Impact of SFAS 133

THE ACCOUNTING REVIEW American Accounting AssociationVol. 88, No. 1 DOI: 10.2308/accr-502642013pp. 233260

The Impact of SFAS 133 on IncomeSmoothing by Banks through Loan Loss

Provisions

Emre KilicGerald J. Lobo

University of Houston

Tharindra Ranasinghe

Singapore Management University

K. Sivaramakrishnan

Rice University

ABSTRACT: We examine the impact of SFAS 133, Accounting for DerivativeInstruments and Hedging Activities, on the reporting behavior of commercial banks andthe informativeness of their financial statements. We argue that, because mandatoryrecognition of hedge ineffectiveness under SFAS 133 reduced banks ability to smoothincome through derivatives, banks that are more affected by SFAS 133 rely more on loanloss provisions to smooth income. We find evidence consistent with this argument. Wealso find that the increased reliance on loan loss provisions for smoothing income hasimpaired the informativeness of loan loss provisions for future loan defaults and bankstock returns.

Keywords: SFAS 133; income smoothing; hedging, derivatives; loan loss provisions.

Data Availability: The data are available from public sources.

I. INTRODUCTION

This study examines the impact of SFAS 133 (FASB 1998), Accounting for Derivative

Instruments and Hedging Activities, on the reporting behavior of commercial banks and the

informativeness of their financial statements. Extant research suggests that firms view

hedging and reporting discretion as substitute mechanisms available for smoothing income (Barton

2001; Pincus and Rajgopal 2002). Prior to SFAS 133, SFAS 52 (FASB 1981) and SFAS 80 (FASB

We gratefully acknowledge the helpful comments of Giri Kanagaretnam, Mort Pincus, and workshop participants atBrock University, Singapore Management University, University of Colorado at Boulder, Oklahoma State University,and the 2010 AAA Annual Meeting.

Editors note: Accepted by Steven Kachelmeier.

Submitted: August 2010Accepted: July 2012

Published Online: July 2012

233

1984) allowed considerable room for judgment in assessing hedge effectiveness for accounting

purposes. In particular, because the ineffective portions of hedges were generally ignored in the

reporting of income (Ryan 2007; Ahmed et al. 2011), banks had the latitude to time the recognition

of gains and losses from ineffective hedges with the purpose of reducing income volatility.

Consequently, firms did not have to resort to the use of reporting discretion in non-derivative items

as much to achieve their smoothing objectives.

SFAS 133 changed accounting for derivatives substantially by forcing recognition of (1) allhedging derivatives at their fair values, and (2) any hedge ineffectiveness in income as it occurs. As

a result, the hedging derivative gains/losses recognized in income by derivative user banks almost

quadrupled following the adoption of SFAS 133 (Table 2), with as much as 20 percent of those

gains/losses arising from ineffective hedging. Clearly, hedge ineffectiveness increases income

volatility because gains and losses from hedges and hedged items are not offsetting (} 22, SFAS133).1 Ostensibly, the Bankers Roundtable anticipated such income effects and opposed the draft

proposal of the standard back in 1997 by noting in a letter to the FASB that:

The draft proposes an unworkable framework that would introduce artificial and

inappropriate volatility on the financial statements of corporations that safely use

derivatives for hedging activities. (Bankers Roundtable 1997)

Thus, SFAS 133 would arguably inhibit the use of derivatives in smoothing income. This

possibility raises at least two interesting research questions regarding the impact of SFAS 133 on

the reporting behavior of managers, and the consequent impact on the informativeness of financial

statements. In particular:

1. Did firms using derivative instruments for hedging significantly alter their reporting

behavior post-SFAS 133?

2. Notwithstanding any changes in reporting behavior, by enacting SFAS 133, did the FASB

achieve its intended purpose of making financial statements more transparent to users?

We address these questions in the context of commercial banks. We focus on banks because

derivative instruments present a natural way for banks to hedge exposures of their financial assets

and liabilities to interest rate and exchange rate risks. Evidence indicates that bank managers, on

average, exhibit a proclivity to smooth reported income (Wahlen 1994; Collins et al. 1995;

Kanagaretnam et al. 2003; Kanagaretnam et al. 2004; Liu and Ryan 2006).

There are two potential ways in which banks can respond to the increased volatility from

ineffective hedging in the post-SFAS 133 period. They can rely more on their reporting discretion

with respect to loan loss provisions (LLP), and/or they can simply become more effective hedgers.

Therefore, whether and to what extent banks have increased their reliance on LLP for smoothing

can only be addressed through empirical examination, which is the focus of this study.

A direct indicator of the extent to which SFAS 133 has affected banks is the magnitude of gains

and losses from ineffective hedging reported in 10-K filings. Our findings are consistent with the

hypothesis that discretionary LLP is positively associated with hedging derivative gains/losses

post-SFAS 133 for affected banks (i.e., banks that use derivatives and report gains/losses fromineffective hedging), but not so for unaffected banks (i.e., banks that use derivatives and do notreport gains/losses from ineffective hedging). While SFAS 133 may have changed the hedging

behavior itself by forcing banks to hedge more effectively, our findings raise the possibility that the

rigidity of SFAS 133 with respect to the recognition of hedging derivative gains/losses has resulted

1 Our examination of bank response letters to the Exposure Draft for SFAS 133 reveals that almost all banksspecifically point out the unfavorable impact of SFAS 133 on income volatility. Also, Singh (2004) reports thattwo-thirds of all the response letters discuss the impact of SFAS 133 on income.

234 Kilic, Lobo, Ranasinghe, and Sivaramakrishnan

The Accounting ReviewJanuary 2013

in at least some banks (affected banks) resorting to a greater use of LLP for their income-smoothingobjectives.

Moreover, if banks were indeed relying more on LLP for smoothing income post-SFAS-133,

then LLP should be less informative about future loan defaults (charge-offs) for affected banksrelative to unaffected banks and non-user banks (i.e., banks that do not use derivatives). We findsupport for this hypothesis as well. Collectively, these results suggest that the level of earnings

management through LLP has increased in the post-SFAS-133 period for affected banks but not forunaffected banks or for non-user banks.

Next, we examine whether the market is able to discern this shift in reporting behavior

following the adoption of SFAS 133. Our results indicate that the positive association between

discretionary LLP and market returns is significantly lower in the post-SFAS 133 period compared

to the pre-SFAS 133 period for affected banks than for unaffected banks and for non-user banks.2

That is, the increased use of LLP for smoothing income appears to have impaired its

informativeness from the markets perspective. Additionally, we do not detect any statistically

significant change in the relation between returns and hedging derivative gains/losses following the

adoption of SFAS 133. Thus, we are unable to reject the null hypothesis that the informativeness of

these gains and losses has not increased post-SFAS 133, even though the standard has effectively

curtailed much of the discretion banks had in timing these gains and losses. These results are robust

to a number of sensitivity checks and a variety of alternative model specifications.

Our study adds to the literature in at least two important ways. First, it provides evidence on

how SFAS 133 affected reporting behavior, an issue unexamined by prior research. While it is not

possible to directly assess the extent to which banks might have used LLP to offset hedge

ineffectiveness prior to SFAS 133, a variety of tests in this study collectively indicate that bank

managers increase their reliance on discretionary LLP to counteract the undesirable effects ofSFAS 133s recognition requirements. Second, prior evidence indicates that SFAS 133 has altered

the pricing of bank derivatives in equity markets (Ahmed et al. 2006) and bond markets (Ahmed et

al. 2011). Our study adds to those findings by showing that, for banks, the effects of SFAS 133 go

beyond derivatives; SFAS 133 also altered the information content of a non-derivative financial

statement component (i.e., LLP) and the pricing of that component in equity markets.

We discuss the institutional background and develop our hypotheses in Section II, describe the

data, sample selection, and research design in Section III, present and discuss results in Section IV,

and provide concluding remarks in Section V.

II. BACKGROUND AND HYPOTHESES

Hedge accounting helps banks to avoid income volatility and smooth income by synchronizing

the timing of recognition of gains and losses on the hedged item and the hedging derivative and

recognize offsetting gains and losses concurrently in income.

Prior to SFAS 133, accounting for derivatives was guided by SFAS 52, Foreign CurrencyTranslation, and SFAS 80, Accounting for Futures Contracts, and by Emerging Issues Task Force(EITF 1984) Issue No. 84-36, which addressed the accounting for interest rate swaps and some

hedging activities not covered by SFAS 52 or SFAS 80. These pronouncements were incomplete

(SFAS 133, } 235) and inconsistent (SFAS 133, } 236).3 In the pre-SFAS 133 period, even thoughthe ineffective portion of derivative hedges would flow through the income statement, they were not

2 The positive association between bank stock returns and discretionary LLP is consistent with the findings of priorstudies (e.g., Beaver et al. 1989; Liu and Ryan 1995) and the argument presented in Wahlen (1994) that bankmanagers increase discretionary LLP to signal favorable future cash flow prospects.

3 Rane (1992), Montesi and Lucas (1996), Anson (1999), Gastineau et al. (2001), and SFAS 133 (} 233237)present detailed discussions of these inconsistencies.

