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Usefulness of fair values for predicting banks’ future earnings:

Evidence from other comprehensive income and its components

Brian Bratten

Assistant Professor

Von Allmen School of Accountancy, University of Kentucky

Email: [email protected]

Monika Causholli

Associate Professor

Von Allmen School of Accountancy, University of Kentucky


Urooj Khan

Assistant Professor

Graduate School of Business, Columbia University


Tel: (212) 851 5866

Fax: (212) 316 9219

November 2015

Acknowledgements

We thank B.W. Baer, Gauri Bhat, Patricia Dechow (editor), two anonymous referees, Mark

Evans, Fabrizio Ferri, Leslie Hodder, Sharon Katz, Yuri Loktionov, Doron Nissim, Jeff Payne,

Stephen Penman, Robert Ramsay, Shiva Rajgopal, Miguel Duro Rivas, Joshua Ronen, Ethan

Rouen, Gil Sadka, Dan Stone, Abhishek Varma, Mohan Venkatachalam, Dushyantkumar Vyas,

Dave Ziebart, and seminar participants at Columbia Business School, University of Kentucky,

University of Western Ontario, the 2012 American Accounting Association Annual Meeting, the

2012 Annual Congress of the European Accounting Association, the 23rd annual Conference on

Financial Economics and Accounting, and the 2014 PwC Young Scholars Symposium at the

University of Illinois for helpful comments and suggestions.

mailto:[email protected]



Usefulness of fair values for predicting banks’ future earnings:

Evidence from other comprehensive income and its components

Abstract

This paper examines whether fair value adjustments included in other comprehensive income

(OCI) predict future bank performance. It also examines whether the reliability of these estimates

affects their predictive value. Using a sample of bank holding companies, we find that fair value

adjustments included in OCI can predict earnings both one and two years ahead. However, not

all fair value-related unrealized gains and losses included in OCI have similar implications.

While net unrealized gains and losses on available-for-sale securities are positively associated

with future earnings, net unrealized gains and losses on derivative contracts classified as cash

flow hedges are negatively associated with future earnings. We also find that reliable

measurement of fair values enhances predictive value. Finally, we show that fair value

adjustments recorded in OCI during the 2007–2009 financial crisis predicted future profitability,

contradicting criticism that fair value accounting forced banks to record excessive downward

adjustments.

JEL classification: G21, M41, M48

Keywords : Earnings; Other Comprehensive Income; Fair value; Predictability.

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1. Introduction

We examine whether fair value adjustments embedded in other comprehensive income

(OCI) are useful for predicting future performance in banks. We also examine whether attributes

related to the reliability of fair value adjustments affect their predictive value. Our study is

motivated by the objective of financial reporting as stated in the Financial Accounting Standard

Board’s (FASB) Conceptual Framework, which is to provide decision-useful information that

assists in the prediction of future performance (FASB 2010).

Controversy has surrounded fair value accounting as financial statement users, preparers,

regulators, and others disagree about the extent to which fair values contain information useful to

decision-making. More recently, disagreement about the usefulness of fair values has intensified.

Critics argue that fair value accounting contributed to the 2007–2009 financial crisis by forcing

banks to record unjustified downward adjustments to assets’ fair values, leading to fire sales and

contagion (Bhat et al. 2011; Bowen and Khan 2014; Khan 2015). However, despite the criticism,

the FASB and the International Accounting Standards Board (IASB) believe that the fair value

measurement basis meets the conceptual framework criteria better than other measurement bases,

and both boards have issued standards that increasingly require firms to use fair values in

financial reporting (Barth 2006, 2014).

By definition, fair values are intended to summarize the present value of expected future

cash flows, and proponents of fair value assert that fair values matter for decision-making since

they incorporate current economic conditions and reflect up-to-date expectations (Barth 2014).

On the other hand, critics argue that changes in fair values are transitory and driven by short-

term market movements that have little to do with changes in expectations about future outcomes

(Chisnall 2001). Fair value estimates are also criticized for being more volatile than those based

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on historical or amortized cost (Barth et al.1995; Hodder et al. 2006) and being subject to greater

measurement error, which hurts their reliability and predictive value (Landsman 2007).

Given this continuing debate, our research examines the ability of fair value estimates to

predict future performance. Under the mixed-attribute model prescribed by GAAP, some

changes in fair value estimates of assets and liabilities are included in net income, and other

changes are included in OCI. For example, SFAS 115 and SFAS 133 require inclusion in OCI of

unrealized gains and losses on available-for-sale securities and derivative contracts classified as

cash flow hedges. Therefore our first research question investigates whether fair value estimates

embedded in OCI can be used to predict future performance in banks. Our second research

question investigates whether differences in the reliability of these fair value estimates affect

their predictive value.

There are several reasons why we expect fair value estimates included in OCI to predict

future performance. First, fair value estimates are value relevant (e.g., Barth 1994; Petroni and

Wahlen 1995; Barth et al. 1996; Eccher et al. 1996; Nelson 1996; Venkatachalam 1996; Park et

al. 1999; Barth 2006). Second, unrealized fair value gains and losses included in OCI can predict

future performance due to timing of asset sales. For example, a positive association between

unrealized and realized gains can occur when managers sell assets that previously experienced an

increase in fair value (e.g., Park et al. 1999; Evans et al. 2014). Finally, changes in the fair values

of certain derivative instruments (e.g., derivative contracts classified as cash flow hedges) imply

changes in the prices of the underlying hedged items, which can have implications for future firm

performance (Campbell 2015).

To test our predictions, we focus on banks because a large proportion of banks’ assets are

financial assets, many of which are reported at fair value and were among the earliest assets to be

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subjected to fair value standards. Also, detailed fair value information for banks’ assets and

liabilities is available through their regulatory filings, allowing us to construct a panel of U.S.

bank holding companies (“banks”), both publicly traded and privately held, from 2001 through

2013.

In our tests, we focus on the relation between contemporaneous total OCI, OCI

components, and one- and two-year-ahead bank performance measures. We measure future

performance using two variants of future earnings: (i) pre-tax earnings and (ii) pre-tax earnings

before the provision for loan and lease losses. Our second measure of earnings excludes the

largest accrual in banks and therefore mitigates managerial discretion, resulting in a less biased

measure of performance (Beatty et al 2002).1 We focus on earnings as a measure of performance

rather than future cash flows because earnings is accounting’s summary measure of performance

(Dechow 1994) and is used for a wide range of purposes, including in executive compensation

plans, debt contracts, and initial public offering prospectuses. Moreover, earnings better predict

future cash flows than do current cash flows (Dechow et al. 1998).

To address our second question, we partition our sample along two dimensions: (i)

market-wide liquidity and (ii) the proportion of investment securities guaranteed by the U.S.

government or its agencies. We expect the predictive ability of fair value estimates to be

enhanced when market liquidity and the proportion of investment securities guaranteed by the

U.S. government or its agencies is high, conditions that should result in more reliable fair values.

In additional analyses, we investigate the criticism that fair value accounting forced banks to

record excessive downward adjustments during the 2007–2009 financial crisis by testing the

1 Unlike for nonfinancial firms, estimation of cash flows is problematic in financial firms. As a result, prior studies

have used earnings before taxes and provision for loan losses as a proxy for banks’ cash flows (e.g., Wahlen 1994;

Liu et al. 1997; Kanagaretnam et al. 2014; Altamuro and Beatty 2010; Bischof et al. 2012). In additional tests, we

also perform tests using residual earnings as an alternative measure of performance.

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extent to which fair value adjustments recorded in OCI during the crisis predicted one- and two-

year-ahead performance of banks.

Our findings can be summarized as follows. After controlling for current period net

income, fair value-oriented OCI is incrementally associated with one- and two-year-ahead bank

performance. Decomposing OCI into its components, we find that the net unrealized gains and

losses from available-for-sale securities are positively and significantly associated with future

earnings, while the net unrealized gains and losses from derivatives are negatively related to

future earnings.

Analyses that distinguish between the reliability of fair values provide two additional

insights. First, we find that each dollar of OCI translates into a higher amount of earnings in the

future during periods of higher market-wide liquidity. Second, we find that OCI predicts bank

earnings only for those banks that hold a higher proportion of securities guaranteed by the U.S.

government or its agencies in their investment portfolio. In summary, these findings suggest that

the reliability with which fair values are measured affects their ability to predict future

performance. When we restrict our analysis to the financial crisis years, we continue to find an

association between fair value adjustments and future earnings, suggesting that fair value

adjustments during the crisis predicted future performance. This evidence contradicts claims that

declines in assets’ fair values during the crisis were unrelated to the deterioration in the

underlying fundamentals. Finally, our results are robust to a host of other tests, including

alternative measures of performance, additional controls for banks’ business models, and the

inclusion of bank-fixed effects.

Our study makes several contributions. First, it informs the ongoing debate about the

merits of a fair value accounting-based reporting system. This debate intensified during the

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financial crisis, leading some critics to charge fair value accounting with exacerbating the crisis

because falling market prices required banks to excessively mark down asset values. Many

critics, especially bankers, argued that price movements during the crisis were only temporary

and were not consistent with the actual economic value of the assets (ABA 2008). Several of

these claims are unsubstantiated with empirical evidence. We show that, contrary to this

criticism, fair value adjustments included in OCI during the crisis predicted future bank

performance.

