doktorandenseminar des competence centers corporate finance der universität hohenheim, 27. januar...

Doktorandenseminar des Competence Centers Corporate Financeder Universität Hohenheim, 27. Januar 2006

Good versus Bad Earnings Management:

Is Income Smoothing a Deliverance?

Dipl.-Pol. Matthias Johannsen, MSc.

2

agenda

theoretical considerations conceptual definitions theoretical motivation research question existing literature

set up of empirical investigation research design variable computation hypotheses to be tested

results descriptive statistics hypothesis tests fixed effects panel regressions

references

3

conceptual definitions

earningsmanagement

income smoothing

“purposeful intervention in the external financial reporting process, with the intent of obtaining some private gain (as opposed to say, merely facilitating the neutral operation of the process)” (Schipper 1989: 92)

“actions [by the management of the firm] to dampen fluctuations of the firms’ publicly reported net income”(Trueman/Titman 1988: 127)

note: income smoothing can be achieved by earnings management activities

4

theoretical motivation I opportunistic e.m. and information uncertainty

example

effects increase in current reported earnings increase in net assets reduces future reported residual earnings increase in net assets requires adjusted future depreciation and

hence reduction of future earnings

upward management of reported earnings in order to avoid reporting of loss by reducing depreciation

in general opportunistic earnings management has only a transitory effect

current earning are of little use for predicting future earnings opportunistic earnings management increases information uncertainty

5

theoretical motivation II income smoothing and information uncertainty

example

effects reported earnings follow more closely the general trend reported earnings have lower fluctuations

upward management of reported earnings in case of temporary reduction of earnings

downward management of reported earnings in case of temporary increase of earnings

current earning are very useful for predicting future earnings earnings management to smooth income increases information uncertainty

6

effect of earnings management on the information uncertainty of earnings

research question

opportunistic earnings

management

earnings management to smooth income

no significant

effect

decrease in information uncertainty

increase in information uncertainty

scheme by Guay/Kothari/Watts (1996)

does the market react differently to earnings management depending on the degree of income smoothing present?

7

existing literature I earnings management

verification of opportunistic earnings management Teoh/Welch/Wong (1998), before IPO Burgstahler/Eames (1998), to meet analysts’ forecasts Detzler/Machuga (2002), in cases of non-routine change of CEO

decreasing effect on information uncertainty Francis et al. (2003), earnings management leads to larger cumulative

abnormal returns Marquardt/Wiedmann (2004), earnings management reduces the

explanatory power of in a price-earnings regression Francis et al. (2004), earnings management increases the cost of equity

increasing effect on information uncertainty Subramanyam (1996), discretionary accruals which are often taken as a

measure of earnings management have significant information content in price-earnings regressions

8

existing literature II income smoothing

verification of income smoothing Schmidt (1979), in an older sample for the German Market Kasanen/Kinnunen/Niskanen (1996), in order to sustain a smooth

dividend stream Lim/Lustgarten (2002), for the US

decreasing effect on information uncertainty Bitner/Dolan (1996) show that equity markets pay a premium for shares

of income smoothing firms Zarowin (2002), income smoothing increases the value relevance of

earnings Tucker/Zarowin (2005), income smoothing increases the information

content of reported earnings

9

agenda




references

10

timepost event

period

t2 = end of last fiscal year

t1 = beginof estimation period

the event

t3 = z trading days after end of fiscal year

t0 = x trading days after end of fiscal year

estimation period

research design I event study methodology and its timing

11

to, the event time

research design II structure of the analysis

compare average monthly absolute abnormal returns

separation of sample firm years

earnings management high

earnings management low income smoothing

low

income smoothing high

income smoothing low

income smoothing high

12

according to the model of Dechow/Sloan/Sweeney (1995):