The Impact of SFAS 133 on Income Smoothing by Banks through Loan Loss Provisions 235


easily discernible to financial statement users. Consequently, managers could exercise considerable

discretion in the timing and the recognition of derivative gains and losses (Gastineau et al. 2001;

Marthinsen 2003).4

SFAS 133 imposes stringent rules that require documentation of the entitys risk management

strategy, hedging relationship at inception, and method of assessing hedge effectiveness, thus

substantially eliminating the discretion that managers previously had. In particular, SFAS 133

places restrictions on the assessment and treatment of ineffective hedges. Because the income

effects of the ineffective portions of hedges were generally ignored under prior accounting

standards (Ryan 2007), firms could potentially classify even ineffective (non-offsetting) derivatives

as accounting hedges and time the recognition of gains and losses in order to reduce income

volatility. In contrast, SFAS 133 requires firms to assess the extent to which derivative instruments

are effective at hedging, and mandates immediate recognition of the ineffective portions, thus

making hedge ineffectiveness more apparent than under prior accounting standards.

Given these changes in accounting for hedging derivatives, the income statement is more

exposed to unrealized gains and losses from the ineffective portion of hedging derivatives after

SFAS 133. Consequently, banks may have to look elsewhere to manage income patterns. In

particular, we focus on the use of LLP for earnings management post-SFAS 133 as this is the

largest accrual for banks and prior research has documented that banks use LLP for smoothing

income (Wahlen 1994; Kanagaretnam et al. 2003; Kanagaretnam et al. 2004; Liu and Ryan 2006).

Banks that are more effective in their hedging activities would be less concerned about the

standards impact on income volatility. Therefore, we do not expect such banks (i.e., unaffectedbanks) to change their reporting discretion as much in the post-SFAS 133 period. In contrast, banks

that are relatively ineffective at hedging (i.e., affected banks) are more likely to rely on LLP forsmoothing income following the adoption of SFAS 133. We note that because the ineffective portion

of hedging is not discernible from the financial statements in the pre-SFAS 133 era, it is not possible

to directly assess the extent to which affected banks may have relied on LLP for smoothing purposesin this period. However, as we have noted above, banks had significant discretion in timing and

recognition of gains and losses pre-SFAS 133, and our premise is that they would use this discretion

to achieve their smoothing objectives and therefore not rely so much on LLP for this purpose,

especially given that the use of LLP for earnings management purposes is not costless. Regulators and

auditors are aware of managerial incentives to misuse the inherent discretion in LLP and endeavor to

ensure that LLP estimates closely reflect subsequent actual charge-offs.5 Hence, whether ineffective

hedgers are able to increase their use of LLP post-SFAS 133 is a question that can only be addressed

empirically. Accordingly, we test the following hypothesis:

H1: The pre- to post-SFAS 133 change in income smoothing through LLP is greater for banksthat are less effective at hedging (affected banks) than for banks that are more effective athedging (unaffected banks).

It is plausible that hedging behavior itself might change following the adoption of SFAS 133. In

particular, less effective hedgers are likely to have a stronger incentive to design more effective hedges.

Using a sample of nonfinancial firms, Zhang (2009) finds empirical evidence in support of this notion.

4 For example, in response to the Exposure Draft for SFAS 133, the Hartford Financial Services Group (1997)indicates that accounting guidance for derivatives prior to SFAS 133 requires users to interpret and develop theirown accounting practices (emphasis added).

5 For example, implementation of SFAS 133 coincided with the releases of SECs (2001) Staff Accounting Bulletin102 and Federal Financial Institutions Examination Councils (2001) Policy Statement on Allowance for Loan andLease Losses (ALLL) Methodologies and Documentation for Banks and Savings Institutions. Both of thesereleases attempt to improve the methodology and documentation with respect to LLP and are driven by earningsmanagement as well as capital adequacy concerns (Phillips and Lierley 2001/2002).



If the banks in our study were successful at becoming more effective hedgers post-SFAS 133, then we

would not expect to find support for H1. On the other hand, to the extent hedge ineffectiveness is

unavoidable despite efficient hedging, we would expect to find support for H1.6

Next, we examine whether the informativeness of LLP declined for affected banks followingSFAS 133. LLP, by intent, reflects information about future loan defaults (Beaver et al. 1989;

Wahlen 1994). It is therefore reasonable to posit that use of LLP to smooth hedge ineffectiveness

weakens its usefulness for predicting future loan losses. In a 1994 report, the Government

Accountability Office states that some banks maintained significant amounts of unsupported loan

loss reserves not clearly linked to likely losses (GAO 1994). Therefore, we expect the association

between current-period LLP and next-period loan charge-offs to weaken from pre- to post-SFAS

133 for affected banks. We also expect this association to weaken in the post-SFAS 133 period ashedge ineffectiveness increases. Accordingly, we test the following hypotheses:

H2a: Banks that are less effective at hedging (affected banks) experience a reduction in theassociation between current-period LLP and next-period loan charge-offs from pre- to

post-SFAS 133, as compared to banks that are more effective at hedging (unaffectedbanks).

H2b: Banks that are less effective at hedging (affected banks) experience a reduction in theassociation between current-period LLP and next-period loan charge-offs from pre- to

post-SFAS 133, as compared to banks that do not use derivatives (non-user banks).

H2c: The association between current-period LLP and next-period loan charge-offs decreaseswith hedge ineffectiveness.

We next turn to the question of the impact of SFAS 133 on the informativeness of financial

statements. As noted earlier, banks had considerable discretion in timing the recognition of hedging

derivative gains/losses in the pre-SFAS 133 period. SFAS 133 curtails this discretion and promotes

more timely recognition of hedging derivative gains/losses, which would presumably increase

income volatility, all else equal. While proponents of SFAS 133 did not deny the potential for this

increased volatility, they argued that such increases would merely reflect economic reality that the

prior standards governing hedge accounting failed to capture (Smith et al. 1998). That is, the

ineffective portions of hedges contain information about the extent to which bank hedging policies

are effective.

However, critics argued that this increase in volatility diminishes the usefulness of financial

statements in assessing the fiscal condition of a bank. For example, in a letter to the FASB,

Bankers Roundtable (1997) claims:

The requirement that derivative instruments be reported at fair value is fundamentally

problematic. As many observers have already noted, the result would introduce artificial

volatility into financial statements. This would have a deleterious effect for market

participants attempting to determine the institutions true financial condition.

6 In general, hedge ineffectiveness arises when hedging derivatives and hedged assets/liabilities mismatch in termsof duration, reset dates, currency, or benchmark rates. For instance, partial term hedging strategies are inherentlysubject to timing mismatch, and basis risk hedging strategies are typically subject to benchmark rate mismatch(e.g., when the yield on a hedged asset is based on the U.S. prime rate and the yield on the hedging derivative isbased on the London Interbank Offered Rate). Derivatives are viewed as efficient and low-cost hedging tools (asopposed to replicating portfolio strategies) for banks, even though some ineffectiveness may increase incomevolatility. However, the benefits of managing interest-rate risk and exchange-rate risk using less-than-perfectderivative hedges likely outweigh the costs associated with increased income volatility, especially when banks canpotentially offset that volatility using their discretion over LLP.



Critics also argued that the ineffective portion of hedges as measured under SFAS 133 is transitory in

nature and incapable of providing information about overall effectiveness of hedging instruments and

strategies. For example, Chase Manhattan Corporation states in its response letter to SFAS 133 that:

Recognizing hedge ineffectiveness in income in each period will continually produce

unnecessary and misleading volatility in earnings. (Chase Manhattan Corporation 1997;emphasis added)

In this case, the adoption of SFAS 133 should not have any effect on the informativeness of

hedging derivative gains/losses.

Given these opposing views, we test the following hypothesis:

H3: Banks that are less effective at hedging (affected banks) experience an increase ininformativeness of reported hedging derivative gains/losses from pre- to post-SFAS 133,

as compared to banks that are more effective at hedging (unaffected banks).

Wahlen (1994) reports that bank managers increase discretionary LLP to signal favorable

future cash flow prospects. Consistent with this argument, other studies document that discretionary

LLP is positively associated with bank stock returns (Beaver et al. 1989; Liu and Ryan 1995; Liu et

al. 1997). However, Barth et al. (2003) observe that the signaling value declines as the noise

component increases (i.e., as the signal-to-noise ratio of the component decreases). Therefore, the

informativeness of discretionary LLP with respect to future cash flows can be expected to decline to

the extent bank managers make opportunistic use of LLP (see Kanagaretnam et al. 2009).

Accordingly, if H1 receives empirical support (i.e., banks that are relatively ineffective at hedging

resort more to LLP for smoothing post-SFAS 133), then we should expect a reduction in the

informativeness of discretionary LLP:

H4a: Banks that are less effective at hedging (affected banks) experience a reduction ininformativeness of discretionary LLP from pre- to post-SFAS 133, as compared to banks

that are more effective at hedging (unaffected banks).

H4b: Banks that are less effective at hedging (affected banks) experience a reduction ininformativeness of discretionary LLP from pre- to post-SFAS 133, as compared to banks

that do not use derivatives (non-user banks).

III. DATA, SAMPLE SELECTION, AND RESEARCH DESIGN

Data and Sample Selection

We obtain bank holding company financial data and derivative data from 10-K filings and FR

Y-9C filings, and share price data from the CRSP data files. We restrict the pre-SFAS 133 sample

period to fiscal years 1998, 1999, and 2000, and the post-SFAS 133 sample period to fiscal years

2001, 2002, and 2003 in order to better focus on the changes occurring around the enactment of

SFAS 133.

Given the strong link between bank financial reporting discretion and internal controls as

documented by Altamuro and Beatty (2010), it is critical that our sample banks are subject to the

same regulatory requirements both cross-sectionally and over time. Banks with total assets larger

than $500 million were subject to the internal control and other provisions of the Federal Deposit

Insurance Corporation Improvement Act of 1992 (FDICIA) during our sample period. To ensure

that our sample banks face uniform regulatory requirements, we exclude banks with total assets less

than $500 million that are exempt from FDICIA. Also, Kanagaretnam et al. (2010a) find that

auditor fee dependence is a threat to auditor independence only for small banks. While this finding



does not suggest that earnings management through LLP is prevalent only among smaller banks,

restricting the sample to banks that are not exempt from FDICIA also ensures that affected banks,

unaffected banks and non-user banks do not differ in terms of auditor independence.

