Electronic copy available at: http://ssrn.com/abstract=2448482

Bank Earnings and Regulatory Capital Management using

Available for Sale Securities

Mary E. Barth* Graduate School of Business

Stanford University

Javier Gomez-Biscarri Departamento de Economía y Empresa

Universitat Pompeu Fabra and Barcelona Graduate School of Economics

Ron Kasznik Graduate School of Business

Stanford University

Germán López-Espinosa Departamento de Empresa

Universidad de Navarra

June 2014

* Corresponding author: mbarth@stanford.edu. We appreciate the helpful comments and suggestions from Min Ji Lee, Doron Nissim, Stephen Penman, and seminar participants at the American Accounting Association annual meeting, especially John McInnis the discussant; Barcelona Accounting Seminar Series; European Accounting Association Annual Congress; Spanish Finance Association annual conference, especially Ricardo Gimeno the discussant; Universidad de Navarra; University of Neuchatel; Universitat Pompeu Fabra; Universidad de Valladolid; and the VII International Research Symposium for Accounting Academics (Madrid), especially Pedro J. García the discussant. We also appreciate the data collection efforts of Min Ji Lee. Mary E. Barth and Ron Kasznik acknowledge funding from the Center for Global Business and the Economy at Stanford Graduate School of Business; Germán López-Espinosa (Javier Gomez-Biscarri) acknowledges financial support from the Ministerio de Ciencia e Innovación project ECO2008-02599 (ECO2008-05155) and from the Gobierno de Navarra Jerónimo de Ayanz program (Barcelona GSE).

Electronic copy available at: http://ssrn.com/abstract=2448482

Bank Earnings and Regulatory Capital Management using Available for Sale Securities

ABSTRACT We address banks’ use of available-for-sale (AFS) securities to manage earnings and regulatory capital. Although prior research investigates banks’ use of realized securities gains and losses to smooth earnings and regulatory capital, results are mixed. Creation of AFS securities and enhanced disclosures permit more powerful tests and new insights. We find banks realize gains and losses on AFS securities to smooth earnings and regulatory capital, and banks with more accumulated unrealized gains and losses do so to a greater extent. Banks with negative earnings realize losses to take a big bath, unless they have accumulated unrealized gains that offset the negative earnings. If so, they smooth earnings. Our inferences apply to non-listed and listed banks, which suggests the incentives do not derive solely from public capital market pressures. Our findings reveal the discretion afforded by historical cost-based accounting for AFS securities gains and losses enables banks to manage earnings and regulatory capital. Keywords: Banks, earnings management, available-for-sale securities, realized gains and losses, regulatory capital. JEL Classification: M41, G12.

Electronic copy available at: http://ssrn.com/abstract=2448482

Bank Earnings and Regulatory Capital Management using Available for Sale Securities

1. INTRODUCTION

The question we address is whether, and in what way, banks use available-for-sale (AFS)

securities to manage earnings and regulatory capital.1 AFS securities can be an earnings and

regulatory capital management tool because although these securities are measured at fair value

in the statement of financial position, their unrealized gains and losses are recognized in other

comprehensive income. The gains and losses are recognized in earnings and, largely, in

regulatory capital only when they are realized. We find consistent evidence that banks—both

publicly listed and non-listed—use AFS securities to smooth earnings and regulatory capital,

engage in big bath earnings management, and avoid negative earnings. Taken together, our

findings reveal that the accounting for AFS securities does not eliminate banks’ incentive to use

these securities as an earnings and regulatory capital management tool, and that bank managers

seem to view earnings, and not comprehensive income, as the target for earnings management.

Although prior studies address the question of whether banks use realized securities gains

and losses to smooth earnings and regulatory capital, the studies focus on investment securities,

not AFS securities, and report mixed results. We revisit this question in light of changes in

accounting and disclosure that enable us to conduct more powerful tests and provide new

insights. Key to our research design is Accounting Standards Codification (ASC) Topic 320

(formerly Statement of Financial Accounting Standards No. 115, FASB 1993). ASC Topic 320

requires all entities, including banks, to classify securities as trading, held-to-maturity (HTM), or

available for sale. Trading securities are measured at fair value, and changes in their fair value

are included in earnings and regulatory capital. As a result, there is no incentive to realize

1 We use the term earnings and net income interchangeably.

2

trading securities gains and losses to manage earnings or regulatory capital. Although HTM

securities are measured at amortized cost and, thus, these securities could be used to manage

earnings and regulatory capital, selling HTM securities risks violating the tainting provisions in

ASC Topic 320.

AFS is the largest category of banks’ securities. AFS securities are measured at fair

value in the statement of financial position, with changes in fair value, i.e., unrealized gains and

losses, recognized in other comprehensive income. These gains and losses are only recognized

in earnings when they are realized. This historical cost-based accounting treatment for AFS

securities gains and losses provides the opportunity for banks to engage in earnings and

regulatory capital management by selling these securities and realizing selected gains and losses.

However, if bank managers view comprehensive income as the earnings management target, we

will not observe evidence of this selective selling behavior to manage earnings. Gains and losses

on AFS securities also generally only affect regulatory capital when they are realized. Thus,

banks’ decisions regarding when to sell AFS securities and realize any unrealized gain or loss

also affects regulatory capital.

Banks’ disclosure of accumulated unrealized gains and unrealized losses on AFS

securities permits us to incorporate into our research design the extent to which a bank has

accumulated unrealized gains or losses and, thus, the opportunity to realize gains and losses to

smooth earnings and regulatory capital or to take a big bath. Our research design also benefits

from publicly available quarterly bank regulatory reports that include detailed information about

a bank’s regulatory capital in addition to its securities. Prior research needed to estimate

regulatory capital, which introduced estimation error into the empirical tests. In addition, the

reports cover all banks, publicly listed and non-listed, whereas prior research generally only

3

could include the largest listed banks. Our sample comprises 136,879 bank-quarter observations

for 6,300 banks, with 15,204 (121,675) observations for 728 (5,862) listed (non-listed) banks

from 1996 to 2011.

We base our inferences on estimations of the relation between realized gains and losses

on AFS securities and earnings and regulatory capital, each calculated before the effect of the

realized gains and losses, and control variables identified in prior research. We begin by

focusing on smoothing of earnings and regulatory capital. Consistent with smoothing, we find a

significant negative relation between realized AFS securities gains and losses and pre-gains-and-

losses earnings and regulatory capital. We also find that the relation is significantly more

negative when the bank has more accumulated unrealized gains and losses at the beginning of

the quarter. This finding provides new evidence that banks with more opportunity to smooth

earnings and regulatory capital by realizing heretofore unrealized gains and losses on AFS

securities do so to a greater extent.

We next focus on testing whether banks with negative earnings take a big bath, which

prior research does not consider. We test for big bath earnings management by permitting the

relation between realized gains and losses on AFS securities and earnings before the realized

gains and losses to differ for banks with positive and negative pre-gains-and-losses earnings. We

find evidence of earnings smoothing by banks with positive earnings and evidence of taking a

big bath by banks with negative earnings. In addition, we find that banks with negative earnings

take big baths to a greater extent when they have more accumulated unrealized losses and engage

in earnings smoothing to a greater extent when they have more accumulated unrealized gains.

Estimating the relations separately for publicly listed and non-listed banks reveals that the

findings relating to smoothing earnings and regulatory capital and taking a big bath apply to both

4

types of banks. We also find evidence that non-listed banks with more accumulated unrealized

gains and losses engage to a greater extent in regulatory capital smoothing; we do not find this

evidence for listed banks. These findings contrast with those in prior research that pre-dates

ASC Topic 320, uses considerably smaller samples, and finds weaker evidence of earnings

management for non-listed banks. Our findings reveal that incentives to manage earnings and

regulatory capital do not derive solely from public capital market pressures.

Regarding loss avoidance, we find evidence that listed banks realize just enough AFS

securities gains to offset negative earnings before the realized gains and losses; non-listed banks

do not realize quite enough gains to offset their pre-gains-and-losses negative earnings. We also

find that banks with accumulated unrealized gains on AFS securities that are not large enough to

convert negative earnings before realized AFS securities gains and losses into positive earnings

take a big bath, and banks for which the sum of earnings before realized gains and losses on AFS

securities and accumulated unrealized gains is positive engage in earnings smoothing.

Additional findings support our inferences. First, our inferences are robust to considering

simultaneously loan loss provisions, securitization gains, and changes in equity financing as

alternative earnings and regulatory capital management tools. Second, using the median bank’s

earnings before taxes (regulatory capital) as the earnings (regulatory capital) target reveals

consistent evidence of earnings (regulatory capital) management. Third, consistent with our

expectations, we find no evidence that banks use fair value gains and losses on trading securities

to smooth earnings or regulatory capital.

The remainder of the paper proceeds as follows. Section 2 discusses related research and

explains the basis for our empirical predictions. Section 3 develops the research design. Section

5

4 describes the data and Section 5 presents the results. Section 6 offers a summary and

concluding remarks.

2. INSTITUTIONAL BACKGROUND AND RELATED RESEARCH

2.1 Available-for-sale securities

The accounting for investment securities in the US is specified in FASB Accounting

Standards Codification (ASC) Topic 320.2 Prior to ASC Topic 320, banks classified securities as

trading or investment. Trading securities, which typically represented a small fraction of bank

assets (Barth 1994), were debt securities the bank intended to trade actively, and were measured

at fair value with changes in fair value recognized in earnings. Investment securities, which

represented most bank securities, were securities the bank had the ability and intent to hold to

maturity, and were measured at amortized cost. Although the fair value of investment securities

was disclosed, there was no accounting recognition—either on the statement of financial position

or the statement of income—based on those fair values. Only realized investment securities

gains and losses were recognized in earnings and equity.3

ASC Topic 320 requires all entities, including banks, to categorize securities into one of

three categories. Trading securities are securities that the bank holds for the purpose of selling

them in the near term; this category was largely unchanged by ASC Topic 320. Held-to-maturity

(HTM) securities are debt securities that the bank has the positive intent and ability to hold to

maturity. Although this description is similar to that for investment securities, the classification

criteria in ASC Topic 320 are more stringent. Most notably, ASC Topic 320 introduced

“tainting rules,” under which sales of HTM securities could taint the bank’s assertion that it had

a positive intent to hold the securities to maturity, thereby precluding the bank from using the 2 ASC Topic 320 formerly was Statement of Financial Accounting Standards (SFAS) No. 115 (FASB, 1993). Throughout we refer to ASC Topic 320, rather than SFAS 115. 3 Securities held for sale were measured at the lower of cost or market. Held-for-sale securities comprised a small fraction of banks’ securities.

