ifrs enforcement for banks: the case of value relevance of the fair value hierarchy
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UNIVERSITÀ DEGLI STUDI DI PAVIA
DIPARTIMENTO DI SCIENZE ECONOMICHE ED AZIENDALI
Corso di Laurea Magistrale in International Business and Economics
IFRS ENFORCEMENTS FOR BANKS: THE CASE OF VALUE RELEVANCE OF THE FAIR VALUE HIERARCHY
Relatore: Tesi di Laurea di: Prof. EMANUEL BAGNA CLAUDIA UDROIU Correlatore: Matricola 395642 Prof.ssa CAROLINA CASTAGNETTI
Anno Accademico 2012/2013
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ABSTRACT
La capacità informativa del bilancio di un’azienda rappresenta un’importante
elemento per i soggetti operanti nel mercato. In particolare, la categoria degli
strumenti finanziari misurati al fair value costituisce una componente fondamentale
nella valutazione delle banche. Con l’introduzione della “Gerarchia del fair value”,
suddetti strumenti vengono presentati in nota integrativa secondo una gerarchia
composta di tre livelli, al fine di dettagliare nel migliore dei modi la determinazione
del loro fair value. Oltre all’introduzione della “Gerarchia del fair value”, le autorità
di vigilanza sovranazionali e nazionali emanano i cd. “IFRS Enforcements”, tramite i
quali si raccomanda la massima trasparenza e precisione nella descrizione degli
strumenti finanziari e dei loro componenti.
Le finalità del presente elaborato sono di verificare se l’emanazione di Enforcements
abbia avuto un impatto (positivo) sulla valutazione di tali strumenti e
conseguentemente, sulla valutazione delle banche da parte del mercato. In particolar
modo ci si concentra sulla valutazione degli strumenti il cui fair value è determinato
in base a modelli di stima interni all’azienda, che incorporano informazioni non
pubblicamente disponibili (iscritti al livello 3 della gerarchia).
Dapprima si è dimostrato che le attività e le passività iscritte ai livelli 1 e 2 (le cui
misurazioni provengono teoreticamente da fonti maggiormente attendibili)
contribuiscono in maniera rilevante alla valutazione di mercato delle banche. Tale
risultato vale anche per le passività iscritte a livello 3. Le attività iscritte a livello 3
invece, contribuiscono alla determinazione del prezzo di mercato solamente quando
l’indicatore di profittabilità “Return on tangible equity” non viene considerato. Da
ultimo, viene corroborata l’ipotesi riguardante il miglioramento della valutazione
con riguardo alle passività di livello 3, in virtù dell’emanazione degli Enforcements.
Tali strumenti presentano un impatto sul prezzo di mercato notevole in termini
numerico statistici (coefficiente molto vicino al valore teorico di -1). Inoltre, questo
impatto è maggiore, sia in confronto a risultati precedentemente trovati nella
letteratura, sia in confronto agli effetti attribuiti al livello 1 e al livello 2.
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ABSTRACT
Informativeness of financial statements is of great importance to market
participants. Particularly, financial instruments measured at fair value represent a
relevant category in market valuation of banks. As of requirements of amendments
to IFRS 7, concerning the “Fair value hierarchy”, those instruments have to be
disclosed according to a three level hierarchy, in order to present detailed
information about the determination of related fair values. Along with the hierarchy,
national and supranational authorities issue “IFRS Enforcements”, with the purpose
of drawing more attention on the need of transparency and precision of disclosures
concerning financial instruments.
The aim of this thesis is to determine whether issuance of such Enforcements have
had (positive) effects on investors’ perceived values of these instruments and
consequently, on market values of banks. Focus is especially, on estimation
determined fair values, thus fair values that are calculated through unobservable
assumptions and entity internal estimation methods (level 3).
First of all, it is proved that level 1 and level 2 (theoretically, the most reliable fair
values) assets and liabilities and level 3 liabilities are relevant for explaining market
values. Level 3 assets instead, are value relevant only when the indicator “Return on
tangible equity” is not considered. Secondly, it is proved that IFRS Enforcements
have had a positive impact on investors’ valuation of level 3 liabilities: these
instruments have a large impact (very close to the theoretical value) on the market
value of banks. Moreover, this effect is larger even in comparison with level 1 and
level 2 effects.
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TABLE OF CONTENTS
INTRODUCTION .......................................................................................................................................................... 7
1. REGULATION FRAMEWORK ABOUT FINANCIAL INSTRUMENTS AND FAIR
VALUE MEASUREMENT ........................................................................................................................................ 11
1.1 Measurement of financial assets and financial liabilities ....................................................... 14
1.2 Fair Value Hierarchy ............................................................................................................................. 16
1.3 IFRS Enforcement documents – 2009/2010 ............................................................................... 22
1.4 Bank of Italy, Consob, Isvap Enforcement concerning application of
IAS/IFRS .................................................................................................................................................................. 23
2. RELATED LITERATURE ................................................................................................................................ 25
2.1. Value relevance of fair value accounting ....................................................................................... 25
2.1.1 An European study ........................................................................................................................ 29
2.2. Research concerning value relevance of level 1, 2 and 3 ........................................................ 31
2.2.1. Two European studies ................................................................................................................. 40
2.3. Literature: conclusions ........................................................................................................................ 41
3. STATISTICAL MODEL .................................................................................................................................... 43
3.1. Research questions and hypothesis development .................................................................... 43
3.2. Sample and variables description .................................................................................................... 45
3.3. Statistical regressions ........................................................................................................................... 48
4. RESEARCH RESULTS ..................................................................................................................................... 55
4.1. Regression results (1)........................................................................................................................... 55
4.2. Regression results (2)........................................................................................................................... 58
4.3. Regression results (3)........................................................................................................................... 60
4.4. Summary of regression results ......................................................................................................... 61
CONCLUSIONS ........................................................................................................................................................... 65
REFERENCES ............................................................................................................................................................. 67
APPENDIX ................................................................................................................................................................... 71
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INTRODUCTION
Fair value measurement, the opponent accounting method of amortized cost, has
sustainers and critics. Supporters, on one hand, argue than it improves timeliness
and it provides the true current value of assets and liabilities, thus revealing risks
better than amortized cost. Opponents, on the other hand, claim that this accounting
method provides misleading values due to the possible use of estimation models and
that it increases earnings and equity volatility. Even more so, when it is referred to
financial instruments of banks. Some categories of financial instruments have to be
mandatorily measured at fair value, therefore imprecise measurement lead to wrong
recorded values (both in the Balance Sheet and in the Profit or Loss) and also to
consequences on investors’ allocation decisions. Therefore, information contained in
financial statements has to be presented in such a way, that market participants
perceive it as being precise, free of error, useful and of quality.
The two big accounting standards boards (IASB and FASB), which have in latter
times expanded the use of fair value accounting, have reached their aim of
convergence and consistency, through issuance of very similar financial reporting
standards concerning fair value measurements. Although doubts regarding
interpretation of some of these provisions still remain, both standard setters have
provided detailed general guidance concerning financial instruments, and
concerning the measurement and disclosures of assets and liabilities recorded at fair
value.
In 2007 in the US, had became effective the introduction of the “Fair value
Hierarchy” requiring disclosures of financial assets and liabilities according to a
three level hierarchy. Two years later, in 2009 the IASB too, has introduced almost
identical provisions of a fair value hierarchy in which: level 1 includes quoted prices
in active markets, level 2 includes prices determined with use of observable data
and level 3 includes model estimated values calculated through use of non
observable assumptions. In other words, (when markets are active) level 1 is
expected to reflect the highest reliability of related fundamental values, level 2 lower
reliability and level 3 the lowest. Undoubtedly level 2 and level 3 financial
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instruments have had a relevant role in the 2007 financial crisis given the high
amount of these instruments recorded in financial statements of banks, and their
features of illiquidity. Nevertheless, some of these instruments, at that time where
valued AAA by rating agencies. Level 3 assets are, in other words, the hardest to put
value on, thus are the most illiquid.
The main purpose of accounting regulators is transparency of financial statements
in order to provide usefulness to market participants’ investment decisions.
Therefore are required enhanced disclosures about financial instruments measured
at fair value. Accounting enforcers and authorities, in order to ensure the correct
trend of financial markets, issued also a number of IFRS Enforcements to draw
attention on the vital importance, for transparency purposes, of disclosing in detail
all movements concerning level 3 instruments (additionally to the fair value
hierarchy, entities should disclose also a table of “Movements in and out level 3
instruments”.
Since the introduction of the fair value hierarchy, in the US, a number of researches
has been held on fair value hierarchy, related disclosures in levels and their impact
on the market value of the entity (e.g. Song et al. (2010), Goh et al. (2009), Kolev
(2008)). In Europe, until now only few studies have been done (Fietcher (2010), Di
Martino (2011), Bosch (2012)). Only two of these studies test the effect of each level
of the hierarchy on market values of entities. The question is: do investors take into
account financial instruments disclosed at levels 1, 2 and 3 in valuing an entity? Yes,
they do. It has been demonstrated that the three levels are differently priced by
investors. Most studies have shown that level 3 is the less priced, but some have also
presented good results (a lower discount) for this category. This lower discount of
these level 3 assets could be explained by the unreliability of markets (that is, not
available quoted prices or not active markets) to determine asset values: in this case,
investors could trust more an estimated value. A further explanation could be
referred to the requirements of enhanced disclosures particularly about level 3
instruments (Di Martino (2011)). And detailed disclosures are a consequence of
issuance by national and supranational authorities of IFRS Enforcement documents,
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as mentioned above, in order to ensure a high degree of precision and transparency
of financial statements.
This latter assertion leads to the aim of this thesis. Preliminarily, general value
relevance (that is, useful information) of financial instruments disclosed by levels is
tested. But the primary aim is to test whether enforcements issued by authorities
have had some kind of (positive) effect on the perceptions of investors about level 3
assets, which should be the least reliable ones.
To this purpose, were used data gathered from the 2010 – 2011 annual reports of
European listed banks. The sample has 83 observations. To capture the market value
of banks it was made use of the indicator “Price to tangible book value” (P/TBV), and
all considered variables were scaled by the Tangible Book Value (TBV). Were made
three tests, in order to take into account different effects. Level 1 and level 2 assets
and liabilities resulted to be value relevant for investors, and their impact on P/TBV
is very similar. Level 3 assets are found to be value relevant only in one out of the
three tests, and within this test, related impact on P/TBV is quite interesting. But the
most noticeable outcome is related to level 3 liabilities, which confirm value
relevance in all three tests and what’s more, related impact on the P/TBV is far
larger than impact of level 1 and level 2.
The first chapter presents the framework of accounting regulation concerning
financial instruments classification and provisions about fair value measurements
with focus on fair value hierarchy, related issues and IFRS Enforcements. The second
chapter provides a review of previous literature and findings concerning value
relevance and reliability of fair value and fair value hierarchy, with special focus on
researches held during the financial crisis. The third chapter presents the research
questions, the hypothesis, a description of the sample, of variables and the formal
regressions. The fourth chapter presents the regression results and related
comments.
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1. REGULATION FRAMEWORK ABOUT FINANCIAL INSTRUMENTS AND FAIR VALUE MEASUREMENT
The European Union has agreed on the adoption of International accounting
standards (IAS/IFRS) in 2002. Authorities decided that, starting from 1st January
2005, IAS/IFRS would have mandatorily been applied to consolidated financial
statements of listed companies. Prior to this date, European countries adopted
national General Accepted Accounting Principles (GAAP).
One of the relevant issues, with which IAS/IFRS deal, is fair value measurement.
Particular importance is given to fair value measurement of financial instruments,
given their more and more complex nature and their relevant impact on financial
statements (both on balance sheets and income statements) of banks. Moreover, for
financial institutions, along with the IAS/IFRS adoption, the percentage of financial
instruments measured at fair value have increased (Bagna, 2009). Therefore, in
November 2006, the IASB1 (International Accounting Standards Board) has issued a
discussion paper, on the strength of regulation contained in Financial Accounting
Standard 157 – Fair Value Measurements (FAS 157), in order to express its views
concerning fair value measurements2.
IASB had based its provisions on FAS 157, because of its consistency with other
regulation contained in International Financial Reporting Standards (IFRS)
concerning fair value measurements. Since that year, when also a Memorandum of
Understanding between the IASB and the FASB had been published, the two Boards
have improved their commitment in creating “a common set of high quality global
1 The IFRS foundation is a not-for-profit independent organization, that works in the public interest.
Through IASB, its standard setting body, it develops and issues a set of worldwide accepted
international financial reporting standards (called IAS/IFRS).
2 In September 2006, the FASB (Financial Accounting Standards Board) issued Statement no. 157
“Fair Value Measurements”, related to fair value measurement of assets and liabilities. It deals with
definition, framework for measurement, three-level fair value hierarchy and expanding disclosures
about fair value. Now, under FASB’s new Accounting Standards Codification System, Statement n. 157
has been codified as Topic 820.
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accounting standards”. The purpose was so, to improve convergence between the
two sets of accounting provisions.
In March 2009, the IASB issued amendments to IFRS 7. The name of the document is
“Improving disclosures about financial instruments (Amendments to IFRS 7 -
Financial Instruments: Disclosures)” and it provides a complement to IAS 32 –
“Financial Instruments: Presentation” and to IAS 39 – “Financial Instruments:
Recognition and Measurement” (the latter is going to be replaced by IFRS 9. IFRS 9
was first issued in November 2009 and was afterwards updated. The last update is
of September 2012 and it is effective for annual periods beginning on or after 1
January 2015. Until then, IAS 39 is the standard currently in use).
The scope of amendments to IFRS 7 was bidirectional: on the one hand, it addressed
enhanced disclosure requirements about valuations, methodologies and uncertainty
related to financial instruments recorded at fair value and on the other hand it
enhanced existing disclosure requirements with respect to the nature and the extent
of liquidity risk (IASB, 2009).
The decision of issuance of such provision was driven by requests of users of
financial standards and other interested parties that, given the hard economic
situation of that period, needed “enhanced disclosures” according to IAS 39 -
Financial instruments. Moreover, they asserted that financial statements had to be
improved because their interpretation and application have not been easy, given the
complex nature of some requirements.
As far as concerns fair value measurement disclosures, the document introduced the
“Fair value hierarchy”. According to this hierarchy, classification of assets and
liabilities recorded at fair value is related to the nature of inputs3 used to measure
their prices. IFRS 7 was also amended in October 2010 and in December 2011, in
order to require entities to further enhance disclosures about financial instruments
and about netting arrangements related to financial assets and financial liabilities
respectively.
3 Inputs are the assumptions that market participants would use in pricing an asset or a liability (Cfr.
IFRS 13, Appendix A).