Second, our results complement value relevance research—which has documented an

incremental association between share prices and fair value disclosures related to investment

securities and derivative contracts—by providing direct evidence on whether fair values improve

the prediction of firm performance. Furthermore, our study directly informs on a feature of

accounting estimates desired by standard setters: the ability to predict future outcomes.

Third, we extend the literature on the predictive value of OCI in three important ways.

There is limited and mixed evidence in the literature regarding the predictive ability of OCI for

operating performance. For example, some studies have found that OCI and its components are

value relevant (Barth 1994; Kanagaretnam et al. 2009; Campbell 2015), while others find that

they are not (Dhaliwal et al. 1999) or that they are value relevant but have limited predictive

value (Jones and Smith 2011). By showing that different OCI components have differing

implications for future performance, our results suggest the importance of considering OCI

components individually when making predictions about future performance. Also, departing

from most of these studies, we rely on a sample comprised exclusively of banks for our analyses.

Given the extensive use of fair value estimates in financial reporting for banks, we believe banks

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are best suited to investigate the properties of fair value of estimates.2 Finally, in contrast to

studies that have examined the association between fair value adjustments and future

performance of only one type of financial instrument (e.g., Park et al. 1999; Evans et al. 2014;

Campbell 2015), we use a more comprehensive measure of fair values adjustments.

Our evidence is also relevant to U.S. bank regulators, who have recently adopted the

Basel III Capital Framework recommendations and amended the regulatory capital calculations

to include components of accumulated other comprehensive income (FDIC 2013).3 Critics have

argued that unrealized gains and losses in accumulated other comprehensive income should not

be included in regulatory capital, as these changes are temporary and driven by movements in

interest rates as opposed to changes in credit risks (ABA 2012). Our evidence contradicts this

criticism by showing that certain unrealized gains and losses included in OCI relate to future

bank performance.

The remainder of the paper is organized as follows. Section 2 reviews the literature,

provides institutional background, and presents the hypotheses. Section 3 describes our research

design. Section 4 discusses our sample and the results of our analyses. Section 5 concludes.

2. Literature review and hypotheses development

2.1. Background

An objective of the Conceptual Framework is to provide decision-useful information that

would assist in the assessments of the amounts, timing, and uncertainty of future cash flows and

the prediction of returns on economic resources (FASB 2010). Standard setters such as the FASB

2 For example, Jones and Smith (2011) only have 26 financial firms in their sample, and in this mixed sample, they

find that unrealized gains and losses on available-for-sale securities do not predict future cash flows (p. 2066). 3 Before the adoption of Basel III recommendations, only unrealized gains and losses on equity securities classified

as available-for-sale and foreign currency translation adjustments were included in the calculation of regulatory

capital.

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in the United States believe that incorporating more current information in the financial reports is

a means to achieve this objective. As a result, financial reporting rules in the United States have

become increasingly fair value-based as the FASB has issued several standards requiring

recognition and disclosure of fair values; yet, as discussed in more detail below, regulators,

academics, and practitioners have continued to debate the merits of fair values.4

In theory, fair values represent the most current expectations and changes in expectations

about future performance. Therefore, to the extent that fair values can be measured reliably, fair

value estimates should be useful in predicting future performance (Barth 2000, 2007, 2014; CFA

Institute 2005). However, fair value estimates are more volatile than those determined by other

measurement bases, and changes in fair values are often driven by short-term market movements

that reverse over time. This suggests that fair value estimates are measured with less reliability

and are not likely to predict future outcomes.

Barth (2006) notes that incorporating estimates of the future into the financial statements

affects the nature of reported income and the predictability of future income. Under a full fair

value measurement system, all changes in the fair values of net assets would be incorporated in

net income. This measurement system would result in a comprehensive measure of income—

one that includes all changes in net assets—and would likely result in future income that is more

difficult to predict.5 However, in the current mixed-attribute model, changes in the fair values of

only some assets and liabilities are recognized in earnings, some fair value changes are ignored,

and other values affect net assets and comprehensive income but not contemporaneous net

4 Fair value is defined as “the price that would be received to sell an asset or paid to transfer a liability in an orderly

transaction between market participants at the measurement date” (FASB 2006). 5 Under a full fair value reporting system, earnings would be equal to changes in net assets and thus would be

uninformative about future income because changes in fair value follow a random walk and are unpredictable

(Storey and Storey, 1998; Schipper and Vincent 2003; Nissim and Penman 2008). Comprehensive income is not a

full fair value measure, as it does not include fair value changes in instruments such as held-to-maturity securities,

loans, financial liabilities, and nonterm deposits (Hodder et al. 2006).

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income. Currently, four types of gains and losses are included in OCI: unrealized gains and

losses related to changes in the fair values of investment securities classified as available for sale

(per SFAS 115), unrealized gains and losses related to changes in the fair values of derivative

contracts classified as cash flow hedges (per SFAS 133), foreign currency translation

adjustments (per SFAS 52), and additional minimum pension liability adjustments (per SFAS

87). Thus, under the mixed-attribute model, many of the fair value adjustments for a typical bank

bypass net income and are included in OCI. Therefore, to investigate whether a fair value-based

accounting system better meets the objectives of financial reporting specified in the Conceptual

Framework, we examine whether fair value-based aggregate OCI and its individual components

help predict future performance in banks.

2.2. The predictive ability of fair value information in OCI

The fair-value-related adjustments included in OCI can predict future performance for

several reasons. Ohlson (1999) shows analytically that, while changes of fair values follow a

random walk and do not predict future fair value changes, they could matter for predicting future

performance. For example, to the extent that unrealized gains and losses accumulate over time

before the asset is sold, as is the case under the current mixed-attribute model, the amount of the

unrealized gains and losses can be associated with future firm performance. A few studies that

focus on specific assets find evidence that fair value adjustments related to those assets are

associated with future cash flows and earnings generated specifically from those assets. For

example, using a sample of U.K. firms, Aboody et al. (1999) show that firms’ upward

revaluations of fixed assets relate significantly to changes in future operating income and future

cash from operations. Park et al. (1999) and Evans et al. (2014) find that accumulated fair value

adjustments for investment securities are associated with future income realized from these

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instruments. Similarly, Petroni and Wahlen (1997) show that bond investment fair values are

positively associated with future reported interest income on those investments.

Changes in the fair values of certain derivative contracts can also be informative about

future profitability. Currently, under SFAS 133, firms must record derivative contracts classified

as cash flow hedges at fair value on the balance sheet on a recurring basis, and the related

unrealized gains and losses are included in OCI. When the hedged transaction occurs, the

unrealized gains/losses are reclassified into earnings, and the underlying hedged item also affects

earnings. An unrealized gain (loss) on a derivative contract classified as a cash flow hedge in a

given period suggests that the price of the underlying hedged item has moved in an unfavorable

(favorable) direction and will result in lower (higher) future profitability after the expiration of

the hedge.6 In a sample of nonfinancial firms, Makar et al. (2013) and Campbell (2015) report

that unrealized gains and losses on derivative contracts classified a cash flow hedges are

inversely associated with future profitability and cash flows.

The value relevance literature also documents that fair value adjustments—many of

which are included in OCI—are incrementally relevant to amortized cost estimates, offering

some indirect evidence that equity investors believe fair values matter for the prediction of future

performance.7

Without focusing specifically on the predictive ability of fair values, a related literature

has examined the usefulness of OCI and its components to predict performance. This literature

has largely relied on two approaches to evaluate the usefulness of OCI: (i) examination of the

association between OCI and stock prices and returns or (ii) direct examination of the ability of

6 Firms typically hedge on a rolling basis. At the expiration of the hedge, the firm can buy new hedges. However,

new hedges will only protect the firm against future price changes, not the current price change. 7 See, for example, Barth (1994), Petroni and Wahlen (1995), Barth et al. (1996), Eccher et al. (1996), Nelson

(1996), Venkatachalam (1996), and Park et al. (1999).

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OCI to predict earnings and cash flows. A few studies find that OCI and its components are

value relevant (Barth 1994; Kanagaretnam et al. 2009), while others find contrary evidence

(Dhaliwal et al. 1999). Dhaliwal et al. (1999) find that net income is more strongly associated

with returns than comprehensive income. They fail to find any evidence of comprehensive

income being more strongly associated with future earnings or cash flows. In contrast,

Kanagaretnam et al. (2009) report that comprehensive income is more strongly associated with

one-year-ahead cash flows than net income. However, Kanagaretnam et al. (2009) also find that

net income better predicts future net income relative to comprehensive income and conclude that

components of OCI are poor predictors of profitability due to their transitory nature. It is difficult

to draw definite conclusions based on these studies because the differences in the results could

also be due to differences in their methodology and data (Chambers et al. 2007).