6

HTM category. The third category is available-for-sale (AFS). AFS securities are securities

other than those categorized as trading and held to maturity. Shortly after ASC Topic 320

became effective the FASB offered a brief reclassification period during which banks could

reclassify securities from HTM, i.e., securities previously classified as investment securities, to

AFS (FASB 1995). Most banks did so.4 As a result, AFS replaced investment securities as the

largest category of banks’ securities.

Unlike investment securities, AFS securities are measured at fair value. However,

changes in AFS securities’ fair value, i.e., unrealized gains and losses, are recognized in other

comprehensive income. These gains and losses are only recognized in earnings when the

security is sold or otherwise disposed of, or when impairment is deemed other-than-temporary.

That unrealized gains and losses on AFS securities do not affect earnings admits the possibility

that bank managers selectively sell or dispose of these securities to manage earnings. However,

if bank managers view comprehensive income as the earnings management target, we will not

observe evidence of this selective selling behavior. Accumulated unrealized gains and losses on

AFS securities also are excluded from Tier 1 regulatory capital and, thus, only affect regulatory

capital when they are realized.5 Thus, banks’ decisions regarding when to sell AFS securities

and realize any unrealized gains or losses determines when the gains or losses are included in

regulatory capital as well as earnings.

4 Untabulated statistics based on our sample banks reveal that during this brief reclassification period for approximately 80% (69%) of publicly listed (non-listed) banks the increase in AFS securities, as a percentage of total securities, was within 1% of the decrease in HTM securities. 5 45% of unrealized gains and losses on AFS equity securities are included in Tier II capital. However, untabulated statistics based on our sample of banks reveal that the percentage of equity AFS securities the banks hold is small; the median is zero and the 75th percentile is < 1%. Thus, we proceed as if AFS securities are debt securities. The existence of equity AFS securities weakens the ability to realize gains and losses on such securities to manage regulatory capital, thereby biasing against us finding evidence consistent with banks using AFS securities to manage regulatory capital. Section 5.5.4 considers this possibility.

7

Our tests focus on realized gains and losses on AFS securities as an earnings and

regulatory management tool. Trading securities are measured at fair value, with changes in fair

value, i.e., unrealized gains and losses, recognized in earnings and regulatory capital. Thus,

there is no incentive to realize trading securities gains and losses to manage earnings or

regulatory capital. Because HTM securities are measured at amortized cost, realizing gains and

losses on HTM securities affects earnings. However, selling HTM securities risks violating the

tainting rules, which means that banks are unlikely to use realized gains and losses on HTM

securities as an earnings or regulatory capital management tool.

Using realized gains and losses on AFS securities is a potentially attractive way to

manage earnings and regulatory capital because it is likely less costly than managing accruals or

engaging in other types of real earnings management.6 Although sales of securities involve

transactions costs, such sales are not as subject to ex post scrutiny—such as from auditors—as

accounting accruals because there is a bona fide transaction. Such scrutiny is costly and, coupled

with accounting rules, limits the extent to which banks can manage accruals. Although the

extent to which a bank can realize AFS securities gains and losses to manage earnings and

regulatory capital is limited by the extent to which the bank has unrealized gains and losses, it

avoids the other costs associated with accruals-based earnings management. In addition, unlike

other real earnings management tools, such as engaging in asset securitizations, banks can

essentially eliminate any real effects of the sales of securities, which are typically easily

obtainable financial instruments, by repurchasing the securities shortly—even momentarily—

after their sale. Thus, a bank can realize AFS securities gains and losses without affecting its

6 Real earnings management involves entering into transactions that are reflected in financial reporting (Goel and Thakor 2003). Graham, Harvey, and Rajgopal (2005) reports results of a survey of chief financial officers that indicate real earnings management is more prevalent than accrual-based earnings management.

8

operations or risk profile, which makes securities sales less costly than other real transactions as

earnings and regulatory capital management tools.

Opportunistically realizing gains and losses on AFS securities also has the potential to

affect earnings and regulatory capital to an economically meaningful extent. Nissim and Penman

(2007) and Laux and Leuz (2010) report that banks classify 95% of their non-trading securities

as AFS, which represents 16% of total assets. Although trading securities represent more than

12% of total assets for the largest bank holding companies, trading securities represent almost

0% of total assets for the majority of banks.7

2.2 Related research on earnings management in banks

The question of how entities use accounting discretion to manage earnings and other

performance metrics is of longstanding interest in the accounting literature. The literature on

earnings management is extensive, and offers several incentives for firms to manage earnings,

such as smoothing earnings (e.g., Healy and Wahlen 1999; Dechow, Myers, and Shakespeare

2010b), managing earnings relative to a target (e.g., Burgstahler and Dichev 1997; Degeorge,

Patel, and Zeckhauser, 1999), and recognizing large losses when a loss is unavoidable to increase

the likelihood of positive earnings in the future, i.e., take a big bath (see, e.g., Dechow, Ge, and

Schrand 2010a for a review). All of these incentives apply to banks. In addition, banks are

subject to regulatory capital requirements and, thus, have an incentive to manage regulatory

capital. As a result, banks could manage earnings to accomplish that objective. Beatty and Liao

(2013, particularly section 5) provides a recent review of the large empirical literature on the

banking industry, including that addressing earnings management.

7 Untabulated statistics reveal that, on average, our sample banks classify 86% of their non-trading securities as AFS, which represents 17% of total assets. Although trading securities represent 15% of total assets for the ten largest bank holding companies, they represent less than 0.06% of total assets for the other banks.

9

Because the loan loss provision is a large bank accrual and requires considerable

judgement to determine its amount, many studies investigate whether banks use the provision to

smooth earnings. Some studies find evidence that they do (Ma 1988; Greenawalt and Sinkey

1988; Wahlen 1994; Beatty, Chamberlain, and Magliolo 1995; Collins, Shackelford, and Wahlen

1995; Bhat 1996; Kim and Kross 1998; Kanagaretnam, Lobo, and Mathieu 2003; Pérez, Salas-

Fumás, and Saurina 2008). However, other studies do not (Wetmore and Brick 1994; Ahmed,

Takeda, and Thomas 1999). Wall and Koch (2000) provides a summary of this literature. Kilic,

Lobo, Ranasinghe, and Sivaramakrishnan (2013) finds that adoption of SFAS 133, which

reduces banks’ ability to use derivatives to smooth earnings, is associated with an increase in the

use of the loan loss provision to smooth earnings. The evidence relating to banks using the loan

loss provision to manage regulatory capital is more consistent (Moyer 1990; Beatty et al. 1995;

Ahmed et al. 1999; Pérez et al. 2008). Although we focus on realized AFS securities gains and

losses, we also consider whether the simultaneous use of the loan loss provision as an earnings

and regulatory capital management tool affects our inferences.

DeFond (2010) observes that research on real earnings management is scant. However,

some studies investigate real earnings management by banks and focus on the strategic timing of

asset sales, usually to recognize gains. A few studies find a significant association between

earnings management incentives and asset securitizations (Karaoglu 2005; Dechow and

Shakespeare 2009; Dechow et al. 2010b).8 Although these studies do not consider the sale of

AFS securities, we consider whether the simultaneous use of asset securitizations as an earnings

and regulatory capital management tool affects our inferences.

8 Song and Linsmeier (2010) investigates the use of interest rate swaps to manage net interest income for a sample of 546 bank-year observations for the largest bank holding companies from 1995 to 2002. However, our interest is in earnings and regulatory capital, not net interest income.

10

Several studies consider banks’ use of realized securities gains and losses to manage

earnings and regulatory capital and report mixed evidence. Regardless, all of these studies pre-

date ASC Topic 320 and, thus, the existence of AFS securities. As section 2.1 explains, at the

time of these studies, investment securities were measured at amortized cost. AFS securities, the

focus of our study, are measured at fair value with changes in fair value recognized in other

comprehensive income until realized, when they are recycled into earnings.

Moyer (1990) predicts that banks opportunistically realize securities gains and losses to

increase regulatory capital. Based on a sample of 845 bank-year observations relating to 142

banks from 1981 to 1986, Moyer (1990) finds evidence consistent with banks using realized

securities gains and losses to increase regulatory capital, but only for banks with regulatory

capital below the minimum.9 Based on a sample basically the same as that in Moyer (1990),

Scholes, Wilson, and Wolfson (1990 SWW) finds a significant negative relation between

regulatory capital and realized securities gains and losses, which indicates that banks manage

regulatory capital through the realization of these gains and losses. SWW also finds a significant

negative relation between realized securities gains and losses and the loan loss provision.

Although SWW interprets this finding as consistent with banks using realized securities gains

and losses to smooth earnings by offsetting the earnings effect of the loan loss provision, SWW

does not provide evidence directly supporting this interpretation.

Collins et al. (1995 CSW) investigates the impact of banks’ changing levels of capital

and earnings on several capital-raising decisions, including realizing securities gains and losses.

Based on a sample of 1,760 bank-year observations for 160 banks from 1971 to 1991, CSW

9 This finding is consistent with our regulatory capital smoothing prediction because we predict that banks with lower regulatory capital realize more AFS securities gains. However, very few of our bank-quarter observations have regulatory capital below the required minimum. See footnote 24. Thus, the incentive to avoid violating minimum regulatory capital requirements largely is not present in our sample.

11

estimates bank-specific regressions and reports inconclusive findings regarding whether banks

use realized securities gains and losses to smooth earnings and regulatory capital. CSW finds

that realized securities gains and losses are not consistently negatively related to earnings across

estimation specifications, and are not significantly negatively related to regulatory capital after

1980.