We note that SOX 301 and SOX 302 became effective during our sample period (in 2002).

However, the provisions of both SOX 301 and SOX 302 are modeled after and duplicative of

those already required under FDICIA and, therefore, are unlikely to confound our analyses. In

addition, SOX 404 became effective in 2004, which is not included in our sample period. By

restricting the post-SFAS 133 sample period to 20012003, we reduce the likelihood that any

observed differences in reporting discretion across affected banks, unaffected banks, and non-user

banks are driven by differential internal controls regulations over time.

Of the 2,283 banks with FR Y-9C data, 448 use derivatives. Of these derivative users, we

exclude 250 banks that are privately held and an additional 79 banks that were subject to

acquisitions prior to the implementation of SFAS 133. We also exclude 14 derivative-user banks

that are exempt from FDICIA (i.e., they have less than $500 million in assets). These sample

selection criteria leave us with a final sample of 105 derivative-user banks. Our sample of non-user

banks includes 139 publicly traded banks with more than $500 million in total assets that do not use

hedging derivatives during our sample period.7

As noted earlier, our approach classifies derivative-user banks as affected banks if they report

gains or losses due to hedging ineffectiveness in the income statement after SFAS 133, and as

unaffected banks if they do not report such gains or losses. Accordingly, of the 105 derivative-user

banks, 40 are classified as affected banks and 65 as unaffected banks. The affected bank sample

includes 239 bank-year observations and the unaffected bank sample includes 383 bank-year

observations over the 19982003 period. The non-user bank sample includes 805 bank-year

observations.8

Our analyses are based on derivative-user banks, which are relatively larger and comprise a

relatively small fraction of all banks. However, these sample banks represent a significant portion of

the U.S. banking industry, accounting for 59.7 (62.3) percent of total assets and 71.0 (72.4) percent

of hedging derivatives held by banks in the pre-SFAS 133 (post-SFAS 133) period.

Research Design

H1 posits that banks affected by SFAS 133 are likely to rely more on LLP for smoothing

income following its adoption (relative to banks that are unaffected by the standard). In other

words, H1 predicts a greater positive association between gains/losses from hedging derivatives and

discretionary LLP for affected banks following SFAS 133. We estimate the following regression

model separately for affected banks, unaffected banks, and non-user banks to test this hypothesis:

LLPit a0 a1Postt a2Incit a3Allowanceit a4ChargeOffit a5NonPerformi;t1 a6DNonPerformit a7Loani;t1 a8DLoanit a9Capitalit a10DEBTPi;t1 a11TrdGainLossit a12PosttTrdGainLossit a13Smoothit a14PosttSmoothit a15InctSmoothit a16PosttIncitSmoothit a17HdgDerGainLossit a18PosttHdgDerGainLossit eit:

1

7 All of our derivative-user banks have fiscal years ending on December 31. Accordingly, we require our sample ofnon-user banks to have December 31 fiscal year-ends as well.

8 We are missing five (26) bank-year observations in the unaffected (non-user) bank sample due to missing pricedata in the CRSP files. We also exclude bank-year observations with zero LLP.



Table 1 presents the variable definitions. All continuous variables in Model (1) except

Capital are scaled by beginning total assets. The variable of primary interest, HdgDerGainLoss,

warrants some discussion. In both the pre- and the post-SFAS 133 periods, HdgDerGainLoss

consists of gains and losses recognized in income from derivatives classified as hedging.

However, in the pre-SFAS 133 period, HdgDerGainLoss is not likely to cause income volatility

because lax hedge accounting rules allow banks to time the reporting of gains/losses from

hedging derivatives, most of which are not marked-to-market, by arbitrarily changing which

particular derivative hedged which particular exposure. In contrast, under SFAS 133,

HdgDerGainLoss includes: (1) the effective portion of gains/losses from fair value hedging

derivatives, (2) the effective portion of gains/losses from cash flow hedging derivatives only if net

settlements occur and the hedged transaction affects income in the current period, (3) the

ineffective portion of gains/losses from fair value hedging derivatives, and (4) the ineffective

portion of gains/losses from cash flow hedging derivatives regardless of whether net settlements

occur in the current period.9 The effective component of HdgDerGainLoss does not create

income volatility because the corresponding losses/gains from hedged assets/liabilities are

completely offsetting. However, the ineffective portion of gains/losses has no corresponding

offsetting losses/gains from hedged transactions and, therefore, potentially increases income

volatility that affected banks may wish to smooth.

A positive coefficient on HdgDerGainLoss reflects smoothing via discretionary LLP. The

coefficient on the interaction term Post HdgDerGainLoss, a18, represents the difference inincome-smoothing coefficients (driven by the effect of hedging derivatives on income) between the

post- and pre-SFAS 133 periods. H1 predicts that this incremental smoothing effect is more positive

for affected banks than for unaffected banks.

We classify banks according to the extent (lack) of hedging effectiveness to test this

hypothesis. We classify a bank as affected if it reports derivative gains/losses that include gains/

losses due to hedging ineffectiveness in the post-SFAS 133 period. Affected banks (A) likely have

higher income volatility following SFAS 133 than unaffected banks (U). If affected banks increase

their dependence on LLP to moderate this higher volatility, then we expect aA18; the coefficient onthe interaction term in Model (1) for affected banks, to be more positive than aU18; the correspondinginteraction coefficient for unaffected banks.

Following prior studies (Wahlen 1994; Beaver and Engel 1996; Liu and Ryan 2006;

Kanagaretnam et al. 2004; Kanagaretnam et al. 2010a; Kanagaretnam et al. 2010b), we use

Allowance, ChargeOff, NonPerform, DNonPerform, Loan, and DLoan to control for thenondiscretionary component of LLP. A higher beginning loan loss allowance (Allowance) will

require a lower LLP in the current period. Beaver and Engel (1996) note that current loan charge-

offs (ChargeOff ) can provide information about future loan defaults and are expected to be

positively correlated with LLP that, by definition, contains information about future loan defaults.

Because higher levels of beginning nonperforming loans (NonPerform) and change in

nonperforming loans (DNonPerform) during the current period will require a higher provision inthe current period, we expect NonPerform and DNonPerform to be positively related to LLP. Thesize of the loan portfolio relative to total assets (Loan) varies across banks, and banks with more

assets in the form of loans at the beginning of the period are expected to have higher LLP. Also,

LLP may be positively or negatively related to the change in the amount of loans during the year

(DLoan) depending on the level of default risk associated with incremental loans.

9 Appendix A presents examples of how HdgDerGainLoss is typically calculated.



We control for the use of LLP for capital management and signaling by including Tier 1 capital

(Capital) and next periods change in income before taxes and provisions (DEBTP), respectively.Banks with lower regulatory capital have more incentives to increase LLP to maintain required

minimum capital ratios (Wahlen 1994; Ahmed et al. 1999).10 Therefore, we expect a negative

coefficient on Capital. To the extent banks use LLP to signal future profitability, we expect LLP tobe positively related to DEBTP (Wahlen 1994; Ahmed et al. 1999; Kanagaretnam et al. 2004).

TABLE 1

Variable Definitions

Smoothing Model

LLP loan loss provisions scaled by beginning total assets;Post an indicator variable that equals 1 if the observation belongs to the post-

SFAS 133 period, and 0 otherwise;

Inc an indicator variable that equals 1 if premanaged income (income beforetaxes and provisions) scaled by beginning total assets is above the sample

75th percentile or below the sample 25th percentile, and 0 otherwise;

Allowance beginning allowance for loan losses scaled by beginning total assets;ChargeOff loan charge-offs scaled by beginning total assets;NonPerform beginning nonperforming loans scaled by beginning total assets;DNonPerform change in nonperforming loans scaled by beginning total assets;Loan beginning total loans outstanding scaled by beginning total assets;DLoan change in total loans outstanding scaled by beginning total assets;Capital ratio of actual regulatory capital (Tier I capital) before loan loss reserves to

the minimum required regulatory capital;

DEBTP one-year-ahead change in income before taxes and provisions scaled bybeginning total assets;

TrdGainLoss gains/losses from trading activities scaled by beginning total assets;Smooth income before taxes, provisions, trading gains/losses, and hedging derivative

gains/losses recognized in income scaled by beginning total assets; and

HdgDerGainLoss hedging derivative gains/losses recognized in income scaled by beginningtotal assets.

Reliability of LLP Model

Ineff absolute value of hedge ineffectiveness gains/losses recognized in incomescaled by beginning total assets; and

Size natural logarithm of total assets.Value-Relevance Model

Return annual return from April 1st to March 31st adjusted for the CRSP value-weighted market return;

EBTPD income before taxes, provisions, and hedging derivative gains/losses scaledby beginning market value of equity;

HdgDerGainLossMVE hedging derivative gains/losses recognized in income scaled by beginningmarket value of equity;

NonDiscLLP fitted value from a regression model that includes all the explanatoryvariables except HdgDerGainLoss in Model (1); and

DiscLLP residual value from a regression model that includes all the explanatoryvariables except HdgDerGainLoss in Model (1).

10 We note that allowance for loan losses is excluded from Tier 1 capital after bank capital adequacy requirementschanged in 1990 and included in Tier 2 capital only up to 1.25 percent of risk-adjusted assets. These changessignificantly reduced bank incentives to use LLP for capital management.