Using a sample of nonbanks, Jones and Smith (2011) find that OCI has predictive value

for one-year-ahead earnings but not for longer horizon earnings. Also, OCI predicts cash flows in

some settings but not consistently. We extend their study and this literature by examining the

predictive ability of OCI and its components in banks. Based on the arguments above, we expect

that fair value estimates included in OCI predict future performance and state our first hypothesis

in the alternative form:

Hypothesis 1: Fair value estimates embedded in OCI are predictive of future performance

2.3. The reliability of fair value estimates

The Conceptual Framework lists reliability as one of the two fundamental attributes of

financial information, and many critics have expressed concern that fair values lack reliability

(Barth 2014).8 Landsman (2007) asserts that measurement error and management bias embedded

8 We use the more familiar term “reliability” to capture the construct currently referred to in the Conceptual

Framework as “representational faithfulness”.

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in fair values can inhibit their ability to predict operating performance. Similarly, Barth (2007)

suggests that one of the concerns relating to fair value estimates is the effect of management

incentives, especially on the reliability of estimates for which input prices are not observable.

Prior value relevance studies provide indirect evidence that fair value estimates are less likely to

predict future performance (as captured in share prices) in the presence of measurement error and

bias. While Barth et al. (1996) find that fair value estimates of loans and long-term debt explain

bank share prices beyond related book values, other studies have shown that the fair values of

loans contain substantial measurement error and bias, which in turn reduces their value relevance

(e.g., Nelson 1996; Eccher et al. 1996; Park et al. 1999; Beaver and Venkatachalam 2003;

Nissim 2003). In the same spirit, a few studies investigate the value relevance of fair value

estimates based on the source of information used to calculate fair values as mandated by SFAS

157 (Kolev 2008; Goh et al. 2009; Song et al. 2010). They find that the value relevance of level 1

and level 2 fair value estimates (directly or indirectly observable inputs) is greater than that of

level 3 estimates (unobservable firm-generated inputs). In summary, prior research finds that fair

value estimates are less value relevant when they cannot be measured reliably, implying that

equityholders believe fair value estimates’ predictive value is reduced in such circumstances.

We examine whether the ability of fair value adjustments embedded in OCI to predict

performance varies with the reliability with which fair values of the underlying assets can be

measured. We identify two factors that might affect the reliability of fair values estimates:

market-wide liquidity and the proportion of the bank’s investment securities guaranteed by the

U.S. government or its agencies.

Critics of fair value accounting have expressed concerns that fair value estimates are less

relevant, less reliable, or both during periods of market illiquidity. When asset markets are

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illiquid, volume and level of activity in asset markets significantly decreases and asset prices

reflect the amount of liquidity available in the market rather than the future earnings power of

assets (Allen and Carletti 2008). Thus we expect that fair value estimates embedded in OCI are

likely to have lower predictive value during periods of higher market illiquidity.

Fair value adjustments of securities issued or guaranteed by the U.S. government and its

agencies (agency securities) are likely to be more reliable because agency securities trade in

larger and more liquid markets compared to non-agency securities. Consistent with this

conjecture, unrealized gains and losses of investment securities are more strongly associated with

stock prices for banks with a higher proportion of agency securities (Barth 1994). Also, Bhat et

al. (2011) report that agency securities are subject to significantly lower uncertainty with regard

to default during financial crises, leading to lower price variability. Based on these arguments,

we expect that banks that hold a greater amount of securities issued or guaranteed by the U.S.

government will have a larger percentage of their securities measured more reliably, which in

turn will affect the reliability of fair value adjustments related to these assets included in OCI.

To summarize, we expect that the predictive value of fair value estimates included in OCI

is affected by the reliability with which fair values can be estimated. We state below our

expectation in the alternative form:

Hypothesis 2: The reliability of fair value estimates embedded in OCI enhances their ability to

predict future performance.

3. Research Design and Sample

3.1. The predictive ability of fair value information

Our sample is comprised of all banks, both publicly traded and privately held, that have

FR Y-9C report data available on the Bank Holding Companies Database maintained by the

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Federal Reserve Bank of Chicago during 2001–2013. The Bank Holding Companies (BHC)

Database collects financial data included in FR Y-9C reports filed by BHCs. The FR Y-9C

reports contain information from the balance sheet and income statement and risk-based capital

measures, as well as other reporting schedules. The FR Y-9C report is filed by all BHCs with

total consolidated assets of $500 million or more. In addition, BHCs meeting certain criteria may

be required to file this report. We use 2013 data only to calculate our dependent variable, which

is measured in periods t+1 or t+2.

We combine the FR Y-9C data with data for the components of OCI (which are not

reported in the FR Y-9C) from SNL Financial, which is available for the same period but for

only approximately 44% of the observations in our full sample. In our tests, we compare the

incremental ability of OCI to predict future earnings after controlling for contemporaneous net

income. Compared to net income, OCI is more fair value-oriented because it includes several fair

value adjustments that bypass the income statement but are included in shareholders’ equity

directly (e.g., net unrealized fair value gains and losses on available-for-sale securities and net

unrealized gains and losses on derivative contracts classified as cash flow hedges). If the fair

value information included in these OCI items aids in the prediction of future performance, we

expect that OCI and its components individually will be incrementally predictive of future

performance measures after controlling for current earnings.

3.2. The reliability of fair value estimates

To test the second hypothesis, we partition our sample based on (i) market-wide liquidity,

and (ii) the proportion of investment securities guaranteed by the U.S. government or its

agencies.

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Our proxy for market-wide liquidity is based on Amihud’s (2002) measure of price

response associated with each dollar of trading volume. Specifically, for all stocks listed on

NYSE with positive daily volume, we calculate the ratio of daily absolute return to trading

volume. Next, after eliminating the top and bottom 1 percent observations to remove outliers, we

calculate a market-wide annual liquidity measure, Liquid_Annual, as the daily market-cap-

weighted average of all daily measures. Higher values of Liquid_Annual represent greater

market-wide illiquidity. We partition our sample period into high liquidity years and low

liquidity years based on the median of Liquid_Annual. The years in which Liquid_Annual is

above (below) the median are classified as low liquidity years (high liquidity years).9 If fair value

estimates are less reliable during periods of low market-wide liquidity, we expect to find that fair

value estimates are less predictive of future performance in the low liquidity sample years.

Next, we partition our sample banks based on their holdings of securities issued or

guaranteed by the U.S. government and its agencies. The proportion of U.S. guaranteed

securities is measured as the sum of the amortized cost of investment securities issued or

guaranteed by the U.S. government or its agencies divided by the amortized cost of total

investment securities held by a bank. We run our analyses separately for the high and low public

bank subsamples based on the median proportion of U.S. guaranteed securities.10

9 Arguably, Liquid_Annual can be criticized for only capturing the liquidity of equity markets, as it is estimated

using data of stocks listed on the NYSE, whereas banks trade assets and financial instruments in markets other than

the equity markets. Chordia et al. (2005) document that liquidity co-varies across asset markets with the shocks to

spreads in one market increasing the spreads in the other markets. Thus, we expect that equity market liquidity co-

varies with liquidity in other asset markets. 10 Banks whose holdings of agency securities as a proportion of their total investment securities are above (below)

the sample median are assigned to the high (low) subsample. Only banks with assets in excess of $1 billion are

required to provide the breakdown of their agency securities. This severely limits the data available for this test for

privately held banks, so we perform this analysis only for publicly traded banks.

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3.3. Empirical models

We test for the association between OCI and our two measures of earnings as proxies of

bank performance by following the approach of Altamuro and Beatty (2010) and Kanagaretnam

et al. (2014). Specifically, we augment their models by including OCI and its components and

estimate the following models:

Pre-tax ROAt+1 (t+2) = α +β1Pre-tax ROAt+ β2Log(Assetst) +

β3[Pre-tax ROAt* Log(Assets t)] + β4PtOCI t + fixed-year effects + εt+1 (t+2); (1)

Pre-tax EBPt+1 (t+2) = α +β1Pre-tax ROAt+ β2Log(Assetst) +

β3[Pre-tax ROAt* Log(Assetst)] + β4PtOCI t + fixed-year effecs + εt+1(t+2); (2)

where PtOCI, our variable of interest, is pre-tax other comprehensive income, calculated as the

reported after-tax other comprehensive income (BHCKB511) divided by one minus the

maximum statutory corporate tax rate (35% for all years in our sample), scaled by lagged total

assets.11 Comprehensive income is a more fair value-based reported income number than net

income because several fair value adjustments are included in OCI but not included in net

income. Thus the coefficient on PtOCI captures the incremental value of OCI in predicting

future earnings after controlling for current pre-tax earnings. Following Wahlen (1994), Liu et

al. (1997), Kanagaretnam et al. (2014), Altamuro and Beatty (2010), and Bischof et al. (2012),

Pre-tax ROA is income before taxes (BHCK4301) divided by lagged total assets, whereas pre-tax

earnings before the provision for loan and lease losses, Pre-tax EBP, is calculated as income

before taxes plus the provision for loan losses (BHCK4230), scaled by lagged total assets.12 Pre-

tax ROA and Pre-tax EBP are measured one and two years ahead, denoted by subscripts t+1 and

t+2, respectively. Log(Assets) is the natural log of total assets (BHCK2170). We measure our

11 BHCK item number mnemonics reported in parentheses indicate data items taken from Federal Reserve Board’s

bank holding company database, representing financial data reported on form FR-Y9C. All variables are also

defined in the Appendix. 12 We estimate our measures of performance on a pre-tax basis to avoid confounding effects of tax avoidance on the

relation between fair value adjustments and future operating performance.

