Earnings and Cash Flows in Debt Evaluation by Private Debt Holders

N. Bugra Ozel

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

under the Executive Committee of the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2010

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ABSTRACT

Earnings and Cash Flows in Debt Evaluation by Private Debt Holders

N. Bugra Ozel

This study investigates whether private debt holders focus more on earnings or cash

flows of their borrowers in debt evaluation. I utilize estimates of credit losses and

realizable value of loans as reported by commercial banks in regulatory filings to explore

how private debt holders react to information about borrowers' operating performance. I

find that changes in estimates of credit losses are significantly associated with measures

of borrowers' current and future operating performance, especially with operating income

growth. I also find that when assessing borrowers' performance, private debt holders

consider some accruals (e.g., working capital accruals) more informative than others

(e.g., depreciation). Furthermore, I show that the estimates of credit losses provide

incremental information about borrowers' future short term profitability growth over

several measures of equity investors' expectations (valuation ratios, stock returns, past

growth rates, and analyst forecasts). While this is consistent with the widely held belief

that private debt holders have superior information about their borrowers, it also suggests

that other investors may be ignoring some useful information provided by private debt

holders.

TABLE OF CONTENTS

TABLE OF CONTENTS i

LIST OF FIGURES AND TABLES iii

ACKNOWLEDGEMENTS iv

DEDICATION v

1. INTRODUCTION 1

2. RELATED STUDIES AND BACKGROUND 8

2.1. Review of Literature 9

2.2. Regulations on Loan Loss Reserves 15

2.3. Empirical Predictions 20

3. DATA AND SUMMARY STATISTICS 23

3.1. Sample Formation and Variable Definitions 23

3.2. Summary Statistics 26

4. EMPIRICAL FINDINGS 27

4.1. Earnings vs. Cash Flows 27

4.2. Components of Loan Loss Reserves 30

4.3. High Credit Risk Firms vs. Low Credit Risk Firms 32

4.4. Effect of Financial Condition of Banks 33

4.5. Predictive Ability of Loan Loss Reserves 35

4.6. Robustness Checks 39

4.6.1. Alternative Methods for Calculations 39

4.6.2. Alternative Performance Measures 41

4.6.3. Macroeconomic Variables 41 i

4.7. Discussion of Findings 43

5. CONCLUSION 46

REFERENCES 49

FIGURES 55

TABLES 61

ii

LIST OF FIGURES AND TABLES

FIGURES:

Figure 1: Operating Performance and ALLR over Time 56

Figure 2: Operating Performance and ALLR 57

Figure 3: Distribution of Leverage for Bank and Finance Company Borrowers 58

Figure 4: The Effect of 2000-2003 59

Figure 5: Univariate Regressions of Future Operating Performance on E/P Ratio and

ALLR 60

TABLES:

Table 1: Summary Statistics 62

Table 2: Correlation Matrix 64

Table 3: Regressions of ALLR on Earnings and Cash Flow Growth Variables 65

Table 4: Components of ALLR 67

Table 5: Firm Portfolios Based on Leverage 68

Table 6: Effect of Financial Condition of Lender 69

Table 7: Future Earnings Growth, Proxies of Public Information and ALLR 70

Table 8: Regressions of Change in Nonperforming Commercial Loans on Earnings and

Cash Flow Growth Variables 72

Table 9: Regressions of ALLR on Earnings/Cash Flow Growth Variables and

Macroeconomic Variables 73

iii

ACKNOWLEDGEMENTS

I am indebted to Divya Anantharaman, Bjorn Jorgensen, Nahum Melumad, Doron

Nissim, Stephen Penman and Gil Sadka for their valuable comments and support. I also

thank Sudhakar Balachandran, Shira Cohen, Emre Karaoglu, Jon Kerr, Urooj Khan,

Hanna Lee, Part ha Mohanram, Julian Yeo and Yuan Zhang as well as workshop

participants at Baruch College, Carnegie Mellon University, Columbia University,

INSEAD, London Business School, New York University, The U.S. Securities and

Exchange Commission, and University of California at Los Angeles for their helpful

comments and suggestions. All errors are my own.

iv

DEDICATION

I dedicate this dissertation to my family. Particularly, to my parents, Ahmet and Suzan

Ozel for the encouragement and support they give all the time and to my brother and my

best friend Cagri, whose guidance and advice has always been invaluable to me. I must

also thank my uncle and my grandmother, who have never left my side.

I also dedicate this dissertation to my friends. Especially to Benan Zeki Orbay,Martin

Dumav and Suat Teker for encouraging me to start the doctoral program. A special

feeling of gratitude to Doron Nissim who has been very influential in the development of

my research abilities and very supportive of my work. Finally, I would like to give my

heartfelt thanks to Bjorn Jorgensen, Nahum Melumad and Gil Sadka who have

generously spared their time to help me whenever I needed it.

v

1

EARNINGS AND CASH FLOWS IN DEBT EVALUATION BY PRIVATE DEBT HOLDERS

1. INTRODUCTION

Accounting researchers have extensively studied how equity holders use earnings

and cash flow information in evaluating the future prospects of firms. Complementing

these studies, a growing body of literature focuses on the use of earnings and cash flows

in debt markets.1 This literature provides evidence that secondary market participants

react to accounting-based performance measures and this reaction is different from that of

equity holders (e.g., Easton, Monahan, and Vasvari 2009). Much less work, however, has

been done to understand how private debt holders use earnings and cash flow information

of borrowers in debt evaluation. In fact, private debt holders have an instrumental role in

corporate finance, and differ from other investors in several dimensions. In this study, I

investigate whether private debt holders focus more on earnings or cash flows of the

borrowers when estimating potential credit losses.

Merton (1974) asserts that the value of corporate debt instruments depends on the

risk free rate, the probability of default, and various debt provisions and restrictions (e.g.,

maturity date, coupon rate, and call terms). Of those pricing components, the probability

of default involves the most significant estimation, based primarily on the issuer's

financial reports. Therefore, like stockholders, debt holders monitor the performance of

the firms they invest in, and reassess their investments in the presence of new financial

information. However, the risk-return trade-off for debt holders is different from that for

1 Recently, several studies focused on the use of accounting information in debt markets and produced interesting findings on different fronts (e.g., Dichev and Skinner 2002, Khurana and Raman 2003, Asquith, Beatty and Weber 2005, Ball, Bushman and Vasvari 2008, DeFond and Zhang 2008, Wittenberg-Moerman 2008, Bushman, Smith and Wittenberg-Moerman 2009). Still, the number of studies in this area is relatively small.

2

stockholders. In addition, compared to equity investors, short-term creditors are less

concerned about the long-term prospects of the firms. These distinctions suggest that the

same information may have different implications for debt and equity instruments.

Prior research on equity investors has documented that equity values react to

information about firm's performance (e.g., Ball and Brown 1968) and that cash flows

are more value relevant than accruals. Still, compared to cash flows, earnings have a

stronger association with stock price (e.g., Dechow 1994). Studies have also found that

cash flows are more persistent than accruals (e.g., Sloan 1996), but current earnings can

predict future cash flows better than current cash flows does (e.g., Dechow, Kothari and

Watts 1998). Since debt represents claims on the same assets and profits as equity,

properties and differential implications of cash flows and accruals should also be relevant

for debt holders.

Prior studies on the use of accounting information in debt evaluation have focused

primarily on bondholders, perhaps because corporate bond prices inform on the

probability of default (e.g., Bhojraj and Swaminathan 2009, Easton, Monahan and

Vasvari 2009). Nevertheless, it is also intriguing to study the use of accounting measures

by private debt holders for at least three reasons. First, private debt is the predominant

source of debt financing. For example, the total amount of syndicated loans issued in

2007 was approximately $1,680 billion, compared to $1,127 billion of corporate bond

issues.2 Second, private debt holders are presumed to have access to inside information

(e.g., Fama 1985), and provide more efficient monitoring than equity holders and public

debt holders (e.g., Diamond 1984, 1991). Private debt holders' reaction to financial

2 Loan Pricing Corporation and SIFMA.

3

information may therefore differ from that of the other investors. For instance, if private

debt holders obtain more detailed and timely information regarding borrowers' operating

performance, or if they evaluate borrowers' operating performance more accurately

through the efficient processing of available information, they may anticipate impairment

charges that surprise other investors. Third, private debt is less dependent on borrower

characteristics, such as size or reputation, than is public debt (e.g., James 1987, Diamond

1991, Carey, Post, and Sharpe 1998). These same characteristics are also correlated with

the relative magnitude of accruals and cash flows, which implies that these items may

have different implications for private and public debt. Small companies may be more

dependent on operating cash flows, so that excessive working capital accruals for small

firms may be more suspicious. Empirically, extreme accruals are more common among

young and small firms (e.g., Lev and Nissim 2006, Mashruwala, Rajgopal, and Shevlin

2006).3

For public debt, price is a timely measure of the investors' perception of the

firm's performance. In contrast, for private debt no such metric is readily available. Still,

it is possible to measure private debt holders' reaction to borrowers' cash flow and

accrual information using debt holders' financial disclosures. Specifically, regulators

require most private lenders to disclose comprehensive information about lending

activities, including direct and indirect measures of the changes in the riskiness and value

of debt. The most notable set of such measures is the loan quality information4 provided

3 In addition to these differences, unlike public debt, private debt is not subject to the filing requirements under sections 13 and 15(d) of the Securities Exchange Act of 1934, and is often more senior to public debt in the event of default.

4 Examples of loan quality measures include - but are not limited to - loan loss reserves (estimates of uncollectible amounts of loans), delinquencies (the loans where borrower is

4

by commercial banks, the largest single group of commercial and industrial debt

financers in the economy.5 I employ the disclosures on loan loss reserves—a key loan

quality measure that represents the bank's best estimate of the losses inherent in its loan

portfolio—and total loans to study the reaction of private lenders to new financial

information of borrowers.

I use economy-wide data to conduct the tests. The setting can be viewed as an

economy composed of a representative private lender (i.e., commercial bank) and a

representative publicly traded firm. The firm borrows from the lender and the lender

monitors the firm's financial condition in order to determine whether the firm will be able

to repay the loan. In its financial statements, the lender presents the total amount of loan

given to the firm and the amount reserved for the uncollectible portion of the loan. These

two items reveal the lender's evaluation of the ability of a firm to service its debt at a

given point in time. Within this framework, I examine the association between the

lender's evaluation and the firms' current and future operating performance. While an

economy-wide setting provides several advantages6, it is prudent to acknowledge that

findings based on the aggregates may not apply to each individual bank within the

economy and that a firm-level study could provide valuable insights, as well. However,

commercial banks are not required to present a firm-level breakdown of loans or loan

behind/late in payment), and charge offs (value of loans that are confirmed uncollectible and written off from the books).

5 Carey, Post, and Sharpe (1998) report that banks' share in total C&l loans stayed above 85% historically.

6 Such as the inclusion of public and private banks/corporations, averaging out the effects of bank/firm specific activities, and identifying the results for a representative bank

5

quality measures and, to the best of my knowledge, no other source provides detailed

information on credit evaluation at the individual firm level.

I find that private debt holders' estimates of credit losses are significantly

negatively correlated with borrowers' operating performance. Moreover, when evaluating

credit losses, private debt holders focus primarily on borrowers' operating earnings, not

on operating cash flows. In particular, both the current and future growth rates in

operating earnings are more strongly correlated with changes in loan loss reserves than

are cash flow growth rates. This supports the argument that debt holders consider

accruals informative about the ability of borrowers to service their debt. The result is

especially significant because the loan losses are recognized when the lender has doubts

about the viability of the borrower, and going concern is an underlying assumption of the

accrual concept.

I also find that private debt holders do not weigh all accruals equally. Among the

accounting performance measures tested, growth in operating earnings before

depreciation and amortization has the strongest association with changes in loan loss

reserves. This is consistent with private debt holders regarding certain accruals (e.g.,

working capital) to be more instructive about the borrowers' repayment ability than other

accruals (e.g., depreciation).

When the sample is split into groups based on leverage, an almost monotonic

relation prevails between leverage and the relevance of operating earnings growth for

changes in loan loss reserves. In other words, the earnings growth of high leverage firms

is more strongly correlated with changes in loan loss reserves than is the earnings growth

of low leverage firms. While this result is unsurprising per se, the disturbance of this

6

monotonic relation in the highest leverage group supplements prior research on borrower

characteristics. In particular, it is consistent with the evidence in the finance literature

(e.g., Carey, Post, and Sharpe 1998, Denis and Mihov 2003) that firms in the highest risk

categories tend to borrow from non-bank lenders.

Of the two components of credit losses, confirmed losses (i.e. net charge-offs) are

mainly associated with the current operating performance, especially cash flows, of

borrowers. On the other hand, unconfirmed losses (i.e. provisions for loan losses after

controlling for net charge-offs), are associated with both current and future operating

income of borrowers. This suggests that lenders rely on information about current and

expected future earnings for unconfirmed losses while they focus mainly on borrowers'

current cash flows for confirmed losses.

Finally, I show that changes in loan loss reserves are informative about the future

operating performance of the borrowers even after controlling for measures of public

information including typical valuation ratios, stock returns, analyst forecasts and past

operating performance. The association weakens as future earnings growth is measured

over longer periods. Specifically, in horizons exceeding one year, loan loss reserves

cannot predict future earnings growth whereas the predictive power of earnings to price

(E/P) ratio increases.7 The findings suggest that private debt holders provide some

valuable information about short term future earnings of borrowers. However, given that

loan quality measures are publicly disclosed, it is unclear why this information is not

incorporated into the measures of public information.

7 This is intuitive given that regulatory guidance on loan loss reserves suggests that these reserves should be adequate to cover one year's losses.

7

This study contributes to the literature in several ways. First, using a unique

setting, it explores the use of earnings and cash flow information by private debt holders

in debt evaluation. Accordingly, it complements the studies that examine the use of these

accounting performance measures by equity holders (e.g., Dechow 1994), and by public

debt holders (e.g., Easton, Monahan and Vasvari 2009).

Second, this study provides evidence relating to the debate over the accounting

performance measure that debt holders care more about in debt evaluation.8 Some

academics (e.g., Bhojraj and Swaminathan 2009) implicitly assume that debt holders are

more interested in cash flows than earnings because a firm's ability to repay debt depends

on the amount of cash it generates. Some professionals share the same perspective. For

example Standard and Poor's 2008 Corporate Ratings Criteria notes: "cash flow

adequacy is typically the single most critical aspect of credit rating analysis",9 On the

other hand, several academics (e.g., Watts 2003, Ball, Robin, and Sadka 2008) argue that

timely measures are important for debt holders and that debt holders should focus on

earnings as it is timelier than cash flows. I find evidence consistent this latter view.

Moreover, my findings support the claims that certain accruals are more valuable than

others in debt evaluation. While this may provide a justification for the use of modified

GAAP measures for earnings and cash flows in studies relating to debt holders (e.g.,

James 1995, Carey, Post, and Sharpe 1998, Beaver, McNichols, and Rhie 2005), to the

8 It is important to emphasize that the arguments in the literature, as well as in the current study, relate to the relative usefulness of earnings and cash flows. In other words, the discussion is not about whether debt holders use only earnings or only cash flows but is about which of these measures explain debt holders' reactions better. Clearly, debt holders do not need to choose a single measure and would optimally utilize both earnings and cash flows.

