Delisting returns and their effect on accounting-based market anomalies

Delisting Returns and their Effect onAccounting-Based, Market Anomalies

William Beaver∗, Maureen McNichols†and Richard Price‡

November 27, 2006

Abstract

We show that tests of market efficiency are sensitive to the inclusion of delistingfirm-years. When included, trading strategy returns based on anomaly variables canincrease (for strategies based on earnings, cash flows and the book-to-market ratio) ordecrease (for a strategy based on accruals). This is due to the disproportionate numberof delisting firm-years in the lowest decile of these variables. Delisting firm-years aremost often excluded because the researcher does not correctly incorporate delistingreturns, because delisting return data are missing or because other research designchoices implicitly exclude them.

∗Stanford University†Corresponding Author: mcnichols [email protected], Stanford University‡Rice University. We thank an anonymous referree and Doug Skinner (the editor) for helpful comments.

The authors acknowledge financial support from the Graduate School of Business, Stanford University andthe Jones Graduate School of Management, Rice University.

1

1 Introduction

The treatment of delisting returns has received relatively little attention in the accounting

literature. A delisting return is the return on a security after it has been removed from an

exchange, and is calculated by comparing the security’s value after delisting with the price

on the last trading day. Delistings occur most frequently due to mergers and acquisitions

or poor performance (e.g., bankruptcy). The omission of delisting returns is likely to affect

estimates of portfolio returns because the expected return conditional on the reason for

delisting is not generally zero. In addition, if the market return measure does not include

delisting returns, market and market-adjusted returns will be affected.

To demonstrate the potential impact of excluding delisting returns, we revisit anomalies

based on earnings, accruals, cash flows and the book-to-market ratio (Sloan, 1996; Lakon-

ishok et al., 1994; Bernard and Thomas, 1989, 1990). These studies compare the future

returns of firm-years in high vs. low deciles of different accounting variables. These anoma-

lies lead to questions of market efficiency with respect to fundamental accounting variables.

In this paper, we demonstrate that portfolio returns, as conventionally measured in prior

research, are sensitive to the treatment of delisting returns. We do not take a position on

whether our findings provide evidence in favor of or against market efficiency because our

analysis does not consider transaction costs, among other costs of implementing a trading

strategy. As pointed out by Sloan (1996), findings of market inefficiency in historical data

do not necessarily imply that strategies based on the findings are exploitable. Because firms

that delist are on average highly risky and potentially illiquid, the exploitability of their

returns is an open question.

We find that the exclusion of firms that are delisted in the year t + 1 return accumula-

tion period, hereafter delisting firm-years, does not uniformly increase or decrease portfolio

returns. The effect on inferences about market efficiency depends on the partitioning vari-

able or trading strategy. For portfolios partitioned on earnings, cash flows, and the book-

to-market ratio, the difference between average returns in extreme deciles increases when

2

delisting firm-years are included. In contrast, for portfolios partitioned on accruals, average

returns in the lowest accruals decile decrease significantly when delisting firm-years are in-

cluded, but there is no significant change in the highest accruals decile. These results are

due to the disproportionate concentration of delisting firm-years with very negative returns

in the lowest decile of these variables.

We examine the implications of using size-decile returns (or in general, market returns)

that do not include delisting returns. In general, CRSP market return measures, including

the commonly used Stock File Capitalization Decile Indices, do not include delisting returns.

If researchers include delisting returns in the sample, but do not adjust the corresponding

market return, portfolio returns will be affected.

Three research design choices can result in the inadvertent exclusion of delisting firm-

years. First, requiring future earnings excludes two-thirds of delisting firm-years. Second,

nearly half of all delistings occur outside the date range provided by the CRSP/Compustat

merged database, so valid delisting firm-years are excluded if one does not include matches

outside the CRSP-specified date range. Third, when using monthly delisting returns, re-

searchers unfamiliar with the details of CRSP data who use replacement values for firms

with missing delisting returns will not identify all missing delisting returns because monthly

delisting returns generally contain a partial month return even when the delisting return

is missing.1 Treating partial month returns as valid delisting returns implicitly assumes a

delisting return of zero, which can affect estimated portfolio returns.

Researchers conducting tests of market efficiency should assess the sensitivity of their

findings to the inclusion of delisting firm-years in their sample. Our findings indicate that

inferences concerning market efficiency are sensitive to the treatment of delisting returns.

The magnitude of the effects we document suggests that researchers should carefully consider

whether the exclusion or inclusion of delisting firm-years affects inferences in tests of market

1CRSP provides daily delisting returns, which are the returns attributable to the delisting and monthly

delisting returns, which generally include the return from the beginning of the month to the date of thedelisting, defined by CRSP as the partial month return, and the daily delisting return.

3

efficiency and in other settings.

Section 2 discusses the background and related research. Section 3 discusses the compu-

tation of delisting returns. Section 4 contains descriptive statistics. Section 5 contains the

results of empirical analysis. Section 6 concludes.

2 Background Discussion

We focus on delisting returns for two reasons. First, we are unaware of any prior study that

examines the treatment of delisting returns in the accounting literature and its implications

for research design. The treatment of delisting firm-years varies substantially across studies.

Many papers follow Sloan (1996) and include a description such as the following: “the

delisting return is compounded with the buy-and-hold return and −100% is used as the

delisting return when it is missing and the firm was forced to delist.”2 Xie (2001) does not

describe what is done with missing delisting returns. Hribar and Collins (2002) specifically

state that firms with missing delisting returns are deleted. Mohanram (2004) uses −30%

when delisting returns are missing for reasons related to poor performance. Piotroski (2000)

assumes that all delisting returns are zero. Other papers are silent about how delisting

firm-years are treated (Desai et al., 2004; Zach, 2003; Thomas and Zhang, 2002; Collins and

Hribar, 2000; Zhang, 2005; Khan, 2005; Mashruwala et al., 2006).

Second, delisting firm-years are not uniformly distributed across portfolios commonly

formed on deciles of earnings, accruals, cash flows and the book-to-market ratio, variables of

great interest to accounting researchers. As a result, the treatment of delisting returns can

have a significant impact on estimated returns associated with trading strategies based on

these variables.

2The following papers provide brief explanations of the treatment of delisting returns that are very similarto Sloan (1996): Sun (2003), Kraft et al. (2006) and Dopuch et al. (2005).

4

2.1 Related Research

The accounting and finance literatures document several puzzling patterns in return behav-

ior, including the accrual anomaly (Sloan, 1996), post-earnings announcement drift (Ball and

Brown, 1968; Bernard and Thomas, 1989, 1990), the value-glamour anomaly (Lakonishok

et al., 1994), and the momentum anomaly (Jegadeesh and Titman, 1993). The literature

finds that returns from portfolios partitioned on fundamental variables such as earnings,

accruals, cash flows, the book-to-market ratio and past returns are unexpectedly high or

low.

A number of recent papers examine potential research design problems of tests of market

efficiency. Kothari et al. (2005) show that passive deletion (exclusion of observations that do

not survive the horizon studied) can lead to findings of systematic mispricing. Kraft et al.

(2006) show that portfolio returns to an accruals-based strategy are sensitive to robustness

tests such as trimming. Khan (2005) finds that accounting for additional risk factors causes

the difference in returns between extreme accrual decile portfolios to become insignificant.

Two studies in the finance literature address research design issues associated with delist-

ing returns. Shumway (1997) documents that CRSP data were generally missing delisting

returns for firms with poor performance (this has since been corrected by CRSP). Shumway

and Warther (1999) find that when delistings for performance-related reasons are included,

the size effect that small firms outperform large firms disappears for NASDAQ stocks. Both

Shumway (1997) and Shumway and Warther (1999) suggest that researchers be explicit

about how they handle delisting returns and alert researchers to potential problems with

these data.

Our paper follows the tradition of these two papers in documenting the effect of delisting

returns on anomalies. Our study differs from these studies in three respects. First, our

study addresses several issues relevant to the proper calculation of delisting returns from the

CRSP database. Specifically, researchers who are unaware that monthly delisting returns

contain a partial month return even when the delisting return is missing will fail to correct

5

for the missing return. We find that nearly half of all delistings occur outside the date

range provided by CRSP in the merged CRSP/Compustat database. In addition, if future

earnings are required, nearly two thirds of all delistings are excluded due to lack of earnings

data. Second, we do not find a uniform effect of delisting exclusions on inferences about

market efficiency. Unlike Shumway (1997) and Shumway and Warther (1999), who document

generally decreased returns to trading strategies when delistings are included, most notably

with the size effect, our findings indicate that inclusion of delistings can increase or decrease

the returns to different trading strategies. Third, the exclusion of delisting returns can affect

estimates of market returns. If researchers include delistings in the sample, but do not adjust

the market return measure, market-adjusted returns will be affected.

Besides the inadvertent exclusion of delistings from the sample, subtle research design

choices can also lead to the over-weighting of delistings in the sample. For example, if

all available observations that meet minimum data requirements are used in conducting

analysis, delistings will be over-represented in the most recent fiscal year. The most recent

fiscal year of Compustat data typically will not have the required CRSP data to compute

future returns. However, if the firm delists before the end of the return accumulation period,

the firm will be included in the sample because fewer months of return data are required for

delisting; the latest fiscal year will be composed primarily of delisting firm-years if care is

not taken. Another example of a research design choice that can result in delistings being

over-weighted in analysis is the use of −100% as a replacement value for missing delisting

returns. Replacement values are discussed in the following section.

3 Delisting Returns

A delisting return is the return on a security after it has been removed from a stock exchange.

CRSP provides a three-digit delisting code that explains the nature of the delisting. Most

delistings are classified as mergers (51% of the delistings in our sample, delisting codes 200-

6

299) or dropped delistings3 (44% of the delistings in our sample, delisting codes 500-599).

Delisting returns are computed from liquidation payments or from other information about

the value of the security after delisting. CRSP allows up to ten years after the delisting to

learn the delisting return and updates the records as needed.

