credit risk and ifrs

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Credit Risk and IFRS: The Case of Credit Default Swaps

Gauri Bhat*, Jeffrey L. Callen**, Dan Segal***

October 2011

*Olin Business School, Washington University in St. Louis, One Brookings Drive, MO

63105, USA; [email protected]

**Rotman School of Management, University of Toronto, 105 St. George St., TorontoOntario, Canada M5S 3E6; [email protected]

*** Arison School of Business, Interdisciplinary Center Herzliya, Israel 46510;

[email protected]

Acknowledgements

Bhat gratefully acknowledges the Center for Research in Economics and Strategy

(CRES) at the Olin Business School for financial support. Callen gratefully

acknowledges the Humanities and Social Sciences Research Council of Canada forfinancial support. We thank Nicole Jenkins, Richard Carizosa and workshop participants

at the University of Waterloo, University of Missouri at Columbia, WashingtonUniversity in St Louis, and participants at the 21st Annual Conference on Financial

Economics and Accounting and the 2011 Utah Winter Accounting Conference for helpful

comments.


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Credit Risk and IFRS: The Case of Credit Default Swaps

Abstract

Theory predicts that accounting information transparency affects credit spreads.Given that one of the putative benefits of International Financial Reporting Standards

(IFRS) is transparency of accounting information, motivates this study to evaluate the

impact of IFRS on the pricing of credit spreads in the over-the-counter Credit Default

Swap (CDS) market. Using a difference in differences methodology, with a matched

IFRS-U.S. sample, we find evidence that while earnings are a significant determinant of

credit risk, adoption of IFRS did not change the CDS-earnings relation. Because IFRS

induced transparency did not affect all firms in the same manner, we examine two

dimensions of transparency. First, at the country level, we examine institutional factors

that have been shown to affect the quality of accounting information. We find that

institutional factors have an impact on the CDS-earnings relation but there is no change

in the CDS-earnings relation as a result of IFRS adoption. Second, we examine the

impact of IFRS on firms with low earnings versus firms with high earnings. Consistent

with the prior literature, we find that the CDS-earnings relation is non-linear; earnings are

more credit risk informative for firms with low earnings. However, we find no change in

the CDS-earnings relation in the pre and post adoption periods across levels of earnings.

Key words: Credit Default Swaps, Credit Risk, IFRS

JEL Classification: M40, M41, G13, G20

Data Availability: All data are publicly available.


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1. Introduction

This study evaluates the impact of International Financial Reporting

Standards (IFRS) on the pricing of credit risk in the over-the-counter Credit Default

Swap (CDS) market. The CDS is essentially a pure credit default instrument that

provides a far less noisy measure of credit risk by comparison to corporate bonds or

credit ratings. The intent of this study is to compare the credit risk information conveyed

by IFRS earnings relative to local GAAP earnings for countries which adopted IFRS. Our

study is guided by the theoretical CDS pricing models of Merton (1974) and Duffie and

Lando (2001). The latter model provides justification for the relevance of noisy

accounting information as a determinant of CDS prices. Although accounting plays no

role in the former, the Merton model motivates the need to control for other determinants

of CDS spreads. We focus our analysis on accounting earnings as a proxy for the firms

uncertain wealth and noisy asset dynamics. To the extent that earnings are a meaningful

proxy, the hybrid CDS pricing model of Duffie and Lando (2001) predicts an inverse

relation between CDS spreads and earnings. Indeed, Callen, Livnat, and Segal (2009)

show that U.S. GAAP earnings are an important determinant of CDS spreads. In their

levels analysis, they find that a one percent increase in earnings (normalized by total

assets) reduces CDS spreads by a non-trivial nine percent.

We measure the informativeness of earnings with respect to credit risk by the

earnings coefficient in a regression of CDS spreads on normalized earnings controlling

for other potential determinants of the spread. The more negative the coefficient, the

more informative the credit risk information conveyed by earnings. We use this

coefficient to test the relation between financial statement variables and CDS spreads,

rather than simple correlations, in order to allow for a richer, more powerful analysis.1

In their seminal study, Duffie and Lando (2001) show theoretically that CDS

spreads are negatively correlated with the transparency of accounting information

1Simple correlations potentially obfuscate the transition to IFRS if only because the volatility of many

variables increased in the post-IFRS period consistent with the focus of IFRS on fair value accounting

(Barth, Cram, and Nelson 2001).


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because transparency reduces the noise about the reference firms asset structure and

wealth dynamics. The adoption of IFRS provides a unique research opportunity to

examine the impact of accounting information transparency on the relation between

credit risk and financial statement information, particularly earnings, because the switch

to IFRS for most firms was exogenously mandated by accounting regulators, thereby

mitigating the potential impact of confounding endogenous events.2 In addition, a

relatively large number of firms had to switch to IFRS, thereby providing a reasonable

sample size for testing purposes.

Earnings under IFRS are potentially more transparent than the earnings under

previous local GAAP for the following reasons. First, IFRS is principles based rather

than rules based. Thus, not only does IFRS discourage financial statement engineering

but it encourages companies to adapt their reporting to changes in the business

environment, so that financial information under IFRS better reflects economic reality.

Second, the reporting requirements under IFRS are more comprehensive than those under

many local GAAPs, especially for cash flows, pension obligations, leases and liabilities

of uncertain timing and amount. Cash flow information, in particular, is helpful to

creditors to assess whether the firm will be able to generate sufficient cash flows to

service its debt.3 Third, IFRS emphasizes greater use of fair value accounting than most

of the local GAAPs. Fair value information provides early warnings to investors and

regulators of changes in current market expectations, especially in an environment

characterized by decline in asset prices and increase in risk. These early warning signals

are particularly relevant in the analysis of credit risk because under historical cost, the

recognition of decline in asset prices depends on management discretion and the extent of

conditional conservatism (Linsmeier 2011). Finally, one set of standards across countries

promotes uniformity and comparability, and together with the increased transparency

should allow for better assessment of credit risk. To quote the credit rating agencyMoodys:

2 Our sample excludes firms that adopted IFRS voluntarily before it was mandated.3Firms in Italy and Spain were not required to include a cash flow statement in their annual reports prior tothe adoption of IFRS.


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the adoption of European commission-endorsed IFRS from 2005 by nearly all

publicly traded companies incorporated in the European union should improve the

efficiency and transparency of Moodys analytical processes. (Moodys. Special

Comment. 2004)

However, any discretion accompanying a principles based approach is bound to

be plagued by inconsistent interpretation and application, and potentially compromises

comparability. Indeed, the International Accounting Standards Board (IASB) recognizes

that allowing choices in accounting methods hinders comparability. Furthermore, any

increase in accounting information transparency as a result of IFRS adoption is

potentially contingent on country level institutional factors that complement accounting

standards and shape financial reporting incentives. The extant prior literature documents

that institutional factors are associated with the quality of accounting information (e.g.

Ahmed, Neel, and Wang 2010, Chen, Tang, Jiang and Lin 2010, Alford, Jones, Leftwich

and Zmijewski 1993, Ali and Hwang 2000, Bartov and Goldberg 2001, Bushman and

Piotroski 2006, Ball, Kothari and Robin 2000). Thus, we examine the impact of variation

in institutional factors, including the system of laws (code vs. common law a proxy for

quality of financial statement information)4, the quality of securities law enforcement, the

pervasiveness of earnings management, the extent of differences between local GAAP

and IFRS, and the country-level degree of conservatism, measured by differential

timeliness,5 on the relation between CDS spreads and earnings.

We perform three distinct tests to examine the impact of IFRS on the credit risk

informativeness of earnings. First, we examine the association of earnings with CDS

spreads under both local GAAP (i.e. pre-IFRS) and IFRS, and test whether the adoption

4Code-law accounting affords managers more latitude in timing income recognition thereby obfuscating

the economic performance of the firm. For example, firms create earnings reserves in good years through

excessive impairment charges and provisions, and use these reserves in bad years. The low quality ofaccounting information is attributed primarily to governance practices in code-law countries which are

based on the stakeholder model, whereby the different stakeholders such as employees, government,

lenders and shareholders are able to affect accounting choices. In particular, one of the main accounting

incentives of the various stakeholders is to reduce the volatility of net income, thereby creating a strong

incentive to smooth earnings (Ball, Kothari and Robin 2000).5 While differential timeliness (DT) is also related to country characteristics such as code vs. common law

(Ball, Kothari and Robin 2000, Bushman and Piotroski 2006) and strength of securities law enforcement

(Bushman and Piotroski 2006), we treat DT as an independent feature of the accounting system.


