Electronic copy available at: http://ssrn.com/abstract=1526648

Why Banks Elected SFAS No. 159’s Fair Value Option: Opportunism versus Compliance with the Standard’s Intent

Yao-Lin Chang*

Chi-Chun Liu*

and

Stephen G. Ryan**

January 2011 (First Draft: March 2009)

* National Taiwan University. ** Stern School of Business, New York University (corresponding author, sryan@stern.nyu.edu). Chi-Chun Liu acknowledges research support from the National Science Council of Taiwan (grant no. 97-2410-H-002-048-MY3). We are grateful to accounting seminar participants at Dartmouth College, Duke University, National Chengchi University, National Taiwan University, Texas A&M, and Washington University in St. Louis, particularly Anwer Ahmed, Gauri Bhat, and Mary Lea McAnally, for useful comments.

Electronic copy available at: http://ssrn.com/abstract=1526648

ABSTRACT: We investigate the reasons why banks elected the fair value option (FVO) upon their initial adoption of SFAS No. 159. We propose new hypotheses and provide new evidence regarding whether and how these reasons differed for banks adopting the standard in the first quarter of 2007 (early adopters) versus first quarter of 2008 (regular adopters), as well as across banks’ FVO elections for different types of financial instruments. SFAS No. 159 required firms to record the effect of adopting the standard in retained earnings. Other studies on the FVO find that some early adopters exploited this transition guidance by electing the FVO for AFS securities with cumulative unrealized losses, thereby increasing their future net income. These studies also describe the SEC’s criticism of some early adopters’ FVO elections as inconsistent with the standard’s intent to remedy accounting mismatches for economic hedges. We predict and find that early adopters’ opportunistic behavior exhibited considerably greater variation than found in prior studies. Specifically, we find early adopters with a history of managing earnings through realization of gains and losses on AFS securities were more likely to make opportunistic FVO elections. We show that early adopters with below-median regulatory capital elected the FVO for financial instruments with cumulative unrealized gains, thereby increasing their regulatory capital, opposite to the elections of early adopters with above-median capital. Because of the SEC’s criticism of early adopters, we predict and find that their initial FVO elections complied with SFAS No. 159’s intent. Specifically, we find that regular adopters’ FVO elections are not explained by proxies for their past history of and incentives for opportunistic behavior, but are explained by proxies for accounting mismatches. We find that regular adopters most commonly elected the FVO for loans held for sale. We argue that these elections likely remedied accounting mismatches due to the difficulty of obtaining hedge accounting for hedges of loans held for sale. Moreover, these elections could not exploit SFAS No. 159’s transition guidance to increase future income because of lower of cost and market accounting for loans held for sale. Keywords: Fair value option; Fair value accounting; SFAS No. 159; Banks JEL Classification: G21, M41 Data Availability: All data are available from public sources.

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1. Introduction

We investigate the reasons why U.S. commercial banks (“banks”) elected the fair value

option (“FVO”) for financial instruments upon their adoption of SFAS No. 159, The Fair Value

Option for Financial Assets and Financial Liabilities. We consider whether banks’ FVO

elections reflected exploitation of the standard’s transition guidance or compliance with the

standard’s expressed intent to remedy accounting mismatches for economic hedges. We

distinguish banks adopting SFAS No. 159 in the first quarter of 2007 (early adopters) versus the

first quarter of 2008 (regular adopters), as well as FVO elections for different types of financial

instruments. We hypothesize and provide evidence that early adopters’ opportunistic FVO

elections exhibited considerably greater variation than documented by Song (2008), Henry

(2009), and Guthrie, Irving, and Sokolowsky (2010) (“the FVO literature”). More importantly,

we hypothesize and provide the first evidence that regular adopters complied with SFAS No.

159’s expressed intent.

SFAS No. 159’s transition guidance required firms to record the effect of adopting the

standard in retained earnings. The FVO literature finds that some early adopters exploited the

standard’s transition guidance by electing the FVO option for AFS securities with cumulative

unrealized losses. These elections increased early adopters’ future net income by relieving it of

these losses.

We provide three new findings or insights regarding early adopters incremental to the

FVO literature. First, we predict and find that early adopters with histories of realizing gains and

losses on AFS securities to smooth their net income were more likely to exploit SFAS No. 159’s

transition guidance. This finding indicates the importance of distinguishing firms based on their

histories of managing accounting numbers. Second, we predict and find that early adopters with

below-median regulatory capital elected the FVO for financial instruments with cumulative

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unrealized gains to increase their regulatory capital,1 despite the negative consequences of these

elections for future net income. This is opposite to the elections of early adopters with above-

median regulatory capital. Third, we find that early adopters’ FVO initial elections were most

frequently for AFS securities and debt. We explain why early adopters’ initial FVO elections for

AFS securities and debt were both amenable to exploitation of SFAS No. 159’s transition

guidance and more likely to create than to remedy accounting mismatches.

We provide the three new findings or insights regarding regular adopters. First, we

predict and find that proxies for regular adopters’ histories of and incentives regarding

management of accounting and regulatory capital numbers do not explain their FVO elections,

consistent with these elections not exploiting SFAS No. 159’s transition guidance. Second, we

predict and find that proxies for accounting mismatches explain regular adopters’ FVO elections,

consistent with these elections adhering to SFAS No. 159’s stated intent. Third, we find that

regular adopters most commonly elected the FVO for loans held for sale. We explain that FVO

elections for loans held for sale were likely to remedy accounting mismatches for economic

hedges due to the difficulty of obtaining hedge accounting. We explain that these elections could

not exploit SFAS No. 159’s transition guidance to raise future net income due to lower of cost or

fair value accounting for loans held for sale.

Our results are important because some accounting researchers, standard setters, and

other accounting policymakers have drawn negative inferences about SFAS No. 159’s FVO from

the opportunistic behavior of some early adopters. For example, Song (2008) concludes that

“[o]verall, the FVO seems to induce undesirable effects.”2 Certain FASB board members appear

1 This statement applies to AFS securities, because banks’ Tier 1 regulatory capital excludes accumulated other comprehensive income. 2 Somewhat in contrast, Henry’s (2009) concludes that informal mechanisms arose to help firm implement SFAS No. 159. Guthrie, Irving, and Sokolowsky (2010) conclude that “[w]hile we identify a few firms who appear to adopt the fair value option opportunistically…we find no evidence of systematic and economically meaningful

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to have soured on the FVO due to early adopters’ exploitation of the transition guidance in SFAS

No. 159, a standard they view as principles-based.3 Our results imply that any evaluation by the

FASB of the merits of SFAS No. 159’s FVO relative to the alternative of hedge accounting

should not dwell on early adopters’ opportunistic behavior. In fact, our findings for regular

adopters should apply a fortiori to all banks subsequent to initial adoption, because various

provisions of the standard discussed in Section II make it essentially impossible to manage

accounting or regulatory capital numbers through FVO elections after the period of initial

adoption.

The remainder of the paper is organized as follows. In Section II, we describe the

application of SFAS No. 159’s FVO and transition guidance to different types of financial

instruments, the response of the SEC and other accounting policymakers to early adopters’

exploitation of that transition guidance, and the FVO literature. We develop our hypotheses

along with this discussion. In Section III, we describe our sample, data, variables and empirical

models. In Section IV, we report the results of our main empirical analyses and describe the

specification analyses we conducted. We conclude in Section V.

2. SFAS No. 159, Hypotheses, and the FVO Literature

opportunistic elections for current or future earnings gains in our sample.” Neither of these papers provides empirical evidence that firms’ FVO elections adhered to the standard’s intent, rather than simply did not manipulate the standard’s transition guidance. 3 For example, two FASB members who originally voted for the standard have expressed concerns about the existence or specific features of SFAS No. 159’s FVO. FEI’s Financial Reporting Blog for May 18, 2009 summarizes the discussion at a FASB board meeting that day, stating “some dissatisfaction [was] voiced with respect to the FVO.” The blog quotes two FASB board members who voted for SFAS No. 159. FASB Chairman Robert Herz (now retired from the board) is quoted as stating “My preference would be for no FVO.” Board member Leslie Seidman (now FASB chairman) is quoted as stating “If I’d had my druthers, on transition I would probably [limit the FVO to] each major asset class.” (The bracketed insertion in the Seidman quote is from the blog.)

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SFAS No. 159’s Requirements and Transition Guidance

SFAS No. 159 allows firms to elect to account for most financial instruments and similar

items at fair value with unrealized gains and losses recorded in net income in the period they

occur. Firms’ FVO elections are irrevocable. Firms may elect the FVO only for whole financial

instruments, not for selected risks within financial instruments. Firms may elect the FVO for

individual instruments. AAA (2007) criticizes the optional aspects of the FVO as yielding non-

comparable accounting within portfolios of similar instruments held by a firm as well as across

firms. Two FASB board members dissented from SFAS No. 159 primarily because of this

perceived lack of comparability. The FVO is no worse than the alternative of current hedge

accounting in this regard, however, and paragraphs 17-22 of SFAS No. 159 require detailed

disclosures to mitigate this concern.

In the period they adopted SFAS No. 159, firms could elect the FVO for any financial

instrument they held. As discussed below, this free choice along with the standard’s transition

guidance allowed firms to make opportunistic FVO elections in the adoption period. After

adoption, firms may elect the FVO only at the inception of financial instruments or when certain

specified events trigger a new basis of accounting for those instruments. Firms generally acquire

financial instruments at fair value, and so their FVO elections at the inception of financial

instruments have no immediate effects on their accounting or regulatory capital numbers. For

this reason, it is essentially impossible for firms to make FVO elections to manage the current

levels of their accounting or regulatory capital numbers after the adoption period. Of course, the

volatility of firms’ accounting and regulatory capital numbers in future periods will be affected

by the immediate recognition of unrealized gains and losses as they occur on the elected

financial instruments.

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SFAS No. 159 became effective for firms’ first fiscal year beginning after November 15,

2007. Since all but seven of our sample banks have calendar fiscal years, the effective date of

the standard generally is January 1, 2008 for our regular adopters and non-adopters. Firms could

early adopt SFAS No. 159 as long as they: (1) contemporaneously early adopted SFAS No. 157,

Fair Value Measurements, (2) made the choice within 120 days of the beginning of the fiscal

year, and (3) had not issued financial statements for any quarter of that year. SFAS No. 159’s

effective date for our early adopters generally is January 1, 2007.