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Regarding the combined work of the two Boards, in June 2010 IASB issued a
proposal of requirements of quantitative analysis disclosures relating to
unobservable inputs used to measure fair values. Also FASB exposed amendments to
Topic 820 (former SFAS 157) concerning this issue. Finally, in May 2011 the project
of convergence was completed by the issuance on the part of IASB, of IFRS 13 – Fair
Value Measurements and on the part of FASB, of a revised Topic 820.
All kind of requirements and guidance about fair value measurement and
disclosures about financial and non-financial assets and liabilities which are to be
measured according to fair value hierarchy are now of new issued IFRS 13 concern.4
This standard is effectively applied starting from 1 January 2013 and it represents
the result of the Boards’ cooperative effort in achieving the convergence goal; a
common framework on how to measure fair value for entities around the world had
been completed.5
Moreover, beyond improving convergence between the two sets of accounting
requirements, IFRS 13 seeks (through establishing a single source of guidance) to
reduce the often claimed complexity of application and improve comparability and
consistency in fair value measurements and related disclosures about fair value
hierarchy. There is an important addition to be done: within these provisions, the
definition of fair value doesn’t change6. The purpose is to aloud users of financial
statements (market participants like investors, creditors and other interested
4 See IFRS 7: Par. 27 and Par. 27B dealing with Fair Value Hierarchy have been deleted. Fair Value
Hierarchy is, since May 2011, part of IFRS 13 requirements. However, in this thesis it is going to be
referred to IFRS 7, since IFRS 13 is not applied by banks at the research date (annual reports
2010/2011).
5 Cfr. IFRS 13, Par. IN 7. However some differences between the two set of regulations remain (e.g. US
GAAP does not require a quantitative sensitivity analysis of changes in unobservable inputs of
valuation techniques for level 3 instruments, while IFRS does).
6 Fair value is defined as a sort of exchange price (exit price) under determined market conditions. It
is the price that would be received to sell an asset or paid to transfer a liability in an orderly
transaction (that is, not a forced liquidation or distressed sale) between market participants at the
measurement date (Topic 820, GAAP). IASB’s definition of fair value is: the amount for which an asset
could be exchanged or a liability settled, between knowledgeable, willing parties, in an arm’s length
transaction. So, the fair value is not necessarily equal to the price at which the entity had acquired the
instrument. Therefore, in contrast with historical cost accounting, in a fair value view, past related
transactions or events are relevant only to determine predicted values of future cash flows.
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parties) to be provided with expanded information (disclosures) for a better
assessment of valuation techniques and inputs used by entities to measure their
own assets and liabilities. In other words, the expressed goal of financial accounting
standards is to allow investors and creditors to make proper decisions of “resource
allocation”, being aware of the riskiness of possible investment decisions.
1.1 Measurement of financial assets and financial liabilities
IAS 39, the currently in use accounting standard (as a reminder, it will be replaced
by IFRS 9 starting 1 January 2015), requires financial assets and liabilities to be
initially recognized, when the entity becomes party to the contractual provisions of
the instrument. While initial measurement is at fair value, subsequent measurement
depends on the nature of the instrument.
IAS 39 requires subsequent measurement for financial assets to be classified within
one of the following categories:
1) Financial assets at fair value through profit or loss. These instruments are
divided into two subgroups. The first one is “Held for trading”, which includes
financial assets that are held for selling purposes within a short period of
time. The second one is “Other financial assets designated at fair value
through profit or loss”7. These assets have to be always measured at fair
value and changes of fair values must be recognized in the profit or loss
statement.
2) Loans and receivables. The nature of these instruments is non-derivative and
they presume fixed or determinable payments. They are measured at
amortized cost using the effective interest method.
7 Here the “fair value option” is applied. Fair value option is the possibility of recording financial instruments at fair value, unless they are held for trading within a short period of time.
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3) Held-to-maturity financial assets. They are non-derivative financial assets
with fixed or determinable payments. The entity has an intention and ability
to hold them until maturity and they have to be measured at amortized cost
with the effective interest method.
4) Available for sale financial assets. They are non-derivative financial assets
and have to be measured at fair value and they are all non-derivative
instruments, which are classified neither within held to maturity, financial
assets at fair value through profit or loss or loans and receivables. They are
recorded in the financial statements for an uncertain period of time. Gains or
losses of fair values must be recognized in equity.
Financial liabilities instead, are classified within the following categories:
1) Financial liabilities at fair value through profit or loss. It has 2 subcategories:
“Held for trading” and “Other financial liabilities designated at fair value
through profit or loss”. They have to be measured at fair value and changes of
fair values have to be recognized in the profit or loss statement.
2) Other financial liabilities measured at amortized cost using the effective
interest method. Here are included all financial liabilities that are not
recognized at fair value through profit or loss.
Furthermore, all derivative financial instruments must be accounted for at fair value.
Summarizing, financial assets and liabilities that must be measured at fair value are
held for trading, designated at fair value, available for sale and derivatives8.
Finally, as mentioned above, the “fair value option” (see note 7) may be used for
instruments which are normally recognized at amortized cost, but only if the fair
value is reliably determinable, if the use of this measurement has the purpose of
8“ Available for sale” and “Held to maturity” categories are eliminated in the new IFRS 9.
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reducing an accounting mismatch or when a group of instruments is managed and
its performance is valued by the management on a fair value basis.
All financial instruments recorded at fair value (mandatorily or through fair value
option) have to be disclosed according to the 3 level fair value hierarchy.
1.2 Fair Value Hierarchy
As stated in both sets of accounting standards, IFRS and US GAAP, fair value
measurement reflects current market participants assumptions about the future
economic inflows associated with an asset and future economic outflows associated
with a liability. It attempts to answer a hypothetical question such as: “What are my
assets or liabilities worth today?” (Shaffer, 2010). Since the theoretical definition of
this accounting method makes reference to a market, fair value measurement is not
entity-specific, it rather focuses on market factors. Indeed, the meaning of “fair
value”, used by IASB in its standards, generally is “market price”9.
IFRS 7 requires specific fair value disclosures for classes of financial assets and
liabilities. This is mostly because, even though in a general interpretation “fair value”
means “market price”, sometimes may happen that a market price is not readily
available, or it does not effectively reflect the real value. Therefore, a hypothetical
price (i.e. the cash equivalent of the hypothetical market value) has to be found, and
its calculation is to be based on predictive mathematical models (the so called
“mark-to-model”).
According to methods used to calculate a price for an instrument, entities are
required to classify assets and liabilities into a fair value hierarchy, which states the
following:
9 Therefore, the term “mark-to-market” has often been used as a synonym for “fair value”. With the
issuance of disclosure requirements of the hierarchy, mark-to-market is now generally used to
indicate level 1 financial instruments, while mark-to-model is used to indicate level 3 instruments.
17
• Level 1 assets and liabilities:
Have to be measured based upon unadjusted quoted prices in active markets for
identical assets or liabilities. This is the best evidence of a reliable fair value
measurement. A principal market or the most advantageous market for the
instrument has to be determined10. These prices don’t have to be adjusted, thus if
they are, this would represent an indication of a different pricing method and
consequently, the instrument would be categorized within a lower level of the fair
value hierarchy.
• Level 2 assets and liabilities:
Have to be measured based upon inputs other than quoted prices included in Level
1, directly or indirectly observable (based on market data – e.g. expected volatility,
expected dividend yield, risk-free interest rate). Observable inputs used to measure
level 2 instruments may be quoted prices for similar assets in active markets, quoted
prices for identical assets in inactive markets and market corroborated inputs.
Adjustments to inputs are permitted, but shall not be based on unobservable inputs
which are significant to the valuation in its entirety, otherwise this would lead to the
insertion of the instrument within level 3.
• Level 3 assets and liabilities:
Shall be measured using unobservable inputs, but this is a residual category.
Instruments are classified within level 3, only if relevant observable inputs are not
available. Anyway, any unobservable input should reflect assumptions that market
participants would make in pricing the asset or the liability. Assumptions about risks
have to be taken into consideration: they relate to risks about valuation techniques
and measurement uncertainty, since instruments included in this category are
mostly based on internally developed models.
10 A principal market is the market with the greatest volume and level of activity for the instrument.
The most advantageous market is the market that maximizes the amount that would be received to
sell the asset or minimizes the amount that would be paid to transfer the liability.
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Level 1 measurement is therefore, the best available measure: if a market is active
and orderly transactions take place in it, observed market prices give evidence of the
most precise, objective and fast way to determine fair value of assets. Most
important, pricing according to quoted market prices in active markets guarantees
to third parties, i.e. investors and creditors, that also risk features (e.g. market risk,
liquidity risk, information risk, non-performing risk) are included in the price.
Nevertheless, the issue is not straightforward, when taking into account the meaning
of active markets: “An active market is a market in which transactions for the asset
or liability take place with sufficient frequency and volume to provide pricing
information on a ongoing basis” (IASB, 2011). According to this definition, problems
of reliability may arise, when markets are not active and transactions are not
orderly11. Paragraph 2.1. deals with this issue more in detail.
11 Examples of not orderly transactions are sales deriving from forced liquidations or distressed sales
(e.g. in case of bankruptcy). When transactions are not orderly, there is sufficient time to induce
potential buyers to decrease the price they are willing to pay. Then, in a distressed sale, the seller
may be forced to agree on a price that could be lower than the value of the asset, only because the
LEVEL 1:
- Quoted prices for identical assets in active markets
LEVEL 2:
- Quoted prices for similar assets in active markets
- Quoted prices for identical assets in inactive markets
- Market-corroborated inputs
- Other observable inputs
LEVEL 3:
- Unobservable inputs
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Level 2 and level 3 fair value measurements are determined using market related
inputs or valuation techniques, which should maximize the use of observable inputs
and minimize the use of unobservable ones12. The level within which an asset or a
liability should be settled is determined according to observability and significance
of inputs used to measure it. As an example, if unobservable inputs are used and
they are significant to the entire valuation, instruments are qualified as level 313. If
observable inputs, which don’t require significant adjustments based on
unobservable inputs are used, the asset or the liability shall be qualified within level
2.
For this reason, it is necessary an extensive disclosure in the notes explaining
assumptions and methods used to estimate fair values or changes in methods used
during the current year with respect to the previous year. Concluding, according to
standard setters, a valuation technique should reflect current market conditions and
use risk adjustments (premiums or discounts) that market participants would use in
pricing the asset.
time within which the transaction has to be concluded is shortened (KPMG, First Impressions: Fair
Value Measurement, 2011).
12 Examples of valuation techniques are exposed in IFRS 13: three widely used techniques are the
market approach (use of market prices for identical or similar assets), the cost approach (amount
that would be required currently to replace the service capacity of an asset) and the income approach
(present value of expected future cash flows). Other present value techniques may be used too (e.g.
the discount rate adjustment and the expected cash flows method) (IASB, 2011).
13 IFRS 13 par. 84 states: “ An adjustment to a Level 2 input that is significant to the entire
measurement might result in a fair value measurement categorized within Level 3 of the fair value
hierarchy, if the adjustment uses significant unobservable inputs.”
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Observable inputs, significant inputs and third parties’ pricing
services
Some important issues should be explained more in detail, and these are the
concepts of “observable input”, “significant input” and “third parties’ pricing
services”.
An input is observable if it is developed using market data, such as publicly available
information about actual events or transactions, and that reflects the assumptions
that market participants would use when pricing the asset or the liability. Examples
of markets which provide observable inputs are exchange markets, dealer markets,
principal to principal markets. (IASB, 2011).
As far as concerns the significance of an input, IFRS 7 doesn’t expressively state
what are the conditions according to which precisely to determine whether an input
is significant or not. In any case, if a “lower” input is included in the estimation
procedure of an asset or a liability, this may be significant, otherwise perhaps the
entity wouldn’t have taken into consideration the eventuality of using it. However,
assessing this procedure, requires judgment and careful analysis including analysis
of factors specific to the asset or the liability.
Another issue regards prices obtained from third parties (pricing services or
brokers). Using third parties services to estimate prices does not change the
categorization within the fair value hierarchy. The reasoning is always the same:
inputs matter most. Therefore, it is necessary an understanding of the source, the
third party has used in providing the price. Level 1 sources are quoted prices in
active markets and level 2 and 3 sources use valuation models and adjustments to
observable inputs. Lastly, if after the valuation, there has been a decrease in the
volume or level of activity of the asset or the liability, the entity has to evaluate if
prices provided reflect orderly transactions or if the valuation techniques used to
estimate the price are in line with market participants’ assumptions (IASB, 2009).
21
Concerning third parties valuations of assets and liabilities, a number of debates are
held. For instance, King14’s assertion goes against the general idea about the fair
value hierarchy. From his perspective the hierarchy has a negative critical view
towards valuation techniques that use income approach and cost approach , thus
FASB (and consequently, IASB too) is attributing less reliability than they really
have, to these two methods. He explains that there is no “the” value for an asset or a
liability, because “valuation is an art, not a science”, that is different appraisers will
arrive to different results when valuing the same instrument. This happens because
professional judgment and thus different assumptions are inherent to each valuation
process. And one cannot prove a judgment is correct and univocal. When markets
are not available, at least one among all the assumptions about the past, the future,
the hypothetical markets, the hypothetical use of the asset that hypothetical
knowledgeable market participants would do, will differ across different valuation
methods. He states: “every appraiser has at least one key assumption” (King, 2006).
Other points of view are totally different: there is a general agreement on the
possibility that small adjustments of assumptions in valuation methods may bring to
totally different results. Not only results may differ hugely one from another, but
also their range of possible estimates may be very wide. But in this case, a
measurement cannot be considered reliable. For Ernst & Young a measurement
using a valuation technique is reliable if and only if, through the use of different
reasonable methods and assumptions, not significantly different estimates of fair
value are given. The key explanation is that users of financial statements should be
provided with the most objective information possible, not with management’s view
of what predictions should b. Or at least, the best information about subjective
measurements should be provided. This point of view is more in line with the
objective-oriented point of view of accounting standards regulators.
Therefore, par. 27 and 27B of IFRS 7 amended requires enhanced disclosures
especially about level 3 measurements: valuation techniques and changes in
valuation techniques, a sensitivity analysis of the fair value to changes in
14 King A. is the vice chairman of Marshall & Stevens, an American firm of appraisers. A similar point
of view is expressed by. Martin R. D et al. (2006).
22
unobservable inputs and transfers in and out of each level15. Is then important
disclosing the effect of level 3 measurements on “Profit or loss” or “Other
comprehensive income” and the reconciliations from the opening balance sheets to
the closing balance sheets. Ernst & Young claims that “these criteria are important,
because the IASB believes that all movements in fair value measurement from one
balance sheet to the next deserve to be regarded as components of a company’s
performance (the Comprehensive income) – with the result that changes in fair
value translate directly into performance gains and losses” (Ernst&Young, 2005).
