Fundamental Analysis in Banks: The Use of Financial Statement

Information to Screen Winners from Losers

Presented by

Dr Partha S Mohanram

CPA Ontario Professor of Financial Accounting Professor of Accounting, University of Toronto

#2016/17-08

The views and opinions expressed in this working paper are those of the author(s) and not necessarily those of the School of Accountancy, Singapore Management University.

Fundamental Analysis in Banks: The Use of Financial Statement Information to Screen Winners from Losers

Partha Mohanram Rotman School of Management

University of Toronto partha.mohanram@rotman.utoronto.ca

Sasan Saiy School of Accounting and Finance

University of Waterloo ssaiy@uwaterloo.ca

Dushyantkumar Vyas Dept. of Management – University of Toronto, Mississauga

Rotman School of Management University of Toronto

dushyantkumar.Vyas@rotman.utoronto.ca

February 2016

All errors are our own. We would like to thank Zahn Bozanic, Patricia Dechow, Urooj Khan, Yaniv Konchitchki, Panos Patatoukas, Chandra Seethamraju, Richard Sloan, Xiao-Jun Zhang and seminar participants at the University of California-Berkeley for their comments. Partha Mohanram and Dushyant Vyas wish to acknowledge financial support from SSHRC-Canada.

Fundamental Analysis in Banks: The Use of Financial Statement Information to Screen Winners from Losers

Abstract

Despite the importance of the banking sector to the economy, prior valuation studies in accounting

have tended to generally discard bank stocks. We examine returns to a fundamental analysis based

trading strategy for the U.S. bank stocks, using a bank fundamentals index (BSCORE) based on

thirteen bank specific valuation signals. A long–short strategy based on BSCORE yields positive

hedge returns for all but one year during the 1994–2013 period. Results are robust to partitions

based on size, analyst following and exchange listing status, and persist after adjusting for known

risk factors. Interestingly, we observe especially strong hedge returns during the 2007-2009

financial crisis. We further document a positive relation between BSCORE and future analyst

forecast surprises, earnings announcement period returns, and future performance-based

delistings. Finally, the results are significantly enhanced if we combine the BSCORE strategy with

a relative valuation strategy based on an intrinsic value approach. The results show that

fundamental analysis can provide useful insights for analyzing banks, beyond the usual focus on

metrics such as return on equity (ROE).

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1. Introduction

This study examines the efficacy of financial statement driven fundamental analysis

strategy for screening bank stocks. Despite the importance of the banking sector to the wider

economy, most valuation research in accounting and finance excludes bank stocks. Bank stocks

represent a significant proportion of traded stocks in the stock market. The exclusion of bank stocks

may be partially justified as the financial statement based value drivers are substantially different

for banks as compared to other industries. For example, while working capital accruals are

important for general manufacturing stocks, specific accruals such as loan loss provisions are more

important for banks. Further, as the events pertaining to the financial crisis in 2007–2009 have

shown, there can be considerable uncertainty regarding the valuation of bank stocks. This suggests

that a fundamentals driven approach may be successful in separating winners from losers among

bank stocks in terms of future stock returns.

We build upon prior studies in accounting that document the usefulness of signals

constructed using historical financial statement data in predicting future accounting and stock

return performance (e.g. Lev and Thiagarajan 1993, Abarbanell and Bushee 1997, Bernard and

Thomas 1989, and Sloan 1996 among others). Our broad approach in this paper is similar to

Piotroski (2000), who documents the importance of traditional financial statement analysis in ex–

ante identification of winners and losers among value stocks. Further, we are motivated by

Mohanram (2005), who tailors fundamental analysis contextually for growth stocks. In this paper,

we attempt to tailor fundamental analysis for the context of bank stocks.

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We combine thirteen bank–specific valuation signals to create a bank fundamentals index

(BSCORE). We motivate our signals from the residual income valuation model developed in

Ohlson (1995), Feltham and Ohlson (1995) and other papers. The value of a stock depends on

three factors – the ability to generate profitability in excess of the cost of equity (+), risk (–) and

growth prospects (+). Our choice of signals is also motivated by the guidance in Calomiris and

Nissim (2007) and Koller et al. (2010), who analyze valuation of bank stocks.

In particular, we combine signals that indicate changes pertaining to: (i) overall

profitability (ROE and ROA), (ii) components of profitability (spread, operating expense ratio,

non–interest income, earning assets, and loans to deposits ratio), (iii) credit risk (loan loss

provisions, non–performing loans, and loan loss allowance adequacy), and (iv) indicators of future

growth (revenues, total loans, and trading assets). In the interest of generalizability, we keep the

list of signals parsimonious. We construct the signals in a manner similar to Piotroski (2000) with

an increase coded as 1 and a decrease coded as 0 for positive signals, and the converse for negative

signals. BSCORE is the sum of these 13 individual signals and varies from 0 to 13.

A long–short strategy based on BSCORE quintiles yields an average annualized return of

7.4% during our 1994–2013 sample period. Inconsistent with a risk–based explanation, we observe

positive returns for all but one year (2002) during our sample period (Figure 1). In fact, returns to

the BSCORE trading strategy are the strongest during the financial crisis years 2007–2009, when

tail risk in the banking sector materialized. Further lending robustness to the findings, we

document that the hedge returns are significant across size partitions, and survive controls for

commonly used risk factors. In addition, consistent with a mispricing based explanation, we

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observe a positive relation between BSCORE quintiles and analyst forecast surprises as well as

abnormal returns around subsequent earnings announcements. Also, we document a negative

relationship between BSCORE and performance related delistings.

One criticism of financial statement driven fundamental analysis is that it ignores valuation

– i.e. the fact that firms with poor fundamentals might have lower valuations and firms with strong

fundamentals might have strong valuations. Li and Mohanram (2015) show that combining

measures of quality (FSCORE and GSCORE) with a measure of cheapness, such as the

Value/Price or V/P ratio from Frankel and Lee (1998), significantly strengthens the efficacy of

fundamental analysis. In our analyses, we find that combining the BSCORE trading strategy with

the V/P approach (Frankel and Lee 1998) significantly enhances the hedge returns, with the time

series average increasing from 6.02% to 11.15%.

Our results have obvious implications for researchers and practitioners focusing on

fundamental analysis. They suggest that a simple approach that aggregates signals related to

profitability, growth, and risk can be used to screen bank stocks that are likely to be ex–post

winners and losers in terms of stock returns. Further, our results indicate that the approach can be

implemented practically to generate economically significant hedge returns.

In addition to investors, our paper could also be of potential interest to bank regulators.

According to some estimates, the size of the U.S. banking sector as measured by total banking

assets is as large as the annual GDP1. The importance of banks to overall economic activity was

highlighted rather dramatically in the aftermath of the recent financial crisis that originated within

1 http://www.helgilibrary.com/indicators/index/bank-assets-as-of-gdp

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a subset of the U.S. banks and thereafter developed into a full–fledged U.S. and global economic

downturn. The crisis has also highlighted the surprising inability of investors to judge the

implications of rather simple value and risk drivers such as financial leverage. Accordingly, a

systematic approach that utilizes simple publicly available data to predict performance of this

important subset of the economy is likely to be of interest to market participants, regulators, and

policy–makers alike.

The rest of our paper is organized as follows. Section 2 discusses prior research in both

banking as well as fundamental analysis in order to motivate our approach. Section 3 describes the

individual components used in creation of the BSCORE index, the sample selection process and

descriptive statistics. Section 4 presents the empirical results, while Section 5 concludes.

2. Literature Review

Our paper builds on research from two streams – banking and fundamental analysis. We briefly

describe the relevant research in both of these areas, and use the insights from prior research to

develop our approach towards fundamental analysis in banking.

2.1. Valuation of Bank Stocks

The valuation literature in accounting and finance typically deletes financial sector stocks.

This may partly be due to the fact that banks have a business model that is very different from non-

financial stocks. In contrast to the extant valuation literature, we conjecture that bank stocks are in

fact an ideal laboratory to examine returns to a trading strategy based on fundamental analysis due

to reasons outlined below.

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2.1.1 Banks are “Different”

Modern day banks are inherently different from other industries due to the inherent opacity

or complexity of their balance sheet (Morgan 2002, Macey and O’Hara 2003, Adams and Mehran

2003 and Koller et al. 2010). Morgan (2002) calls banks “black holes of the universe.” This

opacity arises, among other things, due to the limitations of the current accounting models in

conveying information about the extent of credit losses, and the pervasiveness of off–balance sheet

exposures among large banking institutions. Further complicating matter is the extent to which

non–traditional banking activities (such as securitization and investment banking activities) drive

bank value. Macey and O’Hara (2003) state that “Not only are bank balance sheets notoriously

opaque, but as Furfine (2001) points out, rapid developments in technology and increased financial

sophistication have challenged the ability of traditional regulation and supervision to foster a safe

and sound banking system.” This inherent opacity in banks’ financial statements suggests that

while a simple financial statements–based valuation approach might not work as well for banks as

it does for other industries, an approach contextualized for the banking sector may be potentially

fruitful.

Calomiris and Nissim (2007) focus on an activity based valuation of bank holding

companies in the U.S. and document that the valuation drivers identified by their study explain

significant variation in banks’ market–to–book ratios during the 2001–2005 sample period.

Further, they document that residuals from a regression of market–to–book ratio on the activity

based value drivers predict future returns. However, these future returns are found to be impacted

by trading costs. Within the realm of financial institutions, but focussing on insurance companies

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instead, Nissim (2013) examines the accuracy of relative valuation methods in the U.S. insurance

industry, documenting inter alia the dominance of book value multiples over earnings multiples

for that industry.

2.1.2 Banks are Highly Leveraged

As banks are highly leveraged, their valuation is more susceptible to macro–economic and

market sentiment swings (Koller et al. 2010). For example, even though the broader U.S. economy

has pulled out of the recent downturn, investor sentiment regarding bank stocks remains

suppressed compared to the pre–crisis period. It is likely that this characteristic of bank stocks

renders them ripe for exploiting a fundamental analysis–based investment strategy. In other words,

when the broader market is concerned about market and industry-wide factors, a “fundamental

investor” can earn excess returns by screening stocks based on bank–specific value drivers.