SFAS No. 159’s transition guidance contains two features that were amenable to

exploitation. First, paragraph 25 of the standard requires all adopters to record the cumulative

effect of adopting the standard in retained earnings. While the FASB’s reasonable rationale for

this feature is to avoid contaminating net income in the adoption period with gains and losses

that occurred in prior periods, this feature allowed firms to raise their future net income by

electing the FVO for positions with cumulative unrealized losses. This feature also allowed

banks to raise their Tier 1 capital ratio by electing the FVO for financial instruments with

cumulative unrealized gains.

Second, paragraph 30 of SFAS No. 159 allowed early adopters up to 120 days after the

beginning of the adoption quarter to determine the financial instruments for which they initially

elected the FVO. This feature is motivated by the issuance of the standard in February 2007 and

the FASB’s desire to allow early adoption as of the beginning (as opposed to the second quarter)

of fiscal 2007. However, it provided early adopters with the ability to raise their 2007:Q1 and to

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a lesser extent 2007:Q2 net income as well as their Tier 1 capital ratios by electing the FVO for

instruments that experienced unrealized gains during the look-back period.4

The FVO literature finds that some early adopters elected the FVO for financial

instruments, particularly AFS securities, with cumulative unrealized losses at the effective

adoption date and with unrealized gains during the 120-day look-back period. Song (2008) and

Guthrie, Irving, and Sokolowsky (2010) find that this opportunistic behavior is concentrated

among early adopters desiring to meet earnings targets. Although the accounting and banking

literatures often find that banks make accounting choices to increase regulatory capital,5 the

FVO literature provides essentially no evidence that banks exploited SFAS No. 159’s transition

guidance to raise capital.

Beginning in April 2007, the SEC and other accounting policymakers took various

actions to quash the opportunistic behavior exhibited by early adopters. In an April 4, 2007

speech, SEC Deputy Chief Accountant James Kroeker indicated the SEC’s displeasure with

portfolio “rebalancing” or “enhancement” strategies that various investment advisors had

proposed firms use upon adopting SFAS No. 159. In a typical strategy, firms would elect the

FVO for financial instruments with cumulative unrealized losses that they recorded directly in

retained earnings. Firms would then sell the instruments and replace them with similar

instruments for which they would not elect the FVO in order to avoid future net income

volatility. Mr. Kroeker states that “this type of activity does not appear to promote the objective

of the accounting standard. Accordingly, you can expect the SEC staff to continue to have an

4 We do not examine banks’ FVO elections related to the look-back period because the FVO literature appears to fully document the opportunism of these elections (see the following paragraph for a summary of the literature’s findings), and because the look-back period was not available to regular adopters. 5 See, for example, Moyer (1990), Beatty, Chamberlain, and Magliolo, (1995), Kim and Kross (1998), Ahmed Takeda, and Thomas (1999), and Hodder, Kohlbeck, and McAnally (2002).

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interest in such activities.” An April 18, 2007 Center for Audit Quality (CAQ) Alert effectively

reiterated this warning to the broad auditor and financial report preparer communities.6

Henry (2009) identifies 12 firms (11 banks and one lessor) who rescinded or revised their

FVO elections as a result of scrutiny by the SEC or auditors. However, she also discusses how

SunTrust received such scrutiny but did not revise its FVO elections despite appearing to have

exploited SFAS No. 159’s transition guidance.

The FVO literature and discussion above show or strongly suggest two phenomena that

we do not formally state as hypotheses but do demonstrate empirically. First, early adopters’

FVO elections were opportunistic, but regular adopters were deterred from such behavior by the

scrutiny of early adopters FVO elections by the SEC and auditors. Second, early adopters

elected the FVO for financial instruments with cumulative unrealized losses to raise their future

net income.

We propose two new hypotheses regarding early adopters’ opportunistic behavior here

and one more in the next section. First, we predict that the early adopters’ opportunistic FVO

elections described above should be observed primarily for banks with histories of managing

accounting numbers and sufficiently high regulatory capital to render them willing to absorb the

hit to regulatory capital.

[H1] Early adopters with histories of managing accounting numbers and high regulatory capital elected the FVO for financial instruments with cumulative unrealized losses.

6 In addition, in a December 10, 2007 speech, SEC Professional Accounting Fellow Ashley Carpenter stated that the election of the FVO for AFS or HTM securities “does not relieve management of it requirement to assess those securities for other than temporary impairment at the preceding balance sheet date. If an other-than-temporary impairment exists, the impairment loss should be reported in earnings in the period prior to the adoption of Statement 159, and not included in the transition adjustment.” Mr. Carpenter also discussed various issues regarding the classification of securities prior to the election of the FVO.

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Second, we predict that early adopters with a history of managing accounting numbers but with

sufficiently low regulatory capital elected the FVO for financial instruments with cumulative

unrealized gains to raise regulatory capital. [H2] Early adopters with histories of managing accounting numbers and low regulatory capital elected the FVO for financial instruments with cumulative unrealized gains.

We discuss our proxies for history of managing accounting numbers and sufficiently high and

low regulatory capital in Section III.

SFAS No. 159’s Intent

Paragraph 1 of SFAS No. 159 states that the FVO provides “entities with the opportunity

to mitigate volatility in reported net income caused by measuring related assets and liabilities

differently without having to apply complex hedge accounting provisions.” That is, SFAS No.

159’s FVO enables firms to account symmetrically for the two sides of economic hedges in a

simpler fashion than does hedge accounting.

SFAS No. 133, Accounting for Derivative Instruments and Hedging Activities, as

amended and interpreted by a voluminous literature, governs hedge accounting. SFAS No. 133

allows hedge accounting only for highly effective hedges involving derivatives. As discussed

below, the standard’s hedge effectiveness and other requirements make it difficult for many

economic hedges to qualify for hedge accounting.

We determine whether banks’ FVO elections reflect opportunism versus compliance with

SFAS No. 159’s intent based on the answers to two questions. First, do banks elect the FVO as a

substitute for difficult-to-obtain hedge accounting for the elected financial instruments? If so,

banks likely are remedying accounting mismatches, consistent with SFAS No. 159’s intent. In

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contrast, if banks elect the FVO for the hedged items in economic hedges for which it is easy to

obtain hedge accounting or for non-economic hedges, then they likely are behaving

opportunistically. Second, do banks disproportionately elect the FVO for financial instruments

with cumulative unrealized losses or gains? If so, they likely are behaving opportunistically.

Below, we provide the typical answers for these questions for each of the three types of financial

instruments for which banks most commonly elected the FVO: AFS securities and debt for early

adopters and loans held for sale for regular adopters.

We first consider AFS securities and debt, which we consider together due to their

economic similarity. Banks’ derivatives-based hedges of AFS securities and debt usually exhibit

little if any accounting hedge ineffectiveness. This is partly because banks often designate

specific risks within these financial instruments—most commonly benchmark interest rate risk—

as the hedged item.7 Banks usually hedge this benchmark interest rate risk using interest rate

swaps that exhibit close to perfect hedge effectiveness. For this reason, banks generally do not

need to elect SFAS No. 159’s FVO as a substitute for hedge accounting for their hedges of AFS

securities or debt.8

Banks generally could elect the FVO for AFS securities and debt with either cumulative

unrealized losses (or gains), thereby increasing their future net income (Tier 1 regulatory

7 SFAS No. 133 allows hedge accounting for hedges of specific risks within hedged items, such as interest rate risk. SFAS No. 138, Accounting for Certain Derivative Instruments and Certain Hedging Activities-an amendment of FASB Statement No. 133, allows hedge accounting for hedges of benchmark (i.e., Treasury or LIBOR) interest rate risk. 8 Banks’ economic hedges of AFS securities and debt often involve asset-liability management (“ALM”) rather than the use of derivatives. This fact does not change our conclusion that FVO elections for AFS and debt are likely to exhibit opportunistic behavior, however, because SFAS No. 159 allows banks to elect the FVO for one side of an ALM relationship regardless of the accounting for the other side.

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capital).9 Hence, early adopters’ FVO elections for AFS securities and debt likely were

opportunistic.

We now turn to loans held for sale. Banks typically hold loans held for sale only for the

period of time necessary to accumulate enough loans to sell efficiently. For example, mortgages

are banks’ most common type of loans held for sale, and banks typically hold mortgages as held

for sale for about two months. Banks usually hedge loans held for sale by selling forward or

futures contracts with tenors equal to the expected length of the holding period. While these

derivatives-based hedges could in principle qualify for hedge accounting, they typically exhibit

considerable hedge ineffectiveness due to the uncertain length of the holding period. This

uncertain length results partly from fluctuations in the speed that banks accumulate loans held for

sale as well and partly from fluctuations in the receptivity of buyers in the secondary loan

market.10 For this and other lesser reasons,11 banks find it difficult to obtain hedge accounting

for their hedges of loans held for sale. Hence, banks likely elect the FVO for loans held for sale

as a substitute for hedge accounting.12

9While AFS securities are recorded at fair value on the balance sheet under SFAS No. 115, Accounting for Certain Investments in Debt and Equity Securities, unrealized gains and losses are recorded in accumulated other comprehensive income (“AOCI”). The election of this FVO for AFS securities transfers this AOCI to retained earnings. Banks’ Tier 1 capital excludes AOCI, so banks’ FVO elections for AFS securities also affect their Tier 1 capital. 10 To illustrate, assume a bank holds loans held for sale for an expected holding period of two months, but with the actual holding period being one month half the time and three months half the time. Under this assumption, considerable (±50%) hedge ineffectiveness would result if the bank hedged the loans by selling a forward contract with a tenor equal to the average two-month holding period. In fact, this forward contract would not be a sufficiently effective hedge of the loans held for sale for the hedging relationship to qualify for hedge accounting under SFAS No. 133 and current practice, in which highly effective hedges can exhibit no greater than -20% to +25% ineffectiveness. 11 For example, the nonlinear and behavioral prepayment option imbedded in fixed-rate mortgages also yields hedging difficulties. This is particularly true for fixed-rate nonconforming mortgages for which hedging instruments with similar prepayment risks are not readily available. 12 Unlike AFS and debt (see footnote 8), loans held for sale generally are not used in ALM due to their short and variable holding period.