Beatty et al. (1995 BCM) investigates how banks manage regulatory capital and earnings

using realized miscellaneous gains and losses, which is the sum of gains and losses on

investment securities and all other gains, including those on physical assets. A key innovation of

BCM is the simultaneous estimation of a system of equations to consider miscellaneous gains

and losses, in addition to the loan loss provision, equity issues, and other items, as tools to

manage earnings and regulatory capital. BCM’s sample comprises 638 bank-year observations

for 148 banks from 1985 to 1989. Even though BCM finds that miscellaneous gains and losses

are negatively related to earnings and regulatory capital, the relations are not consistently

significant.10,11

Other studies investigate whether the use of realized securities gains and losses to manage

earnings differs for publicly listed and non-listed banks. Beatty and Harris (1999) compares

realized securities gains and losses for 297 publicly listed and 553 non-listed bank-year

observations from 1991 and 1992, and finds that although both groups of banks use realized

securities gains and losses to smooth earnings, the relation is significantly stronger for listed

banks. Using a sample of 4,762 (4,940) yearly observations for 707 publicly listed (1,160 non-

10 For example, BCM tables 6, 7, and 8 reveal a significant negative relation between earnings and miscellaneous gains and losses, which is consistent with using the gains and losses to smooth earnings. However, BCM section 5.3 reports that the relation is not significant after 1987 if the estimating equation includes year fixed effects. 11 Barth, Beaver, and Wolfson (1990) and Ahmed and Takeda (1995) find that the relation between returns and realized securities gains and losses is weaker for banks with lower regulatory capital and earnings, which is consistent with the banks having incentives to manage earnings using realized securities gains and losses.

12

listed) bank holding companies from 1988 to 1998, Beatty, Ke, and Petroni (2002) finds

evidence that publicly listed banks are more likely to use realized securities gains and losses to

transform small decreases in earnings before the gains to small increases in earnings.

Taken together, prior research does not provide consistent evidence that banks use

realized securities gains and losses to smooth earnings and regulatory capital. We re-address this

question in light of the changes that have taken place since the time of this prior research that

should enhance our ability to conduct more powerful tests.12 In particular, the availability of

quarterly bank regulatory reports for all publicly listed and non-listed banks enables us to test our

predictions on a considerably larger sample with greater cross-sectional variation. Whereas

samples in prior studies generally comprise fewer than 2,000 bank-year observations relating to

200 of the largest banks, our sample comprises 136,879 bank-quarter observations relating to

6,300 banks. In addition, the reports include the amount of a bank’s regulatory capital. Prior

research needed to estimate regulatory capital based on its general definition, which likely

introduced estimation error.

Moreover, we provide insights that prior research could not provide. First, we use the

disclosure of accumulated unrealized gains and unrealized losses on AFS securities to take into

account the extent to which a bank has the opportunity to realize gains and losses on AFS

securities, not just the incentive, and to distinguish accumulated unrealized gains and unrealized

losses, thereby permitting us to test for big bath earnings management as well as earnings

smoothing. Second, we exploit the fact that quarterly regulatory reports are available for all

banks—publicly listed and non-listed, and regardless of size—not just large bank holding

12 Lifschutz (2002) is more recent, but is limited in scope. For a sample of 88 bank holding companies from 1997-2000, the study finds that the motivation for bank managers to engage in gains trading is negatively related to the level of earnings before taxes and securities net gains.

13

companies. These reports enable us to conduct powerful tests of banks’ using AFS securities to

manage earnings and regulatory capital for both publicly listed and non-listed banks.

Third, because unrealized gains and losses on AFS securities are recognized in

comprehensive income but not earnings, we provide evidence on whether bank managers view

earnings or comprehensive income as the earnings management target. This, in turn, indicates

whether the change in accounting for these securities removed the incentive for banks to use

these securities as an earnings management tool. Some studies find that other comprehensive

income is value relevant, particularly the unrealized securities gains and losses component (e.g.,

Dhaliwal, Subramanyam, and Trezevant 1999; Biddle and Choi 2006; Chambers, Linsmeier,

Shakespeare, and Sougiannis 2007; Bamber, Jiang, Petroni, and Wang 2010). Except for

Dhaliwal et al. (1999), the sample periods in these studies—as largely is ours—are after the

issuance of ASC Topic 220 (formerly SFAS 130 FASB 1997), which led to more prominent

disclosure of the components of other comprehensive income. Hirst and Hopkins (1998) shows

that more prominent presentation of other comprehensive income decreases the tendency for

firms to manage earnings. As a result, the findings of prior research relating to using realized

securities gains and losses to manage earnings may no longer hold.

3. RESEARCH DESIGN

3.1 Realized gains and losses on AFS securities, earnings, and regulatory capital

Our first two predictions are that banks use realized gains and losses on AFS securities to

smooth earnings and regulatory capital, and the extent to which they do so depends on their

opportunity. To test these predictions, we estimate equations (1a) and (1b).13

2

1 2 3 4 5 6 7

1

1

it it it

it it it it t t t it

RGL NI RegCap

UGL Liquid Int Sec VIX Unemp LIBOR

(1a)

13 For ease of exposition, we use the same notation for coefficients and error terms in each equation.

14

2 3 41 1

1 2 3 4 5 6

1

1 7

it it it it it it it

it it it it t t t it

RGL NI RegCap NI UGL RegCap UGL

UGL Liquid Int Sec VIX Unemp LIBOR

(1b)

RGL is realized gains and losses on AFS securities. NI is net income before taxes and

RGL. RGL and NI are scaled by beginning of quarter total assets. RegCap is the bank’s end of

quarter regulatory capital ratio, which is allowable Tier 1 plus allowable Tier 2 regulatory capital

before RGL and after taxes scaled by risk-weighted assets. i and t denote bank and quarter.

Equations (1a) and (1b), and all specifications that follow, include bank fixed effects. In all

specifications, we cluster standard errors by bank and quarter when constructing t-statistics

(Petersen 2009; Gow, Ormazabal, and Taylor 2010).

To the extent that banks use AFS securities to smooth earnings and regulatory capital, we

predict the coefficients on NI and RegCap, 1 and 2, are negative in equation (1a). Equation

(1b) includes two interaction variables, NI×UGLt−1 and RegCap×UGLt−1, to test whether

smoothing is more pronounced for banks with more accumulated unrealized gains and losses on

AFS securities at the beginning of the quarter, i.e., when banks have more opportunity to engage

in smoothing. To the extent this is the case, we predict 3 and 4 are negative. To the extent

that banks realize gains and losses on AFS securities that were not unrealized at the beginning of

the quarter to smooth earnings and regulatory capital, we also predict the coefficients on NI and

RegCap, 1 and 2, are negative in equation (1b).

Equations (1a) and (1b) both include four variables as controls for bank characteristics

likely associated with realized gains and losses on AFS securities. UGLt−1 is accumulated

unrealized gains and losses on AFS securities; the more accumulated unrealized gains and losses

a bank has at the beginning of the quarter, the more likely it will realize them during the quarter

(Scholes et al. 1990; Beatty and Harris 1999). Thus, we expect the coefficient on UGLt−1, 1, is

15

positive. Liquid is the amount of liquid assets (Beatty et al. 1995), Int is total interest income,

and Sec is the total amount of securities; all three variables are scaled by beginning of quarter

total assets. Because the extent of securities gains and losses likely depends on economic

conditions, equations (1a) and (1b) also include controls for macroeconomic conditions. The

presence of these variables, which vary by quarter, eliminates the need to include quarter fixed

effects. They are VIX, the implied volatility of options on the S&P 500 Index; Unemp, the one-

year-ahead consensus forecast of the US unemployment rate; and LIBOR, the difference between

the London Interbank Offer Rate and overnight indexed swap rates. Because RGL reflects

securities gains and losses, we have no expectations for the coefficients on the control variables

other than UGLt−1.

Our next predictions are that some banks use AFS securities to take a big bath and the

extent to which they do so depends on their opportunity. To test these predictions, we expand

equation (1a) to test whether banks with positive (negative) NI realize more losses (gains) from

AFS securities, which is consistent with earnings smoothing, or whether banks with negative NI

realize more losses, which is consistent with taking a big bath. We also expand equation (1b) to

test whether these relations are more pronounced when banks have more accumulated unrealized

gains or unrealized losses on AFS securities at the beginning of quarter.

We estimate equations (2a) and (2b):

0 1 1

2

1 2

3 4 5 6

3 0 1

7 it it it it it it it

it it it t t t it

DpoRGL negNI NI RegCap UL UG

Liquid Int Sec VI

s

X Unemp LIB R

p s

O

o

(2a)

0 1 2 3 4

5 6 7 8

0 1 2 3 4 5

1

1 1 1 1

1 1 6 7

it it it it it it it

it it it it it it it it

it it it it it t t

RGL NI NI RegCap NI UL

negNI UG posNI UL po

Dpos neg po

sNI UG RegCap UGL

UL UG Liquid Int Sec VIX Unemp LIB

s neg

O

t itR (2b)

16

where Dpos is an indicator variable that equals one if NI is greater than zero, and zero otherwise.

posNI (negNI) is NI if NI is greater (less) than zero, and zero otherwise. UG (UL) is accumulated

unrealized gains (unrealized losses) on AFS securities. All other variables are as previously

defined. We do not partition the regulatory capital variable because no banks have negative

regulatory capital and we are unaware of any incentives for a bank to engage in big bath

regulatory capital management.

In both equations, if banks use AFS securities to smooth earnings, as in equations (1a)

and (1b), we predict the coefficients on negNI and posNI, 1 and 2, are negative. If banks with

negative NI take a big bath, we predict the coefficient on negNI, 1, is positive. In equation (2b),

if banks with negative (positive) NI are more likely to smooth earnings when they have more

accumulated unrealized gains (losses) on AFS securities, we predict the coefficient on

negNI×UGt−1 (posNI×ULt−1), β5 (β6), is negative (positive). If banks with negative NI are more

likely to take a big bath when they have more accumulated unrealized losses on AFS securities,

we predict the coefficient on negNI×ULt−1, β4, is negative; otherwise we have no prediction for

β4. We have no prediction for the coefficient on posNI×UGt−1, β7; we estimate it for completion.

