*Title Page/Author Identifier Page/Abstract
Accounting Anomalies and Fundamental Analysis: A Review of
Recent Research Advances*
Scott Richardson Barclays Global Investors
[email protected] rem Tuna London Business School
[email protected] Peter Wysocki University of Miami School of
Business Administration [email protected]
September 2009 Comments welcomed.
Abstract:This paper surveys recent research advances in the
areas of accounting anomalies fundamental analysis. We use investor
forecasting activity as an organizing framework for the three main
parts of our survey. The first part of the survey highlights recent
research advances. The second part presents findings from a
questionnaire given to investment professionals and academics on
the topics of fundamental analysis and anomalies research. The
final part outlines several new empirical techniques for evaluating
accounting anomalies and suggests directions for future research.
JEL classification: G12; G14; M41 Key words: Accruals; Anomalies;
Forecasting; Fundamental analysis; Market efficiency; Risk
*Manuscript
Accounting Anomalies and Fundamental Analysis: A Review of
Recent Research Advances
September 2009
Abstract:This paper surveys recent research advances in the
areas of accounting anomalies fundamental analysis. We use investor
forecasting activity as an organizing framework for the three main
parts of our survey. The first part of the survey highlights recent
research advances. The second part presents findings from a
questionnaire given to investment professionals and academics on
the topics of fundamental analysis and anomalies research. The
final part outlines several new empirical techniques for evaluating
accounting anomalies and suggests directions for future research.
JEL classification: G12; G14; M41 Key words: Accruals; Anomalies;
Forecasting; Fundamental analysis; Market efficiency; Risk
1. IntroductionObjective The editors of the Journal of
Accounting and Economics gave us the assignment to review the
literature on accounting anomalies and fundamental analysis. Given
the existence of numerous excellent prior literature surveys of
closelyrelated topics such as market anomalies, market efficiency,
fundamental analysis and behavioral finance, we have constructed
our literature survey to complement and fillin-the-gaps left by
related literature surveys. These prior surveys include Barberis
and Thaler (2003), Bauman (1996), Bernard (1989), Byrne and Brooks
(2008), Damodaran (2005), Easton (2009a), Fama (1970), Fama (1991),
Hirshleifer (2001), Keim and Ziemba (2000), Kothari (2001), Lee
(2001), Schwert (2003), and Subrahmanyam (2007). To complement
these literature surveys, we focus on
research studies that: (i) have publication or distribution
dates after the year 1999, (ii) examine accounting-related
anomalies and fundamental analysis geared toward forecasting future
earnings, cash flows and security returns, and (iii) focus on
empirical research methodologies. An underlying theme of our survey
is that information contained in general purpose financial reports
helps investors make better portfolio allocation decisions. To this
end, an investor can use information in general purpose financial
reports to forecast free cash flows for the reporting entity,
estimate the risk of these cash flows, and ultimately make an
assessment of the intrinsic value of the firm which will be
compared to observable market prices. We view this forecasting
activity as the fundamental organizing principle for research on
accounting anomalies and
1
fundamental analysis.1 While we recognize the co-existence of
other accounting properties and objectives, we view forecasting as
a powerful organizing concept for reviewing the recent literature
on accounting anomalies and fundamental analysis. As part of our
review, we adopt a number of complementary approaches to identify,
organize and capture recent advances in this literature. The first
part of our review tabulates a list of the most highly-cited
research studies on accounting anomalies and fundamental analysis
published or distributed since the year 2000. We also organize and
categorize these highly-cited studies by identifying their common
and overlapping citations to earlier papers in the literature. The
second part of our survey presents results from a questionnaire of
investment professionals and accounting academics about their
opinions on investment anomalies and fundamental analysis and how
academic research has informed investment practice. In the final
part of our review, we offer suggestions for future research and
draw on recent conceptual advances from both investment practice
and academic research to demonstrate a more-encompassing definition
and treatment of risk and transaction costs in empirical tests of
equity market anomalies. Specifically, we propose a benchmark
empirical model and then apply it to a case study of the relation
between accruals and future stock returns for a sample of U.S.
firms.2 The primary objective of our review is to produce a
valuable research reference not only for academics and graduate
students, but also for investment professionals. In addition, the
findings from our questionnaire of investment1
We keep the discussion of accounting anomalies and fundamental
analysis distinct from each other as this is how the literature has
evolved. But we note that fundamental analysis could be
characterized as subsuming the accounting anomaly literature (i.e.,
both have primary goals of forecasting earnings and returns).2
We choose the accruals anomaly as our case study because it is
the most frequently-cited accounting anomaly over the period of our
literature review. See section 2 for an analysis of citations and
impact of research studies published since the year 2000.
2
professionals and academics highlight the spillovers from
academic research to professional practice because, relative to
other academic accounting research topics, academic research on
anomalies and fundamental analysis has very direct applications and
intellectual spillovers to actual practice. Accounting anomalies
and fundamental analysis also have direct intellectual connections
to the efficient markets and behavioral finance literatures in
financial economics. Given these linkages, we now briefly summarize
the coverage of prior related literature surveys in accounting and
finance. Coverage of previous literature surveys Literature reviews
of the academic literature on efficient markets have origins going
back to Fama (1970). Given that financial market anomalies and
market efficiency are two sides of single intellectual debate,
prior surveys attempt to capture the tensions in this debate and
give insights about the extent to which markets are informationally
efficient (see, for example Kothari, 2001 and Lee, 2001). Surveys
that summarize the literature in the 1980s and 1990s include Keim
and Ziemba (2000), Hirshleifer (2001), Barberis and Thaler (2003),
and Schwert (2003). More recent surveys that focus on papers in the
finance literature include Subrahmanyam (2007), and Byrne and
Brooks (2008). These surveys cover issues related to market
efficiency, technical, fundamental and event-driven anomalies, and
the now maturing field of behavioral finance. Papers that review
the literature on accounting-based anomalies and fundamental
analysis include Baumans (1996) survey of the fundamental analysis
literature up to the mid-1990s and Kotharis (2001) broad survey of
capital markets research in accounting (with a related discussion
by Lee, 2001). While exhaustive at the time, Kothari (2001) and Lee
(2001) cover the literature only up to the year 2000. Recent
surveys by Damodaran (2005) and Ohlson
3
(2009) provide insightful technical overviews of finance and
accounting valuation models. Similarly, Easton (2009) provides a
literature review of and applications of implied cost of capital
methods which have strong foundations in fundamental analysis.
Below we present summary statistics of the coverage and focus of
prior related surveys to provide a perspective on the coverage (or
lack thereof) of this broad literature. Bauman (Journal of
Accounting Literature, 1996) provides a focused overview of
fundamental analysis research in accounting. He covers 66 papers
that were published between 1938 and 1997 and 40 of these papers
were published in academic accounting journals (including 11 papers
from the Journal of Accounting Research, 9 papers from The
Accounting Review, and 4 papers from the Journal of Accounting and
Economics). Bauman (1996) does not focus on research related to
financial market anomalies. Hirshleifer (Journal of Finance, 2001)
provides a survey of research on investor psychology and asset
pricing. He broadly covers 543 papers published up to the year
2001. Many behavioral finance papers began to be published around
this time and 110 of the papers covered in his survey were either
published or distributed in the years 2000 and 2001.
