18 80,73








316

These findings confirm earlier limited research on the impairment-only approach

under SFAS 142, published by Jarva (2009)1299, Lee (2011)1300, Li et al. (2011)1301,

and Li and Sloan (2012)1302. Jarva (2009) finds evidence that one-year-ahead and

two-year-ahead operating cash flows (year t+1 and year t+2) contain explanatory

power for goodwill write-off decisions in year t.1303 In their regression on year t+1

operating cash flows, the explanatory power of goodwill is highly statistically

significant (p-value < 0,01) whilst for year t+2 a semi-strong impact is found (p-

value < 0,05).1304 Lee (2011) finds mixed results on the ability of goodwill write-offs

to predict future operating cash flows. Not in all regressions that the author performs

the independent variable that measures a goodwill write-off is statistically

significantly related to future cash flows from operation.1305 Li and Sloan (2012)

find also evidence that goodwill write-offs during the post-SFAS 142 regime can

explain future profitability. In their regression, they find a very strong statistically

significant relationship (p-value < 0,01) between goodwill impairment losses1306 in

year t and one-year-ahead ROA (year t+1).

Generally, the findings of the authors mentioned above imply that goodwill write-

offs under SFAS 142 can contain explanatory power about changes in future

financial performance (and that this explanatory power is higher in the post-SFAS

142 regime than before).1307 These findings would also confirm the FASB’s

assertion that the introduction of the impairment-only approach actually allows

disclosing superior information on the underlying economics of goodwill,1308 and

that (some) firms make willingly use of this possibility.

1299 Cf. Jarva (2009), p. 1059. 1300 Cf. Lee (2011), p. 244. 1301 Cf. Li et al. (2011), p. 771. 1302 Cf. Li and Sloan (2012), p. 50. 1303 Cf. Jarva (2009), pp. 1071, 1074. 1304 Cf. Jarva (2009), pp. 1071, 1074. 1305 CF. Lee (2011), pp. 248-249, 251. 1306 Besides the actual write-off amounts, scaled by average total assets, Li and Sloan (2012) also use an indicator variable for impairment losses. 1307 Cf. Lee (2011), p. 236. 1308 Cf. FASB (2014a).


317

In the analysis at hand, the independent variable STOCK_RETURNi,t+1,which

measures the short-term, one-year-ahead annualized stock returns after the goodwill

write-off or non-write-off decision in year t, is not found out to be related to the

goodwill write-off probability in year t. Additionally, the coefficient of the variable

STOCK_RETURNi,t+1 is positive and not negative as originally expected. In all

sixteen variations of the baseline regressions (A.1-A.6, A.9-18), which test the

independent variable STOCK_RETURNi,t+1, positive coefficients of the variables are

identified. Whilst this finding on the positive coefficients is partly surprising, several

other authors noted this observation directly or indirectly in their studies.1309

Chen et al. (2013), for example, argue in their research study that some goodwill

write-off firms with large MTB ratio gaps experience return reversals in the year

after the write-off announcements, as investors might become aware that they

overestimated the impairments in their valuations, and thereby driving stock prices

up again in the subsequent year t+1.1310 Also Li et al. (2010) document that stock

returns of goodwill write-off firms increase over a two month period after the write-

off announcement by approx. 17%, however without giving an explanation for their

observation.1311 Li and Sloan (2012) also show that stock returns of write-off firms

are relatively stable over a six months observation period after the actual write-off

announcement was made. 1312

Besides Chen et al.’s (2013) argument regarding the return reversal in year t+1 due

to overestimated impairments in the stock price in and prior to year t, the findings of

Hirschey and Richardson (2002) and Muller et al. (2009) provide additional insights

on this stock return increase in year t+1. In the study of Hirschey and Richardson

(2002), the authors analyse stock returns of write-off firms before and after goodwill

write-off announcements in US firms. They find that write-off announcements are

often made in combination with restructuring announcements,1313 which often are

considered favourable by market participants, thereby driving stock prices up again

1309 Cf. Chen et al. (2013), p. 19, Li et al. (2010), p. 26, Li and Sloan (2012), p. 41. 1310 Cf. Chen et al. (2013), p. 19. 1311 Cf. Li et al. (2010), p. 26. 1312 Cf. Li and Sloan (2012), p. 41. 1313 Cf. Hirschey and Richardson (2002), p. 183.


318

in the short-term. Muller et al. (2009) also find that write-off firms report more often

the initiation of restructuring programs together with a goodwill write-off than non-

write-off firms.1314 In Muller et al.’s sample (2009), 32,7% of the goodwill write-off

firms report a restructuring program, whilst this is only the case in 14,5% of the non-

write-off firms. The difference in the means of the two subsamples is found out to be

highly statistically significant with a p-value < 0,01.

Empirical findings on future accounting earnings

(EBITDA_MARGIN_CHANGEi,t+1 and EBITDA_MARGIN_CHANGEi,t+2):

Besides examining the relationship between goodwill write-offs in year t and future

stock returns (year t+1 and t+2), the possible link between write-off and non-write-

off decisions in year t and financial performance on the basis of accounting earnings

is investigated. Similar to the SFAS 142-based studies of Jarva (2009)1315, Lee

(2011)1316, Li et al. (2011)1317, and Li and Sloan (2012)1318, a financial metric should

be selected that mirrors the firms’ operational performance in a timely manner and is

less susceptible to managerial manipulation. Based on these considerations, the

change in a firm’s future EBITDA margin (EBITDA_MARGIN_CHANGEi,t+1 and

EBITDA_MARGIN_CHANGEi,t+2), which should act as a proxy for changes in

operating cash flows, was selected. A relative performance metric is better suited

than an absolute performance metric as it is less affected by changes in the size of a

firm or the size of its operations.1319

To understand the effects of future margin changes on the goodwill write-off

probability in year t, further variations of the baseline regressions are analysed (A.7-

A.9) however this time by replacing STOCK_RETURNi,t+n with EBITDA_MARGIN_

CHANGEi,t+n.

1314 Cf. Muller et al. (2009), p. 13. 1315 Cf. Jarva (2009), p. 1084. 1316 Cf. Lee (2011), p. 251. 1317 Cf. Li et al. (2011), p. 769. 1318 Cf. Li and Sloan (2012), p. 50. 1319 Often when firms are in difficult financial situations, restructuring measures are initiated which effect absolute financial performance metrics more than relative financial performance metrics.


319

As expected, the coefficients of the independent variables EBITDA_MARGIN_

CHANGEi,t+1 and EBITDA_MARGIN_CHANGEi,t+2 are both negative in the

regressions (A.7-A.9), meaning that a negative future margin change (i.e. EBITDA

margin decreases) increases the goodwill write-off probability in year t. The effects

of the independent variables which measure changes in future operating

performance are however not statistically significant. With a p-value of 0,066, a

statistically significant influence of the future EBITDA margin change on goodwill

write-off probability in year t is only detected for the variable EBITDA_MARGIN_

CHANGEi,t+2.

The findings of this PhD thesis therefore do not confirm those of Lee (2011)1320 and

Lys et al. (2012)1321 who find that goodwill balances under the impairment-only

approach contain explanatory power regarding future cash flows and accounting

earnings. Lee (2011) studied the relationship between firms’ goodwill balances and

future cash flows from operations before and after the introduction of the

impairment-only approach under SFAS 142, and finds that goodwill under the IOA

better predicts one-year and two-year ahead cash flows from operations than pre-

SFAS 142 goodwill balances which were amortized periodically.1322 The results of

Lys et al. (2012) suggest that goodwill balances under the IOA, if generated by

transactions which created value for shareholders or when written off timely when

transactions destroyed shareholder value, are a predictor of future operating

performance.

One explanation, why the effect from the change of the two-year-ahead EBITDA

margin in the sample of this PhD thesis is not exsting while the effect from two-

year-ahead stock returns is, could be that financial performance measured with

accounting earnings lags behind financial performance measured with stock returns,

a phenomenon frequently termed in academia as “prices lead earnings” 1323, meaning

1320 Cf. Lee (2011), p. 253. 1321 Cf. Lys et al. (2012), p. 2. 1322 Cf. Lee (2011), pp. 248-250. 1323 Kothari (2001), p. 129.


320

that stock prices incorporate faster firm value relevant information than accounting

earnings.1324

1324 Please see also the following papers for a discussion of the “prices lead earnings” hypothesis: Beaver et al. (1980), Beaver et al.(1987), Collins et al. (1987), Freeman (1987), Collins and Kothari (1989), Easton et al. (1992), Kothari (1992), Kothari and Sloan (1992), Warfield and Wild (1992), Collins et al. (1994), Basu (1997), Beaver et al.(1997), Ball et al. (2000), and Liu and Thomas (2000).


321

Table 19: Results of baseline regressions with accounting earnings





(A.7) (A.8) (A.9)



Private information on STOCK_RETURN i,t+2 (-) -2,969 ***

future financial performance (0,933)

STOCK_RETURN i,t+1 (-) 0,022

(0,573)

EBITDA_MARGIN_CHANGE i,t+2 (-) -0,321 -0,339

(0,379) (0,375)

EBITDA_MARGIN_CHANGE i,t+1 (-) -0,289 -0,381

(0,633) (0,804)

SHARE_BUYBACKS i,t (-) -0,293 ** -0,358 *** -0,286 **

(0,132) (0,137) (0,131)



COVENANTS_GW i,t (-) -1,805 *** -2,254 *** -1,791 **

(0,700) (0,781) (0,707)


motives (0,049) (0,050) (0,049)


flexibility (1,386) (1,607) (1,397)


(0,697) (0,789) (0,701)

Control MTB i,t -1,244 -2,342 -1,169

variables (1,246) (1,449) (1,239)

FIRM_SIZE i,t 0,000 0,000 0,000

(0,000) (0,000) (0,000)

GOODWILL_INTENSITY i,t 2,439 5,401 * 2,477

(3,022) (3,236) (3,049)

(Intercept) 7,051 *** 8,739 *** 7,041 ***(1,869) (2,176) (1,909)


[1] Chi-square (d.f.) 111,13 (14) 122,38 (14) 110,88 (14)

-2 Log likelihood 97,95 86,70 98,20








322

Empirical findings on share buybacks (SHARE_BUYBACKSi,t and SHARE_

BUYBACKS_DUMMYi,t):

Across all baseline regressions (A.1, A.3-A.18), the coefficients of the independent

variable SHARE_BUYBACKSi,t, which measures the currency amount of share

buybacks in year t scaled by total revenues in year t, are negative and therefore in

line with the original expectations.1325 The independent variable SHARE_

BUYBACKSi,t is used as a proxy for potential private information on future firm

performance held by senior management teams.

The signs of the coefficients imply that non-write-off firms engage in more share

buyback activities in year t than their write-off counterparts. The signs are in line

with the private information hypothesis as suggested by the IASB. The findings on

the signs of the coefficients are consistent with the observations by Ramanna and

Watts (2012) who performed a similar analysis, however using a dummy variable in

their regressions set to one if share buybacks and/or positive net insider trading are

observable in year t.1326

More importantly in all 17 baseline regressions, with p-values between 0,009 and

0,070, the independent variable SHARE_BUYBACKSi,t is found out to be statistically

significantly related to the actual goodwill write-off or non-write-off decision in

year t. These findings imply that senior managers could have private information on

the future financial performance of the firms and in case being positive hinder them

to write off goodwill. In regression (A.2), the scaled, independent variable

SHARE_BUYBACKSi,t is replaced with the dummy variable SHARE_BUYBACKS_

DUMMYi,t which is set to one if share buybacks are observable in year t and zero

otherwise. This replacement was done to understand whether the scale of share

buybacks in year t is of relevance. This assertion can be confirmed as the factor of

this dummy variable is also negative however not statistically significant.

1325 Please refer to chapter 7.2.3 of the expected signs of explanatory variables in the regressions. 1326 Cf. Ramanna and Watts (2012), p. 774. The authors however measure private information potentially conveyed through net share repurchases and/or positive net insider buying by a single dummy variable and not two individual dummy variables.


323

8.4.1.2 Incentives predicted by agency theory

In the baseline regressions (A.1-A.18), the independent variables used to analyse

incentives predicted by agency theory include variables which study (i) contract-

based motives and (ii) reputation motives. Amongst the variables investigating

contract-based motives are two types of leverage ratios (LEV_RATIOi,t and

LEV_BANK_DEBT_RATIOi,t) and two dichotomous variables associated with

existing debt covenants (COVENANTS_GWi,t and COVENANTSi,t). Reputation

motives are analysed through continuous, independent variables measuring CEO

tenure (CEO_TENUREi,t and CEO_TENURE_TRIMi,t).