total accruals are given by

these are regressed on

the first earnings management measure

= absolute value of the one period ahead forecast error

1

t

tttttt AT

DEPDSTCEQLCTACTTAC

tittiiti

ti

ti

titi

ti

DaDaAT

PPEa

AT

RECSALESa

ATactiTAC ,

1,

,3

1,

,,2

1,1

1,

tiDACEMGMT ,_

variable computation I earnings management variable alternative 1

13

according to the model of Dechow/Dichev (2002):

regress changes in working capital and changes on cash

the residuals are changes in working capital unrelated to past, current and future cash realizations

the second earnings management measure is the standard deviation of all current and past firm specific forecast errors:

tittiiti

ti

ti

ti

ti

ti

ti

ti DaDaAT

CFOa

AT

CFOa

AT

CFOac

AT

WCP,

1,

1,3

1,

,2

1,

1,1

1,

,

titiSDEMGMT

,,_

variable computation II earnings management variable alternative 2

ti,

14

according to the model of Lang/Ready/Yetman (2003): measurement of dampening of fluctuations in performance

In order to keep a large sample modifications are made

(as robustness check, cash from operations and operating income were used without changing the results)

ti

ti

INCOME

SALEStiINCSM

,

,

,

2

2

,

,

,

ti

ti

incomeoperating

operationsfromcash

tiINCSM

variable computation III income smoothing variable

15

unexpected earnings (Ball/Brown (1968) and subsequent)

Momentum effect (Jegadeesh/Titman (1993))

extreme financial performance (own computation)

ii XEX

tititi

XEXSUE

,,

,

2

8,

t

ttii rMOMENTUM

4

,,,,

,

titititi

ti

LEVGZABSCSEGZABSCEQGZABSATGLNZABS

EXTRZ

unexpected earnings X-E[X] are the residuals from an AR(1) specification of earnings

variable computation IV control variables

16

according to the Fama/French (1992, 1996) Three Factor Model

the risk premium is estimated by

abnormal returns, denoted , are the one period ahead forecast errors of the above equation

absolute cumulative abnormal returns are given by

titititftmitfti HMLaSMBaRRaRR ,,3,2,,,1,,

t

ttii AABStomonthsCARABSaverage

0,1

10

variable computation V absolute cumulative abnormal returns

absolute cumulative abnormal returns serve as the proxy variable for information uncertainty

tiA ,

17

hypothesis 1

hypothesis 2

firm years with high (low) degrees of earnings management show high (low) absolute cumulative abnormal returns

firms years with high (low) degrees of income smoothing show low (high) absolute cumulative abnormal returns

hypothesis 3 assuming that hypotheses 1 and 2 cannot be rejected assuming that income smoothing is a special form of

earnings management It is expected that the differences in absolute cumulative

abnormal returns between high and low earnings management firm years disappear for income smoothing firm years

hypotheses to be tested

18

agenda




references

19

Sample Statistic EMGMT_DAC EMGMT_SD INCSM SUE MOMENTUM EXTRZ

EMGMT_DACtotal

below the median

above the median

EMGMT_SDtotal

below the median

above the median

INCSMtotal

below the median

above the median

meanSTDEVmean

STDEVmean

STDEV

meanSTDEVmean

STDEVmeanSTDEV

meanSTDEVmean

STDEVmeanSTDEV

0.14630.22590.03310.02070.25950.2758

0.14690.21480.0780.10330.22250.2722

0.14590.22510.17360.24740.1201

0.85961.9390.38220.87451.32392.4979

0.93262.01670.09570.05111.76952.5949

0.93262.01671.42382.62430.4468

17.039333.884818.954735.30515.123132.2972

14.170626.999517.929732.491610.411619.3583

20.397449.10062.95932.086337.8403

0.11431.09940.19991.04070.02791.1494

0.17041.27170.39611.2561-0.0641.246

0.07351.1073-0.05881.16970.1951

0.03440.43120.04140.34430.02730.5047

0.04650.44190.0390.31850.0540.5388

0.04680.44530.04330.49820.0504

0.30250.52610.20910.30140.39850.6709

0.27890.45010.19070.22850.37660.592

0.3840.71340.4470.7960.3233

0.1988 0.8985 64.8854 1.0319 0.3858 0.6176

descriptive statistics moments of key variables

20

hypothesis test I hypotheses 1 and 2: separation of sample

all observations for EMGMT_DAC

average monthly ABS(CAR)