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of IFRS changed the association between these variables. Second, we examine whether

the association of earnings with CDS spreads subsequent to the adoption of IFRS

depends on the country level institutional factors described above. Third, we also test for

the potential asymmetry (non-linearity) of CDS spreads to high versus low profitability in

light of the evidence in Callen, Livnat and Segal (2009) of a non-linear relation between

CDS spreads and profitability.

We gauge the impact of IFRS on the credit informativeness of earnings using

three samples of firms referenced in CDS contracts: a sample of firms that adopted IFRS;

a sample of U.S. firms; and a matched sample consisting of both IFRS adopters and U.S.

firms. The latter sample allows us to control for potential time-period effects (Meyer

1995, Bertrand, Duflo, and Mullainathan 2004) and other potential correlated omitted

variables (Cram, Karan and Stuart 2009).

Consistent with the findings in Callen, Livnat and Segal (2009) and Das,

Hanouna, and Sarin (2009), we find that earnings are informative about credit risk in

IFRS countries both before and after the adoption of IFRS. However, there is no

statistical difference in the pre and post adoption period, indicating that IFRS had no

impact on the credit informativeness of earnings. These results are robust to the inclusion

of other determinants of credit risk, namely size, leverage, credit rating, volatility of stock

returns, risk free interest rates, and quarter, industry and country fixed-effects. The results

of our second set of tests which condition on institutional factors show that while

earnings are (not) informative about credit risk in pre and post adoption periods in

common (code) law countries, in countries with strong (weak) legal enforcement, in

countries with low (high) earnings management, and in countries with low (high)

differences between local GAAP and IFRS, the adoption of IFRS did not change the

CDS-earnings relation. The results of our third test confirm the asymmetry (non-linearity)

in CDS spreads to high and low earnings, but there is no change in the post-IFRS period

compared to the pre-IFRS period.

We contribute to the extant literature relating to IFRS by studying the impact of

the adoption on the credit markets. The prior literature in this area predominantly focuses


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on the equity markets (Hail and Leuz 2007), but the credit market is no less important for

several reasons. First, the information needs of the equity market may differ from those

of the credit market. Exclusive reliance on the equity market to quantify the impact of

IFRS ignores the fact that the credit market represents a significant source of financing

for the public firms. The CDS market, which is a subset of the credit market, is a multi-

trillion-dollar market. Second, we provide empirical evidence to show whether adoption

of IFRS is potentially significant for creditors and credit rating agencies whose primary

concern is downside risk. Finally, this paper provides additional evidence on the credit

informativeness of earnings using an international sample. While prior literature

discusses the significance of accounting for the credit markets, the empirical evidence on

the credit informativeness of earnings is fairly limited.6 In what follows, Section 2

provides the literature review and develops the hypotheses based primarily on the Merton

(1974) and Duffie-Lando (2001) models of CDS pricing. Section 3 describes the data.

Section 4 presents the empirical results. Section 5 concludes.

2. Literature Review and Hypotheses Development7

The extant research on IFRS adoption is concerned almost exclusively with equity

markets (see for example Daske, Hail, Leuz and Verdi 2007 and Li 2010). But, as the

financial crisis of 2008 has shown, debt markets are no less crucial than equity markets

for the functioning of the financial system in general and the financing of public

corporations in particular. The issue that we address is whether IFRS conveys better

information about the firms credit risk, proxied by the level of CDS spreads, than local

GAAP. In theory, the credit risk information conveyed by IFRS earnings could be

evaluated through debt markets, such as the corporate bond market, rather than through

the CDS market. In the absence of arbitrage opportunities, contractual features (such as

embedded options, covenants, and guarantees), and market frictions, the CDS spread and

6 The information needs of the credit market help to shape the properties of the accounting numbers (Watts

2003) especially because the primary influence on financial reporting comes from debt markets, not equity

markets (Ball 2006).7 We do not summarize the burgeoning literature on IFRS and equity markets in this study.


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the corporate bond yield spreadthe difference between the bond yield and the risk-free

rateare necessarily identical for floating rate corporate debt (Duffie 1999).

Nevertheless, it is precisely because of these latter factorscontractual features and

market frictionsthat CDS instruments offer several advantages over corporate bonds

(and other debt instruments) for analyzing the determinants of credit risk. First, the

finance literature has shown that corporate bond spreads include factors unrelated to

credit risk, such as systematic risk unrelated to default (Elton, Gruber, Agrawal and Mann

2001) and especially illiquidity (Longstaff, Mithal and Neis 2005). Huang and Huang

(2002) conclude that less than 25 percent of the credit spread in corporate bonds is

attributable to credit risk. Second, interest rate risk drives fixed-rate corporate bond yields

for fixed rate debt quite independently of credit risk. Third, in contrast to CDS

instruments, corporate bonds are replete with embedded options, guarantees, and

covenants. Heterogeneity in these features potentially distorts the relationship between

accounting numbers and credit risk in cross-sectional studies. Even more problematic is

that they may generate a spurious relation between earnings and credit risk. For example,

the positive relation between earnings and corporate bond prices could be driven by

earnings-based covenants rather than by credit risk per se. With lower earnings, earnings-

based covenants are more likely to be binding, increasing the probability of technical

bankruptcy and concomitant expected transactions (renegotiation) costs, thereby leading

to reduced bond prices. In contrast, except in rare cases, technical default is not a credit

event in CDS contracts and thus has little impact on CDS spreads. Fourth, the available

empirical evidence indicates that credit risk price discovery takes place first in the CDS

market and only later in the bond and equity markets (Blanco, Brennan and Marsh 2005;

Zhu 2006; Daniels and Jensen 2005;Berndt and Ostrovnaya 2008). The bond markets

lagged reaction potentially distorts empirical studies relating earnings to bond prices.

Fifth, unlike corporate bond yield spreads, no benchmark risk-free rate need be specified

for CDS spreads minimizing potential misspecification of the appropriate risk-free rate

proxy(Houweling and Vorst 2005). Sixth, CDS rates are closely related to the par valueof the reference bond, whereas corporate bond values (including their taxability

characteristics) are affected by coupons. Heterogeneity in coupon rates potentially


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distorts the relationship between earnings and credit risk in cross-sectional studies.

Finally, bond yield spreads are affected by tax differentials in bond pricing. Elton et al.

(2001) document a tax premium of 29 to 73 percent of the corporate bond spread,

depending on the rating.8

2.1 Earnings and CDS Spreads

Of the information provided by financial statements that relates to credit risk, we

focus our analysis on earnings. Earnings can be used by investors to estimate the

reference entitys economic performance and true asset (wealth) dynamics, another

important determinant of credit risk. More specifically, increased profitability of the firm,

as measured by current accounting earnings, should reduce its credit risk since, with

increased profitability, the reference entity is wealthier and less likely to default.

Moreover, accounting studies have shown that current earnings are a good predictor of

future earnings (Finger 1994; Nissim and Penman 2001), future cash flows (Dechow,

Kothari and Watts 1998; Barth, Cram, and Nelson 2001) and firm equity performance

(Dechow 1994). In other words, an increase (decrease) in earnings portends an increase

(decrease) in current and future operating and equity performance and, hence, a reduced

(increased) probability of bankruptcy. Also, earnings comprise a significant portion of the

short-term change in firm assets (via clean surplus) and, therefore, provide information to

investors about the firms asset and wealth dynamics, crucial variables in the estimation

of credit risk (Duffie and Lando 2001). Consistent with these arguments, Callen, Livnat

and Segal (2009) and Das, Hanouna and Sarin (2009) show earnings to be an important

determinant of CDS spreads, indicating that earnings provide relevant information in

assessing firm wealth and asset dynamics.

8 One may still be tempted to argue that since CDS instruments are derivatives whose price depend on the

value of the underlying debt, the role of accounting information in determining the CDS spread is

unclear because the prices and volatilities of the underlying financial instruments are observable.

However, as shown by Duffie and Lando (2001), even noisy accounting information is relevant for the

pricing of CDS because accounting provides information about the firms wealth and asset dynamics and,

hence, about the probability of the occurrence of credit events such as bankruptcy. Also, unlike equities,

even the largest corporate bonds do not trade very often and bond prices are often not observable. In

addition, published bond prices are often stale or simply interpolated.