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Banks generally cannot elect the FVO for loans held for sale with cumulative unrealized

losses because loans held for sale are accounted for at lower of cost or fair value, so unrealized

losses are recognized for accounting purposes as if they were realized. Banks must recognize all

unrealized losses on loan held for sale prior to the adoption of the FVO. Hence, banks cannot

elect the FVO for loans held for sale to raise their future net income. Banks could elect the FVO

for loans held for sale with cumulative unrealized gains to raise their Tier 1 regulatory capital,

however.

Based on the discussion above, we propose two hypotheses regarding whether and when

banks’ FVO option elections complied with SFAS No. 159’s intent. First, we hypothesize that

this compliance varies across the instruments for which banks elected the fair value option.

[H3] Early adopters’ FVO elections for AFS securities and debt exploited SFAS No. 159’s transition guidance. [H4] Regular adopters’ FVO elections for loans held for sale were consistent with SFAS No. 159’s intent.

Second, we hypothesize that banks’ FVO elections are more likely to comply with SFAS

No. 159’s intent when their prior hedge accounting and other accounting treatments for

economic hedges has been ineffective. [H5] Regular adopters were more likely to elect the FVO when they experience greater accounting mismatches.

We discuss our proxies for banks’ accounting mismatches in Section III.

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3. Sample, Data, Variables, and Models

Sample and Data Sources

We restrict our analysis to banks for two reasons. First, this restriction yields a more

homogeneous sample. Compared to non-banks, banks hold more financial instruments and are

more likely to use ALM, other forms of economic hedging, and hedge accounting. Second,

banks provide detailed data about their FVO elections and other attributes in their regulatory FR

Y-9C reports. Both of these reasons are important to our study because we analyze specific

reasons for banks’ FVO elections for specific types of financial instruments. For example,

nonbanks generally do not hold any loans held for sale.

To identify banks that adopted SFAS No. 159, when they did so, and the financial

instruments for which they made their initial FVO elections, we searched all publicly traded

banks’ Form 10-Q filings for 2007:Q1 and 2008:Q1 SEC’s EDGAR database using the

keywords “fair value option,” “FVO,” and “159.” The development of the sample is

summarized in Table 1, Panel A. Of the 371 banks on the EDGAR database in 2007:Q1, 19 have

missing data, 28 early adopted SFAS No. 159, and 324 did not adopt the standard in that quarter.

The 352 observations with non-missing data constitute our 2007:Q1 sample.

Primarily because of termination of securities registration as a result of the financial crisis,

27 banks disappear from the EDGAR database from 2007:Q1 to 2008:Q1. Of the 344 banks on

the EDGAR database in 2008:Q1, 13 have missing data, 27 early adopted SFAS No. 159, 29

regular adopted SFAS No. 159 (excluding one regular adopter with missing data), and 275 did

not elect the FVO that quarter. The 304 observations with non-missing data that are not early

adopters constitute our 2008:Q1 sample.

Table 1 reports the counts of FVO elections in total and by type of financial instruments

for early adopters (Panel B) and regular adopters (Panel C). Of the 28 early adopters, 21 (19)

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elected the FVO for AFS securities (debt), and 13 elected the FVO for both AFS securities and

debt. The dominance of AFS securities and debt in early adopters’ FVO elections is consistent

with hypothesis H3, given the FVO literature’s evidence that some early adopters exploited

SFAS No. 159’s transition guidance. The considerable overlap in the FVO elections for AFS

securities and debt likely reflects the fact that banks typically use both AFS securities and debt in

ALM due to their offsetting risks. The next most frequently elected item is loans held for

investment, with only five elections.

Of the 29 regular adopters, 19 elected the FVO for loans held for sale. The dominance of

loans held for sale in regular adopters’ FVO elections is consistent with hypothesis H4, under the

assumption that regular adopters complied with SFAS No. 159’s intent. There is relatively little

overlap between FVO elections for loans held for sale and other financial instruments; this likely

reflects the fact that banks typically do not use loans held for sale in ALM due to their short and

uncertain holding period. The next most frequently elected item is debt, with only eight

elections.

To be able to make reliable inferences in our analyses of FVO elections by type of

financial instrument, we separately examine only the three most common FVO elections: early

adopters’ elections for AFS securities and debt and regular adopters’ elections for loans held for

sale. At least 19 banks elected the FVO for each of these financial instruments. The next most

common FVO election is debt for regular adopters, with only eight elections.

We collect most of our variables from banks’ regulatory FR Y-9C reports, which U.S.

bank holding companies with total consolidated assets above $500 million are required to file

each quarter with the Federal Reserve. These high quality reports are essentially complete in

terms of the coverage of banks meeting the size criterion and the availability of required

variables. We obtained the fair and carrying values of banks’ financial instruments from their

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Form 10-Ks on the EDGAR database. We gathered net hedge ineffectiveness gains or losses

from quarterly Bank Compustat and daily stock returns from CRSP.

Variables and Empirical Models

We test our hypotheses using logistic regression models in which the dependent variables

are dichotomous variables indicating five distinct types of FVO elections. The explanatory

variables include proxies for accounting mismatches, proxies for opportunism regarding SFAS

No. 159’s transition guidance, and control variables.

The dependent variables are as follows. FVO_2007Q1 takes a value of one for early

adopters and zero otherwise. AFS_2007:Q1 (Debt_2007:Q1) takes a value of one for early

adopters electing the FVO for AFS securities (debt), is missing for early adopters that elected the

FVO only for financial instruments other than AFS securities (debt), and is zero otherwise.

FVO_2008Q1 takes a value of one for regular adopters, is missing for early adopters, and is zero

otherwise. LoanHFS _2008Q1 takes a value of one for regular adopters electing the FVO for

loans held for sale, is missing for early adopters and for regular adopters that selected the FVO

only for financial instruments other than loans held for sale, and is zero otherwise. Panel A of

the Appendix contains the definitions of these dependent variables.

The explanatory variables include four proxies for accounting mismatches. The first two

capture accounting mismatches that occur when the two sides of banks’ ALM or other economic

hedging relationships are accounted for inconsistently. EarV denotes the standard deviation of

quarterly earnings before extraordinary items divided by beginning total assets over the four

quarters prior to the FVO adoption. REcor denotes the correlation between quarterly stock

returns and quarterly earnings divided by beginning total assets and over the four quarters prior

to the FVO adoption. More positive EarV and more negative REcor imply greater accounting

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mismatches, all else being equal. Consistent with hypotheses H4 and H5, we expect regular

adopters’ FVO elections, particularly for loans held for sale, to be positively associated with

EarV and negatively associated with REcor.13

The two remaining accounting mismatch proxies capture banks’ costly or ineffective use

of hedge accounting. Der denotes the notional amount of derivatives divided by total assets at

the beginning of the FVO adoption period; this variable is intended to capture the cost of hedge

accounting. IH_dmy denotes a dummy variable that takes a value of one for banks reporting

gains or losses attributable to accounting hedge ineffectiveness in the year prior to the FVO

adoption and zero otherwise; this variable is intended to capture the ineffectiveness of hedge

accounting. Again consistent with hypotheses H4 and H5, we expect regular adopters’ FVO

elections, particularly for loans held for sale, to be positively associated with both Der and

IH_dmy.14

The explanatory variables include eight proxies for opportunism. As discussed below, we

expect early adopters’ FVO option elections to be associated in predictable directions with many

of these proxies. We expect regular adopters’ FVO elections to have no reliable association with

any of these proxies.

All of the opportunism proxies are based in part or whole on measures of banks’ history

of managing accounting numbers and/or current Tier 1 regulatory capital ratio. We determine

banks’ history of managing accounting numbers using the correlation of quarterly realized net

gains on AFS securities and quarterly income before extraordinary items and realized net gains

13 Song (2008) and Guthrie, Irving, and Sokolowsky (2010) use earnings variability for the same purpose in their studies. No other study on the FVO uses REcor, which the empirical results discussed below suggest is the better proxy. 14 Song (2008) and Guthrie, Irving, and Sokolowsky (2010) use a dummy variable indicating firms’ derivatives use for the same purpose as we use Der. As discussed below, the use of a dummy variable appears to load much of the effect of banks’ derivatives use on firm size, a highly correlated variable. No other study on the FVO has used a variable like IH_dmy that directly captures accounting hedge ineffectiveness.

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on AFS securities over the prior eight quarters for the bank.15 A negative correlation is

consistent with the bank having smoothed its quarterly net income through realization of gains

and losses on AFS securities, i.e., having behaved opportunistically. Hence, we construct the

dichotomous variable OPP, which takes a value of 1 if this correlation is negative, and zero

otherwise.16 We measure banks’ regulatory capital as the dichotomous variable REG_H, which

takes a value of one if the firm’s Tier 1 capital ratio is above the median for the sample banks

that quarter and zero otherwise.

The first two opportunism proxies interact OPP and REG_H. OPP_H denotes OPP

times REG_H. OPP_L equals OPP times (1-REG_H). We expect early adopters’ FVO elections

to be positively associated with both OPP_H and OPP_L. However, we expect early adopters

with OPP_H=1 to elect the FVO for financial instruments with cumulative unrealized losses,

and early adopters with OPP_L=1 to elect the FVO for financial instruments with cumulative

unrealized gains.

The next two opportunism proxies pertain to the availability of financial instruments with

cumulative unrealized gains or losses for the bank. Such availability is necessary for banks to be

able to elect the FVO opportunistically. UGLAFS denotes the difference between the fair value

and carrying value of AFS securities divided by total assets. UGLNFA denotes the difference

between fair value and carrying value of net financial assets other than AFS securities divided by

total assets. We distinguish AFS securities from other net financial assets in part because AFS

15 We use this proxy in part because it is easily measurable (e.g., compared to income smoothing using provisions for loan losses) and in part because we expect realization of gains and losses on AFS securities to be a common form of income management. 16 The correlation of quarterly realized net gains on AFS securities and quarterly income before extraordinary items and realized net gains on AFS securities is distributed fairly symmetrically around zero and takes a wide range of values. Specifically, the quartiles of this correlation are -0.86, -0.36, 0.00, 0.23, and 0.99. This wide range is consistent with considerable cross-sectional variation in banks’ historical proclivity to smooth income through realization of gains and losses on AFS securities.