9 For another example see the Fitch Research's special report named "Cash is King in Bond Analysis".

8

extent that those modified measures include (exclude) accruals that debt holders use, such

studies may over (under) state the role of actual earnings or cash flows.

Finally, this study adds to the evidence that private debt holders possess superior

information about the borrowers. Theoretical studies in finance often argue that due to

reasons such as relationship banking, better processing of information and economies of

scale, compared to public investors, private debt holders have better information about

future prospects of the borrowers (e.g., Sharpe 1990, Diamond 1991). The existing

empirical studies provide indirect support to these claims (e.g., James 1987, Bharath,

Sunder and Sunder 2008). While the present paper corroborates the findings of prior

studies using a more direct test, it also demonstrates that public investors may be

neglecting some useful information that private debt holders disclose.

The remainder of this paper is organized as follows. Section 2 provides

background information and states the empirical predictions. Section 3 describes the data

and provides summary statistics. Section 4 presents the empirical findings. Section 5

summarizes and concludes the paper.

2. RELATED STUDIES AND BACKGROUND

This section has three subsections. Section 2.1 summarizes relevant literature.

Section 2.2 provides an overview of regulations on loan loss reserving. The empirical

predictions of the study are explained in section 2.3.

9

2.1. Review of Literature

In spite of their instrumental role in corporate finance, private debt holders have

received limited attention in accounting literature.10 The extant empirical research

primarily focuses on the use of accounting information in debt contracting (see

Armstrong, Guay and Weber (2009) for a detailed review of contracting literature).11 In

one of the earliest works in this field, Leftwich (1983) finds that the accounting

information used in private debt contracts is systematically different from GAAP

measures. Later studies document that conservative, persistent and relatively

nondiscretionary accounting-based measures are important in debt contracting (e.g.,

Beatty, Weber and Yu 2008, Zhang 2008, Li 2008). They also document that certain

characteristics of borrowers (e.g., accounting quality) and the form in which accounting

information is included in the contract are important determinants of contracting costs

(e.g., Asquith, Beatty, and Weber 2005, Bharath, Sunder and Sunder 2008).

Studies on debt contracting provide somewhat different findings on the use of

earnings and cash flows in contract terms. Dichev and Skinner (2002) show that the most

widely used covenant in debt contracts is debt to cash flow ratio. However, Dichev and

Skinner underline that this ratio does not have a standard definition and metrics used in

the numerator and denominator vary among contracts. Li (2008) also demonstrates that

debt to cash flow covenant is included in the majority of debt contracts, and argues that

10 The number of studies in this field is on the rise since the introduction of Dealscan database. Dichev and Skinner (2002) were among the first to investigate debt contracts using this new data.

11 Some studies also examine the monitoring role of private debt holders (e.g., Beatty, Liao and Weber 2007), and the factors that determine private debt holders' loan related decisions such as loan granting decisions (Libby 1979), and waiver decisions (Chen and Wei 1993).

banks view debt to cash flow ratio as a particularly valuable signal of credit quality. On

the other hand, Asquith, Beatty and Weber (2005) report that performance pricing

provisions often include earnings-based measures.12

Prior research has typically considered debt provisions and restrictions as tools for

reducing agency problems and renegotiation costs rather than as direct measures of risk

or value. For example Gorton and Kahn (2000) provide a theoretical model to

demonstrate that initial terms of loans are not set to price credit risk, but rather are set to

balance bargaining power in later renegotiations.13 Assessment of risk and value of the

lending takes place in periodic evaluations, which are internal events to the lender. In

fact, periodic evaluations are aimed at identifying the borrowers for whom lender's

credit-granting decision may prove erroneous. Accordingly, measures used in contracting

and evaluation processes may differ. For example, prior studies have found that debt

holders often exclude temporary and/or highly discretionary items from debt covenants.

Nevertheless, items such as unusual gains/losses are not necessarily irrelevant when

evaluating borrowers' repayment ability since those can alter the repayment potential of

the debt. The present study's primary focus is to identify how private lenders measure the

performance of the borrower when assessing value and risk of their lending. To

12 Beatty, Dichev and Weber (2002) provide findings consistent with performance pricing complementing debt covenants. On the other hand Zhang (2008) states that it remains unclear whether performance pricing substitutes or complements debt covenants.

13 Waivers of debt covenants can be interpreted as empirical evidences in support of this view. If covenants are used as direct tools for debt evaluation, then debt holders would be reluctant to waive the violations. In contrast, Dichev and Skinner (2002) report that 30% of the firms in their sample have covenant violations and that these violations are frequently waived as most of those firms are not in financial distress. They conclude that debt covenants are used as screening devices. Moreover, Beneish and Press (1995) provide some evidence that firms may default on debt without violating covenants beforehand, even when covenants are not "too slack".

11

effectively address this issue, I test the association of both reported and modified

earnings/cash flow measures with private debt holders' evaluation of potential credit

losses.

The study of use of earnings and cash flows in credit loss evaluation process

relates to diverse strands of literature. One line of research investigates the role of

financial information in failure prediction. Beaver (1966), the study which pioneered

modern studies in this area, finds that cash flow to debt ratio is an early indicator of firm

failure.14 Altaian (1968) is first to introduce multiple discriminant analysis method into

this field and he finds that Beaver's results can be improved by using a larger set of

financial information. Altman's study includes earnings measures but not cash flow

measures due to lack of consistent data. Later studies vary in their use of earnings and

cash flow information (e.g., Deakin 1972, Norton and Smith 1979, Altman 1977, Ohlson

1980).15

The wide variety of ratios used in this literature led to the question of whether

earnings or cash flows is more relevant in failure prediction. Largay and Stickney (1980)

examine the bankruptcy of W.T. Grant Company and demonstrate that cash flow from

operations provided a stronger and earlier signal of the bankruptcy than earnings. Casey

and Bartzcak (1984) provide a careful examination of the usefulness of cash flow

measures and compare them with the predictive ability of accrual based measures.

14 Due to unavailability of cash flow data, early studies often use fund flows, ebida, ebitda and similar measures as proxies for cash flows.

15 Related to failure prediction research, prior studies investigate the types of information credit rating agencies rely on to determine the corporate bond ratings. Similar to research on failure prediction, these studies also differ in their use of earnings and cash flows (e.g., Ang and Patel 1975, Kaplan and Urwitz 1979, Gentry, Newbold and Whitford 1988).

Although the results indicate that cash flow from operations predict bankrupt companies

better than accrual based model, based on overall (bankrupt and non-bankrupt firms)

predictive accuracy, authors conclude that cash flow measures are not useful in failure

prediction.16 Several other researchers studied the question and reached different

conclusions about the usefulness of earnings and cash flows in failure prediction (e.g.,

Gombola et al. 1987, Aziz and Lawson 1989, Mossman et al. 1998). More recently, Jones

and Hensher (2004) investigate a sample of Australian firms and show that cash flow

measures are significant in predicting financial distress.

Extant studies in bankruptcy prediction literature explore the relation between the

default risk and different measures, including accounting variables. However, a better

understanding of how debt holders use those accounting variables in evaluating

borrowers' default risk may provide new directions for this literature. The present paper's

contribution to the literature is twofold. First, this study takes a positive, rather than a

normative, perspective and analyzes private debt holders' approach rather than the

optimal approach in determining credit losses. Second, it provides a convenient

framework for studying private debt holders' reaction to new financial information.

The current study also relates to the literature that focuses on equity holders' and

public debt holders' use of earnings and cash flow information. Following the seminal

16 Casey and Bartzcak (1985) reiterate this conclusion. While consistent with Casey and Bartzcak (1985), Gentry, Newbold and Whitford (1985a, b) also show that three cash flow components (dividends, investments and receivables) provide additional predictive power. White, Sondhi and Fried (2002) discuss two weaknesses of these studies. First, due to costs of bankruptcy, correct prediction of bankrupt firms is more important than correct prediction of non-bankrupt firms. Second, cash dividends, which appear to be a significant predictor of bankruptcy in Gentry, Newbold and Whitford (1985 a) may be capturing the cash generation ability of the firms. It is also important to note that several other researchers (e.g., Gombola and Ketz 1983, Zmijewski 1984, Shumway 2001, Campbell, Hilscher and Szilagyi 2008) criticize the bankruptcy prediction studies for problems in methodologies, models, and sample constructions.

13

paper of Ball and Brown (1968), there has been intensive research on equity holders' use

of accounting information. Several studies examine the relative usefulness of earnings

and cash flows in equity valuation (e.g., Rayburn 1986, Wilson 1987, Livnat and Zarowin

1990, Ohlson 1990, Dechow 1994, Sloan 1996, Lev and Nissim 2004, Penman and

Yehuda 2009). This literature documents that accruals are less value relevant than cash

flows, but earnings explain stock returns better than cash flows. Additionally, findings in

Dechow, Kothari and Watts (1998) and Barth, Cram and Nelson (2001) indicate that

current earnings predict future cash flows better than do current cash flows.

The number of studies that investigate bondholders' use of earnings and cash

flows is relatively small. Early papers in finance literature (e.g., Fisher 1959) document

the relationship between earnings variability and risk premium on bonds. Later studies

provide models for pricing bonds (e.g., Brigham 1966, Van Home 1974) which

essentially relate bond values to accounting earnings. More recent researches, in both

accounting and finance, focus on detailed analysis of bondholders' reaction to earnings

(e.g., Davis, Boatsman and Baskin 1978, Datta and Dhillon 1993, Plummer and Tse

1999, Khurana and Raman 2003). These studies generally find that bond prices react to

earnings related information and as the financial condition of a firm deteriorates, earnings

becomes more relevant for bondholders. To date, the only study that provides a large

sample comparison between the use of earnings and cash flows by public debt holders is

Easton, Monahan and Vasvari (2009). Their study finds that both accruals and cash flow

from operations are correlated with bond returns, especially when firm reports a loss.

An emerging line of research also focuses on the secondary syndicated loan

market (e.g., Wittenberg-Moerman 2008, 2009, Bushman, Smith, Wittenberg-Moerman

14

2009). The loans traded in this market have similarities to both bonds and privately held

debt.17 There are very few studies that examine the use of operating performance

measures in secondary loan market. In a recent study, Allen, Guo and Weintrop (2009)

find loan prices react to earnings, and this reaction takes place around the time syndicate

members receive monthly covenant reports.

Unlike studies on equity holders and secondary debt market participants, research

on private debt holders is yet to examine how this group of stakeholders uses earnings

and cash flow information in evaluation of their investments. Nonetheless the present

study and more generally studies on private debt holders' use of accounting information,

can contribute to literature in at least two respects. First, further research can explain how

demands for and uses of accounting information differ between various groups of

stakeholders. Second, such studies can aid in building a more complete picture of the

direct and indirect effects different stakeholders have on each other.

A third strand of relevant research is the theoretical studies in finance literature

that model banks as possessors of superior information about borrowers. Those studies

often argue that due to reasons such as relationship banking, better processing of

information and economies of scale, banks have better information about future prospects

of the borrowers (e.g., Sharpe 1990, Diamond 1991). Some empirical evidence is

consistent with this assumption. For example, James (1987) finds that the stock market

responds more positively to announcement of bank loans compared to announcement of

other sources of debt. He asserts that banks provide a special service, such as better

17 Secondary trading in loan market is relatively thin but it has been growing fast since 1990s. According to Gadanecz (2004), in 2003 the trading volume in secondary market was equal to nine percent of outstanding syndicated loans. Traded loans differ from non-traded loans in various dimensions, such as seniority, security and maturity (see Wittenberg-Moerman 2008).

15

monitoring, that is not provided by other lenders. Similarly, Bharath, Sunder and Sunder

(2008) find that firms with poor accounting quality prefer bank loans probably because

banks reduce adverse selection costs for borrowers. The current paper adds to the

growing body of evidence on informational superiority of banks. It offers a direct test to

compare expectations of public investors and private debt holders with regard to future

profitability. Similar to previous empirical studies however, it does not distinguish

between the sources of informational differences.

Finally, the paper provides a new dimension to the research conducted at the

economy-wide level. Recently several studies (e.g., Kothari, Lewellen, and Warner 2006,

Sadka 2007, Anilowski, Feng and Skinner 2007, Hirshleifer, Hou, and Teoh 2007, Ball,

Sadka and Sadka 2009, Jorgensen, Li and Sadka 2008, Sadka and Sadka 2009)

investigate various aspects of economy-wide level earnings-stock price relation and

provide interesting results. The present study extends the focus of this literature to private

debt holders.

2.2. Regulations on Loan Loss Reserves

In the US, commercial banks are regulated by several banking regulators

including The Office of Comptroller of the Currency (OCC), The Federal Reserve, and

The Federal Deposit Insurance Corporation (FDIC) in addition to the FASB, IRS and for

publicly traded banks the SEC. The estimation of loan quality measures have long been a

concern for these institutions. Over the last three decades the regulators have spent

considerable effort to have banks report as accurate information about loan quality as

16

possible. Regulations often place a special emphasis on loan loss reserves (a.k.a.

allowance for loan losses) because in contrast to many other measures, estimation of this

measure includes a significant amount of forward looking information and therefore, is

subjective.18 Moreover regulators use this metric as an input for measuring the financial

health of the banks.

Loan loss reserves is a contra asset account on a bank's balance sheet. It is

established and maintained by charges against bank's operating income. Banks often

make additions to this reserve when (i) it becomes likely that a loan or group of loans will

be in part or wholly uncollectible, (ii) an unanticipated charge off occurs for which the

bank did not set aside reserves or (iii) the amount of loans increase. When a loss is

confirmed (e.g., borrower goes bankrupt), the amount is written off from total loans and

from loan loss reserves. Total book value of a bank's loans less the reserves for loan

losses demonstrate the bank's best estimate of the net realizable value of the loan

portfolio as of the financial statement date.

Few banks provided allowance for loan losses prior to 1947; the year the

Commissioner of Internal Revenue differentiated treatment of loan loss reserves from bad

debt reserves of firms in other industries.19 Tax considerations had been the major

determinant of loan loss reserves until to mid-1970s. In 1975, FASB issued FAS 5:

"Accounting for Contingencies", which became the highest hierarchical element in

18 One main concern of FASB is to measure net income for the period. Therefore in FASB's view loan loss reserves are not aimed at identifying expected future losses, but rather at identifying potential losses that will occur due to the events that took place in the current period. In contrast, economists, such as FRB officials, argue that expected loss approach is more meaningful.

19 In 1948 only 38% of the banks were holding loan loss reserves. This figure reached 61% in 1963 and 94% by 1975 (Walter 1991).

17

GAAP relating to loan loss reserves. FAS 5 stated that loss should be recognized when

probable and can be reasonably estimated at the date of financial statement. This core

definition of loss recognition has not changed since then.

Until early 1980s both due to taxation issues and regulator influence, reserving

was often based on arbitrary percentages of total loans or prior years' actual loan losses.

The increase in bank failures during 1980's led regulators to encourage bank managers to

use their own judgment in determining the adequacy of loan loss reserves. Accordingly,

measures are taken to curb the opportunistic use of this discretion. For example in 1986

the Securities and Exchange Commission issued FRR 28 "Loan Loss Allowance

Methodology and Documentation Issues". This statement had the authority of FAS 5 for

the publicly traded banks and was consistent with FAS 5. It underlined that all registrants

are required to provide adequate documentation of procedures followed in reviewing loan

portfolios and in determining amounts of allowances for loan losses. Also in 1986, the

IRS issued the Tax Reform Act which eliminated the rule that had allowed tax deductions

on additions to loan loss reserves in excess of actual charge offs.

