Case of Credit Default Swaps The Author(s) 2014over-the-counter credit default swap (CDS) market. IFRS uses a principles-based approach, emphasizes fair value accounting, and aims

Journal of Accounting,Auditing & Finance

2014, Vol. 29(2) 129–162�The Author(s) 2014

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DOI: 10.1177/0148558X14521205jaf.sagepub.com

Credit Risk and IFRS: TheCase of Credit Default Swaps

Gauri Bhat1, Jeffrey L. Callen2, andDan Segal3,4

Abstract

This study compares the pricing of credit risk information conveyed by accounting numbersunder International Financial Reporting Standards (IFRS) relative to local GenerallyAccepted Accounting Principles (GAAP). We measure the price of credit risk by creditdefault swap (CDS) spreads and focus on three fundamental accounting metrics that informabout credit risk: earnings, leverage, and book value equity. Using a difference-in-differencesmethodology, we find that while earnings, book value, and, to a lesser extent, leverage aresignificant determinants of credit risk pricing both prior to and after IFRS adoption, theadoption of IFRS did not change the credit risk informativeness of these accounting vari-ables as reflected in CDS spreads. This conclusion is robust to controlling for institutionaldifferences among countries as well as a battery of sensitivity analyses.

Keywords

credit default swaps, credit risk, IFRS

Introduction

This study evaluates the impact of the adoption of International Financial Reporting

Standards (IFRS) on the relevance of accounting information in pricing credit risk in the

over-the-counter credit default swap (CDS) market. IFRS uses a principles-based approach,

emphasizes fair value accounting, and aims to promote uniformity and comparability

across countries. We compare the information conveyed by accounting information in pric-

ing credit risk under IFRS relative to local Generally Accepted Accounting Principles

(GAAP) for countries that adopted IFRS. We focus on three fundamental accounting

metrics that inform about credit risk: earnings, leverage, and book value equity. We mea-

sure the credit risk relevance of each of these accounting metrics by reference to their esti-

mated coefficients in a regression of CDS spreads on these metrics, controlling for other

1Washington University in St. Louis, MO, USA2Rotman School of Management, University of Toronto, Ontario, Canada3Interdisciplinary Center, Herzliya, Israel4Singapore Management University, Singapore

Corresponding Author:

Dan Segal, Interdisciplinary Center, Israel, Herzliya 46145, Israel.

Email: [email protected]

potential determinants of the spread.1 This approach is similar to measuring the value rele-

vance of earnings for equity returns by an Earnings Response Coefficient where CDS

spreads replace equity returns.

The role of accounting information in the pricing of credit risk finds theoretical support

in the Duffie and Lando (2001) model, which explicitly acknowledges the relevance of

noisy accounting information as a determinant in the pricing of credit risk. Callen, Livnat,

and Segal (2009) and Das, Hanouna, and Sarin (2009) provide empirical evidence that

accounting information has a role in CDS pricing incremental to market information and

other determinants of CDS spreads. Our study is guided by the above prior research that

speaks to the importance of accounting information for credit markets and the empirical

evidence that directly links accounting information to CDS pricing.

The adoption of IFRS provides a unique research opportunity to examine the impact of

financial statement information on the pricing of credit risk because the switch to IFRS for

most firms was exogenously mandated by accounting regulators, 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.

Whether the credit risk informativeness of accounting information in the pricing of

credit risk has changed under IFRS as compared with prior local GAAP is unclear a priori.

On the one hand, IFRS is principles based rather than rules based, and therefore encourages

companies to adapt their reporting to better reflect the underlying substance of the transac-

tion. Furthermore, IFRS emphasizes greater use of fair value accounting than local GAAP.

Fair value information provides timely early warning signals of changes in current market

expectations, which are particularly relevant for the analysis of credit risk (Linsmeier,

2011). In addition, one set of standards across countries promotes uniformity and compar-

ability and, hence, should allow for better assessment of credit risk especially in the case of

cross-country debt.

On the other hand, by allowing managers to have more judgment and discretion, IFRS

affords greater flexibility for manipulation of accounting information. Furthermore, any dis-

cretion accompanying a principles-based approach is bound to be plagued by inconsistent

interpretation and application, and potentially compromises comparability. As far as fair

value accounting is concerned, in the absence of fairly liquid markets, fair values may not

be meaningful, especially given that the determination of fair values (mark-to-model) is

largely a matter of managerial judgment (Kothari, Ramanna, & Skinner, 2010).

Furthermore, any effect of IFRS adoption is potentially contingent on country-level

institutional factors that complement accounting standards and shape financial reporting

incentives (Ball, 2006). Indeed, prior literature documents that institutional factors are asso-

ciated with the quality of accounting information (e.g., Ahmed, Neel, & Wang, 2012;

Alford, Jones, Leftwich, & Zmijewski, 1993; Ali & Hwang, 2000; Ball, Kothari, & Robin,

2000; Bartov & Goldberg, 2001; Bushman & Piotroski, 2006; Chen, Tang, Jiang, & Lin,

2010). Thus, we also examine the impact of variation in institutional factors on the relation

between CDS spreads and our three accounting metrics, including the system of laws (code

vs. common law—a proxy for quality of financial statement information), the quality of

securities law enforcement, the extent of creditor rights protection, the pervasiveness of

earnings management, the extent of differences between local GAAP and IFRS, and the

country-level degree of conservatism as measured by differential timeliness (DT).3

While IFRS potentially affects financial reporting as a whole and some of the induced

changes in financial reporting affect the quality of disclosures rather than the accounting

numbers directly, we choose to study the impact of the adoption of IFRS on the credit risk

130 Journal of Accounting, Auditing & Finance

relevance of three primary accounting variables: earnings, leverage, and book value. We

perform three distinct tests to examine the impact of IFRS on these three accounting

metrics. First, we examine the association of earnings, leverage, and book value with CDS

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

IFRS changed their association. Second, we examine whether the association of earnings,

leverage, and book value with CDS spreads subsequent to the adoption of IFRS depends on

the country-level institutional factors indicated above. Third, we also test for the potential

asymmetry (non-linearity) of CDS spreads with respect to the levels of earnings, leverage,

and book value equity in light of the evidence in Callen et al. (2009) of a non-linear rela-

tion between CDS spreads and profitability. We also test for asymmetry with respect to

investment/speculative grade debt following Florou, Kosi, and Pope (2012).

Using a sample of 5,893 firm-quarters across 13 countries (with U.S. firms as a control

sample) and difference-in-differences approach, we show that our accounting metrics are

informative in the pricing of credit risk in IFRS countries both before and after the adop-

tion of IFRS, consistent with the findings in Callen et al. (2009) and Das et al. (2009).

However, there is no statistically significant difference in the association of these account-

ing numbers with CDS spreads between the pre- and post-adoption periods, indicating that

IFRS adoption had no impact on the relevance of these accounting metrics in pricing credit

risk. The results of our second set of tests which condition on institutional factors show

that the credit risk relevance of accounting information in pricing CDS spreads depends on

institutional factors. Specifically, earnings, leverage, and book value are credit risk relevant

in common law countries, and in countries with strong legal enforcement, strong creditor

rights, low earnings management, low differences between local GAAP and IFRS, and

countries with high DT. Nevertheless, the adoption of IFRS did not have any impact on the

pricing of credit risk; namely, the relations just described hold both in the pre- and post-

IFRS adoption periods.

We should emphasize that the lack of evidence to support change in the credit risk rele-

vance of these accounting variables does not mean that IFRS adoption failed to produce

any beneficial effects, nor does our evidence necessarily contradict the current evidence

referenced below regarding the impact of IFRS adoptions on debt markets and credit rat-

ings. What the evidence does show is that IFRS adoption had no impact on the relevance

of earnings, book values, and leverage in pricing credit risk (in the CDS market), despite

the relevance of these accounting metrics in pricing credit risk both before and after the

adoption of IFRS.

We contribute to the extant literature on IFRS by studying the impact of IFRS adoption

on credit market pricing. The prior literature in this area focuses predominantly, but not

exclusively, on equity market returns. However, the credit market is no less important than

the equity market for several reasons. First, the informational needs of the equity market

may differ from those of the credit market. Exclusive reliance on the equity market to

quantify the pricing impact of IFRS ignores the fact that credit markets represent a signifi-

cant source of financing for public firms. In particular, the CDS market, which is a subset

of the overall credit market, is a multi-trillion-dollar market. The limited evidence in the

literature supports the conjecture that the adoption of IFRS affected debt markets.

Specifically, Beneish, Miller, and Yohn (2012) 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

Bhat et al. 131

financial risk, consistent with IFRS providing more transparent information. 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. However, using the

CDS market we find that adoption of IFRS did not change the credit risk informativeness

of earnings, leverage, and book value of equity. Second, this article provides evidence on

the informativeness of accounting information in pricing credit risk using an international

sample. While prior literature discusses the significance of accounting for U.S. credit mar-

kets, the empirical evidence on the relevance of accounting numbers for international CDS

pricing is fairly limited. Third, using the mandatory adoption of IFRS, our study establishes

the causal link between the relevance of accounting information and the prediction of

credit default spreads.

In what follows, Section ‘‘CDS and the Pricing of Credit Risk’’ describes the advantages

of using CDS as a proxy for credit risk. Section ‘‘Hypotheses Development’’ provides the

literature review and develops the hypotheses. Section ‘‘Sample Data and Univariate

Empirical Results’’ describes the data. Sections ‘‘Multivariate Empirical Results’’ and

‘‘Further Robustness Checks’’ present the empirical results and sensitivity analyses, respec-

tively. The final section concludes.

CDS and the Pricing of Credit Risk

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

(see, for example, Daske, Hail, Leuz, & Verdi, 2013; 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.

Within the debt markets, we focus on the CDS market. In this section, we discuss the

advantages of using the CDS market in comparison with the bond market and credit ratings

to evaluate credit risk.

CDS Versus Bond Markets

This study evaluates the impact of IFRS on the relevance of accounting information in pric-

ing credit risk by reference to CDS spreads. The credit risk information conveyed by IFRS

earnings could be evaluated instead through corporate bond yield spreads, as was done by

Florou and Kosi (2013). Indeed, absent arbitrage opportunities, contractual features (such

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

the corporate bond yield spread—the difference between the bond yield and the risk-free

rate—are necessarily identical for floating rate corporate debt (Duffie, 1999). Nevertheless,

it is precisely because of the latter factors—contractual features and market frictions—that

CDS instruments offer many advantages over corporate bonds (and other debt instruments)

for analyzing the determinants of credit risk pricing.