All other predictions are as in equations (1a) and (1b). Accordingly, we predict the coefficients

on ULt−1 and UGt−1, 0 and 1, are positive.

3.2 Simultaneous consideration of loan loss provisions, securitizations, and equity financing

Prior research discussed in section 2.2 finds evidence that banks manage earnings and

regulatory capital using the loan loss provision and manage earnings using securitization gains.

Changes in equity financing also can be used as a direct means to manage regulatory capital.

Thus, following Beatty et al. (1995), we next test whether the simultaneous consideration of

17

these earnings and regulatory capital management tools affects our inferences regarding AFS

securities.

We use two-stage least squares regression to estimate systems of equations based on

equations (1a) and (1b).14 For ease of exposition, we present the system based on equation (1b);

the system based on equation (1a) is the same, except that it omits the interaction variables.15

^ ^ ^1 2 1

14^

3 4 5 6

7 81 1 19 0

it it it it it it it it

it it it it it itk k

RGL LLP EqFin NI RegCap NI UGL

RegCap UGL UGL Sec CommonControls

SG

(3a)

3 4 5

5

^ ^1 2

13

1 97 8

it it it it it it

it it it k it itk

LLP EqFin NI RegCap

NPL LLP Sec CommonContr

RG S

ol

L G

s

(3b)

^ ^1 2

10

3 4

6 7

5

1

it it it it it it

it k it itk

SG EqFin NI RegCap

CommonControls

RGL LLP

SG

(3c)

^1 2 3

11

1 1

4

5 6 7

it it it it it

it it k it itk

EqFin RegCap

EqFin Lev CommonCon

RG

tro

L LLP SG

ls

(3d)

LLP is the loan loss provision and SG is securitization gains. EqFin is change in equity other

than that associated with current quarter comprehensive income, which we calculate as the

change in total equity during the quarter minus total comprehensive income. NI^ (RegCap^) is

net income (regulatory capital) before RGL, LLP, and SG, and CommonControls comprises

Liquid, Int, VIX, Unemp, and LIBOR.

Our predictions for the coefficients in equation (3a) are the same as for equation (1b).

For equations (3b) and (3c), if banks use the loan loss provision and securitization gains to

14 To estimate the system, in the first stage, we estimate versions of equation (3a) through (3d) that include as explanatory variables all of the explanatory variables in the system, except for RGL, LLP, EqFin, and SG. In the second stage, we estimate equations (3a) through (3d) replacing RGL, LLP, EqFin, and SG as explanatory variables with their predicted amounts from the first stage. 15 We also estimate, but do not tabulate statistics from, systems of equations similar to equations (3a) through (3d), but analogous to equations (2a) and (2b). Section 5.4 reports the results.

18

smooth earnings and regulatory capital, we predict the coefficients on NI^ and RegCap^, 4 and

5, are negative.16 For equation (3d), to the extent that banks use equity financing to manage

regulatory capital, we predict the coefficient on RegCap^, 4, is positive. We have no predictions

for the coefficients on the alternative earnings and regulatory capital management tools, 1, 2,

and 3, because we have no predictions for which earnings management tools banks use or in

which order.

To identify the system, each equation includes variables omitted from the others.

Equation (3a) includes UGLt−1 because we expect accumulated unrecognized gains and losses on

AFS securities at the beginning of the quarter to be associated with realized gains and losses on

the securities during the quarter. Equation (3b) includes LLPt−1 and ΔNPL, the change in

nonperforming loans and leases scaled by beginning of quarter total assets, because we expect

the bank’s prior quarter loan loss provision and the change in nonperforming loans during the

quarter to be associated with the loan loss provision (Beatty et al. 1995). Equations (3a) and (3b)

include Sec because it captures the bank’s asset composition; banks with higher investment in

securities tend to have relatively lower investment in loans. Equation (3c) (equation (3d))

includes SGt−1 (ΔEqFint−1 and Levt−1) because we expect the bank’s prior quarter securitization

gains (equity financing and leverage) to be associated with the current quarter’s securitization

gains (equity financing).

4. SAMPLE, DATA, AND DESCRIPTIVE STATISTICS

We base our sample on commercial banks and bank holding companies in the US Federal

Reserve Bank of Chicago Bank Regulatory Database, which contains quarterly accounting

information from forms filed by regulated depository financial institutions. Our sample

16 We code LLP as a positive number. Thus, higher LLP corresponds to larger expense.

19

comprises all top holder banks with data required for our tests from 1996 to 2011.17 We exclude

observations from Q1 2008 through Q2 2009 to avoid the potential for the financial crisis to

affect our inferences.18 The sample begins in 1996 when regulatory capital ratios became

publicly available.19 We obtain our macroeconomic variables from the Federal Reserve. The

resulting sample comprises 136,879 bank-quarter observations for 6,300 banks, with 15,204

(121,675) observations for 728 (5,862) publicly listed (non-listed) banks.20

Realized gains and losses on AFS securities, RGL, is reported in line 6b of Schedule HI

(RI) Income Statement for bank holding companies (other commercial banks). Accumulated

unrealized gains and losses on AFS securities, UGL, is the difference between total fair value

and amortized cost for these securities as reported in Schedule HC-B (RC-B) Securities for bank

holding companies (other commercial banks). Accumulated unrealized gains and losses are

reported separately for twenty-one categories of securities. We use the totals across categories to

construct UGL. We use the by-category information to construct UG (UL), accumulated

unrealized gains (losses) on AFS securities. Specifically, if the difference between fair value and

amortized cost for a particular category of securities is positive (negative), we include that

difference in UG (UL).

Table 1 presents distributional statistics for the variables we use in our estimating

equations. Table 1 reveals that, on average, banks realize gains on AFS securities (mean RGL =

0.01), although the median is zero. On average, net income before realized gains and losses on

17 All commercial banks file regulatory reports. Bank holding companies control, directly or indirectly, one or more commercial banks. A bank holding company can control another bank holding company, which in turn controls one or more other commercial banks. The entity at the top of the control chain is called the top holder, which could be a single commercial bank. 18 Nonetheless, untabulated findings from estimating equation (1a) including these observations reveal inferences identical to those revealed by the tabulated findings. 19 1994 is the first year ASC Topic 320 required the classification of securities into trading, available for sale, and held to maturity. Untabulated findings reveal that beginning the sample period in 1994 does not affect our inferences. 20 The sum of 728 and 5,862 exceeds 6,300 because 290 banks changed status during the sample period.

20

AFS securities, NI, also is positive (mean = 0.27; median = 0.32). The mean (median) regulatory

capital ratio before realized gains and losses on AFS securities, RegCap, is 19.20% (15.86%).

Table 1 also reveals that, on average, banks have positive accumulated unrealized gains and

losses on AFS securities (mean UGL = 0.08), but this is the net of unrealized gains (mean UG =

0.26) and unrealized losses (mean UL = −0.18). Untabulated statistics reveal that 57% (42%) of

the observations have positive (negative) UGL and that 81% (70%) of the observations have

positive (negative) UG (UL).

Table 2 presents Pearson (Spearman) correlations above (below) the diagonal. Consistent

with our predictions, the table reveals that realized gains and losses on AFS securities, RGL, is

significantly negatively related to both net income and regulatory capital before RGL, i.e., NI and

RegCap (Pearson corr. = −0.08 and −0.01; Spearman corr. = −0.07 and −0.06). Although almost

all of the correlations are significantly different from zero, most are less than 0.25. The

exceptions are the correlations between unrealized gains and losses on AFS securities, UGL, and

its components unrealized gains, UG, and unrealized losses, UL, and between UG and UL, which

is to be expected. The only other exceptions are the correlations between RegCap and securities,

Sec. Regardless, we rely on multivariate regressions to test our predictions.

5. FINDINGS

5.1 Realized gains and losses on AFS securities, earnings, and regulatory capital

Table 3 presents regression summary statistics from estimating equations (1a) and (1b).

Consistent with predictions, both sets of findings reveal that realized gains and losses on AFS

securities, RGL, is significantly negatively related to net income before the gains and losses, NI

(t-stats. = −3.32 and −3.40) and regulatory capital before the gains and losses, RegCap (t-stats. =

21

−8.06 and −8.12). These findings are consistent with banks using AFS securities to smooth

earnings and regulatory capital.

Also consistent with predictions, table 3 reveals that the coefficients on the interaction

variables in equation (1b), NI×UGLt−1 and RegCap×UGLt−1, are significantly negative (t-stats. =

−4.99 and −3.80). These findings indicate that banks smooth earnings and regulatory capital

using AFS securities to a greater extent when they have more accumulated unrealized gains and

losses at the beginning of the quarter. The findings support the inferences we draw from

equation (1a) by linking accumulated unrealized gains and losses at the beginning of the quarter

to earnings and regulatory capital smoothing during the quarter. In addition, the findings provide

evidence that bank managers perceive earnings, and not comprehensive income, as an earnings

management target. Thus, recognizing unrealized gains and losses on AFS securities in other

comprehensive income did not eliminate banks’ incentive to realize the gains and losses to

manage earnings.

Regarding the control variables, as expected, the coefficients on unrealized gains and

losses on AFS securities at the beginning of the quarter, UGLt−1, are significantly positive in both

equations (t-stats. = 6.95 and 6.23), which indicates that banks realize more gains and losses

during the quarter when they have more accumulated unrealized gains and losses at the

beginning of the quarter. Several of the coefficients on the other control variables are

significantly different from zero.

Table 4 presents summary statistics from estimating equations (2a) and (2b), which

permit the coefficient on NI to differ depending on whether NI is positive or negative. 121,234

(15,534) observations for 6,120 (3,341) banks have positive (negative) NI. Table 4 reveals that

the coefficients on Dpos and posNI are significantly negative (t-stats. range from −3.44 to

22

−5.55). These findings indicate that banks with positive earnings and with more positive

earnings realize fewer gains on AFS securities, or more losses, which is consistent with earnings

smoothing. Table 4 also reveals evidence consistent with banks with negative earnings taking a

big bath, not smoothing earnings. The coefficients on negNI are significantly positive (t-stats. =

4.96 and 3.36), which indicates that banks with more negative earnings realize more losses on

AFS securities, or fewer gains.