Understandably, the vast majority of the papers in this survey are
drawn from finance, economics and psychology journals. Fewer than
10 papers in the survey are from accounting journals. Fundamental
analysis and other accounting-related topics with possible
behavioral foundations are not highlighted in this survey. Schwert
(Handbook of the Economics of Finance, 2003) surveys the finance
literature on anomalies and market efficiency. He covers 107 papers
published in finance and economics journals between 1933-2003,
including 23 papers that were
4
published or distributed between 2000 and 2003. No accounting
papers are included in the survey. In the same handbook, Barberis
and Thaler (2003) survey the behavioral finance literature. They
cover 204 papers between 1933-2003, including 66 papers published
between 2000 and 2004. They only mention one paper published in an
accounting journal (Bernard and Thomas, 1989). Subrahmanyam
(European Financial Management, 2007) provides a review and
synthesis of the behavioral finance literature. He reviews 155
papers published between the years 1979 and 2007, with the majority
of the papers published in the year 2000 or later. The vast
majority of the surveyed papers come from finance journals and only
one cited working paper was eventually published in an accounting
journal. Finally, Byrne and Brooks (Research Foundation of CFA
Institute Monograph, 2008) provide a practitioner-focused survey of
the current state of the art theories and evidence in behavioral
finance. They review 79 papers published between the years 1979 and
2008, with the majority of the papers published in the year 2000 or
later. They include 33 papers published in the Journal of Finance
and 7 papers published in either the Journal of Financial Economics
or the Review of Financial Studies. Only 1 reviewed paper come from
an accounting journal (Journal of Accounting and Economics). A
quick scan of these survey papers reveals where and when the prior
surveys captured innovations in the literature. While Kothari
(2001) and Lee (2001) provide an excellent coverage of research on
anomalies and fundamental analysis in the accounting literature up
until the year 2001, no survey covers papers in the accounting
literature after that year. Furthermore, recent finance surveys on
anomalies focus almost exclusively on behavioral finance and do not
cover accounting anomalies or
5
fundamental analysis. Therefore, one of the goals of our survey
is to fill in some of the gaps of prior literature surveys and
capture research innovations since the year 2000. What we dont
cover Our survey focuses on empirical research on accounting
anomalies and fundamental analysis. However, empirical research is
(or should be) informed by theory, since interpretation of
empirical analysis is impossible without theoretical guidance. As
we stated above, we do not review in detail papers already covered
in prior surveys (especially papers published prior to the year
2000). In addition, within the empirical capital markets area,
there are concurrent Journal of Accounting and Economics survey
papers that may overlap with some of the topics covered in our
survey [see, for example, Beyer, Cohen, Lys and Walther (Corporate
Information Environment, 2009), and Dechow, Ge and Schrand
(Earnings Quality and Earnings Management, 2009). Accordingly, we
do not discuss in detail research papers in these areas, although
we do reference them. Summary of main observations Our first major
observation is based on a citation analysis of recent published and
working papers on accounting anomalies and fundamental analysis.
This citation analysis lets the academic research market speak on
which research papers on accounting anomalies and fundamental
analysis have attracted the attention of other researchers and have
had a meaningful impact on the subsequent literature. While many of
the most highly-cited papers are from finance journals, there are
some very influential papers from accounting journals that are
broadly cited in both accounting and finance journals (see, for
example, Xie, 2001, and Richardson, Sloan, Soliman and Tuna,
2005).
6
Our second major observation is based on a complementary
citation analysis that helps us organize the literature on
accounting anomalies and fundamental analysis. Specifically, we
analyze papers written or published since the year 2000 to identify
common references of prior published research studies. This
approach allows us to identify common themes or clusters of
research topics. Our analysis reveals four main clusters of
overlapping citations to common sets of prior papers. We apply the
following labels to the four clusters of research papers:
Fundamental Analysis, Accruals Anomaly (including related
investment anomalies), Underreaction to Accounting Information
(including PEAD and other forms of momentum), and Pricing Multiples
and Value Anomaly. These four main clusters largely span the
literature. The Fundamental Analysis cluster cites a number of
prior foundational papers including Abarbanell and Bushee (1997 and
1998) and Feltham and Ohlson (1995). The citation foundation of the
Accruals Anomaly cluster is based on the numerous citations to
Sloan (1996) as the underlying prior research study that binds
together this research cluster. The Underreaction to Accounting
Information cluster most often cites Bernard and Thomas (1989,
1990), Foster, Olsen and Shevlin (1984), and Jegadeesh and Titman
(1993) as foundational papers. The Pricing Multiples and Value
Anomalies cluster is bound together by references to the
foundational papers of Basu (1977), Reinganum (1981), Ball (1992),
and Fama and French (1993 and 1995). We then use our forecasting
framework to categorize, evaluate and discuss some of the main
research advances since the year 2000 in each of the four research
clusters. Our framework attempts to provide some unifying structure
to the burgeoning empirical literature on accounting anomalies. We
highlight that many of the anomalies are not unique and, in many
cases, the apparent excess returns to a new anomaly are subsumed by
other existing anomalies (see, for example, Dechow,
7
Richardson and Slaon, 2008, who document that the general
accruals anomaly subsumes the external financing anomaly). We also
explore why and how the anomalies persist in competitive markets,
the robustness of the anomalies, and whether the observed returns
are due to risk or mispricing. Our third major observation arises
from a questionnaire we distributed to investment professionals
(based on a survey of a subset of CFA members) and to accounting
academics who teach and undertake research related to financial
analysis. The questionnaire attempts to capture the important
opinions of the creators and users of research on accounting
anomalies and fundamental analysis. The findings suggest that many
of the conventions and techniques used in academic research differ
from those in the investment community. For example, in contrast to
most empirical academic studies that use either the CAPM or the
Fama-French 3-factor model for risk calibration, most survey
respondents used other types of models. On the other hand,
practitioners appear to have a robust interest in and demand for
new academic research on fundamental analysis and anomalies.
Interestingly, most respondents claimed that earnings or cash flow
momentum has proven to be a successful active investment strategy
in recent years while accounting quality has received less
attention. Respondents also tend to use a range of fundamental
valuation and analysis techniques in their work (including earnings
multiples, book value multiples, cash flow multiples, and
discounted free cash flow models Interestingly, only a small
fraction of respondents frequently used residual income (economic
profit) models for valuation. The survey respondents also indicated
that they get most of their research insights from practitioner
journals such as CFA Magazine, Financial Analysts Journal, and
Journal of Portfolio Management, rather than academic publications
such as the
8
Journal of Financial Economics, Review of Financial Studies,
Journal of Accounting and Economics, Contemporary Accounting
Research, or The Accounting Review. Both the practitioners and
academics who completed our opinion survey placed high importance
to future academic research on: (i) empirical tests of investor
behavior; (ii) empirical tests of asset pricing, risk and factor
models; (iii) empirical research on forecasting firm and industry
fundamentals; and (iv) empirical discovery and investigation or new
anomalies or signals. Next, based on: (i) the prominence of the
accruals anomaly in the recent literature, and (ii) practitioner
interest in future innovations related to empirical tests of
investor behavior and empirical tests of asset pricing, risk and
factor models, we conduct our own empirical analyses to help
advance some concepts and approaches to be considered and applied
in future research studies. Specifically, we provide new insights
on: (i) the time-series variation in the negative relation between
accruals and future returns (specifically, the extent to which this
relation has disappeared, which is consistent with market
learning), and (ii) whether the relation is robust to a more
comprehensive empirical treatment of risk and transaction costs.
Our empirical analysis shows that the negative relation between
accruals and future stock returns has greatly attenuated over time.
In recent years one could conclude that the information in accruals
is now fully priced by the market, which is consistent with the
market learning explanation and inconsistent with the academic
research that has suggested accruals are a priced risk factor. As
discussed in section 5, the time-varying
association between accruals and future stock returns creates a
natural setting where researchers can evaluate the changes in the
macroeconomic environment that prevented / allowed this risk factor
to generate a premium.
9
Finally, we provide suggestions for future research on
accounting anomalies and fundamental analysis. Based on our
citation analysis, literature review, practitioner/academic
questionnaire, and empirical analyses, we identify five major areas
of opportunity. First, there is a lack of research that utilizes
contextual information such as industry, sector and
macro-environmental data to forecast future earnings, cash flow,
risk and value. Second, current research does not fully exploit the
wealth of information contained in general purpose financial
reports but is outside of the primary financial statements. With
the advent of XBRL and improved textual extraction techniques, this
information could be used to improve forecasts of free cash flows,
risk and firm value. Third, there appear to be limitations to
current forecasting techniques and opportunities to overcome these
limitations. Fourth, we discuss the use of accounting information
by external capital providers beyond common equity holders. With
the increased development of credit markets in the last decade
there is now a wealth of data available on credit related
instruments that can be used to help make inferences about the
usefulness of accounting information for a wider set of capital
providers. Fifth, we note that many capital market participants are
using the same information sources to forecast the future and this
has lead to a very crowded space in the investment world. We note
that future research into the (mis)pricing of accounting
information should undertake a more rigorous analysis of risk and
the impact of transaction costs on the implementability of a given
investment idea in a crowded information space with many users
applying the same information and techniques. Outline of the rest
of the paper Section 2 uses citation analysis to identify high
impact papers from the recent literature on anomalies and
fundamental analysis and organize the literature into four10
main research clusters. Section 3 summarizes the results of a
questionnaire of investment professionals and accounting academics
opinions on academic research related to fundamental analysis and
equity market anomalies. Section 4 provides a synthesis of recent
advances in each of research clusters identified above. Section 5
presents a benchmark model for evaluating accounting anomalies
using a moreencompassing definition and treatment of risk and
transaction costs (with a specific case study of the relation
between accruals and future stock returns for a sample of U.S.
firms). Building on findings in section 2-5, we then discuss our
suggestions for future research in section 6. Finally, section 7
summarizes and concludes.