17

securities are accounted for differently, but primarily because the FVO literature finds that early

adopters’ FVO elections for AFS securities were the primary way that they exploited SFAS No.

159’s transition guidance.17 We do not make hypotheses about the direct associations between

UGLAFS and UGLNFA and early adopters’ FVO elections, because we expect these

relationships to depend upon OPP_H and OPP_L.

To capture these indirect associations, the remaining opportunism variables are the four

multiplicative interactions of one of UGLAFS and UGLNFA with one of OPP_H and OPP_L.

We expect early adopters’ initial FVO elections to be negatively associated with UGLAFS ×

OPP_H and UGLNFA × OPP_H, because banks for which OPP_H=1 will elect the FVO for

financial instruments with cumulative unrealized losses to increase future net income. We

expect early adopters’ initial FVO elections to be positively associated with UGLAFS × OPP_L

and UGLNFA × OPP_L, because banks for which OPP_L=1 will elect the FVO for financial

instruments with cumulative unrealized gains to increase regulatory capital.

We include two control variables in the models. We include the log of total assets,

denoted LogTA, to control for the effect of size. Song (2008) and Guthrie, Irving, and

Sokolowsky (2010) find that larger firms are more likely to make FVO elections. Larger firms

are more visible than and differ in numerous other ways from smaller firms that may influence

their FVO elections. Because it is difficult to determine the net implication of the numerous and

pervasive effects of firm size, we make no directional prediction for LogTA despite these

findings.

We include 0-1 year interest rate sensitivity gap, denoted IRSG, to control for banks’

choice to accept interest rate risk economically and/or willingness to absorb the corresponding

17 Song (2008) uses UGLAFS for the same purpose. No other study on the FVO uses UGLNFA.

net income volatility. IRSG may also capture accounting mismatches, due to the mixed-attribute

accounting for financial instruments. Because IRSG may capture various constructs, we do not

make a directional prediction for its association with banks’ FVO elections.

As discussed in the sensitivity tests section, we tried various other control variables

which were insignificant and did not affect our inferences regarding the included explanatory

variables. Panel B of the Appendix contains the definitions of the explanatory variables and

summarizes our predictions for the directions of their associations with FVO elections.

Because our dependent variables are dummy variables indicating different types of FVO

elections, we estimate logistic regressions of these variables on the explanatory variables, i.e.,

).,_,_,_,,_,_,,_,_

,,_,,()variabledummyelectionFVO(Pr

IRSGTALogLOPPUFLAFSHOPPUFLNFAUGLNFALOPPUFLAFSHOPPUGLAFSUGLAFSLOPPHOPP

DerdmyIHREcorEarVfob

××××

= (1)

The sample period for the estimation of equation (1) is 2007:Q1 for the models in which the

dependent variable is FVO_2007Q1, AFS_2007:Q1, or Debt_2007:Q1 and is 2008:Q1 for the

models in which the dependent variable is FVO_2008Q1 or LoanHFS_2008Q1.

We measure all explanatory variables in the period immediately prior to the potential

FVO election under consideration. We winsorize all continuous variables at the 0.5% and 99.5%

tails to reduce the effects of outliers. Our results are robust to conventional alternative

winsorization and deletion rules.

Unless stated differently, we say that an estimated coefficient or other statistic is

significant if the probability of the null hypothesis generating that statistic is 5% or less in a one-

tailed test if we have made a directional prediction and in a two-tailed test otherwise.

18

19

We report several goodness of fit statistics for the estimation of equation (1), the most

interpretable of which is the area under the ROC (receiver operating characteristics) curve. This

statistic is the estimated probability that the model ranks a randomly chosen actual FVO election

higher than a randomly chosen non-FVO election. Random guessing yields an area under the

ROC curve equal to 0.50, while perfect prediction yields a statistic of 1.00. Hosmer and

Lemeshow (2000) state that an area under the ROC curve of 0.70–0.80 (above 0.80) indicates an

acceptable (excellent) model.

4. Empirical Results

Descriptive Statistics

Table 2 reports the means, medians, and standard deviations of the explanatory variables

in equation (1) for various pairings of FVO adopters versus correspondingly defined non-

adopters described below. The table reports the significance levels for these pairings of Student t

and Wilcoxon rank-sum Z tests of differences between the means and medians, respectively, as

well as Snedecor-Cochran F tests of ratios of variances. For comparative purposes, the table

reports these statistics and tests for the non-explanatory variables OPP, UGLAFS×OPP, and

UGLNFA×OPP.

Panel A of Table 2 reports these statistics and tests for early adopters versus non-adopters

in 2007:Q1. Panel B reports them for early adopters that elect the FVO for AFS securities versus

non-adopters for AFS securities in 2007:Q1. Panel C reports them for early adopters that elect

the FVO for debt versus non-adopters for debt in 2007:Q1. Panel D reports them for regular

adopters versus all other observations in 2008:Q1. Panel E reports them for regular adopters that

elect the FVO for loans held for sale versus non-adopters for loans held for sale in 2008:Q1. In

20

Panels B, C, and E, the definitions of non-adopters exclude adopters of the FVO for financial

instruments other than the specific ones under consideration.

The statistics and tests reported in Panel A indicate that early adopters are more likely to

use derivatives (for Der, t=1.81 and Z=2.78) and to report accounting hedge ineffectiveness (for

IH_dmy, Z=2.11) than are other observations in 2007:Q1. These findings are consistent with

early adopters’ FVO elections adhering to SFAS No. 159’s intent. However, there is also

evidence that early adopters with histories of managing accounting numbers and high regulatory

capital (i.e., OPP_H=1) opportunistically elected the FVO for AFS securities with cumulative

unrealized losses (for UGLAFS×OPP_H, t=-2.48 and Z=-2.62) to raise future net income,

consistent with hypothesis H1. In contrast, early adopters with similar histories but low

regulatory capital (i.e., OPP_L=1) opportunistically elected the FVO for debt with cumulative

unrealized gains (for UGLNFA×OPP_L, Z=1.65) to raise regulatory capital, consistent with

hypothesis H2. Notice that the significance of the interactive variables involving OPP are

muted relative to the significance of the corresponding interactive variables involving OPP_H

and OPP_L, because of the different opportunistic behavior of high versus low regulatory capital

early adopters. Panel A also reports evidence that early adopters are larger (for total assets,

t=1.96).

Panels B and C report the descriptive statistics and tests comparing early adopters

electing the FVO for AFS securities and debt to non-adopters for those instruments in 2007:Q1.

While generally similar to the results for early adopters reported in Panel A, these panels indicate

differences between early adopters electing the FVO for the two types of instruments, despite the

substantial overlap in these elections for these instruments reported in Table 1, Panel B. These

differences are not observable in panel A, and so they highlight the importance of separate

analysis of FVO elections by type of financial instrument.

21

Specifically, Panel B reports that early adopters electing the FVO for AFS securities have

a history of managing accounting numbers and higher capital than corresponding non-adopters

(for OPP_H, t=2.17 and Z=2.16), consistent with hypothesis H1. In contrast, Panel C reports

that early adopters electing the FVO for debt also have a history of managing accounting

numbers but have lower capital than corresponding non-adopters (for OPP_L, t=2.12 and

Z=2.11), consistent with hypothesis H2. Panel B reports that early adopters electing the FVO for

AFS securities generally have larger cumulative unrealized losses on AFS securities than

corresponding non-adopters (for UGLAFS, Z=-2.54), again consistent with hypothesis H1. In

contrast, Panel C reports that early adopters electing the FVO for debt generally have larger

cumulative unrealized gains on other net financial assets than corresponding non-adopters (for

UGLNFA, Z=2.02), again consistent with hypothesis H2. As in Panel A, notice that the

significance of the interactive variables involving OPP are muted relative to the significance of

the corresponding interactive variables involving OPP_H and OPP_L.

The statistics and tests reported in Panel D suggest that regular adopters adhered more

strongly to SFAS No. 159’s intent than did early adopters. The differences of the means and

medians of Der (t=2.19, Z=6.88) and IH_dmy (t=3.90, Z=6.82) are considerably more

significantly positive than in Panel A, and the difference of the medians of EarV (Z=1.91) also is

significantly positive. However, there is also some apparent evidence that regular adopters with

histories of managing accounting numbers and low regulatory capital (i.e., OPP_L=1) exploited

SFAS No. 159’s transition guidance. Specifically, regular adopters were more likely than

corresponding non-adopters to have OPP_L=1 (t=2.61 and Z=2.59). Regular adopters with

OPP_L=1 elected the FVO for AFS securities with cumulative unrealized losses (for

22

UGLAFS×OPP_L, t=-2.08 and Z=-2.25) but elected the FVO for net financial assets with

cumulative unrealized gains (for UGLNFA×OPP_L, t=2.17).18

Panel E reports the descriptive statistics and tests comparing regular adopters electing the

FVO for loans held for sale to non-adopters for those instruments in 2008:Q1. Compared to the

results for all regular adopters in Panel D, Panel E reports similarly strong associations with the

accounting mismatch proxies but noticeably weaker associations with the opportunism proxies.

This suggests that regular adopters’ FVO elections for loans held for sale are even more likely to

comply with SFAS No. 159’s intent than their elections for other financial instruments. These

differences again highlight the importance of separate analysis of FVO elections by type of

financial instrument.

Panels A and B of Table 3 report the Pearson correlations of the equation (1) variables for

the 2007:Q1 and 2008:Q1 samples, respectively. We briefly discuss the more interesting

correlations. Log_TA is highly correlated with many of the other explanatory variables,

particularly IH_dmy and Der, indicating the importance of controlling for size. Log_TA is

positively correlated with OPP_L, because larger banks are more likely both to have managed

income and to have lower regulatory capital. IH_dmy and Der are highly positively correlated,

which will tend to work against either variable being significant in the multivariate analysis.

18 We believe these offsetting gains and losses result in part from some regular adopters sponsoring certain types of securitization entities (e.g., structured investment vehicles and asset-backed paper conduits) that they had to consolidate after providing liquidity or other support to the vehicles in late 2007 due to the credit crisis. These entities generally held securities with cumulative unrealized losses and had issued debt that experienced corresponding unrealized gains. Sponsors of the newly consolidated entities frequently elected the FVO option for both the financial assets and financial liabilities of the entities to yield symmetric accounting for those instruments. To the extent this explains the apparently opportunistic behavior described above, that behavior actually is consistent with SFAS No. 159’s intent. Unfortunately, we do not have data on regular adopters’ FVO elections associated with consolidation of securitization entities.