2.1.3 Financial Crisis and the Rationality of Bank Pricing

Finally, the financial crisis period exposed the wild gyrations in the valuation of bank

stocks. If indeed this was a period of where valuations departed from fundamentals, it would also

provide an appropriate setting for the testing of a fundamentals-based investing strategy. Huizinga

and Laeven (2012) focus on the financial crisis period and show, using stock market value as a

benchmark, that banks overstated the value of their distressed (real estate–backed) assets. They

attribute these findings to noncompliance with accounting rules and regulatory forbearance. In a

similar vein, Vyas (2011) shows that financial institutions recorded losses in an untimely manner

compared to the devaluations being implied by the underlying asset markets. Calomiris and Nissim

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(2014) focus on the decline in bank market–to–book ratios during the recent financial crisis, and

show that the declines cannot be fully attributed to delayed recognition of losses on existing

financial instruments.

Based on an examination of 51 large global banks before and after the crisis, Papa and

Peters (2014) document a lag between allowance for loan losses and market values. These findings

echo those reported in Huizinga and Laeven (2012), and Vyas (2011). Papa et al. (2015) focus on

net other comprehensive income (OCI), available–for–sale and cash flow hedge financial

instruments’ gains or losses reported by 44 large global banks during 2006–2013, and document

that not only are losses on OCI more frequent than on the income statement, but that OCI has

incremental economic information content.

2.2 Fundamental Analysis using Financial Statement Analysis

While typically not focusing on bank stocks, there has been an extensive prior literature

focusing on the ability of financial signals to predict future stock returns. Ou and Penman (1989)

show that certain financial ratios can help predict future changes in earnings. Lev and Thiagarajan

(1993) analyze 12 financial signals purportedly used by financial analysts and show that these

signals are correlated with contemporaneous returns and future earnings. Abarbanell and Bushee

(1997) show that an investment strategy based on these signals earns significant abnormal returns.

Two studies that are most relevant to our paper are Piotroski (2000) and Mohanram (2005).

Piotroski (2000) uses financial statement analysis to develop an investment strategy for high BM

or value firms. Piotroski argues that value firms are ideal candidates for the application of financial

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statement analysis as they are often neglected by financial analysts. He combines nine binary

signals based on traditional ratio analysis into a single index called FSCORE. He shows that a

strategy of taking a long position in high FSCORE firms and a short position in low FSCORE

firms generates significant excess returns that are persistent over time, rarely negative, and not

driven by risk. Mohanram (2005) follows a similar approach as Piotroski (2000), but focuses on

low BM or growth stocks. He tailors the ratios to better suit growth stocks. He combines eight

binary signals into a single index called GSCORE, and shows that the GSCORE strategy is

successful in separating winners from losers among low BM firms. In this paper, we will attempt

to tailor fundamental analysis for the context of bank stocks.

3. Research Design, Sample and Descriptive Statistics

3.1. Why Traditional Fundamental Analysis might not be effective in Bank Stocks

The fundamental analysis framework used in prior research can be broadly motivated by the

residual income valuation (RIV) model from Ohlson (1995), and Feltham and Ohlson (1995)

among others. The RIV model characterizes stock price as a function of book value and the present

value of the stream of future expected abnormal earnings. From the RIV model, it is easy to infer

that the value of a stock increases with a firm’s ability to generate abnormal profitability, and

further with the persistence and growth in abnormal profitability. On the other hand, the value of

a stock decreases with (systematic) risk, as future abnormal earnings are discounted further.2

2 The use of residual income valuation by sell side analysts has been growing, as noted by Hand et al. (2015), especially for analysts associated with certain brokerage houses such as Morgan Stanley and J.P. Morgan that have embraced the RIV model. A search of research reports in the banking sector from the Investext database shows that bank stock

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Motivated by this, prior research has identified signals associated with profitability, risk and

growth. Piotroski (2000) applies a variant of the Dupont framework in the subsample of value

stocks and creates an index labeled FSCORE, which combines signals associated with overall

profitability, asset turnover, profit margin, liquidity and solvency. Mohanram (2005) analyzes

growth stocks and also incorporates signals associated with earnings and revenue growth to create

an index called GSCORE.

Applying traditional ratio analysis in banks is problematic because many of the ratios use data

that is either not meaningful or not provided for bank stocks. For instance, the FSCORE metric

incorporates signals related to asset turnover, profit margin, accruals and the current ratio, which

in turn require data items such cost of goods sold, current assets, current liabilities and working

capital accruals. Similarly, the GSCORE metric includes signals related to research and

development, advertising and capital expenditures. Hence, these papers, either explicitly or

implicitly due to the data requirements, exclude bank stocks. This motivates our quest to develop

a customized set of fundamental signals that ex–ante have the potential to be relevant for banks.3

analysts from these brokerages use RIV models to calculate price targets. These analysts routinely forecast ROE (or its variants such as return on economic equity) to arrive at their forecasts of net income / residual income. 3 A naïve attempt to mechanically compute the FSCORE and GSCORE ratios from the Piotroski (2000) and Mohanram (2005) papers in the subset of bank stocks is not very fruitful. FSCORE requires detailed information on components of the income statement and balance sheet, which are often unavailable or inapplicable for banks. GSCORE requires information on items such as R&D, Advertising and Capital expenditures, which are invariably coded as zero (or missing) for banks. Out of our sample of 9,343 observations, FSCORE was computable for only 4574 observations, while GSCORE was computable for 9186 observations. Further, many of the signals were meaningless and were mechanically set to zero or one. Finally, for both FSCORE and GSCORE, we were unable to detect any meaningful relationship with future returns. This corroborates our maintained assumption that traditional ratio based fundamental analysis is inapplicable in banks.

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3.2. Applying Fundamental Analysis to Bank Stocks: Construction of the BSCORE Index

Similar to the approach towards fundamental analysis in non-bank stocks, we look for signals

associated with (abnormal) profitability, risk and growth. Specifically, we categorize these signals

into four buckets — overall profitability, components of profitability, credit risk, and growth. In

particular, we identify the following thirteen signals to screen winners from losers each year among

the population of bank stocks: (i) overall profitability (ROE and ROA), (ii) components of

profitability (spread, operating expense ratio, non-interest income, earning assets, and loans to

deposits ratio), (iii) credit risk (loan loss provisions, non-performing loans, and loan loss allowance

adequacy), and (iv) indicators of future growth (revenues, total loans, and trading assets).4

For each individual metric, we construct an indicator variable that equals one if the

measure improved over the previous year, and zero otherwise. We decide to focus on changes

rather than levels for two reasons. First, this approach mirrors Piotroski (2000) who uses a similar

approach to identify firms with improving fundamentals. Second, this also has the advantage of

the firm serving as its own control, as the level of the ratios can be affected by a number of other

factors including the specific strategic choices a bank has made (e.g. physical branch vs online

banking), segments its operates in, size and geographic location. BSCORE is the sum of all of

these individual scores and has a maximum (minimum) value of 13 (0).5

4 Note that the categorization of signals into four buckets, while beneficial from an expositional point of view, is not necessarily non-overlapping. For example, while the loans to deposits ratio indicates the ability of a bank to deploy a relatively stable source of funding into revenue generating assets, the extent of reliance on deposits as a source of funding also has implications for financial leverage and liquidity risk. 5 We also create a continuous version of BSCORE using the ranks of the variables underlying each signal. The results are broadly similar. We prefer the 0/1 specification we use in the paper because of its simplicity and because of its similarity with screening mechanisms typically used to pick stocks.

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3.2.1 Category 1: Overall Profitability

We employ two metrics to measure a bank’s overall profitability: ROE as a levered measure,

and ROA as an unlevered measure. We use these two correlated but distinct measures because, as

discussed below, leverage can have an ambiguous impact on profitability in banking sector.

1) Return on Equity (ROE): As pointed earlier, observers (e.g., Admati 2011) have argued

that banking analysts (and investors) are fixated on evaluating bank performance using the

accounting return on equity. Accordingly, we use ROE as the first fundamental signal to

screen bank stocks. A potential drawback is that if ROE is primarily driven by leverage,

then its use as a signal of firm value could be questionable during economic downturns

(when banks are more likely to deleverage and forego true value creating activities). Our

first signal (B1) equals 1 when ROE increases from t–1 to t (∆ROEt > 0), and 0 otherwise.

2) Return on Assets (ROA): ROA will be less immune to problems pertaining to leverage

discussed above. Nonetheless, this measure would be prone to business-mix issues (i.e.,

some banks may derive more revenue as fees from underwriting or other banking

activities). Our second signal (B2) equals 1 when ROA increases from t–1 to t (∆ROAt >

0), and 0 otherwise.

3.2.2 Category 2: Components of Profitability

We employ five signals in this category. The first three signals (spread, operating expense

ratio, and non-interest income) are analogous to profit margin, while the remaining two signals

(earning assets and loans to deposits ratio) are measures of asset deployment efficiency.

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3) Spread: We measure the spread on the bank’s loan portfolio as the ratio of net interest

income (interest income minus interest expense) earned during the year to average total

loans. Note that the sign of this signal is ambiguous as higher spread could simply reflect

higher risk on the loan portfolio. Our third signal (B3) equals 1 when the bank’s spread

increases from t–1 to t (∆Spreadt > 0), and 0 otherwise.

4) Operating expense ratio: We use the expense ratio, defined as non-interest expense divided

by total revenue. This ratio measures how large a proportion of revenues gets used up in

operating and administrative expenses. For this ratio, revenues are generally defined as the

sum of net interest income (interest income – interest expense) and non–interest income.

Our fourth signal (B4) equals 1 when expense ratio decreases from t–1 to t

(∆Expense_Ratiot < 0), and 0 otherwise.

5) Non–interest income: It is defined as the ratio of non–interest income to total revenue. This

measure is particularly useful for larger universal banks that generate a significant portion

of their income from non-lending/deposit (non–traditional) activities. These revenues

generally derive from higher value added services (such as investment banking and

brokerage) that are very profitable, or are associated with no direct costs (such as service

fees). Our fifth signal (B5) equals 1 when non-interest income increases from t–1 to t

(∆Noninterest_Incomet > 0), and 0 otherwise.

6) Earning Assets: Banks generate income from inter alia, loans and other investments that

yield interest or dividend income. Accordingly, we include the Earning Assets ratio,

defined as the ratio of earning assets to total assets. We expect that the ability of banks to

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deploy its assets productively should be positively correlated with future realized

performance. Our sixth signal (B6) equals 1 when earning assets increase from t–1 to t

(∆Earning_Assetst > 0), and 0 otherwise.