2. Citation and cluster analysis In this section we utilize well
established techniques to help identify specific high-impact papers
and key research areas related to accounting anomalies and
fundamental analysis. We then group recent research papers into
four clusters based on their common citations to prior studies in
the literature to identify the key topics for our subsequent
literature review.
2.1 Identifying important recent papers on anomalies and
fundamental analysis Our survey focuses on research studies
published or circulated since the year 2000 to complement the prior
literature reviews by Kothari (2001) and Lee (2001). As a starting
point, we let the market speak and use academic citation data to
identify high impact research papers on anomalies and fundamental
analysis. Using citation analysis to quantify research impact has
solid foundations in the accounting literature. There exist a
number of citation-based studies of the prior general accounting
literature including McRae (1974), Brown and Gardner (1985a and
1985b), and11
Brown and Huefner (1994).3 In general, academic citation
analyses utilize the number of citations listed on the ISI Web of
Science and the SSCI (Social Sciences Citation Index).4 However,
this citation data can paint an incomplete and stale picture of
important recent developments and innovations in an academic field.
Moreover, with the advent of the internet and research sites such
as the Social Sciences Research Network (www.ssrn.com) and Research
Papers in Economics (www.repec.org), working papers are quickly and
widely cited by other researchers working papers and published
research papers. Therefore, to capture a broad and timely picture
of recent papers on accounting anomalies and fundamental analysis
literature, we apply the methodology of Keloharju (2008) and
analyze citations using results returned by Google Scholar, a
service that complements the citations generated by the core
journals covered by ISI Web of Science with citations by other
journals and, more importantly, by working papers. The citations on
Google Scholar are timely and include references to and from both
working papers and published papers. We collect the citation data
using the general citation search function of Anne-Wil Harzings
Publish or Perish program, downloadable at http://www.harzing.com/.
This program uses on-line data from Google Scholar to generate a
list of published and working papers cumulative number of citations
to each paper. Given that the cumulative number of citations to a
research study depends not only on impact, but also by the passage
of time since its original circulation or publication, we follow
Schwert (2007) to account for this age effect3
There are also some of citation analyses of sub-fields of
accounting research (see, for example, the citation analysis of the
management accounting literature by Hesford, Lee, Van der Stede and
Young (2007).4
For example, Schwert (2007) uses ISI Web of Science citation
data to rank papers published in the Journal of Financial Economics
between 1974 and 2005 by the number of citations per year.
Citations reported in ISI Web of Science are for published papers
that receive citations from other published papers drawn from a set
of widely-read academic journals.
12
and divide the cumulative number of citations by the number of
years since original circulation or publication of a paper. We
construct a list of the most highly-cited recent papers by first
performing a keyword search on the ssrn.com e-library database to
identify candidate working papers and published papers related that
to financial market anomalies and fundamental analysis.5,6 We then
scan the titles and abstracts of the candidate papers to determine
if they:(i) were posted or published after the year 1999, and (ii)
focus on or have implications for empirical tests of accounting
anomalies and fundamental analysis. We then obtain citation counts
for these papers from Google Scholar using the Publish or Perish
program. We collect citations to both working paper versions and
published versions of each paper and combine duplicate entries to
the same article and correct erroneous title, year, and publication
year information. 2.1.1 Citation impact results For the sake of
brevity, the full list of the most highly-cited research papers on
anomalies and fundamental generated by our search of Google Scholar
can be obtained from the authors directly. At the top of the list,
the ten papers with the highest average number of citations per
year are: 1) Jegadeesh and Titman (Journal of Finance, 2001),
Profitability of momentum strategies: an evaluation of alternative
explanations. 2) Hong, Lim, and Stein (Journal of Finance, 2000),
Bad news travels slowly: size, analyst coverage, and the
profitability of momentum strategies.
5
The keyword search on SSRN included separate searches based on
the following key words in the title or abstract of papers posted
on SSRN: accounting anomaly, fundamental analysis, fundamental
accounting, valuation fundamental, accounting inefficiency, market
inefficiency, earnings drift, price multiple, book market equity,
accruals anomaly, and accounting reaction. We also use the
bibliographic references in these papers to identify other recent
papers on accounting anomalies and fundamental analysis that were
not captured by our initial keyword searches on SSRN.6
The bibliographic references contained in each paper are also
used to classify related research papers and topics. This analysis
is discussed in the next sub-section.
13
3) Diether, Malloy and Scherbina (Journal of Finance, 2002),
Differences of opinion and the cross section of stock return. 4)
Zhang (Journal of Finance, 2005), The value premium. 5) Chan, Chan,
Jegadeesh, and Lakonishok (Journal of Business, 2006), Earnings
quality and stock returns. 6) Lewellen (Journal of Financial
Economics, 2004), Predicting returns with financial ratios. 7)
Zhang (Journal of Finance, 2006), Information uncertainty and stock
returns. 8) Xie (Accounting Review, 2001), The mispricing of
abnormal accruals. 9) Richardson, Sloan, Soliman, and Tuna (Journal
of Accounting and Economics, 2005), Accrual reliability, earnings
persistence and stock prices. 10) Vuolteenaho (Journal of Finance,
2002), What drives firm-level stock returns?
Of the 165 papers, there are 54 papers published in accounting
journals. The 10 papers published in accounting journals with the
highest average citations per year are: 1) Xie (Accounting Review,
2001), The mispricing of abnormal accruals. 2) Richardson, Sloan,
Soliman, and Tuna (Journal of Accounting and Economics, 2005),
Accrual reliability, earnings persistence and stock prices. 3)
Hirshleifer and Teoh (Journal of Accounting and Economics, 2003),
Limited attention, information disclosure, and financial reporting.
4) Khan (Journal of Accounting and Economics, 2008). Are accruals
mispriced evidence from tests of an intertemporal capital asset
pricing model. 5) Mashruwala, Rajgopal, and Shevlin (Journal of
Accounting and Economics, 2006), Why is the accrual anomaly not
arbitraged away? The role of idiosyncratic risk and transaction
costs. 6) Fairfield, Whisenant, and Yohn (The Accounting Review,
2003), Accrued earnings and growth: implications for future
profitability and market mispricing. 7) Beneish, and Vargus (The
Accounting Review, 2002), Insider trading, earnings quality, and
accrual mispricing. 8) Desai, Rajgopal, and Venkatachalam (The
Accounting Review, 2004), Valueglamour and accruals mispricing: one
anomaly or two? 9) Pincus, Rajgopal, and Venkatachalam (The
Accounting Review, 2007), The accrual anomaly: international
evidence. 10) Bartov, Radhakrishnan, and Krisy (The Accounting
Review, 2007), Investor sophistication and patterns in stock
returns after earnings announcements. 2.2 Organizing the
literature: common citations to prior work In the previous
sub-section we used citation analysis of both published papers and
working papers to let the market for academic research reveal which
research papers on accounting anomalies and fundamental analysis
have attracted the attention of other researchers and therefore had
an influenced on the subsequent literature. To14
complement this citation analysis, we organize the literature by
identifying clusters of research papers that have overlapping
references of prior research studies. In order to identify clusters
of papers and topics, we look for common citation patterns across
research papers. We start with the sample of highly-cited papers in
section 2.1 and then gather all citations from these papers to
other research papers. Each unique cited research paper is given an
identifying code. 7 After coding each cited paper, we perform a
k-means cluster analysis of overlapping citations from papers in
our main sample. We limit the number of possible clusters to less
than six to create a tractable mapping of the literature. The
cluster analysis reveals four main clusters of overlapping
citations to common sets of prior papers. Upon examination of
papers in the four main clusters, we assign the clusters the
following labels: Fundamental Analysis, Accrual Anomaly,
Underreaction to Accounting Information including PEAD, Pricing
Multiples and Value Anomaly. These four main categories largely
span the literature. In addition, the four clusters include
subcategories of related studies such as investment anomalies
(falling within the Accruals Anomaly cluster), return momentum
(falling within the Underreaction to Accounting Information
cluster), and information uncertainty (as it relates to
Underreaction to Accounting Information).8
7
This coding process was partially automated and, as a result,
was subject to some errors as some papers in our sample cite the
working paper version of a study, while other papers include a more
upto-date citation of the published version of the same study. In
addition, there are also possible transcription errors by both
authors of the papers and by us in tabulating references to create
the citation database.8
Again, for the sake of brevity, the full tabulation of papers
within each cluster are available from the authors upon
request.