In 1989, bank capital adequacy regulations were substantially changed and loan

loss reserves were removed from Tier I (Primary) Capital calculations. Also as a part of

total capital, loan loss reserves were no longer allowed to account for more than 1.25% of

risk weighted assets. While the regulation did not affect the measurement of potential

loan losses, it affected the incentives of banks. For example Ahmed, Takeda and Thomas

(1999) document that prior to 1990, banks were using loan loss reserves for capital

management purposes; however, after 1990, the strength of this practice diminished

significantly.

18

In 1994, FASB issued FAS 114: "Accounting by Creditors for Impairment of a

Loan". This standard did not change the fundamental recognition criteria provided by

FAS 5, but it required certain methods of measurement for the loans that are individually

considered for impairment.20 In particular, it provided more specific guidance on

measurement and disclosure for (i) loans that are identified for evaluation and

individually deemed to be impaired and (ii) loans that are restructured in a troubled debt

restructuring involving modification of the terms. Today, banks are still required to

estimate their loan loss reserves in accordance with FAS 5 and FAS 114. To ensure the

appropriate application of these standards and to assist banks on important issues about

loan loss reserving, regulators have issued a number of guiding documents since 1994.

Some noteworthy examples include the 1999 Joint Interagency Letter to Financial

Institutions, EITF D-80, 2001 Policy Statement on ALLL Methodologies, and 2006

Interagency Policy Statements on the ALLL.

In spite of several regulations and guidance regarding loan loss reserves, its

estimation still involves substantial judgment.21 The estimation process starts with

categorizing loans based on their segments and risk levels. For loans that are suspect to

losses, banks follow certain methods depending on properties of the loan. Generally, for

loans evaluated under FAS 5 regulators encourage banks to start from historical loss rates

and make adjustments for external factors (such as credit quality, current trends and

changes in industrial, geographical and political environment) and for internal factors

20 FAS 118 amended FAS 114 in 1995. It is primarily about recognition of interest income on impaired loans.

21 In her testimony before the US House of Representatives on July 16, 1999 Donna Tanoue, who was chairman of FDIC then, mentioned : "Establishing appropriate levels for loan loss reserves is an art, not a science"

(such as changes in experience and ability of lending management staff, current nature

and volume of loan portfolio, changes in quality of loan review system).22 For

determining loan loss reserves for loans evaluated under FAS 114, the standard requires

the use of one of three valuation methods: (i) present value of expected future cash flows,

(ii) fair value of collateral or (iii) observable market price of loan. Regulations are silent

about defining a fixed period of losses that the loan loss reserves should cover. However,

since losses should be incurred at the time of recognition, it is likely that reserves are

mainly created for the losses that will be confirmed in the short or medium horizon.

Borrowers' current and expected future operating performances are considered

important signals in determining loan quality.23 OCC Comptroller's Handbook mentions

that loan review systems must not only respond to obvious problems such as delinquency,

but also recognize more subtle warnings such as deterioration in a borrower's financial

statements. It cites reduced profitability and future prospects of borrowers among the

reasons for recognizing loan losses. In SAB No. 102, SEC also suggests that expected

future profitability and cash flows of borrowers should be taken into consideration when

measuring loan quality. Disclosures by commercial banks are aligned with these

requirements. For example in their financial reports many banks cite financial

Regulators stress the importance of providing detailed documentation of all these processes. Federal Reserve BHC Supervision Manual notes: "Examples of underlying supporting evidence could include but not limited to relevant articles from newspapers, other publications that describe economic events affecting particular geographic area, economic reports and data and notes from discussions with borrowers."

23 While regulators require loan losses to be estimated based on current events, not expected future events, they imply that the expected impact of current events on future performance must be incorporated into the estimates.

20

condition/performance of borrowers among the important factors that are used in

determining loan loss reserves.

2.3. Empirical Predictions

Credit losses occur when a borrower no longer has the ability to repay a loan. Due

to accounting conservatism, private debt holders recognize estimates of credit losses

before the loss is confirmed. The estimates depend on debt holder's assessment of

borrower's debt repayment ability, which in turn depends on borrower's operating

performance.

Operating performance measures have different implications about borrower's

debt repayment ability. Cash flows are necessary to service debt. However, earnings are

timelier than cash flows. Anecdotal evidence suggests that private debt holders are more

interested in cash flow information than equity holders do. For example SFAS 95,

"Statement of Cash Flows", states that the majority of respondents to the exposure draft

were commercial lenders and that those respondents claimed: "... creditors are more

exposed to fluctuations in net cash flow from operating activities than to fluctuations in

net income ...". On the other hand, some accounting practitioners, academics and

regulators assert that earnings is a more meaningful figure for debt holders. For instance

FASB's Concept Statement 1 notes: "/Investors, creditors and others] 's interest in an

enterprise's future cash flows . . . leads primarily to an interest in information about its

earnings [comprehensive income] rather than information directly about its cash flows".

21

Easton, Monahan and Vasvari (2009) demonstrate that bond holders react to both

accruals and cash flows. However, as discussed in previous sections of this paper, there

are several reasons why reaction of private debt holders may differ from that of

secondary market participants. Moreover, in the setting of this study, accruals may not be

relevant since recognition of loan losses essentially indicates that the probability of

bankruptcy for the borrower is increasing and accruals are based on going concern

assumption.

In light of arguments supporting both sides, which performance measure

(earnings or cash flows) should receive more weight in debt evaluation is an empirical

question. Therefore, while I expect estimates of potential loan losses to have a negative

correlation with borrowers' operating performance, I do not make a prediction about the

relative strengths of the association with earnings and cash flows.

GAAP performance measures, like earnings and cash flow from operations, are

comprehensive measures and are aimed to satisfy information needs of various

stakeholders. Each component of these measures has different implications about firms'

conditions and debt holders may consider certain items more relevant than others when

evaluating borrowers' debt service ability. Some borrowers fall in this view and provide

customized measures in their financial statements. For example Neiman Marcus's 10-Q

for the third quarter of 2008 reads: "We present the non-GAAP financial measures

EBITDA and Adjusted EBITDA because we ....believe the presentation of these measures

will enhance investors' ability to ... evaluate our ability to service our debt". Another

example is available in Radio Shack Corp.'s 10-K for 2007: "We believe free cash flow is

a relevant indicator of our ability to repay maturing debt, change dividend payments or

fund other uses of capital that management believes will enhance shareholder value"24

Moreover, several studies relating to debt holders (e.g., James 1995, Carey, Post and

Sharpe 1998, Beaver, McNichols and Rhie 2005) implicitly assume that some

components of performance measures are less relevant for debt holders. Accordingly

those studies use modified GAAP measures rather than reported numbers for earnings or

for cash flow from operations. While prior studies show that private debt holders modify

GAAP numbers for contracting purposes (e.g., Lefhvich 1983, El-Gazzar and Pastena

1990, Li 2008), it is unknown whether such modifications take place in estimation of

credit losses. These arguments provide the basis for the second prediction of this study. I

predict that when determining potential credit losses, private debt holders do not value

every component of the GAAP performance measures equally.

The third prediction of this study is based on two observations. First, estimates of

credit losses incorporate forward looking information about borrowers' profitability.

Thus, these estimates should have predictive power on future performance. Second, a

widely held belief holds that private debt holders possess superior information about the

future prospects of the borrowers. In particular, several studies in finance and accounting

argue that private lenders have informational advantage over other investors (e.g., Fama

1985, James 1987, Diamond 1991). However, the proponents of efficient markets would

probably disagree. For example, Black (1975) states that: "[In an efficient market] if the

bank's loan officers have the ability to obtain and identify information on companies that

is not already identified, they would be better off as security analysts".

24 It is important to note that both the names and the calculations of such non-GAAP measures vary among firms, see examples in Mulford and Comiskey (2002) and Mulford and Maloney (2004).

The current setting allows for a natural test of the question: "Do private debt

holders have more information about the future profitability of the borrowers than equity

holders do?" Clearly, it is infeasible to measure the whole information set of either

private debt holders or equity investors. However, it is possible to compare the predictive

ability of private debt holders' estimates (i.e., loan loss reserves) with that of equity

holders' estimates (e.g., valuation ratios, growth forecasts etc.). If public investors utilize

the information provided by private debt holders, then the information content of loan

loss reserves should be subsumed by the estimates of future operating performance based

on public information. Thus, I predict that after controlling for proxies of public

information, loan loss reserves cannot predict future operating performance.

3. DATA AND SUMMARY STATISTICS

3.1. Sample Formation and Variable Definitions

The sample for the empirical tests includes firms with available accounting data

on the Compustat quarterly file from 1990 to 2008.25 Following firms are excluded from

the sample: (i) firms that have fiscal quarter ends in months other than March, June,

September, or December (to match the timing of loan data and firm data) (ii) financial

firms (to avoid any potential mechanical relation between loan quality measures and

performance measures), and (iii) firms that are not incorporated in the US (foreign firms

are likely to borrow from local banks rather than the US banks).

25 Prior to 1989, the necessary cash flow information is unavailable for a representative number of firms and loan loss reserves were extremely volatile in late 1980s due to less developed country crisis.

I use four performance measures in the main tests due to their relevance: (i)

operating income (01), (ii) operating income before depreciation (OIBDP), (iii) cash flow

from operations (CFO), and (iv) cash flow from operations before interest and tax

payments (CFOBIT). I test several other measures and discuss their results in the

robustness checks section. I measure operating performance as growth in these variables.

I calculate growth using two different methods. Under the first method, growth is equal to

the sum of economy-wide income (cash flows) for last four quarters minus the same

figure for prior year's same quarter, divided by the latter. This method allows testing

results using rolling four quarter growth rates. The second method is more suitable for

investigating the association in the short horizon. This method is based on seasonally

differenced quarterly measure, defined as economy-wide income (cash flows) in the

current quarter minus the same figure for four quarters prior, divided by the latter. Hence

to be included in the sample, firms must have data for all relevant quarters. As discussed

in section 2.2, both current performance and expected future performance are cited

among the important factors in determination of loan loss reserves. Accordingly, both

current and future growth rates are included in the tests.

Data on loan quality measures, except for that on provisions for loan losses, are

available in Federal Reserve Economic Database (FRED®).26 Loan loss reserves

information as reported in the database is equal to total loan loss reserves by all

commercial banks divided by total gross loans. Similarly, percentage of nonperforming

commercial loans equals total nonperforming commercial loans divided by total

commercial loans. I calculate the change in these ratios as the difference between the

26 FRED® contains quarterly information from Report of Condition and Income. These filings are mandatory for every national, state member and insured non-member bank.

value of the ratio at time t and that at time t-4. Net charge-offs are equal to total net

charge offs during the quarter divided by total gross loans. Finally, I calculate provisions

for loan losses as total provisions for loan losses divided by total gross loans, using data

from call reports. Since net charge-offs and provisions for loan losses are flow variables,

for each of them current and past three quarters' values are summed up to make the

variable consistent with the loan loss reserves measure.

Proxies for public information used in the study include financial ratios (earnings

to price (E/P), book to market (B/M) and dividend yield (DvYld)), stock returns and

analyst forecasts. E/P ratio (DvYld) is calculated as rolling four quarter trailing aggregate

operating income before depreciation/amortization (dividends) divided by current

aggregate market value. B/M ratio is equal to current aggregate book value divided by

current aggregate market value. Only firms with available data for both numerator and

denominator are included in the calculations. I calculate earnings growth forecasts using

I/B/E/S database. For consistency, I restrict this data to nonfinancial, US firms that have

fiscal quarter ends at March, June, September, or December. Earnings growth forecast for

a period is equal to the sum of current quarter consensus earnings forecasts minus actual

aggregate earnings at quarter t-4, divided by the latter. Because there is not enough time

series data on analysts' operating earnings forecasts, I use net income growth forecasts.

For consensus earnings forecasts, I use the last consensus forecast issued at least 30 days

before the report date. I manually eliminated one influential firm quarter observation,

which is likely to be a data error, from I/B/E/S sample. Inclusion of this observation is

favorable for the results of the paper. Finally, the returns used in the tests are CRSP value

weighted quarterly returns for AMEX, NYSE and NASDAQ stocks.

3.2. Summary Statistics

In Table 1, I report the summary statistics for the data. Quarterly OIBDP (01)

growth is 7.3% (7.65%) for the sample period. This compares to 7.8% reported in

Kothari, Lewellen and Warner (2006) for 1970-2000 period.27 On average, growth rate in

cash flows is higher than the growth rate in earnings in the sample period. Correlation

between concurrent values of OIBDP and CFOBIT is 64% and correlation between

current OIBDP and future CFOBIT is 56% (untabulated). Figure 1 graphically illustrates

the behavior of growth rates over time. High profitability growth during mid-1990s is

followed by negative growth rates in early 2000s.

The average number of firms used in quarterly earnings and cash flow from

operations growth calculations are similar and above 4300 firms per quarter. Data

availability constrains the sample size for cash flow from operations before interest and

tax payments. To ensure the robustness of the results to differences in number of firms, I

replicated all tests first replacing missing observations for interest and tax payments with

zero, second using the sample of firms that has available data for all relevant variables.

Conclusions remain similar.

The average change in loan loss reserves is negative. This might be seen as an

indication of reduction in the credit risk during the sample period although the post-2007

events arguably suggest otherwise. Figure 1 demonstrates that while the change in loan

loss reserves is negative in most of the quarters, in early 1990's, and early 2000's loan

loss reserves grew. There is a noticeable negative association between the growth rates in

operating performance measures and the change in loan loss reserves. This is intuitive

27 The measure of earnings in that study is earnings before extraordinary items.

since banks would recognize loan losses when the borrower's performance falls short of

necessary level to repay debt. Table 2 shows that the correlation is negative for all

operating performance measures and stronger for earnings growth measures. The same

result also holds for the current growth rates (untabulated). The average provisions for

loan losses and net charge offs are very similar for the sample period. This is not

surprising since in the long run provisions should be equal to actual losses. Figure 2

shows the distribution of operating performance on changes in loan loss reserves.

I use three valuation ratios in the study (E/P, DvYld and B/M). E/P ratio has a

mean of approximately 13%. This is lower than the 20% reported in Lewellen (2004) for

1963-2000. Presumably the boom in 1990's and the stock market bubble contribute to

this difference. E/P ratio is negatively correlated with one-quarter-ahead growth rates,

consistent with E/P ratio reflecting future profitability growth. The mean B/M ratio is

34%. Similar to E/P ratio, B/M and DvYld are negatively correlated with one-period-

ahead operating performance whereas analysts' growth forecasts are positively correlated

with it. Quarterly average return on the CRSP value weighted index is 3.1%.

4. EMPIRICAL FINDINGS

4.1. Earnings vs. Cash Flows

The empirical analysis begins with testing the relation between estimates of credit

losses and borrowers' operating performance measures. I use two different models to test

the relation:

28

„ AOP. ^ AOP , ALLRt = a + Pi + P2 — + £

OP t„4 0 P t -3 (Quarterly Differencing)

I L 3 OP - 2 : | O P OP - 2 .OP A L L R t = a + P i * _. ^ ' + P 2 f S l L « ^ + *

5^_-OP A-JOP (Four Qtr. Rolling)

/ LLR \ / LLR \ = ( T o t a l L o a n s j t " ( T O U I L o , P s ) t , 4 where v ' t t _ 4 and OP is aggregate earnings

or cash flows measure.