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Although Callen, Livnat and Segal (2009) and Das, Hanouna and Sarin (2009)

show that earnings are relevant for assessing credit risk, the findings in these studies are

based primarily (but not exclusively) on U.S. reference entities employing U.S. GAAP.

Given the relatively high quality accounting standards together with effective regulation

and securities laws enforcement in the U.S., one cannot generalize these findings to an

international setting where countries differed in their accounting standards prior to IFRS

adoption, and differ subsequently in securities law enforcement, quality of financial

information, and extent of earnings management. These considerations lead to our first

hypothesis:

H1a: CDS spreads are negatively correlated with earnings both pre- and post-

IFRS adoption.

Proponents of IFRS argue that a single set of high quality accounting standards

enhances the transparency and comparability of financial statements. IFRS requirements

are potentially important for evaluating the firms credit risk because they are likely to

result in more transparent statements that provide better information about the firms

ability to pay back its debts. Examples of some of these IFRS requirements include:

comprehensive reporting on cash flows; disclosure of significantly more information

about pension obligations than under local GAAP; disclosure of scale and extent of off-

balance-sheet obligations such as leased assets; information about changes in the amounts

set aside for liabilities of uncertain timing or amount; consolidation of special purpose

entities used for securitization transactions when the substance of the relationship

indicates that they are controlled by the transferor of the asset; and stricter rules for gain

recognition from asset securitization, resulting in more frequent treatment of

securitization as financing transactions. More transparent statements are also likely to

provide debt markets with better estimates of the potential for the firm to recover from a

credit event such as bankruptcy and better estimates of the amounts that debt holders

might recover in case of bankruptcy. Enhanced comparability should allow for easier

and/or better assessment of credit risk, especially when the reference entity and the firm

ensuring itself against the credit risk of the reference entity via CDS instruments reside in

different local GAAP jurisdictions.


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The [limited] evidence in the literature appears to support these conjectures.

Specifically, Beneish, Miller and Yohn (2009) find that IFRS adopting countries attract

more debt investment, and have a lower extent of debt home bias. They also find that

their result is contextual and is driven by adopting countries that have weaker investor

protection and higher financial risk. They argue that their findings indicate that IFRS

adoption reduces the agency costs of debt in countries with less developed investor

protection and greater financial risk, consistent with IFRS providing more transparent

information. Florou and Kossi (2009) examine whether the mandated introduction of

IFRS affects the source and cost of corporate debt. They find that mandatory IFRS

adopters are more likely to issue public bonds than to borrow privately. They also find

that mandatory IFRS adopters pay lower bond yield spreads but not in the case of private

loans. They argue that their findings are consistent with IFRS enhancing the quality and

comparability of accounting information. Their findings also suggest that mandatory

IFRS adoption is especially beneficial for public bond investors, who rely much more on

financial statements and have much less monitoring and renegotiating privileges

compared to private lenders. Finally, they show that the positive consequences of IFRS

for debt financing are present only in countries with stricter enforcement rules, higher

control of corruption and lower financial risk. Christensen, Lee and Walker (2009) show

that mandatory IFRS affects debt contracting. Their results suggest that as a result of the

change to IFRS, the likelihood of debt covenant violations increases, requiring costly

renegotiations between lenders and debtors. These arguments lead to the following

hypothesis:

H1b: CDS spreads are more negatively correlated with earnings post-IFRS

adoption than pre-IFRS adoption.

2.2 Institutional differences, Conditional Conservatism and CDS Spreads

Skeptics often question whether IFRS can credibly provide more transparent

information and raise concerns that the one size fits all approach simply distorts

economic, political and regulatory differences among firms in different jurisdictions.

Furthermore, the implementation of IFRS crucially depends on the effectiveness of


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regulation which is likely to depend in turn on the underlying economic and political

institutions influencing the incentives of the managers and auditors responsible for

financial statement preparation (e.g., Ball, Robin and Wu 2003).

Empirical evidence highlights the role of institutions in moderating the impact of

IFRS adoption on economic constructs. Specifically, Daske et al. (2007) and Li (2010)

show that IFRS has an impact on cost of equity and valuation only in countries with

strong enforcement of securities laws. In a recent working paper, Kosi, Pope and Florou

(2010) find a significant increase in the credit relevance of financial statement

information to firm ratings for mandatory IFRS adopters. The increase is greater than for

a matched sample of U.S. firms, and is more pronounced in countries with strong

enforcement regimes and higher discrepancies between local standards and IFRS. Thus,

IFRS may not improve the accounting quality of credit risk relevant information

uniformly across firms and countries because of additional factors such as legal and

political systems and incentives of financial reporting (Soderstrom and Sun 2011).

Earnings opacity, the reverse of earnings transparency, is a result of complex

interactions among managerial motives, accounting standards, and enforcement of

accounting standards. On one hand managers may intentionally manipulate earnings

leading to less transparent earnings, while on the other hand less transparent earnings

may be a result of lax enforcement or poor accounting standards. Because managements

motives are not observable, earnings transparency at the country level is inherently

difficult to measure. We gauge accounting information transparency at the country level

using factors that affect the quality of the accounting information. Specifically, we use

the following factors in the analysis: origin of the legal system, level of enforcement,

level of earnings management, degree of conditional conservatism, and a measure which

quantifies differences between local GAAP and IFRS.

Accounting standards in code law countries are primarily influenced by

governmental priorities. Political influence on accounting standard setting in code law

countries makes accounting earnings a measure by which to divide profits among various

stakeholders such as governments, shareholders, banks, and labor unions (Ball, Kothari


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and Robin 2000). As a consequence, earnings in code law countries are arguably less

informative about credit risk than earnings in common law countries both pre and post

IFRS adoption.

Enforcement of the legal system also affects accounting quality directly, through

enforcement of accounting standards and litigation against managers and auditors. This

enforcement role of legal systems is especially important in the context of IFRS for two

reasons. First, the IASB issues IFRS but does not have the power to enforce the proper

application of the standards. It is the local legal system of each country where firms are

listed that is responsible for enforcement (Schipper, 2005). Second, IFRS are principles-

based, which means that auditors and accountants need to use judgment and discretion

rather than detailed standards in order to adapt these principles to specific situations

(Ball, 2006). Thus, earnings in countries with high legal enforcement may be more

reflective of credit risk than earnings in countries with low legal enforcement pre and

post IFRS adoption.

Earnings management is a measure of earnings opacity, the opposite of earnings

transparency. While it is difficult to measure earnings transparency directly at the country

level, we focus on distributional properties of reported accounting numbers across

countries and across time as captured by the Leuz et al. (2003) measure of earnings

management at the country level. We focus on this measure because past literature has

identified the existence of earnings management as weakening the link between

accounting performance and the true economic performance of a firm. Thus, one would

expect earnings in countries with high earnings management to be less informative about

credit risk than earnings in countries with low earnings management.

We use the difference between local GAAP and IFRS at the country level to

identify countries which stand to benefit the most from the transition to IFRS. If thedifference between the local GAAP and IFRS is large, the IFRS induced transparency

would be high for that country, the underlying assumption being that IFRS is more

transparent.


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Conditional conservatism should be of particular importance to CDS investors

(and bondholders) who are far more concerned with earnings decreases than earnings

increases (Callen, Livnat and Segal 2009). As emphasized by Watts (2003), conditional

conservative accounting is demanded by debt holders because it reduces managements

ability to artificially increase earnings and asset values. Specifically, when future

negative cash flow shocks are anticipated, conservative accounting requires the firm to

recognize future losses immediately in income, resulting in a concomitant reduction in

asset values. Hence, the degree of conditional conservatism has a direct impact on the

informativeness of earnings in assessing credit risk. The more conditionally conservative

the firm, the more likely is the firms accounting to act as a trip wire regarding

anticipated future negative cash flow shocks that may reduce the firms ability to pay

back its debt. Thus, one should expect ex ante that the more conservative the firm, the

more informative is its earnings about firm credit risk and, hence, the more negative the

relation between earnings and CDS spreads.