15
3. Practitioners and academics opinions on anomalies/fundamental
analysis In addition to our citation analysis of high impact
researcher papers on accounting anomalies and fundamental analysis,
we supplement this with views from the academic and practitioner
communities. In this section, we highlight some of the key
responses received from the academic and practitioner respondents
to the questionnaire. Throughout the rest of our survey, we also
attempt to weave the respondents insights into our review of the
literature (section 4), and into our suggestions for research
(section 6). Past and future demand for research on accounting
anomalies and fundamental analysis potentially is partially
influenced by what is happening in practice. Therefore, to assess
the relevance of past research and help inform directions for
future research, we surveyed investment professionals and academics
to gain a better understanding of how they view the state of the
art on the fundamental analysis and anomalies. Moreover, we wanted
to document any differences in opinions on research between these
two major constituents. Finally, we wished to assess the awareness,
demand for, and use of academic research on accounting anomalies
and fundamental analysis. 3.1 Practitioner questionnaire To survey
the opinions of investment professionals, we worked in cooperation
with the market research group of the CFA Institute to construct
and administer a mini-survey of investment professionals. We
focused on the broad topic of academic research on investment
strategies, accounting anomalies and fundamental analysis. We
constructed the survey questions in order to capture how investment
professionals apply fundamental analysis and other quantitative
techniques in their daily job activities and how academic research
informs their practice. In addition, we included
16
questions about the sources and uses of research information
(including academic research) for their daily job activities. The
market research team from the CFA Institute provided suggestions on
the format of the questions that would maximize the likelihood and
usefulness of survey responses. In spite of our interest to obtain
additional information about the demographics of the practitioner
respondents, the CFA Institute market research team had concerns
about collecting detailed demographic information from respondents.
As a result, the CFA Institute survey did not capture detailed
demographic information from the practitioner respondents. In
addition, we had to work within the CFA resource constraint which
likely limited the final response rate and affected the overall
survey structure. Once the CFA Insitute survey was distributed, we
used a similar format for the academic survey. The practitioner
survey was administered and distributed by the CFA Institute via
e-mail on January 26, 2009. A reminder e-mail was sent to
non-respondents February 10, 2009 and the survey closed on February
12, 2009. The population from which the sample was drawn consisted
of all active members of the CFA Institute, excluding those without
a valid e-mail address and those that requested not to be sent
e-mails or surveys. The sample was generated using a stratified
random sampling technique; this produced a representative sample of
6,000 members to receive the survey based on key demographics (in
this case, region and years holding the CFA charter). The
distribution of the survey sample across these two areas is shown
in the chart below. There were 201 usable responses were obtained,
giving an overall response rate of 3.4%. 3.2 Academic questionnaire
In order to benchmark and contrast the practitioners opinions, we
sent the questionnaire described in section 3.1 to a set of
academics who work and teach in the
17
field of financial analysis. The sample of academics was
identified by randomly selecting: (i) 40 active researchers whose
names appears in the academic references listed at the end of this
paper, and (ii) 40 accounting academics who teach financial
statement analysis (FSA) classes to MBA students. The sample of FSA
teachers was identified from a Google search using the combined
search terms: MBA, Financial Statement Analysis, and Syllabus.9 The
e-mail questionnaire was sent out to the sample of academics in May
and June of 2009. The cutoff for the academics responses was June
30, 2009. As of that date, 63 out of 80 (79%) of the academics in
the sample responded to the survey questions. The number of
academic respondents for each question is listed in Table 1. The
high response rate likely resulted from the fact that the e-mailed
survey directly identified the purpose of the survey (i.e., for the
upcoming Journal of Accounting and Economics Conference) as well as
the likely familiarity of the respondents with the names of the
accounting academics who directly distributed the e-mail survey.
3.3 Analysis of outcomes of survey questions Table 1 provides a
summary tabulation of the responses to each of the survey
questions. The samples consist of (i) 201 practitioner responses to
the questionnaire, and (ii) 63 academic responses to the
questionnaire. The test of difference across the sample mean for
each answer is calculated using a chi-square test of populations of
unequal size and unequal variance. The p-values are adjusted using
Cochran-Coxs approximation of the degrees of freedom for the
unmatched samples.
9
Additional factors influencing the selection of the sample of
FSA teachers includes: (a) the availability of the FSA teachers
valid e-mail address as generated from the Google search criteria,
and (b) the ranking of the FSA teachers website/web presence as
generated by Google (we sequentially gathered e-mail addresses
based on the appearance of web hits generated from the original
Google search criteria).
18
While there are many consistent responses across the sample of
practitioners and academics, we wish to highlight and analyze some
of key differences in views across the two samples of respondents.
Specifically, Question 1 of the survey asked Which risk model is
most appropriate for risk calibration of an equity trading
strategy? There is a large gap between the opinions of academics
and practitioners. While 55% of academics recommend some variation
of the Fama-French 3-factor model, only 29% of practitioners
recommended this approach. The largest fraction of practitioners
(35%) recommended the use of a CAPM model with industry and size
adjustments, while only 7% of academics recommended this approach.
This
observation suggests a striking difference between how academics
and practitioners assess risk. We revisit this issue directly in
section 5 and point to this issue in our suggested directions for
future research in section 6. Another area of major difference of
opinion arises in Question 4 of the survey which focuses on which
techniques had been used and generated successful outcomes for
equity trading strategies. In this area, there are large
differences of opinion in the success of various strategies over
the past decade. While 61% of practitioner respondents claimed that
earnings or cash flow momentum was successful, only 22% of academic
respondents believed that this type of strategy was successful.
Similarly, 57% of practitioner respondents claimed that growth
strategies were successful, while only 22% of academic respondents
believed that growth strategies were successful, and 56% of
respondents claimed that value strategies were successful. On the
other hand, 70% of academic respondents believed that accounting
quality was a successful strategy over the past decade which far
exceeds the 41% of practitioner respondents who believe that this
signal was successful over the same period. These differences in
opinions point to possible differences in: (i) how expected returns
and risks are19
measured, (ii) how trade impact and transactions costs are
quantified and accounted for in trading models, and (iii) how
research data differ across academics and practitioners. We
highlight these issues in section 5 and 6 of this paper and suggest
ways to close the gap between academic and practitioners in the
treatment of risk, trade impact, transactions costs, and data. 4.
Overview of Key Research Papers Our organizing framework highlights
how external investors use accounting information to forecast a
firms future prospects including future earnings, cash flows, risk
and returns. Overall, we view forecasting as the fundamental
principle underlying academic research on accounting anomalies and
fundamental analysis. Given the large number of published and
working papers written since the year 2000, we also attempt to
provide additional structure to the literature by classifying
papers into related research clusters. As discussed in section 2 of
this survey, our citation analysis generates four main clusters of
research topics: Fundamental Analysis, Accruals Anomaly (including
related investment anomalies), Underreaction to Accounting
Information (with a particular emphasis on post-earnings
announcement drift (PEAD)), and Pricing Multiples/Value Anomaly. We
survey key studies in these main areas that have been circulated
since the year 2000. For each area, we highlight various issues
including risk versus mispricing, transactions costs, and limits to
arbitrage that capture the essence of the debate in the
literature.