23

Logistic Regression Estimations

Table 4 reports the results of the logistic regression estimations of equation (1). Panel A

reports the results for the 2007:Q1 sample and the dependent variables FVO_2007Q1 (the

leftmost set of columns), AFS_2007Q1 (the center set of columns), and Debt_2007Q1 (the

rightmost set of columns). Panel B reports the results for the 2008:Q1 sample and the dependent

variables FVO_2008Q1 (the left set of columns) and LoanHFS_2008Q1 (the right set of

columns). The table reports Z statistics and significance levels (1%, 5%, and 10%) for all

coefficients.

The coefficients on the four interactive variables reflect the incremental effects of those

variables beyond the effects of the uninteracted cumulative unrealized gain variables. For

example, the coefficient on UGLAFS×OPP_H reflects the incremental effect of that variable

beyond the effect of UGLAFS. Hence, Table 4 also reports significance levels for the total

coefficients, for example, the sum of the coefficients on UGLAFS and UGLAFS×OPP_H. With

one minor exception noted below, the two sets of significance levels yield identical inferences,

and so we discuss only the first set.

The goodness of fit of the estimated models is generally quite good. By Hosmer and

Lemeshow’s ROC rules of thumb mentioned above, the models for early adopters range from the

upper end of adequate to the lower end of excellent, while the models for regular adopters are all

well into the excellent range.

In 2007:Q1 regressions in Panel A, the only accounting mismatch variable that is reliably

associated with early adopters’ FVO elections is Der, the coefficient on which is significantly

positive for all three dependent variables. Hence, banks that used derivatives more intensively

were more likely to be early adopters. The explanatory power of Der likely is in part attributable

to size-related effects, however, because of the high correlation of Der and Log_TA reported in

24

Table 3, Panel A. We discuss this correlation further below. Overall, these results provide

minimal evidence that early adopters FVO elections adhered to SFAS No. 159’s intent.

In contrast, many of the opportunism variables are significant in Panel A, consistent with

hypothesis H1 There is evidence that early adopters with a history of managing accounting

numbers and high regulatory capital (OPP_H=1) elected the fair value option for AFS securities

and possibly other financial instruments with cumulative unrealized losses to increase future net

income. Specifically, the coefficient on UGLAFS×OPP_H is significantly negative at the 10%

level for the models with dependent variables FVO_2007Q1 and AFS_2007Q1. The coefficient

on UGLNFA×OPP_H is significantly negative for the models with dependent variables

FVO_2007Q1 and AFS_2007Q1, although the significance level of the total coefficient in the

model with the latter dependent variable is only 10%.

Consistent with hypothesis H2, Panel A also reports evidence that early adopters with a

history of managing accounting numbers and low regulatory capital (OPP_L=1) elected the fair

value option for AFS securities, debt, and possibly other financial instruments with cumulative

unrealized gains to increase regulatory capital. Specifically, the coefficients on

UGLAFS×OPP_L are significantly positive for the models with each of the three dependent

variables, although only at the 10% level when Debt_2007Q1 is the dependent variable. In

addition, the coefficient on OPP_L is significantly positive for the models with the dependent

variables FVO_2007Q1 and Debt_2007Q1. Our findings that early adopters opportunistically

elected the FVO for debt and possibly other financial instruments with cumulative unrealized

gains to raise regulatory capital are new given the FVO literature.

The control variables generally are insignificant. Unexpectedly given the findings of the

FVO literature, the coefficient on Log_TA is consistently negative. The reason for this difference

with the results in the FVO literature is the high positive correlation of Log_TA and Der

25

mentioned earlier. If we exclude Der from the model or replace it with a dichotomous dummy

variable for derivatives use, then the coefficient on Log_TA becomes positive. While some of

the papers in the FVO literature control for derivatives use, they do so using a dummy variable

that is less positively correlated with firm size than Der.

In Panel B, regular adopters’ FVO elections are associated in the predicted directions

with both REcor and IH_dmy, consistent with hypothesis H5. The coefficient on REcor is

significantly negative in the models with the dependent variables FVO_2008Q1 and

LoanHFS_2008Q1, although only at the 10% level for the latter. The coefficient on IH_dmy is

significantly positive in the models with both dependent variables. We view the significant

coefficients on these variables as compelling evidence that regular adopters complied with SFAS

No. 159’s intent. Compared to Der, the only accounting mismatch variable significant in

explaining early adopters’ FVO elections, REcor more directly reflects accounting mismatches

for economic hedges and IH_dmy more directly reflects problems in applying hedge accounting.

In contrast to the results for early adopters in Panel A, the only opportunism variable that

is significant for regular adopters in Panel B is UGLNFA×OPP_H in the model with dependent

variable FVO_2008Q1.

Post-Adoption FVO Elections

We expect our findings for regular adopters to hold for all banks FVO elections

subsequent to their adoption of SFAS No. 159, because various provisions of the standard

discussed in Section II make it essentially impossible for firms to manage their accounting or

regulatory capital numbers through FVO elections after the period of initial adoption. In an

attempt to show this, we collected data on early adopters’ FVO elections in the first quarter of

2008 and all banks’ FVO elections in the first quarters of 2009 and 2010. Unfortunately, the

26

FVO election disclosures required by SFAS No. 159 do not allow us to distinguish firms’

preexisting and new FVO elections in a period. Hence, it is essentially impossible to develop

clean tests of the determinants of banks’ new FVO elections for most financial instruments due

to the relatively long contractual lives but high potential for disposition of these instruments.

Because banks generally hold loans held for sale only for a few months, however, it may

be reasonable to assume that all of banks’ reported FVO elections for loans held for sale in a

quarter are new. This assumption is tenuous in 2009:Q1—the peak of the financial crisis, when

holding periods lengthened for all but the highest credit quality loans held for sale—but less so in

2010:Q1. Under this assumption, we found similar results for banks’ FVO elections for loans

held for sale in these quarters subsequent to adoption as those for regular adopters reported in

Table 4, Panel E.

Specification Analyses

We conducted a number of specification tests of equation (1) to ensure the robustness of

our results. First, for the estimations involving FVO elections for AFS securities and debt in

2007:Q1 and loans held for sale in 2008:Q1, we included the prior balance of the corresponding

instrument divided by total assets to ensure that the amount of the instruments did not drive the

FVO elections. The coefficients on all three variables are insignificant.

Second, instead of the dichotomous hedge ineffectiveness variable, IH_dmy, we used a

continuous measure of hedge ineffectiveness, net hedge ineffectiveness gains and losses. We do

not use this continuous measure in our main results because it nets hedge ineffectiveness gains

against ineffectiveness losses. A firm with higher gross hedge ineffectiveness may have lower

net ineffectiveness, and vice versa. For this reason, this measure, even though continuous, is

more limited than IH_dmy in our view. Consistent with this view, the coefficients on the

27

continuous measure of hedge ineffectiveness have the same sign as the ones on our binary

measure but are less significant.

Third, as in the FVO literature, we used a dichotomous dummy variable for derivatives

use instead of our continuous measure, Der. As discussed previously, the use of this dummy

variable loads the effect of derivatives use on our size variable, but it has no other noticeable

effects on the model.

Fourth, instead of total assets, we deflated our continuous variables by share price or

book value of owners’ equity. Fifth, we tried alternative deletion and winsorization rules within

the usual range. Our results are not qualitatively affected either by choice of deflator or

treatment of outliers.

Given the insignificance of EarV in all models, we also deleted this variable from the

models. This has no appreciable effect on any coefficient in any model.

5. Conclusion

In this study, we examine why banks elected SFAS No. 159’s FVO upon their initial

adoption of the standard, and whether the reasons differed for early adopters versus regular

adopters as well as across banks’ FVO elections for different types of financial instruments. We

hypothesize and provide evidence that early adopters behaved opportunistically in more varied

ways than the FVO literature shows. We hypothesize and provide evidence that regular adopters’

elections, particularly for loans held for sale, complied with the standard’s stated intent rather

than exploited its transition guidance.

Our study adds to the FVO literature, which provides evidence that early adopters

disproportionately elected the FVO option for financial instruments with cumulative unrealized

losses in order to record those losses directly in retained earnings under SFAS No. 159’s

28

transition guidance. We refine or explain the FVO option literature’s findings regarding early

adopters in three ways. First, we find that banks with histories of income smoothing through

realization of gains and losses on AFS securities were more likely to make opportunistic FVO

elections, indicating the importance of distinguishing banks on their history of managing

accounting numbers. Second, we show that early adopters’ opportunistic FVO elections for

financial instruments with cumulative unrealized losses were restricted to above-median banks

that were willing to take the hit to regulatory capital. In contrast, early adopters with below-

median regulatory capital elected the FVO for financial instruments with cumulative unrealized

gains. Third, we explain why banks’ FVO initial elections for AFS securities and debt were both

amenable to exploitation of SFAS No. 159’s transition guidance and likely to create accounting

mismatches.

We hypothesize and provide evidence that regular adopters’ elections, particularly for

loans held for sale, complied with the standard’s stated intent rather than exploited its transition

guidance. We predict and find that regular adopters’ FVO elections are associated with a low

correlation between returns and earnings and prior accounting hedge ineffectiveness. Consistent

with this prediction, we find that regular adopters most frequently elected the FVO for loans held

for sale, a financial instrument for which accounting hedges commonly exhibit significant

ineffectiveness or cannot qualify for hedge accounting and for which it is impossible to exploit

SFAS No. 159’s transition guidance to increase future net income. Our results are consistent with

regular adopters’ FVO elections, particularly for loans held for sale, remedying accounting

mismatches for the two sides of economic hedges, consistent with SFAS No. 159’s intent, not

with them exploiting the standard’s transition guidance.

Our findings suggest that any evaluation by the FASB of the merits of SFAS No. 159’s

FVO relative to the alternative of hedge accounting should not dwell on early adopters’

29

opportunistic behavior. In fact, SFAS No. 159’s FVO exhibits three features—election at

inception only, irrevocability, and application to whole financial instruments, not selected risks

—that make it less amenable to management of accounting or regulatory capital numbers than is

current hedge accounting. Although paragraph A3 of SFAS No. 159 indicates that the FASB

“views the fair value option as an interim step that can mitigate existing reporting issues and

expand the use of fair value measurements for financial instruments,” the current summary of the

status of the FASB’s project on the accounting for financial instruments on the FASB website

indicates that this accounting will continue to employ mixed measurement attributes for the

foreseeable future.