I present results of this regression analysis in Table 3. Results for the univariate

regression analysis are similar to those for the multivariate analysis, and thus, are not

reported. Panel A contains the results for the first model (quarterly differencing) and

panel B contains the results for the second model (four quarter rolling window). To

account for the autocorrelation in the errors, I report Newey-West (1987) standard

28 errors.

Table 3 provides three main results. First, in both Panel A and Panel B, all

coefficients on growth rates are negative and, all except one is statistically significant.

This suggests that in the US, commercial banks' estimates of credit losses are negatively

associated with borrowing firms' operating performance measures. Second, earnings

growth measures explain the variation in changes in estimates of credit losses better than

the cash flow growth measures. In Panel A, the regression model with OIBDP growth

rates has an adjusted R-squared of 66% compared to 35% provided by the model with

CFOBIT growth rates. Vuong test indicates that unadjusted R-squared for the two models

are significantly different at 1% level (Z-statistic 3.39). Results in Panel B are similar to

28 The maximum number of lags is set to 4 in Panel A and to 8 in Panel B.

those in Panel A. This suggests that commercial banks' estimates of credit losses relate

more to the earnings of borrowers than to their cash flow. Third, exclusion of certain

items, such as depreciation or cash payments for taxes and interest from performance

measures increases the explanatory power of the models. Specifically, both coefficients

in the regression model with OIBDP growth rates are statistically significant whereas

coefficient on future OI value is statistically insignificant. According to Vuong test,

OIBDP model also has significantly higher explanatory power compared to OI model in

both panels (p value < 0.01). This suggests that information about depreciation is not as

valuable as working capital accruals in estimation of credit losses. Similarly CFOBIT

model has higher explanatory power compared to CFO model, indicating that interest and

tax payments are not as relevant as other operating cash flows in estimation of credit

losses. In fact, CFO model has the lowest explanatory power among all four models.

Table 3 also demonstrates that in earnings-based models, current earnings growth

rates have larger coefficients than future earnings growth rates. In contrast, in cash-flow-

based models, future cash flow growth rates have larger coefficients than those of current

cash flow growth rates. This finding is perhaps a result of conservative accounting and

the correlation between current earnings and future cash flows. To test if cash flow

measures have any incremental explanatory power over earnings measures, the last two

models in both panels include both OIBDP and cash flow growth rates. 29 In those

29 In the four quarter rolling window tests, calculations of current and future growth rates have four overlapping periods, therefore it is prudent to note that collinearity is not a significant concern. The correlations between current and future growth rates are less than 40%. Variance inflation factor is less than 1.15 for all measures and condition number is less than 3.5 for all measures except for CFO model, which has a condition number of 6. These values are considerably lower than the traditional upper threshold of 10. Moreover conclusions using univariate regressions remain the same. Similarly, in regressions that include both earnings and cash flow variables collinearity statistics do not indicate a problem.

30

columns, OIBDP growth rates remain negative and significant while cash flow growth

rates have coefficients indistinguishable from zero. This suggests that OIBDP subsumes

most of the relevant information contained in cash flow rates.

In summary, results in Table 3 lead to three conclusions. First, both earnings-

based and cash-flow-based operating performance measures of the borrowers are

negatively correlated with the estimates of credit losses. Second, operating income

measures explain changes in estimates of potential credit losses better than operating cash

flow measures do. Operating cash flow measures have explanatory power but their

information content is mostly subsumed by operating income measures. Third, not all

items are equally useful in explaining the variation in estimates of credit losses.

Performance measures that exclude certain items (e.g., depreciation/amortization) explain

variation in estimates of credit losses better than the measures which include those items.

4.2. Components of Loan Loss Reserves

Loan loss reserves increase with provision for loan losses and decrease with loan

charge-offs. Charge-offs are mainly related with confirmed losses while provisions are

made for both confirmed and unconfirmed losses. Accordingly it is useful to examine

how these two components that change loan loss reserves are affected by the operating

performance of the borrowers. Table 4 reports the results of this analysis.

In Panel A, confirmed losses (i.e. net charge-offs) are regressed on current and

future operating performance measures of borrowers. First column shows that current

OIBDP measure is significant in explaining confirmed losses while future OIBDP is not.

31

In second column, both current and future CFOBIT measures are significant.

Additionally, the coefficient on current CFOBIT is higher than that on future CFOBIT,

which contrasts with the coefficients on CFOBIT on Table 3. CFOBIT model also has

higher explanatory power compared to OIBDP model however Vuong test fails to reject

the equality of r-squareds. When both OIBDP and CFOBIT are included in the

regression, OIBDP measures become insignificant whereas CFOBIT measures remain

significant. Results are similar for four quarter rolling window tests, except for the

combined test in which current CFOBIT measure also becomes insignificant.

In Panel B, unconfirmed losses are examined. Since provisions for loan losses are

created for both unconfirmed losses and unexpected confirmed losses, in this regression

net charge-offs are included in the models as a control. The first column in Panel B

suggests that current and future OIBDP measures have similar coefficients and both are

related with the unconfirmed losses. In the second column, current CFOBIT measure is

insignificant but future CFOBIT measure is significant, indicating that future cash flows

are correlated with unconfirmed losses. When OIBDP and CFOBIT measures are

included in the same model, coefficients on both OIBDP measures remain significant

while current CFOBIT measure becomes positive and future CFOBIT is insignificant.

Four quarter rolling window tests confirm the same findings.

Overall, table 4 suggests that confirmed loss component of changes in loan loss

reserves is mainly related with the cash flows of the borrowers whereas unconfirmed loss

component is related with operating income. Moreover, it is the current operating

performance of the borrowers that matters more for calculation of the confirmed losses

while for unconfirmed losses both current and future operating performance is important.

32

4.3. High Credit Risk Firms vs. Low Credit Risk Firms

In this section, I extend the analysis in previous section by focusing on firm

portfolios based on credit risk. In particular, firms with high credit risk are more likely to

default on their debt payments. Therefore loan loss reserves should be more strongly

correlated with the operating performance of high credit risk firms rather than with that of

low credit risk firms.

To test this prediction and to refine the findings reported in Table 3, I conduct a

second analysis. In this test, for every quarter, I classify firms into different portfolios

based on leverage.30 Next, I calculate OIBDP growth for each portfolio. Then, similar to

the analysis in Table 3, I run a regression of aggregate changes in loan loss reserves on

each portfolio's current and future earnings growth. Table 5 presents the results of this

second analysis. In Panels A and B I split the sample into two and four leverage

portfolios, respectively.

In Panel A, both quarterly differenced, and four quarter rolling window analysis

shows that earnings growth for high-leverage firms is more significantly correlated with

loan loss reserves than that of low-leverage firms. Coefficients on current earnings

growth for high-leverage firms are about 50% larger than those on low-leverage firms.

Coefficients on future earnings growth for low-leverage firms are insignificant, whereas

for high-leverage firms these coefficients are significant. R-squared of the regression for

high-leverage group is almost twice that of low-leverage group. Vuong test suggests that

30 Following Carey, Post and Sharpe (1998), I define leverage as book value of debt divided by the sum of itself and book value of equity. I exclude firms with negative/missing debt or equity values from the sample. Results are robust when leverage is defined as total liabilities to total assets ratio.

the difference is statistically significant at 1%. These findings suggest that the results in

table 3 are mainly driven by more leveraged firms.

In Panel B, I further split the sample into four leverage portfolios. This more

detailed examination reveals an interesting pattern among the portfolios. In particular, the

explanatory power of the earnings growth rates increases monotonically with leverage

except for the highest leverage portfolio. While it may seem counter intuitive, this

disturbance supports the results of the previous studies in finance literature which have

found that firms in highest risk groups tend to borrow from non-bank lenders. Carey, Post

and Sharpe (1998) report that high leverage firms borrow from finance companies rather

than from banks.31 Figure 3 is taken from their study and demonstrates the borrowing

patterns across different leverage groups. The figure indicates that the distribution of

loans exhibits a symmetric bell shape for banks, in contrast to the markedly skewed

distribution towards the high leverage firms for finance companies. Thus, the disturbance

of the monotonic relation between earnings growth and loan loss reserves in the last

leverage group is not unexpected and in fact, adds to the evidence that firms in the

highest risk groups tend to borrow from sources other than banks.

4.4. Effect of Financial Condition of Banks

Prior research has found some evidence regarding income smoothing and capital

management via loan loss measures (e.g., Wahlen, 1994, Ahmed, Takeda and Thomas

1999). To the extent that these activities are idiosyncratic, the expected effect on the

31 Similarly Denis and Mihov (2003) reports that firms with low credit quality are more likely to borrow from non-bank private lenders.

current results would be zero. However, Liu and Ryan (2006) assert that these activities

may have a systematic component. In particular, authors conclude that in the boom

periods, specifically during 1990s, banks managed loan loss provisions and charge offs

upwards to smooth their earnings.32

To test whether financial condition of banks has any impact on the current findings,

I test whether results differ when on average banks have lower capital ratios or ROAs. I

calculate aggregate ROA for banks as the sum of earnings before provision for loan

losses for all banks in a given quarter divided by the sum of one quarter lagged total

assets for the same banks using data from call reports. For capital ratio, since call reports

do not contain this data until 2001, I use data from Compustat Bank database which

contains data starting 1993. Because the number of banks change dramatically from 1993

to 1994, sample starts from 1994 for capital ratio tests. Capital ratio in a given period is

equal to median capital ratio of banks with available data in that period. I assign each

quarter into one of two categories for each variable using the median value of the variable

for the whole time series (i.e. high capital ratio vs low capital ratio, high ROA vs low

ROA). The results of the tests are reported in table 6.

Capital ratio is negatively associated with the changes in loan loss reserves,

indicating that in periods with lower capital ratio loan loss reserves tend to increase. In

contrast, ROA has a positive relation with changes in loan loss reserves. When high

32 Loan loss reserves increase with loan loss provisions and decrease with charge offs. Accordingly, findings of Liu and Ryan (2006) may contribute to the current results only if banks inflate charge offs more than they do the loan loss provisions. Otherwise, if the upward management of provisions is stronger than that of charge offs, then the loan loss reserves will increase in boom times which would bias against the findings of the current study.

35

capital ratio dummy (takes value 1 if the capital ratio in the quarter is higher than median

capital ratio of the whole sample period, 0 otherwise) and its interaction term with

current OIBDP is included in the regression along with current OIBDP, dummy variable

remains negative and significant whereas interaction term is positive and significant. This

suggests that, loan loss reserves become less sensitive to operating performance of

borrowers when capital ratios are relative high. When high ROA dummy (takes value 1 if

the capital ratio in the quarter is higher than median capital ratio of the whole sample

period, 0 otherwise) and its interaction term with current OIBDP is included in the

regression along with current OIBDP, both the dummy variable and the interaction term

becomes insignificant. With one exception, these results are similar when OIBDP is

measured using four quarter rolling window. The ROA dummy variable becomes positive

and significant when four quarter rolling window is used.

4.5. Predictive Ability of Loan Loss Reserves

Tables 3 reveals that changes in loan loss reserves are significantly associated

with future operating performance of the borrowers. Accordingly, in this section I

analyze whether loan loss reserves provide any information about future operating

performance that is not captured by proxies of public information. I use following models

and report their results in Table 7:

A Q l t + t _ _ _ = « f fcALLRt + fcPlt + c (One-quarter-ahead growth)

= cr+ PiALLR, + p2PI { + e (One-year-ahead growth)

36

where ALLR t= Total Loans

LLR

t .Total Loans

LLR

, 01 is the operating earnings

before depreciation, and PI is the proxy for public information.

In Panel A, the dependent variable is one-quarter-ahead operating income before

depreciation growth. Proxies for expected earnings growth rates include both stock

market based variables (current quarter's stock market returns (VW Return), analysts'

earnings growth forecasts (Growth Forecast)) and credit market based variables (changes

in 10 year t-bond -1 year t-bill yield spread (ATERM), changes in Moody's baa -10 year

t-bond yield spread (ASPREAD)) as well as average of past three quarters' aggregate

earnings growth rate (Average Past Growth).33 Standard errors are calculated using

Newey-West (1987) method with four lags.

The first column in Panel A indicates that ALLR has a decent ability to predict

one-quarter-ahead aggregate OIBDP growth. A one percent increase in loan loss reserves

as a percentage of total loans indicates an approximately forty percent decrease in future

profits. In univariate regressions, coefficients on all proxies are significant. As expected,

coefficients are positive for market returns, analysts' forecasts, and average earnings

growth rate. ATERM and ASPREAD have negative coefficients.34

Conclusions remain similar when alternative measures such as future stock returns (quarter t+1), six month stock returns (from t-1 to t+1) , Moody's baa bond yields, Moody's baa-aaa yield spread, and/or four or five quarter average earnings growth rates are used.

34 These findings complement Kothari, Lewellen and Warner (2006) who find a negative relation between term structure and concurrent earnings growth. They also find a negative relation between market returns and concurrent earnings growth. In the current sample period the correlation between market returns and concurrent earnings growth is positive. However further investigation reveals that this positive relation is driven by 2000-2003 period, and when this period is excluded, the correlation becomes negative.

When ALLR is included into the models, market returns and ATERM become

insignificant. This suggests that ALLR already incorporates the information about one-

quarter-ahead operating performance incorporated into these two variables. An intuitive

result emerges when ALLR and average earnings growth rate variable are included in the

same model. The significance of the average earnings growth variable disappears and the

goodness of fit improves only slightly compared to the regression reported in the first

column. This probably reflects the regulators' requirement that historical data should be

incorporated into the estimation of credit losses. ALLR also provides additional

explanatory power over growth forecasts and ASPREAD. While coefficients on analysts'

earnings growth forecasts and ASPREAD remain significant, the information content of

ALLR is not fully captured by either of these variables.35 Final column shows the results

when ALLR is included in the model together with all proxies.

Panel B reports the results for one-year-ahead earnings growth using four quarter

rolling window. Figure 4 shows the behavior of one-year-ahead OIBDP growth and

aggregate E/P ratio over time. Clearly, the relation between E/P ratio and earnings growth

is significantly distorted between 2000-2003 period. Several events, such as standard

changes, and the burst of dot-com bubble, might contribute to this finding.36 Accordingly,

The use of net income growth estimates as analysts' growth forecast, instead of operating income growth estimates, may add noise to this measure. However, even when actual earnings growth rates are calculated using net income measures as reported in l/B/E/S database, the change in loan loss reserves remains negative and statistically significant. Thus, the result is not attributable to the difference between earnings growth measures. In fact, the use of l/B/E/S earnings growth significantly biases against finding results for loan loss reserves, because as results suggest and as discussed in the robustness checks not all accruals are equally valuable to debt holders.

36 Prior studies, (e.g., Jorgensen, Li and Sadka 2008) also show that this period distorts earnings-return relation, but less so when earnings is measured using operating income. In the current study, it is likely that the shorter length of sample period and the use of rolling window exacerbate the problem.

results for valuation ratios are sensitive to different treatments of this period. Particularly

results for E/P, B/M and DvYld become weaker and often insignificant when this period

is included. On the other hand when the period is excluded, results for ALLR are weaker

and certain econometric problems arise.371 report results for both treatments in Panel B.