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independent variables at time t to investigate whether current fair value adjustments predict

performance one year and two years in the future. Standard errors are clustered by bank.

Empirical models (3) and (4) below examine the association between individual OCI

components and future performance. We decompose OCI into three components representing the

ratio of i) net unrealized gains and losses related to changes in the fair values of investment

securities classified as available for sale (PtOCI-AFS), ii) net unrealized gains and losses related

to changes in the fair values of derivative contracts classified as cash flow hedges (PtOCI-

Derivatives), and iii) all other adjustments included in OCI (e.g., foreign currency translation

adjustments and additional minimum pension liability adjustments) (PtOCI-Other), to total

assets.


β3[Pre-tax ROAt* Log(Assets t)] + β4PtOCI-AFS t + β5PtOCI-Derivativest +

β6PtOCI-Othert + fixed-year effects + εt+1(t+2); (3)


β3[Pre-tax ROAt* Log(Assetst)] + β4PtOCI-AFS t + β5PtOCI-Derivativest +

β6PtOCI-Othert + fixed-year effects + εt+1(t+2). (4)

4. Results

4.1. Descriptive Statistics

Table 1 presents the descriptive statistics for our samples.13 The average (median) bank

has assets of $5.66 ($0.688) billion. Pre-tax ROA is approximately 1.11%, whereas Pre-tax EBP

are about 1.57% of total assets on average. The mean (median) pre-tax other comprehensive

income, PtOCI, is 0.02% (0.00%), suggesting that, on average, fair value adjustments that are

excluded from net income but included in comprehensive income are income increasing.14 In

13 Continuous variables have been winsorized at 1% and 99% to reduce the influence of outliers. 14 Untabulated correlations reveal that pre-tax other comprehensive income (PtOCIt) is positively and significantly

correlated with Pre-tax EBPt+1 and Pre-tax ROAt+1, providing univariate evidence that fair value estimates included

in other comprehensive income predict future earnings.

- 17 -

terms of OCI components, the largest is unrealized gains and losses from available-for-sale

securities, PtOCI – AFS, with a mean (median) of 0.04% (0.02%).

4.2. Fair value accounting and predictability of pre-tax earnings

In this section, we report the results of tests that examine the association between OCI

and future performance measures. The results of equation (1), which estimates the predictive

value of PtOCI for future pre-tax ROA, are shown in Table 2, Panel A, whereas those of equation

(3), which estimates the predictive value of OCI components, are shown in Panel B. The primary

coefficients of interest for hypothesis 1 are PtOCIt in Panel A and PtOCI-AFSt and PtOCI-

Derivativest in Panel B. Results are presented for the prediction of one-year-ahead (left column)

and two-year-ahead (right column) Pre-tax ROA. The regression models include year fixed

effects, and t statistics are based on standard errors clustered by bank. In all of our analyses, the

statistical significance of the coefficients is based on one-tailed p-values for variables for which

we have a predicted sign and two-tailed p-values otherwise. The adjusted R-squared of our

model ranges between 39% (for models using future pre-tax ROA in year t+2) and 54% (for

models using future pre-tax ROA in year t+1), which is comparable to prior research (e.g.,

Altamuro and Beatty 2010). In Panel A, we find that current-year earnings predict future

earnings (the coefficients on Pre-tax ROAt are positive and statistically significant) and larger

banks have relatively higher future earnings (the coefficients on Log(Assetst) are positive and

statistically significant). More importantly, consistent with hypothesis 1, we find that pre-tax

OCI is incrementally associated with both one- and two-year-ahead pre-tax earnings (the

coefficients on PtOCI are positive and statistically significant), suggesting that fair value-

oriented OCI predicts future bank performance.15

15 In untabulated separate regressions of public (private) banks only, the coefficient on PtOCI is 0.1323 (0.1084),

when Pre-tax ROAt+1 is the dependent variable, and 0.0858 (0.1084), when Pre-tax ROAt+2 is the dependent

- 18 -

In Panel B, the results show that net unrealized gains and losses related to available-for-

sale securities are positively related to both one- and two-year-ahead pre-tax earnings (the

coefficients on PtOCI-AFS are positive and statistically significant), whereas net unrealized

gains and losses for derivative contracts classified as cash flow hedges are negatively related to

future earnings (the coefficients on PtOCI-Derivatives are negative and statistically significant).

These coefficients are statistically different from each other in both one- and two-year-ahead

regressions. Finally, while we do not have a prediction about the sign of the coefficient for

PtOCI-Other, the coefficient on net unrealized gains and losses from other OCI components is

negatively and significantly associated with one-year-ahead earnings but not significantly related

to two-year-ahead earnings.16 In conclusion, the evidence in Panel B suggests that unrealized

gains and losses on individual components of OCI have different implications for future

performance of banks.

4.3. Fair value accounting and predictability of pre-tax earnings before provision

In this section, we report the results of tests that examine the association between OCI

and future Pre-tax EBP. The results of equation (2), which estimates the predictive value of

aggregate OCI for future Pre-tax EBP, are shown in Panel A, whereas those of equation (4),

which estimates the predictive value of OCI components, are shown in Panel B of Table 3. As

before, the primary coefficient of interest for hypothesis 1 is on PtOCIt. The adjusted R-squared

variable. However, when we estimate our regressions in a sample comprising both public and private banks and

include an interaction between PtOCI and an indicator variable for public banks, the coefficient on the interaction is

insignificant for both dependent variables. Thus we do not separate public and private banks in our subsequent tests

and tabulate results only for the combined sample. 16 One year ahead, the coefficient on PtOCI-Other is statistically different from PtOCI-AFS (PtOCI-Derivatives) at

the 1% (10%) level. Two years ahead, the coefficient on PtOCI-Other is statistically different from PtOCI-AFS

(PtOCI-Derivatives) at the 10% (1%) level. If we replace PtOCI-Other with the other two OCI components for

which data is available (i.e., foreign currency translation adjustments and additional minimum pension adjustments),

results for PtOCI-AFS and PtOCI-Derivatives are qualitatively similar to those reported in Table 2. The coefficients

on both of the other OCI components are negative and significant for one-year-ahead Pre-tax ROA and statistically

indistinguishable from zero for two-year-ahead Pre-tax ROA.

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of our model ranges from 34% (for models using future pre-tax EBP in year t+2) to 46% (for

models using future pre-tax EBP in year t+1), which is comparable to prior research (e.g.,

Altamuro and Beatty 2010). Similar to the evidence in Table 2, we find that current year earnings

predict future Pre-tax EBP (the coefficients on Pre-tax ROA are positive and statistically

significant) and larger banks have higher Pre-tax EBP (the coefficients on Log(Assetst) are

positive and statistically significant). More importantly, consistent with hypothesis 1, in Panel A,

we find that pre-tax OCI is incrementally associated with one- and two-year-ahead Pre-tax EBP

after controlling for contemporaneous pre-tax earnings (the coefficients on PtOCI are positive

and statistically significant). In Panel B, we find that net unrealized gains and losses related to

available-for-sale securities are positively related to both one- and two-year-ahead Pre-tax EBP

(the coefficients on PtOCI-AFS are positive and statistically significant), whereas the coefficient

on the net unrealized gains and losses for derivative contracts classified as cash flow hedges

(PtOCI-Derivatives) is negatively associated with one- and two-year-ahead Pre-tax EBP. While

the coefficient on PtOCI-Derivatives is statistically significant in the two-year-ahead regressions,

its significance is only marginal in the one-year-ahead regression. As before, the coefficients on

PtOCI-AFS and PtOCI-Derivatives are statistically different from each other in both the one- and

two-year-ahead regressions. The net unrealized gains and losses related to other components are

not significantly related to Pre-tax EBP (the coefficients on PtOCI-Other are negative but not

statistically significant).17

17 If we replace PtOCI-Other with the other two OCI components for which data is available (i.e., foreign currency

translation adjustments and additional minimum pension adjustments), results for PtOCI-AFS and PtOCI-

Derivatives are qualitatively similar to those reported in Table 3. The coefficient on the foreign currency translation

adjustment component is negative and significant (insignificant) for one-year-ahead (two-year-ahead) Pre-tax EBP,

and the coefficients on the pension adjustment are insignificant for both one- and two-year-ahead Pre-tax EBP.

- 20 -

In summary, we find robust evidence in support of H1 in Tables 2 and 3. Our evidence

suggests OCI predicts future bank earnings after controlling for contemporaneous earnings. In

addition, OCI components have different predictive implications for future earnings.