The relation between conditional conservatism and IFRS is complex. On one

hand, one of the major characteristics of IFRS is their anti-conservative nature. The IASB

has explicitly rejected the notion of conservatism in accounting and indicated a

preference for neutral accounting (Watts 2003). This is evident in several IFRS standards

that reduce conservatism by design. For instance, IFRS disallow the completed contract

method, allow for fair value accounting for investment properties, allow for impairment

reversals, and for the revaluation of PP&E and biological assets. Indeed, Ahmed, Neel

and Wang (2010) show that firms exhibited more conservative accruals and more timely

loss recognition in the pre-adoption period. Thus, if IFRS yield financial statements that

are less conservative by comparison to local GAAP, we should expect a weaker inverse

relation between credit spreads and IFRS earnings relative to local GAAP earnings. On

the other hand, the use of fair value accounting under IFRS provides an early warningsignal of declining asset prices and, hence, credit risk. The induced transparency under

IFRS could potentially have a more significant impact on firms who are less conservative

under local GAAP because of the faster recognition of losses under fair value accounting.

These considerations lead to our next set of hypotheses:


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H2a: The relation between CDS spreads and earnings, both pre- and post-

IFRS adoption depends on country-wide institutional differences such as code

law versus common law, regulation enforcement, earnings management, local

GAAP-IFRS differences and conditional conservatism.

H2b: Change in the association between CDS spreads and earnings following

the adoption of IFRS depends on country-wide institutional differences such as

code law versus common law, regulation enforcement, earnings management,

local GAAP-IFRS differences and conditional conservatism.

Although the IFRS literature tend to find that institutional factors affect the

quality of earnings, a caveat is warranted regarding the effect of institutional factors on

the credit risk informativeness of earnings. The extant empirical literature which looks at

the relation between institutional factors and earnings quality focuses on the equity

markets as end-users. These findings do not necessarily extend to credit markets in

general or to the credit risk informativeness of earnings (as measured by CDS spreads) in

particular.

2.3 Non-linearity in CDS Spreads and the earnings relation

While earnings may provide credit risk relevant information in general, the non-

linear payoff function of debt holders, and by extension CDS holders, suggests that CDS

prices will have a stronger reaction to accounting information that presages potential

bankruptcy than to information that presages additional profits. Extant evidence of such

nonlinearities in the corporate bond market is mixed. Datta and Dhillon (1993) find that

corporate bond yields do not react more to (unexpected) losses than to (unexpected)

profits whereas Easton, Monahan and Vasvari (2009) find just such an asymmetry. More

related to our study, Callen, Livnat and Segal (2009) provide evidence supporting this

non-linearity in the CDS market; they show that CDS spreads react more to earnings of

firms with low profitability.9

Following the discussion above related to the differences

9 As pointed out by Callen et al. (2009), firms that reference CDS instruments tend to be large and

successful with few loss quarters. Therefore, we measure asymmetry with reference to the median level of

earnings rather than profits and losses.


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between the U.S. and the international setting, one cannot simply generalize the U.S.

findings on the non-linear relation between CDS prices and earnings to the international

milieu. These considerations lead to our last set of hypotheses:

H3a: The relation between CDS spreads and earnings, both pre- and post-

IFRS adoption, is greater in absolute value for firms with earnings below the

median than for firms with earnings above the median.

H3b: Change in the association between CDS spreads and earnings following

the adoption of IFRS is greater in absolute value for the firms with earnings

below the median than for firms with earnings above the median.

3. Sample Data and Univariate Empirical Results

CDS data, currency exchange rates and interest rates are collected from Thomson

DataStream Navigator. Thomson has Credit Market Analysis (CMA) data covering CDS

contracts for 70 countries from 2003 through 2008 (see Panel A of Table 1). Deleting

CDS indices and keeping CDS contracts of reference entities in countries which adopted

IFRS results in an initial sample of 1,132 firms. We match each of the tickers on

Thomson Financial to obtain financial statement variables. We find a ticker match for

782 firms, out of which 392 are U.S. firms and 390 are firms from across 40 other

countries. Out of these 390 firms, we identify 211 firms in 17 countries that adopted

IFRS in 2005.

For each firm-quarter we obtain the price of CDS contracts with 5-year maturity

issued 45 days after the fiscal-quarter-end. If there are no CDS contracts issued on the

45th day after fiscal quarter-end, we utilize the first CDS contract issued in the range

from 42 to 48 days after the quarter-end. The spread for each CDS contract is derived

from mid-market quotes contributed by investment banks and default-swap brokers. For

each CDS contract, we collect data on its seniority (senior or subordinated), and the

currency of the underlying debt, which in turn determines the currency of the CDS


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contract.10

We convert the CDS quotes to U.S. dollars (USD) using the exchange rate as

of the CDS quote date. We obtain quarterly financial statement data required to compute

market value of equity, ROA, and leverage from the Worldscope database. We download

the financial information in USD wherever available; otherwise, we convert the variables

to USD using the exchange rate as of the fiscal quarter-end. When available we use short-

term credit ratings from S&P to proxy for credit ratings; otherwise, we use long-term

credit ratings. Variable definitions are listed in Appendix A.

We impose the following restrictions on the sample: positive leverage (measured

as short term debt plus long term debt scaled by market value of equity plus total

liabilities), non-missing value for each of the following: market value of equity, return on

assets (computed as income before extraordinary items scaled by total assets), standard

deviation of stock returns (computed on a rolling basis using the most recent 12 monthly

returns with at least 6 data points), and credit rating. In addition, all sample CDS

contracts in this study are limited to senior debt both because there are very few junior

CDS contracts in our initial sample and because their pricing determinants are very

different from senior contracts. Also, given the paucity of voluntary adopters in our data,

we eliminate voluntary adopter observations from our sample. These restrictions reduce

the sample size to 1,699 firm-quarters of 155 firms across 17 countries. For our control

sample, we find 5,295 firm quarters of 308 U.S. firms, which meet the same criteria

above. Finally, we remove firm-quarters for which we could not find a U.S. match (see

below), and end up with a matched sample of 1,663 CDS contract quarters for the two

groups, namely, a treatment group of 155 IFRS firms and a control group of 279 U.S.

firms. To mitigate the effect of outliers, all continuous variables are winsorized at the top

and bottom 1%.

Table 1, Panel A summarizes the sample selection and distribution of the IFRS

and U.S. samples. Panel B lists the number of firms and the number of firm-quarter

observations pre- and post-IFRS adoption by country. Most of the data are from six

countries: Germany, the U.K., France, Sweden, Italy and Australia. The data also indicate

10 Restructuring clauses are only available from 2008. As a result we do not control for this variable in the

analysis.


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that there are significantly more CDS contracts and, hence, sample data, post-IFRS

adoption by comparison to pre-IFRS adoption. This is consistent with the world-wide

secular increase in CDS usage.

(Insert Table 1 about here)

To alleviate concerns regarding time series effects and potential correlated

omitted variables (Meyer 1995, Bertrand, Duflo, and Mullainathan 2004), we use a

matched sample for our empirical tests, where each IFRS CDS observation is matched

with a U.S. CDS observation. We match each IFRS firm-quarter in the sample with a

U.S. domiciled firm-quarter utilising propensity score matching. For each fiscal quarter,

we estimate the propensity score using CDS determinants: profitability, size, leverage,

credit ratings and stock return volatility. We select the U.S. match based on the closest

propensity score without replacement. Our final matched sample comprises 1,132 firm

quarters in the post period and 531 firm quarters in the pre period.


Table 2, Panel A presents descriptive statistics of the main variables used in the

analysis for the IFRS and U.S. matched samples by pre- and post-IFRS adoption periods.

These variables include the CDS spread (CDS_PRM), Return on Assets (ROA), Log

Market Values (SIZE), Leverage (LEV), Standard Deviations of Returns (SD_RET), the

countrys risk-free rate of interest (SPOT), and S&P firm ratings (RATEN). CDS spreads

are significantly higher in the post adoption period for the IFRS firms and the U.S. firms,

indicating an increase in the average level of credit risk. Possible explanations for these

results include the overall impact of the credit crisis and the increase in the number of

reference entities in the post-IFRS period. Comparing the means and the medians by

period, the data show significant variation between the pre and post adoption periods for

IFRS firms as well as U.S. firms for the other variables. Specifically, IFRS firms are

larger in the post period, are more profitable, have lower leverage, higher quality credit

rating (lower credit rating number) and experience greater volatility in equity returns. In

addition, interest rates are also higher in the post period. The U.S. firms exhibit a similar

pattern in all variables. Despite the fact that we have matched the two groups using


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propensity score on profitability, size, leverage, credit ratings and stock return volatility

for each firm quarter, we find that the mean ROA differs significantly between the IFRS

firms and U.S. firms in the pre and post period, and the mean SIZE and LEV differ in the

post period. To address issues arising out of imperfection in the matching process, we use

the control variables in our regressions to soak up the effect of the matched pair

differences (Cram, Karan and Stuart 2009). Finally, the CDS spreads across the two

samples have similar means in the pre- and post-IFRS periods. However, the median

spread in the U.S. is higher (lower) in the pre (post) periods.