7) Loans to Deposits: The last signal we consider in this category is the ratio of loans to

deposits. It measures the ability of a bank to efficiently deploy its primary source of funding

— deposits — to grow its primary earning asset — loans. If the ratio is too low, it means

that the bank has a lot of unused funds and accordingly implies increased inefficiency. Note

that while this ratio is categorized as a component of profitability, it could also be a signal

of liquidity risk — if the ratio is too high, it may impose additional risk or at least liquidity

problems on the bank, especially if a large number of depositors want to withdraw their

deposits simultaneously (although this problem has been sufficiently diminished with the

advent of deposit insurance). Our seventh signal (B7) equals 1 when the ratio of loans to

deposits increases from t–1 to t (∆Loans_Depositst > 0), and 0 otherwise.

3.2.3 Category 3: Credit Risk

The typical recognition sequence for loan losses is (1) recognition of non–accrual or non–

performing status, (2) provision based on historical and current loss experience, (3) charge–off

when the loss is realized, and (4) recovery of previously charged-off loans. Accordingly, we use

the following three metrics.

8) Loan Loss Provisions (LLP): LLP is perhaps the most important accrual for banks — in

terms of absolute magnitude as well as its impact on overall profitability and capital

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adequacy (Beatty and Liao 2011; Liu and Ryan 2006). Accordingly, we define LLP as the

ratio of annual loan loss provision to total loans. Our eighth signal (B8) equals 1 when LLP

decreases from t–1 to t (∆LLPt < 0), and 0 otherwise.

9) Non-performing loans (NPL): We acknowledge the limited horizon problem in loan loss

accruals (due to the incurred loss model). Hence, we also employ a more forward–looking

metric measured as the ratio of non-performing loans to total loans. NPL is a noisy but

perhaps the timeliest measure of the extent of economic losses in a bank’s loan portfolio.

Note that NPL is not reflected in the main body of financial statements, but is found in

notes to the financial statements. Our ninth signal (B9) equals 1 when the level of non–

performing loans decreases from t–1 to t (∆NPLt < 0), and 0 otherwise.

10) Allowance Adequacy: banks with greater loan loss allowance adequacy are generally better

able to absorb expected credit losses without impairing capital during periods of distress

(e.g., Beatty and Liao 2011). Accordingly, we measure allowance adequacy as the ratio of

loan loss allowance to non-performing loans. Our tenth signal (B10) equals 1 when the

ratio of loan loss allowance to non-performing loans increases from t–1 to t

(∆Allowance_Adequacyt > 0), and 0 otherwise.

3.2.4 Category 4: Growth

We employ three signals to measure growth: Change in total revenue measures growth in

overall income, while changes in loans and trading assets measure the bank’s growth in traditional

activities and non–traditional activities, respectively.

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11) Total Revenue: This measure is defined as the sum of net interest income (interest income

– interest expense) and non–interest income, and in itself does not distinguish between the

sources of revenue (traditional vs. non-traditional). Two measures defined below – Loans

and Trading Assets use such decomposition. Our eleventh signal (B11) equals 1 when the

total revenue increases from t–1 to t (∆Revenuest > 0), and 0 otherwise.

12) Loans: Regulators and market participants often evaluate banks on the basis of their ability

to grow their total loan portfolio. On the one hand, increasing the loan base results in

increased revenue, but on the other hand it could also signal increased credit risk. These

concerns generally become acute during periods of financial distress when banks are

reluctant to extend credit due to systemic credit risk fears. Our twelfth signal (B12) equals

1 when the bank’s total loans increase from t–1 to t (∆Loanst > 0), and 0 otherwise.

13) Trading Assets: One measure of a bank’s involvement in non–traditional banking activities

is the extent of reliance on trading activities, measured as trading assets divided by total

assets or as the ratio of trading income to total income. Our thirteenth signal (B13) equals

1 when the bank’s trading assets increase from t–1 to t (∆Trading_Asstest > 0), and 0

otherwise.

3.3. Association of BSCORE with Returns

The signals that constitute BSCORE are simple enough and can be constructed by an investor

who has regular access to basic financial information. In our empirical tests, we will test the

association of BSCORE with future returns. If banks that have strong (weak) fundamentals that

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investors have not completely impounded in stock price, then high (low) BSCORE firms should

earn higher (lower) ex-post returns. If on the other hand, the ratios constituting BSCORE are

already impounded in current price, we should see no relationship with future returns.

We analyze future returns using a one–year horizon, with returns being compounded

beginning four months after the fiscal year end. Our measure of annual returns is labelled RET1,

calculated as buy–and–hold annual returns, with adjustments for delistings as in Shumway (1997).

In addition, we also calculate market–adjusted returns, RETM1, as the difference between RET1

and the compounded value-weighted market return over the same period (VWRETD from CRSP).

It is plausible that the association between BSCORE and returns could be driven by risk. We

will try to address this in a variety of ways, including examining the consistency of returns across

time, examining returns in a variety of partitions, and explicitly controlling for known risk factors.6

3.4.Sample and Descriptive Statistics

We begin our sample construction using all banks with data available in the Bank

COMPUSTAT between 1993 and 2014. Panel A of Table 1 presents our sample selection process.

We restrict the sample to bank–years following 1993 due to limited data availability on Bank

COMPUSTAT in prior years. Accordingly, we begin our sample construction with 17,571 bank–

year observations, corresponding to 2,074 unique banks. Application of further filters pertaining

to the data availability of the BSCORE components decreases the sample to 13,684 (1,775) bank–

6 It is possible that some of the signals identified in this study – especially those pertaining to credit risk — constitute systematic risk factors not already subsumed by extant risk factors.

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year (bank–level) observations. As most banks have December fiscal year ends, we further restrict

the sample to firms that have their fiscal year ends in December, yielding 11,959 (1,466) bank-

year (bank-level) observations. This criterion ensures that the compounding periods for the returns

is identical. Further conditioning on availability of returns on CRSP, along with elimination of

outliers and potential data errors relating to stock price, number of shares outstanding, and size

results in our final sample of 9,343 (1,199) bank–year (bank–level) observations.

Panel B of Table 1 presents descriptive statistics for basic bank characteristics for our

sample as well as for the thirteen signals that construct the BSCORE index. Reflecting the fact that

our sample comprises banks that typically have large asset bases, the average (median) reported

total assets are $ 26.2 bill. ($1.2 bill.). The distribution in terms of size reveals a distinct skewness

towards the right, reflecting the presence of large universal banks in the sample. The distribution

of the other two size variables — revenues and market capitalization — reveals a similar right

skewness, with means that are considerably larger than the median.

The mean (median) ROE is 8.6% (10.3%). In contrast, the mean (median) ROA is 0.8%

(0.9%). The contrast in magnitudes of ROE and ROA is not surprising — banks are in general

highly financially leveraged. Accordingly, their equity base is often very thin, resulting in a much

smaller denominator for ROE compared to ROA. The mean (median) spread is 5.4% (5.2%), with

a modest standard deviation of 1.6% — this reflects the competitive nature of the traditional

banking industry with limited scope for excessive interest margins. The mean (median) operating

expense ratio is 82.7% (79.2%).

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Mean (median) non-interest income of 21.5% (19.7%) shows that non-traditional banking

activities such as those that generate fees and trading commissions drive a substantial one-fifth of

total yearly revenues for an average bank. The mean (median) bank has 88% (89.8%) of its assets

classified as earning assets — in that they represent investments in loans and securities — as

opposed to idle cash balances and other assets such as PP&E. Loans to Deposits ratio exhibits a

mean (median) of 88% (87.5%), suggesting a substantially high proportion of funds raised through

traditional deposit raising activity are deployed into traditional banking assets — loans.

In terms of credit quality metrics, mean (median) annual loan loss provision is 0.6%

(0.3%), while mean (median) non-performing loans are 1.8% (1.0%) of total year end loans

outstanding. Mean (median) allowance adequacy, which is loan loss allowance divided by non-

performing loans is 2.8 (1.34), implying that on average and across the years, outstanding

allowances were more than sufficient to cover expected near term loan losses. However, a closer

examination (untabulated) reveals this adequacy was severely stressed during the recent crisis,

with the ratio consistently dipping below one.

Turning to growth, the average bank-year exhibits robust mean (median) revenue growth

of 12.9% (8.7%), and similar mean (median) loan growth of 13.5% (9.4%). Finally, average

trading assets as a percentage of total assets are quite low at 0.3% (0.0%), reflecting the limited

number of banks that are sophisticated and large enough to have active trading desks.

19

4. Empirical Analyses

4.1. Relation between Individual Signals and Future Returns

In order to provide preliminary evidence on the efficacy of the individual signals in

generating excess returns, we examine the relation between each of the thirteen signals (B1:B13)

and the one-year market adjusted returns (RETM1). Recall that each of the signals, takes on a value

of one for years in which the bank exhibits an improvement in that signal, and zero otherwise. In

particular, we compare the average one-year market adjusted returns for the sample of banks

exhibiting improvement in each of the signals (1) to the sample of banks with no improvement (0).

Table 2 presents the results. According to the last two columns on the right, the differences in

returns are positive and significant for 10 signals except for ∆Spreadt (B3), ∆Noninterest_Incomet

(B5), and ∆Trading_Assetst (B13).

In terms of magnitude, returns for ∆NPLt (B9) and ∆Allowance_Adequacyt (B10) seem to

be the highest, indicating the valuation relevance of credit quality and the ability of a bank to

withstand shocks to credit quality. The insignificance of ∆Spreadt (B3) could be partially explained

by the possibility that our measure of spread can increase either through an increase in net interest

income (a positive signal) or a decrease in assets (which could signal lower growth). The

insignificance of ∆Noninterest_Incomet (B5), and ∆Trading_Assetst (B13) could also signify

greater reliance on non-traditional but risky banking activities. While engagement in these

activities could result in higher fees and trading income, it also increases the overall exposure of

the bank to market risk. Accordingly, our inability to observe significant returns using these

signals is not surprising.

20

We aggregate the thirteen signals (B1:B13) into a single index called BSCORE. Despite

the fact that a few signals do not work at the univariate level, we include all thirteen signals in

BSCORE, as cherry-picking signals that appear to work ex-post would impose look-ahead bias.

4.2. Correlations between Individual Signals, BSCORE and Future Returns

Table 3 presents Pearson and Spearman correlations between BSCORE index, its thirteen

individual component signals (B1:B13), and one year–ahead market adjusted returns (RETM1).