First, the finance literature has shown that corporate bond spreads include factors unre-

lated to credit risk, such as systematic risk unrelated to default (Elton, Gruber, Agrawal, &

Mann, 2001) and especially illiquidity (Longstaff, Mithal, & Neis, 2005).4 Huang and

Huang (2012) conclude that less than 25% of the credit spread in corporate bonds is attri-

butable to credit risk. Second, interest rate risk drives 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 rela-

tion 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. 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 (Berndt & Ostrovnaya,

2008; Blanco, Brennan, & Marsh, 2005; Daniels & Jensen, 2005; Zhu, 2006). The bond mar-

ket’s lagged reaction potentially distorts empirical studies relating earnings to bond yields.

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

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

(Houweling & Vorst, 2005). Sixth, CDS rates are closely related to the par value of the refer-

ence bond, whereas corporate bond values (including their taxability characteristics) are

affected by coupons. Heterogeneity in coupon rates potentially 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% of the corporate bond spread, depending on the rating.5

CDS Versus Credit Ratings

Florou et al. (2012) show that IFRS adoption affects credit ratings. However, by focusing

on credit ratings, their article is conceptually different from ours. Unlike CDS and corpo-

rate bond yield spreads, credit ratings are not market prices. As is well known, in addition

to credit risk, credit ratings reflect rating agency incentives, rating agency competition, and

the ability of rating agencies to predict credit risk well and in unbiased fashion (Becker &

Milbourn, 2011; Bolton, Freixas, & Shapiro, 2012). That is not to say that credit ratings do

not potentially convey information about credit risk pricing. Indeed, we control for credit

ratings in our regressions below. Nevertheless, credit ratings are not market prices (or

market quotes) and therefore it is unclear how effective or timely they are in measuring

credit risk. It is telling that in some of the most egregious bankruptcy cases such as Enron

and WorldCom, credit ratings did not even remotely predict the true credit risk of these

firms. For example, credit ratings for Enron were positive and unchanged up to 4 days

before bankruptcy, whereas CDS spreads began to climb months before. Similarly, CDS

rates began to climb for WorldCom well in advance of rating downgrades (Jorion &

Zhang, 2007). Furthermore, there is compelling evidence that CDS rates anticipate rating

downgrades (Hull, Predescu, & White, 2004; Norden, 2011) and that CDS spreads explain

the cross-sectional variation in primary and secondary bond yields better than credit ratings

(Chava, Ganduri, & Ornthanolai, 2012).

Hypotheses Development

Earnings, Leverage, Book Value, and CDS Spreads

Of the information provided by financial statements that relates to (the pricing of) credit

risk, we focus our analysis on earnings, leverage, and book value. Earnings and book

Bhat et al. 133

values can be used by investors to estimate the reference entity’s economic performance

and true asset (wealth) dynamics, important determinants of credit risk yields (Duffie &

Lando, 2001; Merton, 1974). More specifically, increased profitability of the firm, as mea-

sured by current accounting earnings and book value, should reduce its credit risk as, 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 & Penman, 2001), future cash flows (Barth, Cram, & Nelson, 2001;

Dechow, Kothari, & Watts, 1998), 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. Book value is a measure of minimal firm wealth (Watts, 2003) and a measure

of proximity to default, as firms generally do not declare bankruptcy until the accounting

book value of equity is well below 0. Also, earnings and book value comprise a significant

portion of the short-term change in firm assets (via clean surplus) and, therefore, provide

information to investors about the firm’s asset and wealth dynamics, crucial variables in

the estimation of credit risk (Duffie & Lando, 2001).

Consistent with the seminal study by Merton (1974), structural models imply that lever-

age is one of the main determinants of the likelihood and severity of default. In fact, earn-

ings and leverage are the two variables that have been shown to provide financial distress

information incremental to recent excess stock returns and stock volatility (Shumway,

2001).

Although Callen et al. (2009) and Das et al. (2009) show that accounting information is

relevant for assessing credit risk in the CDS market, 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 United States, one cannot generalize these findings to an interna-

tional setting where countries differed in their accounting standards prior to IFRS adoption,

and differ in securities law enforcement, creditor rights protection, quality of financial

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

hypothesis:

Hypothesis 1a: CDS spreads are associated with accounting numbers (earnings,

leverage, and book value) both pre- and post-IFRS adoption for firms in countries

adopting IFRS.

Prior research suggests that underlying economic and political institutions influence the

incentives of the managers and auditors responsible for financial statement preparation

(e.g., Ball, Robin, & Wu, 2003). Therefore, we gauge the credit risk informativeness of

accounting information controlling for the following institutional factors: origin of the legal

system, level of legal enforcement, level of investor rights protection, level of earnings

management, degree of conditional conservatism, and a measure that quantifies differences

between local GAAP and IFRS.

Political influence on accounting standard setting in code law countries prior to IFRS

promoted the use of accounting metrics in dividing profits among various stakeholders

such as governments, shareholders, banks, and labor unions (Ball et al., 2000). As a result,

accounting information in code law countries was less related to the economic performance

of the firm and, therefore, less informative about credit risk than accounting information in

common law countries.6


Similarly, enforcement of the legal system also affects accounting quality directly,

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

Thus, accounting information in countries with high legal enforcement may be more reflec-

tive of credit risk than accounting information in countries with low legal enforcement pre-

and post-IFRS adoption.

Creditor rights protection (CR) is defined as the extent to which creditors are protected

when the borrowing firm faces financial difficulties, in particular the ability of creditors to

repossess collateral or take over the firm in case of bankruptcy. CR is the result of various

laws and legal mechanisms at the country level, so that there is large variation in CR

across countries (La Porta, Lopez-De-Silanes, Shleifer, & Vishny, 1997). Differences in the

extent of CR may affect the relation between accounting information and CDS because CR

affects the riskiness of debt and the recovery rate in case of bankruptcy. It is plausible that

investors have more incentive to use accounting information to assess credit risk and pro-

tect their interests ex ante if a country’s law is not creditor friendly.

Earnings management generally distorts the informativeness of financial reports. We

focus on distributional properties of reported accounting numbers across countries and

across time as captured by the Leuz, Nanda, and Wysocki (2003) measure of earnings man-

agement at the country level. We focus on this measure because past literature has identi-

fied the existence of earnings management as weakening the link between accounting

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

accounting information in countries with high earnings management to be less informative

about the pricing of credit risk than in countries with low earnings management.

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

countries which are likely to be affected the most from the transition to IFRS. If the differ-

ence between the local GAAP and IFRS is large, informativeness of accounting numbers

should be different than if the difference is small.

Conditional conservatism should be of particular importance to CDS investors (and

bondholders) who are far more concerned with earnings decreases than earnings increases

(Callen et al., 2009). As emphasized by Watts (2003), conditional conservative accounting

is demanded by debt holders because it reduces management’s 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 immedi-

ately in income, resulting in a concomitant reduction in asset values. Hence, the degree of

conditional conservatism has a direct impact on the informativeness of accounting informa-

tion. The more conditionally conservative the firm, the more likely is the firm’s accounting

to act as a trip wire regarding anticipated future negative cash flow shocks that may reduce

the firm’s ability to pay back its debt. Thus, one should expect ex ante that the more con-

servative the country, the more informative is accounting information for the pricing of

credit risk and, hence, the more negative the relation between earnings and CDS spreads.

These considerations lead to our next hypothesis:

Hypothesis 1b: The association between CDS spreads and accounting numbers

(earnings, leverage, book value), both pre- and post-IFRS adoption, depends on

country-wide institutional differences such as code law versus common law, legal

enforcement, creditor rights, earnings management, local GAAP–IFRS differences,

and conditional conservatism.

Bhat et al. 135

Impact of IFRS on the Relation Between CDS and Accounting Variables

To the extent that earnings, leverage, and book value equity are related to credit risk, the

adoption of IFRS may have enhanced the credit risk relevance of these accounting metrics.

The adoption of IFRS had a potentially large impact on the measurement of earnings, lever-

age, and book value (Moody’s, 2004). Moreover, many of these changes are related to

credit risk. Examples include consolidation of special purpose entities used for securitiza-

tion transactions when the substance of the relationship indicates that they are controlled

by the transferor of the asset; stricter rules for gain recognition from asset securitization,

resulting in more frequent treatment of securitization as a financing transaction; greater

usage of fair values especially during periods characterized by increases in credit risk and

declines in asset prices. At the same time, given the concerns related to greater flexibility

and, consequently, potential for earnings manipulation afforded by IFRS and the appropri-

ate implementation of fair value accounting especially for illiquid assets, the adoption of

IFRS may have decreased the credit risk relevance of these accounting variables. These

considerations lead to our next hypothesis:

Hypothesis 2a: The association between CDS spreads and accounting number (earn-

ings, leverage, book value) post-IFRS adoption is different as compared with pre-

IFRS adoption for firms in countries adopting IFRS.

Skeptics often question whether IFRS credibly provides better information than local

GAAP 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 depends crucially on the effectiveness of regulation, which is

likely to depend in turn on the underlying economic and political institutions that influence

the incentives of the managers and auditors responsible for financial statement preparation

(e.g., Ball et al., 2003). Thus, IFRS may not improve the accounting quality of information

relevant for pricing credit risk uniformly across firms and countries because of additional

factors such as legal and political systems and incentives of financial reporting (Soderstrom

& Sun, 2007).7

More specifically, given that the code law countries, unlike common law countries, had

to switch from a local GAAP that was primarily influenced by governmental priorities to a

uniform set of rules under IFRS, the impact of IFRS should be much more pronounced in

code law countries than in common law countries. Similarly, accounting information in

countries with local GAAP that is largely different from IFRS should be more affected by

the adoption of IFRS than countries that have local GAAP which is quite similar to IFRS

to begin with.

Enforcement of the legal system is especially important in the context of IFRS for two

reasons. First, the International Accounting Standards Board (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, which is responsible for enforcement

(Schipper, 1995). Second, IFRS are principles based, which means that auditors and

accountants need to use judgment and discretion rather than detailed standards to adapt

these principles to specific situations (Ball, 2006). Therefore, we should expect countries

with strong legal enforcement and also countries with better creditor rights protection to be

more affected by the adoption of IFRS. Similarly, it is likely that the extent of earnings

management at the country level should also affect the change in credit risk


informativeness of accounting information. Specifically, given the flexibility afforded by

IFRS, we expect that any impact of IFRS on credit risk informativeness would be less pro-

nounced for high earnings management countries.

