This is not the document you are looking for? Use the search form below to find more!

Report home > World & Business

Delisting returns and their effect on accounting-based market anomalies

0.00 (0 votes)
Document Description
We show that tests of market efficiency are sensitive to the inclusion of delisting firm-years. When included, trading strategy returns based on anomaly variables can increase (for strategies based on earnings, cash flows and the book-to-market ratio) or decrease (for a strategy based on accruals). This is due to the disproportionate number of delisting firm-years in the lowest decile of these variables. Delisting firm-years are most often excluded because the researcher does not correctly incorporate delisting returns, because delisting return data are missing or because other research design choices implicitly exclude them.
File Details
  • Added: March, 10th 2010
  • Reads: 818
  • Downloads: 33
  • File size: 272.87kb
  • Pages: 28
  • Tags: accounting, anomalies, delisting returns, accruals
  • content preview
Submitter
  • Username: shinta
  • Name: shinta
  • Documents: 4332
Embed Code:

Add New Comment




Related Documents

Wedding Invitation Designs and Their Impact on Guests

by: oneclickinformation, 4 pages

Wedding invitation designs are what which gets most of the focus and a huge time is spent on them when a wedding is planned.

ANALYSIS OF MICROWAVE DRYING VARIANTS AND THEIR EFFECT ON THE QUALITY OF THE DEHYDRATED PRODUCT

by: shinta, 10 pages

Drying is one of the most important unit operations of food processing; being necessary its study for either the quality evaluation of dried product or its effect on the operational costs. The ...

Personal Relations and their Effect on Behavior in an Organizational Setting: An Experimental Study

by: shinta, 36 pages

We study how personal relations affect performance in organizations. In the experimental game we use a manager has to assign different degrees of decision power to two employees. These ...

take part to ideas. graphic designers and their research on intellectual property

by: Jules, 2 pages

graphic designers and their research on intellectual property

Wettability and Its Effect on Oil Recovery

by: eiffel, 9 pages

Wettability and Its Effect on Oil Recovery

Defining surgical role models and their influence on career choice 2011 Ravindra Fitzgerald

by: Robert, 6 pages

Defining surgical role models and their influence on career choice 2011 Ravindra Fitzgerald

The Mutagenic Activity of Chitosan and its Effect on the Growth of Trichoderma harzianum and Fusarium oxysporum F. Sp. Sesami

by: shinta, 6 pages

Five concentrations of chitosan; 0.38, 0.75, 1.50, 3.00 and 4.50 mg/ml were used to study its effect on the growth of Fusarium oxysporum f sp. Sesami and Trichoderma harzianum. Chitosan ...

Antimutagenic Effect of Curcumin and Its Effect on the Immune Response in Mice

by: shinta, 12 pages

A wide array of antioxidative and anti-inflammatory substances derived from edible plants have been reported to possess chemopreventive and chemoprotective activities. Among the most ...

POPULATION GROWTH AND ITS EFFECT ON ENVIRONMENT IN INDIA

by: daisi, 23 pages

Environmental pollution is one of the serious problems faced by the people in the country. Rapid population growth, industrialization and urbanization in country are adversely affecting the ...

Reports on Hot Drinks Market in Indonesia and Thailand on ReportsnReports.com

by: ohannajohnson, 2 pages

Find hidden opportunities in the most current research data available, understand competitive threats with our detailed market analysis, and plan your corporate strategy with our expert qualitative ...

Content Preview
ARTICLE IN PRESS
Journal of Accounting and Economics 43 (2007) 341–368
www.elsevier.com/locate/jae
Delisting returns and their effect on
accounting-based market anomalies$
William Beavera, Maureen McNicholsa,Ã, Richard Priceb
aGraduate School of Business, Stanford University, USA
bJones Graduate School of Management, Rice University, USA
Received 18 June 2005; received in revised form 28 November 2006; accepted 14 December 2006
Available online 5 January 2007
Abstract
We show that tests of market ef?ciency are sensitive to the inclusion of delisting ?rm-years. When
included, trading strategy returns based on anomaly variables can increase (for strategies based on
earnings, cash ?ows and the book-to-market ratio) or decrease (for a strategy based on accruals).
This is due to the disproportionate number of delisting ?rm-years in the lowest decile of these
variables. Delisting ?rm-years are most often excluded because the researcher does not correctly
incorporate delisting returns, because delisting return data are missing or because other research
design choices implicitly exclude them.
r 2007 Elsevier B.V. All rights reserved.
JEL: G14; M41; G33; G34
Keywords: Accounting; Anomalies; Delisting returns; Accruals
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
$We thank an anonymous referee and Doug Skinner (the editor) for helpful comments. We also gratefully
acknowledge ?nancial support from the Stanford University Graduate School of Business and the Rice University
Jones Graduate School of Management.
ÃCorresponding author. Tel.: +1 650 723 0833; fax: +1 650 725 7979.
E-mail addresses: beaver_william@gsb.stanford.edu (W. Beaver), mcnichols_maureen@gsb.stanford.edu
(M. McNichols), richardp@rice.edu (R. Price).
0165-4101/$ - see front matter r 2007 Elsevier B.V. All rights reserved.
doi:10.1016/j.jacceco.2006.12.002