1.3 IFRS Enforcement documents – 2009/2010
The effects of the financial crisis after 2008 started to be reflected in a higher degree
of uncertainty about financial and economic situation of entities, particularly
financial institutions. Therefore, in 2008 the Committee of European Securities
Regulators (CESR)16, after reviewing IFRS 7 and related disclosures, issued the
statement “Fair value measurement and related disclosures of financial instruments
in illiquid markets”. The purpose of this document is to stress and ensure the proper
use of disclosures when dealing with financial instruments in line with IFRS 7.
Back in 2005, CESR had established a forum where its members and all national
accounting enforcers meet and discuss important issues about enforcements to
accounting standards within European countries. This forum is named EECS
(European Enforcers’ Coordination Sessions) and its main purpose is “[…] to co-
15 A table including movements in and out level 3 should be disclosed. Focus is mainly on level 3
Instruments, since disclosures about level 1 and level 2 can be relatively easy. As indicated above,
Level 3, given its illiquid and uncertain nature, a certain level of judgment and attention to factors
that are specific to each asset or liability. As a matter of fact, the new IFRS 13 par. 94 states that “the
number of classes (of assets and liabilities) may need to be greater for fair value measurements
categorized within level 3” (IASB, 2011).
16 Since January 2011 CESR became ESMA – European Securities and Markets Authorities. Even
though ESMA is independent of the EU institutions, its overall purposes are the “protection of
investors and the insurance of well functioning financial markets in the European Union” (ESMA’s
website). It contributes to the consistent application of IFRS by providing guidelines and
recommendations. Summarizing ESMA’s aim is the supervisory convergence in the EU securities
markets, and issuance of IFRS Enforcements is a relevant part of its activity.
23
ordinate the enforcement activities of member states in order to foster and maintain
the investor confidence” (CESR, 2010).
Financial instruments are a main issue given times of financial crisis, especially
those recorded at fair value. Because of this, during 2009 EECS met several times
and discussed the decisions submitted by its members in order to increase
convergence of national enforcements with respect to IFRS. Afterwards, CESR
published a statement “Application of disclosure requirements related to financial
instruments in the 2008 Financial Statements”. It is a study held on 96 European
financial institutions in order to assess the adequacy of disclosures for financial
instruments. Other identical studies were held in 2009, 2010 and 2011 with
purposes of comparison between results each year. In fact, quality of disclosures on
financial instruments improved year on year, even though further improvements
could be done especially in disclosing information concerning level 3 financial
instruments (ESMA 2012).
1.4 Bank of Italy, Consob, Isvap Enforcement concerning application
of IAS/IFRS
To stay in line with this commitment, in March 2010 an Enforcement document has
been issued in Italy by a coordinating committee of Bank of Italy, Consob and
Isvap17. Specifically, the Enforcement draws attention on disclosures about
impairment test, about contractual provisions on financial debt, debt restructuring
and fair value hierarchy. This document is an appeal to entities to “pay attention” to
IAS/IFRS requirements, in order to provide all necessary detailed information. On
the other side, entities claim the fact that they don’t accomplish requirements in a
satisfactory way, because of the not straightforward interpretation of some
17 Since January 2013, Isvap (Institution for the security of private and of social interest insurances)
has been dissolved. All its powers have been transferred to the new established IVASS (Institution for
the security of insurance companies).
24
expressions used in accounting standards18. In any case, this document recalls, with
regard to fair value hierarchy, the need to correctly determine and disclose the
specific level according to the weight of used inputs (observable and unobservable),
any changes in inputs with respect to the previous period, transfers between level 1
and 2, in and out level 3 and reasons for these transfers and a sensitivity analysis for
level 3 measurements. Therefore, there is no new provision, that this document
requires with respect to IFRS 7: it merely calls attention on the precise and correct
use of the international financial standards when disclosing financial instruments,
since precision is of vital importance for market participants.
As said above, ESMA has already reported improvement of accounting information
year after year since the application of IAS/IFRS in Europe in 2005. But since
financial instruments are a particular asset category on which there was and there is
most focus, the aim of this thesis is to test whether Enforcements of ESMA (along
with the Italian enforcement document) has had some sort of impact on investors’
perceptions about financial assets and liabilities measured at fair value. If it hasn’t,
results are generally going to be in line with results of the literature. If it has, a
smaller discount with respect to the theoretical values of 1 for the assets and -1 for
liabilities is expected for level 3, given the higher confidence of investors after the
issuance of IFRS Enforcements. But firstly, in order to provide a basis of comparison,
previous literature is going to be analyzed
18 This issue is solved step by step also through meetings between ESMA, which provides feedback
about uncertain interpretation, and the IFRS Interpretation Committee (CESR, 2010).
25
2. RELATED LITERATURE
2.1. Value relevance of fair value accounting
IFRS Framework distinguishes between relevance and reliability of information
included in accounting reporting. Information is relevant when it helps investors
and creditors in making their “ressource allocation” decisions, thus when reporting
information is reflected in the value and riskiness of the entity. A measurement is
then considered reliable when it is free of material errors and bias (IASC, 2001).
There are two different views in literature about fair value accounting in which the
trade-off between relevance and reliability is one of the major debates: the fair value
view (supporters of fair value) and the alternative view (opponents to fair value).
The first one argue that fair value improves timeliness and transparency in
comparison with other accounting methods. Indeed, fair value is defined as being
the “exit” price of an asset or a liability: it is not the price at the acquisition date, but
the price for which the asset would be sold or the liability transferred at the
measurement date. This is where fair value gives proof of its timeliness. It has to be
so, since the role of accounting reporting is to serve investors in capital markets; and
achieving this goal is more probable within markets that are orderly (i.e. when
market prices are the best measure of intrinsic fundamental values). In this sense
fair value accounting is laudable for transparency purposes. Of note, for fair value
supporters relevance is of primary importance and reliability comes next
(Whittington, 2008). This claim is, in fact, supported by empirical results of working
papers and researches run on fair value accounting. Value relevance of fair value
assets and liabilities is generally demonstrated19. Concerns of investors and
creditors, instead are reflected through the reliability of those instruments.
This is the reason why arguments against this accounting method exist too. Fair
value involves orderly transactions and market participants, but what if an active
19 Accounting information is considered value relevant when it has the predicted association with
market value of equity, (Song, 2010) thus it is sufficiently significant to be reflected in share prices.
26
market couldn’t be determined? As long as quoted market prices in an active market
are available, there is little room for unobservable inputs and information is
generally reliable, but with no active market and no orderly transactions, market
prices are misleading and different kind of problems may arise. Reliability issues are
to be taken into consideration too. Opponents to fair value accounting claim that this
lack of reliability leads to scarce relevance of assets held for a long period because
their prices are distorted over time and become imprecise. So is reliability “more
important” than value relevance?
According to Ernst and Young, reliability is even considered as a precondition in
order to allow reporting information to be value relevant. Ernst and Young claims
that reliability assumes top significance in situations in which observed market
prices are not available (i.e. level 3 measurements). Thus, they state that the IASB
should explain in accounting standards why relative low reliable measurements
should be considered as relevant to be used for financial reporting purposes
(Ernst&Young, 2005). However, as stated above, most of studies run on this topic
have proved the value relevance of all fair value measurements, even of the least
reliable ones.
The issue could be seen from a simpler point of view: that is, remembering that
perfect and competitive markets exist only theoretically. In line with this shared
opinion, fair value accounting may achieve its goals only from a theoretical point of
view. Having said that, professor Whittington however argues that members of the
alternative critical view, which relates mostly to the real world of market, didn’t
however provide a simple and coherent alternative solution (Whittington, 2008).
Another concern of the alternative view is whether or not fair value accounting plays
an important role in contributing to worsening economic situation in times of
economic downturn (when in fact, markets are anything but perfect; they are rather
illiquid and transactions often are not orderly). Literature has proved that it plays
little or no role20.
20 For instance, at the end of 2008, Citigroup held on its balance sheet a value of level 3 assets equal to
455% of the value of its tangible common equity. Bank of America and JP Morgan were at 121% and
164% respectively. In 2012 things have changed: shares were much lower, of about 40% (see New
27
Basically, the argument of opponents to fair value accounting is that this
measurement method, through asset write-ups in good economic times, leads banks
to increasing their leverage. Then, in downturns they turn to be more vulnerable and
increase the crisis negative effect, also through contagion. Historical cost accounting
instead, doesn’t allow write-ups in good times, therefore doesn’t lead to higher
leverage and vulnerability in bad times. But a bank could however increase its
leverage in booms under historical cost accounting by selling an asset and retaining
a small claim in it. Therefore, the issue of leverage and consequent higher
vulnerability during economic downturns is not necessarily related to fair value
accounting (Laux & Leuz, 2009). On the contrary, it has been argued more than once
that this accounting method helps identifying problems and imminent crisis in
advance.
Concluding, the key issue seems to be the a priori transparency provided by
financial statements, rather than measuring assets at fair value. Procyclical events
may be indeed correlated with transparency and disclosure information, when this
is not enough for investors in order to assess the riskiness of financial instruments.
And this occurs in both cases: when these are or are not recorded at fair value. As
already stated, international accounting regulation expressively states that objective
of financial reporting is to provide useful information in order to allow investors and
creditors, which are the focus group of the IFRS attention, to make their “resource
allocation” decisions. Therefore, it is responsibility of entities, not of standard
setters to determine which is the best way to ensure transparency and, in case of
market downturns, to ensure stability for the industry. (Barth & Landsman, 2010).
Turn back to the inactive markets argument and take into consideration an illiquid
market, in which prices don’t reflect fundamental values of assets and liabilities21.
York Times, may 2012). But is fair value and level 3 assets really part of the financial crisis engine, or
it depends more on transparency of bank financial statements? See e.g. Ryan (2008), Laux and Leuz
(2010), Shaffer (2010). These working papers are in line with fair value accounting view and sustain
the “not responsibility” of fair value in worsening economic downturns.
21 To explain this scenario take as an example the economic and financial crisis in 2007. During this
period there have been assertions that to determine fair values it had been taken into consideration
the last transaction price on the market without paying attention to other issues like whether the
market was or not orderly or whether the transaction was or not driven by a forced sale (Shaffer,
28
Within this scenario, a price has to be estimated either by using observable market-
related inputs (probably distorted) or by using firm internally developed models
which base their procedure on unobservable assumptions (probably biased). In fact,
inherent to estimation procedures (especially those who use managerial
assumptions), are some risks: measurement error, intentional or unintentional
manipulation of data, in one word information risk. The result is that reliability of
estimations is mined. Thus in serious circumstances, estimated fair values may give
rise to perverted balance sheets and income statements.
Concerning this problem, SFAS 157 Fair Value Measurements specifies how to
estimate fair values and limits potential power of managers in manipulating
estimation data. For example, managers are required to adjust observed prices,
when these are provided by not identical assets, to obtain prices which reflect
features specific to their assets. Furthermore, also the IASB Advisory Panel (2008)
emphasizes the fact that market prices cannot be ignored. But this seems to be a
“dog chasing its own tail”, since the issue remains always the same: market prices
yes, but this means unreliable market prices when the market isn’t active or
liquidity risk is high. Many, indeed have argued that although model inputs are
subject to managerial bias, their advantage is that are able to give more reliable
estimates than market inputs when markets are inactive or reflect a higher liquidity
risk (e.g. Ryan (2008), Altamuro&Zhang (2012)). Others, instead have argued that
historical cost accounting could be the best solution (Allen & Carletti, 2007) . Lastly,
some have concluded that when market prices don’t reflect real values, marking-to-
market is of any use; the only way to report relevant information are honest entity-
based disclosures (Burkhardt & Strausz, 2006) .
Actually, from a management-oriented point of view, when marking-to-model is
adopted, tasks of managers are not easy too: “they should somehow determine how
2010). Indeed, during a financial crisis banks may be forced to sell their assets at a price that doesn’t
reflect their fair value, in order to be in line with regulatory capital constrains. On the other hand,
accounting standards setters are quite restrictive about when managers may deviate from observed
market prices: they even stress that an illiquid market is not necessarily a reason for not taking into
consideration observed prices. Anyway “it is difficult to write fair value accounting standards that
provide the flexibility when it is needed and constrain managers’ behavior when it is not needed”
(Laux & Leuz, 2009).
29
hypothetical market participants might use the assets in their own operations and
the assets’ value in use to those firms, so that the price they might pay can be
estimated” (Benston, 2008). And carrying on this process is costly.
2.1.1 An European study
The only study about overall value relevance of fair value instruments held by IFRS
European entities adopters (most studies are held on US GAAP entities adopters) is
exposed in a discussion paper written by Fietcher and Novotny-Farkas (2011). This
research contributes to general fair value accounting research, by testing effects of
fair value accounting on investors’ pricing of banks. These banks are based in
different geographical positions, and the time window is of three years: 2006, 2007,
2008. Here are considered three selected regions: EU15 (European member
countries prior to 1May 2004), other European countries and the rest of the World.
The model distinguishes among categories of financial instruments (available for
sale, held for trading and designated at fair value through profit or loss) and it tests
effects of these categories on the market value of equity of a sample of 322 financial
institutions. Additionally, the authors run a further study controlling for low and
high regulatory quality features. Finally, they test changes in estimated coefficients
at different stages of the financial crisis.
Results are different according to the provenience of banks; the reason may be
because of the different interpretation across countries of the accounting standards.
While in the U.S.A. interpretation of US GAAP is far much likely to be unique all over
the country, in Europe and other countries, previous national GAAP are now
compared with IAS/IFRS regulation. Anyway, fair value is generally value relevant,
but coefficients across institutional factors differ. Moreover, non-mandatorily fair
value adoption (fair value option, which involves the “designated at fair value
through profit or loss” category) is discounted in countries with lower regulatory
30
quality22. Lastly, value relevance of these instruments has decreased with the
worsening of the economic crisis. These results demonstrate that fair value is
relevant for investors’ purposes, but its reliability is weakened in times of economic
instability, and this is in line with other studies concerning fair value accounting.
Remarkable are the results found in EU15 banks: valuation coefficients are not
statistically different from predicted values of 1 and -1 and distinguishing between
low and high regulatory regimes doesn’t provide any difference for the value
relevance of fair value estimates. On the contrary, in the rest of the world the
discount is larger in both cases: for overall fair value with respect to non-fair value
asset and liabilities, and particularly when controlling for low regulatory quality
countries. Furthermore, valuation coefficients decreased earlier in EU15 than in
other countries (already in 2007), which is an evidence of the fact that these EU15
countries have been hit firstly by the economic crisis.
The general idea in literature is that even though, through several empirical
research, has been assessed that fair value measurement is a relevant accounting
method for investors (among the first researchers, Barth, Beaver, & Landsman,
(1996)), they however discount financial instruments book values according to
concerns about lower or higher degree of reliability they perceive about these
assets.23 Critics about fair value claim that even though fair value may be value
relevant during times of market stability, it may lack relevance and reliability during
times of instability. Thus relevance and reliability of fair value accounting method
are seen as a trade-off especially when deviating from market prices: the best
solution would be relying on market quoted prices, but only within an active market.