Equation (2b) reveals that the coefficient on negNI×ULt−1 is significantly negative (t-stat.

= −4.60), which indicates that banks with more negative earnings realize greater net losses when

they have more accumulated unrealized losses at the beginning of the quarter. This finding

reveals that the big bath finding from equation (2a) largely is attributable to negative earnings

banks with larger accumulated unrealized losses. As predicted, the coefficient on negNI×UGt−1

in equation (2b) is significantly negative (t-stat. = −3.09), which indicates that banks with more

negative earnings realize greater net gains on AFS securities when they have more accumulated

unrealized gains at the beginning of the quarter. This is consistent with earnings smoothing by

banks with negative earnings when they have unrealized gains to realize. However, equation

(2b) reveals no evidence that banks with more positive earnings and more accumulated

unrealized losses at the beginning of the quarter smooth earnings; the coefficient on posNI×ULt−1

is not significantly different from zero (t-stat. = −0.77). Together, these findings reveal that the

earnings smoothing finding in table 3 largely is attributable to negative earnings banks with

larger accumulated unrealized gains, not to banks with positive earnings and larger accumulated

unrealized losses.

Regarding the control variables, the coefficients on ULt−1 and UGt−1 are both significantly

positive (t-stats. range from 3.72 to 12.37), which indicates that banks with more accumulated

23

unrealized gains (losses) on AFS securities at the beginning of the quarter realize more gains or

fewer losses (more losses or fewer gains) during the quarter. Inferences relating to all other

coefficients in table 4 are the same as those relating to the corresponding coefficients in table 3.

5.2 Listed versus non-listed banks

Because our tests do not require equity market variables, we are able to test our

predictions on a large sample of publicly listed and non-listed banks. However, to the extent the

incentive to manage earnings derives from public capital market pressures, we expect non-listed

banks to engage in less earnings management (Beatty and Harris 1999; Beatty et al. 2002). To

test whether the inferences we draw from tables 3 and 4 apply to publicly listed and non-listed

banks, we estimate equations (1a) through (2b) separately for each subsample.

Table 5 presents the findings. Panel A presents regression summary statistics relating to

equations (1a) and (1b). Panel A reveals inferences for both categories of banks consistent with

those revealed by table 3. The only exceptions are for listed banks in equation (1b); there is no

evidence that listed banks smooth regulatory capital more when they have more accumulated

unrealized gains and losses on the securities at the beginning of the quarter (t-stat. = –0.01 on

RegCap×UGLt−1) and, although the coefficient on NI×UGLt−1 is significantly negative (t-stat. =

–5.64), the coefficient on NI is not (t-stat. = –1.22).

Panel B of table 5 presents regression summary statistics relating to equations (2a) and

(2b). Panel B reveals inferences for both categories of banks that are the same as those revealed

by table 3, with two exceptions relating to listed banks and equation (2b). First, the coefficient

on posNI×ULt−1 is significantly negative in panel B, whereas it is insignificantly different from

zero in table 4 and in equation (2b) for non-listed banks (t-stat. = –2.82 versus –0.77 and –0.38).

This finding indicates that listed banks with more positive net income and more accumulated

24

unrealized losses realize more AFS securities gains, or fewer losses, which is not consistent with

greater earnings smoothing. Second, the coefficient on RegCap×UGLt−1 is not significantly

different from zero, whereas it is significantly negative in table 4 and in equation (2b) for non-

listed banks (t-stat. = 0.21 versus –4.49 and –4.45). This finding indicates that smoothing of

regulatory capital by listed banks does not depend on the extent to which they have accumulated

unrealized gains and losses.

Perhaps the most notable finding in table 5 is the consistent evidence of non-listed banks

using AFS securities to smooth earnings as well as regulatory capital. If anything, the findings in

table 5 relating to non-listed banks are more consistent with our predictions than those relating to

listed banks. Thus, the table 5 findings suggest that one reason for the relatively weak results in

prior studies relating to banks using realized securities gains and losses to smooth earnings and

regulatory capital is that those studies generally focused on listed banks.21

5.3 Avoiding losses

We expect incentives to manage earnings to be pronounced for banks that would have

reported negative net income without the realization of AFS securities gains (Burgstahler and

Dichev 1997; Degeorge et al. 1999). Thus, we estimate equation (1a) for the full sample and

separately for publicly listed and non-listed banks using observations with negative net income

before realized gains and losses on AFS securities, NI, but with positive net income after the

realized gains and losses, i.e., observations with both NI < 0 and NI + RGL > 0. To the extent

that these banks realize gains on AFS securities to offset negative NI, we expect the coefficient

on NI is negative. However, to test more directly whether banks realize gains on AFS securities

21 Beatty and Harris (1999) finds weaker evidence of earnings smoothing for non-listed banks than for listed banks, but the findings are based on only 553 non-listed bank-year observations from 1991 and 1992. Also, Beatty et al. (2002) finds weaker evidence of small loss avoidance for non-listed banks. That study’s sample also is considerably smaller than ours. We examine loss avoidance in section 5.3.

25

to offset negative NI, we test whether the NI coefficient equals –100, i.e., –100%. Because the

observations are selected based on the sign of NI as the earnings management target, we have no

prediction for the coefficient on RegCap. All other predictions are as in equation (1a).

Table 6, panel A, presents the findings and reveals evidence that banks use realized gains

on AFS securities to offset negative NI. The coefficient on NI is significantly negative for all

samples (t-stats. = –33.06, –14.60, and –29.55). More interesting is that for publicly listed banks

the coefficient on NI is not significantly different from –100 (coef. = –104.10; t-stat. for test of

equality with –100 = –0.57). This finding indicates that listed banks realize just enough gains on

AFS securities to offset negative NI. In contrast, for non-listed banks the coefficient on NI is

significantly different from –100 (coef. = –82.00; t-stat. for test of equality with –100 = 6.49),

which indicates that although non-listed banks realize gains on AFS securities to reduce negative

NI, by 81.98%, they do not realize enough gains to offset it. This somewhat weaker evidence of

loss avoidance for non-listed banks is consistent with the findings in Beatty et al. (2002).

We next estimate equation (2a) separately using observations with NI + UG >= 0 and

with NI + UG < 0. The latter group comprises banks that do not have sufficient accumulated

unrealized gains at the beginning of the quarter to offset negative NI. We predict earnings and

regulatory capital smoothing (taking a big bath) when NI + UG >= 0 (NI + UG < 0) and, thus,

predict the coefficients on posNI and negNI are negative (negNI is positive).

Table 6, panel B, presents the results. As predicted, when NI + UG >= 0 the results are

consistent with earnings smoothing; the coefficients on Dpos, negNI, and posNI are all

significantly negative (t-stats. range from –1.87 to –7.51). Also as predicted, when NI + UG < 0

the results are consistent with taking a big bath; the coefficient on negNI is significantly positive

(t-stat. = 3.11). These findings support the inferences we draw from our primary tests in that

26

they reveal evidence of earnings smoothing when reporting positive earnings is feasible, i.e.,

when NI + UG > 0, and evidence of taking a big bath when it not, i.e., NI + UG < 0. Consistent

with table 4, both groups reveal evidence of regulatory capital smoothing in that the coefficients

on RegCap are negative (t-stats. = –2.18 and = –2.08).

5.4 Simultaneous use of additional earnings and regulatory capital management tools

Table 7 presents regression summary statistics relating to the second-stage estimates for

equations (3a) through (3d); panel A (panel B) presents statistics for the version without (with)

the interaction variables. The first set of columns in panel A reveals that the loan loss provision,

LLP, and changes in equity financing, EqFin, are significantly positively related to realized

AFS securities gains and losses, RGL (t-stats. = 1.84 and 4.07). The positive coefficient on LLP

(EqFin) is consistent with RGL offsetting higher loan loss provisions (being a substitute for

changes in equity financing). The next three sets of columns reveal that the coefficient on RGL

in the LLP (SG; EqFin) equation is significantly negative (positive; positive); the t-statistic is –

3.07 (1.95; 2.07). These findings are evidence that RGL provides incremental explanatory power

for the loan loss provision, securitization gains, and changes in equity financing. The coefficient

on SG is not significantly different from zero in any of the other equations, and the coefficients

on LLP and EqFin are not significant in the SG equation. These findings reveal little

interaction between securitization gains and the other earnings and regulatory capital

management tools.

More importantly for our research question, the coefficients on NI^ and RegCap^ in the

first set of columns are significantly negative, which is consistent with table 3. The t-statistics

are –5.48 and –5.93. Panel B of table 7 reveals the same inferences as table 3, column (2)

regarding NI^×UGLt−1 and RegCap^×UGLt−1 in that coefficients are significantly negative (t-

27

stats. = –5.21 and –3.98).22 Taken together, table 7 reveals that our inferences relating to the use

of AFS securities as an earnings and regulatory capital management tool are unchanged by

simultaneously considering the loan loss provision, securitization gains, and changes in equity

financing as alternative tools.23

5.5. Additional Analyses

5.5.1 Depositors as earnings management incentives for non-listed banks

The incentive for non-listed banks to engage in earnings management could stem from

depositors (Beatty et al. 2002). To test this conjecture, we re-estimated the table 5, panel A,

specifications for non-listed banks after including Dep, the bank’s deposit liability scaled by total

assets, as an additional explanatory variable and interacting it with NI, RegCap, and UGLt−1,

individually and in various combinations. The untabulated findings reveal that the earnings and

regulatory capital smoothing we observe in table 5, panel A, for non-listed banks is associated

with Dep. The coefficients on Dep×NI, Dep×RegCap, Dep×NI×UGLt−1, and

Dep×RegCap×UGLt−1 all are significantly negative (t-stats. range from –2.31 to –5.11), and the

coefficients on NI×UGLt−1 and RegCap×UGLt−1 are not (t-stats. = 0.76 and 0.48). These

findings are consistent with the incentives for non-listed banks to engage in earnings

management stemming from depositors.