We include TrdGainLoss to control for any increase in income volatility from increased

involvement in trading derivatives that banks potentially choose to offset through LLP.11 We also

include Smooth because banks are likely to also use LLP to smooth fluctuations in income arising

from sources other than gains/losses from derivatives, which are explicitly modeled.

Kanagaretnam et al. (2004) document that banks with (relatively) higher deviation of pre-

managed income from the cross-sectional median have a higher propensity to smooth income. To

control for this differential propensity to smooth income, we follow Kanagaretnam et al. (2004) and

include Inc, an indicator variable that equals 1 if the deviation of pre-managed income from the

cross-sectional median is high, and 0 otherwise, interacting this term with Smooth. This allows the

coefficient on Smooth to differ across banks with high- and low-smoothing propensity.

H2a and H2b investigate whether the decrease in informativeness of current-period LLP for

future loan defaults following SFAS 133 is greater for affected banks than for unaffected and for

non-user banks. Following Altamuro and Beatty (2010), we estimate the following model to test

these hypotheses:

ChargeOffi;t1 b0 b1Postt b2LLPit b3PosttLLPit b4NonPerformit b5Sizeit eit:2

All variables are defined in Table 1.

If affected banks increase their dependence on LLP to moderate the higher income volatility

induced by recognition of hedge ineffectiveness, then LLP should become a noisier predictor of

future loan defaults after SFAS 133. Therefore, we expect bA3 ; the coefficient on the interaction termin Model (2) for affected banks, to be more negative than bU3 and b

NU3 ; the corresponding interaction

coefficients for unaffected and non-user banks, respectively. Although such a finding is consistent

with H2a and H2b, it is unclear whether the reduction in informativeness of LLP for future loan

defaults from pre- to post-SFAS 133 is attributable to hedge ineffectiveness. Accordingly, we

conduct a more direct test (H2c) by estimating the following future loan chargeoffs-current LLP

relation conditional on hedge ineffectiveness:

ChargeOffi;t1 p p1Ineff Cit p2LLPCit p3Ineff Cit LLPCit p4NonPerformCitp5SizeCit eit; 3

where the superscript denotes that the variable is centered at its sample mean to mitigate potential

multicollinearity (Aiken and West 1991) and facilitate reliable interpretations. Ineff is the absolute

value of hedge ineffectiveness gain/loss recognized in income scaled by beginning total assets, and

all other variables are defined in Table 1. A negative p3 would support H2c.H3 and H4 investigate the impact of SFAS 133 on the informativeness of hedging derivative

gains/losses and LLP. Following Wahlen (1994), Liu and Ryan (1995), Beaver et al. (1997),

Ahmed et al. (1999), and Kanagaretnam et al. (2009), we estimate the following model to test these

hypotheses:

Returnit v0 v1Postt v2EBTPDit v3PosttEBTPDit v4HdgDerGainLossMVEit v5PosttHdgDerGainLossMVEit v6NonDiscLLPit v7PosttNonDiscLLPit v8DiscLLPit v9PosttDiscLLPit eit: 4

Table 1 presents the variable definitions. We deflate each continuous independent variable in Model

11 The size of bank trading activities in derivatives steadily increased from $31.4 trillion to $67.7 trillion in notionalamount during our sample period (Office of the Comptroller of the Currency 2003).



(4) by beginning market value of equity, and estimate Model (4) for affected banks, unaffectedbanks, and non-user banks.

H3 examines whether the pre- to post-SFAS 133 change in informativeness of hedging

derivative gains/losses is greater for affected banks than for unaffected banks. If SFAS 133 resultsin more timely recognition of derivative gains/losses from hedge ineffectiveness, and ineffective

portions of hedges contain information about the effectiveness of bank hedging policies, then the

coefficient on HdgDerGainLossMVE should increase more for affected banks than for unaffectedbanks post-SFAS 133. Accordingly, v5 should be positive and greater for affected banks than forunaffected banks (i.e., vA5 should be positive and v

A5 should be greater than v

U5 ).

H4a and H4b examine whether the pre- to post-SFAS 133 reduction in informativeness of

discretionary LLP (DiscLLP) is greater for affected banks than for unaffected banks and for non-user banks, respectively. Consistent with investors viewing nondiscretionary LLP (NonDiscLLP) asan expense (Liu and Ryan 1995; Ahmed et al. 1999; Kanagaretnam et al. 2009), we expect the

coefficient on NonDiscLLP to be negative. On the other hand, we expect the coefficient on DiscLLPto be positive, consistent with bank managers increasing discretionary LLP to signal favorable

future cash flow prospects (Beaver et al. 1989; Wahlen 1994; Liu and Ryan 1995; Liu et al. 1997;

Kanagaretnam et al. 2004; Kanagaretnam et al. 2009). If SFAS 133 reduces the signaling value of

discretionary LLP by leading to more income smoothing through discretionary LLP, then the

coefficient on DiscLLP should be less positive post-SFAS 133. In addition, the change in thecoefficient on DiscLLP should be more negative for affected banks than for unaffected banks (H4a)and for non-user banks (H4b). We test these hypotheses by testing whether the coefficient v9, whichreflects the change in informativeness of DiscLLP following SFAS 133, is more negative foraffected banks vA9 than for unaffected banks vU9 and for non-user banks vN9 :

IV. RESULTS

Descriptive Statistics and Univariate Comparisons

Panels A through D of Table 2 present descriptive statistics for LLP, derivatives activity, and

the effect of hedging derivatives on income for the sample banks. SFAS 133 mandates recognition

of all hedging derivatives at fair value. As reported in Panel A, the mean gains/losses from hedging

derivatives recognized in income increases from 1.8 (1.4) percent to 6.5 (6.2) percent of income

before taxes and provisions for affected (unaffected) banks. For affected banks in the post-SFAS133 period, the gains/losses from hedging derivatives recognized in income include gains/losses

from ineffective hedges, which constitute 1.3 percent of income before taxes and provisions, on

average. Thus, for affected banks 20 percent of gains/losses from hedging derivatives recognized inincome in the post-SFAS 133 period are from the ineffective portion of hedges. Untabulated

analysis also indicates that ineffectiveness gains/losses amount to 3.3 percent of net income, on

average, for affected banks. The effect of SFAS 133 is also evident from the statistics presented inPanel C of Table 2. Although the volume of hedging activity remained fairly constant for both

affected and unaffected banks from pre- to post-SFAS 133, the amount of (undeflated) hedgingderivative gains/losses recognized in income more than quadrupled from the pre- to the post-SFAS

133 for both affected and unaffected banks because hedging derivatives are recognized at fair valueby all banks after SFAS 133.12

12 Panel A of Table 2 indicates that the mean ratio of notional amount of hedging derivatives to total assets foraffected (unaffected) banks is 19.0 (8.1) percent before and 19.3 (10.8) percent after SFAS 133. Statistical testsindicate that, for both affected and unaffected banks, the change in the volume of hedging from pre- to post-SFAS133 is not statistically significant.



TABLE 2

Loan Loss Provisions, Derivatives Activity, and Effect of Hedging Derivatives on Income

Panel A: Descriptive Statistics for Affected Banks, Unaffected Banks, and Non-User Banks

Mean Ratio of:Pre-SFAS 133(19982000)

Post-SFAS 133(20012003)

(H0: lPRE lPOST)

t-statistic p-value

Loan Loss Provisions to Income

before Taxes and Provisions

A 0.147 0.173 4.028 0.001U 0.128 0.166 7.655 0.001N 0.139 0.162 6.822 0.001

Hedging Derivative Gains and

Losses Recognized in Income

to Income before Taxes and

Provisions

A 0.018 0.065 8.464 0.001U 0.014 0.062 9.045 0.001N NA NA NA NA

Hedge Ineffectiveness to Income

before Taxes and Provisions

A NA 0.013 NA NAU NA NA NA NAN NA NA NA NA

Unrecognized and Recognized

Hedging Derivative Gains

and Losses to Income before

Taxes and Provisions

A 0.075 0.090 3.722 0.001U 0.055 0.072 4.724 0.001N NA NA NA NA

Notional Amount of Hedging

Derivatives to Total Assets

A 0.190 0.193 0.116 0.886U 0.081 0.108 1.373 0.170N NA NA NA NA

Notional Amount of Trading

Derivatives to Total Assets

A 1.415 1.724 4.783 0.001U 0.207 0.441 7.488 0.001N NA NA NA NA

Affected bank sample (A) consists of 239 bank-year observations. Unaffected bank sample (U) consists of 383 bank-yearobservations. Non-user bank sample (N) consists of 802 bank-year observations. Large user banks consist of the largest25 bank holding companies in terms of total asset size. Seventeen (eight) of the largest 25 banks are classified as affected(unaffected).

Panel B: Mean Ratio of Hedging Derivative Gains and Losses Recognized in Income toIncome before Taxes and Provisions

Pre-SFAS 133 Post-SFAS 133

Large Affected Banks 0.021 0.076

Small Affected Banks 0.017 0.058

Large Unaffected Banks 0.023 0.056

Small Unaffected Banks 0.013 0.063

Panel C: Mean Unscaled Hedging Derivative Gains and Losses Recognized in Income


Large Affected Banks $135,749,510 $651,427,510

Small Affected Banks 11,728,470 58,851,090

Large Unaffected Banks 150,764,100 735,562,180

Small Unaffected Banks 4,202,580 17,765,230

(continued on next page)



Panel B of Table 2 shows that hedging derivatives, which fall within the scope of SFAS133, have economic significance for both large and small derivative user banks in terms of the

effect of hedging derivative gains/losses on income.13 Specifically, the mean gains/losses from

hedging derivatives recognized in income increased from 2.1 (1.7) percent to 7.6 (5.8) percent

of income before taxes and provisions for large (small) affected banks from the pre- to the post-SFAS 133 period. The corresponding change for large (small) unaffected banks is from 2.3(1.3) percent to 5.6 (6.3) percent. Descriptive statistics in Panel D of Table 2 also indicate that

the frequency of gains from hedging derivatives recognized in income is higher than the

frequency of losses for each subgroup of derivative user banks.