4. 4. Reliability of fair value estimates and their predictive ability

In this section, we report the results of the tests of our second hypothesis, which

examines the impact of reliability of fair value estimates on the relation between fair value

adjustments and future earnings.

4.4.1. Market-wide liquidity and the predictive ability of fair value estimates

To examine the impact of market-wide liquidity on the predictive ability of fair value

adjustments with respect to future performance, we estimate equation (1) separately for high

liquidity years (i.e., Liquid_Annual is below the sample period median) and low liquidity years

(i.e., Liquid_Annual is above the sample period median). The results from regressions where

Pre-tax ROA and Pre-tax EBP are measured one year (two years) ahead are presented in Panel A

(B) of Table 4. We continue to find that current year pre-tax earnings are associated with future

performance (for both Pre-tax ROA and Pre-tax EBP) during high as well as low liquidity

periods. With respect to H2, we find that PtOCI has a stronger effect on both future Pre-tax ROA

and Pre-tax EBP measured one and two years ahead during periods of high market-wide

liquidity. While the coefficient on PtOCI is positive and significant for both high and low

liquidity sample periods, the coefficient on PtOCI is almost twice as large during periods of high

market-wide liquidity relative to periods of low market-wide liquidity.18 This evidence is

consistent with H2.

18 In untabulated tests, we combine the high and low liquidity subsamples and interact PtOCI with an indicator for

high liquidity. We predict that the coefficient on this interaction will be positive and significant. Consistent with our

prediction, we find that the coefficient on the interaction of PtOCI with the indicator variable for high liquidity is

positive and statistically significant for each dependent variable.

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4.4.2. Proportion of U.S. agency securities and the predictive ability of fair value estimates

In this section, to test the influence of reliability of fair value estimates on the predictive

ability of fair value adjustments, we partition the subsample of publicly traded banks in our main

sample based on their holdings of agency securities. As mentioned above, only banks with assets

in excess of $1 billion must report the details necessary to determine the extent to which their

investment securities are agency securities. Since a large majority of privately held banks have

less than $1 billion of assets, we cannot reliably estimate the proportion of agency securities in

their investment securities portfolio. Hence we run this analysis only for publicly traded banks.

Banks whose proportion of agency securities in their investment securities portfolio is above

(below) the sample median are classified as “High (Low) Percent of U.S. Guaranteed

Investments” banks. The results from regressions where Pre-tax ROA and Pre-tax EBP are

measured one year (two years) ahead are presented in Panel A (B) of Table 5. We find that the

coefficient on PtOCI is positive and significant in both one- and two-year-ahead regressions

when future performance is measured as Pre-tax ROA only for the subsample of banks with a

higher proportion of U.S. guaranteed securities, suggesting that fair values embedded in OCI

predict next-year bank performance only for banks whose fair value estimates can be measured

more reliably. In regressions where future performance is measured as Pre-tax EBP, the

coefficient on PtOCI is positive and marginally significant only for banks with higher holdings

of agency securities. This provides additional evidence in support of H2. Taken together, the

results reported in Tables 4 and 5 suggest that the predictive ability of fair value estimates for

future performance is enhanced when the fair values can be measured more reliably.

4.5. Additional Tests

- 22 -

During the 2007–2009 financial crisis many criticized fair value accounting because it

forced banks to record downward adjustments to asset values that were not justified by the

deterioration in the underlying fundamentals of the assets. Critics argued that, while many bank

assets were still performing during the financial crisis, fair value accounting required that the

assets be reported at value below their “true value” as some asset markets had become illiquid

(e.g., ABA 2008; Wesbury 2008). Banks argued that these impairments on their investment

securities, many of which were classified as available-for-sale, were temporary, and,

accordingly, they recorded the fair value adjustments in OCI. If the fair value adjustments

recorded during the financial crisis were indeed excessive and temporary, we expect that they

will not predictfuture bank performance. Accordingly, we examine the ability of fair value

adjustments included in OCI to predict future performance during the 2007–2009 financial crisis.

We re-estimate models (1) and (2) for a subsample spanning the years 2007 to 2009.

Laux and Leuz (2010) argue that fair value adjustments recorded during the crisis

reflected the actual underlying economic values of the assets. Our test seeks to provide the first

empirical evidence that complements their conclusions. The results for this analysis are

presented in Table 6. In Panel A (B), future performance is measured by one-year-ahead (two-

year-ahead) Pre-tax ROA and Pre-tax EBP. In all regressions the coefficient on PtOCI is positive

and statistically significant.

In untabulated analysis, we replace PtOCI with its components and re-estimate models

(3) and (4) for the years of the financial crisis. We find that during the crisis, unrealized gains

and losses on available-for-sale securities are positively associated with one- and two-year-ahead

bank earnings, while the unrealized gains and losses from derivative contracts classified as cash

flow hedges are negatively associated with future bank performance. Thus our results suggest

- 23 -

that fair value adjustments recorded in the OCI during the recent crisis were informative about

future bank profitability and consistent with the general conclusions of Laux and Leuz (2010).

These results contradict the claim that, on average, fair value accounting resulted in the recording

of unjustified downward adjustments to asset values.

Additionally, we examine whether the results are similar depending on whether total OCI

is positive or negative (i.e., whether it reflects net increases or net decreases in fair value). To do

so, we divide our sample into two subsamples where OCI is positive versus negative in year t

and then we estimate, in each subsample, equations (1) and (2) examining the association

between PtOCI and each of our four earnings proxies (one- and two-year-ahead pre-tax earnings

and one and two-year-ahead pre-tax earnings before the provision). In total, we estimate eight

separate regressions—four regressions for the positive OCI observations and four for the

negative OCI observations. In these untabulated analyses, we find that the coefficient on PtOCI

is positive and significant in six of the eight regressions. The two exceptions are when Pre-tax

ROAt+2 is the dependent variable in the negative OCI sample and when Pre-tax EBPt+1 is the

dependent variable in the positive OCI sample. In these two cases, the coefficient on PtOCI is

statistically insignificant. To summarize, we find that in general fair value adjustments included

in OCI predict future bank earnings irrespective of whether the net fair value adjustments

included in OCI for the period are positive or negative.

4.6. Robustness tests

We perform several robustness tests. First, we use an alternative measure of performance,

pre-tax residual earnings measured as income before taxes minus 12% times lagged book value

of common equity (BHCK3210), divided by lagged total assets. We use residual earnings as an

additional measure of future performance in our analysis as bank managers have increasingly

- 24 -

incorporated performance metrics that include an adjustment for the return on capital in their

decision-making (Kimball 1998). Also, residual earnings are frequently used in bank valuation

models (e.g., Begley et al. 2006; Kohlbeck and Warfeld 2007). We present the results using this

alternative performance measure in Table 7. Panel A shows the results of the predictive value of

summary OCI for one- and two-year-ahead pre-tax residual earnings, while Panel B shows the

results of individual OCI components. Using this measure, we obtain results that are consistent

with our main results. Specifically, in Panel A, the coefficient on PtOCI is positive and

significant for both one- and two-year-ahead residual earnings. In Panel B, similar to our main

analysis, net unrealized gains and losses on available-for-sale securities are positively associated

with both one and two year-ahead residual earnings, whereas net unrealized gains and losses

from derivative contracts classified as cash flow hedges are negatively associated with future

residual earnings. Also, the coefficients on PtOCI-AFS and PtOCI-Derivatives are statistically

different from each other in both one- and two-year-ahead regressions. Finally, the net unrealized

gains and losses from other OCI components are negatively related to one-year-ahead residual

earnings but not significantly related to two-year-ahead residual earnings.

Second, to alleviate the concern that our documented results could be due to the

differences in the asset holdings and business models of banks, we include several additional

controls and re-run our tests. Specifically, we include bank-year-specific controls for asset

composition (i.e., loans-to-asset ratio), risk buffers (i.e., equity-to-asset ratio), funding structure

(i.e., deposits-to-asset ratio), and asset risk (i.e., nonperforming assets-to-total assets ratio) in

Equations 1 to 4. In an alternate specification, we replace these additional control variables with

bank-fixed effects to control for the business model of the banks. The results of these robustness

tests are qualitatively similar to those reported earlier, and our inferences remain unchanged. The

- 25 -

only exception is that the coefficient on PtOCI-Derivatives is negative but not statistically

significant in the regression including firm fixed effects where one-year-ahead Pre-tax EBP is

the dependent variable.

Finally, in our primary analyses, we follow prior banking and predictive value research

and use assets to scale PtOCI and its components. We re-examine whether our results are

sensitive to the choice of the deflator by re-estimating Equations 1–4 after scaling PtOCI and its

components by the book value of equity (BHCK3210). The results are qualitatively similar to

those reported in Tables 2 and 3. If we instead scale by total interest income (BHCK4107), the

results are also qualitatively similar, with the exception that PtOCI-Derivatives is negative but

not statistically significant when one-year-ahead Pre-tax EBP is the dependent variable.