Empirical findings on financial leverage (LEV_RATIOi,t and LEV_BANK_DEBT_

RATIOi,t):

In the two baseline regressions (A.1-A.4 and A.7-A.18), the coefficients of the

independent variable LEV_RATIOi,t are positive. This observation is not in line with

the original expectation.1327 The sign implies that the higher a sample firm’s

leverage ratio in year t, the higher the goodwill write-off probability in year t.

However, the regressions reveal that the independent variable is not statistically

significantly related to goodwill write-off and non-write-off decisions in year t. In

all regressions which include this independent variable, the impact is statistically

insignificant.

These findings do not confirm those of earlier research studies, either on asset write-

offs in general or goodwill write-offs, by Cotter et al. (1998), Riedl (2004), Beatty

and Weber (2006) and Zang (2008).1328 Cotter et al. (1998) find a statistically

significant influence of leverage on asset write-off decisions in their sample of

Australian firms.1329 So does also Riedl (2004) who focusses on US firms’ write-off

decisions before and after the adoption of SFAS 121 Accounting for the Impairment

1327 Please refer to chapter 7.2.3 of the expected signs of explanatory variables in the regressions. 1328 Cf. Cotter et al. (1998), p. 173, Riedl (2004), p. 843, Beatty and Weber (2006), p. 280, Zang (2008), pp. 38, 53. 1329 Cf. Cotter et al. (1998), p. 173.


324

of Long-Lived Assets.1330 Zang (2008), who performs an analysis of firms adopting

SFAS 142 Goodwill and Other Intangible Assets, also finds that highly leveraged

SFAS 142-adopting firms are less inclined to report transitional goodwill

impairment losses than their counterparts with less leverage.1331 Nevertheless, other

authors also detected a positive and insignificant relationship between the level of

leverage and goodwill write-off probabilities in their samples, similar to the

observations of this PhD thesis. Amongst them are, for example, Siggelkow and

Zülch (2013a)1332 or Glaum et al. (2015)1333.

In the baseline regressions (A.5 and A.6), the general leverage ratio is replaced with

a more specific leverage ratio calculated solely on the basis of bank debt to analyse

the effect of leverage on contract-based motives in the impairment-only approach in

more detail. Similar to the coefficient of the general leverage ratio variable, the sign

of the coefficient of the variable LEV_BANK_DEBT_RATIOi,t is again positive.

More importantly, the leverage variable calculated solely on the basis of existing

bank debt in year t becomes statistically significant in the regressions. With p-values

ranging between 0,001 and 0,002, the effect from the bank debt leverage ratio on

goodwill write-off probability in year t is very stable across the regressions.

Nevertheless it must be added that the interpretation of this variable needs to be

considered in combination of the relatively large observable standard errors.

The positive sign basically suggests that the more bank debt a firm has in year t (as a

ratio of total assets in year t), the higher the probability of a goodwill write-off. This

observation is not in line with the original expectation, as it was assumed on the

basis of agency theory considerations that the coefficient of the bank debt variable

would carry a negative sign. This finding on the positive coefficient implies that

bank debt can actually have a disciplining role regarding the management of

goodwill write-offs for a firm’s senior management. The likely disciplining role of

debt has been noted already earlier by Cotter et al. (1998).1334 The authors argue that

1330 Cf. Riedl (2004), p. 823. 1331 Cf. Zang (2008), pp. 38, 53. 1332 Cf. Siggelkow and Zülch (2013a), p. 42. 1333 Cf. Glaum et al. (2015), p. 50. 1334 Cf. Cotter et al. (1998), p. 173.


325

it is possible that “highly levered firms are more likely to have the value of their

assets under scrutiny by debtholders as part of the firm’s corporate governance

processes (…), and they could be required to writedown overvalued assets as a

consequence of intervention by debt holders.”1335 This reasoning would justify a

positive coefficient in the regression.

Earlier empirical evidence for Cotter et al.’s (1998) argumentation on the association

between the level of leverage and asset write-offs has been provided, for example,

by Strong and Meyer (1987)1336, Elliott and Shaw (1988)1337, and Zucca and

Campbell (1992)1338. Also Glaum et al. (2015) who study the determinants of

goodwill write-offs in firms applying IFRS find that their independent variable

which measures the firm’s level of leverage has a positive coefficient in their

regressions.1339 Due to the highly limited number of research studies that have had a

closer look at individual components of leverage, like bank debt, further

comparisons of the findings of this PhD thesis on the relationship of bank debt and

goodwill write-off decisions is considered difficult.

Empirical findings on accounting covenants (COVENANTS_GWi,t and

COVENANTSi,t):

The second set of independent variables that proxy for contract-based incentives to

manage goodwill write-offs analyses the existence of actual debt covenants. As

mentioned earlier, a potential concern for firms facing possible goodwill write-offs

is that they might impact debt covenants which are based on balance sheet or income

statement ratios.1340 This could represent an issue to firms and their management

1335 Cotter et al. (1998), p. 163. 1336 Cf. Strong and Meyer (1987), p. 650. 1337 Cf. Elliott and Shaw (1988), p. 93. 1338 Cf. Zucca and Campbell (1992), p. 39. 1339 Cf. Glaum et al. (2015), p. 50. See also, for example, Siggelkow and Zülch (2013a), p. 54, who study write-off determinants in German firms applying IFRS. The authors also find that leverage is positively, however statistically insignificantly related to asset write-offs. 1340 Cf. Boennen and Glaum (2014), p. 44.


326

teams as certain types of covenants require maintaining pre-defined levels of

accounting-based ratios.1341

To understand the impact from existing covenants on goodwill write-off decisions,

in a first step the actual covenants in year t were hand collected from firm

disclosures. Building upon this information, the dichotomous variable

COVENANTS_GWi,t was set to one if a firm had debt covenants in year t that would

be impacted by a goodwill write-off. In the sample of this PhD thesis, debt

covenants impacted from a write-off includes primarily (i) leverage ratios (debt to

equity), (ii) equity ratios (equity to assets), (iii) debt ratios (debt to assets), and (iv)

firm-specific variations of those covenants.1342

Similarly, the dummy variable COVENANTSi,t amounts to one if debt covenants

existed in year t, irrespective of whether they would be impacted by a goodwill

write-off or not. Examples for such covenants are (i) interest coverage ratios

(EBITDA to net interest expenses), (ii) ratio of net debt to EBITDA, (iii) amount of

cash held, (iv) consolidated tangible net assets, (v) and firm-specific variations of

those covenants.1343 COVENANTSi,t also include the ones mentioned under

COVENANTS_GWi,t. Given that the variables COVENANTS_GWi,t and

COVENANTSi,t are not independent, the variables are run separately in the baseline

regressions to understand their influence on the goodwill write-off or non-write-off

decisions in year t.

As originally expected, the coefficients of the covenants variables are negative in all

18 baseline regressions (A.1-A.18).1344 The effect of the covenants variable

1341 Cf. Zang (2008), p. 43. 1342 For example: (i) minimum level of consolidated equity, (ii) ratio of non-current assets in the balance sheet not pledged as collateral to unsecured debts, (iii) ratio of borrowings (excluding non-recourse indebtedness) to total shareholders’ funds, (iv) ratio of indebtedness to shareholders’ equity, (v) net debt to shareholder funds. 1343 For example: (i) ratio of funded debt to EBITDA before exceptional items, (ii) ratio of EBITDA before exceptional items less net capital expenditure paid in cash over the sum of scheduled debt repayments plus cash interest, and dividends paid, (iii) ratio of consolidated total net borrowings (consolidated total borrowings less consolidated cash and cash equivalents) to consolidated EBITDA (consolidated net pre-taxation profits), (iv) ratio of net interest charges added to one third of operating lease payments to EBITDA, (v) ratio of net interest bearing debt to EBITDA (adjusted for exceptional items). 1344 Please refer to chapter 7.2.3 of the expected signs of explanatory variables in the regressions.


327

COVENANTS_GWi,t on the observable goodwill write-off or non-write-off decisions

in year t is stronger than the effect of the more general covenants variable

COVENANTSi,t. In the regressions (A.1-A.2, A.4-A.5, and A.7-A.18, p-values

ranging between 0,004 and 0,065 are observable for the independent variable

COVENANTS_GWi,t, whilst those of the variable COVENANTSi,t range between

0,074 and 0,081 in the regressions (A.3 and A.6).

These findings are in line of those of Beatty and Weber (2006)1345 and Zang

(2008),1346 both based on first-time SFAS 142 adopters. Beatty and Weber (2006)

and Zang (2008) also detect negative coefficients for their covenants variables in

their regressions and also observe a statistically significant relationship between

existing covenants and goodwill write-off probability. Beatty and Weber (2006)

come to the conclusion that the goodwill write-off probability for firms adopting

SFAS 142 for the first time is higher if it is unlikely that the firms would breach

existing covenants1347 or when covenants would not get impacted by the write-

off.1348 Similarly Zang (2008) provides evidence that highly leveraged, first-time

SFAS 142 adopters are less likely to recognize a transitional goodwill impairment

loss “when their covenants include the effect of accounting changes and restrictions

on retained earnings and net assets than when their covenants exclude accounting

changes or do not have such restrictions”1349.

Empirical findings on CEO tenure (CEO_TENURE_TRIMi,t and CEO_TENUREi,t):

The variables CEO_TENUREi,t and CEO_TENURE_TRIMi,t measure the years a

CEO has been in office at the time of the goodwill write-off or non-write-off

decision in year t. To calculate these variables, firm disclosures were studies for the

1345 Cf. Beatty and Weber (2006), p. 279. 1346 Cf. Zang (2008), p. 54. 1347 Cf. Beatty and Weber (2006), p. 273. To analyse the effect from existing covenants, Beatty and Weber (2008) analyse slack in net worth covenants (if existing). The variable they use (Net Worth Slack) represents the numerical ranking of covenants slack, calculated as the difference between a firm’s book value of equity less the net worth threshold (as stated in the covenants), divided by the book value of goodwill at the end of the previous financial year. The variable amounts to zero if a firm has no net worth covenants. 1348 Cf. Beatty and Weber (2006), p. 259. 1349 Zang (2008), p. 54.


328

names of the CEOs and the dates when they took office. In all baseline regressions

(A.1-A.9, A.14-A.18), the coefficients of the continuous independent variable

CEO_TENUREi,t are negative, as originally expected. With p-values primarily <

0,05, the influence of a CEO’s tenure on goodwill write-off probability is

statistically significant in all regressions. These findings strongly imply that CEOs

with a shorter tenure are more likely to record a goodwill write-off earlier in their

career than later. Similar results are obtained when using the trimmed, continuous

independent variable CEO_TENURE_TRIMi,t. Here, also a statistically significant

relationship between CEO tenure and goodwill write-off probability is identified in

regression (A.10) with a p-value of 0,024.

Fig. 58: Relationship between CEO tenure and goodwill write-off probability in year t


When analysing CEO tenure in years in the year of the goodwill write-off decision

and the corresponding goodwill write-off probabilities in year t (see figure above), it

becomes obvious that write-off probabilities are higher for CEOs that recently took

office. The highest write-off probabilities are discovered in the first year in office

(69.6%). This write-off probability is approx. 11% higher than the write-off

probability over the entire sample of 58.4%. Still in the second year in office, write-

off probabilities are above the average for the entire sample with 61.9%. Thereafter,

69,6%

61,9%

56,3% 56,4%

52,6%50,0%

40%

50%

60%

70%

1 2 3-5 6-10 11-20 21-40Goo

dwil

l w

rite

-off

pro

babi

lity

(m

ean)

Years in office


329

the likelihood of recording a write-off is below the average probability and further

declines as CEOs are longer in office.

The findings in the multivariate analysis of this PhD thesis are consistent with those

of earlier studies, for example, by Francis et al. (1996)1350, Beatty and Weber

(2006)1351, Masters-Stout et al. (2008)1352, Hamberg et al. (2011)1353, and Zang

(2008)1354. All of them find a statistically significant relationship between CEO

tenure and goodwill write-offs in their regressions. Whilst Beatty and Weber (2006)

also use a continuous independent variable measuring the years of a CEO in office,

Masters-Stout et al. (2008), Hamberg et al. (2011), and Zang (2008) use

dichotomous variables set to one if a CEO change has been observable prior to a

goodwill write-off decision.1355 These authors find statistical significance primarily

at the 95% and 99% confidence levels, very similar to the results of this PhD thesis,

also highlighting reputational concerns of CEOs from “inheriting” goodwill which

was created during the tenure of their predecessors.