Furthermore, even in times of relative market stability pricing of assets using
22 Of note, when an entity decides to record at fair value an instrument, it is aware that changes in fair
values will impact the income statement and the equity, because these changes have to be ascribed in
profit or loss.
23 Studies about value relevance of fair value approach are: Barth (1994); Barth, Beaver and
Landsman (1996); Eccher et al. (1996); Beaver et al. (2003) among others.
Studies of Kolev (2008), Goh (2009), Lev and Zhou (2009), Song (2010) focus on value relevance of
each of the three levels of fair value hierarchy, but they too, demonstrate correlation between stock
prices and total net assets as a whole recorded at fair value too..
31
unobservable assumptions may however lack of reliability and provoke investors’
concern about information assymetry, due to a higher degree of private, and not
public, information.
2.2. Research concerning value relevance of level 1, 2 and 3
Investors and creditors, so, generally perceive as value relevant all information
recorded at fair value.24 Further questions are: how much subjectivity do they
perceive within level 2 and mostly level 3 fair values? To what extent do they
discount these amounts? Do they discount also level 1 asset prices, in times of
economic downturn and not orderly markets?
Until today some studies have been carried on this specific topic25. All of them deal
with information risk and differentials of risk perception across the three levels.
Regression models are created in order to test the link between stock prices of
entities (mostly financial institutions), amount of instruments disclosed within the
fair value hierarchy and other firm fundamentals (such as leverage, other assets,
etc.). Often authors control for firm governance quality, using specific indicators of
“ex-ante” quality, with the purpose of splitting the sample in low and high quality
governance of institutions. This control for “other than firm fundamentals”
demonstrates in the end, that the discont applied by investors for lower levels assets
(e.g. lower correlation between stock price and amount of instruments held at level
2 and mostly level 3) is mitigated for entities that exhibit more “quality features”.
24 A simple, but important assertion to point out is that accounting rules as a whole are value relevant
because we live in an imperfect market. In a perfect market, without information asymmetry,
reporting market values would be superfluous (Beaver, 1981).
25 Excluding Song (2010), Goh (2009), Kolev (2008), Di Martino (2011) and Bosch (2012) few other
authors have dealt with value relevance of fair value disclosed by levels. Baruch&Nan (2009) studied
the impact of the three levels on investors reactions to 44 political and economic events of the last
worldwide financial crisis. Altamuro&Zhang (2012) test the differences between level 2 and level 3 in
reflecting intrinsic value of the mortgage servicing rights. Others are Riedl and Serafiem (2011) and
Liao (2011). All these studies, however lead to results that are directly correlated to the effect of the
last global crisis.
32
Goh, Ng and Yong (2009) focus their study on information risk. Their research
points to find out how come lack of trading for some assets exists, even though firms
are able to provide fair value estimates of their prices. If fair value estimates are
perceived to be the mirror of underlying value of an asset, trading of instruments
should be continuous and frequent. Are marked-to-market assets (level 1) priced
differently from marked-to-model assets (level 2 and level 3)? The answer is yes and
the reasons are concerns of investors about illiquidity and information risk. The
authors run a regression between stock price and assets disclosed under the three
levels of the fair value herarchy, of a selected sample of financial institutions. They
found out first, that the coefficients of levels 1, 2 and 3 are 0.85, 0.63, 0.49
respectively, and second, that the level 2 coefficient estimate is not significantly
different from the level 3 coefficient estimate26.
This result reflects the investors’ discounting of financial instruments, according to
the nature and observability of inputs and assumptions used for measurement. As
predicted, reliability of level 2 and level 3 assets is lower than reliability of level 1
assets, therefore discounting of level 2 and 3 is higher than discounting of level 1.
Theoretically, level 1 shouldn’t be discounted because it reflects observable market
prices, but a reason its coefficient is not equal to one (one dollar of assets is not
priced proportionally to one dollar of share price) also because, the period in which
Goh has run this study was a period of economic downturn: investors were aware
that market prices (i.e. level 1 instruments) didn’t reflect fundamental values of
instruments.
Then the authors run another regression including additional variables reflecting
capital ratios and presence of Big 4 auditors for each bank. Results are coherent with
predictions: level 3 assets for banks with higher capital ratios and audited by one of
the Big 4 auditors has now a higher estimated coefficient. In line with literature,
features like capital ratios, quality of corporate governance and external auditing
26 This study is taken across quarters of 2008. More interestingly, he finds coefficient of level 3
significantly different from 0 in the first quarter (which proofs the value relevance of mark-to-model
fair value prices), but in the following two quarters, this value becomes only marginally different
from 0 (also because of the crisis consequences).
33
services (“quality features”) mitigate concerns of investors about riskiness and
reliability of a bank’s fair value financial assets.
Goh has shown that, given their intrinsic measurement subjectivity, “mark-to-
model” assets (i.e. level 2 and level 3) are discounted with respect to “mark-to-
market” assets (level 1).
Remarkable is that, even if these prices have to be estimated and estimation
procedure lacks in precision by definition, management has a fundamental role
in determining to what extent estimations can be considered as reliable.
Managers make predictions not only about the future performance of the asset,
but also about other variables which are included in the prediction model. As an
example, Schwarz (2011) speaks about certain features that should always be
part of a valuation model when estimating, for example, the value of instruments
composed of subprime mortgages (e.g. likelihood the house prices becomes flat
or decline, the geographical dispersion of the instrument, the location of the
price stagnation, etc.).
The most common way to derive inputs for a model is looking at historical data.
More generally, demand, interest rates, economic growth rates etc., are of great
importance for estimations. Looking at historical data to derive predictions is the
basis, but events that have rarely happened shouldn’t be excluded. Furthermore,
these variables may change frequently, therefore frequent adjustments may be
MANAGEMENT BIAS
MEASUREMENT ERRORS
INACCURATE MODELS
INFORMATION RISK
INVESTORS' DISCOUNT
34
needed. In this case, perceived value of level 3 assets, which require most
adjustments, may be at risk of huge discounts and even of being discarded as
“junk assets”. Is it really so?
Kolev’s (2008) research question goes precisely in this sense. He attempts to find
out if level 3 instruments are really perceived as being “junk assets”, thus being
“marking-to-myth”. He uses word pun to show his purpose of shedding light on
whether investors perceive mark-to-model assets as, actually being of
discarding; if level 2 (whose prices are estimated using both observable and
unobservable inputs) and most of all, level 3 fair values are too unreliable and
therefore not value relevant. His conclusion concerning value relevance of assets
and liabilities recorded at fair value is coherent with the vast majority of studies
about value relevance of fair value: there is evidence of significant positive
association between stock prices and estimations of each of the three levels
independently. There is also evidence of positive correlation between stock price
and total assets recorded at fair value. This means that even though valuations
are not determined according to unadjusted market prices for identical assets,
investors don’t discard marking-to-model assets as being “marking-to-myth”.
Anyway, as predicted, level 2 and 3 coefficient estimates are lower than marking-
to-market coefficient estimates and correlation between stock prices and levels
of the hierarchy are high for level 1, smaller for level 2 and the smallest for level
3. However, using the coefficient of level 1 as a benchmark, the author calculates
a maximum difference between level 1 and 3 estimates of only 35%. As expected,
when running a test of equality between estimated coefficients of level 1 and
estimated coefficients of level 3, the result is negative. This result changes when
running the same test and controlling for two more variables which account for
higher equity capital of the bank and valuation services for level 3 assets
provided by external third parties (these variables account for ex-ante
information quality of banks). A change of point estimates was expected, but only
to a certain extent. What the author finds instead, is that difference between
coefficients of level 1 and coefficients of level 3 is now statistically equal to zero.
This would mean that features which account for the fact that the entity presents
35
high information quality about its assets not only mitigate concerns about level 3
assets, but even remove them totally27. Kolev finally finds out that level 2
estimated coefficients are significantly lower than level 1 estimated coefficients,
but the difference is statistically significant only between level 1 and level 3.
In a study run by Song et al. (2010), a similar result is found: value relevance of
level 1 and 2 is higher than value relevance of level 3. The author finds similar
estimates of level 1 and level 2 coefficients, and even close to the theoretical
value of 1 (“dollar-to-dollar” pricing). He focuses on information asymmetry
between preparers and users of financial statements, thus once more, on
discounts made by investors in valuating reported fair values. Investors again
have concerns about reliability of fair values measured through unobservable
inputs. This effect can be mitigated by the strength of corporate governance: a
weaker corporate governance is positively correlated with greater perceived
information asymmetry. Therefore, for banks with low corporate governance28,
level 1 and 2 appear to be value relevant, while level 3 doesn’t. Song finds
evidence of strong impact of corporate governance on investors’ pricing of level
2 and level 3. Specifically, there is no impact on pricing of level 1 assets, but there
is a relevant increase in level 2 and level 3 point estimates, which brings them
close to the predicted value of 1 (-1 for liabilities).
Similarly to Song, also Liao (2010) investigates whether the fair value hierarchy
can be associated with information asymmetry. The novelty of his research is the
use of quarterly bid-ask spreads as proxies for information asymmetry. That is,
Liao tests whether total net assets and net assets disclosed by levels, are
positively or negatively correlated with the bid-ask spread29. In other words, if
27 This result should be interpreted with caution because there is only a small number of banks which
use third parties’ services in order to estimate fair values of level 3 assets.
28 To describe corporate governance Song uses a standardized variable which takes into
consideration, among others, the number of independent Board members, the number of financial
experts in the audit committee, etc.
29 Using the bid-ask spread as a proxy for information asymmetry is explained by the fact that, when
uninformed investors perceive the risk of information asymmetry, they try to increase the bid-ask
spread to protect themselves against possible losses deriving from trading with more informed
investors. Thus, the bid-ask spread is higher when more information asymmetry is perceived.
36
the accounting standard has the power to improve informativeness of fair value
(in this case, SFAS 157), then the relationship between the bid-ask spread and
the financial instruments differs across hierarchy levels. This hypothesis is
validated: specifically, the relation with the bid-ask spread (information
asymmetry) is positive for all three levels, but the value of this relation changes
from one level to another.
Furthermore, also total net assets as a whole are positively correlated with
information asymmetry. This means that investors perceive some information
asymmetry about all assets recorded at fair value, even about level 1
instruments. This result is similarly interpreted to results of other studies that
didn’t find a “dollar-to-dollar” pricing of all fair value levels (e.g. Song (2010)).
Negative correlation, then is found between information asymmetry and
variables which control for size, stock price and capital ratio Tier I of banks,
which confirms the importance of such ex-ante “quality factors” on investors’
valuation of an entity and its assets.
Riedl and Serafiem (2011), instead estimate the firm equity beta starting from
the CAPM model. Then they decompose it in two parts in order to analyze the
part that captures information asymmetry, thus information risk. They test the
relationship between this measure and the three levels of the hierarchy. SFAS
157 and IFRS 7 require a higher information quality, through enhanced
disclosures for financial instruments, therefore the authors test the link between
improvement of disclosure and the entity’s cost of capital. If the latter is high, it is
a consequence of higher information risk, which means higher beta values. The
authors actually find increasing betas (increasing cost of capital) across
portfolios of assets designed at levels 1, 2 and 3. To the extent that information
risk increases with increasing levels, the conclusion is that level 3 assets lead to a
higher cost of capital with respect to level 1 and level 2.
Secondly, the authors give once more prove of how improving “ex-ante”
information quality (having a higher level of analysts following, lower forecasts
errors and dispertion, and higher market capitalization) mitigates the risk
perceived about assets measured through unobservable inputs. Within this test,
37
no difference is perceived across levels for higher information quality
institutions, while for lower information quality institutions this difference is
significant.
Lev and Zhou (2009) argue that the classification in levels informs on liquidity
risk. They test this claim using correlation between a set of political and
economic events (that occurred during the last four months of 2008) and
changes in raw returns observed on the stock exchange of financial and non-
financial firms. Each event (e.g. statement of Wells Fargo to purchase Wachovia,
Fed cuts on interest rates, Citi Group rescue etc.)30 is classified whether as a
liquidity shrinking or as a liquidity expanding event and has a few trading days
window. Returns on the stock exchange are measured as a mean of daily
cumulative raw returns over the relative time window.
The authors give proof of correlation between stock returns and events.
Particularly, market reactions have been negative for the liquidity-constraint
events and positive for the liquidity-expansive events in both financial and non
financial sectors. As far as concerns the 3 levels hierarchy, for non-financial firms
there has been a worse reaction regarding level 2 and level 3 liabilities with
respect to reaction to level 1, when liquidity-constraint events had occurred. Also
for financial firms, in which most fair value assets are level 2 and 3, reaction has
been more marked, specifically for level 3.31 The opposite holds for liquidity-
expanding events. That said, investors perceive liquidity risk and react
consequently. Thus, they discount the second and third level because those levels
provide information on higher liquidity risk.
Again, concerning illiquid markets, the last US study on reliability of mark-to-
model versus mark-to-market fair values that I am going to mention, goes
partially against (or at least mitigates) the negative results for level 3
instruments, found by Lev and Zhou (2009). It is a test that proves enhanced
30 Five groups of events are used: “Distress”, “Policy”, “Rescue”, “Fed”, “Capital infusion” . While the
first two are liquidity-constraining, the last two are liquidity-expanding.
31 Financial institutions’ liabilities are mostly insured by the Federal Deposit Insurance Corporation
(FDIC). Therefore no reaction was found, for liabilities of financial firms.
38
information capability of mark-to-model methods in inactive, illiquid markets
compared with mark-to-market. That is, when the market is not active and
trading of an instrument is infrequent, use of managerial discretion is more
informative for investors than the sole use of mark-to-market, simply because
referring to market prices doesn’t give a true reflection of fundamental values of
instruments.
Altamuro & Zhang (2012) studied the mortgage servicing rights and the
correlation between the persistence of fees (as a proxy for future cash flows) and
fair value of the mortgage.32 The results are in line with their conjectures: during
periods of infrequent trading and illiquidity, mark-to-model methodology gives a
fair value estimation that is more closely reflected by future cash flows arising
from this instrument. Accounting regulators give less importance to estimation
methodologies and managerial discretion, to the extent that FAS 157 even
emphasize that illiquidity is not a sufficient reason to ignore market prices. But
the matter is that managers have more information than regulators and this
information is reliable, if properly used, when prices provided by markets are
not a reflection of real fundamental values.
Mark-to-model and structured finance
Structured finance33 is another relevant topic when analyzing mark-to-model
assets and liabilities. Structured instruments, by definition, are “unique” because
32 MSR are the rights of serving a mortgage and are sold by the lender to another party. This party
commits himself to collecting monthly payments and forwarding interests and principal to the lender.
MSR market is not small, but it is not considered active because of the infrequent trading of these
instruments. MSR give rise to cash flows for the lender, thus a service fee is paid, to the buyer of
rights, by him. Theoretically the present value of future fee cash flows should be reflected by the MSR
fair value at the balance sheet date.