4.1 Forecasting Framework The organizing framework for our
survey is that investors forecast the level and risk of a firms
free cash flows and then discount the free cash flows to estimate
the value of claims to a firm. If the estimated value and the
observed market value of
20
these claims diverge, then an investor must decide if current
and forecasted future transactions costs and forecasted arbitrage
risk point to a profitable arbitrage opportunity. Finance,
valuation and financial statement analysis textbooks (see, for
example, Penman, 2009, Easton et al., 2009) often use discounted
free cash flow analysis as the basis for determining firm value:
Total Firm Value0 = E0 [ t fcft /t ] (1)
where is the factor used to discount future total free cash
flows (fcf) generated by the firm in periods t=1 . To derive this
value, investors must forecast both future free cash flows and the
risk of these cash flows.10 A future free cash flow (fcf) to the
firm equals its operating profits not used to grow operating asset
(see, for example, Penman and Zhang, 2006, and Easton et al.,
2009).11 Therefore, as long as no components of operating income or
net operating assets are booked directly to equity, fcf in period t
can be is defined as: fcft = oit - noat (2)
where oit equals operating income and noat equals the change in
net operating assets in period t. Alternatively, if the unknown
variable of interest is operating income (oit), then equation (1)
can be restated as: oit = fcft + noat (2)
10
Our forecasting framework focuses total cash flows generated by
the firm (or enterprise) that are then available to all providers
of capital (debt and equity). However, insights from our framework
also flow though to analyzing equityholders claims.11
Operating income available to the enterprise is also commonly
referred to as Net Operating Profit After Tax (see, for example,
Easton et al, 2009).
21
The next question is what determines operating income in period
t? By definition, operating income is: oit = rnoat*noat-1 (3)
where rnoat represents the expected and unexpected flows
generated by beginning of period net operating assets (noat-1). The
recent accounting literature emphasizes that accounting and other
sources of information help investors develop forecasts of the
level (and risk) of the firms future free cash flows and operating
income. Based on equation (2), it can be seen that both operating
income and change in net operating assets are determinants of free
cash flow. Furthermore, equation (3) highlights the role of initial
level net operating assets in determining the level of operating
income over a period. If one uses a simple 1-period forecasting
model and the insights from equations (2) and (3), then next
periods free cash flows or operating income are likely to be
determined by this periods operating income (oit), change in net
operating assets (noat), initial net operating assets (noat-1), and
a Kx1 vector of other current period information (OTHERt):
Et[fcft+1] = g{oit , noat, noat-1, OTHERt} Et[oit+1] = f{oit , noat
, noat-1, OTHERt} (4) (4)
where f{} and g{} are (possibly non-linear) functions that help
forecast future-period flows based on current-period accounting and
non-accounting information.12 The set of non-accounting information
can include information such as current market prices (Pt) and
changes in current market prices (rt) of the firms securities, and
the12
Penman and Zhang (2006) also present a forecasting model for
future operating income, but apply more restriction (less general)
assumptions about the link between current and future accounting
items.
22
accounting and non-accounting information of other firms
(especially related firms such as the same industry as the primary
firm of interest). Equations (4) and (4) can be further generalized
based on the observations that operating assets and operating
income can be: (i) decomposed into their constituent components,
and (ii) these constituent components can provide additional
forecasting power for future cash flows and operating income beyond
the aggregated accounting numbers. Therefore, more generalized
one-period-ahead prediction models of free cash flow and operating
income can be expressed as: Et[oit+1] = F{OICt , NOACt, NOACt-1,
OTHERt} Et[fcft+1] = G{OICt , NOACt, NOACt-1, OTHERt} (4-G)
(4-G)
where OICt is a Mx1 vector of the constituent components of
operating income (oit) and NOACt is a Nx1 vector of the constituent
components of net operating assets (noat) such that oit = m=1,M
oimt , noat = m=1,N noant , and noat-1 = m=1,N noant1.
Again, F{} and G{} are functions that help forecast
future-period flows based on the
vectors of current-period accounting and non-accounting
information. Equation (1) highlights that the value of the firm
(and changes in this value) are derived from forecasts (and changes
in forecasts) of future free cash flows and the risk of these cash
flows. Therefore, the forecasting equations (4-G) and (4-G) suggest
that accounting and non-accounting information in period t have the
ability to predict one-period-ahead security returns (i.e.,
security returns due to risk or mispricing or possibly both).
Therefore, our forecasting framework can be applied to security
returns as: Et[rt+1] = H{OICt , NOACt, NOACt-1, OTHERt}23
(5-G)
where H{} is a function that forecasts next-period security
returns based on currentperiod accounting and non-accounting
information. Again, this generalized framework allows for the
non-accounting information set to include market information such
as current market prices (Pt) and changes in current market prices
(rt) of the firms securities. In addition, forecasts of future
returns can capture both risk and mispricing. In the following
sections, we use these general forecasting equations to present the
forecasting concepts that underlie most recent research studies on
fundamental analysis and accounting anomalies.
4.2. Fundamental Analysis Penman (2004) defines fundamental
analysis as the analysis of information that focuses on valuation.
Fundamental analysis is obviously of critical importance to
investors as they care about how much to pay for an investment and
for how much to sell it. As noted by Kothari (2001), an important
motivation for fundamental analysis research and its use in
practice is to identify mispriced securities relative to their
intrinsic value for investment purposes. Hence, the majority of the
fundamental analysis research in accounting seeks to come up with
better forecasts of earnings or stock returns to assist the
valuation or identification of mispriced securities. As a result,
there is some overlap between research on fundamental analysis and
accounting anomalies discussed later. The beauty of fundamental
analysis is that it is of interest to the believers and
non-believers of market efficiency, as fundamental analysis
research can help us understand the determinants of value which
assists in informed investment decisions and the valuation of
non-publicly traded assets, for which market inefficiency is not a
necessary condition.24
In recent years, fundamental analysis research has generally
focused on forecasting earnings, forecasting stock returns or
estimating a firms cost of capital. Therefore, research studies
based on fundamental analysis can be viewed through the lens of
equations (3G), (3G) and (4G): Et[oit+1] = F{OICt , NOACt, NOACt-1,
OTHERt}, and Et[fcft+1] = G{OICt , NOACt, NOACt-1, OTHERt}, and
Et[rt+1] = H{OICt , NOACt, NOACt-1, OTHERt} Prior to 2000, there
was a flurry of research that used accounting variables (and ratios
of these variables) to predict future returns (see, for example, Ou
and Penman, 1989, Lev and Thiagarajan, 1993, and Abarbanell and
Bushee, 1997). In general, these studies either explicitly or
implicitly took the ideas behind the above equations to develop
prediction models of future returns. The direction of more recent
on fundamentals-based return prediction has focused on context
specific or refined sub-samples of firms where with higher
likelihood of market imperfections which might increase the
ultility of fundamental analysis. For example, Piotroski (2000)
focuses on high B/M firms and demonstrates that, within the high
B/M sample firms, those firms with the strongest fundamentals earn
excess returns that are over 20% greater than firms with the
weakest fundamentals. Similarly, Beneish, Lee and Tarpley (2001)
use a two-stage approach towards financial statement analysis. In
the first stage, they use market based signals to identify likely
extreme performers. In the second stage, they use fundamental
signals to differentiate between winners and losers among the firms
identified as likely extreme performers in the first stage. These
results suggest the possible benefits of carrying out fundamental
analysis in specific sub-samples of firms whose securities are more
likely to be mispriced.25
4.2.1. Under what circumstances do stock prices deviate from
fundamental value? Recent papers attempt to shed light on the type
of stocks in which there will be a large wedge between fundamental
value and market prices. For example, Baker and Wurgler (2006)
define a number of investor sentiment proxies at the aggregate
level. These include share turnover, the closed-end fund discount
and first-day IPO returns. They find that stocks that are difficult
to arbitrage (e.g., small, highly volatile ones) exhibit the
maximum reversals in subsequent months when investor sentiment is
high in a given period. Similarly, Zhang (2006) argues that stocks
with greater information uncertainty (e.g., those which are small
and have low analyst following) exhibit stronger statistical
evidence of mispricing in terms of return predictability based on
ex ante book-to-market ranking cross-sectional regressions. Nagel
(2005) also provides evidence that the mispricing is greatest for
stocks where institutional ownership is lowest; here institutional
ownership is a proxy for the extent to which short-selling
constraints bind (the assumption is that short-selling is cheaper
for institutions). 4.2.2. Putting additional structure on
forecasting activity to derive valuation models As discussed in
Kothari (2001), the residual income model (Ohlson, 1995) has had a
sizable impact on valuation approaches and the application of
fundamental analysis in both academics and practice (see, also
Claus and Thomas, 2001; Gebhardt, Swaminathan and Lee ,2001; Easton
et al., 2002; Baginski and Wahlen, 2003). Ohlson (2005), Ohlson and
Gao (2006), Ohlson (2009) and Easton (2009) provide excellent
overviews of some of the technical and analytical advances in
accountingbased valuation models over the past decade. Within our
forecasting framework, the Ohlson (1995) model and its subsequent
extensions use various simplifying assumptions to place
additional26
structure on the forecasting equations outlined in section 4.1.