Additionally, the effects from CEO tenure on goodwill write-off and non-write-off

decisions are measured with dummy variables in the baselines regressions (A.11-

A.13). The dichotomous variables were set to one if CEO tenure was smaller or

equal to a predefined threshold. In this PhD thesis, these CEO tenures are 1, 2, and 3

years. On this basis the dummy variables

• CEO_TENURE_1st_YEAR_DUMMYi,t,

• CEO_TENURE_1st_or_2nd_YEAR_DUMMYi,t, and

• CEO_TENURE_1st_2nd_or_3rd_YEAR_DUMMYi,t are defined.

1350 Cf. Francis et al. (1996), p. 125. 1351 Cf. Beatty and Weber (2006), p. 280. 1352 Cf. Masters-Stout et al. (2008), p. 1379. 1353 Cf. Hamberg et al. (2011), p. 280. 1354 Cf. Zang (2008), p. 53. 1355 For example, Francis et al. (1996) use a dummy variable set to one in case of a CEO change one year prior to or in the year of the write-off. Masters-Staut et al. (2008) use various dummy variables: (i) CEO tenure is less than 3 years and CEO has been with the firm less than 3 years prior to the appointment, (ii) CEO tenure is less than 3 years and CEO has been with the firm more than 2 years prior to the appointment, and (iii) CEO tenure is less than 3 years. Hamberg et al. (2011) set their dummy variable to one if the CEO in year t-5 is either the CEO or chairman of the board in year t.


330

The substitution of the continuous, independent variable CEO_TENURE_TRIMi,t

with the dummy variables returns very similar results, i.e. the shorter the CEO

tenure, the higher the goodwill write-off probability in year t. In regression (A.11),

the dummy variable CEO_TENURE_1st_YEAR_DUMMYi,t, has a p-value < 0,05,

similar to the p-value of the independent variable CEO_TENURE_1st_or_2nd_

YEAR_DUMMYi,t (also p-value < 0,05). The effect from CEO tenure on goodwill

write-off probability in year t disappears when using a time period of up to 3 years

(i.e. CEO replacement during the preceding three years before the goodwill write-off

or non-write-off decision).


331



Baseline regressions with sensitivities on CEO tenure (A10.-A.13)

(A.1) (A.10) (A.11)





STOCK_RETURN i,t+1 (-) 0,616 0,617 0,680

(0,689) (0,705) (0,689)

SHARE_BUYBACKS i,t (-) -0,344 ** -0,437 ** -0,449 **

(0,136) (0,173) (0,170)

Agency theory- Contract. LEV_RATIO i,t (-) 1,975 2,696 * 2,372


COVENANTS_GW i,t (-) -2,132 *** -1,968 ** -2,136 ***

(0,782) (0,773) (0,772)

Reputation CEO_TENURE i,t (-) -0,101 **

motives (0,049)

CEO_TENURE_TRIM i,t (-) -0,396 **

(0,176)

CEO_TENURE_1st_YEAR_DUMMY i,t (+) 1,883 **

(0,960)


flexibility (1,606) (1,624) (1,576)


(0,788) (0,943) (0,947)

Control MTB i,t -2,310 -0,864 -0,472

variables (1,464) (1,590) (1,517)

FIRM_SIZE i,t 0,000 0,000 0,000

(0,000) (0,000) (0,000)

GOODWILL_INTENSITY i,t 5,458 * 5,123 * 4,588

(3,275) (3,035) (3,150)

(Intercept) 8,388 *** 4,909 *** 3,192 *(2,167) (1,670) (1,648)


[1] Chi-square (d.f.) 122,96 (14) 126,86 (14) 125,51 (14)

-2 Log likelihood 86,12 78,22 79,57








332

Table 20: Results of baseline regressions with sensitivities on CEO tenure




Baseline regressions with sensitivities on CEO tenure (A10.-A.13)

(A.12) (A.13)

Dependent variable: Predicted Est. Est.

GW_WO i,t Independent variable sign (S.E.) sig. (S.E.) sig.

Private information on STOCK_RETURN i,t+2 (-) -2,520 *** -2,285 ***

future financial performance (0,957) (0,857)

STOCK_RETURN i,t+1 (-) 0,842 0,583

(0,746) (0,673)

SHARE_BUYBACKS i,t (-) -0,471 *** -0,399 **

(0,181) (0,168)

Agency theory- Contract. LEV_RATIO i,t (-) 2,963 ** 2,519 *

based incentives motives (1,503) (1,476)

COVENANTS_GW i,t (-) -2,070 *** -1,901 ***

(0,776) (0,731)

Reputation CEO_TENURE_1st_or_2nd_YEAR_DUMMY i,t (+) 1,934 **

motives (0,840)

CEO_TENURE_1st_2nd_or_3rd_YEAR_DUMMY i,t (+) 0,390

(0,641)

Goodwill reporting GOODWILL_HHI i,t (-) -6,497 *** -6,373 ***

flexibility (1,613) (1,532)

CGU_REPORTING_CHANGE i,t (-) -4,947 *** -4,548 ***

(0,959) (0,873)


variables (1,621) (1,517)

FIRM_SIZE i,t 0,000 *** 0,000 **

(0,000) (0,000)

GOODWILL_INTENSITY i,t 4,911 4,276

(3,132) (3,001)

(Intercept) 2,255 3,878 **(1,771) (1,697)

Observations 154 154

[1] Chi-square (d.f.) 126,52 (14) 123,42 (14)

-2 Log likelihood 77,56 83,66

Percent correctly predicted (%) 88,31 85,71

Cox & Snell R Square 0,574 0,557

Industry dummy included YES YES

Year dummies included YES YES




333

8.4.1.3 Goodwill reporting flexibility variables

The goodwill concentration variable GOODWILL_HHIi,t measures the allocation of

goodwill across a firm’s reporting segments in year t. The closer

GOODWILL_HHIi,t is to one, the more concentrated goodwill is in specific

reporting segments. The calculation of this variable is based on a hand collected data

set of information disclosed in the notes of the firms’ financial statements. As

expected, in the baseline regressions (A.1-A.13) the signs of the coefficients of this

independent variable are negative, meaning that higher concentrations of goodwill in

reporting segments reduce goodwill write-off probability in year t. More

importantly, with p-values of 0,000 in all 13 baseline regressions, the influencing

effect of this variable on the observable goodwill write-off probability in year t is

highly statistically significant in all regressions.

Given that similar analyses of the impact of goodwill concentration on goodwill

write-off probability are extremely limited in accounting research, a comparison of

the empirical findings of this PhD thesis is difficult. Nevertheless, the findings of

this PhD thesis on the topic of goodwill concentration confirm the views and

hypotheses of other researchers. This includes, for example, those of Wines et al.

(2007), who argue that the impairment-only approach might motivate senior

executives to allocate goodwill to “cash-generating units at as high a level of

aggregation as possible”1356, as “impairment losses could potentially be avoided by

aggregating units at too high a level”1357. This view is also shared by Carlin et al.

(2010)1358 as well as Finch (2010)1359. Lonergan (2007) sees this concentration also

as a potential issue in the impairment-only approach as acquired goodwill could be

substituted with internally generated goodwill and thereby covering up required

write-offs.1360

1356 Wines et al. (2007), p. 868. 1357 Wines et al. (2007), p. 870. 1358 Cf. Carlin et al. (2010), p. 5. 1359 Cf. Finch (2010), p. 16. 1360 Cf. Lonergan (2007), p. 15. Cf. also Brösel and Klassen (2006), p. 463, Teitler-Feinberg (2006), p. 18, Brösel and Zwirner (2009), p. 196, Pawelzik (2009), p. 60, who argue similarly.


334

Besides the goodwill concentration variable GOODWILL_HHIi,t the dichotomous

variable CGU_REPORTING_CHANGEi,t is included in the baseline regressions

(A.1-A.13). This dummy variable is set to 1 if a firm changed its CGUs or reporting

segments in the year of the goodwill write-off decision (year t). Information on such

changes were hand collected from the firms’ annual reports and analyzed on a year

by year basis. For each observation in the sample, the annual reports of year t and

year t-1 were analysed to understand whether CGUs or reporting segments changed

between t-1 and t.

The coefficients of this dummy variables are found out to be negative in all 13

baseline regressions, in line with the original expectation. Similar to the goodwill

concentration variable GOODWILL_HHIi,t, the effect of the dummy variable

CGU_REPORTING_CHANGEi,t on the observable goodwill write-off probability in

year t is highly statistically significant with p-values of 0,000 in all 13 baseline

regressions. The findings strongly imply that when CGUs or reporting segments

were changed in year t, the corresponding write-off probability is significantly lower

for these firms than for firms which did not change their reporting structure. This

observation is surprising as one could have expected that changes in reporting

structures should be independent of the observable goodwill write-off probability.


335

8.4.2 Sensitivities of the baseline regressions regarding

goodwill reporting flexibility

In the following, additional independent variables are used in the multivariate

analysis of this PhD thesis to analyse the effect from goodwill reporting flexibility

under IAS 36 on goodwill write-off probability in year t. The additional variables

replace the existing variable GOODWILL_HHIi,t used so far in the baseline

regressions (A.1-A.13).

8.4.2.1 Sensitivities on goodwill reporting flexibility

Besides the independent variable GOODWILL_HHIi,t, five additional variables

which proxy for goodwill reporting flexibility under the impairment-only approach

should be tested for their influence on the sample firms’ goodwill write-off

probabilities in year t. These are

• SEGMENT_PROFITABILITYi,t

• SEGMENT_PROFITABILITY _1st+2ndi,t

• SEGMENT_SIZEi,t

• SEGMENT_SIZE _1st+2ndi,t

• SEGMENT_RISKi,t

The variable SEGMENT_PROFITABILITYi,t reflects the percentage of total

goodwill1361 allocated to the most profitable reporting segment with goodwill as of

year-end t, whilst the variable SEGMENT_PROFITABILITY _1st+2ndi,t measures

the percentage of total goodwill allocated to the most and second most profitable

reporting segment with goodwill.1362 The percentage of goodwill allocated to the

largest reporting segment with goodwill is measured by the variable

1361 Book values of goodwill allocated to reporting segments are adjusted for any goodwill write-offs in year t. 1362 Profitability is defined as EBITDA margin as at financial year-end t. Depending on the availability of the metric EBITDA margin in certain industries, alternatively the operating profit margin (adjusted for depreciation and amortization) was used.


336

SEGMENT_SIZEi,t.1363 Correspondingly, the variable SEGMENT_SIZE _1st+2ndi,t

considers the percentage of total goodwill allocated to the largest and second largest

reporting segment with goodwill. The percentage of goodwill allocated to the least

risky reporting segment, identified through a firm’s application of the lowest

discount rate to test the recoverability of goodwill in any reporting segment with

goodwill, is analysed with the independent variable SEGMENT_RISKi,t. Information

on the goodwill allocation was hand collected from the firms’ annual reports. Due to

the relatively high correlations between these five variables, the variables are run

only individually in the regressions.

1363 Size is defined as total revenues as at financial year-end t.


337



Baseline regressions with sensitivities on goodwill reporting flexibility (A.14-A.18)

(A.1) (A.14) (A.15)





STOCK_RETURN i,t+1 (-) 0,616 1,255 * 0,852

(0,689) (0,672) (0,618)


(0,136) (0,132) (0,126)



COVENANTS_GW i,t (-) -2,132 *** -1,470 ** -1,343 **

(0,782) (0,669) (0,681)


motives (0,049) (0,048) (0,048)

Goodwill reporting GOODWILL_HHI i,t (-) -6,493 ***

flexibility (1,606)

SEGMENT_PROFITABILITY i,t (-) -2,508 ***

(0,850)

SEGMENT_SIZE i,t (-) -2,806 ***

(0,983)


(0,788) (0,607) (0,674)

Control MTB i,t -2,310 -2,016 -2,085

variables (1,464) (1,334) (1,299)

FIRM_SIZE i,t 0,000 0,000 * 0,000

(0,000) (0,000) (0,000)

GOODWILL_INTENSITY i,t 5,458 * 4,377 6,641 **

(3,275) (2,776) (2,782)

(Intercept) 8,388 *** 4,197 *** 4,785 ***(2,167) (1,504) (1,636)


[1] Chi-square (d.f.) 122,96 (14) 107,81 (14) 107,35 (14)

-2 Log likelihood 86,12 101,27 101,73








338

Table 21: Results of baseline regressions with sensitivities on goodwill reporting

flexibility




Baseline regressions with sensitivities on goodwill reporting flexibility (A.14-A.18)

(A.16) (A.17) (A.18)



Private information on STOCK_RETURN i,t+2 (-) -2,396 *** -2,037 ** -1,957 **


STOCK_RETURN i,t+1 (-) 1,162 * 0,347 0,733

(0,631) (0,638) (0,602)

SHARE_BUYBACKS i,t (-) -0,341 ** -0,360 ** -0,281 *

(0,141) (0,157) (0,155)



COVENANTS_GW i,t (-) -1,149 * -1,400 ** -1,324 **

(0,623) (0,660) (0,656)

Reputation CEO_TENURE i,t (-) -0,156 ** -0,111 *

motives (0,072) (0,067)

Goodwill reporting SEGMENT_PROFITABILITY_1st+2nd i,t (-) -2,859 ***

flexibility (1,014)

SEGMENT_SIZE_1st+2nd i,t (-) -5,030 ***

(1,498)

SEGMENT_RISK i,t (-) -2,631 ***

(0,996)


(0,772) (0,828) (0,617)

Control MTB i,t -0,718 -0,686 -1,352

variables (1,322) (1,368) (1,375)

FIRM_SIZE i,t 0,000 *** 0,000 ** 0,000

(0,000) (0,000) (0,000)

GOODWILL_INTENSITY i,t 4,793 * 5,973 **

(2,611) (2,878)

(Intercept) 1,667 3,892 ** 3,972 **(1,185) (1,605) (1,695)


[1] Chi-square (d.f.) 105,79 (14) 107,69 (14) 82,14 (12)

-2 Log likelihood 98,29 91,39 92,77

Percent correctly predicted (%) 85,06 85,71 85,16Cox & Snell R Square 0,513 0,534 0,474






339

Table 21 reports on the findings of the multivariate tests when the explanatory

variables GOODWILL_HHIi,t, SEGMENT_PROFITABILITYi,t, SEGMENT_

PROFITABILITY_1st+2ndi,t, SEGMENT_SIZEi,t, SEGMENT_SIZE _1st+2ndi,t,

SEGMENT_RISK,t are tested separately. Regressions (A.14-A.18) replace stepwise

the concentration variable GOODWILL_HHIi,t, with other independent variables

measuring reporting flexibility under IAS 36. With statistical significance at very

high confidence levels, the explanatory variables contain high explanatory power for

the observable goodwill write-off or non-write-off decision in year t.