In the univariate regressions in Panel B, the coefficient on ALLR increases to -15

(to -31 when 2000-2003 is included). This change is in the expected direction since loan

loss reserves, by definition, aim to measure potential credit losses in the short term. When

2000-2003 period is interpolated, E/P and DvYld have negative and significant

coefficients. In the multivariate regressions, results are similar. The coefficient on ALLR

becomes slightly smaller but remains significant when used with valuation ratios. Overall

results in Panel A and Panel B demonstrate that ALLR have significant explanatory

power on OIBDP growth even after controlling for several proxies of public information.

Figure 5 shows the coefficients and the R-squareds from the univariate

regressions of future OIBDP growth on ALLR (E/P ratio) for different horizons. In all of

the regressions 2000-2003 period is excluded and replaced by linearly interpolated data.

Earnings growth over a 2 year horizon is calculated as the geometric mean of earnings

growth in years t+1 and t+2. Panel A demonstrates that the predictive power of ALLR

erodes over time. On the contrary, the predictive power of E/P ratio increases. In the one-

year-ahead earnings regression, predictive power of ALLR and E/P ratio becomes similar,

and in the two-years ahead earnings growth regression E/P ratio has higher explanatory

37 Exclusion of this period causes econometric problems. Specifically, if the gap in the time series is ignored, methods like Newey-West (1987) correction can taint the results and direction of the effect is unclear. To alleviate these problems, linear interpolation method is used to replace the missing data.

39

power than ALLR. Panel B shows that coefficient on ALLR increases from -21 in one

quarter regression to -7 in 2 year regression and becomes insignificant. In contrast,

coefficient on E/P ratio becomes more significant in longer investment horizons. These

results are consistent with ALLR performing well in short term and E/P ratio working

better in longer term.

4.6. Robustness Checks

4.6.1. Alternative Methods for Calculations

Several tests are conducted to test the robustness of the results to alternative

measurement methods. First, I test different measures of earnings growth. Following

prior studies (e.g., Kothari, Lewellen and Warner 2006) I scale earnings changes by (i)

lagged book value and (ii) lagged market value instead of lagged earnings. Results

(untabulated) remain similar for both quarterly differencing and rolling four quarter

window tests.

Second, I conduct tests to check whether firms in Compustat database are

representative of the economy. Jorgensen, Konchitchki, Ozel and Sadka (2009) find that

in the US, the correlation between the ROA for publicly traded firms and that for

privately held firms has been high (69%). Thus, publicly traded firms are likely to be

representative of the economy. Since data source in Jorgensen et al. (2009) do not

provide data for pre-2000 period, I employ an alternative data source, Global Insight

Database, to check whether results are robust to inclusion of private firms. This database

provides data on aggregate earnings and cash flows for all the corporations, including

40

private firms, in the United States.38 When I repeat the tests using this data, results are

weaker but earnings measure still dominates cash flow measure. Thus, the main

conclusion remains robust.

Third, I test the robustness of loan loss reserves measure. Banks report loan loss •jq

reserves for their loan portfolio but not separately for commercial loans. To check if the

noise affects the results, I replicate the tests using non-performing commercial loans.

Loan loss reserve for a loan and the nonperforming status are highly correlated, but loan

loss reserves involve a more significant subjective component. Loans are often classified

as nonperforming when they are regarded as nonaccrual or when they are past due for 90

days or more. I measure non-performing commercial loans as total non-performing

commercial loans divided by total commercial loans at the end of quarter. This measure

is also available in FRED®. The results, reported in Table 8, are similar to those in Table

3. In Table 8, earnings growth variables are mostly significant and the models including

those variables have higher explanatory power than models that include cash flow growth

variables. OIBDP growth rates again outperform OI and cash flow growth rates.

Finally, to ensure that results are not specific to a certain measurement method, I

retest the first two predictions by measuring the variables in ratio form rather than in

growth form. In particular, loan loss reserves is measured as total loan loss reserves

divided by total loans and operating performance is measured as four quarter rolling

38 The drawback of the data is that since Bureau of Economic Analysis (BEA) collects this data for governmental use, it is subject to certain adjustments. Therefore, it is only a noisy proxy for GAAP measures. Moreover, BEA does not provide a breakdown of different components (e.g., accruals) of the measures.

39 In the sample period, the size of outstanding commercial loans has been on average 1.7 times that of consumer loans.

41

performance measure (i.e., earnings or cash flows) divided by beginning of period total

aggregate assets.40 Again, the performance measure calculated using operating income

before depreciation outperforms the other three measures.

4.6.2. Alternative Performance Measures

The main tables present results for four performance measures. I test four

additional performance measures. These are revenues, net income, free cash flows and

total cash flows 41 The results (untabulated) indicate that while revenue and net income

growth rates are significant and negatively related with changes in loan loss reserves,

their coefficients and explanatory powers are weak compared to OIBDP and OI growth

rates. Moreover, neither total cash flows nor free cash flows have a significant negative

relation with loan loss reserves. Overall, these results provide additional evidence that

accruals are relevant in determining loan loss reserves and different earnings components

are not equally weighted in estimation of credit losses.

4.6.3. Macroeconomic Variables

In determination of loan loss reserves, macroeconomic changes are also taken into

consideration. To test whether results are driven by some macroeconomic events, I use

following measures of macroeconomic environment: GDP growth rate, industrial

40 For this test, because of data trend, a time variable is included into the models. Also collinearity becomes a problem therefore conclusion is based on Vuong statistic for explanatory powers of only current (not both current and future) values of performance measures.

41 Aggregate net income and free cash flows are negative in some years. Therefore, to compare these alternative measures with the four measures used in the main tests, I use lagged book value in the denominator for consistency. I calculate free cash flows as cash flow from operations plus cash flow from investments.

42

production growth, change in University of Michigan consumer sentiment index, change

in 10 year T-bond rate, and Case-Shiller Housing Price Index growth rate. First four are

common measures used in the literature for capturing the changes in macroeconomic

environment, and the last measure is included to capture the effects of changes in real

estate prices. Table 9 reports the results of the models.42 Changes are measured as

changes from time t-4 to t and growth rates are calculated using data at time t-4 and t.

Results suggest that the correlation between earnings measures and loan loss reserves

cannot be attributed to macroeconomic changes.

The other macroeconomic variables that are tested but not tabulated include:

changes in Moody's Aaa-Baa yield spread, changes in 1 year t-bill rates, changes in term

structure, quarterly returns on S&P 500/CRSP value weighted portfolio, changes in

University of Michigan consumer sentiment index, returns on MIT/CRE Commercial

Property Index, growth rates of personal disposable income, and growth rates of

commercial and industrial loans. The results are robust to addition of these variables.

I also examine whether economic downturns affect the relation between operating

performance and ALLR. To test this relation, I include a dummy variable for recession

quarters (as defined by NBER) and its interaction term with operating performance

measures to the models in Table 3. Results (untabulated) suggest that findings are not

driven solely by recession periods.

Results using macroeconomic variables should be interpreted with caution. To the extent macroeconomic factors and loan loss reserves are co-determined with corporate performance, addition of these factors will capture information content of corporate performance measures. The effect may not be symmetric for each of the performance measures.

43

4.7. Discussion of Findings

The findings in table 3 indicate that private debt holders focus primarily on

operating earnings of borrowers when evaluating credit losses. In particular, among

various measures of performance, growth in operating earnings before depreciation and

amortization has the highest association with the changes in loan loss reserves. While it is

outside the scope of the current study to explain why debt holders prefer earnings-based

measures over cash flow-based measures, it is pertinent to elaborate on this question. The

straightforward interpretation of the result is that certain, but not all, accruals provide

additional explanatory power about borrower's debt service capacity in the short run. For

example, Stumpp (2000) argues that in a deep recession a company can curtail CAPEX to

pay principal and interest. Accordingly it is legitimate to exclude depreciation from

performance measures when evaluating firms that are in unfavorable financial conditions.

An alternative explanation of the finding is that debt holders use earnings-based

measures to approximate cash flows perhaps because available cash flow information is

not adequate for debt evaluation purposes. This might be the case if, for example, debt

holders are interested in details of operating activities which are often reported only in

accrual basis (e.g., segment disclosures). Assuming that collection of the same

information on cash basis is infeasible or too costly, debt holders may prefer to base their

analysis on income statement information for a consistent and detailed assessment. The

following lines from SFAS 95 also suggest that debt holders demand more information

about cash flows than what regulations currently require: "Both commercial lenders and

equity analysts who responded to the Exposure Draft asked that more detail on cash

flows from operating activities be required. Some said that degree of detail is more

important than manner of presentation The Board understands, however, that

determining operating cash payments in more detail than the minimum specified in

paragraph 27 might involve significant incremental costs over those already required to

apply the indirect method... "

As a third interpretation, debt holders may not be using information in the cash

flow statement optimally. For example, a number of banking professionals contend that

in commercial banking, ascendant definitions of cash flows have not necessarily been the

ones that are the most relevant for the analysis but rather those that are popular among

investment community (e.g., Strischek 2001).

The second main finding is that change in loan loss reserves outperforms proxies

of public information in predicting aggregate operating income growth in the short run.

The result is present only in the short term presumably because of the nature of loan loss

reserves. In particular, although standards do not define a fixed time frame of loss

coverage, regulatory guidance states that coverage of one year's losses is often adequate.

This guidance probably is an important determinant of banks' loan loss reserving

policies.

An interesting question is: "Why do not proxies of public information fully

incorporate the information content of loan loss reserves?" To the extent that proxies

reflect public investors' expectations about future performance, Table 7 suggests that

public investors can improve their estimates of future performance by using information

content of loan loss reserves. At least, two arguments can be made against this claim. The

first argument is based on the anonymity of loan loss reserves. In particular, because

banks are not required to reveal the list of the clients for whom specific reserves are

created for, equity investors are often unable to track the loan loss reserve information

back to the borrower. This obscurity may hinder the use of loan quality information in

equity valuation.43 Although this argument may hold at the firm level, it is not clear why

it is applicable at the aggregate level. In other words, in the absence of firm specific

information, equity holders may still utilize this information by constructing portfolios

that are representative of the economy. Thus, as long as equity investors use information

efficiently, the anonymity of loan loss reserves should not lead to these findings. A

second argument can be made based on the inherent differences between private debt

holders' estimates and equity holders' estimates. Specifically, equity holders are often

concerned about the long term prospects of the firms whereas for private debt holders

shorter term profits are relatively more important. Thus, the estimates of private debt

holders can explain short term performance better than valuation ratios. This explanation

is consistent with the finding that the explanatory power of loan loss reserves erodes as

earnings growth is measured over longer periods. However, the explanation has other

shortcomings; for example, it does not explain why loan loss reserves remain significant

after controlling for analysts' estimates.

In rare instances investors may make inferences from loan quality disclosures. For example, First Union and Bank of America were known to hold two very large pieces of a syndicated loan to Sunbeam Corp. in 2000. In November 14, 2000 when First Union Corp announced that it will raise charge offs due to a large problematic loan but declined to name the borrower, several analysts claimed that the borrower is Sunbeam. At the time, Sunbeam's stock price was already reflecting the significant problems the firm has. But on the day of First Union's announcement Sunbeam's stocks plummeted approximately 8%. Sunbeam's stock price dropped another 9% the next day when Bank of America announced that it too will increase (more than double) the charge offs due to a loan to an "unnamed borrower". Sunbeam went bankrupt in February 2001.

5. CONCLUSION

In this study, I investigate whether private debt holders focus more on earnings or

cash flows of the borrowers when estimating potential credit losses. I find that private

debt holders consider earnings-based measures more relevant than cash flow-based

measures in determination of potential credit losses. While it is mostly the current

performance that is important in explaining confirmed losses, both current and future

performances are important in explaining unconfirmed losses. I also find that private debt

holders do not consider all accruals equally informative about borrowers' debt repayment

ability. Moreover my findings suggest that, compared to public stakeholders, private debt

holders have superior information about borrowers' future operating performance.

This study contributes to the research on debt markets in two key respects. First,

while extant studies mainly focus on secondary debt market participants, this paper

explores the debt evaluation process by private debt holders. Given the significance of the

role private debt holders play in debt markets and the differences that distinguish private

debt holders from other stakeholders, a better understanding of how private debt holders

use accounting information is necessary. Second, the present paper employs a novel

framework for studying private debt holders' reactions to new financial information

about borrowers. Such a framework may prove useful for other studies on debt markets.

My findings provide empirical support to two arguments that are of interest to

both academics and practitioners. First, they are aligned with the view that due to certain

characteristics of earnings, such as timeliness, debt holders would rather focus on

earnings than cash flows. However, this does not necessarily imply that debt holders

47

focus on earnings for those characteristics alone. Alternative explanations are possible;

for example, if borrowers are unable to provide detailed cash flow information for certain

transactions (e.g., because GAAP does not require this information and hence it is not

collected) then private lenders may be using earnings-based measures as proxies of cash

flows. Another explanation that some banking professionals offer is that commercial

bankers may not be using cash flow information optimally in the analysis of credits.

Second, my findings are consistent with the arguments in finance and accounting

literatures that private debt holders have an informational advantage over other investors

who rely only on public information. This finding is perhaps even more interesting

because public disclosures of private debt holders (i.e., loan loss reserves) are used as the

measure of private debt holders' estimates of future performance. This indicates that

equity holders may be neglecting this source of information.

It is important to stress that findings are based on economy-wide level data and as

such reflect the behavior of private debt holders on average. These findings may not

apply to every individual private debt holder within the economy. Moreover, the paper

studies the relative usefulness of earnings and cash flows and the findings cannot be

interpreted as earnings being the only accounting performance measure used in the

estimation of credit losses. Debt holders can and optimally will utilize information from

both income statement and cash flow statement in debt evaluation.

The results raise additional issues for future research. Of particular interest is the

comparative ability of earnings-based measures and cash flow-based measures in

measuring default risk. To date, studies on bankruptcy prediction have produced

conflicting results. More conclusive research is necessary to understand why private debt

48

holders favor earnings over cash flows in debt evaluation. Additional studies might focus

on establishing whether debt financing induces incentives for earnings management, or

on exploring whether the source of financing is an important determinant in accrual

quality. Also, future studies are necessary to explain why equity investors appear to

neglect the information content of loan loss reserves regarding future operating

performance.

49

REFERENCES

Ahmed, A. S., Takeda, C., & Thomas, S. (1999). Bank loan loss provisions: a reexamination of capital management, earnings management and signaling effects. Journal of Accounting and Economics, 28(1), 1-25.

Allen, L., Guo, H., & Weintrop, J. (2008). The information content of quarterly earnings in syndicated bank loan prices. Asia-Pacific Journal of Accounting and Economics, 15 (1), 91-122.

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23 (4), 589-609.

Altman, E. I. (1977). ZETA(TM) analysis : A new model to identify bankruptcy risk of corporations. Journal of Banking and Finance, 1 (1), 29-54.

Ang, J. S., & Patel, K. A. (1975). Bond rating methods: comparison and validation. Journal of Finance , 30 (2), 631-640.

Anilowski, C., Feng, M., & Skinner, D. J. (2007). Does earnings guidance affect market returns? The nature and information content of aggregate earnings guidance. Journal of Accounting and Economics, 44 (1-2), 36-63.

Armstrong, C. S., Guay, W. R., & Weber, J. P. (2009). The role of information and financial reporting. Working Paper.

Asquith, P., Beatty, A., & Weber, J. P. (2005). Performance pricing in bank debt contracts. Journal of Accounting and Economics, 40 (1-3), 101-128.