By construction, BSCORE is positively correlated with each of the thirteen constituent signals. A

closer inspection of the table reveals that BSCORE and its components, with few unremarkable

exceptions, are generally positively and significantly correlated among themselves. It is also

notable that generally, the strongest correlations are obtained when we look at signals related to

ratios within the same “group” – i.e. profitability ratios, components of profitability, credit risk

and growth. Most of the individual BSCORE components also generally exhibit positive and

significant pairwise correlations with annual market adjusted returns. Most importantly, the

Pearson (Spearman) correlation between the BSCORE index and RETM1 at 0.15 (0.13) is positive

and significant at the 1% level. This correlation is also higher than that between the individual

signals and returns, suggesting that there may be a benefit to aggregating the individual signals.

4.3. Analysis of BSCORE-based Hedge Portfolios

Having established the efficacy of the individual signals in generating excess returns, we

now analyze the relationship between BSCORE and future returns. We sort banks into fourteen

portfolios based on their BSCORE levels from 0 to 13. Panel A of Table 4 reports the returns to

21

each of these fourteen portfolios, presenting one year–ahead raw returns (RET1) and market

adjusted returns (RETM1).

The mean and median returns for both RET1 and RETM1 show a monotonic increase as we

move from the BSCORE = 0 portfolio to BSCORE = 13. Specifically, for the BSCORE = 0

portfolio, the mean (median) RET1 is –9.1 % (–43.7%), whereas the mean (median) RETM1 is –

22.1 % (–44.8%). For the BSCORE = 13 portfolio, the mean (median) RET1 is 22.5% (25.5%),

whereas the mean (median) RETM1 is 14% (4.9%). Examining the last two columns on the right

shows that the proportion of bank–years with positive returns also monotonically increases with

BSCORE. In particular, while only 33.3% of the observations have positive one year–ahead raw

returns (RET1) in the lowest BSCORE portfolio, all the bank-year observations in the BSCORE =

13 portfolio show positive returns. This suggests the potential efficacy of a trading strategy that is

long on banks with the highest BSCORE and short on banks with the lowest BSCORE. The

monotonic increase in both RET1 and RETM1 across BSCORE portfolios also holds for the first

and third quartile of returns in each portfolio, suggesting that sorting on BSCORE helps to shift

the entire distribution of returns to the right.

However, there are very few observations in the extreme portfolios in Panel A. In order to

create an implementable strategy, we sort the sample into quintiles and create hedge portfolios

using the top and bottom quintiles (i.e., long on the top quintile, and short on the bottom quintile).7

The results are presented in Panel B of Table 4. We observe mean RET1 (RETM1) of 6.4 % (7.4%),

and median RET1 (RETM1) of 5.1% (5.1%). Examining the last row of Panel B shows that the

7 As BSCORE is a discrete variable whose distribution varies across time, the exact cutoff to determine quintiles varies from year to year, and the quintiles do not necessarily have identical number of observations.

22

differences between the mean and median returns on the long and short portfolios are statistically

significant.

4.4. Partition Analysis

In this section, we partition the sample along a number of dimensions to examine if the

results are robust in different subsets of the population. We consider three partitions – firm size,

analyst following, and listing exchange. These partitions are related to both the information

environment and the implementability of the hedge strategy. The results are presented in Table 5.

Panel A of Table 5 presents the returns by partitions of firm size (market capitalization).

We do not observe any significant discernible pattern of differences in the distribution of hedge

returns across these three size groups. In particular, for small firms, the mean hedge returns RET1

(RETM1) are 5.77% (7.24%). For medium firms, the mean hedge returns RET1 (RETM1) are

5.84% (8.55%), while for large firms, the mean hedge returns RET1 (RETM1) are 7.82% (6.56%).

The last row shows that the return differences are statistically significant in each of the three size

categories. Hence, our results are robust across size partitions. Beyond documenting robustness,

our size partition results also provide comfort regarding the ease of implementation of the

BSCORE-based trading strategy — the finding that results hold even with the small bank stock

partition suggests that transaction costs are unlikely to explain away the returns to BSCORE hedge

strategy.

Panel B of Table 5 presents the returns by partitions of analyst following. The BSCORE

strategy continues to generate positive and statistically significant hedge returns in both partitions.

23

In particular, for the subsample without analyst following, the mean hedge returns RET1 (RETM1)

are 7.00% (7.61%), and for the subsample with analyst following are 5.68% (7.09%). Therefore,

our results are also robust across analyst following partitions.

Panel C of Table 5 reports the returns by partitions of exchange listing status. Specifically,

we partition the sample into two subsamples of firms listed in NYSE/AMEX and NASDAQ/Other

exchanges, respectively. This partition is related to the implementability of the strategies, as

shorting NYSE/AMEX stocks is easier than shorting NASDAQ stocks. The BSCORE strategy

generates positive and statistically significant hedge returns in both partitions. For the

NYSE/AMEX subsample, the mean hedge returns RET1 (RETM1) are 6.57% (5.35%), while for

the NASDAQ/Other subsample, the mean hedge returns RET1 (RETM1) are 6.33% (7.98%). Thus,

our results are robust across exchange listing status as well.

4.5. Results Across Time

To ensure that the BSCORE results documented thus far are not attributable to extreme

return patterns at some points in time or to time clustering of observations, we examine the

performance of BSCORE–based trading strategy for each of the years in our sample period (1994–

2013). In particular, we create long and short portfolios based on the top and bottom deciles of

BSCORE distribution each year. The results are presented in Table 6 and depicted in Figure 1-A.

Table 6 shows that long-short strategy based on BSCORE yields positive hedge returns

(HRET1) for all years during our 1994–2013 sample years except in 2002. Interestingly, hedge

returns reach a peak during the 2007–2009 years when the market was severely affected by the

24

effects of the financial crisis. The consistent performance of the BSCORE strategy over time,

including during the crisis period, seems to suggest that risk is unlikely to be a complete

explanation for our results. In fact, the sample period includes sharp turns in the business cycle

and materialization of tail risk events. Finally, a Sharpe ratio of 1.1 suggests that mean performance

relative to standard deviation remained strong during our sample period.

4.6. Controlling for Identified Risk Factors

The BSCORE strategy could potentially be correlated with common risk factors. In this

section, we examine whether the returns to a BSCORE trading strategy survive controls for

commonly used risk factors in asset pricing tests. Specifically, we run multi–factor portfolio

regressions based on the Fama and French (1993) three–factor model (Table 7-A), Carhart (1997)

four–factor model (Table 7-B), and Fama and French (2015) five–factor model (Table 7-C). We

first create portfolios based on the top, middle 3 and bottom quintiles of BSCORE. We run

calendar-time portfolio regressions using monthly returns for the 12 months after portfolio

formation. The intercept (α) of the regression represents the monthly excess return for each

portfolio. We then consider the hedge return based on a strategy of going long in high BSCORE

firms and short in low BSCORE firms – i.e. the difference in α between the top and bottom

BSCORE quintile portfolios.

Panel A of Table 7 presents the results for the three-factor model. First, we note that the

three Fama–French factors (Rm–Rf, SMB, and HML) load positively and significantly across all

the BSCORE quintiles. Second, we observe a positive (negative) α for the top (bottom). Lastly,

25

the hedge strategy based on top and bottom BSCORE quintiles has a positive α of 0.607 (7.53%

annualized), that is statically significant (t = 4.07).

Panel B of Table 7 presents the results for the four–factor model. Momentum (UMD) loads

positively (0.131) and significantly (t = 4.61) for the hedge strategy, leading to a decline in α to

0.505 (6.23% annualized), but still remaining statistically significant (t = 3.49). Lastly, Panel C

of Table 7 documents the results for the five–factor model. Both the profitability factor (RMW)

and the investment factor (CMA) load significantly, which should not be surprising as the

components of BSCORE focus on both profitability and investment efficiency. The α for the five-

factor model is lower at 0.450 (5.53% annualized), but still statistically significant (t = 2.90). In

summary, results from Table 7 suggest that the efficacy of BSCORE strategy persists after

controlling for common risk factors.

4.7. Future Earnings Announcement Returns, Analyst Surprises, and Performance Delistings

The previous sections document that hedge returns to a long–short trading strategy created

using high and low BSCORE groups persist even after controlling for commonly employed risk

factors, and are consistent over time. To further cement this argument, we explore additional tests

to examine whether market participants are able to impound the future valuation implications

embedded within BSCORE signals immediately. For the mispricing story to hold, it must be the

case that market participants’ reaction to future resolution of uncertainty is positively correlated

with BSCORE. Prior research in accounting has used such tests to lend credence to mispricing

based explanations (e.g. Sloan 1996, Piotroski 2000 and Mohanram 2005). We examine analyst

26

forecast errors, stock market reaction to future earnings announcements, and the extent of

performance related delisting. Table 8 documents the results.

In Panel A of Table 8, the first column presents mean scaled annual forecast surprise for

the following fiscal year (SURP A1), using annual EPS forecasts obtained three months after prior

fiscal year end, scaled by year-end stock price. Analysts’ surprises are more negative for bottom

quintile banks and less negative for the top quintile banks, with the difference in forecast surprise

between the top and bottom BSCORE quintiles being a significant 0.94% (t = 3.03).8 The next

four columns repeat the analyses using quarterly forecasts obtained two months after prior quarter

end and find similar results. The results are consistent with markets being more likely to be

negatively surprised by earnings realizations for low BSCORE firms, and to a lesser extent, with

markets being more likely to be positively surprised for firms with high BSCORE.

The last column of Panel A presents the stock–market reaction around quarterly earnings

announcements in the first year. Quarterly buy–and–hold market–adjusted returns (EA_RET) are

computed for a three–day window (–1 to +1) around earnings announcements, and then summed

across the four quarters. We observe that the quarterly announcement returns increases predictably

and monotonically from the bottom to top BSCORE quintile, and that the return difference

between the top and bottom quintile is 1.05% and statistically significant (t = 3.63). Hence, a high

proportion (1.05/7.40 = 14%) of the annual hedge returns are realized during the 12 trading days

8 The fact that the average forecast surprise is negative for all groups should not be surprising, given that prior research has documented that analysts’ forecasts tend to be too optimistic.

27

surrounding the quarterly earnings announcements, consistent with the stock market reacting to

future earnings information correlated with BSCORE.

In Panel B of Table 8, we use the classification in Shumway (1994) to identify delistings

associated with poor performance in the year after BSCORE computation. Performance delistings

can be viewed as extreme negative return realization events. We predict and find that the

proportion of bank stocks delisted due to performance reasons is significantly higher in the bottom

BSCORE quintile (1.66%) compared with the top BSCORE quintile (0.33%). The difference in

proportions is statistically significant at the 1% level.