• SEGMENT_PROFITABILITYi,t p-value < 0,01 (0,003);

• SEGMENT_PROFITABILITY_1st+2ndi,t p-value < 0,01 (0,005);

• SEGMENT_SIZEi,t p-value < 0,01 (0,004);

• SEGMENT_SIZE_1st+2ndi,t p-value < 0,01 (0,001);

• SEGMENT_RISKi,t p-value < 0,01 (0,008);

Additionally, the coefficients of those variables are all found out to be negative,

implying that all of them reduce the observable goodwill write-off probability in

year t, being in line with the original exectations. This means that the more

concentrated goodwill in larger or more profitable reporting segments in year t is,

the lower the observable goodwill write-off probability in the sample firms in the

same year.

Additionally, in all five regressions (A.14-A.18) the variable which analyses

observable changes of CGU(s) and/or reporting segment(s) between the years t-1

and t (CGU_REPORTING_CHANGEi,t) is found to be highly statistically significant

at a 99% confidence level. This means that changes in the reporting structure of a

firm in year t substantially reduce the observable goodwill write-off probability in

the same year.

Observable goodwill concentrations in reporting segments and corresponding

goodwill write-off probabilities:

In the following it is analysed how goodwill write-off probabilities in year t vary

between different levels of goodwill concentrations in reporting segments. The


340

following goodwill concentration analysis is performed across four distinct

allocation characteristics:

(i) GOODWILL_HHIi,t:

Herfindahl Hirschman index of the firm, calculating the goodwill

concentration over all reporting segments as at financial year-end in the year

of the goodwill write-off decision (financial year t).1364

(ii) SEGMENT_PROFITABILITYi,t:

Represents the percentage of total goodwill1365 allocated to the most

profitable reporting segment with goodwill as of year-end t.1366

(iii) SEGMENT_SIZEi,t:

Represents the percentage of goodwill1367 allocated to the largest reporting

segment with goodwill as of year-end t.1368

(iv) SEGMENT_RISKi,t:

Represents the percentage of goodwill1369 allocated to the reporting segment

with the lowest risk as of year-end t.1370

In a first step, the sample of 154 observations was sorted from the highest to the

lowest level of goodwill concentrations according to each of the four allocation

characteristics stated above. In a next step, the ranked concentration levels were split

in quartiles for each characteristic. In a subsequent step, the mean goodwill write-off

1364 The Herfindahl–Hirschman index (HHI) is calculated for each firm in the sample as: GOODWILL_HHI

i,t=∑j=1 GWj

2 , with n being the number of reporting segments and GWj the ratio of

allocated goodwill to the jth reporting segment to the book value of total goodwill as per financial year-end t (adjusted for any goodwill write-off in year t). This concentration measure does not consider the financial characteristics of the individual reporting segments, like size, profitability or risk. It simply analyzes the overall concentration of goodwill across all segments. 1365 Book values of goodwill allocated to reporting segments are adjusted for any goodwill write-offs in year t. 1366 Profitability is defined as EBITDA margin as at financial year-end t. Depending on the availability of the metric EBITDA margin in certain industries, alternatively the operating profit margin (adjusted for depreciation and amortization) was used. 1367 Book values of goodwill allocated to reporting segments are adjusted for any goodwill write-offs in year t. 1368 Size was defined as total revenues as at financial year-end t. 1369 Book values of goodwill allocated to reporting segments are adjusted for any goodwill write-offs in year t. 1370 Risk was defined as the discount rate used to test the recoverability of goodwill allocated to that reporting segment as at financial year t.


341

probabilities of the individual quartiles in year t were calculated. As expected, the

goodwill write-off probabilities vary substantially between the quartiles of goodwill

concentrations in reporting segments, as the following table and figure show.

Table 22: Observable goodwill concentration in reporting segments and

corresponding goodwill write-off probabilitiesSource: Own illustration.

The firms in the first quartile (top quartile), which includes those with the highest

goodwill concentrations in reporting segments, have across all four categories (HHI,

size, profitability, risk) the lowest write-off probabilities in year t. As displayed in

the table above and figures below, these probabilities amount to 34,2%, 39,5%,

28,9%, and 21,2%, respectively. The goodwill write-off probabilities of the lowest

quartiles are found to be substantially higher with 84,2%, 60,5%, 63,2%, and 71,9%.

To understand whether the write-off probabilities between the first quartile (top

quartile = highest concentrations) and the fourth quartile (lowest quartile = lowest

concentrations) are statistically significantly different, a Student’s t-test was applied

to the goodwill write-off probabilities.

The observable differences between the write-off probabilities under the

concentration characteristics GOODWILL_HHIi,t, SEGMENT_PROFITABILITYi,t,

Goodwill concentration in reporting segments and corresponding write-off probabilities

1st quartile (top)

2nd quartile 3rd quartile 4th quartile (lowest)

p-value(sig.)

[1]

GOODWILL_HHI i,t (mean) 0,964 0,671 0,467 0,293 0,000 ***

Goodwill write-off probabilityi,t (mean) 34,2% 41,0% 74,4% 84,2%

SEGMENT_SIZE i,t (mean) 0,931 0,511 0,154 0,002 0,068 *


SEGMENT_PROFITABILITY i,t (mean) 0,881 0,333 0,079 0,000 0,002 ***


SEGMENT_RISK i,t (mean) 0,944 0,714 0,463 0,125 0,000 ***



[1] Difference in means between observations in the 1st qurtile (top) and 4th quartile (lowest).


342

and SEGMENT_RISKi,t are found to be highly statistically significant at a 99%

confidence level. These findings derived from the univariate and multivariate

analyses strongly support the assertion that goodwill concentration in reporting

segments which possess certain financial characteristics substantially influence

goodwill write-off probabilities.


343

Fig. 59: Goodwill concentration and corresponding goodwill write-off probabilities

in year t

Source: Own illustration

0,964

0,671

0,467

0,29334,2%

41,0%

74,4%

84,2%

0%

20%

40%

60%

80%

100%

0,0

0,2

0,4

0,6

0,8

1,0

1stquartile (top)

2ndquartile

3rdquartile

4thquartile(lowest)

Goodw

ill write-off probability

HH

I (m

ean)

GOODWILL_HHIi,t (mean)

Goodwill write-off probabilityi,t (mean)

0,931

0,511

0,154

0,002

39,5%

61,5%

71,8%

60,5%

0%

20%

40%

60%

80%

100%

0,0

0,2

0,4

0,6

0,8

1,0

1stquartile (top)

2ndquartile

3rdquartile

4thquartile(lowest)

Goodw


Siz

e (m

ean)

SEGMENT_SIZEi,t (mean)


0,881

0,333

0,0790,000

28,9%

76,9%

64,1% 63,2%

0%

20%

40%

60%

80%

100%

0,0

0,2

0,4

0,6

0,8

1,0

1stquartile (top)

2ndquartile

3rdquartile

4thquartile(lowest)

Goodw


Pro

fita

bili

ty (

mea

n)

SEGMENT_PROFITABILITYi,t (mean)


0,944

0,714

0,463

0,125

21,2%

61,3%

75,0% 71,9%

0%

20%

40%

60%

80%

100%

0,0

0,2

0,4

0,6

0,8

1,0

1stquartile (top)

2ndquartile

3rdquartile

4thquartile(lowest)

Goodw


Ris

k (m

ean)

SEGMENT_RISKi,t (mean)



344

8.4.3 Explanatory power of baseline regressions

So far, 18 different baseline regressions were calculated. The R2 of the baseline

regressions (A.1-A.18) range between 0,474 and 0,594 (Cox & Snell R2). The

baseline regressions explain therefore approx. 47-59% of the variation in the

observed outcome (i.e. the actual goodwill write-off and non-write-off decision).1371

The goodness of fit of the model was verified by the application of the Hosmer &

Lemeshow test. The Hosmer & Lemeshow test assists in understanding how well the

data fits the applied model. The test is calculated using the deviance (-2 Log

likelihood) and derives a p-value on the basis of a chi-square distribution. It tests the

null hypothesis that the applied model is a good enough fit for the empirical data

which it uses. The null hypothesis is rejected if the derived p-value is smaller than

0,05 (p-value < 0,05). Consequently, to argue that the applied model is a good fit,

the p-value must exceed 0,05 (p-value > 0,05). The p-values of all 18 baseline

regressions are found out to be greater than 0,05, and therefore fulfil the threshold

criterion.

The 18 baseline regressions (A.1-A.18) including all explanatory variables correctly

predict approx. 84-93% of the actually observable outcomes, compared to the initial

58,4% in the null model (excluding all explanatory variables).

8.4.4 Multivariate results on the basis of subsamples

In the following, subsamples of the original 154 observations are created (S.1-S.10).

The principal reason to do so, is that not for all firms represented in the full sample

of 154 observations the information to calculate certain explanatory variables was

publicly available. In particular, this information relates to the compensation and

bonus structures for all of the firms’ CEOs, their equity ownerships, as well as the

change in their shareholdings in year t.

1371 Nagelkerke R2 range between 0,777 and 0,847.


345

Compared to the baseline regressions, the regressions on the various subsamples

include the additional variables:

• CEO_INSIDER_TRADING_DUMMYi,t

• CEO_INSIDER_TRADINGi,t and

• CEO_INSIDER_TRADINGi,t-1

which are used as additional proxies for possible private information on future

changes in the firms’ financial performance held by the firms’ senior management

teams. Besides that, the variables:

• PERFORMANCE_BONUS_DUMMYi,t

• PERFORMANCE_BONUSi,t and

• PERFORMANCE_BONUS_CHANGEi,t

are used to analyse further agency theory-based incentives related to compensation

and bonus structures of CEOs when writing or not writing off goodwill.

Furthermore, to test the hypotheses related to valuation motives of CEOs with

respect to agency theory-based incentives (i.e. CEOs could be concerned that a

goodwill write-off reduces the market value of the shares they hold due to the

information a write-off conveys1372), the regressions to be analysed in this section

include the variables:

• CEO_EQUITY_OWNERSHIPi,t

• CEO_EQUITY_OWNERSHIP_FIX_COMP%i,t and

• CEO_EQUITY_OWNERSHIP_CSO%i,t

The application of these additional explanatory variables goes in hand with a

reduction of the original sample size due to data availability constraints to calculate

these variables. From the original 154 observations, 126 are analysed in regressions

1372 The information content of goodwill write-offs conveyed to investors poses the risk of individual reputational damages to the firm’s management, as impairments imply that expected cash flows that were assumed to get realized through an acquisition in the past are unable to get realized in the future given the information the firm’s management currently has. In case this represents new information to investors, information on an impaired goodwill will lead to a downward adjustment of future earnings expectations by investors, causing the share price to drop.


346

(S.1, S.8-S.9), 117 in regression (S.10), 115 in regressions (S.2-S.3), 109 in

regression (S.4), and 106 in regressions (S.5-S.7).