Most delisting returns are realized by the end of the month following the delisting. Unt-

abulated descriptive statistics show that of the 4,142 dropped delistings in our sample that

have non-missing monthly delisting returns, 79% of delisting distribution payments are made

in the month of the delisting and 16% are made after the month of the delisting, but within

three months of the delisting. The remaining 5% of delisting payments occur more than

three months after the delisting month. Researchers generally assume that delisting returns

are realized immediately. These statistics suggest that the assumption is usually, although

not always, reasonable.

3.1 Compounding Delisting Returns with Standard Returns

If the researcher requires monthly returns for every month in the range4 [t, t + k] then

firms that delist in this range will be excluded from the sample. In the case of mergers, these

returns are typically significantly positive. In the case of dropped delistings, these returns are

typically significantly negative. To avoid excluding delisting firm-years, the delisting return

can be used as a proxy for the return on the day of the delisting and can be compounded

with standard returns.5 The appendix shows how to do this in detail.

When a security delists, CRSP creates a record for the delisted security that indicates

the delisting date, the reason for the delisting, and the delisting return. CRSP provides daily

delisting returns and monthly delisting returns. Daily delisting returns are straightforward—

3Examples of dropped delistings include bankruptcy, stock price below acceptable level, and insufficientassets, equity, or capital. Also within this range of delisting codes is the more recent phenomenon of firmswho go “dark”, or the choice by firms to delist from the NYSE, AMEX or NASDAQ to avoid filing with theSEC, as discussed in Leuz et al. (2006).

4The range [t, t + k] is the range specified by researchers such as annual returns.5In order to compound delisting returns with standard returns, researchers must separately merge delisting

returns (found in the WRDS “mse” file) with monthly returns (found in the WRDS “msf” file).

7

they contain only the delisting return, or the return given by using the last available price

before delisting and the payment ultimately received by shareholders for the delisted security.

The monthly delisting return generally contains the daily delisting return as well as the

return from the beginning of the month to the date of the delisting. CRSP defines the return

from the beginning of the month to the delisting date as the partial month return, and the

return attributable to the delisting itself as the delisting return.

Usually, the delisting occurs before the last trading day of the month.6 In this case,

the monthly delisting return contains the partial month return and the delisting return.

However, if the delisting occurs on the last trading day of the month, the monthly delisting

return contains only the delisting return because the standard monthly return is not missing.

3.2 Missing Delisting Returns

In some cases, the delisting return is unknown or under investigation by CRSP. Shumway

(1997) and Shumway and Warther (1999) show that the exclusion of firms with missing

delisting returns can significantly affect estimated portfolio returns. Untabulated descriptive

statistics show that 9.4% of monthly delistings in CRSP have missing delisting returns.

Missing delisting returns are overwhelmingly dropped delistings: 94% of missing delisting

returns are dropped delistings, while only 3% of missing delisting returns are merger-related.

When the delisting return is unknown, the daily delisting return is missing. The monthly

delisting return is missing (i.e., has no numeric value) only when the delisting occurs on the

last trading day of the month. Otherwise, the monthly delisting return contains the partial

month return.7

Untabulated descriptive statistics show that in our sample of NYSE, AMEX and NAS-

DAQ firms from 1962-2002, 702 delisting returns are missing in the monthly file. Of these,

645 of the corresponding monthly delisting returns are not literally missing, but contain

6According to CRSP, the last trading day of the month is the last weekday of the month that the marketwas open for exchange.

7Refer to the appendix for a detailed discussion.

8

partial month returns. Because many monthly delisting returns contain only partial month

returns, they should be treated as missing.

Shumway (1997) and Shumway and Warther (1999) suggest using a replacement value to

avoid excluding firm-years with missing delisting returns. The median delisting return for

firms delisted for poor performance reported in both papers is −30%, which can be used as

a replacement value. Shumway and Warther (1999) suggest −55% can be used for NASDAQ

firms.

Rather than using a single replacement value for missing delisting returns, we use multiple

replacement values depending on the nature of the delisting. For our replacement values,

we use the average daily delisting return for the corresponding three-digit delisting code.

We do this because average delisting returns vary significantly for different delisting codes.

Using the information provided by CRSP about the delisting allows us to treat delisting

categories differently. For example, our estimate of the delisting returns for bankrupt firms

with missing delisting returns is different from the estimate for firms that voluntarily delist

(go “dark”).

Specifically, we compute the average daily delisting return for every three-digit delisting

code for all available delistings with non-missing daily delisting returns, and use this as the

replacement value for the missing delisting returns. The replacement value is compounded

with the return from the beginning of the return accumulation period to the delisting date,

as described in the appendix. Sloan (1996) and the subsequent literature that describe how

delistings are treated often use −100% as a replacement value. Since average delisting returns

for the various categories of dropped delistings are generally not this low, this is probably

too extreme an adjustment and likely results in a lower estimate of the return. When we use

−100% as a replacement value, following Sloan (1996), our inferences are largely unchanged,

but returns in the lowest decile of all anomaly variables decrease by up to one percent.

Although our use of multiple replacement values is arguably better than the use of a

single replacement value, the use of any replacement value is an estimate of an unknown

9

return. Researchers should exercise caution and judgment in interpreting results, especially

if results are sensitive to the choice of replacement values.

3.3 Computing Market-Adjusted Returns

An important aspect of the research design for market efficiency studies is the computation

of risk-adjusted returns.8 Much of the literature uses size-adjusted returns, following Sloan

(1996). The sample mean market-adjusted return should equal zero but will not if researchers

apply market-return measures in a manner that weights observations differently from the

weighting of returns in the market index. We discuss four reasons that market-adjusted

returns can be nonzero.9

First, nonzero average market-adjusted returns can result from the treatment of delisting

returns. If delisting firm-years are included in the sample but excluded from the market

return measure, the average sample market-adjusted return can be nonzero. Second, Barber

and Lyon (1997) show that long-run market-adjusted buy-and-hold returns can be signif-

icantly negative. They discuss that this is attributable in large part to the skewness of

the returns distribution. Third, the sample average market-adjusted return can be nonzero

due to differences in the population of firms that is used to create the market return mea-

sure compared to the sample of firms for which enough data are available to conduct the

analysis. If the sample of firms is used as a benchmark for itself, the problem of nonzero

market-adjusted returns is eliminated by construction. However, the average risk-adjusted

returns that would be realized on a sample can be nonzero. Finally, differences in how ob-

servations are weighted or grouped in the construction of market index returns vs. how they

are weighted or grouped in research designs can result in nonzero average market-adjusted

returns.

Addressing all of these issues is beyond the scope of this paper, but we address the first

8We thank the reviewer for suggesting an examination of this issue.9For our sample of firms, the average size-adjusted return is significantly negative over 1987-2002, −0.0063

(t=−2.051), but is insignificant for 1962-2002, −0.0015 (t=−0.803).

10

because it relates to an important effect of the exclusion of delistings. Many market return

measures provided by CRSP do not include delisting returns. In particular, the commonly

used CRSP Stock File Capitalization Decile Indices10 exclude delisting returns. The only

CRSP-supplied return measures that include delisting returns are the CRSP Cap-Based

Portfolio Indices and the CRSP Indices for the S&P 500 Universe.11

In order to avoid excluding delisting returns from decile returns, we adjust the CRSP

Stock File Capitalization Decile Indices to include delisting returns. We use the CRSP decile

assignments and compute decile returns with the CRSP methodology after merging standard

monthly returns, non-missing delisting returns, and replacement values for missing delisting

returns. The decile return is measured as the average return for decile firms, weighted by

the lagged market value of equity.

In addition to providing results using size-adjusted returns, we present results of portfolio

tests with raw returns as a robustness check. The results with raw returns show directly

what happens to average portfolio raw returns without the market adjustment. The extent

to which the risk related to delistings is incorporated in the market return measure is also

important, but is not the primary focus of this paper.

3.4 Using the CRSP/Compustat Merged Database

Many studies merge Compustat and CRSP using the CRSP/Compustat merged database

(CCM). This file provides a direct link between the Compustat primary firm identifier,

GVKEY, and the CRSP primary security identifier, PERMNO, and provides date ranges over

which this link is effective. In cases where a GVKEY links to different securities (PERMNOs)

over its history, CCM provides the link information so that returns can be merged.

A significant number of delisting firm-years are excluded if CCM date ranges are inter-

preted literally. If return data outside the interval are not merged with Compustat, up to

10Wharton Research Data Services (WRDS) provides SAS datasets based on these Stock File Capitaliza-tion Decile Indices. They are the commonly used “ermport” and “mport” files.

11The exclusion of delisting returns from indices is not clearly identified in CRSP documentation, but wascommunicated to us by CRSP technical support.

11

half of all delistings are excluded. The CCM manual states that “If the CRSP data extends

before or after the Compustat data for a company, the last known PERMNO can be used

to identify the issue.” Thus the range in CCM should be appropriately extended to ensure

that valid delistings are merged with Compustat. If Compustat and CRSP are merged using

the CUSIP identifier, roughly 6 to 10% of total observations, including a similar percentage

of the population of delistings, will be lost compared to using CCM, depending on how the

merge is done.12

4 Sample Data

4.1 Sample Period and Variable Definitions

We use two sample periods: (1) 1962-2002, for which we use the “balance sheet” method to

compute cash flows and accruals, and (2) 1987-2002, for which we use the statement of cash

flows for cash flows and accruals measures. We effectively use data from 1961-2004 because

we require lagged assets and twelve-month returns beginning four months after fiscal year-

end. The sample includes all non-ADR NYSE, AMEX and NASDAQ firms that meet data

requirements, excluding banks, insurance and real estate companies (SIC codes between 6000

and 6999).13 We include NASDAQ firms because the incidence of delistings is significantly

greater among NASDAQ firms, and because they are increasingly included in studies in the

anomalies literature.14

We measure earnings, Et, as operating income, DATA178 from Compustat, and in-

12When merging on CUSIP, we use the CNUM and CIC from Compustat and the NCUSIP from CRSP,ensuring that all current and historical CUSIPs are used. Fewer observations are lost with a merge based onthe six-digit CUSIP: the sample size is only 6% smaller than when CCM is used. When using the eight-digitCUSIP, the sample size is roughly 10% smaller. This could be improved depending on what other steps aretaken.