Trading derivatives include customer-related, speculative, and de facto hedging derivatives

(i.e., derivatives that do not qualify for hedge accounting and derivatives that hedge non-

derivative trading positions). The volume of derivative trading activities increased steadily from

pre- to post-SFAS 133 for both affected and unaffected banks, which likely increased income

volatility to the extent that banks did not take offsetting trading positions. As documented by

Ahmed et al. (2011), this increase is driven primarily by the largest 25 banks in terms of total

asset size (hereafter, large banks). Of these 25 large banks, 17 (8) are classified as affected

(unaffected) banks. As reported in Panel A of Table 2, the mean ratio of gross (long plus short)

notional amount of trading derivatives to total assets for affected banks is 1.42 and 1.72 in the

pre- and post-SFAS 133 periods, respectively, indicating that these banks are heavily involved

in derivatives trading. However, an examination of annual reports reveals that long and short

positions are largely offsetting. Indeed, the mean ratio of net (long minus short) notional

amount of trading derivatives to total assets for these banks is only 4.8 percent.14

Table 3 presents descriptive statistics for the variables used in estimating Models (1) through

(4) for affected banks, unaffected banks, and non-user banks. The mean ratio of LLP to beginningtotal assets varies from 0.2 to 0.4 percent and is consistent with prior research.15 Over our sample

period, the average income before taxes and provisions divided by beginning total assets is

TABLE 2 (continued)

Panel D: Frequency of Recognized Hedging Derivative Gains (Losses)


Large Affected Banks 0.667 0.769

(0.333) (0.231)

Small Affected Banks 0.614 0.531

(0.386) (0.469)

Large Unaffected Banks 0.727 0.722

(0.273) (0.278)

Small Unaffected Banks 0.551 0.628

(0.449) (0.372)

13 Large user banks consist of the largest 25 bank holding companies in terms of total asset size.14 Details of this analysis are available from the authors upon request.15 For example, the mean ratio of LLP to beginning total assets reported by Kanagaretnam et al. (2010a) is 0.25

percent. The mean ratio of LLP to beginning total loans reported by Liu and Ryan (2006) is 0.48 percent, which,given that loans constitute around 70 percent of bank total assets on average, corresponds to a LLP to beginningtotal assets ratio of 0.34 percent.



TABLE 3

Descriptive Statistics for Regression Variables

Pre-SFAS 133 (19982000) Post-SFAS 133 (20012003)

Mean (l) Median H0 p-value Mean (l) Median H0 p-value

LLP A 0.003 0.003 lA lU 0.465 0.004 0.003 lA lU 0.163U 0.003 0.002 lU lN 0.142 0.003 0.003 lU lN 0.333N 0.002 0.002 lA lN 0.137 0.003 0.002 lA lN 0.229

Return A 0.098 0.103 lA lU 0.195 0.195 0.181 lA lU 0.082U 0.092 0.094 lU lN 0.143 0.214 0.223 lU lN 0.005N 0.087 0.090 lA lN 0.050 0.173 0.208 lA lN 0.045

Allowance A 0.010 0.010 lA lU 0.562 0.009 0.009 lA lU 0.553U 0.010 0.010 lU lN 0.222 0.009 0.009 lU lN 0.610N 0.009 0.008 lA lN 0.173 0.009 0.008 lA lN 0.401

ChargeOff A 0.004 0.003 lA lU 0.001 0.004 0.003 lA lU 0.128U 0.002 0.002 lU lN 0.626 0.003 0.003 lU lN 0.094N 0.002 0.002 lA lN 0.001 0.002 0.002 lA lN 0.011

NonPerform A 0.004 0.003 lA lU 0.714 0.005 0.004 lA lU 0.143U 0.004 0.003 lU lN 0.127 0.004 0.004 lU lN 0.381N 0.003 0.002 lA lN 0.095 0.004 0.003 lA lN 0.172

DNonPerform A 0.001 0.001 lA lU 0.081 0.000 0.000 lA lU 0.064U 0.000 0.000 lU lN 0.471 0.001 0.000 lU lN 0.298N 0.000 0.000 lA lN 0.103 0.001 0.000 lA lN 0.097

Loan A 0.715 0.724 lA lU 0.462 0.683 0.707 lA lU 0.611U 0.708 0.727 lU lN 0.926 0.668 0.677 lU lN 0.235N 0.707 0.720 lA lN 0.391 0.701 0.709 lA lN 0.427

DLoan A 0.057 0.056 lA lU 0.001 0.044 0.038 lA lU 0.801U 0.082 0.076 lU lN 0.150 0.045 0.040 lU lN 0.158N 0.078 0.075 lA lN 0.012 0.051 0.047 lA lN 0.115

TrdGainLoss A 0.001 0.002 lA lU 0.318 0.002 0.002 lA lU 0.421U 0.001 0.001 lU lN 0.249 0.002 0.002 lU lN 0.275N 0.001 0.001 lA lN 0.399 0.001 0.001 lA lN 0.178

Size A 23.972 24.054 lA lU 0.001 24.184 24.075 lA lU 0.001U 22.179 21.981 lU lN 0.001 22.457 22.299 lU lN 0.001N 20.730 20.532 lA lN 0.001 20.707 20.503 lA lN 0.001

Smooth A 0.019 0.019 lA lU 0.207 0.024 0.023 lA lU 0.308U 0.018 0.019 lU lN 0.092 0.023 0.021 lU lN 0.211N 0.015 0.016 lA lN 0.043 0.020 0.018 lA lN 0.188

HdgDerGainLoss A 0.000 0.001 lA lU 0.116 0.002 0.002 lA lU 0.466U 0.000 0.001 lU lN NA 0.002 0.002 lU lN NAN NA NA lA lN NA NA NA lA lN NA

Affected bank sample (A) consists of 239 bank-year observations. Unaffected bank sample (U) consists of 383 bank-yearobservations. Non-user bank sample (N) consists of 802 bank-year observations. The mean and median values forHdgDerGainLoss in the pre-SFAS 133 period are due to rounding.All variables are defined in Table 1.



approximately 2 percent. The mean annual return ranges between 8.7 (17.3) percent and 9.8 (21.4)

percent in the pre-SFAS 133 (post-SFAS 133) period.16 The descriptive statistics also indicate that

loans account for around 70 percent of total assets on average.17

Income Smoothing through LLP

H1 examines changes from the pre- to the post-SFAS 133 period in the extent to which LLP is

used to smooth gains/losses from hedging derivatives conditional on the (in)effectiveness of banks

hedging activities. Panel A of Table 4 reports results for Model (1), which estimates income

smoothing in the pre- and the post-SFAS 133 periods for affected and unaffected banks. Thecoefficient on HdgDerGainLoss for affected banks is not significantly different from zero (aA17 0.082, p 0.29) in the pre-SFAS 133 period, indicating that these banks did not use LLP to smoothincome fluctuations from hedging derivative gains/losses during this period. This is consistent with

lax hedge accounting rules prior to SFAS 133 that allowed banks to shield their incomes from

undesirable effects of hedging gains/losses. More importantly, the coefficient on Post HdgDerGainLoss for affected banks is significantly greater than zero (aA18 0.138, p , 0.01),indicating that, in the post-SFAS 133 period, banks rely more on LLP for offsetting income

volatility from hedging derivative activities than they did prior to SFAS 133.

The results indicate that unaffected banks also do not rely on LLP for smoothing hedging

derivative gains/losses in the pre-SFAS 133 period (aU17 0.151, p 0.43). More interestingly,unlike affected banks, unaffected banks do not increase their reliance on LLP for smoothing post-SFAS 133. This is indicated by the coefficient on Post HdgDerGainLoss, which is notsignificantly different from zero (aU18 0.052, p0.43). To test H1, we test the difference betweenthe coefficients on Post HdgDerGainLoss for affected banks and unaffected banks. The results ofthis test, reported in Panel B of Table 4, indicate that the pre- to post-SFAS 133 change in income

smoothing through LLP driven by hedging derivative gains/losses is significantly greater for

affected banks than for unaffected banks (p , 0.01). In other words, banks that report hedgeineffectiveness rely more on LLP for smoothing income following SFAS 133, but banks that do not

report any hedge ineffectiveness do not.

For comparison purposes, Table 4 also presents estimation results for a sample of banks that do

not engage in hedging derivative activities (non-user banks). The signs of the coefficients on thecontrol variables in Model (1) for all three subsamples are consistent with expectations and with

prior research (e.g., Wahlen 1994; Kanagaretnam et al. 2004). Allowance is negatively related toLLP, and ChargeOff, NonPerform, DNonperform, and Loan are positively related to LLP, with eachvariable significantly different from zero. Only for the non-user sample is Capital significantlyrelated to LLP, indicating that relatively smaller banks use LLP to manage their capital ratios.DEBTP is positively related to LLP only for affected and unaffected banks, indicating that largerbanks use LLP to signal future profitability.