T for equality to average

T for equality to averageabove the median

below the median

all observations for EMGMT_SD




below the median

all observations for INCSM




below the median

21

Panel A: EMGMT_DAC

Sample Statistic τ_0 τ_0_4 τ_0_6

Panel B: EMGMT_SD


Panel C: INCSM


totalbelow the

medianabove the

median

totalbelow the

medianabove the

median

totalbelow the

medianabove the

median

avg. monthly ABS(CAR)avg. monthly ABS(CAR)

T of H0 ABS(CAR) = total avg.avg. monthly ABS(CAR)

T of H0 ABS(CAR) = total avg.







0.08880.0787

-4.5167***0.1001

3.7467***

0.10250.0847

-4.9234***0.1253

4.0965***

0.08850.1007

4.8748***0.077

-5.803***

τ_0_2

0.06330.0519

-7.721***0.0761

5.6939***

τ_0_2

0.0790.0552

-10.5592***0.1104

6.7129***

τ_0_2

0.06270.0762

6.9412***0.0506

-10.326***

0.05510.0438

-8.9091***0.0678

6.192***

0.07380.0479

-14.5964***0.107

7.5834***

0.05350.066

7.5434***0.0421

-11.9926***

0.04580.0377

-7.6582***0.0548

5.6561***

0.05970.0405

-12.3685***0.0842

7.1775***

0.04470.0539

6.9882***0.0363

-10.1589***

hypothesis test II hypotheses 1 and 2: results

22

hypothesis test III hypothesis 3: separation of sample

observations for EMGMT_DAC if INCSM

is available

Z for: above the median minus below the median

Z for EMGMT_DAC : above the median minus below the median

Z for EMGMT_DAC : above the median minus below the median

INCSM = below the median

INCSM = above the median

observations for EMGMT_SD if INCSM is

available

Z for: above the median minus below the median

Z for EMGMT_SD : above the median minus below the median

Z for EMGMT_SD : above the median minus below the median

INCSM = below the median

INCSM = above the median

23

hypothesis test IV hypothesis 3: results for EMGMT_DAC

Panel A: EMGMT_DAC

Sample Statistic τ_0 τ_0_2 τ_0_4 τ_0_6

total ifINCSM is available

total and INCSM =below the median

total and INCSM =above the

median

0.0215 0.0242 0.0239 0.0171

5.7174*** 9.0167*** 9.9537*** 8.9518***

1.2731 1.4323 1.5199 1.4301

0.0226 0.0289 0.0308 0.022

3.7993*** 6.2943*** 7.4374*** 7372***

1.2729 1.2956 1.4203 1.3951

16.6552***

0.0155 0.0136 0.0115 0.0081

3.3272*** 4.898*** 4.9488*** 4.2926***

1.1866 1.4347 1.3455 1.1731

12.2478***

avg. monthly difference ABS(CAR):above – below EMGMT_DAC

STDEV [ABS(CAR) above] / STDEV [ ABS(CAR) below]



Z statistic of average Z-ratio over all event windows



Z-ratio of H0: difference = 0




24

hypothesis test V hypothesis 3: results for EMGMT_SD

Panel B: EMGMT_SD

Sample Statistic τ_0 τ_0_2 τ_0_4 τ_0_6

total ifINCSM is available

total and INCSM =below the median

total and INCSM =above the

median

avg. monthly difference ABS(CAR):above – below EMGMT_SD

STDEV ABS(CAR) above] / STDEV [ ABS(CAR) below]