5. Conclusion

The increasing use of fair values in financial reporting has sparked an ongoing debate

about the merits of a fair value accounting-based reporting system. We contribute to this debate

by examining whether fair value adjustments embedded in OCI predict future performance in a

sample of banks. We find that fair value-based OCI and its individual components predict future

bank earnings both one and two years ahead. Importantly, different components of OCI have

different implications for future bank profitability, suggesting that not all unrealized gains and

losses included in OCI are similar. Furthermore, the predictive ability of fair value estimates for

future performance is enhanced when the fair values can be measured more reliably. Finally, we

present some of the first empirical evidence contradicting the claims that, during the financial

crisis, fair value accounting forced banks to record excessive downward adjustments to assets’

fair values unrelated to the deterioration in the underlying fundamentals. We find that fair value

- 26 -

adjustments recorded during the crisis predict future bank performance both one and two years

ahead.

Our research informs claims by the FASB and the IASB that fair value accounting meets

the objectives of financial reporting by providing decision-useful information helpful for the

prediction of future performance. Our results are also relevant for U.S. bank regulators, who

have faced criticism for the inclusion of unrealized gains and losses reported in accumulated

other comprehensive income in the calculation of regulatory capital, in line with the

recommendations of Basel III Capital Framework.

As a caveat, care should be exercised in generalizing our findings to firms beyond the

banking industry. Our findings are based on a sample comprised only of banks because banks

have experienced the most immediate and direct impact of the move toward more fair value-

based reporting. Relative to other firms, balance sheets of banks are comprised almost entirely of

financial assets. The fair values of nonfinancial assets may not be estimable with the same

degree of reliability (Barth and Landsman 1995), and this might impact the predictive value of

fair value estimates. Finally, our study speaks to only the mixed-attribute financial reporting

system in use today. It may be the case that our findings do not hold in a “full” fair value

reporting system.

- 27 -

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Appendix – Variable Definitions

Variable Definition Data

Source

Assets ($ MM) Total assets (BHCK2170). FR Y-9C

Pre-tax ROA Income before taxes (BHCK4301) divided by lagged total assets

(BHCK2170).

FR Y-9C

Pre-tax EBP Pre-tax net income (BHCK4301) plus provision for loan losses

(BHCK4230), divided by lagged total assets (BHCK2170).

FR Y-9C

Pre-tax RE Residual earnings divided by lagged total assets. Residual earnings are

calculated as income before taxes (BHCK4301) minus 12% times the

lagged book value of equity (BHCK3210).

FR Y-9C

PtOCI Pre-tax other comprehensive income divided by lagged total assets

(BHCK2170). Pre-tax other comprehensive income is calculated by

dividing other comprehensive income (BHCKB511) by one minus the

statutory tax rate (i.e., 35% during our sample period).

FR Y-9C

PtOCI-

Derivatives

Pre-tax unrealized gains and losses related to changes in the fair value of

derivative contracts classified as cash flow hedges

(CHG_FV_EFF_HEDGE divided by one minus the statutory tax rate)

divided by lagged total assets (BHCK2170).

FR Y-9C,

SNL

Financial

PtOCI-AFS Pre-tax unrealized gains and losses related to changes in fair values of

investment securities classified as available for sale (CHG_UNRL_GAIN

divided by one minus the statutory tax rate) divided by lagged total assets

(BHCK2170).

FR Y-9C,

SNL

Financial

PtOCI-Other All other adjustments included in PtOCI other than PtOCI-AFS and

PtOCI-Derivatives, calculated as [(TOT_OCI minus

CHG_FV_EFF_HEDGE minus CHG_UNRL_GAIN), divided by one

minus the statutory tax rate] divided by lagged total assets (BHCK2170).

FR Y-9C,

SNL

Financial

Liquid_Annual Measure of market-wide liquidity based on Amihud (2002). A ratio of

daily absolute returns to trading volume is calculated for all NYSE stocks

with positive trading volume. Next, a market-wide annual liquidity

measure is calculated as the daily market-cap weighted average of all

daily measures after excluding outliers in the top and bottom 1% of the

sample.

CRSP

Agency_Secs Proportion of securities issued or guaranteed by the U.S. government or

its agencies. Before 2009 [in 2009 and thereafter], this ratio is calculated

as (BHCK0211 + BHCK1289 + BHCK1294 + BHCK1698 + BHCK1703

+ BHCK1714 + BHCK1718 + BHCK1286 + BHCK1291 + BHCK1297

+ BHCK1701 + BHCK1706 + BHCK1716 + BHCK1731) / (BHCK1754

+ BHCK1772)

[(BHCK0211 + BHCK1289 + BHCK1294 + BHCKG300 + BHCKG304

+ BHCKG312 + BHCKG316 + BHCK1286 + BHCK1291 + BHCK1297

+ BHCKG302 + BHCKG306 + BHCKG314 + BHCKG318 +

BHCKK142 + BHCKK150 + BHCKK144 + BHCKK152) / (BHCK1754

+ BHCK1772)].

FR Y-9C

- 33 -

Table 1

Descriptive Statistics

Mean Std. Dev. 1st Pctl. 25th Pctl. Median 75th Pctl. 99th Pctl.

Assetst ($millions) 5,656 26,384 164 383 688 1,461 131,476

Log(Assetst) 13.68 1.40 11.98 12.76 13.38 14.12 18.98

Pre-tax ROAt+1 0.0111 0.0135 -0.0436 0.0073 0.0132 0.0180 0.0361

Pre-tax EBPt+1 0.0157 0.0098 -0.0152 0.0111 0.0160 0.0207 0.0430

Pre-tax REt+1 0.0003 0.0133 -0.0537 -0.0036 0.0023 0.0073 0.0259

PtOCIt 0.0002 0.0046 -0.0120 -0.0017 0.0000 0.0020 0.0133

PtOCI-Derivativest 0.0000 0.0004 -0.0015 0.0000 0.0000 0.0000 0.0015

PtOCI-AFSt 0.0004 0.0037 -0.0087 -0.0012 0.0002 0.0019 0.0106

PtOCI-Othert -0.0001 0.0008 -0.0038 0.0000 0.0000 0.0000 0.0015

Table 1 presents descriptive statistics. For all variables except PtOCI-Derivatives, PtOCI-AFS, PtOCI-Other,

the sample includes 14,781 bank-year observations (2,420 unique banks) from 2001 to 2012, taken from

bank FR Y-9C reports. Assets is equal to total assets. Pre-tax ROA is calculated as income before taxes

divided by lagged total assets. Pre-tax EBP is calculated as pre-tax net income plus the provision for loan

losses, divided by total assets. Pre-tax RE is defined as residual earnings, which is calculated as pre-tax

income minus 12% times the lagged book value of equity, divided by lagged assets. PtOCI is defined as pre-

tax other comprehensive income, which is calculated as other comprehensive income, divided by one minus

the maximum statutory corporate tax rate (35% for all years in our sample), divided by lagged total assets.

PtOCI-Derivatives, PtOCI-AFS, and PtOCI-Other are available for 6,485 bank-year observations (965

unique banks) and are defined similarly to PtOCI but using only the individual components of other

comprehensive income, taken from SNL Financial. All variables have been winsorized at the 1% and 99%

level.

- 34 -

Table 2

Predictability of pre-tax earnings based on fair value-oriented other comprehensive income

Panel A: Predictability of pre-tax earnings using total OCI


β3[Pre-tax ROAt* Log(Assetst)] + β4ptOCIt + year effects + εt+1 (t+2) (1)

Prediction Dep. Var: Pre-tax ROAt+1 Dep. Var: Pre-tax ROAt+2

coef. t-stat coef. t-stat

Intercept

-0.0018 -1.27

-0.0022 -1.16

Pre-tax ROAt (+) 0.8912 10.34 *** 0.8413 7.44 ***

Log(Assetst) (+) 0.0004 4.07 *** 0.0006 4.82 ***

Pre-tax ROAt* Log(Assetst) -0.0161 -2.59 *** -0.0274 -3.38 ***

PtOCIt (+) 0.1136 3.77 *** 0.0928 4.27 ***

Year Fixed Effects

Included

Included

N

14,781

12,145

Adj. R2

0.536

0.385

Panel B: Predictability of pre-tax earnings using OCI components

Pre-tax ROAt+1 (t+2) = α +β1Pre-tax ROAt+ β2Log(Assetst) + β3[Pre-tax ROAt* Log(Assetst)] +

β4ptOCI-AFSt + β5ptOCI-Derivativest +β6ptOCI-Othert + year effects +ε (3)

Prediction Dep. Var: Pre-tax ROAt+1 Dep. Var: Pre-tax ROAt+2


Intercept

-0.0045 -2.15 ** -0.0089 -3.67 ***

Pre-tax ROAt (+) 0.8918 7.09 *** 0.9297 6.38 ***

Log(Assetst) (+) 0.0006 4.03 *** 0.0011 6.33 ***

Pre-tax ROAt* Log(Assetst) -0.0191 -2.13 ** -0.0355 -3.55 ***

PtOCI-AFSt (+) 0.2663 3.69 *** 0.1847 4.35 ***

PtOCI-Derivativest (-) -1.3503 -2.88 *** -1.3429 -2.92 ***

PtOCI-Othert

-0.7252 -3.55 *** -0.2224 -1.29

Year Fixed Effects

Included

Included

N

6,485

5,566

Adj. R2 0.531 0.386

F-test: β4 = β5 F = 29.72*** F = 16.71***

Table 2 presents the results for regressions (1) and (3), testing the ability of fair value adjustments in other

comprehensive income to predict future earnings when the dependent variable is measured at t+1 and t+2.