Before adding the additional independent variables in the regressions (S.2-S.10), the

variables studied in the baseline regressions are analysed for the reduced sample

(S.1). This is done to understand whether the findings obtained in the baseline

regressions also hold true for the reduced sample. The results obtained in (S.1) are

similar to those of (A.1), thereby confirming the original findings on the basis of the

initial sample of 154 observations.

8.4.4.1 Private information variables

Similar to the baseline regressions, the independent variables STOCK_RETURNi,t+2

and SHARE_BUYBACKSi,t, which are used as proxies for potential private

information held by the management teams on the firms’ future financial

performance, are found out to be statistically significantly related to goodwill write-

off probabilities in year t for the subsamples. Again the coefficients carry a negative

sign, implying that the larger the stock returns in year t+2 and the larger share

buybacks in year t, the lower the goodwill write-off probability in year t. For the

variable STOCK_RETURNi,t+2 a statistical significance at the 95% and 99%

confidence level is identified, whilst for the variable SHARE_BUYBACKSi,t

primarily a statistical significance at the 90% and 95% confidence levels is

observable.

Empirical findings on CEO insider trading (CEO_INSIDER_TRADING_DUMMYi,t

and CEO_INSIDER_TRADINGi,t):

In the following, the variable CEO_INSIDER_TRADING_DUMMYi,t is included.

The variable CEO_INSIDER_TRADING_DUMMYi,t is set to 1 if for a firm’s CEO a

positive change in his/her share ownership is observable during year t. The variable

which is used as a proxy for potential private information held by a firm’s senior

management however is not found out to be statistically significantly related to

goodwill write-off probability in year t. Also against the original expectation, the


347

coefficient of this variable is positive in regression (S.2). Similar statistically

insignificant results are observable when the dummy variable

CEO_INSIDER_TRADING_DUMMYi,t is replaced with the continuous variables

CEO_INSIDER_TRADINGi,t and CEO_INSIDER_TRADINGi,t-1 which measure the

percentage change of a CEO’s shareholdings in year t and t-1, respectively.

These findings imply that private information on the firms’ future financial

performance potentially held by CEOs is not necessarily reflected in their insider

trading behaviour. This can have several reasons. Firstly, existing corporate

governance structures in firms and insider trading restrictions could potentially

hinder CEOs to make full personal use of the private information they hold. Firms

might have insider trading policies in place which restrict the number of shares to be

bought or sold, or the timely execution of these trades. Policies might require insider

trades to be approved by the board or respective committees with substantial lead

time. Additionally, share grants are often vested, meaning that shares are blocked for

a certain period of time and cannot be sold immediately. Secondly, trading ahead of

earnings relevant information could cause severe reputational damages for CEOs if

carried out unduly. Consequently, career concerns could restrict CEOs from selling

and buying shares on information that is not or not fully reflected in current share

prices. The threat of severe reputational damages and the related opportunity costs

from such actions (i.e. forgone future salaries when being replacement due a CEO’s

personal enrichment) can outweigh easily the short-term profits from selling or

buying shares ahead of corporate announcements. Thirdly, CEOs might put their

motivations to personally profit from their private information aside through buying

shares in their firms irrespective of the short-term, future financial performance to

comfort investors that their firms are sound investments. By doing so, CEOs might

want to signal shareholders that they are fully committed to their firms and their

implemented strategies which will lead to a higher share price in the future

compared with the current price level. And finally, the variables

CEO_INSIDER_TRADING_DUMMYi,t, CEO_INSIDER_TRADINGi,t, and

CEO_INSIDER_TRADINGi,t-1 might contain some noise and therefore not only

captures CEO insider trades. Often senior executives are rewarded with shares as

part of their annual compensation. This could lead to a positive change in a CEO’s


348

shareholdings without actually buying shares. These share awards represent noise in

the variables used, as they are calculated on the basis of the annual change in shares.


349



Subsamples with additional variables for private information on future financial performance (S.1-S.4)

(S.1) (S.2)



Private information on STOCK_RETURN i,t+2 (-) -3,267 *** -2,269 **


STOCK_RETURN i,t+1 (-) 0,538 0,602

(0,751) (0,755)

SHARE_BUYBACKS i,t (-) -0,414 *** 0,000 ***

(0,158) -

CEO_INSIDER_TRADING_DUMMY i,t (-) 0,013

(0,656)

Agency theory- Contract. LEV_RATIO i,t (-) 1,007 1,156


COVENANTS_GW i,t (-) -2,518 *** -2,765 ***

(0,880) (0,874)

Reputation CEO_TENURE i,t (-) -0,124 ** 0,000 ***

motives (0,062) -




(0,830) (0,928)


variables (1,628) (1,462)

FIRM_SIZE i,t 0,000 0,000

(0,000) (0,000)

(Intercept) 9,883 *** 7,890 ***(2,789) (2,361)


[1] Chi-square (d.f.) 100,76 (13) 84,89 (12)








Note: In regressions (S.2-S.4) the variable CEO_TENURE i,t is excluded as possibilities for insider trading are certainly more limited for

recently installed CEOs than for their more tenured counterparts. Therefore CEO_TENURE i,t is not separately analysed.


350

Table 23: Results of regressions with additional variables for private information on





Subsamples with additional variables for private information on future financial performance (S.1-S.4)

(S.3) (S.4)



Private information on STOCK_RETURN i,t+2 (-) -2,264 ** -5,059 **


STOCK_RETURN i,t+1 (-) 0,573 0,144

(0,735) (1,068)

CEO_INSIDER_TRADING i,t (-) 0,115

(0,269)

CEO_INSIDER_TRADING i,t-1 (-) -0,003

(0,002)

Agency theory- Contract. LEV_RATIO i,t (-) 1,242 1,021


COVENANTS_GW i,t (-) -2,824 *** -2,136 **

(0,884) (0,874)




(0,960) (0,910)


variables (1,470) (1,488)

FIRM_SIZE i,t 0,000 0,000

(0,000) (0,000)

(Intercept) 7,919 *** 5,668 ***(2,335) (1,940)


[1] Chi-square (d.f.) 85,07 (12) 77,19 (11)









351

8.4.4.2 Incentives predicted by agency theory

Empirical findings on CEO performance based compensation

(PERFORMANCE_BONUSi,t, PERFORMANCE_BONUS_DUMMYi,t, and

PERFORMANCE_BONUS_CHANGEi,t):

In a subsequent step, the variables PERFORMANCE_BONUS_DUMMYi,t,

PERFORMANCE_BONUSi,t, and PERFORMANCE_BONUS_CHANGEi,t which

proxy for personal incentives predicted by agency theory are added in the

regressions (S.5-S.7). These explanatory variables measure in how far compensation

concerns could influence the goodwill write-off or non-write-off decisions in the

firms represented in the sample. The dichotomous variable PERFORMANCE_

BONUS_DUMMYi,t is set to 1 if the CEO of a firm received a cash bonus for the

year of the goodwill write-off decision or non-write-off decision (year t), and 0

otherwise. The continuous variable PERFORMANCE_BONUSi,t represents the ratio

between the performance-based cash awards granted to the CEO in % of his/her

fixed salary in the year of the goodwill write-off decision t. The variable

compensation component includes cash-based performance bonuses and excludes

option-based bonuses or restricted share awards. The continuous variable

PERFORMANCE_BONUS_CHANGEi,t measures the percentage change of a CEO’s

performance-based cash awards between years t-1 and t, i.e. the difference between

the variables PERFORMANCE_BONUSi,t and PERFORMANCE_BONUSi,t-1.

Information on the compensation structure and components were hand-collected

from the firms’ annual reports and compensation reports.

In all regressions (S.5-S.7), the coefficients of all cash bonus-based variables which

proxy for compensation concerns of CEOs are negative and in line with the original

expectations. When applying the continuous variables PERFORMANCE_BONUSi,t

and PERFORMANCE_BONUS_CHANGEi,t in the regressions (S.6-S.7), a

statistically significant impact on goodwill write-off probability in year t at the 90%

and 95% confidence level is identified. When using the dummy variable, the effect

is not significant. These findings suggest that senior managers have an incentive to

delay write-offs, especially then when their overall amount of their salary is linked

to the financial performance of the firm. Compensation concerns appear to


352

negatively influence the motivation of CEOs to write off goodwill when actually

economically impaired.

The findings observable in the regressions of this PhD thesis are in line with prior

research performed by Guler (2006)1373, Beatty and Weber (2006)1374 and Ramanna

and Watts (2012)1375 who studied firms applying SFAS 142. In Guler’s (2006)

analysis, the author applies both a logistic and tobit regression to test whether the

amount of a CEO’s bonus in the year prior to the goodwill write-off (year t-1) has an

influence on goodwill write-off decisions and write-off amounts in year t.1376 In his

regressions, Guler (2006) uses as a continuous variable which measures the amount

of a CEO’s bonus, scaled by his/her total salary.1377 In both regressions, a

statistically significant effect from this variable on goodwill write-offs is found (p-

values < 0,10 in both regressions).1378 Similar to the findings of this PhD thesis, the

coefficients of this explanatory variable are negative in all of his regressions.

Beatty and Weber’s (2006) analysis of goodwill write-off decisions of first time

SFAS 142 adopters, using both a probit regression and censored regression1379,

derives results in line with Guler (2006). Including a dichotomous explanatory

variable set to 1 if a firm had an earnings based bonus plan in place in the year prior

to the adoption of SFAS 142 which does not exclude special items like a goodwill

write-off, in both the probit and censored regression the authors find a statistically

significantly negative impact on goodwill write-off probabilities and write-off

amounts with p-values smaller than 0,01 and 0,05, respectively.1380

In the multivariate regressions applied in the study of Ramanna and Watts (2012),

the authors also use a dichotomous variable set to one if a CEO was awarded a cash

1373 Cf. Guler (2006), pp. 43-44. 1374 Cf. Beatty and Weber (2006), pp. 274, 280. 1375 Cf. Ramanna and Watts (2012), pp. 764-765. 1376 Cf. Guler (2006), p. 14. 1377 Cf. Guler (2006), p. 32. 1378 Cf. Guler (2006), pp. 43-44. 1379 In their probit regression, Beatty and Weber (2006) use as the dependent variable the actual observable goodwill write-off decision in year t (dummy), whilst they use in their censored regression the goodwill write-off amount scaled by total goodwill as of the beginning of year t as the dependent variable. 1380 Cf. Beatty and Weber (2006), pp. 274, 280.


353

bonus in the year of the goodwill write-off decision (year t).1381 By studying a

sample of firms applying SFAS 142 and having strong market indications of an

economically impaired goodwill, Ramanna and Watts (2012) find in some of their

regressions a statistically significant negative impact from cash bonus awards in the

year of the goodwill write-off decision on goodwill write-off amounts in the same

year.1382 Observing a statistical significance at the 95% confidence level, the authors

argue that managers’ compensation concerns influence negatively goodwill write-off

decisions.1383

The results derived in this PhD thesis together with the findings of earlier research

studies on SFAS 142 firms suggest that compensation incentives of CEOs can

significantly influence goodwill write-off decisions in the impairment-only

approach, especially then when their overall amount of their salary is linked to the

financial performance of the firm.

1381 Cf. Ramanna and Watts (2012), p. 759. 1382 Cf. Ramanna and Watts (2012), pp. 764-765. 1383 Cf. Ramanna and Watts (2012), p. 772.


354

Table 24: Results of regressions with additional variables for contract-based

incentives




Subsamples with additional variables for contract incentives (S.5-S.7)

(S.5) (S.6) (S.7)



Private information on STOCK_RETURN i,t+2 (-) -2,537 ** -2,281 ** -2,262 **


STOCK_RETURN i,t+1 (-) 0,178 -0,106 0,526

(0,777) (0,735) (0,843)

SHARE_BUYBACKS i,t (-) -0,217 -0,232 -0,487 *

(0,171) (0,185) (0,258)

Agency theory- Contract. PERFORMANCE_BONUS_DUMMY i,t (-) -1,309

based incentives motives (0,881)

PERFORMANCE_BONUS i,t (-) -1,393 *

(0,712)

PERFORMANCE_BONUS_CHANGE i,t (-) -1,739 **

(0,856)

LEV_RATIO i,t (-) 3,291 3,930 * 3,858 *

(2,233) (2,356) (2,288)

COVENANTS_GW i,t (-) -3,858 *** -3,984 *** -3,618 ***

(1,155) (1,188) (1,169)


flexibility (2,034) (2,254) (2,228)


(1,239) (1,428) (1,417)

Control MTB i,t -1,148 -1,161 -2,755

variables (1,824) (1,798) (2,162)

(Intercept) 6,520 *** 6,745 *** 4,974 **(2,193) (2,221) (2,181)


[1] Chi-square (d.f.) 91,09 (11) 89,69 (11) 93,80 (11)

-2 Log likelihood 54,00 53,72 51,29








355

Empirical findings on CEO equity ownership (CEO_EQUITY_OWNERSHIPi,t,

CEO_EQUITY_OWNERSHIP_FIX_COMP%i,t, and CEO_EQUITY_OWNERSHIP_

CSO%i,t):

The next set of explanatory variables which is related to agency theory-based

incentives studies whether larger shareholdings of CEOs in their own firms

influence their goodwill write-off decisions in year t. The shareholdings of a CEO

should be used as a proxy for his/her private wealth tied to the financial performance

of the firm.