Panels B lists the means for the main variables for the 1,663 firm quarters in the

matched sample by country. On the whole, the data show significant variation across

countries. Panel C shows the institutional variables by country. CODE is equal to 1 if the

country has a code law system, and zero otherwise. Out of the 17 countries in our sample,

only 4 countries are common law countries. The RULE column measures the strength of

country legal enforcement based on the year 2005 proxy from Kaufmann, Kraay, and

Mastruzzi (2007). The EM column measures the extent of earnings management as

measured by the country-wide aggregate earnings management score from Leuz, Nanda,

and Wysocki (2003). The DIFF column measures the extent of accounting differences

between local GAAP and IFRS for each country based on the summary score of 21 key

accounting dimensions as computed by Bae, Tan, and Welker (2008). The fifth column

lists average differential timeliness (DT) during the sample period. DT is accounting

conservatism measured using the Basu (1997) differential timeliness (DT) metric at the

country level following Ball, Robin and Sadka (2000). We estimate DT for each country

in the period prior to the adoption of IFRS (2003-2004) and in the period after the

adoption (2006-2008) using the entire cross-section of firms for which we could find the

required data on Datastream.

Panel D presents the correlations between the CDS spread and the primary

determinants of the spread pre- and post-IFRS adoption. All correlations are highly

significant at the 1% level and the signs pre- and post-IFRS are identical. With one

exception, these correlations are also consistent with the underlying theory (Merton 1974;

Duffie and Lando 2001). Specifically, the CDS spread is correlated positively with


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leverage, volatility, and the ratings variable (the higher the ratings number, the lower the

credit rating); the spread is correlated negatively with profitability, size, and the SPOT

rate except in the post period for the IFRS sample. We elaborate more on the positive

correlation between the spread and SPOT in the post period for the IFRS sample below.

4. Multivariate Empirical Results

We analyze the impact of IFRS on CDS premia and the relation between CDS

premia and earnings using a levels analysis. Lack of sufficient appropriate cross-country

data, primarily CDS related data, precludes us from applying changes and event study

analyses to the issue at hand.11

Section 4.1 estimates the general relation between CDS

and ROA (normalized earnings) pre and post-IFRS. Section 4.2 examines whether the

relation depends on country characteristics. Section 4.3 explores whether the relation is

non-linear. Section 4.4 presents additional tests.

4.1 Main Regressions

Based on the predictions of the Merton (1974) and Duffie and Lando (2001)

models, CDS spreads should be decreasing with the reference firms size and ratings

quality and increasing with the reference firms leverage, the volatility of its stock

returns, and the risk-free rate of interest in the economy. In addition, we control for

industry, quarter and country fixed-effects, where applicable. We estimate the regressions

using the following five samples: full IFRS, full U.S., matched IFRS only (consisting of

IFRS firms for which we could find a U.S. match), matched U.S. only (consisting of U.S.

firms that were matched to an IFRS firm), and a matched sample of IFRS-U.S. firms. The

matched sample compares the change in credit risk informativeness pre and post adoption

for IFRS firms with the credit risk informativeness of a control group of U.S. firms(difference in difference with a matched sample design).


11 Only U.S.databases provide sufficient CDS data for such analyses. See Callen, Livnat and Segal (2009).


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Table 3, Panel A shows the panel data regression coefficient estimates for the full

IFRS sample (column 1), the matched IFRS sample (column 2), the full U.S. sample

(column 3) and the matched U.S. sample (column 4). The fifth column (sixth column)

shows the regression for the matched sample of IFRS and U.S. firms (excluding financial

crisis quarters). In all six regressions, we use Ordinary Least Squares and control for

industry and quarter fixed-effects. We also control for country fixed effects in all

regressions involving IFRS firms. Statistical significance is based on robust standard

errors corrected for firm and time clustering (Petersen 2009). Because the ratings

numbers are inversely related to the quality of the firms rating, the CDS spread should

increase with the credit rating (labelled as RATEN). ROA is separated into pre- and post-

IFRS adoption periods for the IFRS and U.S. samples. For example, ROA_PRE_US

(ROA_POST_US) equals ROA for U.S. sample firms if the fiscal quarter-end is in the

period prior to (after) the IFRS adoption period and zero otherwise. The results of F-tests

comparing pre and post coefficient estimates are given below the estimated regressions.

The results are reasonably consistent with the Merton and Duffie-Lando model

predictions for the full IFRS sample as well as for the full U.S. sample.12 Specifically, the

coefficients for ratings, leverage and volatility are positive and significant whereas the

coefficient for size is negative and significant. The coefficient on the risk-free rate

(SPOT_IFRS) is positive and significant which is the only estimated coefficient in this

regression inconsistent with theory. We checked the correlation of CDS spreads with

the spot rate by country to see if the results are driven by specific countries, but we obtain

positive correlations across all countries. One plausible explanation for the positive

correlation is that when interest rates are low, the cost of financing is also low leading to

a lower probability of default.13

The coefficients on the earnings variables are all negative

and highly significant (p-value


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periods, indicating that the adoption of IFRS did not change the credit informativeness of

earnings. Specifically, the coefficient on ROA_POST is not statistically different from

the coefficient on ROA_PRE for the IFRS samples (p-value=0.94 for full IFRS sample

and p-value=0.91 for matched IFRS sample) and the matched samples (p-value=0.923 in

the matched regression and p-value=0.243 in the matched regression excluding the crisis

period). Similar results are obtained for the U.S. sample, with one exception. The

regression using the full U.S. sample shows a significant decrease (p-value=0.022) in the

coefficient on ROA indicating that the credit risk informativeness of earnings declined

for the U.S. sample over the two periods. However the coefficients are not statistically

different in any of the regressions involving the matched U.S. sample. Overall, these

results indicate that although earnings is informative about credit risk both for IFRS

adopters and U.S. firms, the adoption of IFRS did not have a significant impact on the

credit risk informativeness of earnings for IFRS firms.

4.2 Institutional differences, conditional conservatism and CDS spreads

The regressions in Table 3 do not account for certain country and firm-level

factors that might be related to the credit risk informativeness of earnings, especially

when contrasting pre- and post-IFRS adoption periods. Table 4 replicates Table 3 for the

matched sample, controlling separately for code versus common law countries, strong

versus weak legal enforcement countries, the extent of country-level earnings

management, the extent to which IFRS and local GAAP standards differ from each other,

and the extent of differences in differential timeliness across countries. Multicollinearity

concerns induced by the correlations among these conditioning variables and the

extensive breakdowns of earnings pre- and post-IFRS interacted with a relatively large

number of conditioning variables preclude us from controlling for all five additional

factors simultaneously. Instead, we evaluate each of the conditioning variables

separately. Specifically, we condition ROA on the institutional variables, and benchmark

the results against the matched U.S. sample.



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The CODE column of Table 4 conditions earnings on code law versus common

law countries. ROA_PRE_COND (ROA_PRE_NO_COND) equals ROA if the fiscal

quarter-end is in the pre-IFRS period and the country is characterized as a code

(common) law system, and zero otherwise. ROA_POST_COND and

ROA_POST_NO_COND are defined similarly if the fiscal quarter-end is in the post-

IFRS period. The ROA coefficient is negative and insignificant in pre-IFRS (p-

value=0.17) and in post-IFRS periods (p-value=0.54) for code law countries, and

negative and highly significant (p-value


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end is in the post-IFRS period. The coefficient on ROA is negative and significant before

and after IFRS adoption for countries with strong legal enforcement (p-value


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in the post-IFRS (p-value=0.89) periods for countries with large differences across local

GAAP and IFRS. The coefficients on ROA are negative and highly significant (p-

value


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Overall, the evidence from Table 4 suggests that the institutional factors - origin

of law, strength of the legal system, level of earnings management, and difference

between local GAAP and IFRS, affect the CDS-earnings relation, but IFRS was not

consequential for the credit risk informativeness of earnings. These results further

validate the results documented in Table 3 that the adoption of IFRS had no discernable

impact on the credit informativeness of earnings.