33 Structured finance and securitization have two different meanings. Securitization is the process of
pooling similar financial instruments (e.g. loans, mortgages) which are not usually sold in an active
market. Afterwards, securities with claims against these underlying instruments are issued. The aim
is removing them from the balance sheet of the issuer and transferring the risk of default of original
instruments from the issuer to the purchaser. Structured finance instead, concerns the conversion of
these otherwise risky assets, in low risk rated ones, in order to increase the probability of their
39
of the distinctiveness of the underlying instruments upon which they are issued
(e.g. loans, mortgages). Therefore, they always include in their estimation a
certain degree of modeling34. It is a difficult task to find quoted prices for values
of these instruments and as well known, securitization and derivatives (the
structured instruments) have been an important problem of the last financial
crisis.
To shed light on these opaque instruments, Schwarz (2011) creates a simple
model based on tranches of derivatives and related probabilities of default
through which he explains to what extent small errors in modeling structured
instruments can lead to catastrophic consequences. His model basically uses the
present value technique, which is one of the measurement methods that can be
used to estimate level 3 instruments, as provided by SFAS157 and IFRS 7.
Specifically, the most intuitive error is not including systematic risk, which by its
non-diversifiable nature, leads to declines in senior tranches when all underlying
instruments (e.g. subprime mortgages, as of the 2007 financial crisis) go bad.
Indeed, senior tranches go very well when the model doesn’t account for
systematic risk: they have a very low probability of default. But when systematic
risk is taken into account (which is actually a proxy for a bad economic period),
while probability of default of the safest senior tranches increases (even by 25
times), probability of default of junior and mezzanine tranches decreases. The
problem is that lower level tranches are usually held by hedge funds or
institutions which are used to bear high degrees of risk. On the contrary,
superior level tranches, which should be safer, are usually held by more risk-
adverse individual investors.
That’s why a wrong, imprecise model can reduce overall social welfare. A
measurement model has to incorporate all features that help explaining the real
settlement among investors. The purpose is receiving cash and transferring risks from one’s own
balance sheet.
34 In 2003, Warren Buffet claims about derivatives: “The range of derivative contracts is limited only
by the imagination of man (or sometimes, so it seems, of madmen)”. […] It is like “you want to write a
contract speculating on the number of twins to be born in Nebraska in 2020. No problem, at a price
you will easily find an obliging counterparty.”
40
risk it reflects. So, even though a manager or a rating agency doesn’t use private
information and discretion to their own benefit (earnings management), simple
small errors may miss the real explanation of the value of that instrument. The
problem with derivative products is that they magnify risks of underlying
instruments in both ways, upward and downward. That’s why the consequences
may be enormous. And derivatives are always measured at fair value level 2 and
3. They are a time bomb especially for highly leveraged financial institutions.
2.2.1. Two European studies
As already claimed, Di Martino (2011) and Bosch (2012) held the unique
researches on the distinct impact of the three levels on market values of
European entities (specifically, of European banks).
Di Martino (2011) found level 1 instruments to be value relevant, thus providing
useful information to investors. These instruments, particularly present a market
premium of 10% with respect to the total value. In his first test he also found that
level 2 and level 3 instruments are not value relevant, that is not providing
additional quality to information needed by the market. Moreover, of interest is
the negative impact of level 3 instruments (which hypothetically is discounted,
but positive), that is interpreted with a discount of the value of banks for
investors.
Interesting results are found when running a test that included also effects of
further disclosure about level 3 instruments (the required “Movements in and
out level 3 instruments”). Banks which provide this enhanced disclosure invert
the market negative perception about level 3. In other words, these banks
present a higher market value compared with those banks that don’t provide
such further disclosure. In this sense, investors perceive as useful this
information.
Bosch (2012) is the most recent study concerning this topic. He uses a sample of
EU27 banks and make the same kind test about value relevance and reliability of
41
fair value hierarchy. He finds all levels to be value relevant, with level 3 being the
least reliable one.
What is important is that, like Fietcher (2010), he divides the sample in member
countries of EU15 and Other European Countries and finds divergent results
from one sample to another. In particular, while in EU15 all levels are value
relevant and estimated values of each level converge with related expected
values, in other European countries the outcome is different. For these countries,
levels are generally priced less and their estimated values diverge more from the
expected values. This is evidence of the fact that different regulatory qualities
drive different market perceptions.
2.3. Literature: conclusions
Summarizing all the most recent findings, disclosures proposed by IFRS 7 and
SFAS 157 are informative for investors. Value relevance has several times been
tested and the overall conclusion is that enhancing disclosures requirements
enhances information for users of financial statements. Generally, instruments
measured using observable inputs (level 1) are priced more than level 2 and
level 3 instruments. They are not, anyway, priced at 100% their reported value.
The explanation of this result is that the period of time during which almost all
tests have been conducted was a period of inactive markets, characterized by
deviations of prices from their fundamental values. Investors knew that market
prices were distorted. Furthermore, there is a general concern about fair value
financial instruments: as seen above, opinions are conflicting towards fair value
versus other methods of measurement (e.g. amortized cost).
However, pricing differs across levels to the extent that level 1 is anyway, priced
higher than the other two levels. Level 3 for some authors is even discounted as
42
much as level 2 (e.g. Goh (2009)). But generally admitted is that level 3
instruments are discounted the most35.
Also prior to the introduction of the fair value hierarchy, fair value accounting
has been demonstrated to be value relevant, but since the adoption of the new
provisions, investors have had a more precise way to analyze an entity. Another
relevant additional conclusion is that when investors consider also the quality of
corporate governance, and the quality of provenience of reported values, their
risk perception changes. That is, investors price level 3 instruments more, when
an entity performs specific standards of “ex-ante” quality information, with
respect to an entity that doesn’t reach these standards. Some authors even find
that considering “quality features” leads to a perceived value of level 3 assets
equal to the perceived value of level 2 and level 1, thus a total annulment of the
previous higher discount (Song, 2010; Riedl and Serafiem, 2011).
All studies (with exception of the three European studies), have been made in the
U.S.A., using U.S. GAAP provisions as a basis. As already stated at the beginning of
this thesis, IFRS and U.S. GAAP have reached convergence with regard to fair
value measurement and related disclosures, by issuing convergent provisions on
this topic. Therefore, the two sets of principles are more identical than similar
(with few little exceptions), thus U.S. literature is very helpful for comparison
purposes of outcome of the present analysis.
35 As a reminder, Altamuro and Zhang (2012) find mark-to-model as being a good measure in illiquid
markets.
43
3. STATISTICAL MODEL
3.1. Research questions and hypothesis development
The tested model is going to show if, with respect to fair value hierarchy of
financial instruments, recent enforcements of IFRS (for financial information
reported in the 2010 and 2011 annual reports) have had some mitigating impact
on the lower perceived value of fair value instruments. Particularly, level 2 and
level 3 assets as of previous literature are perceived as reflecting less than level 1
the theoretical pricing of “one dollar-for-one dollar”. This is due, as mentioned
above, to the nature of inputs used in measuring fair value. Consequently, the
issue is related discretion of management in using private information to provide
estimates. Discretion of management and measurement errors are more
probable within level 3, but sometimes level 2 and level 3 are seen as a macro
category because of the common deviation from quoted prices in active markets.
There is widely accepted belief that also level 1 is deviating from quoted prices in
active markets, not because quoted prices aren’t used to measure it, but because
during the tested periods markets were not totally active. Therefore, none of the
previous studies has found a full 100% “one dollar for one dollar” valuation for
either level 1, 2 or 3. If it was so, related coefficients would be equal to 1. This is a
theoretical value and it means that an 1% change in value of assets is reflected in
an 1% change in perception of investors about the value of the whole entity.
After 2008 and the start of the financial crisis, as summarized above, European
and national institutions issued enforcements concerning classification and
disclosures of financial instruments as provided by IAS/IFRS. These
enforcements regard 2009 onwards annual reports in order to provide investors
with all relevant information about the financial situation of entities. The stress
is most on financial instruments because of their importance in the crisis and
particularly, in the banking sector. Have these recalls and recommendations
about enhanced precision produced any effects on market capitalizations of
European banks? Have markets perceived enforcements as being useful? Do
44
market participants believe that banks have actually taken into account enforced
provisions and did this attention generated better informing disclosures about
financial instruments?
These are the research questions. Upon these questions, underlying hypothesis
have been computed too. The belief is that the work of ESMA and national
enforcers’ is not useless. More specifically, the supposition is that market
participants generally give credit to institutions that work in order to ensure
transparency in financial markets and believe that their guidelines are taken into
account by entities. Even more so that, if financial institutions wouldn’t comply
with required quality of disclosures, they would fall afoul of sanctions carried on
by “watchdogs” (the supervisory authorities). So, if this hypothesis is accepted,
the result will be that while the perceived values of all instruments disclosed by
levels won’t however fully reflect the amount recorded in the financial
statements (thus, statistically speaking, point estimates of regression coefficients
related to such variables won’t be equal to 1 for assets and -1 for liabilities), the
perceived value of level 3 assets and liabilities will better reflect related recorded
values, than level 2 and level 1 do (thus, level 3 coefficient estimates will be more
close to 1 and -1). This hypothesis is supported by the fact that the enforcements
have special regard to the most opaque, level 3 financial instruments.
Before testing this issue, a more general issue has to be once more tested, that is
the value relevance of all financial instruments recorded at fair value. The belief
is that the result of this preliminary test will be in line with previous literature,
since most authors reached this conclusion, and there is no need to think that
something has meanwhile changed: in other words, investors actually believe
that fair value measurement for financial instruments is value relevant.
Finally, within this assumption, is taken for granted that hierarchy disclosures
required by IFRS 7 are informative for market participants. Different levels
reflect different methods of valuation. This means that across levels, different
discounts are supposed to be found, in line with literature.
45
Formally speaking, the preliminary hypothesis is the following:
(H1) Even though the perceived value of assets and liabilities differs across
levels, all instruments disclosed by levels are perceived to be value relevant.
The main hypothesis of this test is the following:
(H2) Mark-to-model values present, on average, a lower discount for 2010 and
2011 financial statements compared to discounts previously found in literature,
thanks to the recently issued IFRS enforcements concerning the fair value
hierarchy.
3.2. Sample and variables description
Data on accounting values were gathered from both 2010 and 2011 consolidated
financial statements of banks, closing at December 31st, that are directly
downloadable from the entities’ websites. 36
The sample originally was made of 46 European listed banks, but later some
banks were excluded. Three financial institutions didn’t provide all necessary
information and other 2 banks provided useful information only for one of the
two periods, therefore were included only in one of the two yearly data. Finally,
for the research purposes, 42 banks were maintained for the year 2011 and 41
banks for the year 2010. Most number of banks were selected from Italy, United
Kingdom, Spain (see Table 1 for the distribution of banks across countries). The
final sample was computed putting together data for both years. It has 83
observations.
36 Only financial statements of an Italian Mediobanca S.P.A., closes at 31/06.
46
Country Banks Country Banks Italy 8 Austria 2 Spain 6 Germany 2 UK 5 Norway 1
France 4 Belgium 1 Sweden 4 Finland 1
Switzerland 3 Portugal 1 Denmark 3 Ireland 1
Table 1. Number of banks by country.
All financial institutions disclosed information about financial instruments
recorded at fair value in line with IFRS 7, for both years. Subject of the research
are total amounts of fair value financial assets and financial liabilities disclosed
by level. This data, along with the amount of total loans (interbank loans +
customer loans), of other than intangible assets not recorded at fair value, of
customer deposits and of other liabilities, are independent variables in the
statistical regressions subject matter of the research. Market capitalization (as of
31/12/2010 and 31/12/2011) is the dependent variable. The results will show
to what extent amounts of assets and liabilities of European listed financial
institutions in 2010 and 2011 influence the market values of these entities.
In Table 2 are displayed average data about percentage of level 1, 2 and 3 assets
(liabilities) on total amounts of assets at fair value (liabilities at fair value), and
percentage of total assets at fair value (liabilities at fair value) on total amount of
assets (liabilities) as of the balance sheet.
47
ASSETS LIABILITIES
Level 1 Level 2 Level 3 Level 1 Level 2 Level 3
2010 46% 50% 4% 14% 83% 3%
TOT FV 32% 21%
2011 43% 53% 4% 12% 86% 2%
TOT FV 32% 22%
Table 2. Shares of fair value assets (liabilities) by level on total fair value assets (liabilities) and shares
of total fair value assets (liabilities) on total assets (liabilities) as of the balance sheet.
These data are calculated on an average percentage basis across financial
institutions. For example, for the “ASSETS - column” are displayed percentages of
level 1, 2 and 3 on total fair value assets in 2010 and 2011. The same holds for
the “LIABILITIES – column”. “Tot FV” is an average value of total assets recorded
at fair value divided by the amount of total assets as of balance sheets for each
year. Of note, almost 95% of assets disclosed at fair value are distributed
similarly among levels 1 and 2 (level 2 is however prevailing) in both years.
Liabilities, instead present an 85% disclosed at level 2, while level 1 accounts for
a noticeable smaller share. Level 3 instruments represent a small share of all fair
value instruments.
In order to point out the high importance of fair value instruments, table 3
presents data on an average basis about the impact of assets and liabilities,
disclosed by levels, on the tangible book value (common equity less intangible
assets)37.
37 TBV = tangible book value = tangible common equity = common equity – intangible assets.
48
L1 assets/tbv
L2 assets/tbv L3 assets/tbv L1 liab./tbv L2 liab./tbv L3 liab/tbv
Min 0,28x
0,19x 0 0 0,25x 0
Max 11,83x
30,83x 1,90x 7,00x 25,83x 0,98x
Mean 3,03x
5,85x 0,30x 0,75x 5,28x 0,13x
Table 3. Instruments disclosed by level divided by tangible common equity (31/12/2011
data).
There is a reassuring evidence of the lower impact of level 3 instruments on
tangible common equity, with respect to the impact of other levels: on average,
level 3 assets represent 30% of TBV and level 3 liabilities represent 13%. Data
for level 2 assets and liabilities are more alarming (level 2 instruments are about
5 times higher than the tangible common equity). The maximum values within
the sample are even more critical: level 2 assets represent a maximum value
equal to 31 times the TBV, and level 2 liabilities represent a maximum value of
26 the TBV38.
3.3. Statistical regressions
All regression variables are scaled by the tangible book value, otherwise called
net tangible assets, to allow for homoscedasticity and not to account for
differences of size across different bank. The indicator P/TBV is used, because it
reflects the perception of investors about the value of equity. So, if market
capitalization is higher than the tangible book value (and the indicator is higher
than 1), then if the bank would sell all its assets at a price equal to the assets
38To make an example, banks which are settled in Italy, Spain, UK, and France (the first three
countries by number of banks in the sample) on average, hold level 2 assets/TBV with the
following percentages: Italy – 150%, Spain – 181%, UK – 750%, France – 1306%. Furthermore,
37% of the sample (computed with 2011 data) have negative level 2 net assets (assets less
liabilities).