0.0406 0.0552 0.0591 0.0436

6.1202*** 10.7159*** 12.5258*** 11.6522***

1.358 1.7773 2.1748 1.9471

0.0455 0.0647 0.0694 0.0506

4.604*** 7.7967*** 9.422*** 8.7567***

1.5433 1.5335 1.8971 1.7487

20.7142***

0.0281 0.0309 0.0339 0.0257

3.3628*** 5.5974*** 6.7725*** 5.9831***

1.0603 1.6231 1.933 1.7487

14.4842***

25

H1: coefficient on > 0 coefficient on < 0

tittii

i

DaDaEXTRZaMOMENTUMABSa

SUEABSaINCSMaEMGMTacCARABS

,54

321 50_50__0

second estimation: hypothesis 3 (only last three rows of next table)

first estimation: hypotheses 1, 2

tittii

i

DaDaEXTRZaMOMENTUMABSaSUEABSa

INCSMEMGMTaINCSMaEMGMTacCARABS

,654

321 50_50_50_50__0

H1: coefficient on < 050_50_ INCSMEMGMT

50_EMGMT50_INCSM

fixed effects panel regressions I hypotheses 1- 3: model specification

26

fixed effects panel regressions II hypotheses 1- 3: estimation results EMGMT_DAC

IndependentVariable

Dependent Variable

Panel A: EMGMT_DAC

ABS(CAR_0)

ABS(CAR_0_2)

ABS(CAR_0_4)

ABS(CAR_0_6)

intercept

EMGMT_DAC_50

INCSM_50

ABS(SUE)

ABS(MOMENTUM)

EXTRZ

adjusted R2

multiplicative effect: high – low earnings management (low income smoothing)

multiplicative effect: high – low earnings management (high income smoothing)

0.0706*** 0.0568*** 0.0531*** 0.0468***

-0.0008 0.0039 0.004 0.0023

-0.0043 -0.0109** -0.0123*** -0.0085*

0.0129*** 0.0041*** 0.0008 -0.0012

0.0375* 0.022*** 0.0186*** 0.0069

0.0071 0.0049 0.0047** 0.0051**

0.2417 0.4218 0.5027 0.4712

0.0042 -0.0016 -0.0076 -0.0046

-0.0031 0.0048 0.0083 0.0049

0.0011 0.0032 0.0006 0.0003

EMGMT_DAC_50 * INCSM_50

27

fixed effects panel regressions III hypotheses 1- 3: estimation results EMGMT_SD

IndependentVariable

Dependent Variable

Panel B: EMGMT_SD

ABS(CAR_0)

ABS(CAR_0_2)

ABS(CAR_0_4)

ABS(CAR_0_6)

intercept

EMGMT_SD_50

INCSM_50

ABS(SUE)

ABS(MOMENTUM)

EXTRZ

adjusted R2

multiplicative effect: high – low earnings management (low income smoothing)

multiplicative effect: high – low earnings management (high income smoothing)

EMGMT_SD_50 * INCSM_50

0.0725*** 0.0867*** 0.0829*** 0.0745***

0.007 -0.01 -0.0006 -0.0045

0.009 -0.0174 -0.0186*** -0.0149***

0.0098*** -0.0016 -0.0035 -0.0047

0.0303 0.0251*** 0.011* -0.0074

0.0028 -0.0054* -0.001 -0.0007

0.1594 0.3646 0.4564 0.4009

-0.0016 0.003 -0.0086 -0.0128

0.0077 -0.0114 0.0034 0.0016

0.0061 -0.0084 -0.0051 -0.0112

28

agenda




references

29

Ball, Ray, and Philip Brown, 1968, An Empirical Evaluation of Accounting Income Numbers, Journal of Accounting Research, vol. 16, p. 159 – 177.

Bitner, Larry N., and Robert C. Dolan, 1996, Assessing the Relationship between Income Smoothing and the Value of the Firm, Quarterly Journal of Business & Economics, vol. 35, no.1, p. 16 – 35.

Burgstahler, David C., and Michael J. Eames, 1998, Management of earnings and analysts forecasts, University of Washington working paper.

Dechow, Patricia M., and Ilia D. Dichev, 2002, The Quality of Accruals and Earnings: The Role of Accrual Estimation Errors, The Accounting Review, vol. 77, supplement, p. 35 – 59.