22We also estimate systems of equations similar to those based on equations (3a) through (3d), but based on equations (2a) and (2b). The untabulated findings reveal that our inferences relating to the AFS securities generally are the same as those we draw from table 4. The only differences relate to equation (2b). Although the signs are the same as in column (2) of table 4, the coefficient on RegCap^×UGLt−1 is not significantly different from zero (t-stat. = –1.32) and that on posNI^×ULt−1 is (t-stat. = –1.93). The coefficient on posNI^×UGt−1 also is not significantly different from zero (t-stat. = –1.27), but we have no prediction for its sign. 23We also estimated two four-variable systems of equations that include realized gains and losses on held-to-maturity (HTM) securities as an additional tool, but omitted SG. Untabulated findings from estimating these equations reveal inferences relating to AFS securities identical to those based on the tabulated findings.

28

5.5.2 Gross accumulated unrealized gains and losses

As section 4 explains, UG (UL) is accumulated unrealized gains (losses) by category of

AFS securities in regulatory reports. However, listed banks disclose gross accumulated

unrealized gains and losses on AFS securities in Form 10-Q. This disclosure is required

beginning in 2009, but many banks disclosed the amounts earlier. Unfortunately, these data are

not available for non-listed banks or throughout our sample period. However, to gauge the

extent to which UG and UL mask gross accumulated unrealized gains and losses within a

securities category, we hand-collect the gross amounts for 4,071 bank-quarter observations for

461 banks from 2005 to 2011. Untabulated findings reveal that the median ratio of gross

accumulated unrealized gains (losses) on AFS securities to UG (UL) is 1.23 (1.18), which

indicates that UG (UL) understates the gross amounts by 23% (18%). However, untabulated

findings based on estimating equations (2a) and (2b) using the hand-collected data and using UG

and UL on the sample for which the hand-collected data are available reveal identical inferences.

These findings indicate that using UG and UL in our tests in place of the gross accumulated

unrealized gains and losses on AFS securities does not affect our inferences.

5.5.3 Trading securities gains and losses

As section 2.1 explains, we do not expect banks to use trading securities gains and losses

to manage earnings and regulatory capital. To test this conjecture, we re-estimated equation (1a)

using fair value gains and losses on trading securities as the dependent variable and replacing NI

and RegCap with net income and regulatory capital before the fair value gains and losses. We

estimate equation (1a) using the 108,934 observations with available data. Because, by

definition, accumulated unrealized gains and losses on trading securities equals zero, there is no

reason to estimate equation (1b). We also estimate equation (1a) using 3,878 observations from

29

the 150 largest banks based on total assets because the largest banks have the largest trading

securities portfolios. As expected, the untabulated findings reveal no evidence of earnings or

regulatory capital smoothing using trading securities. The t-statistics for the coefficients on the

revised NI and RegCap are –0.92 and 4.16 for the larger sample and 1.68 and 1.89 for the 150

largest banks.

In addition, to ensure that these findings are attributable to trading securities and not

sample composition, we also re-estimated equations (1a) and (1b) on the 108,934 observation

subsample. The untabulated findings reveal inferences identical to those we draw from table 3.

5.5.4 AFS equity securities

As footnote 5 explains, most AFS securities held by our sample banks are debt securities.

However, to the extent that a bank holds AFS equity securities the incentive to use AFS

securities to smooth regulatory capital is diminished. Thus, we re-estimated equations (1a)

through (2b) including an indicator variable, Q5, which equals one if the bank’s ratio of AFS

equity securities to total AFS securities is in the highest quintile across banks in quarter t and

zero otherwise, and Q5 interacted with RegCap, Q5×RegCap. Consistent with banks with more

AFS equity securities engaging in less smoothing of regulatory capital, the coefficient on

Q5×RegCap is significantly positive in all specifications (t-stats. range from 1.74 to 2.26). Also,

in each case, the sum of the coefficients on RegCap and Q5×RegCap is significantly negative,

which is consistent with overall smoothing.

5.5.5 Alternative earnings and regulatory capital targets

Our tests implicitly assume that the targets for earnings management are net income and

regulatory capital before realized gains and losses on AFS securities. In this section, we consider

as alternative targets the median net income and regulatory capital for all banks in the prior

30

quarter.24 Specifically, we estimated equations (1a) through (2b) using [NI – mNI] and [RegCap

– mRegCap] in place of NI and RegCap, where mNI (mRegCap) is the median of net income

before taxes (regulatory capital) for all banks in quarter t – 1.

The untabulated findings relating to equations (1a) and (1b), but using the alternative

targets, reveal inferences regarding earnings smoothing identical to those revealed by table 3.

The coefficients on [NI – mNI] in equation (1a) and [NI – mNI]×UGLt−1 in equation (1b) are

significantly negative (t-stats. = –3.16 and –5.17). However, the inferences regarding regulatory

capital smoothing are somewhat different. Although the untabulated findings reveal that

coefficient on [RegCap – mRegCap] in equation (1a) is significantly negative (t-stat. = –7.37),

that on [RegCap – mRegCap]×UGLt−1 in equation (1b) is significantly positive (t-stat. = 3.35)

whereas it is significantly negative, as predicted, in table 3.

The untabulated findings relating to equation (2a) reveal the same inferences as those

revealed by table 4, except that the coefficient on neg[NI – mNI] is not significantly different

from zero (t-stat. = –0.33). The untabulated findings relating to equation (2b) reveal two

differences regarding the interaction variables for banks with earnings greater than the median

bank. First, the coefficient on pos[NI – mNI]×ULt−1 is significantly positive, as predicted,

whereas that on posNI×ULt−1 is not significantly different from zero in table 4 (t-stat. = 3.54

versus –0.77). Second, the coefficient on pos[NI – mNI]×UGt−1 is not significantly different

from zero, whereas that on posNI×UGt−1 is significantly negative in table 4 (t-stat. = –1.50 versus

–6.67). Recall that we do not have a prediction for the sign of this coefficient. For banks with

earnings less than the median bank, the inferences regarding the interaction variables are the

24 We do not use analyst forecasts of net income because analyst forecasts are not available for many sample banks, particularly non-listed banks. We do not use the 8% required regulatory capital ratio because only 800 observations relating to 223 banks have RegCap less than 8%.

31

same (t-stats. = –5.36 and –4.29 versus –3.09 and –4.60 in table 4). Taken together, the

untabulated findings reveal inferences largely consistent with those we draw from tables 3 and 4.

6. CONCLUSION

The question we address is whether, and in what way, banks use available-for-sale (AFS)

securities to manage earnings and regulatory capital. AFS securities can be an earnings and

regulatory capital management tool because although these securities are measured at fair value

in the statement of financial position, their unrealized gains and losses are recognized in other

comprehensive income. The gains and losses are recognized in earnings and, largely, in

regulatory capital only when they are realized. Although prior studies focus on whether banks

use realized securities gains and losses to smooth earnings and regulatory capital, those studies

yield mixed results. The creation of and enhanced disclosures relating to AFS securities enable

us to conduct more powerful tests of, and provide new insights into, earnings and regulatory

capital management by banks. Our sample comprises 136,879 bank-quarter observations for

6,300 banks, with 15,204 (121,675) observations for 728 (5,862) publicly listed (non-listed)

banks from 1996 to 2011.

We find consistent evidence that banks use AFS securities to smooth earnings and

regulatory capital, and banks with negative earnings use these securities to take a big bath. We

also find that the extent to which banks engage in these behaviors depends on the extent to which

the banks have accumulated unrealized gains and losses on AFS securities to realize. In

addition, banks with negative earnings use realized AFS securities losses to take a big bath,

unless they have accumulated unrealized gains sufficient to offset the negative earnings; in that

case, they smooth earnings. That our earnings management findings apply to non-listed, as well

32

as publicly listed, banks is evidence that the incentives to manage earnings do not derive solely

from public capital market pressures.

Additional findings support our inferences. First, our inferences are robust to

simultaneously considering loan loss provisions, securitization gains, and changes in equity

financing as alternative earnings and regulatory management tools. Second, using the median

bank’s earnings (regulatory capital) as the earnings (regulatory capital) target instead of the

bank’s own amount reveals largely the same inferences. Third, as expected, we find no evidence

of earnings or regulatory capital smoothing using trading securities.

Taken together, our findings provide evidence that banks—both publicly listed and non-

listed—use AFS securities to smooth regulatory capital and, depending on the circumstances,

smooth earnings or take a big bath. Our findings reveal that banks opportunistically use the

discretion afforded to them by accounting standards and regulatory requirements that exclude

some unrealized securities gains and losses from earnings and regulatory capital. If unrealized

gains and losses on all securities were recognized in earnings and regulatory capital, this

discretion would disappear. Thus, although it is impossible to eliminate all ways to

opportunistically manage earnings and regulatory capital, it is possible to eliminate this

particular tool. In addition, our findings reveal that bank managers seem to view earnings, and

not comprehensive income, as the earnings management target. Thus, the inclusion of unrealized

gains and losses on AFS securities in other comprehensive income does not eliminate the

incentive for banks to use AFS securities as an earnings management tool.

33

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37

Appendix A: Variable definitions. Variable Definition

RGL Realized gains and losses on available-for-sale (AFS) securities scaled by beginning of quarter total assets.

NI Net income before taxes and RGL, scaled by beginning of quarter total assets.

RegCap Regulatory capital ratio, i.e., allowable Tier 1 plus Tier 2 regulatory capital before RGL and after taxes, scaled by risk-weighted assets.

UGL Accumulated unrealized gains net of losses on AFS securities, scaled by total assets.

UG Accumulated unrealized gains on AFS securities, scaled by total assets.

UL Accumulated unrealized losses on AFS securities, scaled by total assets.

posNI NI when NI is greater than zero, and zero otherwise.

negNI NI when NI is less than zero, and zero otherwise.

Dposit Indicator variable equals one if NI is greater than zero, and zero otherwise.

Liquid Total liquid assets, scaled by beginning of quarter total assets.

Int Total interest income, scaled by beginning of quarter total assets.

Sec Total amount securities, scaled by beginning of quarter total assets.