13The exclusion of these companies does not significantly affect inferences. Sloan (1996) also excludesbanking and insurance firms due to lack of data availability to compute accruals. The use of historical SICcodes (DATA324, which is only available after 1987) vs. the most recent SIC code (DNUM, which we use),does not change inferences either.

14Inferences also hold when NASDAQ firms are excluded. Although Sloan (1996) includes only NYSE andAMEX firms, other papers include NASDAQ firms (Xie, 2001; Desai et al., 2004).

12

come before extraordinary items, DATA18.15 Cash flows, CFt, are measured using the

balance sheet method (Sloan, 1996) and using the statement of cash flows, excluding cash

flows from extraordinary items and discontinued operations (Hribar and Collins, 2002),

DATA308−DATA124. We compute accruals, ACt, as Et − CFt. When using the balance

sheet method to compute cash flows, we compute accruals with operating income. When

using the statement of cash flows, we compute accruals using income before extraordinary

items. Following Sloan (1996) and most subsequent papers, we deflate all accounting vari-

ables by the average of total assets, DATA6.16

We measure market-adjusted returns in year t+1, URt+1, as twelve-month, size-adjusted,

buy-and-hold returns beginning four months after fiscal year-end. We compute size-decile

returns as described earlier. To avoid excluding delisting firm-years, we use the return from

the beginning of the accumulation period through the delisting date, including the delisting

return, as the proxy for year t + 1 returns, DRt+1. We assume that when a firm is delisted,

the proceeds are reinvested in the same size decile equally among all remaining stocks at

the end of the delisting month. To show the sensitivity of results to delisting firm-years,

the analysis is conducted including and excluding delisting firm-years. As defined earlier, a

delisting firm-year is an observation that delists in the year t+1 return accumulation period.

Thus, fiscal year t is a delisting firm-year if the firm delists in year t + 1.

We form deciles by fiscal year using all observations that meet the specified data require-

ments. Generally, we require the accounting variable in year t (Et, ACt, CFt or BMt) and

the return measure in year t + 1.

4.2 Descriptive Statistics

Table 1 reports the number of delistings in CRSP that merge with Compustat. Panel A

shows that after 1950 there are 18,388 monthly delisting returns in the 2004 CRSP file, of

15Inferences are unchanged whether DATA18 (earnings measure from income statement) or DATA123(earnings measure from the cash flow statement) is used.

16The literature following Sloan (1996) generally deflates by average assets, but papers examining otheranomalies typically deflate by price.

13

which 3,571 do not merge with Compustat because the security (PERMNO) is not in CCM,

leaving 14,817 potential delistings to merge with Compustat. If the date of the fiscal year-end

is required to be within the CCM date range, only 58.7% of delistings merge (of the 14,817

delistings that could merge with Compustat, only 8,701 are within the date range specified

by CCM).17 If the date range of CCM is extended where appropriate (if the PERMNO does

not link to another GVKEY), many more delistings can be merged. An additional 32%

(4,769 of 14,817) of these delistings occur within six months of the end date in the range

specified by CCM. If the date range is extended as far as possible, 98.6% of all delistings

successfully merge (14,613 of 14,817). Care must be taken to ensure that all valid delistings

merge.

Table 1 shows the frequency of delistings by decade in our sample of firms over the period

1962-2002 in Panel B. The left column shows that the average yearly sample size (including

delisting and non-delisting firm-years) increases steadily from the 1960s (average 1,492 firms

per year) to the 1990s (average 5,403 firms per year). The frequency of delistings increases

monotonically over time, from 0.7% in the 1960s, to 10.8% after 2000. The frequency of

merger-related delistings also increases over time, from 0.5% of the sample in the 1960s to

4.4% after 2000. Similarly, the frequency of dropped delistings increases from 0.2% in the

1960s to 6.2% after 2000.

The final two columns of panel B show the percentage of dropped delistings that have

missing delisting returns. In the 1960s, 28% (72%) of dropped delistings have missing

monthly (daily) delisting returns. This generally decreases over time to 2.5% (14.8%) after

2000 for monthly (daily) delisting returns. A significant number of dropped delistings have

missing delisting returns, most notably prior to 1990. Researchers who use daily delisting

returns should be aware that there are more missing daily than monthly delisting returns; if

the date of the delisting payment is greater than 10 trading days after the delisting date, it

can be missing in the daily file, but not in the monthly file. Delistings with missing daily but

17Sample code provided by WRDS requires the date of the fiscal year-end to be within the date rangeprovided by CCM.

14

not monthly delisting returns do not have unknown delisting returns, but they are reported

as missing in the CRSP daily file because the delisting payment is delayed. Rather than

using a replacement value for these delistings, the monthly delisting return can be used with

daily return data to determine the daily delisting return.

Table 2 shows the total number of firm-year observations in the sample over 1962-2002.

The minimum data requirements for our study are current earnings and future returns. Panel

A shows that 153,969 observations meet these requirements. Most of these observations,

143,049, are non-delisting firm-years. Most of the non-delisting firm-year observations have

nonmissing future earnings (142,313 firm-years with nonmissing Et+1 vs. 736 firm-years with

missing Et+1). There is a total of 5,577 merger-related delisting firm-years. Most of these

have missing future earnings (774 firm-years with nonmissing Et+1 vs. 4,803 firm-years with

missing Et+1), so requiring future earnings excludes 86% of all mergers. There is a total of

4,819 dropped delisting firm-years. Many of these firm-years have nonmissing future earnings

(2,849 firm-years with nonmissing Et+1 vs. 1,970 firm-years with missing Et+1), so requiring

future earnings excludes about half of all dropped delistings. Panel B shows that the sample

size decreases by 16,155 (153,969−137,814) when the additional financial statement variables

are required to compute accruals.

In sum, requiring Et+1 results in the exclusion of nearly two-thirds of all delistings. In a

merger, the stock of the acquired firm generally ceases to trade and the acquired firm will

likely stop filing financial reports with the SEC. As a result, there are very few merger-related

delistings that have future earnings. With dropped delistings, a firm often continues to exist

and files its financial reports with the SEC even though it is no longer listed on the NYSE,

AMEX or NASDAQ.

Table 3 shows average delisting returns for the major categories of delistings and the

decile locations of these delistings. Panel A shows that over the 1962-2002 sample period,

there are 5,577 merger-related delistings, 392 exchange delistings, 132 liquidation delistings

and 4,819 dropped delistings. The average size-adjusted return including delisting returns

15

beginning four months after fiscal year-end through the delisting for mergers is 28%, of which

2% is the daily delisting return. In contrast, for dropped delistings the size-adjusted return

is −51%, of which −14% is the daily delisting return.

Table 3 shows the number of merger-related and dropped delisting firm-years in the

deciles of the anomaly variables examined in this paper in Panel B. For delistings due to

mergers in book-to-market (BMt) and earnings (Et) deciles, the number of delisting firm-

years is the lowest in decile 1 (385 in decile 1 of Et, 407 in decile 1 of BMt), but the number of

observations in the remaining deciles fluctuates between 420 and 650. There is an increasing

trend across cash flows (CFt) deciles (335 in decile 1 and 570 in decile 10), and a decreasing

trend across accruals (ACt) deciles (558 in decile 1 and 399 in decile 10); firms with low

accruals and high cash flows are more likely to be acquired.

Dropped delistings are concentrated in extreme deciles. There is a large difference in the

number of observations in the lowest vs. all other deciles for all of the anomaly variables:

for earnings (Et), there are 1,913 in decile 1 vs. 66 in decile 10; for accruals (ACt) there are

1,480 in decile 1 vs. 510 in decile 10; for cash flows (CFt), there are 1,415 in decile 1 vs. 241

in decile 10. With the book-to-market (BMt) ratio, there are significantly more delistings

in the extreme low and high deciles (1,464 in decile 1 and 861 in decile 10 compared to 227

in decile 5).

This pattern is consistent with Piotroski (2000), who finds that firms with “weak current

signals” are more likely to delist for adverse reasons and with Mohanram (2004), who finds

that firms with strong fundamentals are less likely to be delisted for adverse reasons. In

sum, merger-related delistings are comparatively more evenly distributed over the various

anomaly deciles and dropped delistings exhibit substantial concentration in the lowest decile.

Thus excluding dropped delistings is likely to significantly affect results because of the uneven

distribution across deciles, i.e., the returns in the lowest deciles will generally be significantly

higher if delistings are excluded.

Table 4 shows additional descriptive statistics for delisting firm-years by accruals decile.

16

These statistics show that dropped delistings in the lowest accruals decile exhibit a very

high risk of bankruptcy, have consistently negative earnings, have extremely negative special

and extraordinary items and are very highly levered. The reported statistics include: the Z-

Score18 (Altman, 1968); the average percentage of earnings in the past 5 years with negative

net income, estimated for each firm as the number of firm-years with negative earnings

divided by the number of firm-years with nonmissing earnings in the past 5 years; the

average sum of special items and extraordinary items deflated by average assets; and the

average leverage as measured by total liabilities divided by total assets. All variables except

for the percentage of negative earnings in the past 5 years are winsorized at the top and

bottom 1% to reduce the influence of outliers.

Table 4 Panel A shows descriptive statistics for mergers. The Z-Score is positive for all

deciles (2.52 for decile 1, which generally increases to 28.3 for decile 10). The Z-Scores in

Panel A suggest that the average firm that is acquired in a merger is not in immediate danger

of bankruptcy. Firms in the lowest accruals decile have a higher fraction of past earnings that

are negative (0.47 for decile 1 vs. 0.26 in decile 10). The average special and extraordinary

items are increasing across deciles (−0.04 in decile 1 vs. 0 in decile 10). Finally, leverage

does not vary significantly across deciles (0.54 in decile 1, 0.52 in decile 5 and 0.47 in decile

10).