Our study provides just one example of the rich, researchable questions that fair value

options and exploitable transition guidance in accounting standards raise that future research

could address. For example, the international accounting standard IAS 39, Financial

Instruments—Recognition and Measurement, allows a FVO for financial instruments, but only

under the difficult-to-verify condition that FVO elections remedy accounting mismatches.

Fiechter (2010) finds that banks from 42 countries that chose IAS 39’s FVO experienced reduced

income volatility in the cross-section, a finding consistent with our findings that regular adopters

adhered to SFAS No. 159’s intent. Future research could address whether Fiechter’s findings are

attributable to IAS 39’s additional condition, to the standard’s absence of exploitable transition

guidance, to the difficulties firms face in exploiting the FVO on an ongoing basis discussed in

this paper, or to contemporaneous changes in firms’ hedging or investment behavior.

Two other U.S. accounting standards allow FVOs for specific items, SFAS No. 155,

Accounting for Certain Hybrid Financial Instruments—an amendment of FASB Statements No.

133 and 140, for hybrid financial instruments, and SFAS No. 156, Accounting for Servicing of

Financial Assets—an amendment of FASB Statement No. 140, for servicing rights. Hybrid

30

financial instruments and servicing rights both contain hard-to-hedge embedded options that is

similar to the hedging difficulties for loans held for sale discussed in this paper. We predict that

our findings for regular adopters apply to these FVO elections.

Various other accounting standards contain exploitable transition guidance. For example,

the modified retrospective approach to adopting SFAS No. 123(R), Share-based Payment, allows

compensation costs that had not been recognized in net income in prior years to be recorded in

retained earnings. Our findings suggest that whether and how firms exploit such transition

guidance depends on their histories of managing accounting numbers and other relevant firm

characteristics.

Appendix Definitions of Variables and Summary of Equation (1) Coefficient Predictions

).,_,_,_,,_,_,,_,_

,,_,,()variabledummyelectionFVO(Pr

IRSGTALogLOPPUGLNFAHOPPUGLNFAUGLNFALOPPUGLAFSHOPPUGLAFSUGLAFSLOPPHOPP

DerdmyIHREcorEarVfob

××××

=

(1)

Panel A: Dependent Variables Variable Definition FVO_2007Q1 A dummy variable equal to one if a bank initially elected the FVO

in 2007:Q1, and zero otherwise FVO_2008Q1 A dummy variable equal to one if a bank initially elected the FVO

in 2008:Q1, missing if a bank initially elected the FVO in 2007:Q1, and zero otherwise

AFS_2007Q1 A dummy variable equal to one if a bank initially elected the FVO for AFS securities in 2007:Q1, missing if a bank initially elected the FVO for financial instruments other than AFS securities in 2007:Q1, and zero otherwise

Debt_2007Q1 A dummy variable equal to one if a bank initially elected the FVO for financial liabilities (“debt”) in 2007:Q1, missing if a bank initially elected the FVO for financial instruments other than debt in 2007:Q1, and zero otherwise

LoanHFS_2008Q1 A dummy variable equal to one if a bank initially elected the FVO for loans held for sale in 2008:Q1, missing if a bank initially elected the FVO in 2007:Q1 or if a bank initially elected the FVO for financial instruments other than loans held for sale in 2008:Q1, and zero otherwise

Panel B: Explanatory Variables and their Predicted Coefficients

Variable Predicted Definition Coefficient Accounting Mismatch Variables (predicted coefficients are only for regular adopters’ FVO elections, particularly for loans held for sale ) EarV + The standard deviation of quarterly income before extraordinary

items divided by beginning total assets over the prior four quarters

REcor – The correlation between quarterly stock returns and quarterly income before extraordinary items divided by beginning total assets over the prior four quarters

IH_dmy + A dummy variable equal to one if the firm reports gains or losses on ineffective hedges during the prior year, and zero otherwise

Der + The notional amount of derivatives divided by total assets in the prior quarter

31

32

Opportunism Variables (predicted coefficients are only for early adopters’ FVO elections, particularly for AFS securities [the predictions for the variables involving UGLNFA do not apply to these elections] and debt [the predictions for the variables involving UGLAFS do not apply to these elections]) OPP_H + A dummy variable equal to one if the firm’s realized gains or

losses on AFS securities and income before extraordinary items and realized gains or losses on AFS securities over the prior eight quarters are negatively correlated and its Tier 1 risk-based regulatory capital ratio is above the median in the prior quarter and zero otherwise

OPP_L + A dummy variable equal to one if the firm’s quarterly realized gains or losses on AFS securities and quarterly income before extraordinary items and realized gains or losses on AFS securities over the prior eight quarters are negatively correlated and its Tier 1 risk-based regulatory capital ratio is below the median in the prior quarter and zero otherwise

UGLAFS ? Cumulative unrealized gains (losses) on AFS securities divided by total assets in the prior quarter

UGLAFS×OPP_H – See above UGLAFS×OPP_L + See above UGLNFA ? Cumulative unrealized gains (losses) on net financial assets other

than AFS securities divided by total assets in the prior quarter UGLNFA×OPP_H – See above UGLNFA×OPP_L + See above Control Variables Log_TA ? The natural logarithm of total assets in the prior quarter IRSG ? The difference between interest-earning assets and interest-

paying liabilities (including variable-rate preferred stock) that reprice or mature within one year divided by total assets in the prior quarter

a) In the definitions of the explanatory variables, “prior” means prior to the quarter of the potential initial FVO election.

Table 1 Sample Selection and Distribution of FVO Elections

Panel A: Sample Selection 2007:Q1 #Obs 2008:Q1 #Obs

Early Adopters

Non- adopters Total

Regular Adopters

Non- adopters

Total

On EDGAR database 28 343 371 30 314 344 Less early adopters (27) (27) Less missing data 0 (19) (19) (1) (12) (13) Final sample 28 324 352 29 275 304

Panel B: Distribution of Early Adopters’ FVO Elections in Total and by Type of Financial Instrument

FVO_2007Q1=1 AFS_2007Q1=1 Debt_2007Q1=1

Financial Instrument Type # obs % of total #obs % of

total # obs % of total

Total 28 100% 21 100% 19 100%Financial assets: 22 79% 21 100% 13 68%AFS securities 21 75% 21 100% 13 68%HTM securities 2 7% 2 10% 2 11%Loans held for investment 5 18% 5 24% 5 26%Loans held for sale 2 7% 1 5% 1 5%Securities purchased under

resell agreements 3 11% 3 14% 3 16%

Other assets 1 4% 1 5% 1 5%Financial liabilities (“debt”): 19 68% 13 62% 19 100%Securities sold under

repurchase agreements 2 7% 2 10% 2 11%

Deposits 4 14% 4 19% 4 21%Certificates of deposit 2 7% 1 5% 2 11%FHLB advances 5 18% 4 19% 5 26%Short-term borrowings 2 7% 2 10% 2 11%Notes payable 0 0% 0 0% 0 0%Subordinated debt 10 36% 6 29% 10 53%Long-term debt 6 21% 6 29% 6 32%Other liabilities 4 14% 3 14% 4 21%

33

Table 1 (Continued)

Panel C: Distribution of Regular (2008:Q1) Adopters’ FVO Elections in Total and by Types of Financial Instrument

FVO_2008Q1=1 LoanHFS_2008Q1=1

Financial Instrument Type # obs % of total

# obs % of total

Total 29 100% 19 100%Financial assets: 25 86% 19 100%AFS securities 6 21% *** 2 11%HTM securities 0 0% 0 0%Loans held for investment 1 3% 0 0%Loans held for sale 19 66% *** 19 100%Securities purchased under

resell agreements 0 0% 0 0%

Other assets 2 7% 1 5%Financial liabilities (“debt”): 8 28% *** 3 16%Securities sold under

repurchase agreements 0 0% 0 0%

Deposits 0 0% 0 0%Certificates of deposit 5 17% 2 11%FHLB advances 0 0% 0 0%Short-term borrowings 0 0% 0 0%Notes payable 2 7% 1 5%Subordinated debt 1 3% 0 0%Long-term debt 1 3% 0 0%Other liabilities 0 0% 0 0%a) *** indicates significance at the 1% level in two-tailed t tests of differences between

early adopters and regular adopters in the proportions of FVO elections for AFS securities, loans held for sale, and debt (the three most common FVO elections).

34

Table 2 Descriptive Statistics of Explanatory Variables for

Adopters versus Non-adopters in Same Quarter

Panel A: Early Adopters versus Non-adopters in 2007:Q1 Means Medians Std. Dev

Explanatory Variables Adopters

Non-adopters t Test Adopters

Non-adopters

WilcoxonRank-Sum

(Z) test Adopters Non-

adopters F Test EarV 0.0004 0.0005 -1.13 0.0002 0.0002 -0.58 0.0004 0.0007 2.89 ***REcor -0.0851 -0.0397 -0.42 -0.2834 -0.0522 -0.43 0.6309 0.5429 0.74 IH_dmy 0.2143 0.0895 1.55 0.0000 0.0000 2.11 ** 0.4179 0.2859 0.47 ***Der 1.9954 0.1153 1.81 * 0.0147 0.0022 2.78 *** 5.5005 0.5646 0.01 ***OPP 0.7143 0.4753 2.44 ** 1.0000 0.0000 2.42 ** 0.4600 0.5002 1.18 OPP_H 0.3571 0.2222 1.62 0.0000 0.0000 1.62 0.4880 0.4164 0.73 OPP_L 0.3571 0.2531 1.20 0.0000 0.0000 1.20 0.4880 0.4355 0.80 UGLAFS -0.0018 -0.0013 -0.81 -0.0022 -0.0012 -2.03 ** 0.0031 0.0022 0.50 ***UGLAFS×OPP -0.0011 -0.0006 -0.95 -0.0005 0.0000 -1.52 0.0032 0.0018 0.31 ***UGLAFS×OPP_H -0.0013 -0.0003 -2.48 ** 0.0000 0.0000 -2.62 *** 0.0020 0.0014 0.49 ***UGLAFS×OPP_L 0.0002 -0.0002 0.86 0.0000 0.0000 1.35 0.0024 0.0011 0.23 ***UGLNFA 0.0016 0.0011 0.29 0.0016 -0.0022 1.49 0.0088 0.0146 2.75 ***UGLNFA×OPP -0.0001 0.0013 -0.86 0.0000 0.0000 0.48 0.0076 0.0105 1.88 * UGLNFA×OPP_H -0.0008 0.0003 -0.91 0.0000 0.0000 -0.66 0.0066 0.0059 0.78 UGLNFA×OPP_L 0.0007 0.0009 -0.32 0.0000 0.0000 1.65 * 0.0036 0.0087 5.75 ***Total assets ($M) 179,049 12,500 1.96 * 1,623 1,888 0.43 450,507 53,782 0.01 ***IRSG 0.0689 0.0805 -0.48 0.0625 0.0770 -0.45 0.1188 0.1726 2.11 ** # obs 28 324 28 324 28 324 Panel B: Early Adopters for AFS Securities versus Non-adopters for AFS Securities in 2007:Q1 Means Medians Std. Dev