VIX Implied volatility of options on the S&P 500 Index.

Unemp One-year-ahead consensus forecast of the US unemployment rate.

LIBOR Difference between the London Interbank Offer Rate and overnight indexed swap rates.

LLP Quarterly loan loss provision, scaled by beginning of quarter total assets.

ΔEqFin Change in shareholders’ equity other than that associated with comprehensive income. It is computed as change in total equity during the quarter minus reported net income minus change in accumulated other comprehensive income, scaled by beginning of quarter total assets.

SG Securitization gains, i.e., net securitization income, scaled by beginning of quarter total assets.

ΔNPL Change in nonperforming loans and leases, scaled by beginning of quarter total assets.

Lev Total shareholders’ equity scaled by total assets.

NI^ Net income before RGL, LLP, and SG.

RegCap^ Regulatory capital before RGL, LLP, and SG.

38

Table 1: Descriptive statistics

Mean Median StdDev

RGL 0.01 0.00 0.03

NI 0.27 0.32 0.36

RegCap 19.20 15.86 10.83

UGL 0.08 0.03 0.60

UG 0.26 0.09 0.44

UL 0.18 0.03 0.36

Liquid 4.45 3.03 4.86

Int 1.55 1.53 0.36

Sec 25.74 23.08 15.45

VIX 21.10 21.53 5.82

Unemp 5.79 5.14 1.63

LIBOR 0.38 0.24 0.32

See Appendix A for variable definitions. Sample comprises 136,879 bank-quarter observations for 6,300 banks, over the period from Q1 1996 to Q4 2007 and from Q3 2009 to Q4 2011. All values (except for VIX, Unemp, and LIBOR) are multiplied by 100 for expositional convenience.

39

Table 2: Correlation matrix

RGL NI RegCap UGL UG UL Liquid Int Sec RGL 0.08 0.01 0.22 0.21 0.12 0.01 0.09 0.06

NI 0.07 0.03 0.03 0.04 0.00* 0.16 0.21 0.08

RegCap 0.06 0.03 0.04 0.16 0.12 0.20 0.10 0.52

UGL 0.23 0.03 0.04 0.81 0.69 0.01 0.19 0.03

UG 0.25 0.03 0.08 0.85 0.13 0.03 0.19 0.25

UL 0.11 0.01 0.03 0.74 0.44 0.03 0.09 0.26

Liquid 0.09 0.16 0.27 0.05 0.09 0.06 0.10 0.06

Int 0.12 0.27 0.03 0.24 0.31 0.11 0.05 0.17

Sec 0.07 0.06 0.50 0.06 0.24 0.19 0.01 0.13

Pearson (Spearman) correlation coefficients are shown above (below) the diagonal. All correlations are significant at the 0.01 level except for those marked with “*”.

See Appendix A for variable definitions. Sample comprises 136,879 bank-quarter observations for 6,300 banks, over the period from Q1 1996 to Q4 2007 and from Q3 2009 to Q4 2011.

40

Table 3: Summary statistics from regressions of realized gains and losses on AFS securities on proxies for income- and regulatory-capital-related incentives to smooth earnings and/or take a big bath.

Equation (1a) Equation (1b)

Variables Prediction (smoothing) Coefficient t-statistic Coefficient t-statistic

NI 0.420*** 3.32 0.392*** 3.40

RegCap 0.023*** 8.06 -0.022*** 8.12

NI × UGLt1 164.077*** 4.99

RegCap × UGLt1 2.200*** 3.80

UGLt1 + 1.212*** 6.95 2.277*** 6.23

Liquid n/a 0.007 1.28 0.008 1.54

Int n/a 0.383** 2.14 0.365** 2.01

Sec n/a 0.021*** 8.38 0.020*** 8.56

VIX n/a 0.001** 2.37 0.001** 2.05

Unemp n/a 0.001 1.22 0.001 1.03

LIBOR n/a 0.004* 1.94 0.004** 2.00

Bank fixed effects Yes Yes

Adj.R2 5.5% 6.8%

2

1 2 3 4 5 6 7

1

1

it it it

it it it it t t t it

RGL NI RegCap

UGL Liquid Int Sec VIX Unemp LIBOR

(Equation 1a)

2 3 41 1

1 2 3 4 5 6

1

1 7

it it it it it it it

it it it it t t t it

RGL NI RegCap NI UGL RegCap UGL

UGL Liquid Int Sec VIX Unemp LIBOR

(Equation 1b)

41

Table 3 (continued): See Appendix A for variable definitions. All regression estimations include bank fixed effect. t-statistics are based on standard errors clustered by bank and quarter. Coefficient estimates are multiplied by 100 for expositional convenience. Estimations are based on 136,879 bank-quarter observations (6,300 banks) over the period from Q1 1996 to Q4 2007 and from Q3 2009 to Q4 2011. *, **, *** denote significance (based on two-tail tests) at 0.10, 0.05 and 0.01 levels, respectively.

42

Table 4: Summary statistics from regressions of realized gains and losses on AFS securities on proxies for income- and regulatory-capital-related incentives to smooth earnings and/or take a big bath.

Prediction Equation (2a) Equation (2b)

Variables Smoothing Big Bath Coefficient t-statistic Coefficient t-statistic

Dpos 0.007*** 5.08 0.006*** 5.10

negNI + 0.578*** 4.96 0.399*** 3.36

posNI 0.880*** 5.55 0.550*** 3.44

RegCap 0.021*** 6.32 0.020*** 6.56

negNI × ULt1 306.836*** 4.60

negNI × UGt1 188.249*** 3.09

posNI × ULt1 + 21.792 0.77

posNI × UGt1 n/a 177.770*** 6.67

RegCap × UGLt1 2.522*** 4.49

ULt1 + 1.114*** 3.72 1.704*** 3.86

UGt1 + 1.331*** 11.11 2.534*** 12.37

Liquid n/a 0.006 1.20 0.008 1.58

Int n/a 0.273 1.54 0.252 1.41

Sec n/a 0.020*** 6.74 0.019*** 7.29

VIX n/a 0.001** 2.32 0.001* 1.95

Unemp n/a 0.001 1.00 0.001 0.91

LIBOR n/a 0.003 1.60 0.003 1.64

Bank fixed effects Yes Yes Adj.R2 5.9% 7.3%

43

Table 4 (continued):

0 1 1

2

1 2

3 4 5 6

3 0 1

7 it it it it it it it

it it it t t t it

DpoRGL negNI NI RegCap UL UG

Liquid Int Sec VI

s

X Unemp LIB R

p s

O

o

(Equation 2a)

0 1 2 3 4

5 6 7 8

0 1 2 3 4 5

1

1 1 1 1

1 1 6 7

it it it it it it it

it it it it it it it it

it it it it it t t

RGL NI NI RegCap NI UL

negNI UG posNI UL po

Dpos neg po

sNI UG RegCap UGL

UL UG Liquid Int Sec VIX Unemp LIB

s neg

O

t itR (Equation 2b)

See Appendix A for variable definitions. All regression estimations include bank fixed effect. t-statistics are based on standard errors clustered by bank and quarter. Coefficient estimates are multiplied by 100 for expositional convenience. Estimations are based on 136,879 bank-quarter observations (6,300 banks) over the period from Q1 1996 to Q4 2007 and from Q3 2009 to Q4 2011. *, **, *** denote significance (based on two-tail tests) at 0.10, 0.05 and 0.01 levels, respectively.

44

Table 5: Summary statistics from regressions of realized gains and losses on AFS securities on proxies for income- and regulatory-capital-related incentives to smooth earnings and/or take a big bath. Panel A: Equation (1a) Equation (1b)

Prediction Listed Banks Non-Listed Banks Listed Banks Non-Listed Banks

Variables Smoothing Big Bath Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat

NI 0.50** 2.07 0.42*** 3.64 0.23 1.22 0.40*** 3.72

RegCap 0.06*** 3.94 0.02*** 7.29 0.06*** 4.00 -0.02*** 7.20

NI × UGLt1 403.44*** 5.64 147.32*** 5.15

RegCap × UGLt1 0.02 0.01 1.91*** 3.76

UGL + 2.48*** 4.88 1.13*** 7.56 3.58*** 6.20 2.09*** 6.55

Liquid n/a 0.05** 2.20 0.01 1.00 0.05** 2.58 0.01 1.22

Int n/a 0.30 0.75 0.37** 2.27 0.49 1.19 0.35** 2.14

Sec n/a 0.03*** 3.17 0.02*** 7.94 0.03*** 3.35 0.02*** 8.05

VIX n/a 0.01 1.20 0.01** 2.32 0.01** 2.32 0.01** 1.97

Unemp n/a 0.01 0.34 0.01* 1.85 0.01 0.77 0.01* 1.65

LIBOR n/a 0.01 1.20 0.01** 2.32 0.01 0.94 0.01** 2.41

Fixed effects Yes Yes Yes Yes

Adj.R2 8.7% 5.2% 11.4% 6.3%

45

Table 5 (continued):

Panel B: Equation (2a) Equation (2b)

Prediction Listed Banks Non-Listed Banks Listed Banks Non-Listed Banks

Variables Smoothing Big Bath Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat

Dpos 0.01** 2.36 0.01*** 4.97 0.01** 2.41 0.01*** 5.02

negNI + 1.07*** 5.48 0.51*** 3.92 0.76* 1.73 0.36*** 3.27

posNI 2.48*** 5.59 0.72*** 5.39 1.85*** 3.93 0.39*** 2.94

RegCap 0.07*** 4.87 0.02*** 5.82 0.07*** 4.45 0.02*** 6.02

negNI × ULt1 456.48*** 2.33 275.67*** 4.95

negNI × UGt1 223.67** 2.00 181.93*** 2.73

posNI × ULt1 + 291.01*** 2.82 9.36 0.38

posNI × UGt1 n/a 433.20*** 5.01 170.31*** 6.08

RegCap × UGLt1 0.38 0.21 2.21*** 4.45

ULt1 + 3.58*** 3.81 0.98*** 4.07 3.76*** 4.13 1.48*** 4.03

UGt1 + 1.51*** 6.33 1.31*** 10.62 3.23*** 5.85 2.41*** 12.46

Liquid n/a 0.05** 2.17 0.01 0.90 0.05** 2.53 0.01 1.25

Int n/a 0.04 0.11 0.27* 1.65 0.22 0.57 0.25 1.54

Sec n/a 0.04*** 4.23 0.02*** 6.41 0.04*** 4.05 0.02*** 6.77

VIX n/a 0.01** 2.07 0.01** 2.18 0.01*** 2.88 0.01* 1.82

Unemp n/a 0.01 0.47 0.01* 1.64 0.01 1.20 0.01 1.55

LIBOR n/a 0.01 1.60 0.01* 1.96 0.01 1.39 0.01** 2.01

Fixed effects Yes Yes Yes Yes

Adj.R2 10.5% 5.6% 12.8% 6.8%

46

Table 5 (continued): In Panel A we estimate the following models:

2

1 2 3 4 5 6 7

1

1

it it it

it it it it t t t it

RGL NI RegCap

UGL Liquid Int Sec VIX Unemp LIBOR

(Equation 1a)

2 3 41 1

1 2 3 4 5 6

1

1 7

it it it it it it it

it it it it t t t it

RGL NI RegCap NI UGL RegCap UGL

UGL Liquid Int Sec VIX Unemp LIBOR

(Equation 1b)

In Panel B we estimate the following models:

0 1 1

2

1 2

3 4 5 6

3 0 1

7 it it it it it it it

it it it t t t it

DpoRGL negNI NI RegCap UL UG

Liquid Int Sec VI

s

X Unemp LIB R

p s

O

o

(Equation 2a)

0 1 2 3 4

5 6 7 8

0 1 2 3 4 5

1

1 1 1 1

1 1 6 7

it it it it it it it

it it it it it it it it

it it it it it t t

RGL NI NI RegCap NI UL

negNI UG posNI UL po

Dpos neg po

sNI UG RegCap UGL

UL UG Liquid Int Sec VIX Unemp LIB

s neg

O

t itR (Equation 2b)

See Appendix A for variable definitions. All regression estimations include bank fixed effect. t-statistics are based on standard errors clustered by bank and quarter. Coefficient estimates are multiplied by 100 for expositional convenience. Estimations are based on 15,204 (listed) and 121,675 (non-listed) bank-quarter observations (6,300 banks) over the period from Q1 1996 to Q4 2007 and from Q3 2009 to Q4 2011. *, **, *** denote significance (based on two-tail tests) at 0.10, 0.05 and 0.01 levels, respectively.

47

Table 6: Summary statistics from regressions of realized gains/losses on AFS securities on proxies for income- and regulatory-capital-related incentives to smooth earnings and/or take a big bath.

Panel A: Banks with loss avoidance using RGL: Prediction All Banks Listed Banks Non-Listed Banks

Variables Smoothing Big Bath Coefficient t-statistic Coefficient t-statistic Coefficient t-statistic

NI 85.13*** 33.06 104.09*** 14.60 82.00*** 29.55

RegCap n/a 0.05* 1.91 0.09 1.06 0.05* 1.76

UGLt1 + 1.97*** 5.47 0.27 0.23 2.19*** 6.30

Liquid n/a 0.04 1.57 0.08 1.25 0.03 1.18

Int n/a 1.31 1.17 0.57 0.28 1.28* 1.00

Sec n/a 0.04*** 3.01 0.06 0.84 0.03** 2.46

VIX n/a 0.01** 2.38 0.01 0.66 0.01*** 2.92

Unemp n/a 0.01*** 4.01 0.01 1.00 0.01*** 3.77

LIBOR n/a 0.01 0.08 0.02 0.83 0.01 0.22

N 917 156 761

Adj.R2 47.6% 48.5% 47.4%

Test: H0: α1 < 100

t-statistic = 5.78 t-statistic = 0.57 t-statistic = 6.49

48

Table 6 (continued): Panel B: Banks that can and banks that cannot reverse a loss using RGL:

Prediction Banks with NI+UG >= 0 Banks with NI+UG < 0

Variables Smoothing Big Bath Coefficient t-statistic Coefficient t-statistic

Dpos 0.014*** 7.51

negNI + 1.092* 1.87 0.515*** 3.11

posNI 1.122*** 6.61

RegCap 0.007*** 2.18 0.010** 2.08

ULt1 + 0.987*** 4.13 1.432*** 2.78

UGt1 + 1.263*** 11.06 8.647*** 5.73

Liquid n/a 0.005 0.77 0.027*** 2.71

Int n/a 0.097 0.57 0.545* 1.85

Sec n/a 0.020*** 6.41 0.003 0.44

VIX n/a 0.001** 2.30 0.001 0.97

Unemp n/a 0.001 0.19 0.001 1.16

LIBOR n/a 0.002 1.18 0.005 1.20

Bank fixed effects Yes Yes

N 125,567 11,312

Adj.R2 6.6% 7.3%

49

Table 6 (continued): In Panel A we estimate the following model (without bank fixed effect) using observations with negative net income before realized gains and losses on AFS securities but with positive net income after the realized gains and losses, i.e., observations with both NI < 0 and NI + RGL > 0. We estimate this specification for the full sample and separately for publicly listed and non-listed banks.

2

1 2 3 4 5 6 7

1

1

it it it

it it it it t t t it

RGL NI RegCap

UGL Liquid Int Sec VIX Unemp LIBOR

(Equation 1a)

In Panel B we estimate the following model (with bank fixed effect) separately using observations with NI+UG >=0 and with NI+UG< 0. The latter group comprises banks that do not have sufficient accumulated unrealized gains at the beginning of the quarter to offset negative NI:

0 1 1

2

1 2

3 4 5 6

3 0 1

7 it it it it it it it

it it it t t t it

DpoRGL negNI NI RegCap UL UG

Liquid Int Sec VI

s

X Unemp LIB R

p s

O

o

(Equation 2a)

See Appendix A for variable definitions. t-statistics are based on standard errors clustered by bank and quarter. Coefficient estimates are multiplied by 100 for expositional convenience. Equations are estimated over the period from Q1 1996 to Q4 2007 and from Q3 2009 to Q4 2011. *, **, *** denote significance (based on two-tail tests) at 0.10, 0.05 and 0.01 levels, respectively.

50

Table 7: Summary statistics from two-stage least squares regression to estimate system of equations of realized gains/losses on AFS securities on proxies for income- and regulatory-capital-related incentives to manage earnings.

Panel A: RGL LLP SG ΔEqFin

Variables Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat

Endogenous Variables:

RGL 57.22*** 3.07 0.11* 1.95 141.73** 2.07

LLP 0.99* 1.84 0.01 0.22 12.77** 2.10

SG 29.36 1.41 43.87 0.24 497.85 1.37

ΔEqFin 1.21*** 4.07 1.60 0.82 0.01 0.06

Experimental Variables:

NI^ 0.83*** 5.48 10.34*** 4.80 0.01 1.18

RegCap^ 0.04*** 5.93 0.05 0.99 0.01** 2.19 2.88*** 13.10

Equation Identifiers:

UGLt1 1.75*** 5.96

Sec 0.01*** 3.31 0.12*** 5.24

ΔNPL 2.21*** 2.82

LLPt1 24.49*** 5.50

SGt1 55.78*** 12.41

ΔEqFint1 0.42 0.45

Lev 4.83*** 12.72

CommonControls Included Included Included Included

Fixed effects Included Included Included Included

51

Panel B:

RGL LLP SG ΔEqFinVariables Coeff. t-stat Coeff. t-stat Coeff. t-stat Coeff. t-stat

Endogenous Variables:

RGL 46.59*** 3.00 0.13** 2.00 152.07** 2.41

LLP 1.11** 1.99 0.01 0.19 13.19** 2.15

SG 24.85 1.25 38.90 0.21 493.36 1.36

ΔEqFin 1.35*** 4.45 1.82 0.93 0.01 0.04

Experimental Variables:

NI^ 0.63*** 5.00 10.26*** 4.75 0.01 1.16

RegCap^ 0.03*** 5.62 0.05 1.00 0.01** 2.22 2.88*** 13.11

NI^ × UGLt1 190.19*** 5.21

RegCap^ × UGLt1 4.07*** 3.98

Equation Identifiers:

UGLt1 3.37*** 6.06

Sec 0.01*** 2.95 0.12*** 5.38

ΔNPL 2.21*** 2.82

LLPt1 24.47*** 5.49

SGt1 55.78*** 12.41

ΔEqFint1 0.42 0.46

Lev 4.83*** 12.71

CommonControls Included Included Included Included

Fixed effects Included Included Included Included

52

Table 7 (continued): Two-stage least squares regression the following system of equations:

^ ^ ^1 2 1

14^

3 4 5 6

7 81 1 19 0

it it it it it it it it

it it it it it itk k

RGL LLP EqFin NI RegCap NI UGL

RegCap UGL UGL Sec CommonControls

SG

(3a)

3 4 5

5

^ ^1 2

13

1 97 8

it it it it it it

it it it k it itk

LLP EqFin NI RegCap

NPL LLP Sec CommonContr

RG S

ol

L G

s

(3b)

^ ^1 2

10

3 4

6 7

5

1

it it it it it it

it k it itk

SG EqFin NI RegCap

CommonControls

RGL LLP

SG

(3c)

^1 2 3

11

1 1

4

5 6 7

it it it it it

it it k it itk

EqFin RegCap

EqFin Lev CommonCon

RG

tro

L LLP SG

ls

(3d)

In panel A we estimate the system without NI^ × UGLt1 and RegCap^ × UGLt1 in (3a). In panel B we estimate the full model. CommonControls are Liquid, Int, VIX, Unemp, and LIBOR. See Appendix A for variable definitions. t-statistics are based on standard errors clustered by bank and quarter. Coefficient estimates are multiplied by 100 for expositional convenience. Estimations are based on 93,858 bank-quarter observations with all available data to estimate (3a)-(3d) over the period from Q1 1996 to Q4 2007 and from Q3 2009 to Q4 2011. *, **, *** denote significance (based on two-tail tests) at 0.10, 0.05 and 0.01 levels, respectively.