4.3 Asymmetry in CDS-earnings relation

Callen, Livnat and Segal (2009) show that credit risk, as measured by CDS

premia, is more sensitive to earnings decreases than earnings increases, which is

consistent with a non-linear payoff function for debt holders. To explore the possibility

that the impact of IFRS is more pronounced for firms with lower profitability, we

estimate our regressions partitioning the sample firms according to the level of ROA.

Table 5, column 1 shows the regression results for the IFRS matched sample only with

pre and post ROA further partitioned based on the median level of earnings by quarter.

ROA_PRE_IFRS_HIGH (ROA_PRE_IFRS_LOW) equals ROA if the period is pre

IFRS, the observation belongs to the IFRS sample and ROA is above (below) the median

ROA. ROA_POST_IFRS_HIGH (ROA_POST_IFRS_LOW) is defined similarly if the

fiscal quarter-end is in the post IFRS period. Table 5, Column 1 shows that ROA

estimated coefficients are negative and significant before and after IFRS adoption for

firms with earnings below (p-values


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described. Overall, these findings suggest that there is asymmetry in the CDS-earnings

relation and that earnings are more credit risk relevant for firms with earnings below the

median. However, IFRS adoption is inconsequential to the CDS-earnings relation

whether earnings are below or above median.

4.4 Robustness Checks

We perform several additional tests to ensure that our results are robust. First,

following Cram, Karan and Stuart (2010), we use a dummy variable for each matched

pair in the matched sample regressions (1,663 additional dummies). While the coefficient

on ROA_PRE_IFRS is negative (-1.767) but insignificant (p-value=0.27), and the

ROA_POST_IFRS is negative (-3.140) and significant (p-value=0.03), the difference

between these coefficients is not statistically significant, confirming our main results that

adoption of IFRS did not have an impact on the CDS-earnings relation. Furthermore,

untabulated results related to institutional differences, level of differential timeliness and

the level of earnings are similar to those presented in Table 4 and Table 5. Second, we

use an alternative research design for the matched sample. Rather than employing a

difference in differences design with dummies for matched pairs, we estimate the

regressions based on differences between the matched pairs for dependent and

independent variables. The results are very similar to those reported. Third, we check the

results at country level for three countries in our sample which have at least 20 firms and

50 firm-quarters in the pre and post IFRS periods, namely Australia, France and United

Kingdom. We estimate the regressions at the country level (similar to column 2 in Table

3) and with a matched U.S. sample (similar to column 5 in Table 3). While both sets of

tests reveal wide variation of the estimated ROA coefficients among these three

countries, they indicate that the coefficient on ROA_POST_IFRS is not significantly

different from the coefficient on ROA_PRE_IFRS. Fourth, we use the log of book values

as an alternative proxy for size. Our main result that IFRS did not have an impact on the

CDS-earnings relation remains unchanged for each of these robustness checks.


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5. Conclusion

The proponents of IFRS argue that one of the major benefits of IFRS is increased

financial statement transparency. If so, the adoption of IFRS provides a unique setting for

examining how changes in transparency affect the credit risk informativeness of

accounting numbers. Our analysis indicates that earnings are a significant determinant of

credit risk, but the adoption of IFRS did not change the overall credit informativeness of

earnings. While it is difficult to measure transparency at the country level, the extant

prior literature documents that institutional factors affect accounting information quality

and hence arguably transparency. Therefore, we investigate whether credit risk

informativeness is affected by country-level institutional factors, including code law vs.

common law, strength of security law enforcement, prevalence of earning management,IFRS-local GAAP differences, and differential timeliness. We find that earnings are

informative about credit risk in pre as well as post adoption periods for common law

countries, countries with strong legal enforcement, countries with low earnings

management, countries with low differences between local GAAP and IFRS, and

countries with both high and low differential timeliness. However, similar to the results

related to earnings in general, IFRS adoption does not appear to have an impact on the

credit informativeness of earnings even when controlling for institutional differences. We

also explore the asymmetry in the credit risk informativeness of earnings and find that

while earnings of low profitability firms are more credit risk relevant than the earnings of

the firms with high profitability, IFRS does not have any impact on the asymmetry.


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Appendix A - Variable Definitions

Variable Description Source

CDS_PRM Log of CDS spread (mid-point between bid and ask) from the daily quotes, 45 days

from the fiscal quarter end

Datastream

CODE Indicator value equal to 1 if the firm belongs to a country characterized by the codelaw system; 0 otherwise

COMMON Indicator value equal to 1 if the firm belongs to a country characterized by the

common law system; 0 otherwise

COND Indicator variable equal to 1 if firm belongs to a country

- characterized by code law system; 0 otherwise- in country with above median RULE, 0 otherwise- in country with above median EM, 0 otherwise- in country with above median DIFF, 0 otherwise- in country with above median DT, 0 otherwise

DIFF Accounting differences between local GAAP and IFRS

based on the summary score of 21 key accounting dimensions. Higher value indicates

larger differences between local GAAP and IFRS.

Bae, Tan &

Welker (2008)

DT Accounting conservatism measured using the Basu (1997) differential timeliness(DT) metric at the country level following Ball, Robin and Sadka (2000).

Datastream

EM Country-wide aggregate earnings management score. Higher value indicates higher

extent of earnings management

Leuz, Nanda &

Wysocki (2003)

IFRS Indicator variable equal to 1 if firm is from country that adopted IFRS; 0 otherwise

LEV Short term debt plus long term debt scaled by market value of

equity plus total liabilities

Thomson Financial

NO_COND Indicator variable equal to 1 if firm belongs to a country

- characterized by common law system; 0 otherwise- in country with below median RULE, 0 otherwise- in country with below median EM, 0 otherwise- in country with below median DIFF, 0 otherwise- in country with below median DT, 0 otherwise

POST Indicator variable equal to 1 if observation is post 2004; 0 otherwise

PRE Indicator variable equal to 1 if observation is pre 2005; 0 otherwise

RATEN Credit rating, takes numerical values from 1 to 20, 1 being the highest for ratingAAA+ and 20 being the lowest for rating CCC.

Standard & Poors

ROA Income before extraordinary items scaled by total assets Thomson Financial

RULE Strength of legal enforcement. Higher value indicates stronger enforcement Kaufmann, Kraay, and

Mastruzzi (2007)

SD_RET Standard Deviation of the most recent 12 monthly returns (with at least 6 data points) Thomson Financial

SIZE Log of market value Thomson Financial

SPOT Annualized 3-month Treasury-Bill rate Datastream

U.S. Indicator variable equal to 1 if U.S. firms; 0 otherwise Thomson Financial

HIGH Indicator variable equal to 1 if ROA is above median

LOW Indicator variable equal to 1 if ROA is below median


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Table 1 Sample Selection and Distribution

Panel A: Sample selection by number of firms and CDS contract quartersNon-U.S. U.S. Total

Firms

CDS

ContractQuarters

Firms

CDS

ContractQuarters

Firms

CDS

ContractQuarters

CDS prices available on datastream 1,382

Firms with data available on ThomsonFinancial

390(41countries)

392 782

Firms from countries which adoptedIFRS in 2005

211(17 countries)

392 603

Final Full Sample(delete observations with missingfinancial statement data includingcredit rating)

155(17 countries)

1,699 308 5,295 463 6,994

Final Matched Sample 155(17 countries)

1,663 279 1,663 434 3,326

Panel B: Number of firms, firm quarters and CDS contract quarters by country

FIRMS FIRM-QUARTERS

PRE POST PRE POST

AUSTRALIA 23 26 55 81

BELGIUM 2 3 6 29

DENMARK 2 2 14 22

FINLAND 2 4 15 42

FRANCE 22 26 85 136

GERMANY 1 18 5 159

HONGKONG 5 9 16 40

IRELAND 3 3 11 17

ITALY 9 9 34 98

NETHERLANDS 7 7 45 63

NORWAY 1 4 3 14

POLAND 2 2 19 21

PORTUGAL 1 1 8 10

SPAIN 6 7 45 66

SWEDEN 12 13 91 137

SWITZERLAND 1 8 4 55

UNITED KINGDOM 23 26 75 142

IFRS TOTAL 122 168 531 1,132

UNITED STATES 215 294 531 1,132

GRAND TOTAL 3,326

Notes Table 1:Panel A describes the sample selection process. Panel B shows the number of firms, the number of firm-

quarter observations and the number of CDS contracts before and after IFRS adoption by country for the

the matched sample.


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Table 2 - Descriptive Statistics

Panel A: Descriptive Statistics of Main Variables for matched IFRS and U.S. sample (n=3,326)

IFRS SAMPLE

N=1,663

U.S. SAMPLE

N=1,663

IFRS -

U.S.