49
value recorded on its balance sheet, would have a gain39. Finally, using the
tangible book value provides a more cautious measure with respect to the simple
book value40.
With these variables initially three regressions were created. One that gathers
together results for both years and other two that account for each year
separately.
1. The first regression, that controls exclusively for amounts of assets and
liabilities is, therefore, the following:
Pi/TBVi = α + β1*AFV1i/TBVi + β2*AFV2i/TBVi + β3*AFV3i/TBVi +
β4*LOANSi/TBVi + β5*OTHAi/TBVi + β6*LFV1i/TBVi + β7*LFV2i/TBVi +
β8*LFV3i/TBVi + β9*DEPi/TBVi + β10*OTHLi/TBVi + ε
Where:
P is the share price multiplied by the total shares (total market capitalization of
the bank). AFV1(LFV1), AFV2(LFV2), AFV3(LFV3) are assets (liabilities) at level
1, level 2, level 3, LOANS are the interbank loans + net loans41, OTHA are other
assets (i.e. not disclosed at fair value) excluding the intangible assets, DEP are the
total customer deposits, OTHL are remaining liabilities not disclosed at fair
value.
2. The second regression, which accounts also for each year separately is:
39 Of note, in the present sample, P/TBV values range between 0.22 and 2.92 in 2011 and
between 0.26 and 3.66 in 2010.
40 This measure gives an advantage in the sense that it can be used also in the future (to
determine, e.g. the price of the entity), when the goodwill or the brands won’t necessarily have
the same value. This implies that it is more difficult for intangible assets to have a precise value,
because this value can change easily.
41 Net loans are total loans to customers reduced by possible default losses and unearned interest
income.
50
Pi/TBVi = α + β1*AFV1i/TBVi + β2*AFV2i/TBVi + β3*AFV3i/TBVi +
β4*LOANSi/TBVi + β5*OTHAi/TBVi + β6*LFV1i/TBVi + β7*LFV2i/TBVi +
β8*LFV3i/TBVi + β9*DEPi/TBVi + β10*OTHLi/TBVi + β11*YEAR + ε
Where YEAR is a dummy variable, that is equal to 0 for the 2010 data, and equal
to 1 for the 2011 data.
3. The third regression takes into account the impact of YEAR and of an
additional variable (ROTE):
Pi/TBVi = α + β1*AFV1i/TBVi + β2*AFV2i/TBVi + β3*AFV3i/TBVi +
β4*LOANSi/TBVi + β5*OTHAi/TBVi + β6*LFV1i/TBVi + β7*LFV2i/TBVi +
β8*LFV3i/TBVi + β9*DEPi/TBVi + β10*OTHLi/TBVi + β10*ROTEi + β11*YEAR + ε
Where ROTE is the Return on Tangible Equity. It measures how well an entity is
producing profit with the invested equity, not accounting for intangibles.
Investors usually compare similar companies, by comparing their ROTE rates in
order to see which one performs better. ROTE is computed with the following
formula:
NET INCOME
ROTE =
COMMON EQUITY – INTANGIBLES
Using hypothesis summarized above and the regression equations, testing and
accepting the first hypothesis (H1) means that coefficient estimates assigned to
assets and liabilities of each level (β1, β2, β3, β6, β7, β8) are statistically significant.
Testing and accepting the second hypothesis (H2), means that coefficient
estimates of level 3 instruments is higher than coefficient estimates of levels 2
and 1 or, at least they have a reasonable lower discount compared with
discounts resulting from previous literature.
Table 2 summarizes the predicted signs of all the model variables.
51
VARIABLE (1) PREDICTED
SIGN (2) PREDICTED
SIGN (3) PREDICTED
SIGN AFV1/TBV + + + AFV2/TBV + + + AFV3/TBV + + +
LOANS/TBV + + + OTHA/TBV + + + LFV1/TBV - - - LFV2/TBV - - - LFV3/TBV - - - DEP/TBV - - -
OTHL/TBV - - - YEAR +/- +/- ROTE +
Table 4. Predicted signs of variables.
In tables 2 and 3 are presented descriptive statistics of variables.
Numbers show that on average the indicator “Price to tangible book value”
(P/TBV) is 66% higher in 2010 compared to 2011. This implies that market
participants have put a higher discount on banks during 2011, perhaps due to a
global negative view of investors’ confidence.
Standard deviation of this variable is also noticeably higher in 2010. This implies
that the indicator varies within the sample more in 2010 than in 2011, and this
higher variance could partially explain the increase in investors’ valuation of
banks in 2010.
Mean of LFV1/TBV and LFV3/TBV are less than 1 in both years, while on average
level 2 liabilities are approximately 5 times higher than the tangible book value
(for 2010 the value is equal to 4.68 and for 2011 to 5.28). This gives evidence of
the high amount of level 2 liabilities that banks generally hold. As far as concerns
assets, amounts of level 2 is also high, but level 1 accounts for a substantial share
too (it is not so for liabilities).
52
Descriptive statistics*
P/TBV LOANS AFV1 AFV2 AFV3 OTHA DEP LFV1 LFV2 LFV3 OTHL ROTE
Mean 0.7823 15.2089 3.0271 5.8543 0.2982 1.0803 9.1095 0.7498 5.2808 0.1261 7.8750 0.0065
Median 0.6512 14.1356 2.3844 3.5084 0.1538 1.1330 8.3652 0.2989 2.7603 0.0216 7.3337 0.0594
StdDev 0.5245 6.1889 2.0388 6.2330 0.3800 2.7174 3.4344 1.2541 5.5312 0.2260 4.6330 0.1570
Min 0.2216 6.9938 0.2784 0.1912 0.0000 -4.4663 1.8332 0.0000 0.2482 0.0000 1.1702 -0.6019
Max 2.9183 44.1371 11.8343 30.8309 1.9037 8.7061 21.9468 7.0022 25.8314 0.9832 27.5055 0.1524
P1% 0.23 7.09 0.59 0.22 0.00 -4.36 2.34 0.00 0.27 0.00 1.35 -0,4936
P99% 2.51 35.67 10.14 26.23 1.64 8.70 19.51 5.62 22.95 0.89 23.49 0,1477
Table 5. Descriptive statistics – 2011.
P/TBV LOANS AFV1 AFV2 AFV3 OTHA DEP LFV1 LFV2 LFV3 OTHL ROTE
Mean 1.1812 15.2009 3.3900 5.3212 0.3092 1.0889 9.2659 0.9021 4.6752 0.1447 7.8977 0,0880
Median 1.0931 15.1193 2.6831 2.8473 0.1622 1.0511 9.4327 0.3360 2.8045 0.0319 7.1075 0,0905
StdDev 0.7011 5.7655 2.4170 5.7579 0.4231 3.1607 3.4189 1.5594 4.9380 0.2573 4.3855 0,0658
Min 0.2649 6.7298 0.3419 0.2459 0.0000 -7.3681 1.8153 0.0000 0.1711 0.0000 0.2757 -0,1703
Max 3.6599 38.9075 12.7764 29.4672 2.0775 9.0641 21.8754 8.7825 23.7799 1.2451 23.2499 0,2091
P1% 0.29 7.28 0.55 0.25 0.00 -6.24 2.25 0.00 0.19 0.00 1.00 -0,1096
P99% 3.54 33.14 11.60 23.22 1.81 8.82 19.55 7.04 19.64 1.01 20.88 0,2029
Table 6. Descriptive statistics – 2010.
*All variables are scaled by TBV.
53
On average, level 1 instruments (both assets and liabilities) have decreased from
one year to another and interestingly level 2 instruments have increased.
Particularly, from 2010 to 2011, level 1 assets decrease by 12% and level 1 liabilities
decrease by 20%. Level 2 assets and liabilities increase by 9% and 11% respectively.
Evidence of these changes can also be drawn from percentages presented in table 2.
Level 3 instruments have basically remained the same.
Of note is also that no banks have no level 2 liabilities, while there is at least one
bank that has no level 1 (and no level 3) liabilities. Since for the purpose of the test,
data are gathered together from both years, descriptive statistics for these data (of
note, all variables are still scaled by the tangible book value) as a whole, is
summarized in Table 7.
P/TBV LOANS AFV1 AFV2 AFV3 OTHA DEP LFV1 LFV2 LFV3 OTHL ROTE
Mean 0,98 15,20 3,21 5,59 0,30 1,08 9,19 0,83 4,98 0,14 7,89 0,05
Median 0,87 14,33 2,43 3,04 0,16 1,12 8,86 0,34 2,80 0,03 7,13 0,08
Std dev 0,65 5,95 2,23 5,97 0,40 2,93 3,41 1,41 5,22 0,24 4,48 0,13
Min 0,22 6,73 0,28 0,19 0,00 -7,37 1,82 0,00 0,17 0,00 0,28 -0,60
Max 3,66 44,14 12,78 30,83 2,08 9,06 21,95 8,78 25,83 1,25 27,51 0,21
Table 7. Descriptive statistics – all data.
55
4. RESEARCH RESULTS
A formal statistical regression has been run using Excel 2007. As already mentioned,
observations for years 2010 and 2011 were used together in order to enhance the
sample size. Firstly, were not made any controls to keep into account for different
years. Next, a dummy variable was introduced. Finally, also the return on tangible
equity ratio (ROTE) was included in the model. In the following figure, results of (1)
are briefly presented.
4.1. Regression results (1)
In the following table, results of the first regression (1) are briefly summarized:
R squared: 23%
Adj. R squared:13%
VARIABLE Coefficients Signif.
Intercept 0,160 78%
LOANS/TBV 0,546 6%
AFV1/TBV 0,707 2%
AFV2/TBV 0,687 2%
AFV3/TBV 0,778 7%
OTHA/TBV 0,533 6%
DEP/TBV -0,582 6%
LFV1/TBV -0,681 3%
LFV2/TBV -0,694 2%
LFV3/TBV -0,983 8%
OTHL/TBV -0,623 4%
Table 8. Regression results (1).
56
First of all, the total fitting of the regression (R2) is not very high: R square is 23%
and corrected R square is 13%, even though statistics for the overall significance of
the model (F statistics) has a good value (see tables in the appendix for detailed
regression results). Moreover, also the individual significance of each variable (with
the exception of the intercept) presents good values, with significance values of level
1 and 2 coefficients that perform the best: these estimates are significant at about
98% level.
Some preliminary considerations are to be pointed out about coefficients of variable
“LOANS/TBV” (Loans) and “DEP/TBV” (Customer deposits).
Loans present a β that is equal to 0.546, lower than the theoretical value of 1, in
other words a 50% discount. This implies a lower than “dollar-to-dollar” pricing of
these assets. Loans are recorded at amortized cost, and cannot be devalued unless a
default probability arises (the so called “non-performing loans”). Prerequisite of
devaluation is an objective evidence of one or more occurred events after the initial
recognition of the asset (IAS 39). If a probability of default arises, the loan value is
calculated with a higher interest rate. This higher rate is given by an increased
spread and an increased risk. In such a case, the value of the loan would decrease.
The loan has to be recorded in the balance sheet, but as already said, it has to be
valued at amortized cost (that is a higher value than its fair value calculated with the
increased interest rate). Therefore, if don’t occur sure events, that imply the
probability that the loan could become a non-performing loan, the loan will be
recorded at a higher value than its real underlying value. The differential between
the amortized cost and the loan fair value has to be recorded in “bad debts
provision” and the value of the certain payable amount is given by the residual part
(the difference between amortized cost and the amount in “bad debts provision”).
Concluding, loans are discounted because their book value may be higher than their
real value.
As far as concerns “Customer deposits”, the related β coefficient estimate is equal to
-0,582. These liabilities too, are not priced in line with their book value, therefore
market put a discount also upon this category. This is due to the fact that banks have
a benefit of gain by paying to customers interest rates on their deposits, that are
57
lower compared to the market interest rate. Thereafter at this latter interest rate,
banks do investments (issue loans). That is, financial institutions, in order to obtain
a gain, issue loans and invest themselves at a higher rate than the interest rate they
remunerate customers for doing business (opening deposits) with them. Market
participants perceive the benefit that banks obtain by offering low interest rates on
deposits, therefore they price this category with a discount.
Focusing on results for coefficients estimates of level 3 instruments (in the
regression equation, β3 and β8), remarkable is the highest value of liabilities
estimated coefficient, (almost equal to -1) with respect to coefficients of level 2 and
level 1. Of note is that level 1 and level 2 liabilities have very similar coefficients (and
very similar standard errors), suggesting that level 3 are considered far more
different with respect to the first two levels of the hierarchy.
As for the asset category (β1, β2, β3), differentials between level 1 and 2, and level 3
is smaller. Here coefficient estimates of level 1 and 2 are 0,707 and 0,687
respectively - once again they display very similar values - while level 3 coefficient is
higher (0,778) but not so high as for liabilities.
Of note is that level 3 standard errors are higher than standard errors of level 1 and
level 2, and also the significance test (p-value – “signif” column) shows that
estimates of level 3 are significant at a 92% level for liabilities (93% level for assets),
with a p-value of 0.08 (0.07 for assets).
The good significance level of all coefficients confirms the preliminary hypothesis
(H1) within a global 92% confidence level: all fair value instruments are value
relevant for investors’ purposes. Moreover, the predicted negative sign for liabilities
and positive for assets is in line with results. Coefficients, as predicted are not equal
to their theoretical value of 1 (-1), although level 3 liabilities coefficient is very close
to this value. However this proximity with the theoretical value (coefficient of level 3
liabilities is - 0,94) is to be interpreted cautiously, because of the higher standard
errors and p-values with respect to other variables.
However, this high coefficient estimate for level 3 would lead to not refusing also the
second hypothesis (H2). There is evidence of a good improvement in investors’
58
valuation of level 3 liabilities for the years 2010 and 2011: the coefficient is higher
than the coefficient related to level 1 and level 2, and it is close to the theoretical
value. Also level 3 assets present a good estimate (0.778), higher than coefficients of
level 1 and level 2. This improvement is supposed to be correlated with commitment
of ESMA and national enforcers and the issuance of IFRS enforcements, with
particular regard to level 3 fair value disclosures. As a consequence of enforcements,
entities disclose in detail each component related to level 3 instruments, in order to
improve reliability of this category.
4.2. Regression results (2)
Results of the second regression (2), which accounts for different year effects, are
briefly summarized below42:
R squared: 29%
Adj. R squared: 19%
VARIABLE Coefficients Signif.