4.8. Additional Conditioning on Likely Overvaluation or Undervaluation (V/P)

The preceding analyses demonstrate that BSCORE can be used to effectively screen

between high and low quality bank stocks. However, future return performance is a function of

both future realized bank performance and its current valuation. This is especially likely to be

salient for many banks that are highly visible and followed by market participants for a variety of

political and regulatory reasons. Accordingly, we try to condition all of the preceding analyses on

current bank valuation. The expectation is that all of the previously documented strong results for

high BSCORE firms are especially pronounced for those banks that are in addition currently

under–valued (at time t). Conversely, we expect that the previously documented weak results for

low BSCORE firms will be especially pronounced for those banks that are currently over-valued.

This expectation also follows from the results in Li and Mohanram (2015), who show that

combining ratio analysis based methods of fundamental analysis with intrinsic value based

methods dramatically increases hedge returns.

28

To determine the extent of current undervaluation or overvaluation, we rely on the research

methodology in Frankel and Lee (1998) to calculate the Value to Price or V/P metric. We use the

cross-sectional forecasting approach from Li and Mohanram (2014) to ensure that we do not lose

observations for firms without analyst following. The details of the cross-sectional forecasting and

the V/P estimation is presented in Appendix B.

The results of this conditioning analysis are presented in Table 9 and depicted in Figure 1-

B. First, Panel A repeats the analysis in Table 4-B, except the long portfolio comprises of banks

within the topmost BSCORE quintile and above median V/P ratio (i.e., undervalued firms with

high fundamental quality), and the short portfolio comprises of the bottommost quintile and below

median V/P ratio (i.e., overvalued low fundamental quality firms). Table 9-A shows both the one

year–ahead raw returns (RET1) and one year-ahead market adjusted returns (RETM1). The spread

in RET1 increases rather dramatically from 6.4% in Table 4B to 11.8% in Table 9-A. A similar

trend is observed for the spread in RETM1. It increases from 7.45% in Table 4-B to 12.2% in Table

9-A.9

Panel B of Table 9 performs the same time partition analysis as in Table 6, except that the

long portfolio comprises of banks within the topmost BSCORE quintile and above median V/P

ratio, and the short portfolio comprises of the bottommost BSCORE quintile and below median

9 One concern might be that the stronger hedge returns shown here are merely an artefact of smaller sample size – i.e. the fact that we are focusing on more extreme high and low BSCORE firms. To test this conjecture, in untabulated analyses, we examine hedge returns to deciles of BSCORE, so that the sample size for the long/short portfolios is comparable to quintiles of BSCORE further conditioned by V/P. We find that the hedge returns increase modestly to 6.56% for RET1 and 8.03% for RETM1, far less than the hedge returns for the combination of BSCORE and V/P. Finally, we also examine the average BSCORE for low and high V/P partitions. Among low BSCORE firms, the average BSCORE for low V/P firms (3.57) is only marginally lower than the average BSCORE for high V/P firms (3.78). Among high BSCORE firms, the average BSCORE for high V/P firms at 9.89 is actually lower than the average BSCORE for low V/P firms at 9.97. This suggests that the V/P screen provides information orthogonal to BSCORE.

29

V/P ratio. Table 9-B also reveals a dramatic improvement in hedge returns over time compared to

Table 6. Specifically, we find that the hedge returns (HRET1) are positive in all of the years in our

sample. Furthermore, our previous finding in Table 5 that fundamental analysis gains importance

during years of macroeconomic uncertainty (2007–2009) is strengthened and now holds even for

the earlier recession in 2001. Finally, the Sharpe ratio improves from 1.1 to 1.4 using this strategy.

Lastly, Panel C of Table 9 repeats the risk factor analysis as in Table 7 after conditioning

the BSCORE portfolios on V/P. We find that the monthly α for the Fama–French (1993) three–

factor, the Carhart (1997) four-factor, and the Fama–French (2015) five–factor models increases

from 0.607, 0.505, and 0.450 in Table 7 to 0.868, 0.767, and 0.790 in Table 9-C, respectively. The

α from these tests corresponds to 10.93%, 9.60% and 9.90% in terms of annualized returns. All in

all, the results in Table 9 show that the efficacy of the BSCORE trading strategy can be enhanced

by additionally incorporating intrinsic value.

4.9. Comparison with Strategies based Solely on Summary Profitability Ratios

An examination of the correlation matrix in Table 3 suggests that BSCORE is highly

correlated with the signals associated with ROE (B1, correlation 0.67) or ROA (B2, correlation

0.70). Is it therefore likely that results similar to what we obtain with BSCORE could ostensibly

be obtained by using just these ratios? Indeed, ROE is very often used as a focal ratio in the analysis

of bank profitability. To test this, we run some additional (untabulated) tests focusing on portfolios

based on ROE and ROA.

30

To ensure comparability with our BSCORE portfolios, we form quintiles based on the

change in ROE and ROA respectively. We then replicate the tests in Table 4, 6 and 7 using these

metrics. We find that while strategies based on ROE and ROA do generate positive hedge returns,

the performance of the strategies is inferior to that of the BSCORE strategy, with lower and more

inconsistent hedge returns. For instance, the ROE based strategy generates average annual hedge

returns of 4.82% (compared to 6.02% for BSCORE) with a Sharpe ratio of 0.67 (compared to 1.17

for BSCORE), with negative hedge returns in five years out of 20. An ROA based strategy

generates average hedge returns of 3.81% with a Sharpe ratio of 0.55, with negative returns in six

years out of 20. Hence, it is clear that the more comprehensive BSCORE strategy outperforms

strategies based on a single metric of performance such as ROE or ROA. This suggests that there

is merit in looking at bank ratios at a finer level, focusing on the drivers of profitability, risk and

growth.

5. Conclusion

This study examines returns to a fundamental analysis based trading strategy for U.S. bank

stocks. We ex–ante identify thirteen bank fundamental signals related to profitability, risk and

growth to create an index of bank fundamental strength (BSCORE). We find that a long-short

strategy based on BSCORE yields positive market adjusted hedge returns of 7.4% during our

1994–2013 sample period. The results are consistent across partitions based on size, analyst

following and exchange listing status. Inconsistent with a risk-based explanation, positive hedge

returns are obtained for all but one year during the sample period. Hedge returns remain positive

across size partitions, and survive risk adjustment based on Fama–French (1993) three–factor,

31

Carhart (1997) four–factor, and Fama–French (2015) five–factor models. Further lending credence

to a mispricing rather than risk-based explanation, we observe a positive relation between

BSCORE quintiles and analyst forecast surprises around earnings announcements. We also find

evidence that the pattern of excess returns around subsequent earnings announcements correlates

with BSCORE. Further corroborating these findings, we report a negative association between

BSCORE quintiles and extreme negative return realization events as measured by future

performance delistings. Finally, the returns to a BSCORE-based strategy are enhanced when we

combine this approach with ex-ante measures of under- or over-valuation based on intrinsic value.

It is interesting to observe that while returns to fundamentals-based trading strategies such

as the accruals-based strategies seem to have diminished over time for non-financial firms, their

importance for banks remains intact and, in fact, peaked during the recent financial crisis. This is

consistent with the concept of adaptively efficient markets introduced by Grossman and Stiglitz

(1980) – i.e. the notion that markets may have a blind spot but once this is pointed out, the markets

adapt and become efficient. Green et al. (2011) and Mohanram (2014) show that returns to the

accruals anomaly decline once investors and financial analysts respectively pay greater attention

to accruals. Given that banks have not been widely analyzed using an approach such as that

developed in this paper, it is not surprising to find continued strong returns to fundamental

strategies in bank stocks.

This study adds to the recent regulatory policy debate on bank performance measurement

and valuation during the crisis, by showing that a simple approach using publicly available data

can be used to predict near term bank performance. Our results demonstrate that there are valuable

32

signals related to profitability, risk and growth embedded within past financial reports that can

serve as barometers of bank health. Our study demonstrates that fundamental analysis is especially

important for banks during crisis periods.

Finally, our results provide credence to observations by Admati (2011), and Moussu and

Petit-Romec (2013), among others, who have commented on the excessive fixation of bank

managers and analysts on ROE as the central performance metric. In particular, Moussu and Petit-

Romec (2013) document that ROE is often enhanced by leverage, and that pre–crisis ROE is

strongly correlated with value destruction during the financial crisis of the past decade. We

conjecture that this excessive focus on ROE could come at the expense of ignoring some other

fundamental performance signals that present a more nuanced picture of expected future

performance.

33

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Appendix A: Variable Definitions

Variable Definition

ROE

(B1) Return on equity, calculated as net income divided by shareholders’ equity. B1 equals 1 when ROE increases (∆ROEt > 0), and 0 otherwise.

ROA

(B2) Return on assets, calculated as net income divided by total assets. B2 equals 1 when ROA increases (∆ROAt > 0), and 0 otherwise.

Spread

(B3)

Spread on the bank’s loan portfolio measured as the ratio of net interest income earned during the year to average total loans. B3 equals 1 when spread increases (∆Spreadt > 0), and 0 otherwise.

Expense_Ratio

(B4) Operating expense ratio, calculated as non–interest expense divided by total revenue. B4 equals 1 when expense ratio decreases (∆Expense_Ratiot < 0), and 0 otherwise.

Noninterest_Income (B5)

Non-interest income, calculated as the ratio of non–interest income to total revenue. B5 equals 1 when non–interest income increases (∆Noninterest_Incomet > 0), and 0 otherwise.

Earning_Assets

(B6) Earning assets, calculated as the ratio of earning assets to total assets. B6 equals 1 when earning assets increases (∆Earning_Assetst > 0), and 0 otherwise.

Loans_Deposits

(B7) Loans to deposits, calculated as the ratio of total loans to total deposits. B7 equals 1 when the ratio of loans to deposits increases (∆Loans_Depositst > 0), and 0 otherwise.

LLP

(B8)

Loan loss provision, calculated as the ratio of annual loan loss provision to total loans. B8 equals 1 when the level of loan loss provisions decreases (∆LLPt < 0), and 0 otherwise.

NPL

(B9)

Non–performing loans, calculated as the ratio of non–performing loans to total loans. B9 equals 1 when the level of non–performing loans decreases (∆NPLt < 0), and 0 otherwise.