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 expli-

citly rejected the notion of conservatism in accounting and indicated a preference for neu-

tral 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 property, plant and equipment (PP&E) and biological assets. Indeed,

Ahmed et al. (2012) 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 with local GAAP, we should expect a weaker inverse

relation between credit spreads and accounting information under IFRS relative to local

GAAP earnings. On the other hand, the use of fair value accounting under IFRS provides

an early warning signal of declining asset prices and, hence, credit risk. The induced infor-

mativeness of fair values under IFRS could potentially have a more significant impact on

firms that are less conservative under local GAAP because of the faster recognition of

losses under fair value accounting.

These considerations lead to our next hypotheses:

Hypothesis 2b: Change in the association between CDS spreads and accounting

numbers following the adoption of IFRS depends on country-wide institutional dif-

ferences such as code law versus common law, legal enforcement, creditor rights,

earnings management, local GAAP–IFRS differences, and conditional

conservatism.

Although the IFRS literature tends to find that institutional factors affect the quality of

accounting information, a caveat is warranted regarding the effect of institutional factors on

the credit risk informativeness of accounting numbers in pricing CDS instruments. The

extant empirical literature that looks at the relation between institutional factors and

accounting quality focuses almost exclusively on the equity markets as end users. Recent

evidence by Florou and Kosi (2013) and Florou et al. (2012) extend the analysis to the cor-

porate bond market and to credit ratings. These findings do not necessarily apply to deriva-

tive credit markets such as the CDS market.

Non-Linearity in CDS Spreads and the Earnings Relation

While earnings may provide credit risk relevant pricing 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 bank-

ruptcy than to information that presages additional profits. Extant evidence of such non-

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

rate 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 et al. (2009) provide evidence supporting non-linearity in the CDS

market; they show that CDS spreads react more to earnings of firms with low profitability.8

Following the discussion above regarding the differences between the U.S. and the

Bhat et al. 137

international setting, one cannot simply generalize the U.S. findings on the non-linear rela-

tion between CDS spreads and the three accounting metrics to the international milieu.

These considerations lead to our last set of hypotheses:

Hypothesis 3a: The association between CDS spreads and each of earnings, book

value, and leverage is greater (smaller) in absolute value for firms with earnings

and book value (leverage) below the median than for firms with these accounting

numbers above the median both pre- and post-IFRS adoption for firms in countries

adopting IFRS.

Hypothesis 3b: Change in the association between CDS spreads and accounting

metrics following the adoption of IFRS is greater (smaller) in absolute value for

firms with earnings and book value (leverage) below the median than for firms

with these accounting numbers above the median.

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 that adopted IFRS

result in an initial sample of 1,392 firms. We match each of the ticker symbols with

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 a 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.9 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 contract.10 We obtain

quarterly financial statement data required to compute market value of equity, return on

assets (ROA), leverage, and book value from the Worldscope database. We download the

financial information in U.S. dollars wherever available; otherwise, we convert the vari-

ables to U.S. dollars using the exchange rate as of the fiscal quarter-end. When available

we use short-term credit ratings from Standard & Poor’s (S&P) to proxy for credit ratings;

otherwise, we use long-term credit ratings. Variable definitions are listed in the appendix.

We impose the following restrictions on the sample: positive book value, positive lever-

age (measured as short-term debt plus long-term debt scaled by market value of equity plus

total liabilities), and non-missing values for each of the following: market value of equity,

ROA (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 six data points), bid–ask spread of the CDS instrument, 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 and the need to reduce any endogeneities arising out of


Tab

le1.

Sam

ple

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cial

390

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whic

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in2005

211

392

603

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ain

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121

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308

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6,6

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Final

full

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105

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234

4,6

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ters

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ters

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sam

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sam

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Pre

Post

Pre

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Aust

ralia

16

54

79

49

67

Den

mar

k2

14

22

13

18

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nd

215

20

15

15

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81

135

78

117

Hong

Kong

516

25

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23

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and

311

17

11

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34

101

31

82

The

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lands

745

67

39

58

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nd

215

23

11

17

Spai

n6

45

61

41

52

Swed

en11

81

118

76

93

The

United

Kin

gdom

23

76

127

70

104

IFR

Sto

tal

105

487

795

449

661

The

United

Stat

es234

1,1

28

3,4

83

449

661

Gra

nd

tota

l5,8

93

2,2

20

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show

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ions,

and

the

num

ber

ofC

DS

contr

acts

bef

ore

and

afte

rIF

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adoption

by

countr

y.C

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ault

swap

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RS

=In

tern

atio

nal

Finan

cial

Rep

ort

ing

Stan

dar

ds.

139

IFRS adoption, we eliminate voluntary adopter observations from our sample. These

restrictions reduce the sample size to 1,401 firm-quarters of 121 firms across 17 countries.

For our control sample, we find 5,278 firm-quarters of 308 U.S. firms, which meet the

same criteria above. The data also indicate that there are significantly more CDS contracts

and, hence, sample data, in the post-IFRS period in comparison with the pre-IFRS period.

This is consistent with the worldwide secular increase in CDS usage. Hence, we require

that each firm has at least one observation in the pre- and post-IFRS adoption period. This

restriction reduces the IFRS sample to 1,282 firm-quarters of 105 firms across 12 countries

and the U.S. sample to 4,611 firm-quarters of 234 firms. To mitigate the effect of outliers,

all continuous variables are winsorized at the top and bottom 1%.

Table 1, 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 five countries:

Australia, France, Italy, Sweden, and the United Kingdom.

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

for the IFRS and U.S. samples by pre- and post-IFRS adoption periods. These variables

include the log of the CDS spread (CDSPRM), Return on Assets (ROA), Log Book Values

(BV), Leverage (LEV), Bid–ask spreads (BID–ASK), Standard Deviations of Returns

(SDRET), the country’s risk-free rate of interest (SPOT), and S&P firm ratings (RATING).

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 med-

ians by period, the data show significant variation between the pre- and post-adoption peri-

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

are larger, are more profitable, have lower leverage, higher bid–ask spread, and experience

greater volatility in equity returns in the post period. In addition, interest rates are also

higher in the post-period. The U.S. firms exhibit a similar pattern in all variables except for

volatility which is lower in the post period. We also find that the IFRS firms and U.S.

firms differ in the pre- and post-periods. Specifically, the CDS spread is higher in the

United States in both periods. In addition, in both periods, U.S firms are smaller, and have

lower profitability and leverage, and have lower credit rating (the higher the ratings

number, the lower the credit rating) and greater return volatility.

Panel B shows the means of the main variables for the 5,893 firm-quarters by country.

On the whole, the data show significant variation across countries. Panel C presents the

institutional variables by country. CODE is equal to 1 if the country has a code law system

and 0 otherwise. Out of the 12 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 CR column is the

creditor rights protection index computed by La Porta et al. (1997). The EM column mea-

sures the extent of earnings management as measured by the country-wide aggregate earn-

ings management score from Leuz et al. (2003). The DIFF column measures the extent of

accounting differences between local GAAP and IFRS for each country based on the sum-

mary score of 21 key accounting dimensions as computed by Bae, Tan, and Welker (2008).

The fifth column lists average DT during the sample period. DT is accounting conservatism

measured using the Basu (1997) DT metric at the country level following Ball et al.

(2003). 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.


Table 2. Descriptive Statistics.

Panel A: Descriptive Statistics of Main Variables for Full IFRS and U.S. Sample (n = 5,893).

IFRS sample N = 1,282 U.S. sample N = 4,611

N = 487 N = 1,128 Significance of IFRS–U.S.

Mean Median SD Mean Median SD Mean Median

PRECDSPRM 3.851 3.697 0.804 3.975 3.821 0.926 *** ***ROA 0.017 0.013 0.023 0.014 0.012 0.016 ***LEV 0.244 0.234 0.124 0.217 0.195 0.134 *** ***BV 8.428 8.457 1.099 8.028 8.025 1.144 *** ***BID–ASK 6.930 5.000 6.480 8.035 5.000 7.434 *** ***RATING 9.466 9.000 3.252 9.846 9.000 3.366 ** ***SDRET 0.061 0.055 0.026 0.072 0.065 0.033 *** ***SPOT 2.863 2.146 1.324 1.651 1.460 0.717 *** ***

N = 795 N = 3,483

Mean Median SD Mean Median SD Mean Median

POSTCDSPRM 4.144 4.098 0.983 4.173 4.016 1.048 *ROA 0.020 0.015 0.024 0.014 0.013 0.017 *** ***LEV 0.231 0.205 0.131 0.203 0.179 0.128 *** ***BV 8.701 8.704 1.237 8.286 8.203 1.149 *** ***BID–ASK 9.017 5.961 8.641 8.831 5.000 7.955 ***RATING 9.250 9.000 3.277 10.062 9.000 3.114 *** ***SDRET 0.065 0.061 0.026 0.068 0.061 0.032 ***SPOT 4.363 4.530 1.322 4.286 4.880 1.133 * ***

Significance of pre–postCDSPRM *** *** *** ***ROA ** ***LEV * *** *** **BV *** ** *** ***BID–ASK *** *** ***RATING *SDRET *** *** *** ***SPOT *** *** *** ***

Panel B: Mean of Main Variables by Country for the Full Sample (n = 5,893).

Country n CDSPRM ROA LEV BV BID–ASK RATING SDRET SPOT

Australia 133 3.632 0.039 0.168 8.126 7.554 8.519 0.060 6.035Denmark 36 3.754 0.007 0.450 8.774 11.247 10.472 0.045 3.320Finland 35 5.330 0.007 0.282 7.635 16.830 14.343 0.082 3.062France 216 4.191 0.017 0.215 9.313 7.404 9.556 0.065 3.218Hong Kong 41 3.676 0.036 0.219 9.781 8.410 10.122 0.060 4.925Ireland 28 3.307 0.005 0.362 8.699 8.970 8.000 0.059 3.173Italy 135 3.997 0.006 0.315 8.916 8.434 9.015 0.058 3.502The Netherlands 112 3.941 0.016 0.165 8.852 5.866 9.205 0.062 3.245Poland 38 3.596 0.003 0.330 6.136 6.221 10.842 0.105 3.445Spain 106 4.028 0.013 0.296 9.086 7.373 8.925 0.056 3.129Sweden 199 3.839 0.020 0.189 8.010 7.632 8.487 0.064 2.821The United Kingdom 203 4.473 0.027 0.224 8.453 9.936 9.626 0.067 4.866The United States 4,611 4.124 0.014 0.206 8.223 8.637 10.009 0.069 3.642

(continued)

Bhat et al. 141

Panel D lists 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. The signs pre- and post-IFRS are identical and consistent with the underlying theory

(Duffie & Lando, 2001; Merton, 1974), with the one exception that SPOT in the pre-period

for the IFRS sample is not significant. Specifically, the CDS spread is correlated positively

with leverage, bid–ask spread, volatility, and the ratings variable; the spread is correlated

negatively with earnings, book value, 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.