To begin with, the individual variables CEO_EQUITY_OWNERSHIPi,t, CEO_

EQUITY_OWNERSHIP_FIX_COMP%i,t, and CEO_EQUITY_OWNERSHIP_

CSO%i,t are calculated. CEO_EQUITY_OWNERSHIPi,t represents the EUR amount

of a CEO’s shareholdings at the end of year t, i.e. the year of the goodwill write-off

or non-write-off decision. The continuous variable CEO_EQUITY_OWNERSHIP_

FIX_COMP%i,t measures the market value of a CEO’s shareholdings as a

percentage of his/her fixed salary, both as of the end of year t. The final variable

related to a CEO’s shareholdings studies the effect of the number of shares held by a

CEO as a percentage of the total number of common shares outstanding, both

measured at the end of year t (CEO_EQUITY_OWNERSHIP_CSO%i,t).

As outlined above, on the basis of agency theory-based considerations, one could

have expected that the larger the private wealth of a CEO being tied to the

performance of the firm, the harder the individual might want to abstain from

writing off goodwill.1384 Whilst one finds that the coefficient of the independent

variable CEO_EQUITY_OWNERSHIPi,t is negative in regression (S.8), in line with

the original expectation and thereby implying a hindering impact on goodwill write-

off decisions, the overall impact on goodwill write-off probability in year t is not

found out to be statistically significant. Contrary to the original expectation, the

signs of the coefficients of the two additional independent variables


CSO%i,t are positive in the regressions (S.9-S.10). Similar to the independent

1384 Cf. Oberholzer-Gee and Wulf (2012), p. 1, Healy (1985), p. 95.


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variable CEO_EQUITY_OWNERSHIPi,t, the impact of the independent variables


CSO%i,t on goodwill write-off decisions in year t is not found out to be statistically

significant. On the basis of these findings, private wealth concerns of CEOs,

irrespective of how measured, seem not to be related to goodwill write-off decisions

in the sample firms.

The finding on the positive sign of the coefficient CEO_EQUITY_OWNERSHIP_

CSO%i,t in this PhD thesis are however in line with the findings of AbuGhazaleh et

al. (2011). The authors find in their tobit regression a statistically significant positive

relationship between the number of common shares held by executive directors as a

percentage of shares outstanding and goodwill write-offs (p-value < 0,05).1385 This

means that on the basis of AbuGhazaleh et al.’s (2011) sample, a larger equity

ownership of senior executives would result in larger goodwill write-offs.1386 The

authors also reason on the basis of agency theory and agency cost considerations

that equity ownership actually restricts managerial opportunism.1387

The negative factor of CEO_EQUITY_OWNERSHIPi,t in regression (S.8) is also in

line with the findings of Guler (2006). The study of Guler (2006) found a

statistically significant negative relationship between equity-like securities held by a

CEO and goodwill write-off decisions. Studying a sample of firms applying SFAS

142, Guler’s (2006) logistic and tobit regressions find that in-the-money stock

options in year t-11388 reduce goodwill write-off probability and goodwill write-off

amounts under SFAS 142 in year t.1389 In his logistic regression, Guler (2006) finds

a highly statistically significant effect from in-the-money stock options with a p-

1385 Cf. AbuGhazaleh et al. (2011), p. 183. As the dependent variable, the authors use the goodwill write-off amount in year t, scaled by total assets as of year t-1. 1386 Cf. AbuGhazaleh et al. (2011), p. 183. 1387 Cf. AbuGhazaleh et al. (2011), p. 182. 1388 Cf. Guler (2006), p. 32, who uses the explanatory variable In-the-money options t-1 to salaryt-1 which measures the value of a CEO’s exercisable in-the-money options as of year t-1, scaled by his/her salary as of the same year. 1389 Cf. Guler (2006), pp. 2, 30.


357

value of 0,013, whilst in the tobit regression the effect is not as strong (p-value <

0,10).1390

On the basis of the mixed findings of earlier research studies performed by Guler

(2006) and AbuGhazaleh et al. (2011) as well as the regression results presented in

this PhD thesis, the relationship between the shareholdings of CEOs which act as a

proxy for their private wealth tied to the performance of the firm and goodwill write-

off probability is not conclusive, and therefore certainly requires addition

investigation in accounting research.

1390 Cf. Guler (2006), p. 44.


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Table 25: Results of regressions with variables for valuation motives

Source: Own illustration



Subsamples with variables for valuation motives (S.8-S.10)

(S.8) (S.9) (S.10)





STOCK_RETURN i,t+1 (-) 0,609 0,690 0,032

(0,737) (0,724) (0,752)


(0,160) (0,162) (0,185)



COVENANTS_GW i,t (-) -2,244 *** -2,200 *** -2,932 ***

(0,823) (0,818) (1,001)

Valuation CEO_EQUITY_OWNERSHIP i,t (-) -0,016

motives (0,037)

CEO_EQUITY_OWNERSHIP_FIX_COMP% i,t (-) 0,104

(1,305)

CEO_EQUITY_OWNERSHIP_CSO% i,t (-) 0,004

(0,009)


flexibility (1,674) (1,669) (1,977)


(0,792) (0,794) (0,955)

Control MTB i,t -2,479 -2,637 * -2,081

variables (1,627) (1,594) (1,715)

FIRM_SIZE i,t 0,000 0,000 0,000

(0,000) (0,000) (0,000)

(Intercept) 8,309 *** 8,433 *** 8,246 ***(2,512) (2,506) (2,675)


[1] Chi-square (d.f.) 97,38 (13) 97,99 (13) 97,19 (12)

-2 Log likelihood 74,71 61,11 74,90








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Empirical findings on covenants (COVENANTS_GWi,t):

Like in the baseline regressions, the independent variable COVENANTS_GWi,t is

tested in the subsamples for its influence on goodwill write-off probability in year t.

The dichotomous variable COVENANTS_GWi,t is set to 1 if in year t the firm under

analysis had debt covenants in place which would be directly affected by a goodwill

write-off. The variable therefore proxies for the potential negative consequences

from breaching debt covenants when writing off goodwill. Negative consequences

from breaching debt covenants can include stronger monitoring from creditors,

restrictions of managerial discretion and negative funding rate consequences.1391

Similar to the baseline regressions, in the subsamples negative coefficients of the

independent variable COVENANTS_GWi,t are observed in all regressions (S.1-S.10).

Furthermore, in all regressions a highly statistically significant effect from this

variable on goodwill write-off probability in year t is found (primarily at the 99%

confidence level). The findings of the influence on goodwill write-off probabilities

are in line with earlier research studies on firms applying SFAS 142; in particular

those of Beatty and Weber (2006) and Zang (2008) who also find that existing debt

covenants can influence goodwill write-off decisions. As described in the literature

review section, Beatty and Weber (2006) studied catch up impairment charges for

firms applying SFAS 142 for the first time. By using a probit regression on goodwill

write-off and non-write-off decisions and a censored regression on the goodwill

write-off amounts, the authors find that firms with existing debt covenants delay

goodwill impairments to future periods.1392 Their results provide evidence with a

statistical significance at the 90% and 95% confidence level. Also in Zang’s (2008)

analysis of goodwill write-off decisions of first time SFAS 142 adopters, the author

1391 Cf. Zang (2008), p. 45. 1392 Cf. Beatty and Weber (2006), p. 273. To analyse the effect from existing covenants Beatty and Weber (2006) analyse slack in net worth covenants (if existing). The variable they use (Net Worth Slack) represents the numerical ranking of covenants slack, calculated as the difference between a firm’s book value of equity less the net worth threshold (as stated in the covenants), divided by the book value of goodwill at the end of the previous financial year. The variable amounts to zero if a firm has no net worth covenants.


360

finds in his tobit model that goodwill write-off amounts are statistically significantly

related to existing debt covenants at a 95% level.1393

On this topic, however, it needs to be added that existing covenants that would be

impacted by a goodwill write-off, like leverage or gearing ratios, do not

automatically pose a threat to a firm and therefore do not have inevitably always a

strong influence on goodwill write-off probability. This holds particularly true when

a firm has substantial headroom between the required ratios stated in the debt

contract and the actual ratios that would result from taking a write-off. One can

think of the following example: firm A has to keep up always a leverage ratio of 2,0,

defined as the book value of debt to book value of equity. Before a possible

goodwill write-off, the firm has a leverage ratio of 1,0. By writing off goodwill, the

actual leverage ratio would increase to 1,5. Consequently, by writing off goodwill,

the debt covenant would not be breached due to the existing headroom between the

actual and required ratio. This reasoning has already been described by Beatty and

Weber (2006) who introduced the term “covenant slack” in the discussion of

determinants of goodwill write-offs.1394 Therefore it could actual be that the

headroom a firm has between the required and actual ratio represents a better proxy

for the risk emerging from covenants than the pure existence of a covenant that

would7 be impacted by a goodwill write-off.

8.4.5 Explanatory power of the regressions based on

subsamples

On the basis of various subsamples, nine different variations of the baseline model

were calculated (S.2-S.10), containing additional independent variables compared

with the baseline regressions. The additional variables related to a CEO’s insider

trading behaviour on the basis of potential private information, compensation

concerns of CEOs in the year of the goodwill write-off decision as well as CEO

1393 Cf. Zang (2008), p. 53. In his tobit regression, Zang (2008) uses the continuous explanatory variable LEVERAGE*D_Restrict, which is the product of a firm’s leverage and a dummy variable which is set to 1 if a firm has a debt covenant in place that would be affected by a goodwill write-off, and 0 otherwise. 1394 Cf. Beatty and Weber (2006), p. 264.


361

equity ownership. The R2 of the regressions (S.2-S.10) range between 0,507 and

0,587 (Cox & Snell R2). These regressions therefore explain approx. 51-59% of the

variation in the observed outcome (i.e. the actual goodwill write-off and non-write-

off decision).

In accordance to the baseline regressions, the goodness of fit of the model was

verified by the application of the Hosmer & Lemeshow test. The Hosmer &

Lemeshow test assists in understanding how well the data fits the applied model.

The test is calculated using the deviance (-2 Log likelihood) and derives a p-value

on the basis of a chi-square distribution. It tests the null hypothesis that the applied

model is a good enough fit for the empirical data which it uses. The null hypothesis

is rejected if the derived p-value is smaller than 0,05 (p-value < 0,05). Consequently,

to argue that the applied model is a good fit, the p-value must exceed 0,05 (p-value >

0,05). The p-values of all ten regressions on the basis of the respective subsamples

are found out to be greater than 0,05, and therefore fulfill the Hosmer & Lemeshow

threshold criterion.

The ten regressions (S.1-S.10) correctly predict approx. 83-90% of the actual

observable outcomes, compared to the 57,0% in the null model (excluding all

explanatory variables).1395

8.4.6 Limitations of research findings

Although the sample, on which basis the multivariate analyses were carried out,

covers a broad spectrum of 136 firms, i.e. observations from 18 different industries

in 17 European countries between 2005 to 2014, the performed analyses and the

corresponding results described in this PhD thesis need to be considered in the

context of how the sample was constructed. Due to the applied sampling

methodology, the analysed sample might not be representative for the universe of

firms applying IFRS. This is primarily due to the following reasons:

1395 Compared to 58,4% in the null model of the baseline regressions (A.1-A.17).


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(1) Sample consists exclusively of public firms and excludes private firms applying

IFRS:

The firms in the sample were selected on the basis of very strong, externally

observable indicators of an economically impaired goodwill. In the case at

hand, this was a MTB < 1 over two consecutive financial year-ends. By

definition of this indicator, a MTB ratio is only observable for public firms.

The sample therefore does not include private firms. It can however be

questioned whether the results derived on the basis of private firms would be

different from those of public firms.

Agency theory-based incentives might be equally present for senior

management teams of private firms. This includes the variable tested in this

PhD thesis (i) debt covenants, (ii) CEO tenure, and (iii) bonus concerns.

Additionally, the permissible reporting flexibility of IAS 36, i.e. how goodwill

gets allocated to reporting segments, is independent of whether a firm is public

or private. Therefore, it can be reasoned that CEOs of private firms would

make equally use of this reporting flexibility, most likely leading to similar

results.