Pre-tax ROA is calculated as pre-tax net income, divided by lagged total assets. Assets is total assets. In

Panel A, PtOCI is equal to pre-tax other comprehensive income, calculated as the reported after-tax other

comprehensive income divided by one minus the maximum statutory corporate tax rate of 35%, divided by

lagged total assets. In Panel B, PtOCI-Derivatives, PtOCI-AFS, and PtOCI-Other, available for a subsample,

are defined similarly to PtOCI but using only the individual components of other comprehensive income. t-

statistics are based on standard errors clustered by firm. One-tailed tests of significance are used where

predictions have been made. ***, **, * indicate statistical significance at the 1, 5, and 10 % levels,

respectively.

- 35 -

Table 3

Predictability of pre-tax earnings before the provision for loan losses (EBP) based on OCI

Panel A: Predictability of pre-tax EBP using total OCI


β3[Pre-tax ROAt* Log(Assetst)] + β4ptOCIt + year effects + εt+1 (t+2) (2)

Prediction Dep. Var: Pre-tax EBPt+1 Dep. Var: Pre-tax EBPt+2


Intercept

-0.0062 -3.32 *** -0.0052 -2.54 **

Pre-tax ROAt (+) 0.9304 9.05 *** 0.8010 6.86 ***

Log(Assetst) (+) 0.0009 7.12 *** 0.0010 6.87 ***

Pre-tax ROAt* Log(Assetst)

-0.0288 -3.85 *** -0.0268 -3.17 ***

PtOCIt (+) 0.1059 3.94 *** 0.0902 4.60 ***

Year Fixed Effects

Included

Included

N

14,781

12,145

Adj. R2

0.461

0.440

Panel B: Predictability of pre-tax EBP using OCI components

Pre-tax EBPt+1 (t+2) = α +β1Pre-tax ROAt+ β2Log(Assetst) + β3[Pre-tax ROAt* Log(Assetst)] +

β4ptOCI-AFSt + β5ptOCI-Derivativest +β6ptOCI-Othert + year effects +ε (4)

Prediction Dep. Var: Pre-tax EBPt+1 Dep. Var: Pre-tax EBPt+2


Intercept

-0.0124 -5.84 *** -0.0130 -6.07 ***

Pre-tax ROAt (+) 1.0640 8.42 *** 0.9706 7.21 ***

Log(Assetst) (+) 0.0014 9.27 *** 0.0015 10.04 ***


-0.0424 -4.77 *** -0.0417 -4.44 ***

PtOCI-AFSt (+) 0.2427 4.11 *** 0.1762 5.15 ***

PtOCI-Derivativest (-) -0.4032 -1.62 * -0.6634 -2.25 **

PtOCI-Othert

-0.1848 -1.08

-0.0460 -0.41

Year Fixed Effects

Included

Included

N

6,485

5,566

Adj. R2 0.349 0.335

F-test: β4 = β5 F = 8.05*** F = 10.23***

Table 3 presents the results for regressions (2) and (4) testing the ability of fair value adjustments in other

comprehensive income to predict future earnings before the provision when the dependent variable is

measured at t+1 and t+2. Pre-tax EBP is calculated as pre-tax net income plus the provision for loan

losses, divided by lagged total assets. Pre-tax ROA is calculated as pre-tax net income, divided by lagged

total assets. Assets is total assets. In Panel A, PtOCI is equal to pre-tax other comprehensive income,

calculated as the reported after-tax other comprehensive income divided by 1 minus the maximum

statutory corporate tax rate of 35%, divided by lagged total assets; in Panel B, PtOCIt-Derivatives, PtOCIt-

AFS, and PtOCIt-Other, available for a subsample, are defined similarly to PtOCI but using only the

individual components of other comprehensive income. t-statistics are based on standard errors clustered

by firm. One-tailed tests of significance are used where predictions have been made. ***, **, * indicate

statistical significance at the 1, 5, and 10 % levels, respectively.

- 36 -

Table 4

Predictability of pre-tax earnings and EBP in periods of high versus low liquidity

Panel A: One-year-ahead predictability of pre-tax earnings and EBP by periods of high vs. low liquidity

Pre-tax ROAt+1 or Pre-tax EBPt+1 = α +β1Pre-tax ROAt+ β2Log(Assetst) + β3[Pre-tax ROAt* Log(Assetst)] + β4PtOCIt + year effects + εt+1 (t+2)

Dependent Variable = Pre-tax ROAt+1

Dependent Variable = Pre-tax EBPt +1

Prediction High Liquidity Low Liquidity High Liquidity Low Liquidity

coef. t-stat coef. t-stat coef. t-stat coef. t-stat

Intercept

0.0056 2.16 ** -0.0077 -3.99 *** 0.0055 2.10 ** -0.0078 -3.73 ***

Pre-tax ROAt (+) 0.6874 3.96 *** 1.0071 9.64 *** 0.5381 2.83 *** 1.1263 10.88 ***

Log(Assetst) (+) -0.0001 -0.61

0.0007 5.59 *** 0.0001 0.41

0.0015 9.92 ***


-0.0016 -0.13

-0.0243 -3.23 *** 0.0028 0.20

-0.0450 -6.00 ***

PtOCIt (+) 0.1740 4.47 *** 0.0866 2.23 ** 0.1312 3.80 *** 0.0963 2.86 ***

Year Fixed Effects

Yes

Yes

Yes

Yes

N

5,696

9,085

5,696

9,085

Adj. R2

0.482

0.564

0.511

0.443

- 37 -

Table 4 (continued)

Predictability of pre-tax earnings and EBP in periods of high versus low liquidity

Panel B: Two-year-ahead predictability of pre-tax earnings and EBP by periods of high vs. low liquidity

Pre-tax ROAt+2 or Pre-tax EBPt+2 = α +β1Pre-tax ROAt+ β2Log(Assetst) + β3[Pre-tax ROAt* Log(Assetst)] + β4PtOCIt + year effects εt+1 (t+2)



Prediction High Liquidity Low Liquidity High Liquidity Low Liquidity


Intercept

0.0082 2.31 ** -0.0094 -4.07 *** 0.0047 1.59

-0.0070 -2.97 ***

Pre-tax ROAt (+) 0.6257 2.40 *** 0.9734 7.98 *** 0.3668 1.62 * 0.9823 8.10 ***

Log(Assetst) (+) -0.0001 -0.28

0.0011 6.69 *** 0.0003 1.37 * 0.0014 8.10 ***


-0.0155 -0.83

-0.0347 -3.97 *** 0.0043 0.26

-0.0398 -4.56 ***

PtOCIt (+) 0.1334 3.01 *** 0.0724 3.52 *** 0.1220 3.08 *** 0.0746 3.94 ***

Year Fixed Effects

Yes

Yes

Yes

Yes

N

4,478

7,667

4,478

7,667

Adj. R2

0.340

0.460

0.329

0.395

Table 4 presents the results for regressions (1) and (3) testing the ability of fair value adjustments in other comprehensive income to predict one-year-

ahead earnings (in Panel A) and two-year-ahead earnings (in Panel B) across periods of high vs. low market-wide liquidity. Pre-tax ROA is calculated as

pre-tax net income, divided by lagged total assets. Pre-tax EBP is calculated as pre-tax net income plus the provision for loan losses, divided by lagged

total assets. Assets is total assets. PtOCI is equal to pre-tax other comprehensive income, calculated as the reported after-tax other comprehensive income

divided by 1 minus the maximum statutory corporate tax rate of 35%, divided by lagged total assets. The sample is partitioned based on years of high

liquidity (t = 2005, 2006, 2007, 2010, 2011, 2012, and 2013) and low liquidity (t = 2001, 2002, 2003, 2004, 2008, and 2009). Sample years are classified

as high and low liquidity based on Liquid_Annual. See the appendix for details on the estimation of Liquid_Annual. t-statistics are based on standard

errors clustered by firm. One-tailed tests of significance are used where predictions have been made. ***, **, * indicate statistical significance at the 1, 5,

and 10 % levels, respectively.