Allowance_Adequacy (B10)

Allowance adequacy, calculated as the ratio of loan loss allowance to non–performing loans. B10 equals 1 when the ratio of loan loss allowance to non-performing loans increases (∆Allowance_Adequacyt > 0), and 0 otherwise.

Revenues

(B11)

Total Revenue, calculated as the sum of net interest income (interest income – interest expense) and non-interest income. B11 equals 1 when the total revenue increases (∆Revenuest > 0), and 0 otherwise.

Loans

(B12) Loans is defined as the total loans of the bank. B12 equals 1 when the bank’s total loans increases (∆Loanst > 0), and 0 otherwise.

Trading_Assets

(B13)

Trading assets, calculated as the ratio of trading assets to total assets or ratio of trading income to total income. B13 equals 1 when the bank’s trading assets increases (∆Trading_Asstest > 0), and 0 otherwise.

RET1 Buy-and-hold returns using a one-year horizon starting on Apr 1st, adjusted for delisting returns consistent with Shumway (1997).

RETM1

Buy-and-hold returns using a one-year horizon starting on Apr 1st, adjusted for delisting returns consistent with Shumway (1997),in excess of the buy-and-hold value weighted market return.

36

Appendix B: Estimation of the V/P Ratio

From Li and Mohanram (2014), we forecast future earnings using the following model:

Et+τ = χ0 + χ1*NegEt + χ2*Et + χ3*NegEt*Et + χ4*Bt + χ5*TACCt + ε (τ = 1 to 5)

We implement this model using data from COMPUSTAT. Et is earnings per share before

special and extraordinary items ((ib-spi)/csho); NegEt is an indicator variable for loss firms; Bt is

book value of equity per share (ceq/csho); TACCt is total accrual per share calculated following

Richardson et al. (2005), i.e., (ΔWC+ ΔNCO+ΔFIN)/csho, where WC is (act-che)-(lct-dlc); NCO

is (at-act-ivao)-(lt-lct-dltt); and FIN is (ivst+ivao)-(dltt+dlc+pstk).

We estimate this cross-sectional model using all available observations over the past ten

years. This ensures that the earnings forecasts are strictly out of sample. We estimate the model as

of June 30 of each year. To further reduce look-ahead bias, we assume that financial information

for firms with fiscal year ending (FYE) in April to June is not available on June 30. In other words,

only the financials of firms with FYE from April of year t-1 to March of year t are used for

estimation of year t. For each firm and each year t in our sample, we compute earnings forecasts

for year t+1 to year t+5 by multiplying the independent variables in year t with the pooled

regression coefficients estimated using the previous ten years of data. This method only requires a

firm have non-missing independent variables in year t to estimate its future earnings. As a result,

the survivorship bias is kept to a minimum.

Using the cross-sectional forecasts thus obtained, we estimate the intrinsic value of a firm

using the residual income valuation model:

𝑉𝑉𝑡𝑡 = 𝐵𝐵𝑡𝑡 + �𝐸𝐸𝑡𝑡[𝑁𝑁𝑁𝑁𝑡𝑡+𝑖𝑖 − (𝑟𝑟𝑒𝑒𝐵𝐵𝑡𝑡+𝑖𝑖−1)]

(1 + 𝑟𝑟𝑒𝑒)𝑖𝑖

∞

𝑖𝑖=1

= 𝐵𝐵𝑡𝑡 + �𝐸𝐸𝑡𝑡[(𝑅𝑅𝑅𝑅𝐸𝐸𝑡𝑡+𝑖𝑖 − 𝑟𝑟𝑒𝑒)𝐵𝐵𝑡𝑡+𝑖𝑖−1]

(1 + 𝑟𝑟𝑒𝑒)𝑖𝑖

∞

𝑖𝑖=1

where 𝑉𝑉𝑡𝑡 is the stock’s fundamental value at time t, Bt is the book value of equity per share at time

t , Et[.] is expectation based on information available at time t; NIt+i is earnings before special and

extraordinary items per share for period t+i; re is the cost of equity capital, and ROEt+i is the after–

tax return on book equity for period t+i.

37

To implement the model, we estimate the firm’s future earnings per share from t+1 to t+5

using the methodology discussed above. We compute book value of equity and return on equity in

each period assuming clean surplus accounting: Bt+I = Bt+i-1 + (1−k) × NIt+i and ROEt+i = NIt+I

/ Bt+i −1, where k is the estimated payout ratio. The payout ratio (k) is set to dividend divided by

net income (dvc/(ib–spi)) in year t for firms with positive earnings, or dividend in t divided by 6%

of total assets (dvc/(6% × at)) for firms with negative earnings. If k is greater (less) than one (zero),

we set it to one (zero).

We assume that abnormal earnings stay constant after the forecast horizon to estimate

terminal value. We use the risk-free rate (yield on the ten-year U.S. treasury) plus 5% as the cost

of equity capital (re), which is cross–sectionally constant but varies across time.

38

Figure 1-A: Returns to BSCORE Strategy across Time

Figure 1-B: Returns to BSCORE Strategy combined with V/P across Time

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

19941995199619971998199920002001200220032004200520062007200820092010201120122013

Hedg

e Re

turn

s (%

)

Year

0%

5%

10%

15%

20%

25%

30%

35%

19941995199619971998199920002001200220032004200520062007200820092010201120122013

Hedg

e Re

turn

s (%

)

Year

39

TABLE 1. Sample Selection and Descriptive Statistics

Panel A presents the sample selection procedure. Panel B presents descriptive statistics. Please see Appendix A for the definition of the variables.

Panel A: Sample Selection

Criterion Bank-Years

Unique Banks

Observations between 1993-2013 on Bank COMPUSTAT 17,571 2,074

Availability of information to compute the variables underlying B1:B13 13,974 1,805

Availability of lagged information to compute B1:B13 and BSCORE 13,684 1,775

December Fiscal Year End firms only 11,959 1,466

Availability of Future returns on CRSP 9,937 1,251

Stock Price >= $1, Shares Outstanding >= 1 million, Total Assets >= $100

million and Market Capitalization >= $10 million

9,343 1,199

FINAL SAMPLE 9,343 1,199

Panel B: Descriptive Statistics (N=9,343)

Variable Mean Min Q1 Median Q3 Max Stdev

Total Assets 26288.4 100.0 553.4 1209.5 3741.2 3771200 180680

Revenues 991.8 2.8 23.6 52.3 162.3 119643 6015

Market Capitalization 2593.2 10.1 59.1 151.3 553.7 238675 13481

ROE 8.6% –74.1% 6.2% 10.3% 13.6% 26.5% 10.4%

ROA 0.8% –4.3% 0.6% 0.9% 1.2% 2.4% 0.8%

Spread 5.4% 2.1% 4.3% 5.2% 6.1% 13.0% 1.6%

Expense_Ratio 82.7% 50.9% 74.0% 79.2% 85.7% 214.1% 19.5%

Non_Interest_Income 21.5% –2.6% 12.9% 19.7% 27.9% 73.7% 13.6%

Earning_Assets 88.0% 51.4% 85.9% 89.8% 92.5% 97.3% 7.1%

Loans_Deposits 88.0% 35.3% 76.1% 87.5% 98.5% 150.8% 19.4%

LLP 0.6% –0.3% 0.2% 0.3% 0.6% 5.5% 0.8%

NPL 1.8% 0.0% 0.5% 1.0% 2.0% 15.0% 2.3%

Allowance_Adequacy 2.80 0.15 0.70 1.34 2.58 41.74 5.42

Revenue Growth 12.9% –35.1% 1.6% 8.7% 18.1% 129.7% 22.0%

Loan Growth 13.5% –24.5% 2.5% 9.4% 19.4% 107.3% 19.5%

Trading Assets 0.3% 0.0% 0.0% 0.0% 0.0% 16.2% 1.7%

40

TABLE 2. Relation between Individual Signals and Future Returns

This table presents the mean one-year market adjusted returns (RETM1) for the two binary values of the individual signals for the

sample of banks. For definitions of B1:B13 as well as RETM1, please see the Appendix A. t–statistic for difference in means is

from a two–sample t–test. */**/*** represent statistical significance using 2 tailed tests at 10%/ 5%/ 1% levels.

SIGNAL (0) (1)

N Mean

RETM1

N Mean RETM1

(1) – (0) t–statistic

Category 1: Overall Profitability

B1: ∆ROEt > 0 4627 –0.31% 4716 6.10% 6.40% 8.48***

B2: ∆ROAt > 0 4710 0.14% 4633 5.76% 5.62% 7.46***

Category 2: Components of Profitability

B3: ∆Spreadt > 0 5503 3.38% 3840 2.28% –1.09% –1.43

B4: ∆Expense_Ratiot < 0 4471 –0.37% 4872 5.95% 6.33% 8.32***

B5: ∆Noninterest_Incomet > 0 4554 3.47% 4789 2.41% –1.05% –1.39

B6: ∆Earning_Assetst > 0 4662 1.66% 4681 4.19% 2.53% 3.35***

B7: ∆Loans_Depositst > 0 4174 0.27% 5169 5.07% 4.80% 6.31***

Category 3: Credit Risk

B8: ∆LLPt < 0 4488 0.41% 4855 5.25% 4.83% 6.34***

B9: ∆NPLt < 0 4202 –1.81% 5141 6.80% 8.61% 11.19***

B10: ∆Allowance_Adequacyt > 0 4363 –1.22% 4980 6.56% 7.78% 10.25***

Category 4: Growth

B11: ∆Revenuest > 0 1885 –0.37% 7458 3.76% 4.13% 4.14***

B12: ∆Loanst > 0 1721 1.23% 7622 3.31% 2.08% 2.00**

B13: ∆Trading_Assetst > 0 8654 2.87% 689 3.65% 0.79% 0.51

41

TABLE 3. Correlations between Individual Signals, BSCORE, and Future Returns

This table presents correlations between the BSCORE index, the individual signal, and future stock returns for the sample of banks. BSCORE is the sum of the signals B1:B13. For definitions of B1:B13 as well as RETM1, please see the Appendix A. Coefficients above the diagonal are Pearson and those below diagonal are Spearman rank-order correlations. */**/*** represent statistical significance at 10%/ 5%/ 1% levels.