Table 2. (Continued)

Panel C: Institutional Variables by Country for the IFRS Sample.

CODE RULE CR EM DIFF DT

Australia 0 1.81 3 24.8 4 0.285Denmark 1 2.03 2 216.0 11 0.236Finland 1 1.95 1 212.0 14 0.216France 1 1.31 0 213.5 12 0.191Hong Kong 0 1.45 4 219.5 3 0.103Ireland 0 1.62 1 25.1 1 0.025Italy 1 0.37 2 224.8 12 0.457Netherlands 1 1.75 3 216.5 4 0.154Poland 1 NA 1 NA 12 0.190Spain 1 1.10 2 218.6 14 0.406Sweden 1 1.86 1 26.8 10 0.279United Kingdom 0 1.73 4 27.0 1 0.292Median 1.73 2 213.5 10.5 0.226

Panel D: Correlations With CDS Spread (CDSPRM) for the Full Sample (n = 5,893).

IFRS sample (N = 1,282) U.S. sample (N = 4,611)

Pre Post Pre Post

ROA 20.174*** 20.099*** 20.423*** 20.420***LEV 0.197*** 0.162*** 0.518*** 0.493***BV 20.158*** 20.093*** 20.404*** 20.418***BID–ASK 0.613*** 0.725*** 0.655*** 0.742***RATING 0.449*** 0.306*** 0.445*** 0.446***SDRET 0.425*** 0.382*** 0.498*** 0.538***SPOT 20.026 0.252*** 20.085*** 20.300***

Note. 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 full 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. full sample by country. Panel C presents the

institutional factors by country, origin of legal system (CODE), strength of enforcement (RULE), creditor rights

(CR), level of earnings management (EM), difference between local GAAP and IFRS (DIFF), and differential

timeliness (DT). Panel D presents sample correlation of CDSPRM (log of CDS spread in basis points) with the

selected variables for the IFRS and U.S. full sample by adoption periods. Variable definitions are provided in the

appendix. IFRS = International Financial Reporting Standards; NA = not applicable.

***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively, using two-tailed tests.


Multivariate Empirical Results

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

earnings, leverage, and book value using a levels analysis. Lack of sufficient cross-country

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

yses to the issue at hand.11 Section ‘‘Main Regressions’’ estimates the general relation

between CDS spreads and ROA, leverage (LEV), and book value equity (BV), including

controls, pre- and post-IFRS. Section ‘‘Institutional Differences, Conditional Conservatism

and CDS Spreads’’ examines whether the relation depends on country characteristics.

Section ‘‘Asymmetry in CDS-Earnings Relation’’ explores whether the relation is non-

linear.

Main Regressions

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

spreads should be decreasing with the reference firm’s ratings quality and wealth (as mea-

sured by earnings and book value) and increasing with the reference firm’s leverage, stock

return volatility, bid–ask spread, and the risk-free rate of interest in the economy.

We regress CDS spreads on their determinants using ordinary least squares (OLS), con-

trolling for industry and quarter fixed effects in all regressions. 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 firm’s rating, the CDS spread

should increase with RATING. We define two indicator variables: POST equals 1 if the

observation falls in the post-IFRS adoption period and 0 otherwise; IFRS equals 1 if the

firm belongs to the IFRS sample and 0 otherwise. Table 3, Panel A shows the marginal

impact of POST or IFRS on the association between CDS spreads and the accounting vari-

ables, whereas Table 3, Panel B shows the total coefficient on each of the accounting vari-

ables in each period for each sample.

Table 3, Panel A, shows the regression coefficient estimates for the full IFRS sample

without (with) adoption period interaction terms in column 1 (2). The third column shows

the regression for the pooled IFRS and U.S. samples inclusive of adoption period and IFRS

interaction terms.

Two potential limitations of the analysis in column (3) are that the U.S. sample size

dominates the IFRS sample size and that U.S. firms have different profitability, leverage,

and book values. To alleviate these concerns and others regarding time series effects and

potential correlated omitted variables (Bertrand, Duflo, & Mullainathan, 2004; Meyer,

1995), column (4) replicates the regression analysis using a matched sample, 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 utilizing propensity score

matching. For each fiscal quarter, we estimate the propensity score using CDS determi-

nants: bid–ask spread, credit ratings, and stock return volatility.12 We select the U.S. match

based on the closest propensity score without replacement. Our final matched sample is

comprised of 1,110 firm-quarters (661 and 449 in the post- and pre-periods, respectively)

for 105 IFRS firms and a control group of 197 U.S. firms.

The results of Table 3 are reasonably consistent with the Merton and Duffie-Lando

model predictions for all regressions. The coefficients on the bid–ask spread, ratings, and

volatility are positive and highly significant in all four regressions as predicted, while the

Bhat et al. 143

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

Panel A: Regression Analysis for IFRS, U.S., and Both Samples.

Variables

IFRS IFRS IFRS and U.S. IFRS and U.S. Matched

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

ROA 23.284*** (.000) 23.601*** (.002) 210.040*** (.000) 210.023*** (.000)ROA 3 IFRS 6.993*** (.004) 7.390*** (.005)ROA 3 POST 0.594 (.644) 3.090 (.105) 2.486 (.294)ROA 3 POST 3 IFRS 22.993 (.190) 21.432 (.600)LEV 0.330 (.306) 0.261 (.498) 1.286*** (.001) 0.995** (.010)LEV 3 IFRS 20.531 (.253) 20.511 (.257)LEV 3 POST 0.099 (.709) 0.474* (.094) 0.591* (.091)LEV 3 POST 3 IFRS 20.635 (.112) 20.769* (.095)BV 20.069** (.014) 20.087** (.019) 20.045 (.284) 20.091** (.044)BV 3 IFRS 20.061 (.277) 0.016 (.774)BV 3 POST 0.027 (.370) 20.056 (.106) 20.007 (.848)BV 3 POST 3 IFRS 0.084* (.067) 0.024 (.654)BID–ASK 0.047*** (.000) 0.047*** (.000) 0.047*** (.000) 0.050*** (.000)RATING 0.036*** (.000) 0.036*** (.000) 0.034*** (.000) 0.027*** (.000)SDRET 3.084*** (.009) 3.162*** (.007) 4.200*** (.000) 5.035*** (.000)SPOT 0.028 (.488) 0.025 (.534) 0.002 (.895) 20.007 (.661)POST 20.421 (.117) 0.366 (.265) 20.197 (.581)IFRS 0.331 (.515) 20.272 (.591)POST 3 IFRS 20.535 (.204) 20.049 (.919)Constant 3.129*** (.000) 3.288*** (.000) 3.105*** (.000) 3.517*** (.000)Fixed effects

Industry Yes Yes Yes YesCountry Yes Yes Yes YesQuarter Yes Yes Yes YesObservations 1,282 1,282 5,893 2,220R2 .792 .793 .752 .745

Panel B: Comparison of Coefficients From Panel A.

Coefficients

IFRS IFRS IFRS and U.S. IFRS and U.S. Matched

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

ROA IFRS PRE 23.601*** 23.046** 22.633**ROA IFRS POST 23.008*** 22.949** 21.579LEV IFRS PRE 0.261 0.755* 0.484LEV IFRS POST 0.360 0.593* 0.306BV IFRS PRE 20.087** 20.106** 20.075**BV IFRS POST 20.059** 20.077** 20.058**ROA U.S. PRE 210.040*** 210.023***ROA U.S. POST 26.950*** 27.536***LEV U.S. PRE 1.286*** 0.995**LEV U.S. POST 1.760*** 1.586***BV U.S. PRE 20.045 20.091**BV U.S. POST 20.102*** 20.098***

Note. Panel A shows the regression estimates of the CDS spread on its determinants. Column 1 (column 2) shows

the regressions using the full IFRS sample. Column 3 shows the full sample results using both IFRS and U.S. firms.

Column 4 replicates column 3 for a matched sample of IFRS and U.S. firms. Panel B shows the comparison of the

regression coefficients based on Panel A. Variables are defined in the appendix. p Values are in parentheses. All

regressions are estimated using OLS with robust standard errors corrected for firm and time clustering. CDS =

credit default swap; IFRS = International Financial Reporting Standards; OLS = ordinary least squares.



interest rate coefficient is not significant. Focusing first on the base model IFRS regression

(column 1), the accounting variables of earnings (ROA) and book value equity (BV) are

significant and have the predicted signs; CDS spreads decrease with earnings and book

value. Leverage is not significant.13

Column 2, Panel B, shows that earnings and book value are highly significant for the

IFRS sample both in the pre- and post-periods, but leverage is not significant. The results

for the pooled U.S./IFRS sample (column 3) are similar except that leverage is now posi-

tive and marginally significant for the IFRS sample. The results of the matched sample

(column 4) show similar results for U.S. firms, whereas the results for IFRS firms are

weaker; earnings is significantly related to CDS spreads in the pre-period but only margin-

ally in the post-period (p value = .15). The coefficient on leverage is not significant both in

the pre- and post-periods, and the coefficient on book value is negative and significant in

both periods. To summarize, overall the IFRS sample results indicate that earnings (at least

in the pre-period) and book values are credit risk relevant but not leverage.

Importantly, columns 2, 3, and 4 of Panel A indicate that the estimated coefficients for

the accounting variables in the post-period are not significantly different from the pre-

period for the IFRS sample. Focusing first on earnings, column 2 shows that the interaction

of the post-indicator with ROA is not significantly different from 0 for the IFRS sample.

The pooled sample (column 3) provides similar results. Specifically, the positive and signif-

icant coefficient on the interaction variable of ROA and IFRS implies that the coefficient

on ROA for the U.S. sample is significantly greater in absolute value than for the IFRS

sample in the pre-period. The positive but marginally significant (p value = .105) coeffi-

cient on the interaction variable of ROA and POST suggests that the credit relevance of

ROA has decreased in the post-period for the U.S. sample. More importantly, the coeffi-

cient on the interaction of POST, IFRS, and ROA is not significant (p value = .19), indicat-

ing that the association between the CDS spread and ROA in the post-period for the IFRS

sample is not significantly different from the pre-IFRS period association. The results for

the matched sample (column 4) are similar, except that there is no change in the credit rele-

vance of ROA in the post-period for the U.S. sample.