- 38 -

Table 5

Predictability of pre-tax earnings and EBP in publicly traded banks based on characteristics of investment securities

Panel A: One-year-ahead predictability of pre-tax earnings and EBP by the extent of U.S. government guaranteed investment securities

Pre-tax ROAt+1 or Pre-tax EBPt+1 = α +β1Pre-tax ROAt+ β2Log(Assetst) + β3[Pre-tax ROAt* Log(Assetst)] + β4PtOCIt + year effects + εt+1 (t+2)



Percent of U.S. Guaranteed Investments Percent of U.S. Guaranteed Investments

Prediction High Low High Low


Intercept

-0.0058 -1.30

-0.0041 -0.71

-0.0106 -2.32 ** -0.0084 -1.48

Pre-tax ROAt (+) 0.7354 2.59 *** 0.7917 3.45 *** 0.8524 2.87 *** 0.8350 3.21 ***

Log(Assetst) (+) 0.0005 1.61 * 0.0001 0.25

0.0010 2.93 *** 0.0006 1.73 **

Pre-tax ROAt* Log(Assetst) -0.0089 -0.47

0.0016 0.10

-0.0217 -1.08

-0.0084 -0.47

PtOCIt (+) 0.3088 2.66 *** 0.0954 1.22

0.1624 1.56 * 0.0937 1.24

Year Fixed Effects

Yes

Yes

Yes

Yes

N

1,644

1,650

1,644

1,650

Adj. R2

0.456

0.557

0.438

0.532

- 39 -

Table 5 (continued)

Predictability of pre-tax earnings and EBP in publicly traded banks based on characteristics of investment securities

Panel B: Two-year-ahead predictability of pre-tax earnings and EBP by the extent of U.S. government guaranteed investment securities

Pre-tax ROAt+2 or Pre-tax EBPt+2 = α +β1Pre-tax ROAt+ β2Log(Assetst) + β3[Pre-tax ROAt* Log(Assetst)] + β4PtOCIt + year effects εt+1 (t+2)



Percent of U.S. Guaranteed Investments

Percent of U.S. Guaranteed Investments

Prediction High Low High Low


Intercept

-0.0085 -1.32

0.0090 1.31

-0.0140 -2.61 *** 0.0007 0.12

Pre-tax ROAt (+) 0.7539 2.02 ** 0.1196 0.38

1.0305 3.18 *** 0.4582 1.47 *

Log(Assetst) (+) 0.0011 2.57 *** -0.0002 -0.50

0.0016 4.33 *** 0.0004 1.12


-0.0276 -1.08

0.0352 1.59

-0.0433 -1.94 * 0.0111 0.52

PtOCIt (+) 0.1933 1.73 ** 0.0389 0.38

0.1019 1.30 * 0.0457 0.49

Year Fixed Effects

Yes

Yes

Yes

Yes

N

1,530

1,558

1,530

1,558

Adj. R2

0.355

0.454

0.299

0.396

Table 5 presents the results for regressions (1) and (3) testing the ability of fair value adjustments in other comprehensive income to predict one-year-

ahead earnings (in Panel A) and two-year-ahead earnings (in Panel B) as a function of the extent of U.S. guaranteed investments among publicly traded

banks. Pre-tax ROA is calculated as pre-tax net income, divided by lagged total assets. Pre-tax EBP is calculated as pre-tax net income plus the provision

for loan losses, divided by lagged total assets. Assets is total assets. PtOCI is equal to pre-tax other comprehensive income, calculated as the reported

after-tax other comprehensive income divided by 1 minus the maximum statutory corporate tax rate of 35%, divided by lagged total assets. Banks whose

proportion of agency securities in their investment securities portfolio is above (below) the median are classified as High (Low) percent of U.S.

Guaranteed Investment Banks. t-statistics are based on standard errors clustered by firm. One-tailed tests of significance are used where predictions have

been made. ***, **, * indicate statistical significance at the 1, 5, and 10 % levels, respectively.

- 40 -

Table 6

Predictability of other comprehensive income during the financial crisis

Panel A: One-year-ahead predictability of earnings using total OCI

Pre-tax ROAt+1 or Pre-tax EBPt+1 = α +β1Pre-tax ROAt+ β2Log(Assetst) +

β3[Pre-tax ROAt* Log(Assetst)] + β4PtOCIt + year effects + εt+1

Prediction Dep. Var: Pre-tax ROAt+1 Dep. Var: Pre-tax EBPt+1


Intercept

-0.0012 -0.39

-0.0017 -0.62

Pre-tax ROAt (+) 1.1123 4.70 *** 1.0878 6.42 ***

Log(Assetst) (+) 0.0003 1.28

0.0010 5.10 ***


-0.0326 -1.95 * -0.0482 -3.99 ***

PtOCIt (+) 0.2128 3.20 *** 0.2270 4.15 ***

Year Fixed Effects

Yes

Yes

N

2,831

2,831

Adj. R2

0.508

0.437

Panel B: Two-year-ahead predictability of earnings using total OCI

Pre-tax ROAt+2 or Pre-tax EBPt+2 = α +β1Pre-tax ROAt+ β2Log(Assetst) +

β3[Pre-tax ROAt* Log(Assetst)] + β4PtOCIt + year effects + εt+2

Prediction Dep. Var: Pre-tax ROAt+2 Dep. Var: Pre-tax EBPt+2


Intercept

-0.0056 -1.73 * -0.0070 -2.71 ***

Pre-tax ROAt (+) 0.8519 3.70 *** 0.6051 3.77 ***

Log(Assetst) (+) 0.0008 3.57 *** 0.0013 7.34 ***


-0.0301 -1.87 * -0.0199 -1.77 *

PtOCIt (+) 0.1716 4.16 *** 0.1637 4.73 ***

Year Fixed Effects

Yes

Yes

N

2,664

2,664

Adj. R2

0.247

0.277

Table 6 presents the results for regressions (1) and (2) testing the ability of fair value adjustments in

other comprehensive income to predict future earnings where the dependent variable is measured at t+1

(Panel A) and t+2 (Panel B), using only observations where t=2007, 2008, and 2009. Pre-tax ROA is

calculated as pre-tax net income, divided by lagged total assets. Pre-tax EBP is calculated as pre-tax net

income plus the provision for loan losses, divided by lagged total assets. Assets is total assets. PtOCI is

equal to pre-tax other comprehensive income, calculated as the reported after-tax other comprehensive

income divided by 1 minus the maximum statutory corporate tax rate of 35%, divided by lagged total

assets. t-statistics are based on standard errors clustered by firm. One-tailed tests of significance are used

where predictions have been made. ***, **, * indicate statistical significance at the 1, 5, and 10 % levels,

respectively.

- 41 -

Table 7

Alternative measure of performance: Predictability of pre-tax residual earnings (RE) based on OCI

Panel A: Predictability of pre-tax residual earnings using total OCI

Pre-tax REt+1 (t+2) = α +β1Pre-tax ROAt+ β2Log(Assetst)

β3[Pre-tax ROAt* Log(Assetst)] + β4ptOCIt + εt

Prediction Dep. Var: Pre-tax REt+1 Dep. Var: REt+2


Intercept

-0.0110 -5.75 *** -0.0107 -4.68 ***

Pre-tax ROAt (+) 0.4123 4.22 *** 0.3451 2.84 ***

Log(Assetst) (+) 0.0003 2.25 ** 0.0005 2.95 ***


0.0113 1.61

0.0014 0.16

PtOCIt (+) 0.0828 2.80 *** 0.0558 2.67 ***

Year Fixed Effects

Included

Included

N

14,781

12,145

Adj. R2

0.467

0.340

Panel B: Predictability of pre-tax residual earnings using OCI components

Pre-tax REt+1 (t+2) = α +β1Pre-tax ROAt+ β2Log(Assetst) + β3[Pre-tax ROAt* Log(Assetst)] +

β4ptOCI-AFSt + β5ptOCI-Derivativest +β6ptOCI-Othert +εt

Prediction Dep. Var: Pre-tax REt+1 Dep. Var: REt+2


Intercept

-0.0143 -5.65 *** -0.0182 -6.88 ***

Pre-tax ROAt (+) 0.4964 3.65 *** 0.5198 3.42 ***

Log(Assetst) (+) 0.0005 2.78 *** 0.0009 5.09 ***


0.0034 0.35

-0.0122 -1.17

PtOCI-AFSt (+) 0.2103 2.95 *** 0.1478 3.54 ***

PtOCI-Derivativest (-) -1.5023 -3.11 *** -1.4852 -3.23 ***

PtOCI-Othert

-0.6547 -3.12 *** -0.1395 -0.83

Year Fixed Effects

Included

Included

N

6,485

5,566

Adj. R2 0.484 0.361

F-test: β4 = β5 F = 30.29*** F = 18.88***

Table 7 presents the results for the above regressions testing the ability of fair value adjustments in other

comprehensive income to predict future earnings where the dependent variable is measured at t+1 and t+2.

Pre-tax RE is calculated as pre-tax net income minus 12% times the lagged book value of common equity,

divided by lagged total assets. Pre-tax ROA is calculated as pre-tax net income, divided by lagged total assets.

Assets is total assets. In Panel A, PtOCI is equal to pre-tax other comprehensive income, calculated as the

reported after-tax other comprehensive income divided by 1 minus the maximum statutory corporate tax rate

of 35%, divided by lagged total assets. In Panel B, PtOCIt-Derivatives, PtOCIt-AFS, and PtOCIt-Other,

available for a subsample, are defined similarly to PtOCI but using only the individual components of other

comprehensive income. t statistics are based on standard errors clustered by firm. One-tailed tests of

significance are used where predictions have been made. ***, **, * indicate statistical significance at the 1, 5,

and 10 % levels, respectively.