Aziz, A., & Lawson, G. H. (1989). Cash flow reporting and financial distress models: Testing of hypotheses. Financial Management, 18 (1), 55-63.

Ball, R., & Brown, P. (1968). An empirical evaluation of accounting income numbers. Journal of Accounting Research , 6 (2), 159-177.

Ball, R., Robin, A., & Sadka, G. (2008). Is financial reporting shaped by equity markets or by debt markets? An international study of timeliness and conservatism. The Review of Accounting Studies , 13 (2-3), 168-205.

Ball, R., Sadka, G., & Sadka, R. (2009). Aggregate earnings and asset prices. Journal of Accounting Research , 47 (5), 1097-1133.

Ball, R., Bushman, R. M., & Vasvari, F. P. (2008). The debt-contracting value of accounting information and loan syndicate structure. Journal of Accounting Research , 46 (2), 247-287.

Barth, M. E., Cram, D. P., & Nelson, K. K. (2001). Accruals and the prediction of future cash flows. The Accounting Review, 76 (1), 27-58.

Beatty, A., Dichev, I. D., & Weber, J. P. (2002). The role and characteristics of accounting-based performance pricing in private debt contracts. Working Paper.

Beatty, A., Weber, J. P., & Yu, J. J. (2008). Conservatism and debt. Journal of Accounting and Economics , 45 (2-3), 154-174.

50

Beatty, A., Liao, S., & Weber, J. P. (2008). Evidence on the determinants and economic consequences of delegated monitoring. Working Paper.

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.

Beaver, W. H., McNichols, M. F., & Rhie, J. W. (2005). Have financial statements become less informative? Evidence from the ability of financial ratios to predict bankruptcy. Review of Accounting Studies , 10 (1), 93-122.

Beneish, M. D., & Press, E. (1995). Interrelation among events of default. Contemporary Accounting Research, 12 (1), 57-84.

Bharath, S., Sunder, J., & Sunder, S. (2008). Accounting quality and debt contracting. The Accounting Review, 83(1), 1-28.

Bhojraj, S., & Swaminathan, B. (2009). How does the corporate bond market value capital investments and accruals? Review of Accounting Studies, 14 (1), 31-62.

Black, F. (1975). Bank funds management in an efficient market. Journal of Financial Economics, 2 (4), 323-339.

Brigham, E. F. (1966). An analysis of convertible debentures: Theory and some empirical evidence. Journal of Finance, 21 (1), 35-54.

Bushman, R. M., Smith, A. J., & Wittenberg-Moerman, R. (2008). Price discovery and dissemination of private information by loan syndicate participants. Working Paper.

Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. Journal of Finance , 63 (6), 2899-2939.

Carey, M., Post, M., & Sharpe, S. A. (1998). Does corporate lending by banks and finance companies differ? Evidence on specialization in private debt contracting. The Journal of Finance, 53 (3), 845-878.

Casey, C. J., & Bartczak, N. J. (1984). Cash flow-It's not the bottom line. Harvard Business Review, 62 (4), 61-66.

Casey, C. J., & Bartczak, N. J. (1985). Using operating cash flow data to predict financial distress: some extensions. Journal of Accounting Research , 23 (1), 384-401.

Chen, K. C., & Wei, K. C. (1993). Creditors' decisions to waive violations of accounting-based debt covenants. The Accounting Review, 68 (2), 218-232.

Datta, S., & Dhillon, U. S. (1993). Bond and stock market response to unexpected earnings announcements. Journal of Financial and Quantitative Analysis, 28 (4), 565-577.

Davis, D. W., Boatsman, J. R., & Baskin, E. F. (1978). On generalizing stock market research to a broader class of markets. The Accounting Review, 53 (1), 1-10.

Deakin, E. B. (1972). A discriminant analysis of predictors of business failure. Journal of Accounting Research , 10 (1), 167-179.

Dechow, P. M. (1994). Accounting earnings and cash flows as measures of firm performance: The role of accounting accrual. Journal of Accounting and Economics, 18 {1), 3-42.

51

Dechow, P. M., Kothari, S. P., & Watts, R. L. (1998). The relation between earnings and cash flows. Journal of Accounting and Economics, 25 (2), 133-168.

DeFond, M. L., & Zhang, J. (2008). The information content of earnings surprises in the corporate bond market. Working Paper.

Denis, D. J., & Mihov, V. T. (2003). The choice among bank debt, non-bank private debt, and public debt: evidence from new corporate borrowings. Journal of Financial Economics , 70 (1), 3-28.

Diamond, D. W. (1984). Financial intermediation and delegated monitoring. The Review of Economic Studies, 51 (3), 393-414.

Diamond, D. W. (1991). Monitoring and reputation: The choice between bank loans and directly placed debt. Journal of Political Economy, 99 (4), 689-721.

Dichev, I. D., & Skinner, D. J. (2002). Large-sample evidence on the debt covenant hypothesis. Journal of Accounting Research, 40 (4), 1091-1123.

Easton, P. D., Monahan, S. J., & Vasvari, F. P. (2009). Initial evidence on the role of accounting earnings in the bond market. Journal of Accounting Research, 47 (3), 721-766.

El-Gazzar, S., & Pastena, V. (1990). Negotiated accounting rules in private financial contracts. Journal of Accounting and Economics, 12 (4), 381-396.

Fama, E. F. (1985). What's different about banks? Journal of Monetary Economics , 15 {1), 29-39.

Federal Reserve Bank. (2002). Bank Holding Companies Supervision Manual.

Fisher, L. (1959). Determinants of risk premium on corporate bonds. Journal of Political Economy , 67(3), 217-237.

Fitch Research. (1994). Cash is King in Bond Analysis. Special Report.

Gadanecz, B. (2004). The syndicated loan market: structure, development, and implications. BIS Quarterly Review,December, 75-89.

Gentry, J. A., Newbold, P., & Whitford, D. T. (1985a). Classifying bankrupt firms with funds flow components. Journal of Accounting Research, 23(1), 146-160.

Gentry, J. A., Newbold, P., & Whitford, D. T. (1985b). Predicting bankruptcy: If cash flow's not the bottom Line, what is? Financial Analysts Journal, 41 (5), 47-56.

Gentry, J. A., Newbold, P., & Whitford, D. T. (1988). Predicting industrial bond ratings with a probit model and funds flow components. Financial Review, 23 (3), 269-286.

Gombola, M. J., & Ketz, J. E. (1983). A note on cash flow and classification patterns of financial ratios. The Accounting Review, 58 (1), 105-114.

Gombola, M. J., Haskins, M. E., Ketz, J. E., & Williams, D. D. (1987). Cash Flow in Bankruptcy Prediction. Financial Management, 16 (4), 55-65.

52

Gorton, G., & Kahn, J. (2000). The design of bank loan contracts. Review of Financial Studies , 13 (2), 331-364.

Hirshleifer, D. A., Hou, K., & Teoh, S. H. (2008). Accruals,cash flows and aggregate stock returns. Journal of Financial Economics , 91 (3), 389-406.

James, C. (1987). Some evidence on the uniqueness of bank loans. Journal of Financial Economics, 19 (2), 217-235.

James, C. (1995). When do banks take equity in debt restructurings? The Review of Financial Studies, 8(4), 1209-1234.

Jones, S., & Hensher, D. A. (2004). Predicting firm financial distress: a mixed logit model. Accounting Review, 79 (4), 1011-1038.

Jorgensen, B. N., Konchitchki, Y., Ozel, B. N., & Sadka, G. (2009). The legal environment and the differential performance of publicly-traded and privately-held firms. Working Paper.

Jorgensen, B. N., Li, J., & Sadka, G. (2008). Are accounting standards diversifiable? Evidence of the aggregate effects of standards. Working Paper.

Kaplan, R. S., & Urwitz, G. (1979). Statistical models of bond ratings: A methodological inquiry. Journal of Business, 52 (2), 231-261.

Khurana, I. K., & Raman, K. K. (2003). Are fundamentals priced in the bond market? Contemporary Accounting Research , 20 (3), 465-494.

Kothari, S. P., Lewellen, J. W., & Warner, J. B. (2006). Stock returns, aggregate earnings surprises and behavioral finance. Journal of Financial Economics, 79 (3), 537-568.

Largay, J. A., & Stickney, C. P. (1980). Cash flows, ratio analysis and the W.T. Grant Company bankruptcy. Financial Analysts Journal, 36 (4), 51-54.

Leftwich, R. (1983). Accounting information in private markets: Evidence from private lending agreements. The Accounting Review, 58 (1), 23-42.

Lev, B., & Nissim, D. (2004). Taxable income, future earnings, and equity values. The Accounting Review, 79(4), 1039-1074.

Lev, B., & Nissim, D. (2006). The persistence of the accruals anomaly. Contemporary Accounting Research , 23 (1), 193-226.

Lewellen, J. (2004). Predicting returns with financial ratios. Journal of Financial Economics , 74 (2), 209-235.

Li, N. (2008). Negotiated measurement rules in debt contracts. Working Paper.

Libby, R. (1979). The impact of uncertainty reporting on the loan decision. Journal of Accounting Research , 17, 35-57.

Liu, C., & Ryan, S. G. (2006). Income smoothing over the business cycle: Changes in banks' coordinated management of provisions for loan losses and loan charge-offs from the pre-1990 bust to the 1990s boom. The Accounting Review, 81 (2), 421-441.

53

Livnat, J., & Zarowin, P. (1990). The incremental information content of cash-flow components. Journal of Accounting and Economics, 13 (1), 25-46.

Mashruwala, C., Rajgopal, S., & Shevlin, T. J. (2006). Why is the accrual anomaly not arbitraged away? The role of idiosyncratic risk and transaction costs. Journal of Accounting and Economics, 42(1-2), 3-33.

Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance , 29 (2), 449-470.

Mossman, C. E., Bell, G. G., Swartz, L. M., & Turtle, H. (1998). An empirical comparison of bankruptcy models. Financial Review, 33 (2), 35-54.

Mulford, C. W., & Comiskey, E. E. (2002). The financial numbers game: detecting creative accounting practices. John Wiley & Sons, Inc.

Mulford, C. W., & Maloney, D. K. (2004). Corporate reporting practices for free cash flow. http://faculty.babson.edu/halsey/acc7500/Cash%20flow%20-%20free%20cash%20flow%20computation%20survey.pdf.

Newey, W. K., & West, K. D. (1987). A simple, positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix. Econometrica, 55 (3), 703-708.

Norton, C. L., & Smith, R. E. (1979). A comparison of general price level and historical cost financial statements in the prediction of bankruptcy. The Accounting Review, 54 (1), 72-87.

Office of Comptroller of Currency. (1998). Allowance for Loan and Lease Losses. Comptroller's Handbook.

Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy . Journal of Accounting Research, 18 (1) 109-131.

Ohlson, J. A. (1990). A synthesis of security valuation theory and the role of dividends, cash flows, and earnings. Contemporary Accounting Research, 6 (2), 648-676.

Penman, S. H., & Yehuda, N. (2009). The pricing of earnings and cash flows and an affirmation of accrual accounting. Review of Accounting Studies , 14 (4), 453-479.

Plummer, C. E., & Tse, S. Y. (1999). The effect of limited liability on the informativeness of earnings: Evidence from the stock and bond markets. Contemporary Accounting Research , 16 (3), 541-574.

Rayburn, J. (1986). The association of operating cash flow and accruals with security returns. Journal of Accounting Research , 24, 112-133.

Sadka, G. (2007). Understanding stock price volatility: The role of earnings. Journal of Accounting Research , 45 (1), 199-228.

Sadka, G., & Sadka, R. (2009). Predictability and the earnings-returns relation. Journal of Financial Economics, 94 (1), 87-106.

Sharpe, S. A. (1990). Asymmetric information, bank lending and implicit contracts: A stylized model of customer relationships. Journal of Finance, 45 (4), 1069-1087.

54

Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74(1), 101-124.

Sloan, R. G. (1996). Do stock prices fully reflect information in accruals and cash flows about future earnings? The Accounting Review, 71 (3), 289-316.

Standard and Poor's. (2008). Corporate Ratings Criteria.

Strischek, D. (2001). E-B-l-T-D-A it doesn't spell "Cash Flow". RMA Journal, 84 (3), 30-40.

Stumpp, P. M. (2000). Putting EBITDA in perspective : Ten critical failings of EBITDA as the principal determinant of cash flow. Moody's Investors Service.

Van Home, J. C. (1974). Financial management and policy. Englewood Cliffs, N.J.: Prentice Hall.

Wahlen, J. M. (1994). The nature of information in commercial bank loan loss disclosures. The Accounting Review, 69 (3), 455-478.

Walter, J. R. (1991). Loan loss reserves. Economic Review, 20-30.

Watts, R. L. (2003). Conservatism in accounting part I: Explanations and implications. Accounting Horizons, 77(3), 207-221.

White, G. I., Sondhi, A. C., & Fried, D. (2002). The Analysis and Use of Financial Statements. John Wiley & Sons, Inc.

Wilson, P. G. (1987). The incremental information content of the accrual and funds components of earnings after controlling for earnings. The Accounting Review, 62 (2), 293-322.

Wittenberg-Moerman, R. (2008). The role of information asymmetry and financial reporting quality in debt trading: Evidence from the secondary loan market. Journal of Accounting and Economics, 46 (2-3), 240-260.

Wittenberg-Moerman, R. (2009). The impact of information asymmetry on debt pricing and maturity. Working Paper.

Zhang, J. (2008). The contracting benefits of accounting conservatism to lenders and borrowers. Journal of Accounting and Economics, 45 (1), 27-54.

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research , 22, 59-82.

FIGURES

56

Figure 1: Operating Performance and ALLR over Time

Panel A: Growth in Operating Income before Dep. and ALLR

0.4

0.2

1 v V

-0.2

-0.4

•ALLR . — <_ Growth in Operating Inc. Bef. Dep. (Quarterly, Current)

Panel B: Growth in Cash Flow from Operations before Interest and Taxes and ALLR

0.4

0.2

-0.4

i r % I r t / V * 1 \ * I . I V

t . A * A/ \

i » V

1

i

r ! J r / i f h iV # » \ i « i"» i % %

-ALLR • Growth in CFO Bef. interest & Tax {Quarterly, Current)

For illustration purposes ALLR values are multiplied by 100.

Figure 2: Operating Performance and ALLR

57

Growth in CFO

t -0 .2

Growth in CFOBIT

pf» 0.2 «p.3 I % •

• • •

0.4

0.3

• #

• 0.2

AL

LR

• V I •

_ - J

• • #

-0.3 -0.2 •

-0.1

-0.2

%

0.3 0.4

Growth in OIBDP

58

Figure 3: Distribution of Leverage for Bank and Finance Company Borrowers

0.3

O Bank Borrowers

• Finance Company Borrowers

0-.1 .1-2 .2.3 .3.4 4-5 .5.6

Leverage

.6-7 7-.8

This figure compares the distributions of leverage for bank borrowers and finance company borrowers. Leverage is measured as the book value of debt divided by the sum of itself and book equity. The figure is taken from Carey, Post and Sharpe (1998) study. Loan data for their study is taken from Loan Pricing Corporation, SEC filings and leverage data is taken from Compustat. White (Black) bars indicate the loans given to that group of leveraged companies as a percentage of total loans issued by banks (finance corporations).