The coefficients on leverage and its interactions indicate that association between lever-

age and CDS spreads in the pre-period is similar for the U.S. and IFRS samples. In con-

trast, the association of CDS spreads with leverage is significantly (p value \ .1) higher for

the U.S. sample in the post-period, and there is a marginal decline in the association (p

value = .112) for the IFRS sample. Panel A shows that the coefficients on leverage in the

post-period are just marginally smaller than the coefficients in the pre-period for the IFRS

sample in the pooled regression (p value = .112) and in the matched sample regression (p

value = .095). Although the decrease in the leverage coefficient for the IFRS sample is

marginally significant in the matched sample, Panel B indicates that the overall coefficient

on leverage for the IFRS sample is not significant both in the pre- and post-periods for the

matched sample.

Although the coefficient on the interaction of book value and IFRS is not significant in

column 3, the results in Panel B show that book value is credit risk relevant for the IFRS

sample in the pre-period (p value \ 5%). The interaction variable of POST and book value

for the U.S. sample is negative and marginally significant (p value = .106) and the coeffi-

cient on book value as per Panel B is negative and significant (p value \ 1%), thus indicat-

ing that book value is more credit risk informative in the post-period for the U.S. sample

relative to the pre-period in the pooled regression. The pooled regression indicates a

decrease in the credit risk informativeness of book value in the post-period for the IFRS

Bhat et al. 145

sample, but the results for both the IFRS sample and the matched sample suggest that there

is no change in the credit risk informativeness of book value between the two periods.

Taken together, the results in Table 3 indicate that although earnings and book value

are informative about credit risk for IFRS adopters and earnings, leverage and book

values are informative about credit risk for U.S. firms, the adoption of IFRS did not have

a significant impact on the credit risk informativeness of these three accounting metrics

for IFRS firms.14

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, leverage, and book values,

especially when contrasting pre- and post-IFRS adoption periods. Table 4 replicates Table

3 for the IFRS sample, controlling separately for code versus common law countries,

strong versus weak legal enforcement countries, strong versus weak creditor rights protec-

tion, 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 DT across coun-

tries. Multicollinearity concerns induced by the correlations among these conditioning vari-

ables and the extensive breakdowns of the accounting variables pre- and post-IFRS

interacted with a relatively large number of conditioning variables preclude us from con-

trolling for all six additional factors simultaneously. Instead, we evaluate each of the condi-

tioning variables separately.

The CODE column of Panels A and B of Table 4 conditions the accounting metrics on

code law versus common law countries. COND equals 1 if the country is characterized as

having a code law system and 0 otherwise. Thus, the coefficients on the accounting vari-

ables represent the association between these variables and CDS spreads in common law

countries. The results indicate that the legal system has an impact on the credit risk infor-

mativeness of accounting information. Whereas earnings, leverage (in the post-period), and

book value of equity are all significantly associated with credit risk and in the predicted

direction for common law countries, none of the accounting variables are associated with

credit risk for code law countries. These results suggest that the origin of the legal system

is related to the credit risk informativeness of accounting information, with higher credit

risk informativeness of accounting metrics in common law countries, consistent with the

conjecture that the accounting in common law countries prior to IFRS better reflected eco-

nomic reality in comparison with code law countries. However, the adoption of IFRS did

not affect the credit risk informativeness of accounting information in either legal system;

the coefficients on the accounting variables in the post-period are similar to (not signifi-

cantly different from) the pre-period. These results suggest that the adoption of IFRS did

not affect differences in the quality of accounting information across the two regimes.

The RULE column conditions the accounting variables on the strength of legal enforce-

ment. We partition the accounting variables pre- and post-IFRS based on the legal enforce-

ment score. COND equals 1 if the strength of the legal enforcement is above the median

and 0 otherwise. Given the COND specification, the coefficients on the accounting vari-

ables represent the association between these variables and CDS spreads in countries with

weak legal enforcement in the pre-period. None of the coefficients on the accounting vari-

ables are significant, indicating that accounting information in countries with weak legal

enforcement is not credit risk informative. Although the interaction between these variables

and the COND variable is not significant, Panel B shows that the association between CDS


Tab

le4.

The

Det

erm

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ore

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and

Five

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elA

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ysis

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mple

Bas

edon

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ors

.

CO

ND

CO

DE

RU

LEC

REM

DIF

FD

T

VA

RIA

BLE

S(1

)(2

)(3

)(4

)(5

)(6

)

RO

A2

6.8

78***

(.005)

22.7

87

(.174)

21.4

54

(.346)

25.5

58***

(.009)

25.1

77***

(.007)

20.9

90

(.432)

RO

A3

CO

ND

4.8

77*

(.077)

22.5

12

(.345)

25.1

67**

(.043)

2.9

19

(.282)

4.1

06*

(.083)

27.2

02***

(.006)

RO

A3

PO

ST2.5

35

(.270)

0.7

06

(.762)

21.0

59

(.651)

0.4

22

(.804)

1.1

12

(.493)

20.2

25

(.908)

RO

A3

PO

ST3

CO

ND

22.7

03

(.383)

20.4

20

(.869)

2.5

39

(.383)

0.9

51

(.698)

0.5

08

(.870)

1.3

60

(.609)

LEV

0.6

80

(.280)

0.7

75

(.113)

0.6

85

(.168)

0.4

39

(.477)

0.5

07

(.309)

0.4

53

(.359)

LEV

3C

ON

D2

0.2

40

(.748)

20.4

71

(.442)

20.2

71

(.704)

0.0

15

(.983)

20.4

11

(.547)

20.2

11

(.779)

LEV

3PO

ST0.3

79

(.566)

20.1

61

(.642)

20.0

32

(.927)

0.3

06

(.545)

0.3

47

(.431)

0.3

07

(.376)

LEV

3PO

ST3

CO

ND

20.3

75

(.598)

0.2

81

(.570)

0.0

76

(.881)

20.3

54

(.536)

20.3

43

(.522)

20.2

90

(.583)

BV

20.1

20**

(.038)

20.0

50

(.177)

20.0

32

(.459)

20.1

38*

(.055)

20.1

06**

(.027)

20.0

31

(.459)

BV

3C

ON

D0.0

75

(.292)

20.1

06

(.135)

20.1

13*

(.067)

0.0

80

(.287)

0.0

42

(.522)

20.1

02*

(.100)

BV

3PO

ST0.0

22

(.578)

0.0

47

(.120)

0.0

46

(.142)

0.0

21

(.597)

0.0

34

(.338)

0.0

54

(.126)

BV

3PO

ST3

CO

ND

0.0

27

(.290)

0.0

01

(.943)

20.0

06

(.758)

0.0

10

(.585)

0.0

05

(.767)

20.0

03

(.885)

BID

–A

SK0.0

48***

(.000)

0.0

48***

(.000)

0.0

48***

(.000)

0.0

47***

(.000)

0.0

48***

(.000)

0.0

45***

(.000)

RA

TIN

G0.0

41***

(.000)

0.0

38***

(.001)

0.0

41***

(.000)

0.0

36***

(.001)

0.0

37***

(.000)

0.0

43***

(.000)

SDR

ET

3.7

64***

(.007)

3.5

87***

(.006)

3.7

46***

(.005)

4.6

71***

(.000)

3.6

29***

(.007)

4.2

69***

(.001)

SPO

T0.0

42

(.329)

0.0

42

(.225)

0.0

39

(.343)

0.0

42

(.279)

0.0

56

(.112)

20.0

05

(.878)

PO

ST2

0.4

62*

(.098)

20.5

63**

(.031)

20.4

58

(.109)

20.4

17

(.157)

20.5

17*

(.071)

20.5

73*

(.057)

CO

ND

20.5

86

(.359)

0.8

07

(.207)

0.9

80*

(.086)

20.5

55

(.402)

20.0

28

(.963)

1.1

62**

(.039)

Const

ant

3.8

74***

(.000)

3.3

79***

(.000)

3.1

34***

(.000)

3.9

42***

(.000)

3.5

86***

(.000)

3.0

99***

(.000)

Fixe

def

fect

sIn

dust

ryYe

sYe

sYe

sYe

sYe

sYe

sQ

uar

ter

Yes

Yes

Yes

Yes

Yes

Yes

Obse

rvat

ions

1,2

82

1,2

82

1,2

82

1,2

44

1,2

82

1,2

82

R2

.760

.766

.761

.764

.768

.762

(con

tinue

d)

147

Tab

le4.

(continued

)

Pan

elB

:C

om

par

ison

ofC

oef

ficie

nts

From

Pan

elA

.

CO

ND

CO

DE

RU

LEC

REM

DIF

FD

T

CO

EFF

ICIE

NT

S(1

)(2

)(3

)(4

)(5

)(6

)

RO

AC

ON

DPR

E2

2.0

01

25.2

9***

26.6

2***

22.6

39

21.0

71

28.1

92***

RO

AC

ON

DPO

ST2

2.1

69

25.0

13***

25.1

40***

21.2

66

0.5

49

27.0

57***

LEV

CO

ND

PR

E0.4

40

0.3

04

0.4

15

0.4

54

0.0

95

0.2

42

LEV

CO

ND

PO

ST0.4

44

0.4

25

0.4

59

0.4

06

0.0

99

0.2

59

BV

CO

ND

PR

E2

0.0

45

20.1

56**

20.1

45***

20.0

57

20.0

63

20.1

33***

BV

CO

ND

PO

ST0.0

03

20.1

08**

20.1

05**

20.0

25

20.0

23

20.0

82*

RO

AN

OC

ON

DPR

E2

6.8

78***

22.7

87

21.4

54

25.5

58***

25.1

77***

20.9

90

RO

AN

OC

ON

DPO

ST2

4.3

43***

22.0

80

22.5

13

25.1

36***

24.0

64***

21.2

15

LEV

NO

CO

ND

PR

E0.6

80

0.7

75

0.6

85

0.4

39

0.5

07

0.4

53

LEV

NO

CO

ND

PO

ST1.0

59*

0.6

15

0.0

78

0.7

45

0.8

54*

0.7

60*

BV

NO

CO

ND

PR

E2

0.1

20**

20.0

50

20.0

32

20.1

38*

20.1

06**

20.0

31

BV

NO

CO

ND

PO

ST2

0.0

98*

20.0

03

0.0

14

20.1

16**

20.0

71*

0.0

23

Not

e.Pan

elA

show

sth

ere

gres

sions

ofth

eC

DS

spre

adon

its

det

erm

inan

tsco

nditio

nin

gfo

rin

stitutional

fact

ors

by

countr

yusi

ng

the

full

IFR

Ssa

mple

only

.C

olu

mns

1to

6co

n-

ditio

n(C

ON

D)

on

the

ori

gin

of

lega

lsy

stem

(CO

DE),

stre

ngt

hof

enfo

rcem

ent

(RU

LE),

cred

itor

righ

tsin

dex

(CR

),le

velofea

rnin

gsm

anag

emen

t(E

M),

diff

eren

cebet

wee

nlo

cal

GA

AP

and

IFR

S(D

IFF)

,an

dD

T,re

spec

tive

ly.Pan

elB

show

sth

eco

mpar

ison

of

the

regr

essi

on

coef

ficie

nts

bas

edon

Pan

elA

.Var

iable

sar

edef

ined

inth

eap

pen

dix

.p

Val

ues

are

inpar

enth

eses

.C

DS

=cr

edit

def

ault

swap

;IF

RS

=In

tern

atio

nal

Finan

cial

Rep

ort

ing

Stan

dar

ds;

GA

AP

=G

ener

ally

Acc

epte

dA

ccounting

Pri

nci

ple

s;D

T=

diff

eren

tial

tim

elin

ess;

OLS

=ord

inar

yle

ast

squar

es;RO

A=

retu

rnon

asse

ts.