Table 4 also shows that the coefficient on TrdGainLoss and the coefficient on Post TrdGainLoss do not significantly differ from zero for any of the three bank subsamples. Theseresults that indicate trading activities do not appear to influence discretionary reporting behavior,

despite the increased involvement in trading activities post-SFAS 133. These are results in line with

our finding that the long and short positions in trading derivatives are largely offsetting in nature

16 The difference in average annual returns between pre- and post-SFAS 133 periods is primarily attributable toaverage negative returns in 1999 and highly positive returns in 2003.

17 The mean and median HdgDerGainLoss in the pre-SFAS 133 period for affected and unaffected banks and theminimum NonPerform for all subsamples are reported as 0.000 due to rounding.



TABLE 4

Tests of Income Smoothing Behavior of Banks before and after SFAS 133

Panel A: Results of Regressions of LLP on Determinants of LLP

Model 1:LLPit a0 a1Postt a2Incit a3Allowanceit a4ChargeOffit a5NonPerformi;t1

a6DNonPerformit a7Loani;t1 a8DLoanit a9Capitalit a10DEBTPi;t1 a11TrdGainLossit a12PosttTrdGainLossit a13Smoothit a14PosttSmoothit a15InctSmoothit a16PosttIncitSmoothit a17HdgDerGainLossit a18PosttHdgDerGainLossit eit

Affected Users (A) Unaffected Users (U) Non-Users (N)

Coeff.Estimate p-value



Post 0.001 0.942 0.001 0.257 0.001 0.107Inc 0.002 0.051 0.001 0.784 0.001 0.006Allowance 0.067 0.058 0.100 , 0.001 0.171 , 0.001ChargeOff 0.884 , 0.001 0.899 , 0.001 0.840 , 0.001NonPerform 0.044 0.025 0.053 0.003 0.043 , 0.001DNonPerform 0.279 , 0.001 0.262 , 0.001 0.129 , 0.001Loan 0.002 0.019 0.002 0.005 0.003 , 0.001DLoan 0.002 0.096 0.001 0.217 0.001 0.842Capital 0.008 0.352 0.001 0.552 0.003 0.013DEBTP 0.030 0.004 0.027 0.045 0.001 0.292TrdGainLoss 0.074 0.431 0.128 0.414 0.006 0.932Post TrdGainLoss 0.040 0.520 0.104 0.277 0.034 0.676Smooth 0.125 0.027 0.118 0.002 0.131 0.002Post Smooth 0.014 0.611 0.023 0.176 0.011 0.322Inc Smooth 0.112 0.040 0.080 0.029 0.073 0.011Post Inc Smooth 0.006 0.712 0.003 0.774 0.003 0.643HdgDerGainLoss 0.082 0.286 0.151 0.432Post HdgDerGainLoss 0.138 0.002 0.052 0.428No. of Observations 239 383 805

F-value 88.95 , 0.001 90.59 , 0.001 52.08 , 0.001Adj. R2 0.822 0.823 0.755

Panel B: Test of H1

p-value

H1 aA18 . aU18 0.001

A denotes affected bank sample, U denotes unaffected bank sample, and N denotes non-user bank sample. The sampleconsists of bank-year observations from 1998 to 2003. All results are based on standard errors clustered simultaneouslyby bank and by year. All p-values are based on two-tailed t-tests.All variables are defined in Table 1.



(Table 2). These results are also consistent with the findings of Liu et al. (2004) and Ahmed et al.

(2011) that banks do not offset trading gains/losses with nontrading gains/losses.The coefficient on Smooth is significantly positive for all three subsamples, consistent with the

broad findings of prior research that banks use LLP to smooth income (Wahlen 1994;

Kanagaretnam et al. 2004; Liu and Ryan 2006). The significant and positive coefficients on Inc Smooth for all three subsamples show that, consistent with Kanagaretnam et al. (2004), bankswith greater deviation of income before taxes and provisions from the cross-sectional median have a

higher propensity to smooth income. The insignificant coefficients on Post Smooth and Post Inc Smooth indicate that propensity to smooth income arising from activities other than hedgingderivatives and trading activities is not significantly different from pre- to post-SFAS 133 for either

banks with high- or banks with low-smoothing incentives.

Model (1) implicitly assumes that it is the hedge ineffectiveness component of

HdgDerGainLoss introduced by SFAS 133 that increases income volatility, and affected banksuse LLP to smooth this component. To confirm the validity of this premise, we re-estimate Model

(1) after decomposing HdgDerGainLoss into effective (HdgDerGainLossEFF) and ineffective(HdgDerGainLossINEFF) components for affected banks in the post-SFAS 133 period. Untabulatedresults show that the coefficient on HdgDerGainLossINEFF is positive and significant (gINEFF 0.713; p , 0.01) and the coefficient on HdgDerGainLossEFF is insignificant (gEFF 0.074; p 0.32), confirming that our income smoothing results for affected banks presented in Table 4 aredriven by the hedge ineffectiveness component of HdgDerGainLoss introduced by SFAS 133.

Taken together, our results support the hypothesis that, because SFAS 133 introduced volatility in

banks income by requiring recognition of gains/losses from the ineffective portion of hedges, affectedbanks reacted to this constraint by increasing their reliance on LLP to achieve smooth income.

Note, however, from Table 3 that affected banks are, on average, larger than unaffected andnon-user banks. Given the evidence in Kanagaretnam et al. (2010a) that auditors of large banks arerelatively independent (i.e., there is no association between audit fees and discretionary LLP), a

question arises as to how independent auditors might permit such opportunistic use of LLP. We

offer two reasons why large banks have room for discretion despite the closer scrutiny of

independent auditors. First, banking industry statistics show that large banks have a significantly

greater proportion of heterogeneous loans (commercial and industrial loans, direct lease financing,

commercial real estate loans, and foreign loans) in their loan portfolios,18 and Liu and Ryan (1995)

document that banks have more discretion over heterogeneous loans, all else equal. Moreover,

Fields et al. (2004) observe that large banks engage in a higher volume of other complex loan-

related transactions (e.g., collateralization, syndication, and securitization of loans). Therefore, it is

relatively more difficult for the auditor to determine where exactly discretion is being exercised

when loan portfolios are complex and heterogeneous.

Second, an inspection of Big 4 auditor manuals reveals that the actual audit procedures for loan

loss estimates give banks room for discretion. Specifically, auditors typically establish rules to be used

in assessing the materiality of misstatements, in line with the guidelines described in AU Section 350

of the AICPA Professional Standards for tolerable misstatement or tolerable error.19 Discussions

18 The statistics are based on regulatory filings for our sample period (19982003) and show that loans comprise60.1 percent of total assets for both large and small banks. The proportion of heterogeneous loans in the loanportfolio is 63.1 percent for large banks and 49.9 percent for small banks. For our sample, which consists of largebanks only, loans comprise 70 percent of total assets, and the proportion of heterogeneous loans in the loanportfolio is 68.2 percent.

19 Typically, when net income is used to determine a benchmark, the materiality threshold is set between 5 and 10percent of net income. When total assets are used to determine a benchmark, the materiality threshold is typicallyset between 0.5 and 1 percent of total assets. A widely known formula used by KPMG to determine materiality is1.6 Max [Total Assets, Revenue]0.667 (Petroni and Beasley 1996; Blokdijk et al. 2003).



in the AICPA Professional Standards suggest that the resulting acceptable range for the allowance

balance is typically between 10 to 20 percent of the account balance for an independent and fair audit,

which allows banks considerable room to exercise discretion over LLP. Kanagaretnam et al.s (2010a)

finding that large bank auditors are independent might then be viewed as suggesting that if large

banks report an allowance outside the acceptable range, then an independent auditor will propose an

audit adjustment and/or report a misstatement. In our study, the magnitude of discretionary LLP

attributable to smoothing of hedge ineffectiveness equals 3.4 percent of the allowance, on average.

This represents an SFAS 133-induced shift in discretionary reporting behavior of only 17 to 34

percent of the width of the acceptable range for the allowance. Therefore, we could observe incomesmoothing by large banks even though there is no association between audit fees and discretionary

LLP as documented by Kanagaretnam et al. (2010a).20,21 In sum, these arguments suggest that large

banks have some room for material and opportunistic use of LLP despite being under the scrutiny of

large and relatively independent auditors.

Reliability of Current LLP for Future Loan Charge-Offs

H2a and H2b posit a greater pre- to post-SFAS 133 reduction in reliability of current periods

LLP for next periods loan charge-offs for affected banks than for unaffected banks and for non-user banks. The results of this test, reported in Table 5, indicate a strong positive relation betweencurrent LLP and future loan charge-offs for all three subsamples in the pre-SFAS 133 period. The

coefficients on LLP for the affected banks, unaffected banks, and non-user banks are positive andsignificant (p , 0.01), indicating that in the pre-SFAS 133 period, LLP serves as a reliablepredictor of future loan charge-offs. The coefficient on Post LLP, b3, provides an estimate of thepre- to post-SFAS 133 change in the current LLP-future loan charge-offs relation. b3 is significantlynegative for affected banks (bA3 0.078, p 0.08) but not for unaffected banks (b

U3 0.004, p

0.59) and non-user banks (bN3 0.004, p0.38). These results indicate that the reliability of currentLLP for future loan charge-offs decreased following SFAS 133 for banks that were ineffective at

hedging, but not for banks that were effective at hedging or for banks that did not engage in hedging

activity.

Consistent with this reasoning, Panel C of Table 5 shows that the pre- to post-SFAS 133

change is significantly more negative for affected banks than for unaffected banks (p 0.01) andnon-user banks (p 0.05). The rationale underlying H2a and H2b is that, if affected banks resortmore to LLP for smoothing after SFAS 133 than do unaffected banks and non-user banks, thenreported LLP will be a less reliable predictor of future loan charge-offs after SFAS 133 for affectedbanks.