Explanatory Variables Adopters

Non-adopters t Test Adopters

Non-adopters

WilcoxonRank-Sum

(Z) test Adopters Non-

adopters F Test EarV 0.0004 0.0005 -0.75 0.0002 0.0002 -0.45 0.0005 0.0007 2.34 ** REcor -0.1096 -0.0397 -0.56 -0.3485 -0.0522 -0.58 0.6549 0.5429 0.69 IH_dmy 0.1905 0.0895 1.13 0.0000 0.0000 1.52 0.4024 0.2859 0.50 ** Der 2.5608 0.1153 1.79 * 0.0166 0.0022 2.27 ** 6.2750 0.5646 0.01 ***OPP 0.71429 0.4753 2.13 ** 1.0000 0.0000 2.12 ** 0.4629 0.5002 1.17 OPP_H 0.4286 0.2222 2.17 ** 0.0000 0.0000 2.16 ** 0.5071 0.4164 0.67 OPP_L 0.2857 0.2531 0.33 0.0000 0.0000 0.33 0.4629 0.4355 0.88 UGLAFS -0.0020 -0.0013 -0.97 -0.0025 -0.0012 -2.54 ** 0.0035 0.0022 0.42 ***UGLAFS×OPP -0.0014 -0.0006 -1.12 -0.0012 0.0000 -2.49 ** 0.0036 0.0018 0.25 ***UGLAFS×OPP_H -0.0016 -0.0003 -2.63 ** 0.0000 0.0000 -3.08 *** 0.0022 0.0014 0.41 ***UGLAFS×OPP_L 0.0002 -0.0002 0.70 0.0000 0.0000 0.44 0.0027 0.0011 0.18 ***UGLNFA 0.0008 0.0011 -0.14 0.0015 -0.0022 0.88 0.0092 0.0146 2.53 ** UFLNFA×OPP -0.0006 0.0013 -0.80 0.0000 0.0000 0.17 0.0080 0.0105 1.72 UGLNFA×OPP_H -0.0006 0.0003 -0.56 0.0000 0.0000 -0.39 0.0075 0.0059 0.60 * UGLNFA×OPP_L 0.0000 0.0009 -1.25 0.0000 0.0000 1.01 0.0026 0.0087 11.27 ***Total assets ($M) 214,092 12,500 1.81 * 1,265 1,888 -0.30 509,029 53,782 0.01 ***IRSG 0.0829 0.0805 0.11 0.0510 0.0770 -0.08 0.0847 0.1726 4.15 ***# obs 21 324 21 324 21 324

35

Table 2 (Continued)

Panel C: Early Adopters for Debt versus Non-adopters for Debt in 2007:Q1 Means Medians Std. Dev

Explanatory Variables Adopters

Non-adopters t Test Adopters

Non-adopters

WilcoxonRank-Sum

(Z) test Adopters Non-

adopters F Test EarV 0.0005 -0.26 0.0002 0.0002 0.35 0.0005 0.0007 2.18 * REcor -0.1733 -0.0397 -1.03 -0.3299 -0.0522 -1.05 0.6211 0.5429 0.76 IH_dmy 0.2632 0.0895 1.65 0.0000 0.0000 2.46 ** 0.4524 0.2859 0.40 ***Der 2.8405 0.1153 1.81 * 0.0128 0.0022 2.71 *** 6.5490 0.5646 0.01 ***OPP 0.6316 0.4753 1.32 1.0000 0.0000 1.32 0.4956 0.5002 1.02 OPP_H 0.1579 0.2222 -0.66 0.0000 0.0000 -0.66 0.3746 0.4164 1.24 OPP_L 0.4737 0.2531 2.12 ** 0.0000 0.0000 2.11 ** 0.5130 0.4355 0.72 UGLAFS -0.0011 -0.0013 0.15 -0.0019 -0.0012 -0.69 0.0034 0.0022 0.44 ***UGLAFS×OPP -0.0004 -0.0006 0.23 0.0000 0.0000 0.12 0.0033 0.0018 0.30 ***UGLAFS×OPP_H -0.0005 -0.0003 -0.57 0.0000 0.0000 -0.04 0.0015 0.0014 0.87 UGLAFS×OPP_L 0.0001 -0.0002 0.55 0.0000 0.0000 0.56 0.0029 0.0011 0.16 ***UGLNFA 0.0038 0.0011 1.21 0.0017 -0.0022 2.02 ** 0.0091 0.0146 2.60 ** UGLNFA×OPP 0.0012 0.0013 -0.03 0.0000 0.0000 0.78 0.0079 0.0105 1.75 UGLNFA×OPP_H 0.0003 0.0003 -0.02 0.0000 0.0000 -0.27 0.0066 0.0059 0.78 UGLNFA×OPP_L 0.0009 0.0009 -0.05 0.0000 0.0000 1.45 0.0044 0.0087 3.90 ***Total assets ($M) 238,032 12,500 1.85 * 3,496 1,888 1.54 530,650 53,782 0.01 ***IRSG 0.0737 0.0805 -0.17 0.0656 0.0770 -0.16 0.1402 0.1726 1.51 # obs 19 324 19 324 19 324 Panel D: Regular Adopters versus Non-adopters in 2008:Q1 Means Medians Std. Dev

Explanatory Variables Adopters

Non-adopters t Test Adopters

Non-adopters

WilcoxonRank-Sum

(Z) test Adopters Non-

adopters F Test EarV 0.0011 0.0010 0.41 0.0005 0.0004 1.91 * 0.0011 0.0019 2.96 ***REcor 0.2803 0.2207 0.49 0.3139 0.4165 0.64 0.5802 0.6215 1.15 IH_dmy 0.4138 0.0473 3.90 *** 0.0000 0.0000 6.82 *** 0.5012 0.2126 0.18 ***Der 0.4982 0.0643 2.19 ** 0.1798 0.0017 6.88 *** 1.0603 0.3785 0.13 ***OPP 0.5172 0.4764 0.42 1.0000 0.0000 0.42 0.5086 0.5004 0.97 OPP_H 0.0690 0.2473 -3.27 *** 0.0000 0.0000 -2.17 ** 0.2579 0.4322 2.81 ***OPP_L 0.4483 0.2291 2.61 *** 0.0000 0.0000 2.59 *** 0.5061 0.4210 0.69 UGLAFS -0.0006 -0.0001 -1.44 -0.0003 0.0001 -2.24 ** 0.0017 0.0020 1.45 UGLAFS×OPP -0.0005 -0.0001 -1.88 ** 0.0000 0.0000 -2.01 ** 0.0012 0.0015 1.68 * UGLAFS×OPP_H -0.0000 -0.0001 0.24 0.0000 0.0000 -0.31 0.0002 0.0013 31.87 ***UGLAFS×OPP_L -0.0005 -0.0000 -2.08 ** 0.0000 0.0000 -2.25 ** 0.0012 0.0008 0.52 ***UGLNFA 0.0119 0.0047 1.92 * 0.0042 0.0017 1.95 * 0.0196 0.0131 0.44 ***UGLNFA×OPP 0.0108 0.0030 2.10 ** 0.0000 0.0000 1.61 0.0197 0.0107 0.30 ***UGLNFA×OPP_H 0.0022 0.0015 0.35 0.0000 0.0000 0.51 0.0112 0.0085 0.57 ** UGLNFA×OPP_L 0.0086 0.0015 2.17 ** 0.0000 0.0000 1.36 0.0173 0.0069 0.16 ***Total assets ($M) 81,766 5,441 2.83 *** 15,923 1,654 6.23 *** 145,153 15,749 0.01 ***IRSG 0.1719 0.0638 3.20 *** 0.1858 0.0597 3.16 *** 0.1594 0.1742 1.19 # obs 29 275 29 275 29 275

36

37

Table 2 (Continued)

Panel E: Regular Adopters for Loans Held for Sale versus Non-adopters for Loans Held for Sale in 2008:Q1 Means Medians Std. Dev