Table 4 Panel B shows descriptive statistics for dropped delistings. The Z-Score is sig-

nificantly negative for the lowest decile, but fluctuates for the remaining deciles (−5.08 for

decile 1, 3.40 for decile 5 and 2.06 for decile 10). The Z-score for dropped delistings in the

lowest accruals decile is very different than the Z-score for mergers in Panel A. This is not

surprising since bankruptcies are included in the set of dropped delistings. Dropped delist-

ings have a much higher fraction of past earnings that are negative (0.77 for decile 1 and

0.67 for decile 10). Special and extraordinary items are significantly negative for all deciles,

18We use the original coefficients reported by Altman (1968) as follows: Z-Score = 1.2 × Working Capi-tal/Assets + 1.4 × Retained Earnings/Assets + 3.3 × EBIT/Assets + 0.6 × Market Value of Equity/TotalLiabilities + 0.999 × Revenue/Assets. Altman (1968) finds that firms with a Z-Score below 2.675 exhibitgreater risk of bankruptcy.

17

but are the most negative for the lowest decile (−0.09 for decile 1 vs. −0.03 for decile 10).

Finally, leverage is higher for dropped delistings than for mergers, and is highest for the

lowest decile (1.04 for decile 1 vs. 0.60 for decile 10).

Table 5 shows the average anomaly decile firm size and the impact of delistings on size-

decile returns. Panel A shows the average firm size, as measured by the market value of

equity at the beginning of the year t+1 return accumulation period, across the deciles of the

anomaly variables examined in this paper. With earnings and cash flows, there is a strictly

monotonic increase in average firm size across deciles: for earnings (Et), average firm size

increases from $64 million in decile 1 to $1,974 million in decile 10; for cash flows (CFt), it

increases from $71 million to $1,923 million. With accruals (ACt), extreme deciles consist of

smaller firms ($272 million and $226 million in deciles 1 and 10 respectively) with larger firms

in middle deciles ($1,372 million in decile 5). With the book-to-market ratio (BMt), larger

firms are generally in low deciles ($1,329 and $1,958 million in deciles 1 and 2 respectively)

with small firms in the high deciles ($82 million in decile 10).

Table 5 Panel B compares CRSP size-decile (Stock File Capitalization Decile) returns to

the size-decile returns we compute that include delisting returns. We compound returns over

calendar years after 1990 because the number of delistings is greater in this period, although

the pattern is observable over the entire time period. CRSP decile returns and the adjusted

decile returns are both monotonically decreasing across size deciles: CRSP size-decile returns

are 31% in the lowest size decile and 12.2% in the highest whereas the size-decile returns that

are adjusted to include delisting returns are 27.3% in the lowest size decile and 12.2% in the

highest. The most significant differences between CRSP size-decile returns and the adjusted

size-decile returns are in the lowest deciles (CRSP returns are 3.7% and 1.2% higher in size

deciles 1 and 2 respectively). These results are consistent with the descriptive statistics in

Panel B of Table 3 and Panel A of table 5. Since dropped delistings are concentrated in

the lowest deciles of earnings, cash flows and accruals, and the firms in the lowest deciles

of these variables are generally smaller, the exclusion of missing delisting returns from the

18

size-decile returns has the most significant impact in the lowest size deciles.

5 Empirical Analysis

We conduct two types of tests to demonstrate the impact of delisting returns on empirical

analysis of anomalies. First, we estimate regressions of future size-adjusted returns on earn-

ings, accruals and cash flows following Beaver and McNichols (2001). Second, we conduct

an analysis of future returns with portfolios formed on the level of earnings, accruals, and

cash flows, all deflated by average assets, following Sloan (1996), and on the book-to-market

ratio, following Lakonishok et al. (1994).

5.1 Empirical Methods

We employ the following regression models which regress year t + 1 size-adjusted returns on

year t accounting variables:

URt+1 = α + βEt + εt+1 (1)

URt+1 = α + γCFt + δACt + εt+1 (2)

Market efficiency implies that β = 0 and that γ = δ = 0, i.e., future risk-adjusted

returns should not be predictably related to current publicly available information. We do

not trim the variables in these regressions to provide consistency between regression results

and portfolio tests, which also use untrimmed data. Inferences are not significantly affected

if the data are trimmed or if models are estimated in ranks. Sloan (1996) does not trim.

Our portfolio tests are based on decile portfolios formed using the following variables at

time t: earnings (Et), accruals (ACt), cash flows (CFt) and the book-to-market ratio (BMt).

Average returns are computed for each decile. Market efficiency implies that risk-adjusted

19

returns should be insignificant for each decile.

The Mishkin (1983) model is also used in the literature, following Sloan (1996). We do

not tabulate findings using the Mishkin framework because it requires future earnings and

consequently, two-thirds of delisting firm-years would be excluded. Regressing future returns

directly on current accounting variables avoids excluding delisting firm-years, which is the

focus of our study.

5.2 Results

5.2.1 Return Regressions

Table 6 presents the estimation results for equation (1). Panel A shows that when delisting

firm-years are excluded, the estimated coefficient on income before extraordinary items over

1987-2002 is marginally significant, 0.019 (t=1.94), and the estimated coefficient on operating

income over 1962-2002 is significant, 0.045 (t=5.57). Panel B shows that when delisting

firm-years are included, the coefficient on income before extraordinary items over 1987-2002

increases in magnitude and significance to 0.089 (t=10.6) and the coefficient on operating

income over 1962-2002 increases to 0.117 (t=16.61).

These findings suggest that stock prices do not completely reflect the future implica-

tions of current earnings. This is inconsistent with Sloan (1996), who finds that the market

correctly estimates earnings persistence. The difference in results can be attributed to differ-

ences in research design. We regress future size-adjusted returns directly on current earnings

while Sloan (1996) uses the Mishkin framework, which requires future earnings and there-

fore excludes a significant number of observations, including delisting firm-years. We also

include firms traded on the NASDAQ, which are excluded from Sloan (1996). Finally, our

sample time period extends beyond the time period of Sloan (1996) and, consequently, in-

cludes many more delistings. Our results are consistent with Bernard and Thomas (1989,

1990), Abarbanell and Bernard (1992) and Collins and Hribar (2000), who find mispricing

of earnings.

20

Table 7 contains the estimation results for equation (2). This analysis controls for the

incremental effect of cash flows (CFt) and accruals (ACt) for each other. This is important

because the two components of earnings are significantly negatively correlated. In Panel

A, when delisting firm-years are excluded, the estimated coefficients are significant for both

sample periods. For the 1987-2002 sample period using cash flow statement data, the esti-

mates are 0.092 (t=6.58) for CFt and -0.066 (t=−4.61) for ACt. For the 1962-2002 sample

period, using the balance sheet method, the estimated coefficients are 0.075 (t=8.72) for CFt

and −0.206 (t=−12.15) for ACt. Panel B shows that when delisting firm-years are included,

cash flow coefficient magnitudes increase and become more significant while accrual coeffi-

cient magnitudes become insignificant or less significant. For the 1987-2002 sample period,

the coefficients are 0.168 (t=13.62) for CFt and −0.005 (t=−0.39) for ACt. Over 1962-2002,

the coefficients are 0.147 (t=19.19) for CFt and −0.07 (t=−4.79) for ACt.

These results show that the cash flow effect gets stronger, while the accrual effect becomes

weaker when delistings are included. The insignificance of accruals in the regression for

1987-2002 is consistent with Desai et al. (2004), who find that in the presence of other

fundamental variables, cash flows is highly significant, while accruals is insignificant.19 Our

results suggest that the insignificance of accruals in the multivariate regression is due, in

large part, to delisting firm-years. Our results provide an explanation for why the different

designs of Desai et al. (2004) and Sloan (1996) lead to different conclusions about investors’

ability to process accruals.

5.2.2 Portfolio Tests of Earnings Deciles

Results of portfolio tests of all anomaly variables are presented with size-adjusted returns

and raw returns. Inferences are consistent with both measures of returns, demonstrating the

robustness of the results. The results with raw returns show the effect of excluding delisting

firm-years on portfolio returns without an adjustment for expected returns. We use size-

19Although not discussed in their paper, we confirmed by correspondence that Desai et al. (2004) doinclude delisting returns. Also, Desai et al. (2004) do not require future earnings as Sloan (1996) does.

21

adjusted returns as our measure of market-adjusted returns because it is commonly used in

the literature. Since all raw decile returns are significantly positive, we do not report the

associated t-statistics in the tables.

Table 8 shows the results of portfolio tests of earnings deciles. Panel A shows that when

delisting firm-years are excluded, the difference between extreme deciles is insignificant over

1987-2002 (using income before extraordinary items) and significant over 1962-2002 (using

operating income). Over 1987-2002, both extreme deciles, and the difference between them,

0.01 (t=0.52), are insignificant. Over 1962-2002 the difference is 0.043 (t=4.02): decile 1 is

−0.028 (t=−2.93) and decile 10 is 0.015 (t=3.04).

Panel B shows that when delisting firm-years are included, the difference between extreme

deciles increases in magnitude and significance. Over 1987-2002, the difference is 0.107

(t=6.11): decile 1 is −0.083 (t=−5.44) and decile 10 is 0.024 (t=2.79). Over 1962-2002 the

difference is 0.108 (t=10.92): decile 1 is −0.085 (t=−9.83) and decile 10 is 0.023 (t=4.79).

The same effect is observable in raw returns. Over both time periods there is a significant

decrease in the returns of the lowest decile. Returns in decile 1 decrease from 0.177 in Panel

A to 0.066 in Panel B over 1987-2002 and from 0.15 in Panel A to 0.078 in Panel B over

1962-2002. Also, over both time periods, the largest return of any decile in Panel B is decile

10: 0.16 over 1987-2002 and 0.167 over 1962-2002. The difference between extreme deciles

of raw returns is positive and significant: 0.094 over 1987-2002 and 0.089 over 1962-2002.

The regression results presented in Table 6 are consistent with the results presented in

Table 8. Both analyses show a significant earnings effect whether delisting firm-years are

included or excluded, and that the effect becomes stronger in magnitude and significance

when delisting firm-years are included, over both time periods.