These structured forecasting equations are then used to create
valuation models. All valuation models are theoretically the same
and are merely transformations of the discounted free cash flow
model (or a discounted dividend model) with varying assumptions and
data requirements. However, the applicability and utility of a
given valuation model depends on the plausibility of the
assumptions underlying the model and the quality and availability
of empirical data required by the model. Recent important advances
in this area include both refinements of the valuation models and
application of these models. A particularly-interesting example of
a recent valuation refinement is the OJ model presented in Ohlson
and JuettnerNauroth (2005). This model focuses on abnormal earnings
growth (the OJ model) with no clean surplus accounting requirement
that is generally found in previous models (such as the Ohlson,
1995). The OJ model differs from a traditional residual income
model by specifying earnings per share as the fundamental
forecasting benchmark. The proliferation of valuation models has
spawned a growing debate about the superiority, applicability and
empirical properties of various models (see, for example, Francis
et al. 2000; Lundholm and OKeefe 2001; Penman 2001; Courteau et al.
2001; Richardson and Tinaikar 2004; Juettner-Nauroth and Skogsvik,
2005; Chen, Long and Shelly, 2008). These studies have compared the
bias and accuracy of different valuation models. Not surprisingly,
the various benchmarking studies conclude that different
implementation techniques and the different underlying assumptions
of various valuation models lead to different abilities of the
models to predict future returns.
27
Existing studies generally argue that no single accounting-based
valuation model has dominant empirical properties. However, the OJ
model has some advantages over traditional the residual income
models. Specifically, Ohlson (2005) analyzes a number of situations
and concludes that truncation errors of terminal streams are
smaller and less frequent under the OJ model compared to a
traditional residual income model which relies on book equity as a
performance benchmark. This implies that a finite-term OJ model
will likely outperform a finite-term residual income model. Ohlson
(2005) shows that capitalized earnings under the OJ model better
capture the market value of equity than the book value of equity in
a world of conservative accounting. Chen et al. (2005) also argue
that the OJ model is better able to handle the dirty accounting
systems observed in the real world because the OJ model does not
reply on the clean surplus assumption which is fundamental to the
residual income model. Ali et al. (2003) compare the ability of
different valuation measures to predict future abnormal returns.
They find that all of the valuation measures, including the OJ
model, have the ability to predict future returns, and that the
incremental contribution of the OJ model is significant in
regressions of future returns on the value-price and B/M ratios.
These findings suggest that the OJ model has some ability to
predict future abnormal stock returns. 4.2.3. Determining Implied
Cost of Capital Using Fundamentals Another approach investors use
to forecast expected future returns is model and then estimate a
firms cost of capital. Most empirical asset-pricing studies tend to
rely on realized stock returns as a proxy for ex ante expected
stock returns because expected stock returns are not directly
observable. However, these estimates are
28
problematic because the estimates are imprecise (see, for
example, Fama and French, 1997). To address some of the limitations
of asset-pricing methods used to determine a firms cost of capital,
recent accounting and finance studies (e.g., Claus and Thomas,
2001; Gebhardt, Lee, and Swaminathan, 2001; Pstor, Sinha, and
Swaminathan, 2007, and Easton, 2009) propose an alternative
approach to estimate expected returns: the implied or imputed
equity cost of capital. The implied equity cost of capital of a
company is the internal rate of return (IRR) that equates the
companys stock price to the present value of all expected cash
flows available to equity-holders. In other words, it is the
discount rate that the market uses to discount the expected cash
flows of the company This implied cost of capital approach relies
heavily on forecasting a firms future cash flows. From a practical
perspective, much of the work on implied equity cost of capital
uses analysts forecasts of future earnings (rather than free cash
flow to equity holders) as the key forecasting variable. The major
advantage of the implied cost of capital approach to risk
measurement is that it does not have to rely on noisy realized
returns or on a specific asset pricing model other than that
investors use a discounted future cash flows (dividends) to derive
fundamental value. Therefore, the implied cost of capital approach
applies standard fundamental valuation techniques and uses observed
market prices and forecasts of earnings (cash flows) to derive the
markets assessment of the equity risk (cost of capital) of a firm.
For the firm as a whole, we can apply valuation equation (1) to a
situation where investors observe total firm value in period t,
forecast future FCFs, and then solve for r=-1: Total Firm Valuet =
t FCFt /()t Reverse Engineer Equation 1
29
Given the simple and practical foundations of the implied cost
of capital approach, it has been used in many recent studies
related to empirical asset pricing (e.g., Chava and Purnanandam,
2007; Chen and Zhao, 2007; Pstor, Sinha, and Swaminathan, 2007) and
also applied in other settings where cost of capital is an
important market outcome (e.g., Francis, Khurana, and Pereira,
2005; Hail and Leuz, 2006). On the other hand, other recent studies
suggest that the empirical outputs derived from the implied cost of
capital approach are noisy, flawed or biased. Several studies
attempt to correlate ex ante implied cost of capital of a firm with
a companys observed ex post stock returns (e.g., Easton and
Monahan, 2005; Guay, Kothari, and Shu, 2005). Overall, these
studies find that the ex ante implied cost of capital has a low
association with future realized returns and, therefore, the
implied cost of capital estimates are poor measures of a firms
expected equity returns. Easton and Monahan (2005) show that
implied cost of capital estimates are negatively correlated with ex
post observed stock returns and they suggest that the problem
arises from the quality of analysts earnings forecasts used to
calculate the implied cost of equity capital. There are other
potential problems with implied cost of capital estimates that rely
on analysts forecasts of future earnings. For example, analysts
earnings forecasts may not capture the markets forecasts of future
cash flow. While analysts earnings forecasts are widely followed,
they also appear to be inherently biased. There is a long
literature that suggests that analysts forecasts are biased at
various horizons (see, for example, Richardson, Teoh and Wysocki,
2003 and Easton and Sommers, 2007). In general, analysts medium and
long-horizon earnings forecasts tend to be too optimistic. Also,
analysts tend to cover relatively few firms and available forecasts
tend to be for near-term earnings such as earnings for the coming
quarter or fiscal year. There are also apparent biases in which
firms are covered by analysts, for30
example, financially distressed firms are often not covered or
are dropped by analysts (Deither, Malloy, and Scherbina, 2002). To
address some of the limitations of the implied cost of capital
approach, Hou, van Dijk, and Zhang (2009) outline a novel method to
estimate a firms implied equity cost of capital. They build on Fama
and French (2000, 2008), Hou and Robinson (2006), and Hou and van
Dijk (2008) and apply a cross-sectional model to forecast the
earnings of individual firms. In essence they apply fundamental
analysis techniques to forecasts future income using a simplified
version of model (3-G): Et[oit+1] = F{OICt , NOACt, NOACt-1,
OTHERt} Their approach uses a cross-sectional model to capture
across-firm variation in future profitability using
publicly-available accounting (and other) information at the time
of the forecast. Hou et al (2009) then use these earnings forecasts
as inputs for a discounted residual income model to estimate
implied cost of capital. An advantage of this forecasting
methodology is that it does not rely upon analysts forecasts to
generate cost of capital estimates. An interesting aspect of this
approach is that is has foundations in the fundamental analysis
literature and it fits well with our view that the common principle
underlying this literature is forecasting. Following Easton and
Monahan (2005), Hou et al. (2009) assess the reliability of their
model-based implied cost of capital estimates by testing their
correlation with future observed stock returns. Hou et al. (2009)
show that the cost of capital estimates are significantly
positively correlated with future stock returns. They also show
that the greater reliability of their forecasting-model-based
estimates of implied cost of capital arises from the improved
earnings forecasts generated by their cross-sectional model.