ARTICLE IN PRESS
342
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
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 ?ows and the book-to-market ratio (Sloan, 1996;
Lakonishok et al., 1994; Bernard and Thomas, 1989, 1990). These studies compare the future
returns of ?rm-years in high vs. low deciles of different accounting variables. These anomalies
lead to questions of market ef?ciency 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 ?ndings provide evidence in favor of or against market ef?ciency because our analysis does
not consider transaction costs, among other costs of implementing a trading strategy. As pointed
out by Sloan (1996), ?ndings of market inef?ciency in historical data do not necessarily imply
that strategies based on the ?ndings are exploitable. Because ?rms that delist are on average
highly risky and potentially illiquid, the exploitability of their returns is an open question.
We ?nd that the exclusion of ?rms that are delisted in the return accumulation period,
hereafter delisting ?rm-years, does not uniformly increase or decrease portfolio returns.
The effect on inferences about market ef?ciency depends on the partitioning variable or
trading strategy. For portfolios partitioned on earnings, cash ?ows, and the book-to-
market ratio, the difference between average returns in extreme deciles increases when
delisting ?rm-years are included. In contrast, for portfolios partitioned on accruals,
average returns in the lowest accruals decile decrease signi?cantly when delisting ?rm-years
are included, but there is no signi?cant change in the highest accruals decile. These results
are due to the disproportionate concentration of delisting ?rm-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 ?rm-
years. First, requiring future earnings excludes two-thirds of delisting ?rm-years. Second,
nearly half of all delistings occur outside the date range provided by the CRSP/Compustat
merged database, so valid delisting ?rm-years are excluded if one does not include matches
outside the CRSP-speci?ed date range. Third, when using monthly delisting returns,
researchers unfamiliar with the details of CRSP data who use replacement values for ?rms
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.
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 the delisting, de?ned by
CRSP as the partial month return, and the daily delisting return.

ARTICLE IN PRESS
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
343
Researchers conducting tests of market ef?ciency should assess the sensitivity of their
?ndings to the inclusion of delisting ?rm-years in their sample. Our ?ndings indicate that
inferences concerning market ef?ciency 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 ?rm-years affects inferences in tests
of market ef?ciency and in other settings.
Section 2 discusses the background and related research. Section 3 discusses the
computation 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 ?rm-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 À100% is
used as the delisting return when it is missing and the ?rm was forced to delist.’’2 Xie (2001)
does not describe what is done with missing delisting returns. Hribar and Collins (2002)
speci?cally state that ?rms with missing delisting returns are deleted. Mohanram (2004) uses
À30% 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
?rm-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 ?rm-years are not uniformly distributed across portfolios commonly
formed on deciles of earnings, accruals, cash ?ows and the book-to-market ratio, variables
of great interest to accounting researchers. As a result, the treatment of delisting returns
can have a signi?cant impact on estimated returns associated with trading strategies based
on these variables.
2.1. Related research
The accounting and ?nance literatures document several puzzling patterns in return
behavior, 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 ?nds that returns from portfolios partitioned on fundamental variables such as
earnings, accruals, cash ?ows, 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 ef?ciency. Kothari et al. (2005) show that passive deletion (exclusion of
observations that do not survive the horizon studied) can lead to ?ndings 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) ?nds that accounting for
2The following papers provide brief explanations of the treatment of delisting returns that are very similar to
Sloan (1996): Sun (2003), Kraft et al. (2006) and Dopuch et al. (2005).