5.2.3 Portfolio Tests of Accruals Deciles

Table 9 shows the results of portfolio tests of accruals deciles. Panel A shows that when

delisting firm-years are excluded, the difference between extreme deciles is significant over

22

both time periods. Over 1987-2002, using accruals computed with cash flow statement data,

the difference is 0.139 (t=6.94) and decile returns are generally decreasing across deciles

(decile 1 is 0.066 and decile 10 is −0.073). Over 1962-2002, the difference is 0.135 (t=11.04)

and, again, decile returns are generally decreasing across deciles (decile 1 is 0.055 and decile

10 is −0.080).


deciles decreases in magnitude and significance. Over 1987-2002, the difference between ex-

treme deciles decreases to 0.063 (t=3.44). The lowest ACt decile return is now insignificantly

negative, -0.024 (t=−1.62). The difference between extreme deciles remains significant be-

cause of the large negative returns in decile 10, −0.087 (t=−8.46). Over 1962-2002, the

difference is 9.7% (t=8.43). Again, the most significant change across deciles is in the lowest

decile: returns in decile 1, 0.007 (t=0.77), are insignificant while returns in the highest decile

increase in absolute magnitude and significance, −0.09 (t=−12.84).




1962-2002. The difference between extreme deciles of raw returns decreases from 0.149 to

0.059 over 1987-2002 and from 0.139 to 0.093 over 1962-2002.

The regression results in Table 7 are largely consistent with the results presented in Table

9. Both tests suggest that the accrual effect is sensitive to the inclusion of delisting firm-

years in the sample. Both tests show that the accrual effect is significant when delisting

firm-years are excluded, and that the effect weakens as delisting firm-years are added, due

to the disproportionate number of dropped delistings in the lowest accruals decile.

5.2.4 Portfolio Tests of Cash Flow Deciles

Table 10 shows the results of portfolio tests of cash flow deciles. Panel A shows that when

delisting firm-years are excluded, the difference between extreme deciles is significant over

23

both time periods. Over 1987-2002, using cash flows from the cash flow statement, the

difference is 0.10 (t=5.47) and decile returns are generally increasing across deciles (decile 1

is −0.04 and decile 10 is 0.059). Over 1962-2002, the difference is 0.113 (t=9.98) and, again,

the decile returns are generally increasing across deciles (decile 1 is −0.064 and decile 10 is

0.049).


deciles increases in magnitude and significance. Over 1987-2002, the difference between

extreme deciles is 0.165 (t=9.78). The main difference is that returns in the lowest decile

fall to −0.103 (t=−7.22). Over 1962-2002, the difference between extreme deciles is 0.159

(t=15.06), which is attributable to returns in the lowest decile falling to −0.109 (t=−12.13).




1962-2002. The difference between extreme deciles of raw returns increases from 0.07 to

0.146 over 1987-2002 and from 0.094 to 0.146 over 1962-2002.

The regression results in Table 7 are consistent with the results presented in Table 10.

They suggest that the cash flow effect is not driven by the exclusion of delisting firm-years.

In fact, both tests show that the effect is stronger when delistings are included.

5.2.5 The Value-Glamour Anomaly: Portfolio Tests of BMt Deciles

The value-glamour anomaly is one of the most prominent anomalies and is studied by both

accounting and finance academics. The value-glamour anomaly documented by Lakonishok

et al. (1994) includes analysis with the book-to-market ratio, earnings, cash flows and sales

growth. In this paper, we focus on the book-to-market ratio, earnings, cash flows and

accruals.

Table 11 presents average returns by BMt decile. Panel A shows the size-adjusted returns

when delisting firm-years are excluded. The difference between extreme deciles is 0.158

24

(t=9.33) over 1987-2002 (decile 1 is −0.044 and decile 10 is 0.113) and 0.143 (t=13.68) over

1962-2002 (decile 1 is −0.057 and decile 10 is 0.086). Panel B shows that when delisting

firm-years are included, the difference between extreme deciles increases. The difference is

0.185 (t=11.91) over 1987-2002 (decile 1 is −0.11 and decile 10 is 0.075) and 0.168 (t=17.11)

over 1962-2002 (decile 1 is −0.101 and decile 10 is 0.067). The effect in the lowest BMt decile

strengthens, but the effect in the highest BMt decile weakens. Both are due to the inclusion

of a disproportionate number of dropped delistings in both the highest and lowest deciles

relative to the other deciles, as seen earlier in the discussion of Table 3. The book-to-market

effect gets stronger overall with the inclusion of delisting firm-years because there are more

dropped delistings in the lowest BMt decile than in the highest BMt decile.

The same effect is observable in raw returns. Over both time periods, there is a significant

decrease in the returns of the lowest and highest deciles. Returns in decile 1 decrease from

0.102 in Panel A to 0.024 in Panel B over 1987-2002 and from 0.083 in Panel A to 0.03

in Panel B over 1962-2002. Returns in decile 10 decrease from 0.301 in Panel A to 0.246

in Panel B over 1987-2002 and from 0.272 in Panel A to 0.242 in Panel B over 1962-2002.

The difference between extreme deciles of raw returns increases from 0.199 to 0.222 over

1987-2002 and from 0.189 to 0.212 over 1962-2002.

6 Conclusion

We investigate the sensitivity of tests of market efficiency to the treatment of delisting firm-

years in the settings of Sloan (1996) and Lakonishok et al. (1994). These studies test whether

the market is efficient with respect to earnings, accruals, cash flows and the book-to-market

ratio. Treatment of delisting firm-years in the literature varies greatly and the accounting

literature has not addressed delisting returns and the impact they can have on inferences

about market efficiency in a systematic way.

Our study raises the concern that incomplete understanding of CRSP data or design

25

choices results in the inadvertent exclusion of delisting firm-years, which can significantly

affect estimates of portfolio returns. Our study also raises the concern that including delisting

firm-years but using a market return measure that does not include delistings can also affect

market-adjusted returns and estimated portfolio returns. We propose using a size-decile

return measure, or more generally, a market return measure, that includes delisting returns.

We identify three issues that can result in the inadvertent exclusion of delisting returns.

First, requiring future earnings excludes two-thirds of delisting firm-years. Second, many

delistings occur outside the date range provided by the CRSP/Compustat merged database;

delistings will be excluded if care is not taken when merging Compustat and CRSP. Third,

monthly delisting returns are rarely missing because they contain partial month returns even

when the delisting return is missing. Researchers should check monthly delisting returns to

ensure that replacement values are compounded with partial month returns when necessary.

We find that the exclusion of delisting firm-years does not uniformly increase or decrease

portfolio returns. The effect on inferences of market efficiency depends on the setting. For

portfolios partitioned on earnings, cash flows, and the book-to-market ratio, the difference

between average returns in extreme deciles increases when delisting firm-years are included.

In contrast, for portfolios partitioned on accruals, average returns in the lowest accruals decile

decrease significantly when delisting firm-years are included, but there is no significant change

in the highest accruals decile. These results are due to the disproportionate concentration

of delisting firm-years with very negative returns in the lowest decile of these variables.

In this paper, we demonstrate that portfolio returns, as conventionally measured in prior

research, are sensitive to the treatment of delisting returns. We do not take a position on

whether our findings are evidence in favor of or against market efficiency because our analysis

does not consider transaction costs, among other costs of implementing a trading strategy.

As pointed out by Sloan (1996), findings of market inefficiency in historical data do not

necessarily imply that strategies based on the findings are exploitable. Because firms that

delist are on average highly risky and potentially illiquid, the exploitability of their returns

26

is an open question.

Researchers conducting tests of market efficiency should assess the sensitivity of their

findings to the inclusion of delisting firm-years in their sample. Our findings indicate that

inferences concerning market efficiency are sensitive to the treatment of delisting returns.

The magnitude of the effects we document suggests that researchers should carefully consider

whether the exclusion or inclusion of delisting firm-years affects inferences in tests of market

efficiency and in other settings.

27

A Computing Returns

When computing returns, the typical method of computing long-run, buy-and-hold returns is

to use monthly returns as follows, where Rt,t+k indicates the return from the end of month20

t to the end of month t + k, where rt+i represents the return in month t + i:

Rt,t+k =

k∏

i=1

(1 + rt+i) − 1 (3)

A.1 Delisting Returns

In order to compute Rt,t+k, it is necessary to have return data in every month from t + 1 to

t + k. When a firm is delisted, it is likely that returns will be available only for a subset of

t + 1 to t + k, and if care is not taken, the firm-year observation will be excluded from the

sample.

To avoid excluding delisted firms, the delisting return can be used as a proxy for the

monthly return, and the delisting proceeds can be assumed to be reinvested. To illustrate,

suppose that a firm delists at the beginning of month t + j where 0 < j ≤ k. The delisting

return can be compounded with monthly returns as follows where DRt,t+k is the return over

the range [t, t + k] including all available standard monthly returns along with the delisting

return, drt+j:

DRt,t+k = (1 + Rt,t+j−1)(1 + drt+j) − 1 (4)

DRt,t+k can be used as a proxy for Rt,t+k.

20Months should be interpreted as a month-year combination; For example returns can be computed fromJanuary 1, 1999 to December 31, 1999 or from June 1, 1999 to May 31, 2000.

28

A.1.1 The Timing of the Delisting

The previous section makes the simplifying assumption that the delisting occurs at the

beginning of the month. In reality, the delisting can occur any day of the month. We now

re-express DRt,t+k incorporating the varied timing of the delisting. Now let drd,t+j represent

the delisting return, which occurs on day d of month t + j. The return measure should

include the available daily returns in month t + j to the delisting date. We first define the

partial month return, pmrd,t+j, as follows where reti,t+j represents the return on day i of

month t + j:

pmrd,t+j =d∏

i=1

(1 + reti,t+j) − 1 (5)

Given this definition of the partial month return, DRt,t+k can be written more concisely

as follows where the delisting return is now written as drd,t+j to indicate the day of delisting:

DRt,t+k = (1 + Rt,t+j−1)(1 + pmrd,t+j)(1 + drd,t+j) − 1 (6)

The first term, (1 + Rt,t+j−1), is the return for all months before the month of the delisting,

t + j. The second term, (1 + pmrd,t+j), is the partial month return or the return from the

beginning of the month until the delisting in month t+ j. The last term, (1+ drd,t+j), is the

delisting return.