(2) Sample consists of liquid public firms for which it can be argued that the MTB

ratio below 1 is a better proxy for an economically impaired goodwill than for

illiquid firms:

As described earlier, the sample used for the regressions consist of constituents

of the STOXX® Europe 600 Index, which contains highly liquid stocks. The

more liquid a stock is, the higher the possibility that a firm’s market

capitalization is a meaningful proxy for the firm’s fair value of total net assets

(or also termed intrinsic value). For less liquid firms, i.e. which have a

relatively low free float and corresponding low trading volume, it might

become difficult to argue for an economically impaired goodwill once one

observes a MTB < 1, as the market capitalization might not be a sound

indication of the firm’s fair value. Results on the basis of less liquid firms


363

might be different as the argument for an economically impaired goodwill does

not hold.

(3) Country differences:

The analysis of this PhD thesis is based on a pooled sample of 136 firms from

17 different European countries. National enforcement of accounting standards

and legal enforcement of investor rights might be different between those

countries.1396 Different levels of enforcements could influence the possibilities

of senior management teams to manage goodwill write-offs, i.e. accelerating or

delaying goodwill write-offs to strive for personal incentives. Consequently the

findings presented in the regressions stated above might differ between the 17

countries. Due to the relatively small sample size of 154 observations, a

separate analysis by country or country clusters was not performed.

8.5 Summary of research findings and their

implications

The analyses of this PhD thesis are based on a sample of European firms for which

very strong indicators of an economically impaired goodwill were observable.

Hypotheses are formed on the basis of the fundamental questions (i) why certain

firms and their CEOs are less or more likely to take goodwill write-offs and (ii)

whether goodwill reporting flexibility in the impairment-only approach influences

goodwill write-off probabilities. On the basis of the original sample of 154

observations and various subsamples, this PhD thesis finds confirmation of several

of the pronounced hypotheses. The strengths of the confirmations however vary.

Trying to understand why not all of the firms wrote off goodwill despite a highly

likely economically impaired goodwill, various sets of variables related to (i)

potential private information on a firm’s future financial performance held by a

firm’s management, (ii) incentives predicted by agency theory, as well as (iii)

1396 See Leuz et al. (2003), pp. 516-517.


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goodwill reporting flexibility under IAS 36 are studied for their influence on

goodwill write-off decisions.

Table 26: Overview on confirmations of hypotheses Source: Own illustration

Applying binary logistic regressions, several variables which proxy for private

information on the firm’s future performance as well as agency theory-based

incentives are found out to be statistically significantly related to goodwill write-off

probabilities in year t and therefore have the ability to influence or even hinder

write-offs.

Determinants of goodwill write-off and non-write-off decisions - Confirmation of hypotheses

Overview on results in multivariate regressions

strength Sub- strength

Baseline of variables samples of variables

Research area Hypothesis regressions (proxies) regressions (proxies)

H1: Stock returns ✓ strong ✓ strong

H2: Share buybacks ✓ medium ✓ medium

H3: CEO insider trading

Reputation H4: CEO tenure ✓ medium ✓ medium

Contract-based H5: CEO compensation ✓ medium

H6: Accounting covenants ✓ strong ✓ strong

H7: Financial leverage

Valuation H8: CEO equity ownership

H9: Goodwill concentration ✓ strong ✓ strong

H10: Segments’ size ✓ strong

H11: Segments’ profitability ✓ strong

H12: CGU/reporting changes ✓ strong ✓ strong

Key: ✓ confirmation of hypothesis

no confirmation of hypothesis

strong most observations in the regressions: statistical significance at the 95 and 99% confidence level.

medium most observations in the regressions: statistical significance at the 90 and 95% confidence level.

Goodwill

reporting flexibility

Confirmation of hypotheses

Private information

on future financial

performance

Agency theory-

based incentives


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8.5.1 Goodwill write-off decisions and private

information on firms’ future financial

performance

On the basis of the results derived from the sample firms, one comes to the

conclusion that private information on a firm’s future performance held by senior

management can influence goodwill write-off decisions. In the binary logistic

regressions, the variable which measures two-years-ahead stock returns has a

statistically significant effect on the goodwill write-off probability in year t. The

findings imply that the higher stock returns are in the future, the lower the goodwill

write-off probability in year t, thereby confirming Hypothesis 1. The findings

therefore imply that basically goodwill write-offs and non-write-offs in the sample

firms reveal some information on the firms’ future financial performance.

Nevertheless on that topic it needs to be added that several other variables which

measure future financial performance like one-year-ahead stock returns and changes

in accounting earnings in year t+1 and t+2 are not statistically significantly related

to goodwill write-off probability in year t. This could have several reasons. Firstly,

the change in financial performance requires time and is not immediately observable

in terms of stock returns in year t+1. This reasoning would explain why one-year-

ahead stock returns do not differ between the goodwill write-off and non-write-off

subsamples. Secondly, as described above, it could be that financial performance

measured in terms of accounting earnings lags behind financial performance

measured in terms of stock returns (“prices lead earnings”1397), meaning that stock

prices incorporate faster firm value relevant information than accounting earnings in

year t+2. This would explain why two-years-ahead stock returns hold a greater

explanatory power in the regressions than changes in accounting earnings in the

same year (t+2).

That senior management teams possess private information on the firms’ future

financial performance suggests also the variable which measures the scale of share

1397 Kothari (2001), p. 129.


366

buybacks in year t. Non-write-off firms buy back more shares in the year of the

goodwill write-off or non-write-off decision than their write-off counterparts. This

share buyback behavior implies that senior management teams of non-write-off

firms are of the opinion that their firms are partly undervalued on stock markets,

thereby indirectly confirming the private information hypothesis on changes in

future financial performance. Consequently, these finding provide support for

Hypothesis 3. No support however is found for the insider trading Hypothesis 2

which assumed that goodwill write-off probability in year t is lower for firms whose

CEOs increase their stockholdings in the same year than for firms whose CEOs do

not increase their stockholdings. Possible reasons for this observation could be that

corporate governance structures in firms and reputational concerns could hinder

CEOs to make full private use of the private information they hold on future

financial performance.

Implications:

Leaving aside the results emerging from agency theory-based incentives to be

discussed further below and looking only at the observable relationship between

future changes in financial performance and goodwill write-off probability in year t

as well as the findings from the variables measuring positive or negative private

information held by senior executives (Hypotheses 1 and 3), one could argue that the

impairment-only approach principally works in accordance to the IASB’s

argumentation for introducing the impairment-only approach. Further, it can be

argued that goodwill write-off and non-write-off decisions have some information

value to addressees of financial statements.

Nevertheless on this topic it needs to be mentioned that not writing off goodwill

would only be justified from the viewpoint of the IASB if a firm’s performance

would recover in the future to an extent that goodwill is fully recovered again, i.e.

recoverable amount > carrying amount. As initially stated, the sample firms were

selected on the basis of very strong indicators of an impaired goodwill, i.e. MTB <

1. A MTB ratio below 1 suggests that the market values of a firm’s assets are below

their book values, and therefore requiring a goodwill write-off. On the basis of this

reasoning, one could argue that not recognizing a write-off would only be acceptable


367

if the MTB ratio improves in the near future to an extent that it is larger than 1. This

however is not the case in the sample on average. As described in chapter 8.1.2, over

the following two years, i.e. t+1 and t+2, the mean MTB ratio of the non-write-off

subsample firms is still well below 1, strongly suggesting that a write-off would be

required in year t. Consequently, despite the confirmatory findings of the

relationship between future changes in financial performance and goodwill write-off

probability in year t, a write-off would have been actually necessary in the majority

of the cases as the MTB ratio does not substantially improve after the goodwill non-

write-off decision in year t.

8.5.2 Goodwill write-off decisions and agency theory-

based incentives

Besides the observable relationship between future changes in financial performance

and goodwill write-off probability in year t, in the sample it is also found that

goodwill write-off and non-write-of decisions are associated with agency theory-

based incentives, which by definition should be unrelated to a firm’s future financial

performance and therefore under the assertion of the IASB be unrelated to goodwill

write-off probabilities. On the basis of the IASB’s private information disclosure

assertion, one would have expected that those variables measuring agency theory-

based incentives would not have significant explanatory power for goodwill write-

off probability; however they have, as found out in this PhD thesis.

This PhD thesis finds confirmation for Hypothesis 4 which assumed that CEO tenure

is statistically significantly related to goodwill write-off probability in year t. The

findings of the analyses document that goodwill write-off probability is higher for

firms with CEOs that have been in office for a shorter time period than for firms

with CEOs who have been in office longer. The analyses also find confirmation of

the CEO compensation concern Hypothesis 5. Compensation concerns of CEOs can

substantially influence goodwill write-off decisions.


368

As found out in the multivariate analyses of this PhD thesis, cash bonuses1398 are

lower for CEOs of goodwill write-off firms than for CEOs who did not write off

goodwill. Furthermore, goodwill write-offs trigger negative changes in the overall

variable cash compensation of senior executives between year t-1 and t.

Consequently, CEOs have a personal interest not to write off goodwill, so that their

salaries do not decrease from the previous year’s levels. Additionally, existing debt

covenants are found out to be an influencing factor of goodwill write-off decisions.

In the vast majority of regressions, the dummy variable which measures the

existence of covenants which would be impacted by a goodwill write-off holds

explanatory power regarding goodwill write-off probability in year t, thereby

confirming Hypothesis 6. The findings on the write-off probability reducing effects

from CEO tenure, compensation concerns, as well as debt covenants support the

view that agency theory-based incentives play a decisive role in the goodwill write-

off decision making process. No confirmations, however, are found for the financial

leverage and CEO equity ownership Hypotheses 7 and 8 in this PhD thesis.

Implications:

The findings of this PhD thesis suggest that personal interests of a firm’s senior

management team can work against the outright execution of the accounting

standard as originally hoped for by the IASB. The results from the regressions imply

that CEOs make use of the impairment-only approach to strive for personal

incentives by, for example, writing off goodwill early once installed newly in office

or by not writing off goodwill to limit the impacts on their compensation. These

results stand in strong contract to the IASB’s private information hypothesis as they

support the view that senior managers on average make use of the unverifiable

discretion in the impairment-only approach to manage financial reporting

opportunistically and thereby striving for personal incentives. By doing so,

executives transfer wealth from shareholders and bondholders to themselves through

delaying or accelerating goodwill write-offs. Consequently, the impairment-only

approach can be considered as being too flexible and thereby catering the personal

motivation of senior executives to achieve their desired outcomes.

1398 In terms of fix salaries.


369

8.5.3 Goodwill write-off decisions and goodwill

reporting flexibility

Amongst the most interesting findings of this PhD thesis are that goodwill

concentration levels in reporting segments are found to be a determining predictor of

goodwill write-off probability in the sample. The effect is observable in various

subsamples and across all regressions and highly statistically significant. The

influence of goodwill allocations to reporting segments on goodwill write-off

probabilities has not received much attention in goodwill accounting research so far;

most likely due to the time-consuming data collection requirements that come along

with such analyses.

This PhD thesis finds that goodwill write-off probabilities are substantially lower for

firms with a high concentration of goodwill in certain reporting segments compared

to firms with low concentration levels. This observation is very interesting as in the

strict sense of the impairment-only accounting standard IAS 36 one could have

expected that goodwill allocation to reporting segments should be unrelated to the

observable write-off probabilities. This however is not the case. Consequently, the

analyses provide strong confirmation of the goodwill concentration Hypothesis 9.

When looking at the distinctive characteristics of the reporting segments to which

acquired goodwill was allocated and the related write-off probabilities, it becomes

observable that non-write-off firms have larger portions of goodwill in more

profitable and/or larger reporting segments than their write-off counterparts. Here

again, the results are highly statistically significant in the regressions and therefore

confirm Hypotheses 10 and 11 which stated that goodwill write-off probabilities are

lower for firms that have allocated greater portions of goodwill to larger or more

profitable reporting segments than for firms that have allocated smaller portions of

goodwill to such reporting segments. Furthermore, the analyses of this PhD thesis

reveal that changes in CGU structures and reporting segments in year t statistically

significantly reduce goodwill write-off probabilities in the same year, providing

support for Hypothesis 12.


370

Implications:

The findings on the statistically significant negative relationship between

concentration effects and write-off probabilities strongly imply that in larger

reporting segments substitution effects between internally generated goodwill and

acquired goodwill occur. This means that in larger reporting segments it is

potentially more difficult than in smaller reporting segments to isolate cash flows

from acquired assets (incl. goodwill) than from non-acquired, pre-existing assets.

This certainly represents an issue in the outright execution of the impairment-only

approach in goodwill accounting.