Therefore, there appears to be promise in using this type of
forecasting31
methodology for future research on implied cost of capital and
other fundamentalsbased research. 4.3. Accruals Anomaly The
accruals anomaly originally documented by Sloan (1996) suggests
that firms with high (low) reported accruals in a fiscal period
tend to have abnormally low (high) stock returns in subsequent
periods. Accruals are non-cash accounting items which are added to
operating cash flows to generate a firms current reported
accounting income. Sloans original paper hypothesizes that
investors naively fixate on bottom line income and they do not
appear to understand that: (i) earnings is composed of both
operating cash flows and (non-cash) accruals, and (ii) the cash
flow and accrual components of earnings have different abilities to
predict future earnings. In particular, innovations to accruals
tend to reverse in future periods and investors do not appear to
understand this time-series property when they develop their
forecasts of future earnings and cash flows and therefore set
current stock prices. Sloan (1996) defines accruals using changes
in balance sheet items and measures total accruals (ACC) as changes
in non-cash working capital minus depreciation expense scaled by
average total assets: ACC (CA CASH) (CL STD TP) DEP (6)
where CA is the change in current assets (Compustat annual item
4), CASH is the change in cash or cash equivalents (Compustat
annual item 1), CL is the change in current liabilities (Compustat
annual item 5), STD is the change in debt included in current
liabilities (Compustat annual item 34), TP is the change in income
taxes
32
payable (Compustat annual item 71), and DEP is depreciation and
amortization expense (Compustat annual item 14). Sloans main
finding that firms with high (low) reported accruals tend to have
abnormally low (high) future stock returns has inspired a vast body
of follow-up work that attempts to understand the underlying causes
of this anomaly. Sloans original paper hypothesizes that nave
investor fixation on bottom line earnings and that investors do not
understand the differential persistence of the cash flow and
accrual components of earnings. In particular, innovations to
accruals tend to reverse in future periods and investors do not
appear to understand this time-series property when they develop
their forecasts of future earnings and cash flows. Using our
forecasting framework, we restate the accruals anomaly as follows:
Investors attempt to forecast a firms operating performance using
current reported earnings and changes in net operating assets to
generate these forecasts. However, Within the original Sloan (1996)
framework, equations (3-G) and (4-G) are used in reduced form where
oit cfot and accrualst are the only components of OICt and NOACt
used in the forecasting exercise. Therefore, equations (3-G) and
(4-G) are reduced to: Et[oit+1] = F{cfot , accrualst } Et[rt+1] =
H{cfot , accrualst } where cfot captures operating cash flows in
period t and accruals captures a specific subset of change in net
operating assets in period t. The evidence presented in Sloan
(1996) suggests that, investors do not properly weight the
components of oit (namely, cfot and accrualst) in generating their
forecasts. Much of the follow-up work on Sloan
33
(1996) essentially captures refinements to the forecasting of
future earnings, cash flows, risk and returns. 4.3.1. Possible
(non-risk) explanations for the accruals anomaly The first category
of papers examines the reasons for the accrual anomaly. Sloans
original paper hypothesizes that nave investor fixation on bottom
line earnings and that investors do not understand the differential
persistence of the cash flow and accrual components of current
earnings in helping forecast future earnings and cash flows. Recent
examples of papers that directly evaluate whether this
hypothesis is empirically supported include Ali, Hwang, and
Trombley (2000), Zach (2005), Kothari, Lutskina, and Nikolaev
(2006), and Hirshleifer, Hou, Teoh, and Zhang (2004). The first
three of this set of papers do not find support for the investor
fixation hypothesis. More specifically, Ali, Hwang, and Trombley
(2000) find that abnormal returns are not lower for that are
followed by sophisticated investors who might better understand the
properties of accruals (such firms include the largest firms, those
with high analyst following, and those with high institutional
ownership). They also document that the association between
accruals and future stock returns is not a function of transaction
costs, transaction volume, or stock price. Consequently, they
conclude that the nave investor fixation hypothesis cannot explain
the accrual anomaly. Employing different sets of analyses, Zach
(2005) concludes similarly. Finding no evidence of accrual
reversals or overreaction, he argues that investor fixation could
not be the reason for the accrual anomaly. Kothari, Lutskina, and
Nikolaev (2006) find that the agency theory of overvalued equity,
not investors fixation on accruals, explains the accrual anomaly.
They state that overvalued firms have incentives to remain
overvalued, whereas undervalued firms have no incentives to prolong
their undervaluation, which results in an asymmetric relation
between the34
accruals and past and future returns. Kothari et al. interpret
analysts greater degree of optimism and the distortion of insider
trading and investment financing in high accrual firms to be
consistent with the agency theory of overvalued equity. Though the
findings in the aforementioned papers do not support Sloans
investor fixation hypothesis, Hirshleifer, Hou, Teoh, and Zhang
(2004) document that, limited attention of investors who focus on
accounting profitability without taking into consideration the
other factors in forecasting future cash profitability, could
explain the mispricing of net operating assets scaled by total
assets, which is consistent with the investor fixation hypothesis.
A second explanation for the accrual anomaly is offered by Xie
(2001), which finds that the anomaly is attributable to the
mispricing of discretionary accruals as a consequence of
overestimating the persistence of the discretionary accruals.
Within our forecasting framework, Xie (2001) further subdivides the
components of the NOACt vector into finer components that have
differential predictive ability for future earnings. Chan, Chan,
Jegadeesh and Lakonishok (2006) essentially replicate Sloan (1996)
and find that firms with earnings increases accompanied by high
accruals have lower future stock returns. Based on a variety of
additional analyses, they conclude that most of the evidence is
consistent with accruals capturing the earnings management
activities of the management, consistent with the findings in Xie
(2001). Richardson, Sloan, Soliman and Tuna (2005) bring a
different
perspective to the debate and argue that investors do not
understand the lower persistence of less reliable accruals, which
leads to incorrect investor forecasts of future earnings and cash
flows and to their mispricing of current accounting realizations.
Within our forecasting framework, Richardson et al (2005) use an
extended decomposition of the noat to identify components (ie, a
NOACt vector) that35
exhibit high versus low reliability in predicting future
operating income. After ranking the components of noat according to
their reliability, they find that the magnitude of the accrual
anomaly is greater for the less reliable accruals. This finding is
also consistent with that of Xie (2001) as discretionary accruals
are expected to be less reliable and therefore less persistent. Yet
more papers examine other potential reasons for the lower
persistence of accruals. On the one hand, Beneish and Vargus (2002)
find that the lower persistence of accruals is consistent with
earnings management. They document that the lower persistence of
income increasing accruals in the presence of insider selling is
partially attributable to earnings management. On the other hand,
Fairfield, Whisenant, and Yohn (2003) argue that the lower
persistence of accruals maybe due to the effect of growth on
profitability as they document that accruals covary more with
invested capital, the denominator used in the computation of
profitability, than cash flow does. Again, within our framework
they apply a version of equations (3-G) and (4-G) to capture the
broader effect of change in net operating assets as a possible
driver of the accruals anomaly. Bringing another perspective to the
earnings persistence debate, Dechow and Ge (2006), show that
earnings persistence is a function of both the sign and the
magnitude of accruals. They find that accruals increase (decrease)
the persistence of earnings compared to cash flows in high (low)
accrual firms. Dechow and Ge
document that the lower persistence of earnings in low accrual
firms is due to special items. Low accrual firms with special items
have higher future returns than other low accrual firms consistent
with investors not understanding that special items are
transitory.