A.1.2 Delistings as Recorded in CRSP

Within CRSP, the return data needed to compute DRt,t+k are first, the monthly return data,

rt+i, where 0 < i ≤ j and second, the monthly delisting return mdrt+j, which is different

from the delisting return itself. One important, but subtle, point is that CRSP implicitly

distinguishes between delistings that occur before the last trading day of the month and

those that occur on the last trading day of the month.

29

When the delisting occurs before the last trading day of the month, the monthly delisting

return includes the delisting return and the partial month return. However, if the delisting

occurs on the last trading day of the month, the monthly delisting return includes only the

delisting return. In this case, the stock price is available at the beginning and the end of

the month, and so the monthly return is actually not missing in month t + j, which is the

month of the delisting. To clarify, the monthly delisting return in month t+ j, mdrt+j, from

CRSP is defined as follows where eom is the last trading day of the month (end of month):

mdrt+j = (1 + pmrd,t+j)(1 + drd,t+j) − 1 if d < eom (7)

= drd,t+j if d = eom (8)

Given the preceding discussion, the formulas for computing delisting returns using CRSP

data are straightforward. If the delisting occurs before the last trading day of the month

then DRt,t+k is computed as follows:

DRt,t+k = (1 + Rt,t+j−1)(1 + mdrt+j) − 1 (9)

In this case, mdrt+j includes the partial monthly return and the delisting return. Monthly

return data are only available until month t + j − 1, the month prior to the delisting.

If the delisting occurs on the last trading day of the month then DRt,t+k is computed as

follows:

DRt,t+k = (1 + Rt,t+j)(1 + mdrt+j) − 1 (10)

The difference between this expression and the previous expression is very minor, but im-

portant. In this case, since the delisting occurs on the last day of the month, a monthly

return can be computed in month t + j, the month of the delisting. The monthly delisting

return in this case only holds the delisting return itself.

30

A.2 Missing Delisting Returns

If delisting returns are missing, then computing DRt,t+k is not possible. Rather than exclud-

ing the firm-year observation, a replacement value for the delisting return can be used.

Assuming return data are available until the delisting occurs, a proxy for DRt,t+k can be

computed as follows, where rv is the replacement value used for drd,t+j:

DRt,t+k = (1 + Rt,t+j−1)(1 + pmrd,t+j)(1 + rv) − 1 (11)

Since the monthly delisting return contains the partial month return when the delisting

occurs before the end of the month, a missing delisting return does not always result in a

missing value for the monthly delisting return, mdrt+k. Consider the following:

mdrt+j = pmrd,t+j if d < eom and drd,t+j is missing (12)

= missing if d = eom and drd,t+j is missing (13)

Given this discussion, DRt,t+k should be computed as follows with CRSP data when the

delisting occurs before the end of the month.

DRt,t+k = (1 + Rt,t+j−1)(1 + mdrt+j)(1 + rv) − 1 (14)

It is important to note that mdrt+j in this case is only the partial monthly return. Even

though mdrt+j is not literally missing, the delisting return is missing.

When the delisting occurs at the end of the month, DRt,t+k is computed as follows:

DRt,t+k = (1 + Rt,t+j)(1 + rv) − 1 (15)

CRSP identifies monthly delisting returns that contain partial month returns but missing

delisting returns by setting the amount after delisting (dlamt) to a nonzero amount and the

31

delisting payment date (dlpdt) to a value less than or equal to the delisting date. Refer to

CRSP documentation for more detailed guidance.

A.3 A Note about Using Log Returns

Since in practice returns are computed using a natural log transformation, we make note

of an empirical issue especially relevant when computing delisting returns. The typical log

transformation of returns follows where lrt+i = log(1 + rt+i):

log(Rt,t+k + 1) = log(

k∏

i=1

(1 + rt+i)) =

k∑

i=1

(lrt+i) (16)

and

Rt,t+k = eP

k

i=1lrt+i − 1 (17)

Care must be taken when computing DRt,t+k. A log transformation of mdrt+j can cause

problems when the delisting return is −100%. Software packages give an undefined value21

to log(0), resulting in the potential exclusion of these delistings.

21The log of 0 (log(−100%+1)) is negative infinity. There are 360 delistings post 1950 that have a delistingreturn of −100%. SAS returns a missing value for log(0); although missing values are treated as negativeinfinity in SAS, exp(log(0)) returns a missing value.

32

References

Abarbanell, J., Bernard, V., July 1992. Tests of analysts’ overreaction/underreaction to

earnings information as an explanation for anomalous stock price behavior. Journal of

Finance 47 (3), 1181–1207.

Altman, E., September 1968. Financial ratios, discriminant analysis and the prediction of

corporate bankruptcy. Journal of Finance 23 (4), 589–609.

Ball, R., Brown, P., Autumn 1968. An empirical evaluation of accounting income numbers.

Journal of Accounting Research 6 (2), 159–178.

Barber, B., Lyon, J., Month 1997. Detecting long-run abnormal stock returns: The empirical

power and specification of test statistics. Journal of Financial Economics 43 (3), 341–372.

Beaver, W., McNichols, M., June 2001. Do stock prices of property casualty insurers fully

reflect information about earnings, accruals, cash flows, and development? Review of

Accounting Studies 6 (2-3), 197–220.

Bernard, V., Thomas, J., 1989. Post-earnings-announcement drift: Delayed price response

or risk premium. Journal of Accounting Research 27 (3), 1–36.

Bernard, V., Thomas, J., December 1990. Evidence that stock prices do not fully reflect the

implications of current earnings for future returns. Journal of Accounting and Economics

13 (4), 305–340.

Collins, D., Hribar, P., February 2000. Earnings-based and accrual-based market anomalies:

one effect or two? Journal of Accounting and Economics 29 (1), 101–123.

Desai, H., Rajgopal, S., Venkatachalam, M., April 2004. Value-glamour and accruals mis-

pricing: One anomaly or two? The Accounting Review 79 (2), 335–385.

Dopuch, N., Seethamraju, C., Xu, W., 2005. The accrual anomaly within the context of

profit and loss firms, working Paper, Washington University in St. Louis, St. Louis, MO.

33

Hribar, P., Collins, D., March 2002. Errors in estimating accruals: Implications for empirical

research. Journal of Accounting Research 40 (1), 105–135.

Jegadeesh, N., Titman, S., March 1993. Returns to buying winners and selling losers: Im-

plications for stock market efficiency. Journal of Finance 48 (1), 65–91.

Khan, M., 2005. Are accruals really mispriced? Evidence from tests of an intertemporal cap-

ital asset pricing model, working Paper, University of Toronto, Toronto, Ontario, Canada.

Kothari, S., Sabino, J., Zach, T., February 2005. Implications of survival and data trimming

for tests of market efficiency. Journal of Accounting and Economics 39 (1), 129–161.

Kraft, A., Leone, A., Wasley, C., May 2006. An analysis of the theories and explanations

offered for the mispricing of accruals and accrual components. Journal of Accounting

Research 44 (2), 297–339.

Lakonishok, J., Shleifer, A., Vishny, R., December 1994. Contrarian investment, extrapola-

tion, and risk. Journal of Finance 49 (5), 1541–1578.

Leuz, C., Triantis, A., Wang, T., March 2006. Why do firms go dark? Causes and economic

consequences of voluntary SEC deregistrations. AFA 2006 Boston Meeting.

Mashruwala, C., Rajgopal, S., Shevlin, T., October 2006. Why is the accrual anomaly not ar-

bitraged away? The role of idiosyncratic risk and transaction costs. Journal of Accounting

and Economics 42 (1-2).

Mishkin, F., 1983. A Rational Expectations Approach to Macroeconomics. The University

of Chicago Press for the National Bureau of Economic Research, Chicago, IL.

Mohanram, P., 2004. Separating winners from losers among low book-to-market stocks using

financial statement analysis, working Paper, Columbia University, New York, NY.

Piotroski, J., 2000. Value investing: The use of historical financial statement information to

separate winners from losers. Journal of Accounting Research 38 (3), 1–41.

34

Shumway, T., March 1997. The delisting bias in CRSP data. Journal of Finance 52 (1),

327–340.

Shumway, T., Warther, V., December 1999. The delisting bias in CRSP’s Nasdaq data and

its implications for the size effect. Journal of Finance 54 (6), 2361–2389.

Sloan, R., July 1996. Do stock prices fully reflect information in accruals and cash flows

about future earnings? The Accounting Review 71 (3), 289–315.

Sun, Y., 2003. Analysis of accrual mispricing, working Paper, Washington University in St.

Louis, St. Louis, MO.

Thomas, J., Zhang, H., June 2002. Inventory changes and future returns. Review of Ac-

counting Studies 7 (2–3), 163–187.

Xie, H., July 2001. The mispricing of abnormal accruals. The Accounting Review 76 (3),

357–373.

Zach, T., 2003. Inside the ‘accrual anomaly’, working Paper, Washington University in St.

Louis, St. Louis, MO.

Zhang, X. F., 2005. What causes the accrual anomaly–growth or earnings persistence?,

working Paper, The University of Chicago, Chicago, IL.

35

Table 1Number and Frequency of Occurrence of Delistings

Panel A shows the number of monthly delisting returns post 1950 in theCRSP database, the number that merges with Compustat and explains why alldelisting returns do not merge using the CRSP/Compustat merged database(CCM). Extending the date range means extending the CCM link end date asfar as appropriate to allow delistings outside the range provided by CCM tobe included in the sample. Panel B shows the frequency of delistings over thesample period, 1962-2002. Merger-related delistings include delisting codes200-299. Dropped delistings include delisting codes 500-599. Average samplesize over each decade is shown as well as the average percentage of delistingsin that decade. The final column shows the percentage of dropped delistingswith missing delisting returns (dr) in both the daily and monthly files.