***,**,

and*

den

ote

sign

ifica

nce

atth

e1%

,5%

,an

d10%

leve

ls,

resp

ective

ly,

usi

ng

two-t

aile

dte

sts.

All

regr

essi

ons

are

estim

ated

usi

ng

OLS

with

robus

tst

andar

der

rors

cor-

rect

edfo

rfir

man

dtim

ecl

ust

erin

g.T

he

num

ber

of

obse

rvat

ions

inth

eEM

colu

mn

issm

alle

rbec

ause

we

do

not

hav

eth

eEM

mea

sure

for

Pola

nd.

The

RO

Ava

riab

les

inea

ch

colu

mn

are

def

ined

asfo

llow

s(t

he

LEV

and

BV

vari

able

sar

edef

ined

sim

ilarl

y):

The

colu

mn

labe

led

‘‘CO

DE’’

conditio

ns

on

Code

vers

us

Com

mon

law

countr

ies.

CO

ND

=1

ifco

de

law

countr

yan

d0

oth

erw

ise.

RO

AC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifco

de

law

and

0oth

erw

ise.

RO

AC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifco

de

law

and

0oth

erw

ise.

RO

AN

OC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifco

mm

on

law

and

0oth

erw

ise.

RO

AN

OC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifco

mm

on

law

and

0oth

erw

ise.

The

colu

mn

labe

led

‘‘RU

LE’’

conditio

ns

on

the

stre

ngt

hofle

galen

forc

emen

t.Val

ues

above

(bel

ow

)th

em

edia

nre

pre

sent

countr

ies

with

stro

ng

(wea

k)le

galen

forc

emen

t.

CO

ND

=1

ifst

rong

lega

len

forc

emen

tan

d0

oth

erw

ise.

RO

AC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifst

rong

lega

len

forc

emen

tan

d0

oth

erw

ise.

RO

AC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifst

rong

lega

len

forc

emen

tan

d0

oth

erw

ise.

148

RO

AN

OC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifw

eak

lega

len

forc

emen

tan

d0

oth

erw

ise.

RO

AN

OC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifw

eak

lega

len

forc

emen

tan

d0

oth

erw

ise.

The

colu

mn

den

ote

dby

‘‘CR

’’co

nditio

ns

on

the

leve

lof

cred

itor

righ

tsin

dex

com

put

edby

LaPo

rta,

Lopez

-De-

Sila

nes

,Sh

leife

r,an

dV

ishn

y(1

997

)fo

rea

chco

untr

y.W

ecl

assi

fy

each

countr

yto

hig

h(low

)C

Rif

the

estim

ated

CR

isab

ove

(bel

ow

)th

em

edia

n.

CO

ND

=1

ifth

eco

untr

y-ye

aris

clas

sifie

das

hig

hC

Ran

d0

oth

erw

ise.

RO

AC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifhig

hC

Ran

d0

oth

erw

ise.

RO

AC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifhig

hC

Ran

d0

oth

erw

ise.

RO

AN

OC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

iflo

wC

Ran

d0

oth

erw

ise.

RO

AN

OC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

iflo

wC

Ran

d0

oth

erw

ise.

The

colu

mn

labe

led

‘‘EM

’’co

nditio

ns

on

the

exte

ntofea

rnin

gsm

anag

emen

tpri

or

toth

ead

option

ofIF

RS.

Val

ues

above

(bel

ow

)th

em

edia

nre

pre

sent

countr

ies

with

hig

h(low

)

earn

ings

man

agem

ent.

CO

ND

=1

ifhig

hea

rnin

gsm

anag

emen

tan

d0

oth

erw

ise.

RO

AC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifhig

hea

rnin

gsm

anag

emen

tan

d0

oth

erw

ise.

RO

AC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifhig

hea

rnin

gsm

anag

emen

tan

d0

oth

erw

ise.

RO

AN

OC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

iflo

wea

rnin

gsm

anag

emen

tan

d0

oth

erw

ise.

RO

AN

OC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

iflo

wea

rnin

gsm

anag

emen

tan

d0

oth

erw

ise.

The

colu

mn

den

ote

d‘‘D

IFF’

’co

nditio

ns

on

the

exte

nt

tow

hic

hth

ere

are

diff

eren

ces

bet

wee

nlo

calG

AA

Pan

dIF

RS.

Val

ues

above

(bel

ow

)th

em

edia

nre

pre

sent

countr

ies

with

larg

e(s

mal

l)IF

RS-

loca

lG

AA

Pdiff

eren

ces.

CO

ND

=1

ifla

rge

acco

unting

diff

eren

ces

and

0oth

erw

ise.

RO

AC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifla

rge

acco

unting

diff

eren

ces

and

0oth

erw

ise.

RO

AC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifla

rge

acco

unt

ing

diff

eren

ces

and

0oth

erw

ise.

RO

AN

OC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifsm

allac

count

ing

diff

eren

ces

and

0oth

erw

ise.

RO

AN

OC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifsm

allac

counting

diff

eren

ces

and

0oth

erw

ise.

The

colu

mn

den

ote

dby

‘‘DT

’’co

nditio

ns

on

the

leve

lof

diff

eren

tial

tim

elin

ess.

We

estim

ate

DT

for

each

countr

yse

par

atel

yin

the

pre

(post

)IF

RS

per

iods

of

2002-

2004

(2006

-

2008)

usi

ng

the

Bas

u(1

997

)sp

ecifi

cation

for

those

firm

sin

each

countr

yfo

rw

hic

hea

rnin

gs,re

turn

s,an

dm

arke

tva

lues

are

avai

lable

on

Dat

astr

eam

.W

ecl

assi

fyea

chco

untr

y

tohig

h(low

)D

Tif

the

estim

ated

DT

isab

ove

(bel

ow

)th

em

edia

n.

CO

ND

=1

ifth

eco

untr

y-ye

aris

clas

sifie

das

hig

hD

Tan

d0

oth

erw

ise.

RO

AC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

ifhig

hD

Tan

d0

oth

erw

ise.

RO

AC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

ifhig

hD

Tan

d0

oth

erw

ise.

RO

AN

OC

ON

DPR

E=

RO

Apre

-IFR

Sad

option

iflo

wD

Tan

d0

oth

erw

ise.

RO

AN

OC

ON

DPO

ST=

RO

Apost

-IFR

Sad

option

iflo

wD

Tan

d0

oth

erw

ise.

149

spreads and each of earnings and book value for strong enforcement countries is significant

at the 1% and 5% level, respectively. The interaction of the accounting variables with the

POST dummy is not statistically significant, implying that the association between account-

ing information and credit risk remains significant (not significant) for strong (weak) legal

enforcement countries. Hence, the results indicate that the credit risk informativeness of

accounting information depends on the strength of legal enforcement, but that the adoption

of IFRS did not have an impact on the credit risk informativeness of accounting informa-

tion irrespective of the strength of legal enforcement.

The CR column of Table 4 conditions the accounting variables on the extent of creditor

rights at the country-year level. We partition the accounting variables pre- and post-IFRS

based on the creditor rights score of La Porta et al. (1997). COND equals 1 if the strength

of creditor rights is above the median and 0 otherwise. Given the COND specification, the

coefficients on the accounting variables represent the association between these variables

and CDS spreads in countries with weak creditor rights in the pre-period. The results indi-

cate that accounting variables are not credit informative in low CR countries both pre- and

post-IFRS. In contrast, earnings and book value are credit risk informative in high CR

countries both pre- and post-IFRS. Furthermore, the relation [or lack thereof] between CDS

and accounting variables did not change from the pre- to the post-periods for the high and

low CR countries.

The EM column of Table 4 conditions the accounting metrics on the extent of

earnings management. Values above (below) the median represent countries with high

(low) earnings management. COND equals 1 (0) if the observation belongs to a country

with high (low) earnings management. Hence, here the coefficients on the accounting

variables represent the association between these variables and CDS spreads in countries

with low earnings management in the pre-period. The results indicate that there is a nega-

tive and significant association between credit risk and each of earnings and book value

of equity in countries with low earnings management both in the pre- and post-periods.

In contrast, accounting information is not credit risk informative for countries with high

earnings management both in the pre- and post-periods. Furthermore, the association

between accounting information and credit risk in the post-period is not different

from the pre-period for both high and low earnings management countries. Overall, the

results suggest that earnings and book value convey credit risk relevant information only

when the level of earnings management is low, and that again IFRS adoption is not

consequential.

The DIFF column of Table 4 conditions the accounting variables on the extent of

accounting differences between local GAAP and IFRS. Values above (below) the median

represent countries with high (low) local GAAP–IFRS differences. COND equals 1 (0) if

the observation belongs to a country with high (low) local GAAP–IFRS difference. Thus,

the coefficients on the accounting variables represent the association between these vari-

ables and CDS spreads in countries with low GAAP–IFRS differences. The results indicate

that earnings and book value of equity are negatively associated with credit risk both in the

pre- and post-periods for countries with low GAAP–IFRS differences. In addition, leverage

is significantly associated with credit risk in these countries in the post-period only. In con-

trast, none of the accounting variables are significantly associated with credit risk both in

the pre- and post-periods for countries with high GAAP–IFRS differences. Because this

analysis conditions directly on the differences between local GAAP and IFRS, these


findings provide strong evidence that the adoption of IFRS had no impact on the credit risk

informativeness of accounting information.

The DT column of Table 4 conditions the accounting metrics on the extent of condi-

tional conservatism measured at the country-year level.15 We rank countries according to

the DT measure in the pre- and post-IFRS periods. COND takes the value of 1 for countries

with DT above the median and 0 otherwise. Thus, the coefficients on the accounting vari-

ables represent the association between these variables and CDS spreads in countries with

low conservatism. We find that earnings and book value of equity are negatively and sig-

nificantly associated with credit risk for high conservatism countries, but not for low con-

servatism countries. More importantly, with the exception of leverage in the post-period for

low conservatism countries, there is no change in the association between accounting infor-

mation and credit risk in either sample.