Mean p

value

IFRS -

U.S.

Median p

value

N=531 N=531

PRE

MEAN MEDIAN STD MEAN MEDIAN STD

CDS_PRM 3.860 3.697 0.834 3.893 3.773 0.878 0.527 0.021

ROA 0.016 0.012 0.022 0.013 0.010 0.015 0.012 0.022

SIZE 9.140 9.099 1.138 9.106 8.986 1.225 0.639 0.010

LEV 0.249 0.241 0.128 0.247 0.238 0.142 0.808 0.427

RATEN 9.429 9.000 3.280 9.593 9.000 3.279 0.416 0.000

SD_RET 0.063 0.056 0.028 0.065 0.056 0.035 0.279 0.380

SPOT 2.788 2.146 1.305 2.488 2.450 1.210 0.000 0.000

N=N=1,132 N=1,132

POST

Mean Median Std Mean Median Std

CDS_PRM 4.136 4.102 1.019 4.068 3.932 1.051 0.121 0.000

ROA 0.018 0.013 0.023 0.016 0.014 0.017 0.002 0.467

SIZE 9.683 9.695 1.132 9.596 9.603 1.222 0.078 0.055LEV 0.232 0.207 0.133 0.221 0.199 0.139 0.071 0.225

RATEN 8.943 9.000 3.106 9.143 9.000 3.044 0.123 0.002

SD_RET 0.065 0.060 0.026 0.067 0.059 0.034 0.386 0.137

SPOT 4.211 4.150 1.324 4.573 5.030 1.065 0.000 0.000

POST-

PRE

POST-

PRE

p value

POST-

PRE

p value

POST-

PRE

p value

POST-

PRE

p value

CDS_PRM 0.000 0.000 0.001 0.001

ROA 0.034 0.043 0.001 0.000 SIZE 0.000 0.000 0.000 0.000 LEV 0.014 0.001 0.001 0.005 RATEN 0.003 0.472 0.006 0.001 SD_RET 0.091 0.005 0.444 0.138 SPOT 0.000 0.000 0.000 0.000


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Table 2 contd

Panel B: Mean of Main variables by country for the matched sample (n=3,326)

COUNTRY N CDS_PRM ROA SIZE LEV RATEN SD_RET SPOT

AUSTRALIA 136 3.637 0.040 9.046 0.170 8.588 0.061 6.039BELGIUM 35 3.509 0.013 9.871 0.176 8.229 0.050 3.709

DENMARK 36 3.750 0.007 9.580 0.451 10.472 0.046 3.320FINLAND 57 4.830 0.015 8.988 0.227 11.193 0.074 3.415

FRANCE 221 4.234 0.016 9.938 0.220 9.665 0.066 3.245GERMANY 164 4.245 0.007 9.952 0.255 8.049 0.068 3.946

HONG KONG 56 4.029 0.035 9.314 0.230 10.393 0.070 5.119IRELAND 28 3.305 0.005 9.356 0.362 8.000 0.059 3.173

ITALY 132 3.992 0.006 9.414 0.315 9.061 0.058 3.476NETHERLANDS 108 4.006 0.016 9.869 0.170 9.157 0.063 3.235

NORWAY 17 4.967 0.006 9.023 0.303 8.765 0.083 4.811

POLAND 40 3.653 0.003 7.005 0.340 11.075 0.107 3.155PORTUGAL 18 3.194 0.002 9.209 0.401 8.500 0.069 3.031SPAIN 111 4.014 0.013 9.909 0.302 8.910 0.057 3.200

SWEDEN 228 3.837 0.019 9.099 0.186 8.539 0.065 2.803SWITZERLAND 59 3.830 0.020 10.305 0.235 8.424 0.058 2.139UNITED KINGDOM 217 4.422 0.027 9.584 0.213 9.378 0.066 4.840UNITED STATES 1,663 4.012 0.015 9.440 0.229 9.287 0.066 3.907

Panel C: Institutional variables by country for the IFRS sample

CODE RULE EM DIFF DT

AUSTRALIA 0 1.81 -4.8 4 0.285

BELGIUM 1 1.45 -19.5 13 0.080

DENMARK 1 2.03 -16.0 11 0.236

FINLAND 1 1.95 -12.0 14 0.216

FRANCE 1 1.31 -13.5 12 0.191

GERMANY 1 1.77 -21.5 11 0.269

HONG KONG 0 1.45 -19.5 3 0.103

IRELAND 0 1.62 -5.1 1 0.025

ITALY 1 0.37 -24.8 12 0.457

NETHERLANDS 1 1.75 -16.5 4 0.154

NORWAY 1 2.02 -5.8 7 0.271

POLAND 1 NA NA 12 0.190

PORTUGAL 1 NA -25.1 13 0.430

SPAIN 1 1.10 -18.6 14 0.406

SWEDEN 1 1.86 -6.8 10 0.279

SWITZERLAND 1 1.96 -22.0 12 0.259

UNITED KINGDOM 0 1.73 -7.0 1 0.292

MEDIAN 1.75 -16.25 11 0.259


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Panel D: Correlations with CDS spread (CDS_PRM) for the matched sample (n=3,326)

IFRS SAMPLE U.S. SAMPLE

PRE POST PRE POST

ROA (0.190) (0.082) (0.445) (0.370)

SIZE (0.315) (0.225) (0.513) (0.418)

LEV 0.219 0.202 0.532 0.452

RATEN 0.480 0.250 0.407 0.339

SD_RET 0.433 0.367 0.445 0.469

SPOT (0.025) 0.351 (0.169) (0.323)

Notes Table 2:

Panel A presents means, medians, p-values of the difference between means, and p-values of the difference

between medians of the main variables for the matched IFRS and U.S. sample by adoption periods. Panel

B presents means of the main variables in the analysis for the IFRS and the U.S. matched sample by

country. Panel C presents the institutional factors by country, origin of legal system (CODE), strength of

enforcement (RULE), level of earnings management (EM), difference between local GAAP and IFRS(DIFF) and differential timeliness (DT). Panel D presents sample correlation of CDS_PRM (log of CDS

spread) with the selected variables for the IFRS and U.S. matched sample by adoption periods. Variable

definitions are provided in Appendix A.


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Table 3 - The Determinants of CDS Spreads Before and After IFRS Adoption

IFRS U.S. IFRS AND U.S.

VARIABLES ALL MATCHED ALL MATCHED

MATCHED MATCHED

EXCL. CRISIS

(1) (2) (3) (4) (5) (6)

ROA_PRE_IFRS -4.248*** -4.153*** -3.334*** -4.004***(0.002) (0.002) (0.007) (0.003)

ROA_POST_IFRS -4.140*** -3.990*** -3.213** -2.444

(0.003) (0.004) (0.018) (0.103)

ROA_PRE_US -7.877*** -6.382** -7.332*** -8.714***

(0.000) (0.014) (0.003) (0.001)

ROA_POST_US -4.680*** -6.007*** -8.709*** -9.656***(0.000) (0.000) (0.000) (0.000)

SIZE -0.260*** -0.260*** -0.204*** -0.174*** -0.218*** -0.249***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

LEV 0.901** 0.948** 2.006*** 2.256*** 1.535*** 1.111***

(0.020) (0.015) (0.000) (0.000) (0.000) (0.000)

RATEN 0.052*** 0.052*** 0.057*** 0.036*** 0.045*** 0.050***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

SD_RET 3.288** 3.450** 5.581*** 6.067*** 5.318*** 6.295***

(0.020) (0.013) (0.000) (0.000) (0.000) (0.000)

SPOT 0.094* 0.096* -0.034 -0.084 0.026 0.035(0.055) (0.055) (0.416) (0.160) (0.169) (0.227)

POST 4.963*** 4.886*** 4.530*** 4.125*** 4.742*** 4.846***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

PRE 5.066*** 4.999*** 4.553*** 3.965*** 4.731*** 4.838***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

COUNTRY DUMMIES YES YES YES YES

INDUSTRY DUMMIES YES YES YES YES YES YES

QUARTER DUMMIES YES YES YES YES YES YES

OBSERVATIONS 1,699 1,663 5,295 1,663 3,326 2,358

R-SQUARED 0.983 0.983 0.982 0.981 0.981 0.981

F-TESTS

ROA_PRE_IFRS=ROA_POST_IFRS 0.005 0.012 0.009 1.364

(0.943) (0.912) (0.923) (0.243)

ROA_PRE_US=ROA_POST_US 5.286 0.019 0.384 0.155

(0.022) (0.891) (0.536) (0.694)

Notes to Table 3:Table 3 shows the regression estimates of the CDS spread on its determinants. Column 1 (Column 2) shows

the regressions using the full IFRS (matched IFRS) samples. Column 3 (Column 4) shows the regressions

using the full U.S. (matched U.S.) samples. Column 5 (Column 6) shows the matched sample results

(excluding the crisis quarters). The CDS spread determinants include profitability (ROA), log of market

value (SIZE), leverage (LEV), credit rating (RATEN), volatility of returns (SD_RET), the country risk-free

interest rate (SPOT). The PRE (POST) indicator variable takes the value of 1 if the fiscal quarter is in the

pre-adoption (post-adoption) period and zero otherwise. Variables are defined in Appendix A. Allregressions control for industry, fiscal quarter, and country fixed-effects (not reported). Two-tailed p-

values are in parentheses. All regressions are estimated using OLS with robust standard errors corrected for

firm and time clustering.