Intercept 0,577 33%
YEAR -0,326 2%
LOANS/TBV 0,418 14%
AFV1/TBV 0,566 5%
AFV2/TBV 0,550 7%
AFV3/TBV 0,609 15%
OTHA/TBV 0,405 15%
DEP/TBV -0,449 13%
LFV1/TBV -0,546 7%
LFV2/TBV -0,551 7%
LFV3/TBV -0,856 12%
OTHL/TBV -0,491 9%
Table 9. Regression results (2).
42 Of note, YEAR attributes 0 to data gathered from 2010 annual reports, and 1 to data gathered from
2011 annual reports.
59
With the introduction of a dummy variable which accounts for different years, R2
and adjusted R2 of the model improve by 6%. The overall significance (F statistics)
improves too. This result implies the benefit of using multiple variable regression:
with the inclusion of new variables, total model explaining power is enhanced.
However, globally now coefficients are less significant with respect to the first
regression. Particularly, only dummy YEAR, level 1 and level 2 and OTHL present
significance at a 90% level.
Looking at point estimates, coefficients of “LOANS” and of “DEP” now present even a
higher discount, but their individual significance is lower with respect to the first
test (p-values are 0.14 and 0.13 respectively). Dummy YEAR presents a negative
value (-0,326), which would imply that, if all other coefficients were equal to 0, value
of the dependent variable (P/TBV) would be directly attributed only to the intercept
and to this negative value. In other words, in 2011, it is estimated a higher discount
of market values of banks, compared to 2010. In this case, no precise conclusions
can be drawn, since the intercept is not significant.
Coefficients of level 1 and level 2 instruments remain highly significant with respect
to other variables, in line with the first regression. Their point estimates remain
once more very similar (equal to about 0.55 both for assets and liabilities).
Significance of level 3 instruments, instead worsen and also point estimates are
lower. Specifically, for level 3 assets difference between related coefficient and level
1 and 2 coefficients is only of 5% (for level 3 liabilities, difference is of 30%).
60
4.3. Regression results (3)
Results of the third regression, which accounts for each year separately, and also for
the effects of return on tangible equity, are briefly summarized below:
R squared: 34%
Adj. R squared: 23%
VARIABLES Coefficients Signif.
Intercept 0,456 43%
ROTE 1,256 3%
YEAR -0,231 9%
LOANS/TBV 0,413 14%
AFV1/TBV 0,544 5%
AFV2/TBV 0,511 8%
AFV3/TBV 0,512 22%
OTHA/TBV 0,396 15%
DEP/TBV -0,441 13%
LFV1/TBV -0,511 9%
LFV2/TBV -0,509 8%
LFV3/TBV -0,864 11%
OTHL/TBV -0,477 9%
Table 10. Regression results (3).
Coefficient of ROTE is higher than 1 and its significance level is good (p-value 0.03).
This implies that “Return on tangible equity” is a good indicator for market
participants to settle a price on the entity.
LOANS and DEP confirm their discounts and present almost identical values of point
estimates and significance levels, similarly to regression (2). Dummy YEAR is also
significant at 91% level and confirms its negative corrective impact of the intercept.
Focusing on results about fair value assets and liabilities, is remarkable the
worsening of the significance related to level 3 assets compared to previous
regressions: the related p-value is equal to 0.22, thus it cannot be considered
significant at a 90% level, like all other fair value instruments. Level 1, level 2 assets
and level 3 liabilities have similar estimates and p-values to those found in
regression (2). Finally, level 1 and level 2 liabilities present a discount of 50%
61
(higher than in previous regressions) at a 91% and 92% significance level,
respectively.
Concluding, general results as far as concerns fair value instruments don’t change
drastically, with the exception of level 3 assets variable which turns to be
statistically not significant at a 90% confidence interval. On one hand, introduction
of a new variables improve general fitting of the model, on the other hand
coefficients (particularly those related to level 3) become individually significant at a
lower level.
4.4. Summary of regression results
Summarizing, coefficient estimates of level 1 and level 2 instruments are very close
one to each other in all three regressions. In order to gather approximate estimate
differentials across the three levels, the following table displays features of
coefficients of level 3 instruments, compared to level 1 and level 243.
ASSETS LIABILITIES
Regression Difference
between L3 and (L1 and L2)
Difference between L3 and
(L1 and L2)
(1) Higher than L1
and L2 of ~ 0,08
Higher than L1
and L2 of ~ 0,30
(2) Higher than L1
and L2 of ~ 0,05
Higher than L1
and L2 of ~ 0,30
(3) Like L2, lower
than L1 (but p-
value=0,22)
Higher than L1
and L2 of ~ 0,36
Table 11. Coefficient approximate differences.
Difference is evident for liabilities, level 3 presents a lower investors’ discount of
about 30% compared to level 1 and level 2 (thus, an improvement of the level 3
impact on market value of banks).
43 Results are presented in absolute values.
62
After the three tests, general conclusions can be drawn:
Further variables introduction improves the overall explaining power of the model
variance. Precisely, R2 improves by 10% in the third test compared to the first one.
Significance of individual variables however, generally performs better when
including less variables.
Loans and deposits are always discounted and ROTE proves a high positive impact on
market values.
Dummy YEAR has a negative estimated coefficient, therefore proves a market
discount for 2011, compared with 2010. Further precise conclusions cannot be
drawn, since the intercept is not significative. Therefore it is not possible to correctly
quantify this discount.
Level 1 and level 2 instruments have very similar coefficient estimates, with similar
standard errors. This result is in line with results found in the study run by Song et
al. (2010), and Goh et al. (2009).
Level 3 assets have the lowest significance value, suggesting that value relevance of
these instruments is lower than value relevance of other levels. However, out of the
first test, level 3 assets presented a significance level. With introduction of further
variables, related p-value worsened.
Level 3 liabilities instead, perform better and present a coefficient estimate very
close to its theoretical value (-1). Therefore, this would be evidence of higher
heterogeneity in investors’ valuation of fair value liabilities, than of assets. Putting it
differently, valuation of instruments is less similar across levels for liabilities than
for assets (although p-value related to the latter is higher).
Seems like ESMA and national enforcers have generated (through IFRS
enforcements) a positive impact on investors’ perception about level 3 liabilities,
which are supposed to be disclosed more in detail than prior to the issuance of such
enforcements. This result is partially in line with results found by De Martino G.
(2012). He initially finds value relevance evidence related to level 1 only. Moreover,
63
he even finds a negative impact of level 3 instruments on value of banks. But
secondly, with the introduction of a new variable, that accounted for enhanced
disclosures about level 3 instruments44, related coefficient turned out to be
significant and positive. So, enhanced disclosures about these instruments (which
are a direct consequence of IFRS enforcements) decrease the information
asymmetry and totally change the final outcome.
Regarding the two hypothesis of this research, as far as concerns liabilities, (H1)
(concerning the value relevance of fair value instruments) and (H2) (concerning the
improvement of investors’ pricing of level 3 after the issuance of enforcements) are
accepted at approximately 90% significance level. Related coefficient estimates are
higher than those of level 1 and level 2, and close to 1 (-1).
For assets, value relevance of level 3 is not verified in all three tests. An
improvement in pricing level 3 assets (H2) with respect to previous literature is
seen by looking at coefficient point estimates, but p-values of 0.15 and 0.22 may run
against the value relevance of these instruments.
A partial explanation for the lower value relevance of level 3 assets45, may be related
to the introduction of the new variable “Return on tangible equity”. ROTE is directly
and positively correlated with P/TBV (Bagna, 2012) and perhaps, it incorporates a
large share of significance (thus, value relevance) of level 3 assets. In other words,
when market participants perceive great importance of ROTE for their economic
decisions, it seems like they attribute level 3 assets no additional useful information.
Moreover, ROTE already accounts for changes in fair values46.
In any case, the high impact of level 3 liabilities (and its relatively good p-value) is
still remarkable, both in numerical terms and as far as concerns its economic
interpretation. It seems like IFRS Enforcements are a widely helpful instrument for
investors.
44 Disclosure regarding the “Movements in and out of level 3 category”.
45 Is to remember that in the first regression, level 3 assets were even more significant and value
relevant than level 3 liabilities.
46 Which, as a remainder, have to be recorded in the profit or loss statement.
65
CONCLUSIONS
In recent years, fair value accounting has acquired remarkable importance
compared to prior the adoption of international financial reporting standards.
Particularly, International Accounting Standards Board has judged that enhanced
disclosures concerning fair value financial instruments were needed, given their
complex nature and their weight, especially on balance sheets and income
statements of financial institutions. Starting from November 2009, European entities
disclose financial instruments in the notes of consolidated balance sheets, through a
three level hierarchy, that classifies fair value assets and liabilities according to the
nature of inputs (publicly observable or non observable) used to determine related
values. Particularly, top priority is given to observable market prices for identical
instruments (level 1). If market evolution cannot be considered regular,
determination of fair value is to be done by considering the best available
observable assumptions (level 2). Finally, if this is possible neither, private
management’s information along with internally developed fair value estimates are
used (level 3).
Literature has generally demonstrated the value relevance of instruments recorded
at fair value (that is, they provide useful information to investors for purposes of
entity valuation). Reliability of reported values is a more complex issue when
dealing with fair value accounting, especially with regard to fair value hierarchy.
Therefore, in order to ensure informativeness of reported values, were issued a
number of documents stressing the importance of proper disclosures and drawing
attention on precision and transparency, especially when disclosing level 3 (the
most opaque) assets and liabilities. These documents are the so called IFRS
Enforcements.
The aim of this thesis was to test whether purposes of IFRS enforcements,
concerning disclosures of fair value hierarchy, has had a positive impact on
investors’ valuation of a sample of European banks in 2010 and 2011.
66
This research, however presents some caveats. One element regards the gathering of
data. Financial institutions present financial information in many different ways,
within the sample. While some banks disclose instruments precisely, for some
others for instance, pooling financial instruments in order to classify them within
the hierarchy is not straightforward.
Another element is given by the reduction of the sample size, because not all banks
provided all necessary information. Therefore, increasing the sample size could have
provided individual variable significance at a higher confidence interval
Nevertheless, three different statistical tests were made and, regarding level 1 and
level 2 instruments, results show that their impacts on the market value of banks are
similar: both are value relevant and both are discounted by investors, of
approximately 30% (out of the first test) and of 50% (out of the second and the third
test).
As far as concerns level 3 liabilities, were found convergent results too. In other
words, the outcome of all three tests is coherent with the research hypothesis:
investors perceive level 3 liabilities as being value relevant, and price them with a
lower discount than prior to the issuance of IFRS Enforcements (while previous
literature found higher discounts of level 3 compared to discounts of the other two
levels, in the present research it is found a lower discount for level 3, compared to
the other two levels).
As far as concerns assets, related value relevance was not fully confirmed in two out
of the three tests. This result may partially be attributed to the “Return on tangible
equity” ratio, which accounts for a great share in bank valuations. Thus on the one
side, when investors account for this profitability indicator, in pricing of an entity,
while they account for level 1 and level 2 assets and liabilities and also for level 3
liabilities, they may not fully account for level 3 assets, despite IFRS enforcements.
On the other side, ROTE already incorporates elements of fair value, since gains and
losses of fair values have to be recorded in the income statement.
67
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APPENDIX
(1) P/TBV = α + β1*AFV1/TBV + β2*AFV2/TBV + β3*AFV3/TBV + β4*LOANS/TBV +
β5*OTHA/TBV + β6*LFV1/TBV + β7*LFV2/TBV + β8*LFV3/TBV + β9*DEP/TBV +
β10*OTHL/TBV + ε
Multiple R 0,4837230
Squared R 0,2339879
Corrected squared R 0,1275973
Std. Error 0,6036107
Obs. 83
VARIANCE ANALYSIS
DF SQ MQ F Signif. F
Regression 10 8,013166642 0,801316664 2,199329437 0,027233417
Residual 72 26,23290483 0,3643459
Total 82 34,24607148
VARIABLE Coefficients Std. Error Stat. t Signif. Lower 95% Upper 95%
Intercept 0,160 0,582 0,276 78% -0,999 1,320
LOANS/TBV 0,546 0,287 1,902 6% -0,026 1,119
AFV1/TBV 0,707 0,290 2,439 2% 0,129 1,284
AFV2/TBV 0,687 0,300 2,290 2% 0,089 1,285
AFV3/TBV 0,778 0,428 1,819 7% -0,075 1,631
OTHA/TBV 0,533 0,283 1,884 6% -0,031 1,096
DEP/TBV -0,582 0,301 -1,935 6% -1,181 0,018
LFV1/TBV -0,681 0,306 -2,227 3% -1,291 -0,071
LFV2/TBV -0,694 0,300 -2,311 2% -1,292 -0,095
LFV3/TBV -0,983 0,558 -1,762 8% -2,094 0,129
OTHL/TBV -0,623 0,291 -2,138 4% -1,203 -0,042
Table 12. Regression results – Equation (1).
72
(2) Pi/TBV i = α + β1*AFV1 i/TBV i + β2*AFV2 i/TBV i + β3*AFV3 i/TBV i + β4*LOANS i/TBV i +
β5*OTHA i/TBV i + β6*LFV1 i/TBV i + β7*LFV2 i/TBV i + β8*LFV3 i/TBV i + β9*DEPi/TBV i +
β10*OTHL i/TBV i + β11*YEAR + ε
Multiple R 0,5428154
Squared R 0,2946485
Corrected squared R 0,1853687
Std. Error 0,5832826
Obs. 83
VARIANCE ANALYSIS
DF SQ MQ F Signif. F
Regression 11 10,09055 0,917323 2,696276 0,00588334
Residual 71 24,15552 0,340219
Total 82 34,24607
VARIABLE Coefficients Std. Error Stat t Signif. Lower 95% Upper 95%
Intercept 0,577 0,587 0,984 33% -0,593 1,748
YEAR -0,326 0,132 -2,471 2% -0,589 -0,063
LOANS/TBV 0,418 0,282 1,481 14% -0,145 0,981
AFV1/TBV 0,566 0,286 1,983 5% -0,003 1,136
AFV2/TBV 0,550 0,295 1,862 7% -0,039 1,138
AFV3/TBV 0,609 0,419 1,453 15% -0,227 1,445
OTHA/TBV 0,405 0,278 1,458 15% -0,149 0,960
DEP/TBV -0,449 0,296 -1,519 13% -1,038 0,140
LFV1/TBV -0,546 0,301 -1,818 7% -1,146 0,053
LFV2/TBV -0,551 0,296 -1,863 7% -1,141 0,039
LFV3/TBV -0,856 0,541 -1,581 12% -1,935 0,223
OTHL/TBV -0,491 0,286 -1,713 9% -1,062 0,080
Table 13. Regression results – Equation (2).