Panel A: Number of Delistings that Merge with CompustatTotal Delistings in CRSP 18,388PERMNO is not in CCM 3,571Potential Delistings to Merge with Compustat 14,817Delistings within CCM Date Range 8,701Delistings ≤ 6 Months after CCM Date Range 4,769Delistings Merge after Extending Date Range 14,613Delistings do not Merge after Extending Date Range 258

Panel B: Frequency of Delistings by DecadeAverage Percentage of

Average Average Percentage of Dropped Delistings withTime Sample Sample Delisted Missing Delisting ReturnsPeriod Size All Mergers Dropped Monthly dr Daily dr

1962-69 1,492 0.7% 0.5% 0.2% 28.0% 72.0%1970-79 3,162 3.6% 2.4% 0.9% 41.9% 67.4%1980-89 4,202 7.6% 3.7% 3.1% 29.2% 37.7%1990-99 5,403 9.1% 4.8% 4.3% 6.5% 16.3%2000-02 4,789 10.8% 4.4% 6.2% 2.5% 14.8%

36

Table 2Sample Size with and without Delistings

This table shows the number of observations in the sample including NYSE,AMEX and NASDAQ firms over 1962-2002. Current operating Income, Et,and either year t + 1 size-adjusted returns (URt+1) or year t + 1 delistingreturns are required; the return accumulation period begins four monthsafter the fiscal year-end and continues for twelve months. Panel A showsthe total number firm-years in the sample, grouped by non-delisting anddelisting firm-years (observations that are delisted in the year t + 1 returnperiod). The number of each group with nonmissing and missing futureearnings (Et+1) is also presented. Panel B in addition requires current cashflows (CFt) and accruals (ACt = Et−CFt). Cash flows are computed usingbalance sheet data as in Sloan (1996). The last column of the table showsthe percentage of each group that has missing Et+1.

Panel A: Sample Size with EarningsNonmissing Missing Missing/

Firm-Years Et+1 Et+1 Total TotalNon-Delisting 142,313 736 143,049 0.01

DelistingMerger-Related (200-299) 774 4,803 5,577 0.86Dropped (500-599) 2,849 1,970 4,819 0.41Other 146 378 524 0.72

Delisting Subtotal 3,769 7,151 10,920 0.65

Total Firm-Years 146,082 7,887 153,969 0.05

Panel B: Sample Size with Cash Flows and AccrualsNonmissing Missing Missing/

Firm-Years Et+1 Et+1 Total TotalNon-Delisting 126,823 658 127,481 0.01

DelistingMerger-Related (200-299) 723 4,587 5,310 0.86Dropped (500-599) 2,685 1,852 4,537 0.41Other 134 352 486 0.72

Delisting Subtotal 3,542 6,791 10,333 0.66

Total Firm-Years 130,365 7,449 137,814 0.05

37

Table 3Average Delisting Returns and Decile Concentrations of Delistings

Panel A shows the number of delistings in the sample over 1962-2002for NYSE, AMEX and NASDAQ firms. Average size-adjusted returnsare also shown including monthly returns prior to delisting with thedelisting in year t + 1 (UR + dr) and the (daily) delisting return (dr)by itself. Panel B shows the distribution of delistings across deciles.Deciles are formed by fiscal year with the following variables: operatingincome (Et); accruals (ACt) and cash flows (CFt) computed using thebalance sheet method; and the book-to-market ratio (BMt). CRSPgroups delistings as follows: 200-299 is mergers; 300-399 is exchanges;400-499 is liquidations; 500-599 is dropped securities.

Panel A: Delistings by CategoryCategory n UR + dr dr

200-299 5,577 0.28 0.02300-399 392 0.28 0.01400-499 132 0.18 0.16500-599 4,819 −0.51 −0.14

Panel B: Decile Concentrations of DelistingsDecile Et ACt CFt BMt

Mergers: Delisting Codes 200-2991 385 558 335 4072 506 646 446 4203 567 579 497 5024 576 549 517 5245 602 539 560 6436 623 508 573 6207 591 507 575 5588 610 538 612 6089 589 487 625 65010 528 399 570 559Dropped Securities: Delisting Codes 500-5991 1,913 1,480 1,415 1,4642 1,057 622 786 4253 713 397 590 3144 387 264 420 2605 257 218 297 2276 160 247 250 2187 107 257 221 2338 94 254 153 2989 65 288 164 40810 66 510 241 861

38

Table 4Descriptive Statistics by Accruals Decile for Delisting Firm-Years

This table shows additional descriptive statistics by accruals decile fordelisting firm-years (observations that are delisted in the year t + 1return period). The averages of the following variables are presented:Altman’s Z-Score; the average percentage of earnings in the past 5 yearswith negative net income (NI < 0); the average sum of special itemsand extraordinary items deflated by average assets (SI + EI); and theaverage leverage as measured by total liabilities divided by total assets.All variables except for the percentage of negative earnings in the past5 years (NI < 0) are winsorized at the top and bottom 1% to reducethe influence of outliers. Panel A shows descriptive statistics for merg-ers (delisting codes 200-299). Panel B shows descriptive statistics fordropped delistings (delisting codes 500-599). The sample is pooled over1962-2002 and includes NYSE, AMEX and NASDAQ firms. Accrualsdeciles are assigned by fiscal year.

Panel A: Mergers (Codes 200-299)Decile Z-Score NI < 0 SI + EI Leverage n

1 2.52 0.47 −0.04 0.54 5582 3.42 0.34 −0.03 0.51 6463 3.60 0.30 −0.02 0.53 5794 3.30 0.27 −0.01 0.51 5495 3.79 0.22 −0.01 0.52 5396 4.04 0.23 −0.01 0.51 5087 5.69 0.21 −0.01 0.46 5078 6.04 0.21 −0.01 0.47 5389 5.85 0.23 −0.01 0.43 48710 28.30 0.26 0.00 0.47 399

Panel B: Dropped Delistings (Codes 500-599)Decile Z-Score NI < 0 SI + EI Leverage n

1 −5.08 0.77 −0.09 1.04 1,4802 −0.12 0.73 −0.05 0.70 6223 0.59 0.68 −0.04 0.67 3974 −0.19 0.69 −0.04 0.68 2645 3.40 0.63 −0.05 0.61 2186 1.60 0.65 −0.03 0.60 2477 3.77 0.66 −0.07 0.60 2578 2.05 0.64 −0.01 0.59 2549 −0.07 0.67 −0.04 0.58 28810 2.06 0.67 −0.03 0.60 510

39

Table 5Decile Firm Size and the Impact of Delistings on Size-Decile Returns

Panel A shows the average market value of equity of firms in the decilesexamined in this paper: Earnings (Et), Accruals (ACt), Cash Flows(CFt) and the book-to-market ratio (BMt). The sample is pooled over1962-2002 and includes NYSE, AMEX and NASDAQ firms. Deciles areassigned by fiscal year. The average market value of equity is reportedfor each decile of the corresponding variable. Panel B shows the differ-ence between CRSP size-decile (stock file capitalization decile) indexreturns and size-decile index returns that are adjusted to include delist-ing returns, as discussed in the paper. Returns are compounded overcalendar years. We report the average index returns from 1990-2004for size-deciles of NYSE, AMEX and NASDAQ firms.

Panel A: Average Decile Firm SizeDecile Et ACt CFt BMt

1 64 272 71 1,3292 131 581 125 1,9583 232 1,159 196 1,2404 502 1,208 397 1,0625 734 1,372 542 8126 931 1,152 768 6987 1,081 961 1,095 5498 1,196 755 1,392 4099 1,359 474 1,658 21410 1,974 226 1,923 82

Panel B: Comparing CRSP Decile Returnsto Decile Returns that Include Delistings

CRSP Decile Adjusted DecileSize Decile Return Return Difference

1 0.310 0.273 0.0372 0.239 0.226 0.0123 0.196 0.190 0.0054 0.180 0.177 0.0035 0.176 0.174 0.0026 0.157 0.157 0.0017 0.142 0.142 0.0008 0.139 0.139 0.0009 0.133 0.133 0.00010 0.122 0.122 0.000

40

Table 6Return Regressions with Earnings

This table shows the results of the regression of futuresize-adjusted returns (URt+1) on current earnings (Et).The earnings measure used is income before extraordi-nary items over the time period 1987-2002 and oper-ating income over 1962-2002. The measure of returnsis the twelve-month, size-adjusted return beginning fourmonths after the fiscal year-end. Delisting firm-years (ob-servations that are delisted in the year t+1 return period)are excluded in Panel A and included in Panel B. Vari-ables are not trimmed for this analysis, but inferencesare essentially unchanged when the top and bottom 1%of all variables are trimmed.

URt+1 = α + βEt + εt (1)

1987-2002 1962-2002Income Before Operating

Extraordinary Items Income

Panel A: Excluding Delisting Firm-YearsEst. T-Value Est. T-Value

α 0.007 2.26 0.001 0.71β 0.019 1.94 0.045 5.57n 74,542 143,049R2

a 0.000 0.000

Panel B: Including Delisting Firm-YearsEst. T-Value Est. T-Value

α −0.001 −0.24 −0.006 −3.22β 0.089 10.60 0.117 16.61n 82,208 153,969R2

a 0.001 0.002

41

Table 7Return Regressions with Cash Flows and Accruals

This table shows the results of the regression of future size-adjusted returns (URt+1) on current cash flows (CFt) and ac-cruals (ACt). Cash flows and accruals are computed with thebalance sheet method over 1962-2002 and with cash flow state-ment data over 1987-2002. The measure of returns is the twelve-month, size-adjusted return beginning four months after the fis-cal year-end. Delisting firm-years (observations that are delistedin the year t + 1 return period) are excluded in Panel A and in-cluded in Panel B. Variables are not trimmed for this analysis,but inferences are essentially unchanged when the top and bot-tom 1% of all variables are trimmed.