B1 B2 B3 B4 B5 B6 B7 B8 B9 B10 B11 B12 B13 BSCORE RETM1

B1 0.70*** 0.04*** 0.64*** 0.01 0.15*** 0.16*** 0.12*** -0.01 0.04*** 0.19*** 0.00 0.18*** 0.66*** 0.09***

B2 0.70*** 0.03*** 0.71*** 0.01 0.14*** 0.24*** 0.12*** 0.00 0.06*** 0.20*** -0.03*** 0.19*** 0.68*** 0.08***

B3 0.04*** 0.03*** 0.03** 0.29*** 0.08*** -0.10*** 0.05*** -0.01 -0.07*** 0.05*** 0.12*** -0.01 0.30*** 0.03***

B4 0.64*** 0.71*** 0.03** 0.00 0.17*** 0.10*** 0.13*** -0.01 -0.02* 0.28*** 0.01 0.13*** 0.65*** 0.09***

B5 0.01 0.01 0.29*** 0.00 0.13*** -0.30*** 0.08*** -0.01 -0.07*** 0.05*** 0.36*** 0.06*** 0.31*** 0.07***

B6 0.15*** 0.14*** 0.08*** 0.17*** 0.13*** -0.08*** 0.79*** 0.01 -0.06*** 0.25*** 0.10*** 0.02* 0.55*** 0.12***

B7 0.16*** 0.24*** -0.10*** 0.10*** -0.30*** -0.08*** -0.01 0.00 -0.10*** -0.10*** -0.32*** 0.13*** 0.13***

B8 0.12*** 0.12*** 0.05*** 0.13*** 0.08*** 0.79*** -0.01 -0.01 -0.05*** 0.17*** 0.04*** 0.02 0.50*** 0.11***

B9 -0.01 0.00 -0.01 -0.01 -0.01 0.01 0.00 -0.01 -0.01 -0.01 -0.01 0.00 0.09*** 0.01

B10 0.04*** 0.06*** -0.07*** -0.02* -0.07*** -0.06*** -0.10*** -0.05*** -0.01 -0.05*** -0.06*** 0.14*** 0.15*** -0.01

B11 0.19*** 0.20*** 0.05*** 0.28*** 0.05*** 0.25*** -0.10*** 0.17*** -0.01 -0.05*** 0.04*** -0.05*** 0.41*** 0.07***

B12 0.00 -0.03*** 0.12*** 0.01 0.36*** 0.10*** -0.32*** 0.04*** -0.01 -0.06*** 0.04*** 0.29*** 0.26*** 0.02**

B13 0.18*** 0.19*** -0.01 0.13*** 0.06*** 0.02* 0.13*** 0.02 0.00 0.14*** -0.05*** 0.29*** 0.38*** 0.05***

BSCORE 0.67*** 0.70*** 0.29*** 0.66*** 0.30*** 0.54*** 0.13*** 0.50*** 0.09*** 0.14*** 0.41*** 0.23*** 0.35*** 0.15***

RETM1 0.09*** 0.08*** 0.03** 0.09*** 0.05*** 0.12*** -0.02* 0.10*** 0.00 -0.01 0.07*** 0.02 0.04*** 0.13***

42

TABLE 4. Returns to an Investment Strategy Based on BSCORE for Banks This table presents the distribution of returns based on the level of BSCORE. BSCORE is the sum of the signals B1:B13. For definitions of B1:B13 as well as RET1 and RETM1, see the Appendix A. In Panel B, in each year, the sample is divided into quintiles based on the level of BSCORE. As BSCORE is a discrete measure, the cutoff to determine quintiles varies by year, and the quintiles may not equal exactly 20% of the sample. t–statistic for difference in means (z–statistic for differences in medians) is from a two–sample t–test (Wilcoxon test). */**/*** represent statistical significance using 2 tailed tests at 10%/ 5%/ 1% levels.

Panel A: Distribution of Returns by BSCORE

BSCORE N Mean RET1

Mean RETM1

Q1 RET1

Q1 RETM1

Median RET1

Median RETM1

Q3 RET1

Q3 RETM1

% positive RET1

% positive RETM1

0 18 –9.1% –22.1% –72.5% –76.7% –43.7% –44.8% 7.6% –10.0% 33.3% 22.2% 1 125 –7.5% –24.8% –46.5% –60.0% –7.7% –18.1% 23.3% 11.3% 43.2% 34.4% 2 249 8.6% –7.1% –20.6% –31.1% 4.9% –8.1% 30.4% 15.0% 56.2% 40.6% 3 579 2.8% –7.5% –22.7% –31.8% 3.8% –9.7% 26.0% 13.7% 54.8% 38.2% 4 807 6.7% –3.8% –14.9% –24.4% 6.2% –5.0% 28.2% 17.1% 57.1% 44.0% 5 1078 10.0% 0.2% –10.7% –20.2% 8.6% –1.4% 32.0% 20.5% 61.3% 48.5% 6 1271 14.8% 4.4% –4.9% –16.0% 11.3% 1.2% 35.8% 21.9% 67.0% 51.8% 7 1348 16.6% 6.5% –3.4% –13.7% 12.2% 2.3% 36.2% 24.4% 68.6% 53.4% 8 1338 17.4% 5.8% –2.7% –14.8% 14.7% 1.9% 36.3% 25.2% 71.5% 52.3% 9 1157 18.6% 6.6% –3.2% –13.2% 14.0% 2.1% 37.4% 23.6% 71.0% 52.7%

10 892 19.0% 7.0% –1.9% –12.2% 14.6% 2.4% 36.3% 22.6% 72.4% 53.1% 11 408 18.5% 7.1% –1.5% –10.7% 13.7% 3.3% 36.1% 21.6% 73.0% 55.6% 12 69 21.3% 10.6% –2.8% –12.7% 13.1% 2.3% 40.4% 26.4% 69.6% 53.6% 13 4 22.5% 14.0% 14.6% –6.8% 25.5% 4.9% 30.5% 34.7% 100.0% 50.0%

Panel B: Distribution of Returns by Quintiles BSCORE

BSCORE Quintile

N Mean RET1

Mean RETM1

Q1 RET1

Q1 RETM1

Median RET1

Median RETM1

Q3 RET1

Q3 RETM1

% positive RET1

% positive RETM1

Bottom 1862 10.9% –1.0% –10.6% –21.4% 9.0% –2.7% 32.7% 18.7% 61.9% 46.8% Middle 3 5683 14.0% 3.1% –6.3% –17.1% 11.0% 0.1% 34.4% 22.0% 66.2% 50.1% Top 1798 17.3% 6.4% –3.2% –13.4% 14.1% 2.4% 35.7% 23.8% 70.9% 53.2% Top – Bottom 6.4% 7.4% 5.1% 5.1% 8.9% 6.4% t–stat/z–stat 4.92*** 5.96*** 5.10*** 5.79*** 5.75*** 3.87***

43

TABLE 5. Returns to BSCORE Strategy Partitioned by Size, Analyst Following, and Exchange Listing Status

This table presents the distribution of returns based on quintiles of BSCORE, further partitioned by measures of size, analyst following and exchange listing status. BSCORE is the sum of the signals B1:B13. For definitions of B1:B13, RET1, and RETM1, see the Appendix A. The sample is divided into quintiles based on the level of BSCORE. As BSCORE is a discrete measure, the cutoff to determine quintiles varies by year, and the quintiles may not equal exactly 20% of the sample. In Panel A, the sample is further partitioned into three groups based on market capitalization at fiscal year–end (prcc_f*csho). In Panel B, the sample is further partitioned into two groups based on analyst following. In Panel C, the sample is further partitioned into two groups based on exchange listing status. t–statistic for difference in means is from a two–sample t–test. */**/*** represent statistical significance using 2 tailed tests at 10%/ 5%/ 1% levels.

Panel A: Partitions based on Market Capitalization Small firms Medium firms Large Firms

Quintile N RET1 RETM1 N RET1 RETM1 N RET1 RETM1

Bottom 682 12.18% 0.11% 616 10.70% –2.19% 564 9.54% –1.05%

Middle 3 1841 15.51% 4.88% 1899 13.57% 2.80% 1943 12.98% 1.75%

Top 586 17.95% 7.36% 604 16.54% 6.36% 608 17.36% 5.51%

Top – Bottom 5.77% 7.24% 5.84% 8.55% 7.82% 6.56%

t–stat 2.40** 3.11*** 2.78*** 4.09*** 3.52*** 3.34***

Panel B: Partitions based on Analyst Following No Following Following

Quintile N RET1 RETM1 N RET1 RETM1

Bottom 952 9.78% –1.81% 910 12.05% –0.16%

Middle 3 2680 14.33% 4.08% 3004 13.69% 2.25%

Top 855 16.78% 5.81% 943 17.73% 6.93%

Top – Bottom 7.00% 7.61% 5.68% 7.09%

t–stat 3.65*** 4.06*** 3.23*** 4.36***

Panel C: Partitions based on Exchange Listing Status NYSE/AMEX NASDAQ/Other

Quintile N RET1 RETM1 N RET1 RETM1

Bottom 409 11.11% –0.04% 1453 10.83% –1.27%

Middle 3 1195 15.93% 4.64% 4488 13.48% 2.71%

Top 404 17.69% 5.31% 1394 17.16% 6.71%

Top – Bottom 6.57% 5.35% 6.33% 7.98%

t–stat 2.28** 2.04** 4.35*** 5.67***

44

TABLE 6. Hedge Returns across Time This table presents the distribution of hedge returns across time. BSCORE is the sum of the signals B1:B13. For definitions of B1:B13 as well as RET1, see the Appendix A. The sample is divided into quintiles based on the level of BSCORE. As BSCORE is a discrete measure, the cutoff to determine quintiles varies by year, and the quintiles may not equal exactly 20% of the sample. The hedge return HRET1 is the difference in the mean raw return (RET1) between the top and bottom quintiles of BSCORE. Sharpe ratio is the ratio of time series mean hedge return to standard deviation. t-statistic is the ratio of time series mean hedge return to standard error. *** represent statistical significance using 2 tailed tests at 1% levels.