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Table 4 - The Determinants of CDS Spreads Before and After IFRS Adoption and

Five Conditioning Variables

VARIABLES CODE RULE EM DIFF DT

(1) (2) (3) (4) (5)

ROA_PRE_COND -2.666 -5.158*** -2.277 -0.270 -5.134***(0.165) (0.000) (0.309) (0.912) (0.001)

ROA_POST_COND -1.194 -4.648*** 0.348 0.355 -4.816***

(0.542) (0.000) (0.868) (0.891) (0.001)

ROA_PRE_NO_COND -6.195*** -2.038 -5.518*** -6.006*** -3.500

(0.000) (0.427) (0.000) (0.000) (0.100)

ROA_POST_NO_COND -6.532*** -0.950 -6.268*** -5.953*** -2.921

(0.000) (0.760) (0.000) (0.000) (0.112)

ROA_PRE_US -8.858*** -9.102*** -8.865*** -8.919*** -9.042***

(0.000) (0.000) (0.000) (0.000) (0.000)

ROA_POST_US -10.479*** -10.494*** -10.222*** -10.501*** -10.564***

(0.000) (0.000) (0.000) (0.000) (0.000)

SIZE -0.172*** -0.171*** -0.196*** -0.175*** -0.169***

(0.000) (0.000) (0.000) (0.000) (0.000)

LEV 1.704*** 1.675*** 1.589*** 1.663*** 1.700***

(0.000) (0.000) (0.000) (0.000) (0.000)

RATEN 0.050*** 0.049*** 0.048*** 0.049*** 0.049***(0.000) (0.000) (0.000) (0.000) (0.000)

SD_RET 5.410*** 5.379*** 5.904*** 5.418*** 5.396***

(0.000) (0.000) (0.000) (0.000) (0.000)

SPOT 0.026 0.015 0.017 0.023 0.017

(0.159) (0.393) (0.356) (0.201) (0.349)

POST 4.291*** 4.306*** 4.532*** 4.326*** 4.285***

(0.000) (0.000) (0.000) (0.000) (0.000)PRE 4.263*** 4.291*** 4.505*** 4.301*** 4.262***

(0.000) (0.000) (0.000) (0.000) (0.000)

COUNTRY DUMMIES YES YES YES YES YES

INDUSTRY DUMMIES YES YES YES YES YES

QUARTER DUMMIES YES YES YES YES YES

OBSERVATIONS 3,326 3,326 3,286 3,326 3,326

R-SQUARED 0.979 0.979 0.979 0.979 0.979

F-TESTSROA_PRE_COND=ROA_POST_COND 0.418 0.134 1.245 0.048 0.044

(0.518) (0.715) (0.265) (0.827) (0.834)

ROA_PRE_NO_COND=ROA_POST_NO_COND 0.056 0.137 0.313 0.002 0.061

(0.813) (0.711) (0.576) (0.969) (0.804)

ROA_PRE_COND=ROA_PRE_NO_COND 2.208 1.435 1.780 4.613 0.556

(0.138) (0.232) (0.183) (0.032) (0.456)

ROA_POST_COND=ROA_POST_NO_COND 5.978 1.532 9.483 5.786 0.935

(0.481) (0.547) (0.556) (0.491) (0.506)

ROA_PRE_US=ROA_POST_US 0.497 0.364 0.347 0.475 0.442

(0.481) (0.547) (0.556) (0.491) (0.506)

ROA_PRE_US=ROA_PRE_COND 6.373 3.106 6.121 8.922 2.738

(0.012) (0.079) (0.014) (0.003) (0.099)

ROA_PRE_US=ROA_PRE_NO_COND 1.101 5.578 1.994 1.584 4.710(0.295) (0.019) (0.159) (0.209) (0.031)

ROA_POST_US=ROA_POST_COND 21.92 13.35 23.55 18.46 10.35

(0.000) (0.000) (0.000) (0.000) (0.001)

ROA_POST_US=ROA_POST_NO_COND 4.369 9.718 5.563 7.104 14.92

(0.037) (0.002) (0.019) (0.008) (0.000)


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Notes to Table 4:

Table 4 shows the regressions of the CDS spread on its determinants conditioning for institutional factors by country

using the matched IFRS and U.S. samples. Columns 1 to 5 condition (COND) on the origin of legal system (CODE),strength of enforcement (RULE), level of earnings management (EM), difference between local GAAP and IFRS(DIFF) and differential timeliness (DT), respectively and show the regression of the CDS spread on profitability

(ROA), log of market value (SIZE), leverage (LEV), credit rating (RATEN), volatility of returns (SD_RET), the

country risk-free interest rate (SPOT). The constant PRE and POST indicator variables take the value of 1 if the fiscalquarter is in the pre-adoption and post-adoption period, respectively, and zero otherwise. Pre and Post are trifurcated

into three separate coefficients for IFRS countries with COND equal to 1, IFRS countries with NON_COND equal to 1,and the U.S. Variables are defined in Appendix A. All regressions control for industry and fiscal quarter (not reported).Two-tailed p-values are in parentheses. All regressions are estimated using OLS with robust standard errors correctedfor firm and time clustering and multiple CDS contracts per firm. The number of observations in the EM column issmaller because we do not have the EM measure for Poland. The ROA variables in each column are defined as follows:

The column labeled CODE conditions on Code vs. Common law countries.COND=1 if code law country and zero otherwiseROA_PRE_COND=ROA pre-IFRS adoption if code law and zero otherwise.

ROA_POST_COND=ROA post-IFRS adoption if code law and zero otherwise.ROA_PRE_NO_COND=ROA pre-IFRS adoption if common law and zero otherwise.ROA_POST_NO_COND=ROA post-IFRS adoption if common law and zero otherwise.

The column labeled RULE conditions on the strength of legal enforcement. Values above (below) the median representcountries with strong (weak) legal enforcement.COND=1 if strong legal enforcement and zero otherwiseROA_PRE_COND=ROA pre-IFRS adoption if strong legal enforcement and zero otherwise.

ROA_POST_COND=ROA post-IFRS adoption if strong legal enforcement and zero otherwise.ROA_PRE_NO_COND=ROA pre-IFRS adoption if weak legal enforcement and zero otherwise.

ROA_POST_NO_COND=ROA post-IFRS adoption if weak legal enforcement and zero otherwise.

The column labeled EM conditions on extent of earnings management prior to the adoption of IFRS. Values above(below) the median represent countries with high (low) earnings management.

COND=1 if high earnings management and zero otherwiseROA_PRE_COND=ROA pre-IFRS adoption if high earnings management and zero otherwise.ROA_POST_COND=ROA post-IFRS adoption if high earnings management and zero otherwise.ROA_PRE_NO_COND=ROA pre-IFRS adoption if low earnings management and zero otherwise.

ROA_POST_NO_COND=ROA post-IFRS adoption if low earnings management and zero otherwise.

The column denoted DIFF conditions on the extent to which there are differences between local GAAP and IFRS.Values above (below) the median represent countries with large (small) IFRS-local GAAP differences.

COND=1 if large accounting differences and zero otherwiseROA_PRE_COND=ROA pre-IFRS adoption if large accounting differences and zero otherwise.ROA_POST_COND=ROA post-IFRS adoption if large accounting differences and zero otherwise.ROA_PRE_NO_COND

credit risk and ifrs

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