59

Figure 4: The Effect of 2000-2003

60

Figure 5: Univariate Regressions of Future Operating Performance on E/P Ratio and ALLR

Panel A: Adjusted R-Squareds

Adj. R-Squareds F r o ^ Univariate

Regressions

30.00%

25.00%

20.00%

15.00%

10.00%

5.00%

0.00% 1/4 1 2 Horizon of Future Earnings Growth (Years)

Panel B: Coefficients (t-statistics)

Coefficients Q.OO (t-statistics) From

Univariate

Regressions -0.10

- 0 . 2 0

-0.30

-0.40

-0.50

- 0 . 6 0

-0.70

- 0 .80

Horizon of Future Earnings Growth (Years)

This figure compares the R-squareds (Panel A) and coefficients (Panel B) from univariate regressions of growth in future OIBDP on ALLR and E/P ratio. Growth is calculated for three different horizons: one quarter ahead, one year ahead and two years ahead. Two years ahead growth is equal to geometric mean of growth in year t and year t+1. Rolling four quarter window is used for calculating annual growth rates. 2000-2003 period is replaced with interpolated data in all regressions. Coefficients on change in LLR are divided by 100. Adjusted R-Squareds are from OLS regressions. Standard errors (shown in parenthesis in Panel B) are calculated using Newey-West (1987) method.

* E/P "ALLR

61

TABLES

Earnings Growth

Cash Flow Growth

Loan Quality Ratios

Public Information

Returns

Table 1: Summary Statistics

^ Average No of Firms Mean St Dev Min Max

Operating Inc. Before Depreciation (Q) 68 4342 7.312 7.246 -10.825 20.728 Operating Income (Q) 68 5021 7.650 11.534 -24.585 27.581 Operating Inc. Before Depreciation (Roll) 65 3723 7.053 6.685 -7.368 17.191 Operating Income (Roll) 65 4422 7.351 10.601 -20.499 22.473

Cash Flow From Ops. Bef. Interest and Taxes (Q) 68 1709 9.774 9.091 -12.620 28.535 Cash Flow From Ops. (Q) 68 4451 8.874 9.051 -8.557 33.409 Cash Flow From Ops. Bef. Interest and Taxes (Roll) Cash Flow From Ops. (Roll)

65 1393 9.186 7.476 -2.816 26.544 Cash Flow From Ops. Bef. Interest and Taxes (Roll) Cash Flow From Ops. (Roll) 65 4049 8.183 4.700 -0 . 000 21.627

A Loan Loss Reserves/G. Loans 68 -0.076 0.133 -0.27 0.25 Net Charge offs/G.Loans 68 - 0.750 0.299 0.383 1.465 Provision For Loan Losses/G. Loans 68 - 0.758 0.314 0.409 1.539 A Nonperforming Loans/G. Loans (Commercial) 68 - -0.194 0.644 -1.39 1.16

E/P Ratio 68 3793 13.433 3.218 8.157 20.653 B/MRatio 68 4573 34.129 6.744 20.957 49.648 Dividend Yield 68 4301 1.826 0.626 0.906 3.269 Earnings Growth Forecasts 68 1780 10.728 13.878 -30.323 36.054

Value Weighted Returns (Q) 68 7563 3.120 7.718 -16.640 21.289

CT> N3

The table reports the average, standard deviation and the extreme values of aggregate earnings growth, aggregate cash flow growth, change in aggregate loan quality ratios, proxies for public information and market returns. All variables are in percentages, except N, number of observations and average number of firms per year. Aggregate earnings is measured using two alternative metrics: i) Operating Income Before Depreciation (OIBDP), ii) Operating income (OI). Aggregate cash flows is also measured using two alternative metrics: i) Cash Flow From Operations (CFO) and Cash Flow From Operations Before Interest and Tax Payments (CFOBIT). Growth in the earnings variable is calculated using two methods: i) seasonal differencing (i.e., growth in aggregate earnings is equal to aggregate earnings for quarter t minus aggregate earnings for quarter t-4, divided by the latter) ii) four quarter rolling window (i.e., growth in aggregate earnings at time t is equal to sum of aggregate earnings for last four quarters (from t-3 to t) minus the same value calculated at the quarter t-4, divided by the latter). Growth in cash flow from operations is calculated similarly. Aggregate E/P (Dividend Yield) is calculated as sum of four quarter-rolling aggregate operating income before depreciation (dividends) divided by total market value of firms at the end of the given period. Aggregate B/M ratio is calculated as aggregate book value of the firms at the end of quarter t divided by the aggregate market value of the firms at the end of quarter t. Accounting data is taken from Compustat North America database. Change in loan loss reserves/total gross loans and non-performing commercial loans/total commercial loans are measured as change in ratio from quarter t-4 to quarter t. Provisions for loan losses/total loans and net charge offs/total loans are measured as the sum of current and past three quarters' values of these ratios. Loan quality data, except provisions for loan losses, are available in Federal Reserve Economic Database (FRED). Data for provisions for loan losses are extracted from call reports and aggregated. Earnings growth forecasts are calculated using analyst forecast data from I/B/E/S database. Earnings growth forecast for a period is equal to aggregate consensus earnings forecasts for quarter t minus aggregate actual earnings in quarter t-4 divided by the latter. For consensus earnings forecasts, the last consensus forecast issued at least 30 days before the report date is taken. Returns are CRSP quarterly value weighted returns for AMEX, NYSE, and NASDAQ stocks. Sample consists of firms with March, June, September or December fiscal quarter ends. Financial firms (sic>5999 & sic<7000) and non US firms are excluded from the earnings, cash flows, earnings forecast and valuation ratio calculations. In calculation of growth variables only firms with available data for all relevant quarters are used, where applicable.

Table 2: Correlation Matrix

ALLR FOIBDP FOI FCFOBIT FCFO E/P B/M DvYld ALLR (Q) (Q) (Q) (Q)

E/P B/M DvYld

ALLR 1.000 FOIBDP(Q) -0.704 1.000

FOI(Q) -0.502 0.836 1.000 FCFOBIT(Q) -0.506 0.643 0.433 1.000

FCFO(Q) -0.187 0.360 0.230 0.478 1.000 E/P -0.015 -0.073 -0.020 -0.214 -0.018 1.000 B/M 0.184 -0.196 0.010 -0.317 -0.103 0.857 1.000

DvYld 0.033 -0.096 -0.004 -0.228 -0.062 0.951 0.864 1.000 Growth Forecast -0.576 0.727 0.653 0.434 0.034 0.071 -0.057 0.065

V.W. Returns (Q) -0.200 0.174 0.117 0.096 0.017 0.027 -0.131 0.139

Growth V.W. Returns Forecast (Q)

1.000 0.103 1.000

The table reports correlations among the change in aggregate loan loss reserves, aggregate one-quarter-ahead earnings growth, aggregate one-quarter-ahead cash flow growth, market returns and proxies for public information. Aggregate earnings is measured using two alternative metrics: i) Operating Income Before Depreciation (OIBDP), ii) Operating income (OI). Aggregate cash flows is also measured using two alternative metrics: i) Cash Flow From Operations (CFO) and Cash Flow From Operations Before Interest and Tax payments (CFOBIT). One-quarter-ahead growth rate in the earnings variable is calculated using quarterly seasonal differencing (i.e., growth in aggregate earnings is equal to aggregate earnings in quarter t minus aggregate earnings in quarter t-4, divided by the latter). Growth in cash flow from operations is calculated similarly. Aggregate E/P (Dividend Yield) is calculated as sum of 4 period-rolling window aggregate operating income before depreciation (dividends) divided by total market value of firms at the end of the given period. Aggregate B/M ratio is calculated as aggregate book value of the firms at the end of quarter t divided by the aggregate market value of the firms at the end of quarter t. Accounting data is taken from Compustat North America database. Change in loan quality ratios is measured as change in ratio from quarter t-4 to quarter t. Loan quality data is available in Federal Reserve Economic Database (FRED). Earnings growth forecasts are calculated using analyst forecast data from I/B/E/S database. Earnings growth forecast for a period is equal to aggregate consensus earnings forecasts for quarter t minus aggregate actual earnings in quarter t-4 divided by the latter. For consensus earnings forecasts, the last consensus forecast issued at least 30 days before the report date is taken. Returns are CRSP quarterly value weighted returns for AMEX, NYSE, and NASDAQ stocks. Sample consists of firms with March, June, September or December fiscal quarter ends. Financial firms (sic>5999 & sic<7000) and non US firms are excluded from the earnings, cash flows, earnings forecast and valuation ratio calculations. In calculation of growth variables only firms with available data for all relevant quarters are used, where applicable.

CT)

65

Table 3: Regressions of ALLR on Earnings and Cash Flow Growth Variables

Panel A: Quarterly Differenced Data

Current OIBDP Growth

Future OIBDP Growth

Current 01 Growth

Future OI Growth

(1) (2) (3) (4) (5) (6) -0.983 -1.007 -1.033 (-5.50) (-6.08) (-5.28) -0.572 -0.518 -0.571 (-4.27) (-3.13) (-3.62)

-0.737 (-3.89) -0.000 (-0.00)

Current CFOBIT -0.443 0.040 Growth (-3.14) (0.25)

Future CFOBIT Growth

Current CFO Growth

Future CFO Growth

Intercept

Adj. R-Squared

-0.577 -0.071 (-2.94) (-0.41)

-0.348 0.124 (-2.46) (1.14) -0.334 -0.032 (-1.92) (-0.27)

0.034 -0.023 0.021 -0.019 0.035 0.029 (1.72) (-1.08) (0.54) (-0.44) (1.34) (1.04)

66.19% 41.88% 35.05% 7.84% 65.26% 66.30%

66

Panel B: Four Quarter Rolling Window

Current OIBDP Growth

Future OIBDP Growth

Current OI Growth

Future OI Growth

(1) (2) (3) (4) (5) (6) -1.359 -1.848 -1.511 (-9.63) (-8.38) (-12.38) -0.825 -0.555 -0.757 (-5.76) (-1.87) (-5.36)

-0.955 (-10.81) -0.189 (-1.10)

Current CFOBIT -0.462 0.498 Growth (-2.60) (2.34)

Future CFOBIT Growth

Current CFO Growth

Future CFO Growth

Intercept

-1.004 -0.072 (-2.67) (-0.35)

-1.083 0.410 (-3.59) (1.71) -1.386 -0.153 (-2.62) (-0.80)

0.078 0.011 0.054 0.123 0.053 0.064 (6.10) (0.10) (1.31) (1.62) (3.44) (2.53)

Adj. R-Squared 83.63% 71.01% 53.13% 34.08% 85.57% 85.81%

This table reports the slope estimates, t-statistics (in parenthesis) and adjusted r-squareds when change in loan loss reserves is regressed on current and future earnings and cash flow growth rates. Panel A reports the results of the regression with seasonally differenced quarterly data as explanatory variables (E.g., aggregate earnings for quarter t minus aggregate earnings for quarter t-4 divided by the latter). Specifically, following model is used:

M L R T = p 1 _ L + p , _ i i i + E Ur t_4 U r ( - j where OP is either earnings or cash flows variable. Panel B reports the results of the regression with four quarter rolling window data as explanatory variables. (E.g., sum of aggregate earnings for four quarters ending at quarter t minus sum of aggregate earnings for four quarters ending at quarter t-4 divided by the latter). Specifically, following model is used:

dLLRt — « + fc _ + P 2 +

Definitions of variables are available in the notes to Table 1. Adjusted r-squareds are from OLS regressions. Standard errors are calculated using Newey-West (1987) methodology. All coefficients are multiplied by 100.

67

Table 4: Components of ALLR

Panel A: Net Charge-Offs

Quarterly

Current OIBDP Growth

Future OIBDP Growth

Current CFOBIT Growth

Future CFOBIT Growth

Intercept

Adj. R-Squared

Four Quarter Rolling (1) (2) (3) (4) (5) (6)

-2.243 -1.041 -3.191 -1.464 (-2.86) (-1.14) (-3.54) (-1.37) 0.167 0.540 0.855 2.179 (0.31) (0.78) (1.46) (3.06)

-1.369 -1.028 -2.376 -0.923 (-4.02) (-2.34) (-4.10) (-1.20) -0.956 -0.909 -0.862 -1.994 (-2.42) (-1.78) (-1.65) (-3.48)

0.904 0.983 0.980 0.907 1.052 0.959 (8.30) (8.80) (8.47) (8.33) (8.46) (9.60)

24.16% 34.14% 33.97% 52.92% 57.52% 63.23%

Panel B: Provisions for Loan Losses

Quarterly

Current OIBDP Growth

Future OIBDP Growth

Current CFOBIT Growth

Future CFOBIT Growth

Net Charge-Offs

Intercept

Adj. R-Squared

Four Quarter Rolling (1) (2) (3) (4) (5) (6)

-0.586 -0.792 -0.882 -1.349 (-3.08) (-4.71) (-3.48) (-4.91) -0.556 -0.548 -0.756 -0.671 (-4.49) (-3.76) (-5.74) (-2.13)

-0.107 0.258 -0.052 0.559 (-0.71) (2.36) (-0.21) (2.96) -0.373 0.063 -0.740 0.122 (-2.36) (0.52) (-2.47) (0.46)

0.880 0.933 0.907 0.827 0.886 0.878 (17.24) (11.28) (17.01) (13.96) (12.37) (14.38) 0.178 0.102 0.141 0.248 0.156 0.174 (3.36) (1.14) (2.23) (3.77) (1.55) (2.47)

95.68% 91.94% 95.87% 96.62% 93.20% 97.16%

This table reports the slope estimates, t-statistics (in parenthesis) and adjusted r-squareds when components of change in loan loss reserves is regressed on current and future earnings and cash flow growth rates. In Panel A, the dependent variable is net charge offs, and in Panel B it is provisions for loan losses. Definitions of variables are available in the notes to Table 1. Adjusted r-squareds are from OLS regressions. Standard errors are calculated using Newey-West (1987) methodology. All coefficients, except those on net charge-offs, are multiplied by 100.

68

Table 5: Firm Portfolios Based on Leverage

Panel A: Two Portfolios

Quarterly Four Quarter Rolling Low High Low High

Current OIBDP -0.645 -0.901 -0.802 -1.297 Growth (-2.80) (-5.45) (-7.93) (-7.54)

Future OIBDP -0.001 -0.811 -0.198 -1.127 Growth (-0.01) (-4.78) (-1.03) (-5.11)

Intercept -0.009 0.029 0.031 0.061 Intercept (-0.33) (1.40) (0.83) (3.94) Adj. R-Squared 33.42% 65.02% 57.59% 78.35%

Panel B: Four Portfolios

Quarterly Four Quarter Rolling 1

(Low) 2 3 4 (High) 1 (Low) 2 3 4

(High) Current OIBDP -0.266 -0.558 -1.008 -0.617 -0.473 -0.709 -1.345 -0.944

Growth (-2.16) (-2.46) (-4.06) (-4.24) (-7.37) (-5.80) (-7.54) (-6.05) Future OIBDP 0.033 -0.065 -0.405 -0.674 0.068 -0.277 -0.671 -1.256

Growth (0.27) (-0.32) (-1.65) (-3.73) (0.69) (-1.36) (-3.90) (-2.77)

Intercept -0.051 -0.012 0.024 -0.010 -0.031 0.028 0.058 0.022 Intercept (-1.92) (-0.42) (1.11) (-0.40) (-1.17) (0.61) (4.15) (0.84) Adj. R-Squared 8.94% 32.07% 59.46% 42.47% 41.44% 58.72% 76.76% 62.28%

This table reports the slope estimates, t-statistics (in parenthesis) and adjusted r-squareds when change in loan loss reserves is regressed on current and future aggregate OIBDP growth rates of firms in different leverage portfolios. In every quarter, firms are classified into different portfolios based on leverage where leverage is defined as book value of debt divided by the sum of itself and book value of equity. Earnings growth for each portfolio is calculated using both seasonally differenced quarterly data (E.g., aggregate earnings for quarter t minus aggregate earnings for quarter t-4 divided by the latter) and four quarter rolling window data (E.g., sum of aggregate earnings for four quarters ending at quarter t minus sum of aggregate earnings for four quarters ending at quarter t-4 divided by the latter). Panel A reports the results of the regressions when sample is split into two leverage portfolios, and Panel B reports the results of the regressions when sample is split into four leverage portfolios. Adjusted r-squareds are from OLS regressions. Standard errors are calculated using Newey-West (1987) methodology. All coefficients are multiplied by 100.