YEAR NLONG NSHORT

RET1 (top BSCORE quintile)

RET1 (bottom BSCORE quintile) HRET1

1994 107 77 39.7% 36.6% 3.1%

1995 122 119 34.2% 31.2% 3.1%

1996 89 100 67.9% 57.9% 10.1%

1997 100 83 –10.2% –15.0% 4.8%

1998 71 114 –7.7% –11.1% 3.4%

1999 120 66 29.7% 22.2% 7.5%

2000 105 124 35.1% 31.0% 4.1%

2001 84 101 18.8% 10.0% 8.8%

2002 84 121 45.8% 47.8% –1.9%

2003 107 100 7.5% 3.1% 4.4%

2004 86 130 16.2% 9.0% 7.2%

2005 90 105 1.5% 0.3% 1.1%

2006 91 68 –1.6% –22.2% 0.6%

2007 81 108 –38.1% –55.3% 17.3%

2008 81 103 24.4% 12.2% 12.2%

2009 97 68 13.9% –3.6% 17.5%

2010 53 76 6.7% 1.4% 5.3%

2011 53 59 28.0% 23.2% 4.7%

2012 92 71 27.8% 21.1% 6.8%

2013 85 69 5.6% 5.1% 0.5% Analysis of Hedge Returns across time Mean Hedge Returns 6.02% Std. Dev of Hedge Returns 5.16% Sharpe Ratio 1.168 t-statistic 5.22*** Min. Hedge Return -1.91% Max Hedge Returns 17.54% Years with negative returns 1 out of 20

45

TABLE 7. Comparison of Excess Returns after Controlling for Risk Factors

BSCORE is the sum of the signals B1:B13. For definitions of B1:B13 see the Appendix A. Calendar-time regression are run for quintiles based on BSCORE for the twelve months starting on the April of the year after fiscal year end. We consider three portfolios – the top quintile, the three middle quintiles and the bottom quintile. The regression has 236 observations from April 1995 till December of 2014. The average monthly return for each quintile portfolio is regressed on various combinations of the market–factor (Rm – Rf), the size factor (SMB), the book–to–market factor (HML), the momentum factor (UMD), the profitability factor (RMW), and the investment factor (CMA). In addition, we also run a hedge regression with the hedge return between the top and bottom quintiles as the dependent variable. Figures in italics are t–statistics. */**/*** represent statistical significance using 2 tailed tests at 10%/ 5%/ 1% levels.

BSCORE Quintile α Rm – Rf SMB HML UMD RMW CMA Adj. R2

Panel A: Fama French 3-Factor Model

Bottom Quintile –0.185 0.745 0.345 0.747 55.3%

–0.81 14.25*** 4.91*** 10.07***

Middle Quintiles 0.198 0.691 0.286 0.731 64.2%

1.11 17.09*** 5.26*** 12.75***

Top Quintile 0.422 0.625 0.258 0.621 62.1%

2.55*** 16.59*** 5.09*** 11.61***

Top - Bottom 0.607 –0.120 –0.087 –0.126 6.91%

4.07*** –3.53*** –1.91* –2.62***

Panel B: Carhart 4-Factor Model

Bottom Quintile –0.079 0.689 0.366 0.703 –0.136 56.8%

–0.35 12.62*** 5.27*** 9.46*** –3.03***

Middle Quintiles 0.242 0.667 0.294 0.713 –0.057 64.4%

1.35 15.60*** 5.41*** 12.23*** –1.61

Top Quintile 0.426 0.623 0.259 0.620 –0.005 61.9%

2.54** 15.53*** 5.07*** 11.34*** –0.16

Top - Bottom 0.505 –0.066 –0.107 –0.084 0.131 14.26%

3.49*** –1.91* –2.44** –1.78*** 4.61***

Panel C: Fama French 5-Factor Model

Bottom Quintile –0.186 0.744 0.379 0.778 0.068 –0.113 55.1%

–0.76 12.25*** 4.64*** 7.24*** 0.56 –0.74 Middle Quintiles 0.083 0.734 0.358 0.659 0.216 0.040 64.7%

0.44 15.78*** 5.72*** 8.01*** 2.34** 0.34 Top Quintile 0.264 0.685 0.345 0.511 0.272 0.098 63.4%

1.53 15.95*** 5.97*** 6.72*** 3.18*** 0.91 Top - Bottom 0.450 –0.059 –0.034 –0.267 0.204 0.211 9.81%

2.90*** –1.53 –0.66 –3.90*** 2.65*** 2.18**

46

TABLE 8. Relation between BSCORE and Future Forecast Surprises, Announcement Period Returns and Delistings

BSCORE is the sum of the signals B1:B13. For definitions of B1:B13 see the Appendix A. The sample is partitioned each year into quintiles based on BSCORE. We consider three portfolios – the top quintile, the three middle quintiles and the bottom quintile. EA_RET is the average market–adjusted return around earnings announcements, using the 12 trading days around the earnings announcement dates (RDQ) in the following year, using the CRSP value–weighted index for market returns. SURP A1 is the annual forecast surprise, measured as the difference between actual EPS and consensus mean annual EPS forecast issued three months after fiscal year end. SURP Q1–Q4 is the quarterly forecast surprise, measured as the difference between actual quarterly EPS and consensus quarterly EPS, measured two months after prior quarter end. All surprise variables are scaled by stock price at the end of the fiscal year. For Panel B, we use the classification in Shumway (1994) to identify delistings associated with poor performance in the year after BSCORE computation.

Panel A: Analyst Forecast Surprises and Earnings Announcement period returns BSCORE Quintile

N SURP A1

N SURP

Q1 SURP

Q2 SURP

Q3 SURP

Q4 N EA_RET

Bottom Quintile 910 –1.00% 1024 –0.20% –0.39% –0.19% -0.46% 1807 -0.46%

Middle Quintiles 3004 –0.50% 3344 –0.05% –0.10% –0.14% –0.25% 5572 0.29%

Top Quintile 943 –0.06% 1066 0.03% –0.09% –0.06% –0.10% 1757 0.59%

Top – Bottom 0.94% 0.23% 0.30% 0.14% 0.36% 1.05%

t–stat 7.09*** 3.03*** 3.40*** 1.76* 4.24*** 3.63***

Panel B: Performance Delistings

BSCORE Quintile N Proportion Delisted

Bottom Quintile 1862 1.66%

Middle Quintiles 5683 0.39%

Top Quintile 1798 0.33%

Top – Bottom -1.33%

t–stat -4.08***

47

TABLE 9. Returns to an Investment Strategy based on BSCORE Interacted with V/P

This tables repeats the analysis from prior tables, by further conditioning on the V/P ratio. For all firms in the sample, the V/P ratio is calculated using the methodology described in Appendix B. Firms in the top quintile are included only if they have above median V/P ratios, while firms in the bottom quintiles are only considered if they have below median V/P ratios. Please see headers to Tables 4, 6 and 7 for details.

Panel A: Distribution of Returns by Quintiles BSCORE, further Conditioned on V/P

BSCORE Quintile

N Mean RET1

Mean RETM1

Bottom (and below median V/P) 912 8.7% –3.5%

Middle 3 5683 14.0% 3.1%

Top (and above median V/P) 895 20.5% 8.7%

Top – Bottom 11.8% 12.2%

t-stat/z-stat 6.47*** 6.87***

Panel B: Hedge Returns across Time, further Conditioned on V/P

FYEAR NLONG NSHORT RET1

(top BSCORE quintile) RET1

(bottom BSCORE quintile) HRET1

1994 59 42 41.7% 29.1% 12.7% 1995 66 63 37.7% 27.9% 9.7% 1996 47 48 74.7% 58.5% 16.2% 1997 56 44 –13.3% –15.7% 2.4% 1998 30 58 –3.6% –15.1% 11.6% 1999 55 28 37.9% 22.6% 15.3% 2000 53 61 39.5% 22.9% 16.6% 2001 41 51 27.8% 3.8% 24.0% 2002 42 60 51.4% 41.7% 9.7% 2003 53 55 6.3% 2.0% 4.3% 2004 34 51 13.2% 12.9% 0.3% 2005 38 40 4.5% 0.3% 4.2% 2006 42 27 –19.3% –27.7% 8.4% 2007 38 46 –46.2% –50.8% 4.6% 2008 43 44 19.9% –11.9% 31.8% 2009 55 48 17.1% –1.8% 18.8% 2010 28 41 8.3% 0.0% 8.3% 2011 25 27 45.2% 28.6% 16.6% 2012 52 43 29.0% 21.3% 7.7% 2013 38 35 4.7% 4.7% 0.0%

48

Table 9 (continued)

Analysis of Hedge Returns across time Mean Hedge Returns 11.15% Std. Dev of Hedge Returns 8.09% Sharpe Ratio 1.38 t-statistic 6.17*** Min. Hedge Return 0.00% Max Hedge Returns 31.80% Years with negative returns 0 out of 20

Panel C: Controlling for Risk Factors, further Conditioned on V/P

BSCORE quintile α Rm – Rf SMB HML UMD RMW CMA Adj. R2 Fama French 3–Factor Model Bottom Quintile & below median V/P

–0.326 0.722 0.267 0.662 43.4% –1.20 11.68*** 3.21*** 7.54***

Middle Quintiles 0.176 0.695 0.294 0.725 65.2% 1.01 17.51*** 5.52*** 12.89*** Top Quintile & above median V/P

0.542 0.620 0.308 0.670 57.7% 2.91*** 14.61*** 5.41*** 11.12***

Top – Bottom 0.868 –0.102 0.042 0.008 1.08% 4.31*** –2.23** 0.67 0.12 Carhart 4–Factor Model Bottom Quintile & below median V/P

–0.202 0.657 0.291 0.611 –0.158 45.2% –0.75 10.17*** 3.55*** 6.94*** –2.98***

Middle Quintiles 0.220 0.671 0.303 0.707 –0.057 65.5% 1.25 15.99*** 5.68*** 12.36*** –1.65* Top Quintile & above median V/P

0.564 0.609 0.313 0.661 –0.028 57.6% 2.99*** 13.48*** 5.45*** 10.75*** –0.76

Top – Bottom 0.767 –0.049 0.021 0.050 0.130 5.05% 3.85*** –1.02 0.35 0.77 3.31*** Fama French 5–Factor Model Bottom Quintile & below median V/P

–0.341 0.727 0.310 0.684 0.094 –0.108 43.1% –1.18 10.11*** 3.21*** 5.38*** 0.66 –0.60

Middle Quintiles 0.064 0.737 0.363 0.654 0.206 0.044 65.7% 0.35 16.12*** 5.90*** 8.09*** 2.27*** 0.38 Top Quintile & above median V/P

0.449 0.655 0.370 0.615 0.181 0.022 57.9% 2.28** 13.35*** 5.61*** 7.08*** 1.86* 0.18

Top – Bottom 0.790 –0.072 0.061 –0.069 0.087 0.130 0.73% 3.70*** –1.35 0.85 –0.73 0.82 0.97