ARTICLE IN PRESS
344
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
additional risk factors causes the difference in returns between extreme accruals decile
portfolios to become insigni?cant.
Two studies in the ?nance literature address research design issues associated with
delisting returns. Shumway (1997) documents that CRSP data were generally missing
delisting returns for ?rms with poor performance (this has since been corrected by CRSP).
Shumway and Warther (1999) ?nd that when delistings for performance-related reasons
are included, the size effect that small ?rms outperform large ?rms 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. Speci?cally, researchers who are unaware that monthly delisting returns contain a
partial month return even when the delisting return is missing will fail to correct for the
missing return. We ?nd 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 ?nd a uniform effect of delisting exclusions on inferences about market
ef?ciency. 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 ?ndings 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 ?scal year. The most recent
?scal year of Compustat data typically will not have the required CRSP data to compute
future returns. However, if the ?rm delists before the end of the return accumulation
period, the ?rm will be included in the sample because fewer months of return data are
required for delisting; the latest ?scal year will be composed primarily of delisting ?rm-
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 À100% 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 classi?ed as mergers (51% of the delistings in our sample,
delisting codes 200–299) or dropped delistings3 (44% of the delistings in our sample,
delisting codes 500–599). Delisting returns are computed from liquidation payments or
3Examples of dropped delistings include bankruptcy, stock price below acceptable level, and insuf?cient assets,
equity, or capital. Also within this range of delisting codes is the more recent phenomenon of ?rms who go

ARTICLE IN PRESS
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
345
from other information about the value of the security after delisting. CRSP allows up to
10 years after the delisting to learn the delisting return and updates the records as needed.
Most delisting returns are realized soon after the delisting. Untabulated descriptive
statistics show that of the 4,142 dropped delistings in our sample that have nonmissing
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 ½t; t þ k? then
?rms that delist in this range will be excluded from the sample. In the case of mergers, these
returns are typically signi?cantly positive. In the case of dropped delistings, these returns
are typically signi?cantly negative. To avoid excluding delisting ?rm-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—
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 de?nes 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 ?rms with
missing delisting returns can signi?cantly affect estimated portfolio returns. Untabulated
descriptive statistics show that 9.4% of monthly delistings in CRSP have missing delisting
(footnote continued)
‘‘dark’’, or the choice by ?rms to delist from the NYSE, AMEX or NASDAQ to avoid ?ling with the SEC, as
discussed in Leuz et al. (2006).
4The range ½t; t þ k? is the range speci?ed 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’’ ?le) with monthly returns (found in the WRDS ‘‘msf’’ ?le).
6According to CRSP, the last trading day of the month is the last weekday of the month that the market was
open for exchange.

ARTICLE IN PRESS
346
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
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
NASDAQ ?rms from 1962 to 2002, 702 delisting returns are missing in the monthly ?le. Of
these, 645 of the corresponding monthly delisting returns are not literally missing, but
contain 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 ?rm-years with missing delisting returns. The median delisting return
for ?rms delisted for poor performance reported in both papers is À30%, which can be
used as a replacement value. Shumway and Warther (1999) suggest À55% can be used for
NASDAQ ?rms.
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 signi?cantly 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 ?rms with missing delisting returns is different from the estimate for ?rms that
voluntarily delist (go ‘‘dark’’).
Speci?cally, we compute the average daily delisting return for every three-digit delisting
code for all available delistings with nonmissing 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 À100% 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
À100% 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 1%.
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
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 ef?ciency studies is the
computation of risk-adjusted returns.8 Much of the literature uses size-adjusted returns,
7Refer to the Appendix for a detailed discussion.
8We thank the reviewer for suggesting an examination of this issue.

ARTICLE IN PRESS
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
347
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 ?rm-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 signi?cantly
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 ?rms that is used to create the market-return measure
compared to the sample of ?rms for which enough data are available to conduct the analysis.
If the sample of ?rms 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 observations 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 ?rst
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, nonmissing delisting returns, and replacement values for
missing delisting returns. The decile return is measured as the average return for decile
?rms, 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 ?le provides a direct link between the Compustat primary ?rm
identi?er, GVKEY, and the CRSP primary security identi?er, PERMNO, and provides
date ranges over which this link is effective. In cases where a GVKEY links to different
9For our sample of ?rms, the average size-adjusted return is signi?cantly negative over 1987–2002,
À0:0063 ðt ¼ À2:051Þ, but is insigni?cant for 1962–2002, À0:0015 ðt ¼ À0:803Þ.
10Wharton Research Data Services (WRDS) provides SAS data sets based on these Stock File Capitalization
Decile Indices. They are the commonly used ‘‘ermport’’ and ‘‘mport’’ ?les.
11The exclusion of delisting returns from indices is not clearly identi?ed in CRSP documentation, but was
communicated to us by CRSP technical support.