H2c posits that the relation between current-period LLP and next-period loan charge-offs

weakens as hedge ineffectiveness increases. We report the estimation results of Model (3) in Panel

20 On the other hand, if the auditor is not independent (for small banks as in Kanagaretnam et al. [2010a]), theneither a misstatement will not be reported or the auditor will intentionally set a wider acceptable range to allowthe bank to manage LLP. Therefore, we could observe income smoothing by small banks as well as a positiveassociation between audit fees and discretionary LLP (i.e., lack of auditor independence) as reported inKanagaretnam et al. (2010a). Indeed, although small banks have more transparent and less complex assetportfolios from the auditors standpoint and therefore less room for discretion, we find in additional analyses thatsmall banks smooth income through LLP as much as large banks do, providing further evidence consistent withKanagaretnam et al. (2010a) that small bank auditors are not independent.

21 We estimate the income-smoothing model using the sample banks in Kanagaretnam et al. (2010a). We include abinary variable (SMALL), which equals 1 if the observation belongs to a small bank, and 0 otherwise. We allowthe coefficient on EBTP to vary for large and small banks by interacting SMALL with EBTP. Thus, if thecoefficient on SMALL EBTP is reliably different from zero, then it indicates that the extent of smoothing differsbetween large and small banks in the sample used in Kanagaretnam et al. (2010a). The results indicate that largeand small banks do not differ in terms of the extent of income smoothing through LLP.



TABLE 5

Tests of the Reliability of Bank Loan Loss Provisions before and after SFAS 133

Panel A: Results of Charge-Off Regressions

Model 2:ChargeOffi;t1 b0 b1Postt b2LLPit b3PosttLLPit b4NonPerformit b5Sizeit eit





Post 0.001 0.847 0.001 0.544 0.001 0.581LLP 0.814 , 0.001 0.766 , 0.001 0.808 , 0.001Post LLP 0.078 0.083 0.004 0.590 0.004 0.384NonPerform 0.163 , 0.001 0.101 , 0.001 0.131 ,0.001Size 0.001 , 0.001 0.001 , 0.001 0.001 ,0.001

No. of Observations 239 383 805

F-value 78.57 , 0.001 85.91 , 0.001 137.21 , 0.001Adj. R2 0.592 0.594 0.581

Panel B: Results of Charge-Off Regressions for Affected Banks (A) after SFAS 133

Model 3:ChargeOffi;t1 p0 p1Ineff Cit p2LLPCit p3Ineff Cit LLPCit p4NonPerformCit p5SizeCit

eitCoefficient Estimate p-value

Ineff C 4.586 0.078LLPC 0.838 , 0.001Ineff C LLPC 131.444 0.068NonPerformC 0.061 0.033SizeC 0.001 0.004

No. of Observations 120

F-Value 95.22 , 0.001Adj. R2 0.692

Panel C: Tests of H2a, H2b, and H2c

p-value

H2a bA3 , bU3 0.009

H2b bA3 , bN3 0.047

H2c pA3 , 0 0.068

A denotes affected bank sample, U denotes unaffected bank sample, and N denotes non-user bank sample. The sample forModel (2) consists of bank-year observations from 1998 to 2003, and the sample for Model (3) consists of bank-yearobservations from 2001 to 2003. All results are based on standard errors clustered simultaneously by bank and by year.All p-values are based on two-tailed t-tests. All variables in Model (3) are centered at their sample means.All variables are defined in Table 1.



B of Table 5 and the results of the test in Panel C. The significantly negative coefficient on Ineff C LLPC (pA3 131.444, p 0.07) shows that the reliability of LLP is decreasing with the magnitudeof hedge ineffectiveness, confirming that the differential change in the reliability of LLP for affectedand unaffected banks documented in Panel A is driven by hedge ineffectiveness recognized inincome, as required by SFAS 133.22

Informativeness of Discretionary LLP

H3 examines the change in informativeness of hedging derivative gains/losses following SFAS

133, and H4 examines the change in informativeness of discretionary LLP following SFAS 133,

conditional on the effect of SFAS 133 on banks discretionary reporting behavior. We estimate

Model (4) for affected banks, unaffected banks, and non-user banks to test these hypotheses andreport the results in Table 6.

As expected, income before provisions, taxes, and hedging gains/losses (EBTPD) aresignificantly positively related to bank returns for all three subsamples, indicating that EBTPD issignificantly informative in the pre-SFAS 133 period. Because EBTPD is not affected by thepronouncements of SFAS 133, we would not expect this relation to be any different in the post-

SFAS 133 period. Indeed, the coefficients on Post EBTPD are insignificant for all threesubsamples.

The variable HdgDerGainLossMVE is positively associated with bank returns for both affectedbanks and unaffected banks (i.e., v4 is significant for both subsamples), indicating that hedgingderivative gains/losses are informative in the pre-SFAS 133 period. However, for both subsamples,

this informativeness does not increase in the post-SFAS 133 period; we are unable to reject the null

that v5 is zero for both affected and unaffected banks (p 0.39 and p 0.33, respectively).Moreover, as Panel B of Table 6 indicates, we are unable to reject the null that v5 is not different foraffected banks and unaffected banks at conventional significance levels (p 0.41). In other words,our results do not lend support to the hypothesis that the increase in informativeness of hedging

derivative gains/losses is any greater for affected banks relative to unaffected banks (H3). As wenoted earlier, one plausible explanation is that the ineffective portion of hedging derivative gains/

losses is transitory in nature and does not convey information about the extent to which bank

hedging policies are effective.

Our test of H1 indicates greater reliance on LLP for smoothing income following SFAS 133 for

affected banks than for unaffected banks. H4 tests whether the post-SFAS 133 informativeness ofdiscretionary LLP is reduced more for affected banks than for unaffected banks and non-user banks.The results in Table 6 are consistent with this hypothesis. The coefficient on DiscLLP is positiveand significant for all subsamples, as expected. The coefficient on Post DiscLLP for affectedbanks is negative and significant (vA9 0.094, p 0.06), indicating a reduction in informativenessof discretionary LLP for affected banks following implementation of SFAS 133. By contrast, we donot observe a significant reduction in informativeness of discretionary LLP for unaffected banks (vU90.002, p 0.40) and for non-user banks (vN9 0.015, p 0.53). In addition, as reported in PanelB of Table 6, the coefficient on Post DiscLLP for affected banks is significantly lower than thecorresponding coefficients for unaffected banks (p 0.03) and non-user banks (p 0.01).Consistent with prior research (e.g., Beaver et al. 1989; Wahlen 1994; Liu and Ryan 1995; Liu et al.

1997; Kanagaretnam et al. 2004), the results in Table 6 also show that nondiscretionary LLP

(NonDiscLLP) is significantly negatively related to returns for all subsamples, and this relation isnot reliably different in the post-SFAS 133 period.

22 Note that, although the coefficient on the interaction term appears large, the effect of the interaction term on thedependent variable is relatively small because the magnitude of the interaction term is very small by construction.



Collectively, the results in Table 6 indicate that, although SFAS 133 did not significantly alter

the informativeness of hedging derivative gains/losses, it significantly reduced the informativeness

of discretionary LLP for banks that are more affected by the standard. If enhancing the

informativeness of financial statements is a broad goal of any accounting standard, then these

results question whether SFAS 133 meets that goal. While the standard may have increased the

disclosure relating to derivatives usage, it does not appear to have had a positive effect on the value-

relevance of aggregate income measures.

TABLE 6

Tests of the Association between Returns and Income Components before and after SFAS 133

Panel A: Results of Regressions of Return on Income Components

Model 4:Returnit v0 v1Postt v2EBTPDit v3PosttEBTPDit v4HdgDerGainLossMVEit

v5PosttHdgDerGainLossMVEit v6NonDiscLLPit v7PosttNonDiscLLPit v8DiscLLPit v9PosttDiscLLPit eit





Post 0.077 0.005 0.065 0.002 0.042 , 0.001EBTPD 1.804 0.007 1.865 0.001 1.778 0.011Post EBTPD 0.022 0.305 0.020 0.441 0.001 0.557HdgDerGainLossMVE 0.963 0.079 0.900 0.035Post HdgDerGainLossMVE 0.049 0.392 0.039 0.325NonDiscLLP 1.295 0.019 1.381 0.030 1.202 0.008Post NonDiscLLP 0.020 0.369 0.051 0.466 0.007 0.447DiscLLP 0.784 0.038 0.811 0.050 0.703 0.062Post DiscLLP 0.094 0.064 0.002 0.396 0.015 0.532No. of Observations 239 383 805

F-value 67.42 , 0.001 56.1 , 0.001 73.08 , 0.001Adj. R2 0.340 0.322 0.351

Panel B: Tests of H3, H4a, and H4b

p-value

H3 vA5 . vU5 0.405

H4a vA9 , vU9 0.025

H4b vA9 , vN9 0.012

A denotes affected bank sample, U denotes unaffected bank sample, and N denotes non-user bank sample. The sampleconsists of bank-year observations from 1998 to 2003. All results are based on standard errors clustered simultaneouslyby bank and by year. All p-values are based on two-tailed t-tests.All variables are defined in Table 1.



Additional Analyses of the Impact of SFAS 133 on Income Smoothing

Our primary analyses focus on the effects of income volatility caused by ineffectiveness gains/

losses from derivatives still classified as hedging under SFAS 133. Another potential source of

SFAS 133-driven income volatility is the ineffectiveness from de facto hedging derivatives that areclassified as trading due to the stricter criteria under SFAS 133. SFAS 133 disallows hedgeaccounting for derivatives hedging certain risks associated with specific assets and liabilities.