73
(3) Pi/TBV i = α + β1*AFV1 i/TBV i + β2*AFV2 i/TBV i + β3*AFV3 i/TBV i + β4*LOANS i/TBV i +
β5*OTHA i/TBV i + β6*LFV1 i/TBV i + β7*LFV2 i/TBV i + β8*LFV3 i/TBV i + β9*DEPi/TBV i +
β10*OTHL i/TBV i + β10*ROTEi + β11*YEAR + ε
Multiple R 0,5837667
Squared R 0,3407836
Corrected squared R 0,2277751
Std. Error 0,5678980
Obs. 83
VARIANCE ANALYSIS
DF SQ MQ F Signif. F
Regression 12 11,67049905 0,972541588 3,015556366 0,00187011
Residual 70 22,57557242 0,322508177
Total 82 34,24607148
VARIABLES Coefficients Std. Error Stat t Signif. Lower 95% Upper 95%
Intercept 0,456 0,574 0,794 43% -0,689 1,601
ROTE 1,256 0,567 2,213 3% 0,124 2,387
YEAR -0,231 0,136 -1,707 9% -0,502 0,039
LOANS/TBV 0,413 0,275 1,503 14% -0,135 0,961
AFV1/TBV 0,544 0,278 1,953 5% -0,012 1,099
AFV2/TBV 0,511 0,288 1,774 8% -0,063 1,085
AFV3/TBV 0,512 0,410 1,247 22% -0,307 1,331
OTHA/TBV 0,396 0,271 1,463 15% -0,144 0,936
DEP/TBV -0,441 0,288 -1,531 13% -1,014 0,133
LFV1/TBV -0,511 0,293 -1,742 9% -1,095 0,074
LFV2/TBV -0,509 0,289 -1,763 8% -1,084 0,067
LFV3/TBV -0,864 0,527 -1,639 11% -1,915 0,187
OTHL/TBV -0,477 0,279 -1,711 9% -1,034 0,079
Table 14. Regression results – Equation (3).
74
LEVEL/TOT. FV ASSETS LEVEL/TOT. FV LIABILITIES
BANK A.L1/TOT A.L2/TOT A.L3/TOT L.L1/TOT L.L2/TOT L.L3/TOT
Banca Monte dei Paschi di Siena S.p.A. 49,19% 49,79% 1,02% 1,43% 98,40% 0,17%
Banca Popolare dell'Emilia Romagna S.C.A.R.L. 71,99% 15,57% 12,43% 0,02% 98,13% 1,85%
Banca Popolare di Milano S.C.A.R.L. 73,11% 19,43% 7,46% 6,13% 90,84% 3,03%
Banco Bilbao Vizcaya Argentaria S.A. 58,14% 40,69% 1,17% 12,49% 87,45% 0,06%
Banco de Sabadell S.A. 75,70% 22,03% 2,28% 0,00% 86,75% 13,25%
Banco Espirito Santo S/A 39,02% 59,86% 1,12% 0,00% 100,00% 0,00%
Banco Popolare S.C. 69,80% 26,72% 3,49% 59,40% 40,60% 0,01%
Banco Popular Espanol S.A. 77,75% 13,45% 8,80% 0,68% 62,88% 36,43%
Banco Santander S.A. 49,89% 49,36% 0,75% 11,08% 88,78% 0,14%
Bank of Ireland Ord Stk EUR0.64 73,79% 25,33% 0,88% 0,00% 96,59% 3,41%
Barclays PLC 11,86% 83,97% 4,17% 5,70% 92,57% 1,73%
BNP Paribas S.A. 35,96% 60,95% 3,09% 16,69% 78,65% 4,65%
Commerzbank AG 18,40% 79,60% 2,00% 9,54% 89,88% 0,58%
Credit Agricole S.A. 48,11% 49,85% 2,04% 8,56% 90,34% 1,09%
Credit Suisse Group AG 41,08% 50,24% 8,69% 25,47% 67,35% 7,18%
Danske Bank A/S 37,39% 61,70% 0,91% 65,23% 33,94% 0,83%
Deutsche Bank AG 11,80% 84,19% 4,01% 6,59% 91,90% 1,51%
DNB ASA 40,40% 35,87% 23,72% 0,00% 99,92% 0,08%
Erste Group Bank AG 43,53% 55,76% 0,71% 1,23% 98,75% 0,02%
HSBC Holdings PLC 43,66% 54,60% 1,74% 22,99% 74,57% 2,44%
Intesa Sanpaolo S.p.A. 60,93% 37,06% 2,01% 10,04% 88,90% 1,06%
Julius Baer Gruppe AG 50,40% 49,60% 0,00% 46,66% 53,34% 0,00%
Jyske Bank A/S 69,87% 29,30% 0,83% 1,77% 98,23% 0,00%
KBC Group N.V. 67,19% 28,22% 4,59% 12,83% 69,68% 17,48%
Lloyds Banking Group PLC 51,50% 45,51% 2,99% 1,31% 98,31% 0,37%
Mediobanca Banca di Credito Finanziario 59,68% 31,99% 8,33% 19,33% 67,37% 13,31%
National Bank of Greece S.A. 28,99% 69,59% 1,41% 0,23% 99,46% 0,30%
Natixis 32,83% 60,06% 7,11% 16,44% 82,97% 0,60%
Nordea Bank AB 26,10% 70,77% 3,13% 19,66% 79,20% 1,14%
Pohjola Bank Plc 65,48% 31,45% 3,07% 1,17% 96,11% 2,72%
Raiffeisen Bank International AG 52,48% 46,64% 0,88% 16,48% 83,52% 0,00%
Royal Bank of Scotland Group Plc 16,60% 81,40% 2,00% 5,53% 93,72% 0,75%
Skandinaviska Enskilda Banken AB 38,01% 59,96% 2,04% 31,61% 59,49% 8,90%
Societe Generale 42,83% 54,24% 2,93% 4,16% 89,50% 6,34%
Standard Chartered PLC 35,85% 62,54% 1,61% 9,64% 89,48% 0,88%
Svenska Handelsbanken A 59,64% 39,18% 1,17% 47,12% 52,78% 0,10%
Swedbank AB 24,01% 75,91% 0,08% 25,82% 74,17% 0,00%
Sydbank A/S 4,68% 93,71% 1,62% 0,12% 99,88% 0,00%
UBS AG 24,97% 71,50% 3,53% 8,17% 87,47% 4,35%
UniCredit S.p.A. 38,61% 56,56% 4,83% 10,38% 86,64% 2,98%
Unione di Banche Italiane SCpA 80,37% 18,01% 1,62% 18,79% 81,21% 0,00%
Table 15. First three columns: shares of level 1, level 2, level 3 assets on total fair value assets. Last
three columns: shares of level 1, level 2, level 3 liabilities on total fair value liabilities. (data from
2010 annual reports)
75
LEVEL/TOTAL FV ASSETS LEVEL/TOTAL FV LIABILITIES
BANK A.L1/TOT A.L2/TOT A.L3/TOT L.L1/TOT L.L2/TOT L.L3/TOT
Banca Monte dei Paschi di Siena S.p.A. 49,55% 49,56% 0,90% 3,70% 96,21% 0,09%
Banca Popolare dell'Emilia Romagna S.C.A.R.L. 75,55% 13,43% 11,02% 0,00% 98,53% 1,47%
Banca Popolare di Milano S.C.A.R.L. 67,85% 24,21% 7,94% 2,07% 94,99% 2,93%
Banco Bilbao Vizcaya Argentaria S.A. 49,30% 49,40% 1,30% 10,41% 89,55% 0,04%
Banco de Sabadell S.A. 77,99% 20,16% 1,85% 3,91% 83,27% 12,82%
Banco Espirito Santo S/A 30,26% 68,27% 1,46% 0,00% 100,00% 0,00%
Banco Popolare S.C. 64,73% 32,41% 2,86% 51,30% 48,70% 0,00%
Banco Popular Espanol S.A. 75,63% 15,96% 8,42% 0,00% 93,84% 6,16%
Banco Santander S.A. 40,76% 58,88% 0,36% 8,39% 91,47% 0,14%
Bank of Ireland Ord Stk EUR0.64 69,28% 27,78% 2,94% 0,02% 97,31% 2,67%
BANKIA S.A. 46,08% 46,80% 7,13% 1,86% 98,14% 0,00%
Barclays PLC 12,44% 83,54% 4,02% 4,30% 93,93% 1,77%
BNP Paribas S.A. 27,15% 69,63% 3,22% 14,43% 81,21% 4,36%
CaixaBank S.A. 64,50% 33,38% 2,13% 15,57% 84,39% 0,04%
Commerzbank AG 24,96% 72,81% 2,23% 8,27% 91,02% 0,71%
Credit Agricole S.A. 36,87% 61,08% 2,06% 6,69% 92,76% 0,55%
Credit Suisse Group AG 35,34% 55,93% 8,74% 22,19% 71,82% 5,99%
Danske Bank A/S 34,67% 64,05% 1,28% 54,74% 43,83% 1,43%
Deutsche Bank AG 9,53% 86,90% 3,57% 4,65% 94,05% 1,30%
DNB ASA 16,69% 66,29% 17,02% 0,00% 99,94% 0,06%
Erste Group Bank AG 42,63% 56,91% 0,47% 0,74% 99,24% 0,02%
HSBC Holdings PLC 38,80% 59,44% 1,75% 18,33% 80,01% 1,65%
Intesa Sanpaolo S.p.A. 57,80% 40,16% 2,04% 5,31% 93,48% 1,21%
Julius Baer Gruppe AG 62,41% 37,59% 0,00% 24,49% 75,51% 0,00%
Jyske Bank A/S 56,26% 42,83% 0,91% 1,05% 98,95% 0,00%
KBC Group N.V. 61,37% 30,05% 8,57% 13,15% 67,78% 19,07%
Lloyds Banking Group PLC 52,94% 43,91% 3,15% 3,85% 95,20% 0,95%
Mediobanca Banca di Credito Finanziario S.p.A. 61,14% 28,11% 10,74% 38,92% 45,44% 15,64%
Natixis 23,32% 72,81% 3,87% 12,29% 87,63% 0,08%
Nordea Bank AB 23,55% 74,57% 1,88% 14,63% 84,92% 0,45%
Pohjola Bank Plc 58,22% 39,35% 2,43% 0,69% 96,53% 2,77%
Raiffeisen Bank International AG 49,87% 49,52% 0,60% 4,84% 94,35% 0,81%
Royal Bank of Scotland Group Plc 14,44% 83,70% 1,86% 4,60% 94,56% 0,83%
Skandinaviska Enskilda Banken AB 35,19% 63,94% 0,87% 26,49% 71,50% 2,00%
Societe Generale 34,85% 62,86% 2,28% 3,73% 91,05% 5,23%
Standard Chartered PLC 29,81% 68,25% 1,94% 6,01% 93,58% 0,42%
Svenska Handelsbanken A 51,98% 47,47% 0,55% 39,08% 60,89% 0,04%
Swedbank AB 23,15% 76,80% 0,05% 27,52% 72,48% 0,00%
Sydbank A/S 3,61% 94,86% 1,53% 0,07% 99,93% 0,00%
UBS AG 19,41% 77,08% 3,51% 5,50% 90,70% 3,80%
UniCredit S.p.A. 26,74% 67,93% 5,33% 6,44% 90,42% 3,14%
Unione di Banche Italiane SCpA 75,57% 22,72% 1,71% 15,63% 84,37% 0,00%
Table 16. First three columns: shares of level 1, level 2, level 3 assets on total fair value assets. Last
three columns: shares of level 1, level 2, level 3 liabilities on total fair value liabilities (data from 2011
annual reports).
76
2010 2011
BANK FVA/TA FVL/TL FVA/TA FVL/TL
Banca Monte dei Paschi di Siena S.p.A. 23,04% 25,39% 23,20% 23,15%
Banca Popolare dell'Emilia Romagna 7,39% 5,30% 8,17% 7,81%
Banca Popolare di Milano S.C.A.R.L. 22,75% 3,73% 20,87% 5,84%
Banco Bilbao Vizcaya Argentaria S.A. 22,72% 7,86% 22,71% 10,01%
Banco de Sabadell S.A. 12,33% 1,27% 14,64% 1,53%
Banco Espirito Santo S/A 22,68% 15,70% 22,39% 19,56%
Banco Popolare S.C. 13,01% 24,74% 14,44% 24,60%
Banco Popular Espanol S.A. 10,69% 1,68% 9,69% 2,05%
Banco Santander S.A. 23,88% 18,03% 23,07% 17,01%
Bank of Ireland Ord Stk EUR0.64 19,22% 12,75% 16,51% 14,11%
BANKIA S.A. - - 27,10% 9,95%
Barclays PLC 46,71% 40,35% 50,95% 44,17%
BNP Paribas S.A. 52,97% 38,36% 52,03% 41,34%
CaixaBank S.A. - - 22,13% 5,62%
Commerzbank AG 39,06% 30,63% 36,63% 30,96%
Credit Agricole S.A. 41,60% 23,95% 43,58% 28,33%
Credit Suisse Group AG 56,52% 42,77% 51,49% 40,35%
Danske Bank A/S 53,03% 35,42% 58,15% 40,16%
Deutsche Bank AG 61,07% 46,37% 61,62% 48,94%
DNB ASA 29,12% 30,05% 32,14% 33,51%
Erste Group Bank AG 16,34% 4,56% 18,30% 5,86%
HSBC Holdings PLC 43,35% 28,15% 42,52% 29,14%
Intesa Sanpaolo S.p.A. 26,78% 12,76% 27,10% 13,52%
Julius Baer Gruppe AG 45,14% 18,50% 34,70% 13,21%
Jyske Bank A/S 38,57% 17,15% 35,55% 16,25%
KBC Group N.V. 33,87% 19,69% 28,07% 21,41%
Lloyds Banking Group PLC 25,20% 7,30% 25,03% 9,01%
Mediobanca Banca di Credito Finanziario S.p.A. 32,95% 9,79% 30,05% 11,84%
National Bank of Greece 15,65% 3,89% - -
Natixis 42,61% 36,46% 55,99% 47,96%
Nordea Bank AB 45,58% 35,56% 52,13% 39,98%
Pohjola Bank Plc 28,27% 6,08% 30,81% 8,92%
Raiffeisen Bank International AG 13,50% 7,92% 15,56% 10,18%
Royal Bank of Scotland Group Plc 54,08% 46,52% 58,55% 52,84%
Skandinaviska Enskilda Banken AB 23,06% 10,67% 22,89% 11,35%
Societe Generale 49,38% 33,20% 46,32% 34,97%
Standard Chartered PLC 28,23% 14,11% 28,80% 15,34%
Svenska Handelsbanken A 14,76% 10,04% 13,44% 9,29%
Swedbank AB 51,52% 26,44% 47,22% 22,91%
Sydbank A/S 46,24% 27,73% 48,19% 32,61%
UBS AG 52,71% 44,86% 50,23% 45,42%
UniCredit S.p.A. 23,25% 14,51% 25,22% 15,74%
Unione di Banche Italiane SCpA 10,51% 1,84% 9,34% 2,34%
Table 17. Shares of fair value assets (fair value liabilities) on total assets (total liabilities).
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