The findings on the higher concentration of goodwill in more profitable reporting

segments and the thereof resulting lower goodwill write-off probabilities also allow

for interpretation. It is likely that the positive performance of the more profitable

reporting segment to which large portions of goodwill were allocated can conceal

pending goodwill impairments, as a firm’s management could, for example, argue to

the firm’s auditors that acquired assets perform similarly as the reporting segment to

which they were allocated. Difficulties in separating the various cash flow streams

emerging from acquired and pre-existing assets have also to be mentioned in this

respect. Generally, one can think of the following scenario: In case a firm’s senior

management wants to reduce the risk of a goodwill write-off in the subsequent years

after an acquisition, it might want to allocate as much goodwill as possible to larger

and/or more profitable reporting segment. This strategy certainly depends on the

auditor’s approval as the audit team usually reviews such allocations.

Changes in reporting structures (CGUs and/or reporting segments) also reduce the

sample firms’ goodwill write-off probability. This finding implies that altering the

structure on which basis the recoverability of goodwill is tested makes it harder for

auditors to identify pending impairment losses which actually should have been

recognized. Generally in practice one can observe that changes in reporting

structures lead to a lower number of CGUs and reporting segments. This suggests

that in case of altered reporting structures also goodwill substitution effects occur,

however this time between the merged CGUs and/or reporting segments.


371

On the basis of the above described findings regarding an observable relationship

between goodwill reporting flexibility (concentration and reporting structure

changes) as well as agency theory-based incentives and goodwill write-off

probability, it can certainly be discussed whether the impairment-only approach in

its current form fully serves the interests of investors as the goodwill balance on a

firm’s balance sheet might often be over- or understated, and thereby not accurately

reflecting a firm’s fair value of net assets.

Appendix

372

Appendix

Appendix

373

Appendix I:List of sample firms

List of sample firms

Company Country Supersector (STOXX) Observations

A.P. Møller - Mærsk A/S DK Industrial Goods & Services 1

AEGON N.V. NL Insurance 1

ageas SA/NV NL Insurance 1

Air France-KLM SA FR Travel & Leisure 2

Airbus Group N.V. FR Industrial Goods & Services 1

Alcatel-Lucent FR Technology 2

Allianz SE DE Insurance 1

Alpha Bank A.E. GR Banks 1

Anglo American plc GB Basic Resources 1

ArcelorMittal LU Basic Resources 1

Arkema S.A. FR Chemicals 1

Ashtead Group plc GB Industrial Goods & Services 1

Aviva plc GB Insurance 1

AXA Group FR Insurance 1

Bâloise Holding AG CH Insurance 1

Banca Monte dei Paschi di Siena IT Banks 1

Banca popolare dell'Emilia Romagna IT Banks 1

Banca Popolare di Milano Scarl IT Banks 1

Banca Popolare di Sondrio IT Banks 1

Banco Bilbao Vizcaya Argentaria, S.A. ES Banks 1

Banco Comercial Português S.A. PT Banks 1

Banco de Sabadell, S.A. ES Banks 1

Banco Popolare Societa Cooperativa IT Banks 2

Banco Popular Espanol S.A. ES Banks 1

Appendix

374

Banco Santander, S.A. ES Banks 1

Bankinter, S.A. ES Banks 1

Barratt Developments plc GB Personal & Household Goods 1

BNP Paribas SA FR Banks 1

Bollore FR Industrial Goods & Services 1

Bouygues SA FR Construction & Materials 1

CaixaBank, S.A. ES Banks 1

CNP Assurances Société anonyme FR Insurance 1

Cofinimmo S.A. BE Real Estate 1

Commerzbank AG DE Banks 1

Compagnie de Saint-Gobain S.A. FR Construction & Materials 1

Credit Agricole S.A. FR Banks 1

Danske Bank A/S DK Banks 1

Delta Lloyd N.V. NL Insurance 1

Deutsche Bank AG DE Banks 1

Deutsche Lufthansa AG DE Travel & Leisure 2

DNB ASA NO Banks 1

DS Smith Plc GB Industrial Goods & Services 1

E.ON SE DE Utilities 1

Endesa SA ES Utilities 1

Enel SpA IT Utilities 1

Erste Group Bank AG AT Banks 2

Groupe Delhaize SA BE Retail 1

Eurazeo FR Industrial Goods & Services 1

Eurobank Ergasias S.A. GR Banks 2

Exor S.p.A. IT Financial Services 1

Fiat Chrysler Automobiles N.V. IT Automobiles & Parts 1

Finmeccanica SpA IT Industrial Goods & Services 1

Fonciere des Regions FR Real Estate 2

Friends Life Group Limited GB Insurance 1

GAGFAH S.A. LU Real Estate 1

GDF SUEZ S.A. FR Utilities 1

Appendix

375

Georg Fischer AG CH Industrial Goods & Services 1

Groupe Bruxelles Lambert BE Financial Services 1

HeidelbergCement AG DE Construction & Materials 1

Helvetia Holding AG CH Insurance 1

Home Retail Group plc GB Retail 1

Huhtamaki Oyj FI Industrial Goods & Services 1

Iberdrola, S.A. ES Utilities 1

Immofinanz AG AT Real Estate 1

ING Groep N.V. NL Banks 1

Intesa Sanpaolo S.p.A. IT Banks 1

intu properties plc GB Real Estate 2

Investment AB Kinnevik SE Financial Services 1

Investor AB SE Financial Services 1

Jyske Bank A/S DK Banks 2

KBC Group NV BE Banks 1

Kingfisher plc GB Retail 1

Lafarge S.A. FR Construction & Materials 1

Lagardere SCA FR Media 1

Land Securities Group plc GB Real Estate 1

Lloyds Banking Group plc GB Banks 1

Man Group plc GB Financial Services 1

Marine Harvest ASA NO Food & Beverages 1

Melrose Industries PLC GB Industrial Goods & Services 1

Merlin Entertainments plc GB Travel & Leisure 1

Mondi plc GB Basic Resources 1

Münchener Rück AG DE Insurance 1

National Bank of Greece S.A. GR Banks 1

Natixis FR Banks 2

Neste Oil Corp. FI Oil & Gas 1

Norsk Hydro ASA NO Basic Resources 2

OC Oerlikon Corporation AG CH Industrial Goods & Services 1

Old Mutual plc GB Insurance 1

Appendix

376

OMV Aktiengesellschaft AT Oil & Gas 1

Ophir Energy Plc GB Oil & Gas 1

Orange FR Telecommunications 1

Outokumpu Oyj FI Basic Resources 2

Pargesa Holding SA CH Financial Services 1

Persimmon plc GB Personal & Household Goods 1

Peugeot S.A. FR Automobiles & Parts 1

Phoenix Group Holdings Plc GB Insurance 2

Piraeus Bank S.A. GR Banks 1

Raiffeisen Bank International AG AT Banks 1

Renault Société Anonym FR Automobiles & Parts 1

Repsol, S.A. ES Oil & Gas 1

Rexel SA FR Industrial Goods & Services 1

RSA Insurance Group plc GB Insurance 1

SCOR SE FR Insurance 2

SEGRO plc GB Real Estate 1

Skandinaviska Enskilda Banken AB SE Banks 1

Smurfit Kappa Group plc IE Industrial Goods & Services 2

Societe Generale Group FR Banks 1

Stora Enso Oyj FI Basic Resources 2

Storebrand ASA NO Insurance 1

Suez Environnement Company SA FR Utilities 1

Swedbank AB (publ) SE Banks 1

Swiss Life Holding AG CH Insurance 1

Swiss Prime Site AG CH Real Estate 1

Sydbank A/S DK Banks 1

Taylor Wimpey plc GB Personal & Household Goods 1

Telecom Italia S.p.A. IT Telecommunications 1

TGS Nopec Geophysical Co. ASA NO Oil & Gas 1

The Bank of Ireland IE Banks 1

The Royal Bank of Scotland Group GB Banks 1

Thomas Cook Group plc GB Travel & Leisure 1

Appendix

377

ThyssenKrupp AG DE Industrial Goods & Services 1

Travis Perkins plc GB Industrial Goods & Services 1

TUI AG GB Travel & Leisure 2

Unibail-Rodamco SE FR Real Estate 1

UniCredit S.p.A. IT Banks 1

Unione di Banche Italiane Scpa IT Banks 1

UPM-Kymmene Oyj FI Basic Resources 1

Veolia Environnement S.A. FR Utilities 1

Vestas Wind Systems A/S DK Oil & Gas 1

Vienna Insurance Group AT Insurance 1

Vodafone Group Plc GB Telecommunications 2

Voestalpine AG AT Basic Resources 1

Volkswagen AG DE Automobiles & Parts 1

Wereldhave NV NL Real Estate 1

Wolseley plc GB Industrial Goods & Services 1

Zurich Insurance Group AG CH Insurance 1

Table 27: List of sample firms Source: Own illustration

Appendix

378

Appendix II: Multicollinearity diagnostics in baseline

regressions and regressions on the basis of subsamples

As outlined in chapter 8.3, multicollinearity in the multivariate regression models is

tested through the analysis of the variance inflation factor (VIF) of each independent

variable in the individual regressions presented above. According to Midi et al.

(2010), “(m)ulticollinearity can cause unstable estimates and inaccurate variances

which affects confidence intervals and hypothesis tests. The existence of collinearity

inflates the variances of the parameter estimates, and consequently incorrect

inferences about relationships between explanatory and response variables”1399. The

VIF provides insights on how much the standard error of a variable in a regression

could be inflated by collinearity. Generally, multicollinearity in multivariate

regression models is considered a cause of concern if the so-called variance inflation

factor exceeds 10.1400 Due to the possibility of collinearity in the multivariate

regression models, the VIFs of each variable are calculated and analyzed for

significant levels. However as the table below show, the VIFs of the variables range

predominately between 1 and 2.

1399 Midi et al. (2010), p. 253. 1400 Cf. O’Brien, R. (2007), p. 688, Menard (1995), p. 66, Neter et al. (1989), p. 409, Kennedy (1992), p. 183.

Appendix

379

Key: VIF = Variance inflation factor

Table 28: Multicollinearity diagnostics Source: Own illustration

Multicollinearity diagnostics in Baseline regressions Multicollinearity diagnostics in regressions based on subsamples

Regression min max min max Regression min max min max

A.1 0,606 0,914 1,094 1,651 S.1 0,649 0,922 1,085 1,540

A.2 0,609 0,884 1,131 1,643 S.2 0,574 0,887 1,127 1,743

A.3 0,612 0,902 1,109 1,633 S.3 0,615 0,827 1,209 1,627

A.4 0,449 0,921 1,086 2,225 S.4 0,591 0,880 1,137 1,691

A.5 0,599 0,913 1,096 1,669 S.5 0,666 0,945 1,058 1,502

A.6 0,608 0,900 1,111 1,645 S.6 0,656 0,943 1,060 1,524

A.7 0,621 0,937 1,067 1,610 S.7 0,665 0,933 1,072 1,504

A.8 0,605 0,933 1,072 1,652 S.8 0,651 0,911 1,097 1,537

A.9 0,621 0,934 1,071 1,611 S.9 0,589 0,922 1,085 1,698

A.10 0,604 0,914 1,094 1,655 S.10 0,643 0,940 1,064 1,556

A.11 0,606 0,915 1,093 1,651

A.12 0,608 0,915 1,093 1,646

A.13 0,606 0,909 1,100 1,650

A.14 0,608 0,916 1,092 1,644

A.15 0,641 0,906 1,104 1,561

A.16 0,629 0,915 1,092 1,590

A.17 0,642 0,916 1,092 1,558

A.18 0,615 0,913 1,095 1,627

Tolerance

Collinearity Statistics

VIF

Collinearity Statistics

Tolerance VIF

Appendix

380

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Curriculum Vitae

Korbinian Eichner

Professional experience

KPMG AG 2014-

Corporate Finance, Business Valuation, Zurich

University of St. Gallen (HSG) 2013-2014

Institute of Accounting, Control and Auditing

Research Associate to Prof. Dr. Peter Leibfried, MBA, CPA

UBS AG, Investment Bank 2011-2013

Investment Bank Finance, Equity Securities Division, Zurich

KPMG AG 2007-2009

Corporate Finance, Business Valuation, Munich

Education

University of St. Gallen (HSG) , Switzerland 2013-2016

PhD studies in Management, Specialization in Accounting

University of St. Gallen (HSG), Switzerland 2009-2011

M.A. HSG in Banking and Finance, Specialization in Corporate Finance

Wharton School, University of Pennsylvania, USA 2009

Courses in Corporate Finance and Accounting

Heilbronn University, Germany 2002-2006

BSc equivalent in Business Administration, Specialization in

Accounting & Finance