Overall, the evidence in Table 4 suggests that the institutional factors—origin of law,

strength of the legal system, creditor protection, level of earnings management, difference

between local GAAP and IFRS, and degree of DT—affect the credit risk relevance of earn-

ings, leverage, and book value of equity. However, IFRS adoption was not consequential

for the credit risk informativeness of any of these metrics. These results further validate the

results documented in Table 3 that the adoption of IFRS had no discernible impact on the

credit risk informativeness of accounting information.16

Asymmetry in CDS-Earnings Relation

Callen et al. (2009) show that credit risk, as measured by CDS spreads, is more sensitive to

earnings decreases than earnings increases, which is consistent with a non-linear payoff

function for debt holders. We extend this non-linearity analysis to include leverage and

book values. To explore the possibility that the impact of IFRS is more pronounced for

firms with lower earnings, higher leverage, and smaller book value of equity, we estimate

our regressions partitioning the sample firms according to the level of ROA, LEV, and BV.

In Table 5, columns 1, 2, and 3 show the regression results for the IFRS sample partitioned

based on the median level of earnings, leverage, and book value by quarter, respectively. In

each of the columns, HIGH takes the value of 1 if partitioning variable is greater than the

median and 0 otherwise.

Column 1 shows that earnings are credit relevant only when earnings are below the

median both in the pre- and post-periods. The coefficient on LEV is not significant both in

the pre- and post-periods regardless of the level of earnings. The coefficient on BV is nega-

tive and significant in the pre-period for both high and low earnings firms. The coefficient

remains negative and significant in the post-period only for firms with below median earn-

ings. Hence, these results are consistent with the findings in Callen et al. (2009) and indi-

cate that the credit risk relevance of earnings and book value is more pronounced for firms

with greater credit risk (proxied by below median earnings). Importantly, the adoption of

IFRS again seems to be inconsequential.

Column 2 shows the results when we partition the sample based on leverage.

Accounting information is credit risk relevant for firms with below median leverage both

in the pre- and post-periods. In contrast, only book value is significantly associated with

credit risk for high leverage firms. These findings suggest that proximity to default is the

primary credit relevant information for high credit risk (i.e., above median leverage) firms.

Similar to the results in column 1, the adoption of IFRS did not have an impact on the

Bhat et al. 151

Table 5. The Determinants of CDS Spreads Before and After IFRS Adoption Conditional onEarnings, Leverage, and BV.

Panel A: Regression Analysis Conditional on Level of Earnings, Leverage, and Book Value.

HIGH HIGH ROA HIGH LEV HIGH BV

Variables (1) (2) (3)

ROA 29.623*** (.002) 22.954*** (.009) 22.067 (.198)ROA 3 HIGH 8.696*** (.010) 20.074 (.974) 22.529 (.263)ROA 3 POST 4.523 (.142) 20.507 (.642) 0.972 (.658)ROA 3 POST 3 HIGH 24.719 (.170) 2.391 (.309) 21.100 (.660)LEV 0.133 (.733) 1.134** (.031) 1.028** (.015)LEV 3 HIGH 0.301 (.513) 21.054* (.080) 21.261*** (.003)LEV 3 POST 0.038 (.890) 20.403 (.422) 20.094 (.810)LEV 3 POST 3 HIGH 0.181 (.709) 0.119 (.856) 0.263 (.568)BV 20.091** (.047) 20.079** (.037) 20.098* (.061)BV 3 HIGH 0.014 (.704) 20.029 (.483) 20.059 (.410)BV 3 POST 0.023 (.487) 0.030 (.299) 0.027 (.488)BV 3 POST 3 HIGH 0.002 (.902) 0.013 (.520) 20.000 (.986)BID–ASK 0.047*** (.000) 0.047*** (.000) 0.046*** (.000)RATING 0.035*** (.000) 0.037*** (.000) 0.034*** (.000)SDRET 3.031*** (.008) 3.200*** (.004) 3.093*** (.005)SPOT 0.018 (.645) 0.028 (.505) 0.026 (.509)POST 20.413 (.141) 20.409 (.114) 20.405 (.226)HIGH 20.361 (.189) 0.422 (.273) 0.943 (.127)Constant 3.485*** (.000) 3.092*** (.000) 3.189*** (.000)Fixed effects

Industry Yes Yes YesCountry Yes Yes YesQuarter Yes Yes YesObservations 1,282 1,282 1,282

R2 .796 .798 .799


High High ROA High LEV High BV

Variables (1) (2) (3)

ROA HIGH PRE 20.928 23.028 24.596**ROA HIGH POST 21.124 21.145 24.723***LEV HIGH PRE 0.434 0.080 20.234LEV HIGH POST 0.653 20.203 20.064BV HIGH PRE 20.076** 20.108** 20.157**BV HIGH POST 20.051 20.065* 20.130***ROA LOW PRE 29.623*** 22.954*** 22.067ROA LOW POST 25.100** 23.462*** 21.095LEV LOW PRE 0.133 1.134** 1.028**LEV LOW POST 0.171 0.732* 0.933***BV LOW PRE 20.091** 20.079** 20.098*BV LOW POST 20.067* 20.049 20.071

Note. Panel A shows the regressions of the CDS spread on its determinants conditioning for high versus low ROA,

LEV, and BV for the full IFRS sample only. The HIGH variable is equal to 1 if ROA, LEV, and BV are above the

median in columns 1, 2, and 3, respectively. Panel B shows the comparison of the regression coefficients based on

Panel A. Variables are defined in the appendix. p Values are in parentheses. All regressions are estimated using

OLS with robust standard errors corrected for firm and time clustering. CDS = credit default swap; IFRS =

International Financial Reporting Standards; OLS = ordinary least squares.



relation between accounting information and credit risk conditional on the magnitude of

leverage.

Column 3 reports the results when we partition the sample based on book value of equity.

We find that earnings and book value provide credit relevant information in the pre- and

post-periods for firms with above median book value of equity, which are firms with low

credit risk. In contrast, leverage is the only credit relevant accounting variable for firms with

below median book value of equity, implying that leverage plays a crucial role in the pricing

of CDS when proximity to default is high (i.e., the firm is closer to default). These results

hold for both the pre- and post-periods, again implying that the adoption of IFRS did not

affect the relation between the pricing of credit risk and accounting information.

Taken together, the relation between accounting information and CDS spreads appears

to be non-linear and depends on the level of credit risk, as proxied by the level of earnings,

leverage, and book value. Furthermore, this relation is not affected by the adoption of

IFRS, thereby providing additional support to our previous findings that the adoption of

IFRS did not affect the credit risk informativeness of accounting information.

Further Robustness Checks

To further validate our results, we undertake a series of robustness checks as follows:

Crisis Quarters

To ensure that our results are not driven simply by the financial crisis of 2007-2008, we

examine our results after excluding the financial crisis quarters. In July 2007, market illi-

quidity became increasingly broad and severe in several distinct waves over time, and

extended well beyond the market for subprime positions (Ryan, 2008). Hence, we define

the financial crisis period as the quarters starting from the third quarter of 2007 and ending

in the fourth quarter of 2008.

Column 1 of Table 6 reports the results when we exclude the financial crisis period. The

results are very similar to column 4 of Table 3 with two exceptions, the coefficient on

book value is more negative in the post-period for the U.S. sample and the coefficient on

leverage is positive and significant in the post-period for the IFRS sample. Importantly, the

change in the coefficients for the IFRS sample in the post-period is not significant.

Excluding U.K. Firms

We examine whether the results are affected by the inclusion of U.K. firms. These firms dom-

inate our sample (largest in terms of number of firms) and the United Kingdom is a common

law country, has high RULE, low earnings management, low DIFF, and high DT. Thus, U.K.

firms are potentially similar to our control sample of U.S. firms, and, hence, the adoption of

IFRS is expected to have a low impact on U.K. firms. Results excluding U.K. firms are pre-

sented in column 2 of Table 6. The results indicate that none of the accounting variables are

related to CDS spreads both in the pre- and post-periods for the IFRS sample, and with the

exception of leverage, the change in the coefficients for the IFRS sample in the post-period is

not significant. These results again suggest that IFRS adoption is not consequential.17

Bhat et al. 153

Table 6. Matching Sample Analysis.

Panel A: Regression Analysis Using Matched Sample Excluding Crisis Period, U.K. Firms, and AllowingControls to Vary.

Variables

EXCL. crisis EXCL. UK Controls vary

(1) (2) (3)

ROA 210.307*** (.000) 210.634*** (.000) 210.448*** (.000)ROA 3 IFRS 7.007*** (.010) 9.418*** (.001) 7.759*** (.003)ROA 3 POST 0.984 (.720) 2.962 (.271) 3.585 (.138)ROA 3 POST 3 IFRS 1.795 (.571) 22.846 (.355) 22.427 (.397)LEV 0.853** (.033) 0.978** (.016) 0.919** (.016)LEV 3 IFRS 20.406 (.362) 20.632 (.171) 20.323 (.484)LEV 3 POST 0.187 (.563) 0.681* (.080) 0.604* (.083)LEV 3 POST 3 IFRS 20.026 (.954) 20.841* (.094) 20.915** (.049)BV 20.078* (.087) 20.088* (.070) 20.080* (.063)BV 3 IFRS 0.000 (.993) 0.027 (.643) 0.009 (.868)BV 3 POST 20.104** (.011) 20.010 (.807) 20.009 (.820)BV 3 POST 3 IFRS 0.050 (.365) 0.027 (.649) 0.013 (.794)BID–ASK 0.049*** (.000) 0.046*** (.000)RATING 0.033*** (.001) 0.032*** (.000)SDRET 4.810*** (.000) 5.165*** (.000)SPOT 0.007 (.809) 0.010 (.605)POST 0.708* (.067) 20.188 (.633) 0.038 (.929)IFRS 20.176 (.730) 20.381 (.489) 20.171 (.749)POST 3 IFRS 20.404 (.428) 20.002 (.997) 0.217 (.693)Constant 3.322*** (.000) 3.443*** (.000) 3.247*** (.000)Fixed effects

Industry Yes Yes YesCountry Yes Yes YesQuarter Yes Yes YesObservations 1,684 1,872 2,220R2 .700 .750 .751


Coefficients

EXCL. crisis EXCL. U.K. Controls vary

(1) (2) (3)

ROA IFRS PRE 23.300*** 21.216 22.689**ROA IFRS POST 20.521 21.100 21.530LEV IFRS PRE 0.447 0.346 0.596*LEV IFRS POST 0.607* 0.187 0.286BV IFRS PRE 20.077* 20.060 20.071*BV IFRS POST 20.132*** 20.044 20.067**ROA U.S. PRE 210.307*** 210.634*** 210.448***ROA U.S. POST 29.323*** 27.672*** 26.863***LEV U.S. PRE 0.853** 0.978** 0.919**LEV U.S. POST 1.039*** 1.659*** 1.524***BV U.S. PRE 20.078* 20.088* 20.080*BV U.S. POST 20.182*** 20.098*** 20.088***

Note. Panel A shows the regression estimates of the CDS spread on its determinants for the matched IFRS andU.S. sample. Column 1 replicates column 6 of Table 3 excluding the crisis quarters defined as the period betweenthe third quarter of 2007 to fourth quarter of 2008. Column 2 replicates column 6 of Table 3 excluding firms fromU.K. and matched U.S. firms. Column 3 replicates column 6 of Table 3 allowing each of the four control variables(BID–ASK, RATING, SDRET, SPOT) to vary by period (PRE vs. POST). Panel B shows the comparison of theregression coefficients based on Panel A. Variables are defined in the appendix. p Values are in parentheses. Allregressions are estimated using OLS with robust standard errors corrected for firm and time clustering. CDS =credit default swap; IFRS = International Financial Reporting Standards; OLS = ordinary least squares.***, **, and * denote significance at the 1%, 5%, and 10% levels, respectively, using two-tailed tests.