Explanatory Variables Adopters

Non-adopters t Test Adopters

Non-adopters

WilcoxonRank-Sum

(Z) test Adopters Non-

adopters F Test EarV 0.0011 0.0010 0.65 0.0007 0.0004 2.42 ** 0.0011 0.0019 2.98 ***REcor 0.3248 0.2207 0.71 0.571 0.4165 1.10 0.6384 0.6215 0.95 IH_dmy 0.4737 0.0473 3.60 *** 0.0000 0.0000 6.82 *** 0.5130 0.2126 0.17 ***Der 0.6406 0.0643 1.95 * 0.2408 0.0017 6.17 *** 1.2823 0.3785 0.09 ***OPP 0.5790 0.4764 0.86 1.0000 0.0000 0.86 0.5073 0.5004 0.97 OPP_H 0.0526 0.2473 -3.31 *** 0.0000 0.0000 -1.93 * 0.2294 0.4322 3.55 ***OPP_L 0.5263 0.2291 2.93 *** 1.0000 0.0000 2.90 *** 0.5130 0.4210 0.67 UGLAFS -0.0007 -0.0001 -1.32 -0.0003 0.0001 -1.68 * 0.0016 0.0020 1.67 UGLAFS×OPP -0.0005 -0.0001 -1.10 0.0000 0.0000 -0.95 0.0013 0.0015 1.29 UGLAFS×OPP_H 0.0000 -0.0001 0.83 0.0000 0.0000 0.18 0.0000 0.0013 3800.00 ***UGLAFS×OPP_L -0.0005 -0.0000 -1.47 0.0000 0.0000 -1.27 0.0013 0.0008 0.40 ***UGLNFA 0.0100 0.0047 1.22 0.0039 0.0017 1.32 0.0184 0.0131 0.50 ** UGLNFA×OPP 0.0094 0.0030 1.53 0.0000 0.0000 1.42 0.0180 0.0107 0.35 ***UGLNFA×OPP_H 0.0002 0.0015 -2.40 ** 0.0000 0.0000 0.22 0.0007 0.0085 146.01 ***UGLNFA×OPP_L 0.0092 0.0015 1.84 * 0.0000 0.0000 1.48 0.0181 0.0069 0.15 ***Total assets ($M) 86,980 5,441 2.65 ** 33,018 1,654 5.41 *** 133,836 15,749 0.01 ***IRSG 0.1596 0.0638 2.34 ** 0.1858 0.0597 2.53 ** 0.1499 0.1742 1.35 # obs 19 275 19 275 19 275 a) In Panels A-C, “non-adopters” refers to firms that did not adopt the FVO in 2007:Q1 for

any financial instrument in Panel A, for AFS securities in Panel B, and for debt in Panel C. These non-adopters include regular adopters. In Panels D and E, “non-adopters” refers to non-early-adopter firms that did not adopt the FVO in 2008:Q1 for any financial instrument in Panel D and for loans held for sale in Panel E.

b) All variables are defined in the Appendix except for OPP, which is a dummy variable equal to one if the firm’s Tier 1 risk-based regulatory capital ratio is above the median in the prior quarter and zero otherwise, and total assets.

c) Continuous variables are winsorized at 0.5% and 99.5%. d) ***, **, and * indicate that tests for differences in values for adopters are significantly

different from nonadopters at a two-tailed p-value ≤ 0.01, 0.05, and 0.10, respectively. T tests are reported for means, Wilcoxon rank-sum tests are reported for medians, and Snedecor and Cochran (1983) F tests are reported for standard deviations. The F tests compare the ratios of variances and so are significant if the F statistics are sufficiently large or small.

Table 3 Pearson Correlations of Equation (1) Explanatory Variables for 2007:Q1 and 2008:Q1 Samples

Panel A: 2007:Q1

EarV REcor IH_dmy Der OPP_H OPP_L UGLAFS UGLAFS×OPP_H

UGLAFS×OPP_L UGLNFA UGLNFA

×OPP_HUGLNFA×OPP_L Log_TA

EarV REcor 0.01 IH_dmy 0.10 0.01 Der 0.00 -0.07 0.37 OPP_H -0.05 0.05 -0.03 -0.06 OPP_L -0.02 0.02 0.21 0.16 -0.33 UGLAFS -0.07 -0.09 0.02 0.01 -0.09 0.14 UGLAFS×OPP_H -0.09 -0.12 -0.01 0.02 -0.48 0.16 0.54 UGLAFS×OPP_L -0.01 0.00 -0.02 -0.04 0.09 -0.26 0.48 -0.04 UGLNFA -0.01 -0.02 0.09 0.04 0.00 0.10 -0.02 -0.10 -0.04 UGLNFA×OPP_H 0.00 0.03 0.00 -0.01 0.07 -0.02 -0.16 -0.27 0.01 0.41 UGLNFA×OPP_L 0.04 -0.03 0.11 0.05 -0.06 0.18 0.02 0.03 -0.06 0.59 0.00 Log_TA 0.08 -0.03 0.52 0.50 -0.12 0.29 0.09 0.06 0.04 0.13 -0.01 0.19 IRSG 0.00 -0.03 0.16 0.06 0.00 0.02 0.03 0.05 -0.05 -0.02 -0.05 0.05 0.22

38

39

Table 3 (Continued) Panel B: 2008:Q1

EarV REcor IH_dmy Der OPP_H OPP_L UGLAFS UGLAFS×OPP_H

UGLAFS×OPP_L UGLNFA UGLNFA

×OPP_HUGLNFA×OPP_L Log_TA

EarV REcor 0.11 IH_dmy 0.07 0.14 Der 0.02 0.00 0.30 OPP_H -0.15 -0.19 -0.08 -0.01 OPP_L 0.05 0.07 0.08 0.03 -0.32 UGLAFS -0.03 -0.03 -0.11 -0.19 -0.04 -0.03 UGLAFS×OPP_H 0.02 0.06 0.01 -0.23 -0.09 0.03 0.59 UGLAFS×OPP_L -0.04 -0.02 0.02 -0.04 0.04 -0.11 0.43 0.00 UGLNFA -0.03 0.12 0.15 0.04 0.05 0.14 -0.03 0.06 -0.05 UGLNFA×OPP_H -0.06 0.04 0.02 -0.03 0.32 -0.10 0.05 0.07 0.01 0.58 UGLNFA×OPP_L 0.01 0.06 0.16 0.09 -0.14 0.44 -0.04 0.01 -0.11 0.56 -0.04 Log_TA 0.07 0.18 0.46 0.47 -0.16 0.27 -0.11 0.00 -0.14 0.17 -0.04 0.28 IRSG 0.12 0.16 0.15 0.07 -0.21 0.17 0.04 0.12 -0.06 0.05 -0.06 0.10 0.26 a) All variables are defined in the Appendix. Each continuous variable is winsorized at 0.5% and 99.5% tails to reduce the effect of outliers. b) Figures in bold indicate that the correlation coefficients are significant at the 5% level or higher.

Table 4 Logistic Regression Estimation of Equation (1)

).,_,_,_,,_,_,,_,_,,_,,()variabledummyelectionFVO(Pr

IRSGTALogLOPPUFLAFSHOPPUFLNFAUGLNFALOPPUFLAFSHOPPUGLAFSUGLAFSLOPPHOPPDerdmyIHREcorEarVfob××××

=

(1)

Panel A: FVO Elections in 2007:Q1 FVO_2007Q1 AFS_2007Q1 Debt_2007Q1

Explanatory variable

Pred. sign Coef. Z-value Pred.

sign Coef. Z-value Pred. sign Coef. Z-value

EarV ? -466.81 -1.23 ? -367.65 -0.91 ? -92.66 -0.25 REcor ? -0.29 -0.72 ? -0.37 -0.77 ? -0.36 -0.75 IH_dmy ? 0.34 0.41 ? -0.47 -0.34 ? 0.18 0.19 Der ? 0.36 2.11 ** ? 0.59 2.52 ** ? 0.34 2.07 ** OPP_H + -0.19 -0.18 + -0.17 -0.15 + -0.43 -0.32 OPP_L + 1.41 1.88 ** + 0.87 0.93 + 1.36 1.74 ** UGLAFS ? -306.41 -1.48 ? -267.24 -1.13 ? -265.96 -1.24 UGLAFS×OPP_H – -412.89 -1.32 */*** – -485.37 -1.44 */*** ? -192.09 -0.48 UGLAFS×OPP_L + 532.24 2.16 **/** + 644.70 2.18 **/** ? 467.59 1.84 **/** UGLNFA ? 30.08 1.34 ? 28.05 1.08 ? 33.72 1.46 UGLNFA×OPP_H – -96.96 -2.18 **/** ? -91.55 -1.97 **/* – -61.41 -1.04 UGLNFA×OPP_L + -37.26 -1.05 ? -56.66 -1.05 + -43.37 -1.16 Log_TA ? -0.17 -0.86 ? -0.45 -1.65 * ? -0.12 -0.52 IRSG ? -0.03 -0.02 ? 1.31 0.80 ? -0.29 -0.18 Intercept -1.14 -0.39 2.49 0.65 -2.06 -0.61 # adopters 28 21 19 # total observations 352 345 343 Likelihood ratio X2 39.77 42.86 25.38 P-value 0.0003 0.0001 0.031 Pseudo R2 20.35% 27.08% 17.28% Area under ROC curve 0.78 0.80 0.75

40

41

Table 4 (Continued) Panel B: FVO Elections in 2008:Q1 FVO_2008Q1 LoanHFS_2008Q1 Explanatory variable

Pred. sign

Coef. Z-value Pred. sign

Coef. Z-value

EarV + -109.26 -0.57 + -108.56 -0.43 REcor – -0.81 -1.80 ** – -0.82 -1.53 * IH_dmy + 1.47 2.14 ** + 1.80 2.25 ** Der + -0.29 -0.73 + -0.10 -0.21 OPP_H ? -2.08 -1.45 ? -1.02 -0.89 OPP_L ? -0.71 -1.01 ? -0.28 -0.35 UGLAFS ? 29.44 0.15 ? 56.07 0.22 UGLAFS×OPP_H ? -146.82 -0.40 ? 176.01 0.36 UGLAFS×OPP_L ? -341.56 -1.17 ? -306.17 -0.87 UGLNFA ? -37.91 -1.01 ? -47.32 -1.03 UGLNFA×OPP_H ? 109.77 2.08 **/** ? 34.49 0.40 UGLNFA×OPP_L ? 63.41 1.45 ? 66.77 1.27 Log_TA ? 0.89 3.98 *** ? 0.88 3.17 *** IRSG ? 2.38 1.40 ? 0.81 0.37 Intercept -16.09 -4.71 -16.35 -3.89 Number of adopters 29 19 Number of total observations 304 294 Likelihood ratio X2 39.77 56.51 P-value 0.0000 0.0000 Pseudo R2 39.93% 40.13% Area under ROC curve 0.90 0.91 a) All variables are defined in the Appendix. b) Each continuous variable is winsorized at 0.5% and 99.5%. c) ***, **, * indicates coefficients significant at the 0.01, 0.05, and 0.10 levels, respectively, in a one-tailed test if the coefficient has the predicted sign and a two-tailed test otherwise. For the four interactive variables (e.g., UGLAFS×OPP_H), the second significance level is for the total coefficient (e.g., for the sum of the coefficients on UGLAFS and UGLAFS×OPP_H). d) The Area under the ROC (Receiver Operating Characteristics) curve statistic is the estimated probability that the model ranks a randomly chosen actual FVO election higher than a randomly chosen non-FVO election.

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