Dechow, Patricia M., Richard G. Sloan, and Amy P. Sweeney, 1995, Detecting Earnings Management,, The Accounting Review, vol. 70, no. 2, p. 193 – 225.

references I

30

Detzler, Miranda Lam, and Susan M. Machuga, 2002, Earnings Management Surrounding Top Executive Turnover in Japanese Firms, Review of Pacific Basin Financial Markets and Policies, vol. 5, no. 3, p. 343 – 371.

Fama, Eugene F., and Kenneth R. French, 1992, The Cross-Section of Expected Stock Returns, The Journal of Finance, vol. 47, no. 2, p. 427 – 465.

Fama, E.; French, K.; 1996; Multifactor Explanations of Asset Pricing Anomalies, Journal of Finance, vol. 51, no.1, p. 55.

Francis, Jennifer, Ryan LaFond, Per Olsson, and Katherine Schipper, 2003, Accounting Anomalies and Information Uncertainty, Duke University Fuqua School of Business working paper.

Francis, Jennifer, Ryan LaFond, Per Olsson, and Katherine Schipper, 2004, The Market Pricing of Accruals Quality, Stockholm Institute of Financial Research working paper.

references II

31

Guay, Wayne R., S. P. Kothari, and Ross L. Watts, 1996, A Market-Based Evaluation of Discretionary Accrual Models, Journal of Accounting Research, vol. 34, supplement, p. 83 – 105.

Jegadeesh, Narasimhan, and Sheridan Titman, 1993, Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency, Journal of Finance, vol. 48, no. 1, p. 65 – 91.

Kasanen, Eero, Juha Kinnunen, and Jyrki Niskanen, 1996, Dividend-based earnings management: Empirical evidence from Finland, Journal of Accounting and Economics, vol. 22, p. 283 – 312.

Lang, Mark, Jana Smith Raedy, and Michelle Higgins Yetman, 2003, How Representative Are Firms That Are Cross-Listed in the United States? An Analysis of Accounting Quality, Journal of Accounting Research, vol. 41, no. 2, p. 363 – 386.

references III

32

Lim, Steve C., and Steven Lustgarten, 2002, Testing for Income Smoothing Using the Backing Out Method: A Review of Specification Issues, Review of Quantitative Finance and Accounting, vol. 19, p. 273 – 290.

Marquardt, Carol A., and Christine I. Wiedman, 2004, The Effect of Earnings Management on the Value Relevance of Accounting Information, Journal of Business Finance & Accounting, vol. 31 (3) & (4), p. 297 – 332.

Schipper, Katherine, 1989, Commentary on Earnings Management, Accounting Horizons, vol. 3, issue 4, p. 91 – 102.

Schmidt, Franz, Bilanzpolitik deutscher Aktiengesellschaften, Empirische Analyse des Gewinnglättungsverhaltens, (Gabler, Wiesbaden, 1979).

Subramanyam, K. R., 1996, The pricing of discretionary accruals, Journal of Accounting and Economics, vol. 22, p. 249 – 281.

references IV

33

Teoh, Siew Hong, Ivo Welch, and T. J. Wong, 1998, Earnings Management and the Long-Run Market Performance of Initial Public Offerings, The Journal of Finance, vol. 53, no. 6, p. 1935 – 1974.

Trueman, Brett, and Sheridan Titman, 1988, An Explanation for Accounting Income Smoothing, Journal of Accounting Research, vol. 26, supplement, p. 127 – 143.

Tucker X. Jenny, and Paul Zarowin, 2005, Dose Income Smoothing Improve Earnings Informativeness?, University of Florida, Warrington College of Business and New York University, Stern School of Business Working Paper.

Zarowin, Paul, 2002, Does Income Smoothing Make Stock Prices More Informative?, New York University, Stern School of Business working Paper.

references V

34

Thank you very much for your attention.

doktorandenseminar des competence centers corporate finance der universität hohenheim, 27. januar...

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