Interacting Control Variables

The coefficients on the control variables are constrained to be equal in the post- and pre-

IFRS periods and for IFRS firms and U.S. firms in the main regressions of Table 3.

However, it is possible that the relation between the CDS spread and all of its determinants

varies by sample (IFRS vs. U.S) and period. Hence, we allow the coefficients on our con-

trol variables BID–ASK, RATING, SDRET, and SPOT to vary by sample and period.

Column 3 of Table 4 shows the results when we interact the control variables with the

IFRS and POST dummies. The results indicate that earnings and leverage are significantly

related to CDS spreads in the pre-period, but become insignificant in the post-period (p

value of coefficient on ROA = .18 and p value of coefficient on LEV = .36). Book value is

significant both in the pre- and post-periods.

Asymmetry in CDS-Earnings Relation Based on Investment Grade

Following Florou et al. (2012), we also partition the sample based on investment grade

versus low grade debt. Investment grade debt is defined as having a credit rating of

BBB1 and above. All other debt is low grade. This division between investment and low

grade reflects the fact that firms on which CDS instruments are written tend to be of

higher grade than the Compustat average. This division also breaks the observations

fairly evenly between the two categories. The (untabulated) results indicate that earnings

and book value are significant both pre- and post-IFRS for speculative rating, and book

value is significant for investment grade firms. Similar to the results above, the adoption

of IFRS did not affect the association of the accounting variables with CDS spreads for

both samples.18

Conclusion

The adoption of IFRS provides a unique research opportunity to examine the impact of

financial statement informativeness on credit risk because the switch to IFRS for most

firms was exogenously mandated by accounting regulators, mitigating the potential

impact of confounding endogenous events. Our analysis indicates that earnings, book

value, and, to a lesser extent, leverage are significant pricing determinants of credit risk

both pre- and post-IFRS, but that the adoption of IFRS did not change the overall credit

risk informativeness of these three accounting metrics. We also find that institutional

factors—origin of law, strength of the legal system, level of earnings management, differ-

ence between local GAAP and IFRS, degree of DT, and creditor protection—affect the

credit risk relevance of earnings, leverage, and book value of equity. However, IFRS

adoption was not consequential for the credit risk informativeness of any of these metrics.

The lack of impact of IFRS adoption is robust to excluding financial crisis quarters,

excluding U.K. or French firms, allowing all control variables to vary by period and by

IFRS and U.S. samples, breaking down earnings into cash flows and accruals, and for dif-

ferent CDS maturities. Overall, in contrast to the evidence of the impact of IFRS on

equity holders and credit ratings, our results indicate that the adoption of IFRS is not con-

sequential for the relevance of fundamental accounting numbers in pricing credit risk in

the CDS market.

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Acknowledgments

We wish to acknowledge the anonymous reviewer of this Journal for detailed and constructive com-

ments. We wish to thank workshop participants at the University of Missouri-Columbia, University of

Notre Dame, University of Waterloo, Washington University in St. Louis, and the Hebrew University

of Jerusalem. We also wish to thank conference participants at the 2012 Winter Global Conference

on Business and Finance, 2011 University of Minnesota Empirical Accounting Research Conference,

2011 Utah Winter Accounting Conference Program, and the 21st Annual Conference on Financial

Economics & Accounting. We also with to acknowledge Richard Carrizosa the discussant at the 2011

Utah conference and Nicole Jenkins the discussant at the 21st Annual Conference on Financial

Economics & Accounting.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/

or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or

publication of this article: Callen acknowledges the Social Sciences and Humanities Council of

Canada for generous funding of this project. Bhat acknowledges funding from the Center for

Research in Economics and Strategy (CRES) at the Olin Business School.

Notes

1. In this article, the term credit risk relevance refers to the relevance of accounting information in

the pricing of credit risk.

2. Our sample excludes firms that adopted International Financial Reporting Standards (IFRS)

voluntarily.

3. While differential timeliness (DT) is also related to country characteristics such as code versus

common law (Ball, Kothari, & Robin, 2000; Bushman & Piotroski, 2006) and strength of securi-

ties law enforcement (Bushman & Piotroski, 2006), we treat DT as an independent feature of the

accounting system.

4. These are relative statements. Credit default swap (CDS) markets also suffer from potential

illiquidity—indeed, we control for the bid–ask spread in our empirical tests—but far less so than

corporate bond markets.

5. Also, unlike equities, even the largest corporate bonds do not trade very often and bond prices

are often not observable. Instead, published bond prices are often stale or simply interpolated.

One may still be tempted to argue that as CDS instruments are derivatives whose price depends

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 firm’s

wealth and asset dynamics and, hence, about the probability of the occurrence of credit events

such as bankruptcy.

6. Code-law accounting affords managers more latitude in timing income recognition, thereby

obfuscating the economic performance of the firm. 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 et al., 2000). This can be accomplished, for example,

by firms creating earnings reserves in good years through excessive impairment charges and pro-

visions, and using these reserves in bad years. These are promoted by the government’s stake-

holder policy.


7. Empirical evidence highlights the role of institutions in moderating the impact of IFRS adoption

on economic constructs. Specifically, Li (2010) and Daske, Hail, Leuz, and Verdi (2013) show that

IFRS has an impact on the cost of equity and valuation only in countries with strong enforcement

of securities laws. Florou, Kosi, and Pope (2012) 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.

8. As pointed out by Callen, Livnat, and Segal (2009), firms that reference CDS instruments tend

to be large and successful with few loss quarters. Therefore, we measure asymmetry with refer-

ence to the median level of earnings rather than profits and losses.

9. Like many other CDS studies, we focus our analysis on 5-year maturities because they are the

most common and the most liquid. Results for other maturities are similar (see Note 14).

10. Restructuring clauses are only available from 2008. As a result, we cannot control for this vari-

able in the analysis.

11. Only U.S. databases provide sufficient CDS data for such analyses (see Callen et al., 2009).

12. We report the results for the matched sample based on propensity score matching excluding the

accounting variables—earnings, leverage, and book value—as we are interested in the effect of

IFRS on these variables. However, we ran an alternate analysis, using propensity score matching

on all six variables: earnings, leverage, book value, bid–ask spread, credit ratings, and stock

return volatility. The results of the alternate analysis are similar.

13. Absent industry fixed effects, leverage in the IFRS base regression is positive and significant.

Leverage is known to be highly industry specific.

14. Consonant with the CDS literature, the analysis above focused on 5-year maturities, the most

common CDS maturity. We also replicated our analysis for maturities from 1 to 10 years and

obtained qualitatively similar results. Importantly, irrespective of the maturity, IFRS had no

impact on the credit risk relevance of our three accounting metrics.

15. In untabulated results, we find that DT decreased significantly after the adoption of IFRS; mean

(median) DT in the pre- and post-IFRS periods is 0.33 (0.33) and 0.15 (0.19), respectively. This

is consistent with the anti-conservatism tendency of IFRS.

16. Christensen, Hail, and Leuz (2013) show that the IFRS-related liquidity benefits in the equity

market are attributable to changes in financial reporting enforcement by some countries. Hence,

we perform two tests to mitigate concerns that the results that we attribute to the mandatory

IFRS adoption on the CDS market may in fact be driven by concurrent country institutional

reforms. First, similar to Florou and Kosi (2013), we condition on whether the firm belongs to a

country which is a member of the European Union or not. Second, similar to Florou and Kosi,

we condition our regression analysis on whether the firm belongs to a country whose local super-

visory authorities shifted from reactively reviewing financial statements to a proactively review-

ing them at the time of mandatory IFRS adoption; these countries include Finland, Germany, the

Netherlands, Norway, and the United Kingdom (Christensen et al., 2013). The (untabulated)

results show that IFRS had no impact on the credit risk relevance of our three accounting

metrics.

17. We examine whether the results are affected by the inclusion of French firms. French firms dom-

inate our sample in terms of firm-quarter observations. The results are virtually identical to those

reported in column 2 and indicate again that the adoption of IFRS did not affect the association

between CDS and each of earnings and book value.

18. Because debt holders may be more concerned with cash flows than earnings, we also break down

earnings into cash flow and accrual components and test whether IFRS had an impact on credit

risk relevance of cash flows and accruals. Callen et al. (2009) find that both cash flows and

accruals are equally significant in pricing U.S. CDSs. We break down earnings (return on assets

[ROA]) into cash flows and accruals (both deflated by total assets) in three different ways. First,

we use the statement of cash flows to obtain cash from operations and then subtract this number

Bhat et al. 159

from earnings before extraordinary items to obtain accruals. Second, we estimate cash flows as

earnings before extraordinary items plus depreciation and define accruals as depreciation. Third,

we define cash flow as earnings before extraordinary items plus depreciation plus net current assets

and define accruals as depreciation plus net current assets. Each method yields different sample

sizes with the largest sample obtained from the statement of cash flows (955 observations). The

(untabulated) results are mixed regarding the relative importance of cash flows versus accruals in

pricing of credit risk. However, uniformly, across all three specifications, we observe that adoption

of IFRS did not change the credit risk relevance of cash flows and accruals.

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Case of Credit Default Swaps The Author(s) 2014over-the-counter credit default swap (CDS) market. IFRS uses a principles-based approach, emphasizes fair value accounting, and aims