69

Table 6: Effect of Financial Condition of Lender

Quarterly Four Quarter Rolling (1) (2) (3) (4) (5) (6) (7) (8)

Current OIBDP -1.496 -1.313 -1.311 -1.686 -1.384 -1.610 Growth (-5.68) (-4.85) (-3.05) (-6.08) (-9.21) (-3.75)

Capital Ratio -0.188 (-2.08)

High Capital Ratio -0.118 -0.109 -0.085 -0.092 Dummy (-4.78) (-2.53) (-2.83) (-2.43)

High Capital Dummy 0.765 0.572 0.468 0.407 * OIBDP Growth (3.13) (1.69) (1.74) (1.39)

Bank ROA 1.270 Bank ROA (2.32)

High ROA Dummy 0.050 (1.42)

0.051 (0.92)

0.064 (2.39)

0.043 (0.73)

High ROA Dummy * -0.084 0.002 -0.141 0.132 OIBDP Growth (-0.30) (0.00) (-0.89) (0.34)

Intercept 1.915 -0.006 0.050 -0.002 0.016 0.067 -0.009 0.042 Intercept (1.94) (-2.56) (2.03) (-0.06) (0.26) (2.50) (-0.37) (0.61) Adj. R-Squared 16.36% 19.36% 55.52% 57.91% 60.09% 60.23% 64.82% 66.14%

This table reports the slope estimates, t-statistics (in parenthesis) and adjusted r-squareds when components of changes in loan loss reserves is regressed on current and future earnings and cash flow growth rates. Panel A reports the results of the regression with four quarter aggregate net charge offs as dependent variable. Specifically, following model is used for quarterly regressions:

40Pt AOP.+1 ,VC0, = a + B, 4- 8 , i l l 4- c t 3

1 OP^_4 O P | _ j

and for four quarter rolling window regressions:

where OP is either earnings or cash flows variable.

Panel B reports the results of the regression with four quarter aggregate provision for loan losses as dependent variable. Definitions of variables are available in the notes to Table 1. Adjusted r-squareds are from OLS regressions. Standard errors are calculated using Newey-West (1987) methodology. All coefficients are multiplied by 100.

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Adj

. R-S

quar

ed

53.8

2%

1.67

%

46.7

0%

52.0

5%

30.3

4%

20.9

5%

53.3

0%

54.5

6%

65.8

7%

53.4

2%

56.7

5%

67.1

5%

-Vl

o

71

Panel B: Annual Earnings Growth

Including 2000-2003 One-Year-Ahead OIBDP Growth

(1) (2) (3) (4) (5) (6) (7) ALLR -30.754 -30.758 -30.889 -31.230 ALLR (-5.62) (-5.61) (-5.36) (-5.14)

E/P -0.029 -0.035 E/P (-0.06) (-0.11)

DvYld 0.201 (0.07)

0.534 (0.33)

B/M -0.076 0.049 B/M (-0.39) (0.33)

Intercept 0.054 0.083 0.075 0.105 0.058 0.044 0.036 Intercept (4.10) (1.12) (1.35) (1.52) (1.14) (1.08) (0.60) Adj. R-Squared 42.18% 0.00% 0.00% 0.00% 41.27% 41.55% 41.54%

Interpolating 2000-2003 1 Year Ahead OIBDP Growth (Four Quarter Rolling)

(1) (2) (3) (4) (5) (6) (7) ALLR -14.558 -14.637 -13.790 -12.758 ALLR (-1.67) (-2.27) (-2.14) (-1.65)

E/P -0.643 -0.646 E/P (-1.78) (-2.67)

DvYld -3.177 (-1.74)

-3.029 (-2.29)

B/M -0.214 (-1.20)

-0.163 (-1.14)

Intercept 0.088 0.187 0.158 0.173 0.175 0.144 0.145 Intercept (5.94) (4.22) (5.24) (2.70) (5.05) (5.28) (2.79) Adj. R-Squared 16.55% 20.27% 18.77% 9.22% 37.63% 33.96% 21.28%

This table reports the results of the regression of future growth in operating earnings on proxies of public information and change in loan loss reserves. One-quarter-ahead OIBDP growth is equal to aggregate earnings for quarter t minus aggregate earnings for quarter t-4 divided by the latter (Panel A). In particular, Panel A reports the results of the following model:

0IBBPt_3 = « + 0iAlLR t + p2PI, + e

where OI is the operating earnings before depreciation, and PI is the proxy for public information. One-year-ahead OIBDP growth is equal to sum of aggregate earnings for four quarters ending at quarter t minus sum of aggregate earnings for four quarters ending at quarter t-4 divided by the latter (Panel B). In particular, Panel B reports the results of the following model:

OIBDP, - OIBDP, a + p.ALLRt + p2PI, + t St-i u , u l " s

OIBDP* Definition of variables is available in the notes to Table 1. Adjusted r-squareds are from OLS regressions. Standard errors are calculated using Newey-West (1987) methodology.

72

Table 8: Regressions of Change in Nonperforming Commercial Loans on Earnings and Cash Flow Growth Variables

Quarterly Four Quarter Rolling (1) (2) (3) (4) (5) (6) (7) (8)

Current OIBDP -2.230 -2.069 Growth (-1.86) (-1.06)

Future OIBDP -3.833 -5.687 Growth (-3.04) (-3.57)

Current OI -1.689 -2.267 Growth (-2.08) (-2.22)

Future OI -1.629 -2.554 Growth (-1.58) (-2.52)

Current CFOBIT -0.429 1.372 Growth (-0.42) (0.71)

Future CFOBIT -1.217 -3.272 Growth (-1.23) (-1.49)

Current CFO -0.963 -0.106 Growth (-0.97) (-0.04)

Future CFO -0.650 -2.269 Growth (-0.71) (-0.76)

Intercept 0.253 0.062 -0.034 -0.055 0.372 0.155 -0.005 -0.047 Intercept (2.13) (0.59) (-0.12) (-0.21) (1.80) (0.98) (-0.13) (-0.09) Adj. R-Squared 38.40% 28.88% 1.13% 0.00% 38.67% 33.29% 10.76% 0.00%

This table reports the slope estimates, t-statistics (in parenthesis) and adjusted r-squareds when change in nonperforming commercial loans is regressed on current and future earnings and cash flow growth rates. Panel A reports the results of the regression with seasonally differenced quarterly data as explanatory variables (E.g., aggregate earnings for quarter t minus aggregate earnings for quarter t-4 divided by the latter). Specifically, following model is used:

„ £GP. A O P ^ i 0*t_4 OP,_3

where OP is either earnings or cash flows variable. Panel B reports the results of the regression with four quarter rolling window data as explanatory variables. (E.g., sum of aggregate earnings for four quarters ending at quarter t minus sum of aggregate earnings for four quarters ending at quarter t-4 divided by the latter). Specifically, following model is used:

S-s OP5 - nzi OP* S T F GPS - S - 3 OP S iNPL, = « + Pi * 1 „ J z — e

Definition of variables is available in the notes to Table 1. Adjusted r-squareds are from OLS regressions. Standard errors are calculated using Newey-West (1987) methodology. All coefficients are multiplied by 100.

Tab

le 9

: Reg

ress

ions

of A

LLR

on

Ear

ning

s/C

ash

Flow

Gro

wth

Var

iabl

es a

nd M

acro

econ

omic

Var

iabl

es

-0.9

66

-0.8

93

-0.9

81

-1.0

83

-0.9

73

-0.9

74

(-4.

27)

(-4.

21)

(-5.

45)

(-5.

46)

(-5.

30)

(-4.

88)

Qua

rter

ly

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(1

1)

(12)

C

urre

nt

OIB

DP

Gro

wth

Fu

ture

OIB

DP

-0.5

58

-0.5

23

-0.5

75

-0.6

58

-0.5

65

-0.5

97

Gro

wth

(-

3.19

) (-

3.81

) (-

4.29

) (-

3.27

) (-

3.98

) (-

3.24

) C

urre

nt

CFO

BIT

G

row

th

Futu

re

CFO

BIT

G

row

th

GD

P G

row

th

Ind.

Pro

duct

ion

Gro

wth

A

Con

sum

er

Sent

imen

t

A T

-bon

d

AC

RE

I

Inte

rcep

t

Adj

. R-S

quar

ed

-0.2

45

-0.3

11

-0.4

41

-0.4

45

-0.4

35

-0.2

60

(-1.

35)

(-2.

02)

(-2.

95)

(-2.

50)

(-2.

76)

(-1.

45)

-0.3

81

-0.4

36

-0.5

81

-0.5

82

-0.5

66

-0.4

14

(-2.

25)

(-2.

89)

(-3.

12)

(-2.

57)

(-2.

81)

(-2.

23)

-0.2

31

-4.4

09

0.22

7 -1

.803

(-

0.13

) (-

2.39

) (0

.09)

(-

0.75

) -0

.494

-1

.914

-0

.006

-0

.016

(-

1.27

) (-

3.71

) (-

0.86

) (-

2.14

) 0.

095

0.11

7 0.

006

0.01

8 (0

.68)

(0

.52)

(0

.04)

(0

.11)

0.

024

0.00

1 0.

022

0.01

3 (2

.08)

(0

.03)

(1

.35)

(0

.49)

-0

.094

-0

.091

-0

.170

-0

.216

(-

0.32

) (-

0.21

) (-

0.50

) (-

0.69

) 0.

044

0.21

8 0.

038

0.04

9 0.

034

.020

4 0.

054

0.02

2 0.

038

0.02

4 0.

054

0.14

3 (0

.56)

(2

.38)

(2

.14)

(1

.65)

(1

.72)

(0

.53)

(1

.96)

(0

.40)

(1

.74)

(0

.56)

(0

.49)

(1

.19)

65

.67%

46

.57%

66

.40%

50

.87%

65

.91%

34

.41%

67

.85%

34

.02%

65

.80%

34

.14%

69

.24%

50

.33%

•vl

oo

(1)

(2)

(3)

(4)

-1.4

47

-1.3

02

(-12

.99)

(-

10.1

4)

Cur

rent

O

IBD

P G

row

th

Futu

re O

IBD

P -0

.870

-0

.790

G

row

th

(-5.

21)

(-5.

67)

Cur

rent

C

FOB

IT

Gro

wth

Fu

ture

C

FOB

IT

Gro

wth

GD

P G

row

th

-0.3

75

-0.3

72

(-1.

85)

(-2.

12)

-0.8

95

-0.8

60

(-2.

44)

(-2.

75)

0.91

8 -1

.588

(1

.19)

(-

1.04

) In

d. P

rodu

ctio

n -0

.319

-1

.251

G

row

th

(-1.

07)

(-3.

48)

A C

onsu

mer

Se

ntim

ent

A T

-bon

d

AC

RE

I 0.

038

0.12

2 0.

081

0.07

0 in

terc

ept

(Q

(L4

7)

(7 2

0)

(2.5

0)

Adj

. R-S

quar

ed

83.7

9%

53.6

8%

83.7

5%

59.4

4%

Four

Qua

rter

Rol

ling

(5)

(6)

(7)

(8)

(9)

(10)

(1

1)

(12)

-1

.352

-1

.418

-1

.397

-1

.464

(-

9.58

) (-

12.3

3)

(-11

.89)

(-

11.2

8)

-0.8

60

-0.8

95

-0.8

41

-0.9

43

(-5.

95)

(-6.

44)

(-6.

57)

(-7.

55)

-0.4

65

-0.4

76

-0.5

04

-0.4

38

(-2.

71)

(-2.

34)

(-3.

52)

(-2.

72)

-0.9

96

-1.0

21

-1.1

03

-0.9

61

(-2.

62)

(-2.

51)

(-2.

71)

(-2.

35)

0.95

6 0.

296

(0.9

7)

(0.1

4)

-0.0

03

-0.0

12

(-0.

59)

(-2.

24)

0.10

1 -0

.091

0.

124

-0.0

28

(1.2

7)

(-0.

65)

(1.6

5)

(-0.

19)

0.01

4 0.

004

0.00

9 0.

008

(1.3

9)

(0.2

5)

(0.7

7)

(0.4

3)

0.29

8 0.

534

0.26

5 0.

201

(1.5

3)

(1.1

8)

(1.0

5)

(0.4

3)

0.08

1 0.

054

0.09

2 0.

058

0.06

4 0.

034

0.03

8 0.

056

(6.1

8)

(1.2

9)

(6.6

1)

(1.0

7)

(4.1

5)

(0.9

1)

(0.8

0)

(0.5

0)

83.6

0%

52.5

3%

84.3

6%

52.3

9%

84.6

5%

56.1

3%

85.2

3%

57.2

8%

The

tabl

e re

ports

the

slop

e es

timat

es,

(t-st

atis

tics)

and

adj

uste

d r-

squa

reds

whe

n ch

ange

in lo

an lo

ss r

eser

ves

is re

gres

sed

on c

urre

nt a

nd fu

ture

ea

rnin

gs a

nd c

ash

flow

gro

wth

rat

es a

nd m

acro

econ

omic

var

iabl

es.

Pane

l A

repo

rts t

he r

esul

ts o

f th

e re

gres

sion

with

sea

sona

lly d

iffer

ence

d qu

arte

rly d

ata

as e

xpla

nato

ry v

aria

bles

. Spe

cific

ally

, fol

low

ing

mod

el is

use

d:

iiop.

a

op

t+1

Mm

t =

a +

Pi —

J.

+ p 2

—+

PJM

F, +

c

t-4

°"

t-

3

whe

re O

P is

eith

er e

arni

ngs

or c

ash

flow

s va

riabl

e an

d M

F is

the

mac

roec

onom

ic v

aria

ble.

Pa

nel B

repo

rts th

e re

sults

of t

he re

gres

sion

with

four

qua

rter

rollin

g w

indo

w d

ata

as e

xpla

nato

ry v

aria

bles

. Spe

cific

ally

, fol

low

ing

mod

el is

use

d:

MX

Rt=

«

+ M

ll

U

°P*

+ h

^ °F

' +

iW

+

«

Adju

sted

r-s

quar

eds

are

from

OLS

reg

ress

ions

. St

anda

rd e

rrors

are

cal

cula

ted

usin

g N

ewey

-Wes

t (1

987)

met

hodo

logy

. A

ll co

effic

ient

s ar

e m

ultip

lied

by 1

00.

Ul