ARTICLE IN PRESS
348
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
securities (PERMNOs) over its history, CCM provides the link information so that returns
can be merged.
A signi?cant number of delisting ?rm-years are excluded if CCM date ranges are
interpreted literally. If return data outside the interval are not merged with Compustat, up
to 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 identi?er, 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 de?nitions
We use two sample periods: (1) 1962–2002, for which we use the ‘‘balance sheet’’ method
to compute cash ?ows and accruals, and (2) 1987–2002, for which we use the statement of
cash ?ows for cash ?ows and accruals measures. We effectively use data from 1961 to 2004
because we require lagged assets and 12-month returns beginning four months after ?scal
year-end. The sample includes all non-ADR NYSE, AMEX and NASDAQ ?rms that
meet data requirements, excluding banks, insurance and real estate companies (SIC codes
between 6000 and 6999).13 We include NASDAQ ?rms because the incidence of delistings
is signi?cantly greater among NASDAQ ?rms, and because they are increasingly included
in studies in the anomalies literature.14
We measure earnings, Et, as operating income, DATA178 from Compustat, and income
before extraordinary items, DATA18.15 Cash ?ows, CF t, are measured using the balance
sheet method (Sloan, 1996) and using the statement of cash ?ows, excluding cash ?ows
from extraordinary items and discontinued operations (Hribar and Collins, 2002),
DATA308–DATA124. We compute accruals, ACt, as Et À CF t. When using the balance
sheet method to compute cash ?ows, we compute accruals with operating income. When
using the statement of cash ?ows, we compute accruals using income before extraordinary
items. Following Sloan (1996) and most subsequent papers, we de?ate all accounting
variables by the average of total assets, DATA6.16
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 on the
six-digit CUSIP: the sample size is only 6% smaller than when CCM is used. When using the eight-digit CUSIP,
the sample size is roughly 10% smaller. This could be improved depending on what other steps are taken.
13The exclusion of these companies does not signi?cantly affect inferences. Sloan (1996) also excludes banking
and insurance ?rms due to lack of data availability to compute accruals. The use of historical SIC codes
(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 ?rms are excluded. Although Sloan (1996) includes only NYSE and
AMEX ?rms, other papers include NASDAQ ?rms (Xie, 2001; Desai et al., 2004).
15Inferences are unchanged whether DATA18 (earnings measure from income statement) or DATA123
(earnings measure from the cash ?ow statement) is used.
16The literature following Sloan (1996) generally de?ates by average assets, but papers examining other
anomalies typically de?ate by price.

ARTICLE IN PRESS
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
349
We measure market-adjusted returns in year t þ 1, URtþ1, as 12-month, size-adjusted,
buy-and-hold returns beginning four months after ?scal year-end. We compute size-decile
returns as described earlier. To avoid excluding delisting ?rm-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 þ 1 returns, DRtþ1. We assume that when a ?rm 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 ?rm-years, the
analysis is conducted including and excluding delisting ?rm-years. As de?ned earlier, a
delisting ?rm-year is an observation that delists in the return accumulation period. Fiscal
year t is a delisting ?rm-year if the ?rm delists in year t þ 1.
We form deciles by ?scal year using all observations that meet the speci?ed data
requirements. Generally, we require the accounting variable in year t (Et; ACt; CF t or
BMt) and the return measure in year t þ 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 ?le, of
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 ?scal
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 speci?ed 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 speci?ed 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 ?rms over the
period 1962–2002 in Panel B. The left column shows that the average yearly sample size
(including delisting and nondelisting ?rm-years) increases steadily from the 1960s (average
1,492 ?rms per year) to the 1990s (average 5,403 ?rms 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 ?nal 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 signi?cant 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 ?le, but not in the monthly ?le. Delistings with missing daily but
17Sample code provided by WRDS requires the date of the ?scal year-end to be within the date range provided
by CCM.