URt+1 = α0 + γCFt + δACt + εt (2)

1987-2002 1962-2002CF and AC from CF and AC from

Cash Flow Statement Balance Sheet Method

Panel A: Excluding Delisting Firm-YearsEst. T-Value Est. T-Value

α 0.003 0.77 −0.006 −2.64γ 0.092 6.58 0.075 8.72δ −0.066 −4.61 −0.206 −12.15n 69,684 127,481R2

a 0.001 0.002

Panel B: Including Delisting Firm-YearsEst. T-Value Est. T-Value

α −0.007 −2.12 −0.012 −5.68γ 0.168 13.62 0.147 19.19δ −0.005 −0.39 −0.070 −4.79n 76,798 137,814R2

a 0.002 0.003

42

Table 8Returns of Earnings Deciles

This table shows returns by earnings decile. Data are ranked by fiscal year earnings(Et) deflated by average assets. Average returns are computed for each decile. Incomebefore extraordinary items (EI) is used over 1987-2002 and operating income is usedover 1962-2002. The return measures used are raw returns (Rt+1) and size-adjustedreturns (URt+1) starting four months after the fiscal year-end compounded for twelvemonths. Delisting firm-years (observations that are delisted in the year t + 1 returnperiod) are excluded in Panel A and included in Panel B.

1987-2002 1962-2002Income Before EI Operating Income

Panel A: Excluding Delisting Firm-YearsEt Mean Mean Mean Mean

Decile Rt+1 URt+1 T-Value Rt+1 URt+1 T-Value1 0.177 0.006 0.34 0.150 −0.028 −2.932 0.216 0.045 3.21 0.180 0.008 0.953 0.169 0.006 0.51 0.157 −0.006 −0.844 0.158 −0.001 −0.06 0.173 0.015 2.495 0.161 0.004 0.58 0.164 0.008 1.486 0.144 −0.009 −1.11 0.162 0.008 1.757 0.148 0.000 0.01 0.156 0.001 0.148 0.143 −0.003 −0.47 0.161 0.010 2.219 0.142 0.001 0.14 0.154 0.005 1.0010 0.157 0.016 1.75 0.163 0.015 3.04

10 − 1 −0.020 0.010 0.52 0.013 0.043 4.02n 74,542 74,542 143,049 143,049

Panel B: Including Delisting Firm-YearsEt Mean Mean Mean Mean

Decile Rt+1 URt+1 T-Value Rt+1 URt+1 T-Value1 0.066 −0.083 −5.44 0.078 −0.085 −9.832 0.142 −0.016 −1.24 0.145 −0.017 −2.013 0.142 −0.012 −1.13 0.142 −0.013 −1.954 0.151 −0.003 −0.30 0.163 0.010 1.695 0.153 0.003 0.40 0.161 0.008 1.536 0.151 0.001 0.13 0.166 0.015 3.327 0.151 0.006 0.78 0.160 0.010 2.118 0.151 0.009 1.43 0.166 0.019 4.369 0.147 0.009 1.38 0.160 0.014 3.1510 0.160 0.024 2.79 0.167 0.023 4.79

10 − 1 0.094 0.107 6.11 0.089 0.108 10.92n 82,208 82,208 153,969 153,969

43

Table 9Returns of Accruals Deciles

This table shows returns by accruals decile. Data are ranked by fiscal year on accruals(ACt) deflated by average assets. Average returns are computed for each decile. Cashflow statement data are used for 1987-2002 and the balance sheet method is used for1962-2002. The return measures used are raw returns (Rt+1) and size-adjusted returns(URt+1) starting four months after the fiscal year-end compounded for twelve months.Delisting firm-years (observations that are delisted in the year t + 1 return period)are excluded in Panel A and included in Panel B.

1987-2002 1962-2002Cash Flow Statement Balance Sheet Method

Panel A: Excluding Delisting Firm-YearsACt Mean Mean Mean Mean

Decile Rt+1 URt+1 T-Value Rt+1 URt+1 T-Value1 0.239 0.066 3.97 0.222 0.055 5.632 0.217 0.054 4.56 0.197 0.035 4.863 0.206 0.047 4.39 0.187 0.031 4.984 0.172 0.017 1.87 0.179 0.023 3.825 0.158 0.006 0.76 0.165 0.012 2.126 0.167 0.015 1.62 0.164 0.012 2.097 0.159 0.014 1.46 0.163 0.012 1.778 0.125 −0.024 −2.63 0.140 −0.015 −2.389 0.114 −0.039 −3.86 0.119 −0.041 −6.7710 0.091 −0.073 −6.56 0.083 −0.080 −10.88

1 − 10 0.149 0.139 7.17 0.139 0.135 12.57n 69,684 69,684 127,481 127,481

Panel B: Including Delisting Firm-YearsACt Mean Mean Mean Mean

Decile Rt+1 URt+1 T-Value Rt+1 URt+1 T-Value1 0.129 −0.024 −1.62 0.161 0.007 0.772 0.193 0.039 3.39 0.179 0.026 3.743 0.182 0.028 2.80 0.179 0.029 4.794 0.160 0.011 1.23 0.170 0.020 3.535 0.148 0.000 0.01 0.168 0.018 3.236 0.169 0.023 2.66 0.164 0.017 3.057 0.157 0.016 1.75 0.161 0.014 2.258 0.121 −0.021 −2.52 0.140 −0.010 −1.799 0.110 −0.038 −4.04 0.115 −0.040 −6.8710 0.070 −0.087 −8.46 0.068 −0.090 −12.84

1 − 10 0.059 0.063 3.59 0.093 0.097 9.75n 76,798 76,798 137,814 137,814

44

Table 10Returns of Cash Flow Deciles

This table shows returns by cash flow decile. Data are ranked by fiscal year on cashflows (CFt) deflated by average assets. Average returns are computed for each decile.Cash flow statement data are used for 1987-2002 and the balance sheet method is usedfor 1962-2002. The return measures used are raw returns (Rt+1) and size-adjustedreturns (URt+1) starting four months after the fiscal year-end compounded for twelvemonths. Delisting firm-years (observations that are delisted in the year t + 1 returnperiod) are excluded in Panel A and included in Panel B.

1987-2002 1962-2002Cash Flow Statement Balance Sheet Method

Panel A: Excluding Delisting Firm-YearsCFt Mean Mean Mean Mean

Decile Rt+1 URt+1 T-Value Rt+1 URt+1 T-Value1 0.136 −0.040 −2.59 0.108 −0.064 −6.582 0.150 −0.024 −1.78 0.135 −0.035 −4.183 0.148 −0.020 −1.66 0.142 −0.020 −2.684 0.149 −0.009 −0.88 0.151 −0.005 −0.755 0.156 0.007 0.75 0.153 −0.002 −0.276 0.173 0.023 2.61 0.173 0.019 3.337 0.184 0.033 3.88 0.178 0.025 5.188 0.170 0.024 2.50 0.190 0.039 6.419 0.179 0.031 3.82 0.187 0.036 6.6310 0.205 0.059 6.27 0.202 0.049 8.46

10 − 1 0.070 0.100 5.15 0.094 0.113 10.55n 69,684 69,684 127,481 127,481

Panel B: Including Delisting Firm-YearsCFt Mean Mean Mean Mean

Decile Rt+1 URt+1 T-Value Rt+1 URt+1 T-Value1 0.057 −0.103 −7.22 0.051 −0.109 −12.132 0.088 −0.076 −6.21 0.113 −0.048 −5.973 0.122 −0.036 −3.15 0.129 −0.028 −3.954 0.126 −0.027 −2.65 0.141 −0.009 −1.455 0.141 −0.004 −0.46 0.147 −0.003 −0.546 0.173 0.030 3.48 0.169 0.020 3.637 0.178 0.032 3.99 0.174 0.027 5.658 0.173 0.031 3.46 0.193 0.046 7.869 0.178 0.036 4.78 0.192 0.046 8.6610 0.203 0.062 6.88 0.196 0.050 9.01

10 − 1 0.146 0.165 9.41 0.146 0.159 16.00n 76,798 76,798 137,814 137,814

45

Table 11Returns of Book-to-Market Deciles

This table shows returns by book-to-market decile for 1987-2002 and 1962-2002. Dataare ranked by fiscal year on the book-to-market ratio (BMt). The average return iscomputed for each decile. The book-to-market ratio is computed using the bookvalue of common equity and the market value of equity at fiscal year-end. The returnmeasures used are raw returns (Rt+1) and size-adjusted returns (URt+1) starting fourmonths after fiscal year-end compounded for twelve months. Delisting firm-years(observations that are delisted in the year t + 1 return period) are excluded in PanelA and included in Panel B.

1987-2002 1962-2002

Panel A: Excluding Delisting Firm-YearsBMt Mean Mean Mean MeanDecile Rt+1 URt+1 T-Value Rt+1 URt+1 T-Value

1 0.102 −0.045 −3.78 0.083 −0.057 −7.622 0.088 −0.053 −5.57 0.092 −0.047 −7.853 0.097 −0.050 −5.74 0.106 −0.039 −7.094 0.136 −0.010 −1.05 0.133 −0.012 −2.045 0.137 −0.009 −1.03 0.141 −0.007 −1.186 0.154 0.003 0.29 0.161 0.008 1.287 0.174 0.019 2.04 0.177 0.019 3.458 0.186 0.024 2.25 0.189 0.023 3.639 0.246 0.074 6.01 0.230 0.056 7.7710 0.301 0.114 9.41 0.272 0.086 11.73

10 − 1 0.199 0.158 8.20 0.189 0.143 13.34n 74,087 74,087 139,164 139,164

Panel B: Including Delisting Firm-YearsBMt Mean Mean Mean MeanDecile Rt+1 URt+1 T-Value Rt+1 URt+1 T-Value

1 0.024 −0.110 −10.23 0.030 −0.101 −14.482 0.077 −0.057 −6.16 0.085 −0.049 −8.253 0.083 −0.058 −6.99 0.097 −0.043 −8.154 0.127 −0.015 −1.68 0.131 −0.012 −2.045 0.129 −0.011 −1.42 0.138 −0.005 −0.926 0.153 0.007 0.84 0.161 0.013 2.287 0.166 0.016 1.89 0.174 0.021 3.938 0.183 0.026 2.53 0.189 0.028 4.469 0.227 0.062 5.43 0.223 0.055 8.0410 0.246 0.075 6.74 0.242 0.067 9.71

10 − 1 0.222 0.185 10.58 0.212 0.168 16.96n 81,755 81,755 150,046 150,046

46

Delisting returns and their effect on accounting-based market anomalies

Documents

Transcript of Delisting returns and their effect on accounting-based market anomalies