Examples include derivatives that hedge the interest-rate risk in held-to-maturity securities and

macro-hedge a portfolio of held-to-maturity securities.

Since recognizing the hedging derivative at fair value but the hedged security at cost would

create excessive income volatility, SFAS 133 allows banks to transfer such hedged securities, and

the derivatives hedging these securities, to the trading category (SFAS 133, } 54) so that gains andlosses from the hedged securities and hedging derivatives can be offsetting. The transition from

cost-based to market-based reporting of such transferred securities following the adoption of SFAS

133 results in a one-time reporting of unrealized holding gains/losses that are reported as thecumulative effect on net income of adopting SFAS 133.

The gains/losses from reclassified securities and derivatives are recognized in income

immediately as part of trading gains/losses, and only the ineffective portion increases income

volatility. To the extent that banks manage LLP to offset the volatility caused by ineffectiveness

from de facto hedges reclassified as trading, the pre- to post-SFAS 133 change in incomesmoothing through LLP should be greater for banks reporting a non-zero transitional amount

(reclassifying banks) than for banks reporting a zero transitional amount (non-reclassifying banks).In smoothing tests, this hypothesized change will manifest as a greater positive association between

LLP and TrdGainLoss in the post-SFAS 133 period for reclassifying banks.We partition our sample into reclassifying and non-reclassifying banks and test H1 through H4.

One caveat is that banks do not separately disclose the amount of trading gains/losses due to

ineffectiveness from de facto hedges classified as trading. Therefore, although the existence of suchgains/losses is highly probable for the reclassifying banks, the exact amount of these gains/lossesand the notional amount of the reclassified derivatives typically are not disclosed. We find evidence

consistent with reclassifying banks using LLP to offset volatility from derivatives reclassified underSFAS 133. Specifically, our (untabulated) results show that the smoothing hypothesis holds for

reclassifying banks with larger transitional gains/losses (i.e., banks with transitional gains/lossesabove the sample median).23 One plausible explanation for not finding similar results for banks with

small transitional gains/losses is that it is easier for these banks to offset SFAS 133-induced trading

income volatility within the trading portfolio (as suggested by Liu et al. [2004] and Ahmed et al.

[2011]) because the magnitude of SFAS 133-induced trading income volatility is small.

Robustness Checks

Control for Self-Selection

Endogeneity induced by self-selection bias is a concern in much of derivatives research

because users (heavy users) of derivatives are likely to systematically differ from non-users

(sporadic users) (Barton 2001; Pincus and Rajgopal 2002). Indeed, the results of the tests of

differences presented in Table 3 show that affected, unaffected, and non-user banks differ in certainaspects. However, this is unlikely to be a major concern in our study due to the difference-in-

differences approach adopted in our research design. That is, we analyze changes in the coefficients

of interest within a partition from the pre- to post-SFAS 133 period and the comparative magnitude

23 The results of these analyses are not tabulated for brevity.



of those changes across partitions. If the implementation of SFAS 133 is assumed to be anexogenous shock, then our results cannot be attributed to self-selection issues.

Nevertheless, we control for potential systematic differences (in terms of size, executive

compensation plans, tax convexity, profitability, market, and credit risk exposures, etc.) across

affected, unaffected, and non-user banks using a standard extension of the Heckman two-stageprocedure generalized to polychotomous choices developed by Lee (1983).

Finance theory posits that the probability of financial distress (Smith and Stulz 1985), the level

of risk faced by a bank and the costs of managing that risk (Geczy et al. 1997), CEO risk-taking

incentives (Tufano 1996; Rogers 2002), and tax incentives (Nance et al. 1993; Dolde 1995; Mian

1996) affect both the decision to use derivatives and the extent of use. Consequently, we conjecture

that derivative use is positively related to bank size (Size), leverage (Leverage), interest rate risk(Gap), nonperforming loans (NonPerform), liquidity (Liquidity) and tax convexity (Convexity), andnegatively related to net interest margin (Margin) and managerial incentives to take risk (Incentive).We specify unaffected banks as the base category in the first-stage multinomial logit model.Including Inverse Mills ratios (Iml) obtained from the multinomial logit model in our tests yieldsresults (untabulated) that are qualitatively similar to our primary results.24

Alternative Model Specifications and Samples

Our results are robust to various alternative model specifications and samples. For example, our

inferences are unaltered when the variables in Model (1) are scaled by alternative scalars including

beginning total loans (as in Liu and Ryan [2006]), average loans outstanding (as in Ahmed et al.

[1999]), and beginning book value of equity (as in Kanagaretnam et al. [2004]).

As discussed in Liu and Ryan (1995), provisions for homogeneous loans, which are small and

infrequently renegotiated, are based on statistical analyses or historical data at the portfolio level,

whereas provisions for heterogeneous loans, which are large and frequently renegotiated, are based

on judgment on a loan-by-loan basis. Because banks have differential ability to exercise discretion

over provisions for homogeneous and heterogeneous loans, the composition of the loan portfolio, in

addition to its size, likely affects LLP. Consequently, we also test H1 after allowing the relationbetween LLP and each component of the loan portfolio (i.e., homogeneous and heterogeneousloans) to differ. Our results for Models (1) through (4) remain qualitatively unchanged.

Prior research indicates that auditor reputation has a moderating effect on the opportunistic use

of managerial discretion in LLP (Kanagaretnam et al. 2009, 2010a; Kanagaretnam et al. 2010b).

The two measures of auditor reputation used are auditor type (Big 5 versus non-Big 5) and auditor

expertise (industry specialization).25 We note that 89 percent of the affected and unaffected banks inour sample are audited by Big 5 auditors. To examine whether the differential inferences for

affected banks and unaffected banks are driven by differences in auditor type (expertise), we re-runour tests after excluding banks audited by non-Big 5 auditors (banks audited by KPMG). We reach

the same conclusions.

Influence of Extreme Values

Given the relatively small sample sizes, particularly for affected banks, we examine whetherour inferences are influenced by extreme values. We do so by re-estimating Models (1), (2), and (3)

after deleting observations in the outside 1 percent of either tail of the sample distribution of each

24 The results of the multinomial logit model estimation are available upon request.25 Following Kanagaretnam et al. (2010b), we identify KPMG as the banking industry specialist based on the

analysis of the Government Accountability Office (2003).



variable. Our inferences remain unchanged, suggesting that our results are not driven by extreme

values.

V. CONCLUSION

SFAS 133 is among the most intensely debated accounting standards in recent times.

Proponents of the standard welcomed it as a comprehensive approach to reporting risk management

activities that addresses concerns about incompleteness, inconsistencies, and ambiguities of prior

standards. On the other hand, SFAS 133 came under heavy criticism from industry, which argued

that the new rules would introduce artificial volatility to financial reports. Prior research on SFAS

133 highlights its positive effects on the firm and the capital markets, including increasing the

transparency of derivative financial instruments (Ahmed et al. 2006), inducing better risk

management practices (Zhang 2009), and increasing the risk-relevance of accounting measures of

derivative exposures (Ahmed et al. 2011).

Unlike those studies, ours is the first study to reveal that the adoption of SFAS 133 appears to

have resulted in unintended negative consequences as well. Using a sample of U.S. bank holding

companies over the period of 19982003, we find that banks likely to be more affected by SFAS

133 achieved a greater degree of income smoothing from offsetting LLP following its adoption. We

also find that the informativeness of LLP deteriorated for these banks subsequent to SFAS 133 (i.e.,

the association of LLP with future loan defaults and contemporaneous stock returns became

weaker). We do not find evidence that SFAS 133 altered the informativeness of hedging derivative

gains/losses for bank holding companies. Our study reinforces the notion that the efficacy of a given

regulation cannot be assessed on a standalone basis without giving due consideration to its

unintended consequences.

Even as FASB continues its quest for improving transparency and making financial statements

representationally faithful, our analysis reaffirms the collective wisdom in the extant earnings

management literature that managers continue to seek alternate avenues to achieve their reporting

objectives. We believe that our findings are particularly timely given the recent exposure draft on

accounting for financial instruments (FASB 2010), wherein FASB proposes to substantially expand

the scope of fair value reporting to cover almost all financial instruments including commercial and

consumer loans.

REFERENCES

Ahmed, A. S., C. Takeda, and S. Thomas. 1999. Bank loan loss provisions: A reexamination of capital

management, earnings management and signaling effects. Journal of Accounting and Economics 28

(1): 125.

Ahmed, A. S., E. Kilic, and G. Lobo. 2006. Does recognition versus disclosure matter? Evidence from

value-relevance of banks recognized and disclosed derivative financial instruments. The AccountingReview 81 (3): 567588.

Ahmed, A. S., E. Kilic, and G. Lobo. 2011. Effects of SFAS 133 on the risk relevance of accounting

measures of banks derivative exposures. The Accounting Review 86 (3): 769804.Aiken, L. S., and S. G. West. 1991. Multiple Regression: Testing and Interpreting Interactions. Newbury

Park, CA: Sage Publications.

Altamuro, J., and A. Beatty. 2010. How does internal control regulation affect financial reporting? Journalof Accounting and Economics 49 (1/2): 5874.

Anson, M. J. P. 1999. Accounting and Tax Rules for Derivatives. New Hope, PA: Frank J. FabozziAssociates.

Bankers Roundtable. 1997. Comment Letter to the Exposure Draft for SFAS No. 133. Letter No. 24.Norwalk, CT: FASB.



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