ARTICLE IN PRESS
350
W. Beaver et al. / Journal of Accounting and Economics 43 (2007) 341–368
Table 1
Number and frequency of occurrence of delistings
Panel A: number of delistings that merge with Compustat
Total delistings in CRSP
18,388
PERMNO is not in CCM
3,571
Potential delistings to merge with Compustat
14,817
Delistings within CCM date range
8,701
Delistings p6 months after CCM date range
4,769
Delistings merge after extending date range
14,613
Delistings do not merge after extending date range
258
Panel B: frequency of delistings by decade
Time
Average
Average percentage of sample delisted
Average percentage of dropped delistings
period
sample
with missing delisting returns
size
All (%)
Mergers (%)
Dropped
Monthly dr (%)
Daily dr (%)
(%)
1962–1969
1,492
0.7
0.5
0.2
28.0
72.0
1970–1979
3,162
3.6
2.4
0.9
41.9
67.4
1980–1989
4,202
7.6
3.7
3.1
29.2
37.7
1990–1999
5,403
9.1
4.8
4.3
6.5
16.3
2000–2002
4,789
10.8
4.4
6.2
2.5
14.8
Panel A shows the number of monthly delisting returns post 1950 in the CRSP database, the number that merges
with Compustat and explains why all delisting returns do not merge using the CRSP/Compustat merged database
(CCM). Extending the date range means extending the CCM link end date as far as appropriate to allow delistings
outside the range provided by CCM to be included in the sample. Panel B shows the frequency of delistings over
the sample period, 1962–2002. Merger-related delistings include delisting codes 200–299. Dropped delistings
include delisting codes 500–599. Average sample size over each decade is shown as well as the average percentage
of delistings in that decade. The ?nal columns show the percentage of dropped delistings with missing delisting
returns (dr) in both the daily and monthly ?les.
not monthly delisting returns do not have unknown delisting returns, but they are reported
as missing in the CRSP daily ?le 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 ?rm-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 nondelisting ?rm-years. Most of the nondelisting ?rm-year
observations have nonmissing future earnings (142,313 ?rm-years with nonmissing Etþ1 vs.
736 ?rm-years with missing Etþ1). There is a total of 5,577 merger-related delisting ?rm-
years. Most of these have missing future earnings (774 ?rm-years with nonmissing Etþ1 vs.
4,803 ?rm-years with missing Etþ1), so requiring future earnings excludes 86% of all
mergers. There is a total of 4,819 dropped delisting ?rm-years. Many of these ?rm-years
have nonmissing future earnings (2,849 ?rm-years with nonmissing Etþ1 vs. 1,970 ?rm-
years with missing Etþ1), so requiring future earnings excludes about half of all dropped
delistings. Panel B shows that the sample size decreases by 16,155 ð153; 969 À 137; 814Þ
when the additional ?nancial statement variables are required to compute accruals.

Document Outline
  • Delisting returns and their effect on accounting-based market anomalies
    • Introduction
    • Background discussion
      • Related research
    • Delisting returns
      • Compounding delisting returns with standard returns
      • Missing delisting returns
      • Computing market-adjusted returns
      • Using the CRSP/Compustat merged database
    • Sample data
      • Sample period and variable definitions
      • Descriptive statistics
    • Empirical analysis
      • Empirical methods
      • Results
        • Return regressions
        • Portfolio tests of earnings deciles
        • Portfolio tests of accruals deciles
        • Portfolio tests of cash flow deciles
        • The value-glamour anomaly: portfolio tests of BMt deciles
    • Conclusion
    • Computing returns
      • Delisting returns
        • The timing of the delisting
        • Delistings as recorded in CRSP
      • Missing delisting returns
      • A note about using log returns
    • References

Download
Delisting returns and their effect on accounting-based market anomalies

 

 

Your download will begin in a moment.
If it doesn't, click here to try again.

Share Delisting returns and their effect on accounting-based market anomalies to:

Insert your wordpress URL:

example:

http://myblog.wordpress.com/
or
http://myblog.com/

Share Delisting returns and their effect on accounting-based market anomalies as:

From:

To:

Share Delisting returns and their effect on accounting-based market anomalies.

Enter two words as shown below. If you cannot read the words, click the refresh icon.

loading

Share Delisting returns and their effect on accounting-based market anomalies as:

Copy html code above and paste to your web page.

loading