36
In summary, these studies provide useful examples of how the
literature has evolved over the past decade. Overall, each paper
has attempted to provide insights into the problems that investors
have in using current accounting information to correctly forecast
future earnings, cash flows and returns. The primary explanation
for the negative relation between accruals and future stock returns
(holding aside the issue of risk) is that capital market
participants fail to correctly utilize accrual information in their
forecasts of future earnings (and cash flows). 4.3.2. Is the
accruals anomaly distinct from other anomalies? The answer to this
question is not conclusive, but the majority of the evidence
supports the view that accruals anomaly is distinct and is
incremental to other previously-documented anomalies. There is a
large list of papers that study this question with an aim to
document whether the accruals anomaly is subsumed by other
anomalies. Collins and Hribar (2000) document that the accrual
anomaly is distinct from the post-earnings announcement drift
anomaly documented by Bernard and Thomas (1989). While both
anomalies appear to be distinct in generating future stock returns,
they both have the basis of incorrect investor responses to current
accounting information to generate forecasts of future earnings,
cash flows and returns. Barth and Hutton (2004) show that the
predictive ability of accruals for future returns is not subsumed
by the predictive ability of analysts forecast revisions. In a
similar line of research, Cheng and Thomas (2006) document that the
accrual anomaly is distinct from the value-glamour anomaly.
However, Fairfield, Whisenant, and Yohn (2003) argue that accruals
anomaly is a special case of a more general growth anomaly (see
also section 4.3). They find that both accruals and growth in
long-term net operating assets (components of net operating assets)
have similar negative associations with future return on assets,
and that the market seems to overvalue them similarly.37
Related, Zhangs (2007) findings also suggest that the accrual
anomaly is attributable to investment / growth information
contained in accruals measured as the covariation between accruals
and employee growth, rather than investors misunderstanding of the
implications of accruals for earnings persistence. He finds that
for industries and firms where accruals and employee growth covary
more, accruals have a stronger power in predicting future returns.
Challenging the conclusion that growth is the sole explanation for
the accruals anomaly, Richardson, Sloan, Soliman, and Tuna (2006)
find that the temporary accounting distortions also play an
important role in explaining the lower persistence of accruals in
addition to growth-based explanations. Again, this debate revolves
around the issue of which factors influence or distort how
investors generate their forecasts of future earnings, cash flows
and returns. This debate leads to our next subsection which deals
with the relation between the accruals anomaly and the
growth/investment anomaly, which is identified as a sub-category of
research related to accounting accruals. 4.3.3. Relation between
accruals anomaly and investment anomaly Over the past decade, there
have been numerous studies investigating the association between a
firms corporate asset investment and disinvestment actions and
future stock returns. The findings suggest that corporate events
associated with the expansion of a firms scale and its assets
(i.e., acquisitions, public equity offerings, public debt
offerings, and bank loan initiations) tend to be followed by
periods of abnormally low long-run stock returns. On the other
hand, corporate events associated with decreases in the scale of
the firm and asset contraction (i.e., spinoffs, share repurchases,
debt prepayments, and other payouts) tend to be followed by periods
of
38
abnormally high long-run stock returns.13 In this section, we
discuss the links between this literature and the accruals
literature. In addition to these long-run event studies, other work
documents a negative relationship between various forms of
corporate investment and the cross-section of returns. For example,
an increase in accruals, capital investment, and sales growth
rates, and external financing tends to be negatively correlated
with subsequent stock returns. Recent studies include Fairfield,
Whisenant, and Yohn (2003), Richardson and Sloan (2003), Titman,
Wei and Xie (2004), and Hirshleifer, Hou, Teoh, and Zhang (2004).
More recent research (see, for example, Richardson, Sloan, Soliman,
and Tuna, 2006, and Cooper, Gulen and Schill, 2008) presents
additional evidence on the debate over whether growth is fairly
priced in the cross-section of future stock returns by introducing
and fine-tuning measures of firm growth. In addition, these studies
attempt to understand the underlying sources of firm-level growth
effects. The refined measures of firm growth are motivated by the
observation that prior studies on the effects of growth on returns
use components of a firms total investment or financing activities,
and often ignore the larger picture of potential total asset growth
effects of comprehensive firm investment and disinvestment. Cooper
et al (2008) use a general measure of firm asset growth, the
year-onyear percentage change in total assets and a panel of U.S.
stock returns. They document a negative correlation between firm
asset growth and subsequent firm
13
References include acquisitions (Asquith, 1983; Agrawal Jaffe,
and Mandelker, 1992; Loughran and Vijh, 1997, Rau and Vermaelen,
1998), public equity offerings (Ibbotson, 1975; Loughran and
Ritter, 1995), public debt offerings (Spiess and Affleck-Graves,
1999); bank loan initiations (Billet, Flannery, and Garfinkel,
2006), spinoffs (Cusatis, Miles, and Woolridge, 1993; McConnell and
Ovtchinnikov, 2004), share repurchases (Lakonishok and Vermaelen,
1990; Ikenberry, Lakonishok, and Vermaelen, 1995), debt prepayments
(Affleck-Graves and Miller, 2003), and dividend initiations
(Michaely, Thaler, and Womack, 1995).
39
abnormal returns. They find that asset growth remains
significant in explaining future stock returns that include
book-to-market ratios, firm capitalization, short- and longhorizon
lagged returns, and other growth measures (including growth in
sales from Lakonishok, Shleifer, and Vishny (1994), growth in
capital investment from Titman, Wei and Xie (2004), accruals from
Sloan (1996), and a cumulative accruals measure (net operating
assets) from Hirshleifer, Hou, Teoh, and Zhang (2004).14 In a
related line of research that focuses on the association between
investments and future stock returns, Titman, Wei, and Xie (2004)
find that companies that increase their investments the most have
significantly lower returns than their benchmark over the next five
years. Anderson and Garcia-Feijoo (2006) document similar findings,
namely the future returns are significantly lower for firms that
have accelerated their investments. Titman et al. (2004) interpret
this finding as consistent with investors underreaction to
increased investments for empire building purposes, after
documenting that the abnormal returns are concentrated around
earnings announcements. Dechow, Richardson, and Sloan (2008)
document that the accruals anomaly subsumes the external financing
anomaly and offer an alternative interpretation to the investment
anomalies literature. Dechow et al. (2008) document that the
accruals anomaly subsumes the external financing anomaly and they
find that it is the use of the external financing proceeds that
predicts future returns, rather than raising or distributing
financing as suggested by the earlier studies and reviewed by
Ritter (2003). Dechow et al. (2008) also find that firms with high
accruals have lower
14
Richardson, Sloan, and Tuna (2006) show that the cumulative
accrual measure is simply an algebraic transformation of the change
in net operating assets documented in Richardson et al. (2005).
Thus, the claim that the cumulative accruals measure captures past
changes in net operating assets is misleading.
40
earnings persistence and lower future stock returns even if they
use internally generated funds instead of raising external
finances, unlike firms that expend the externally raised cash or
swap equity for debt, which are not as likely to be mispriced. In
summary, the accrual anomaly literature has evolved over time to
make clear explicit links with other anomaly papers, most
noticeably the financing and investing anomalies. Recent research
suggests that the accrual anomaly actually subsumes these related
anomalies. Overall, it appears the investors have difficulty
interpreting the performance of firms that have significant changes
in net operating assets (i.e. accruals) and then using this
information to generate forecasts of future earnings, cash flows
and returns. 4.3.4. Why is the accruals anomaly not arbitraged
away? There are three potential answers to this question: (i)
because there are limits to arbitrage and large transaction costs
that prevent this, (ii) because accruals is a risk factor, and
(iii) market has not learnt yet and investors continue mispricing
the accruals. Although these answers are discussed in the context
of accruals anomaly, they also apply to other accounting anomalies.
4.3.4.1 Limits to arbitrage and transaction costs: Mashruwala,
Rajgopal, and Shevlin (2006) find that the accruals anomaly is
concentrated in firms with high idiosyncratic return volatility,
low price, and low volume, suggesting that transaction costs may
provide an obstacle for investors to trade away the accrual
anomaly. Similarly, Lev and Nissim (2006) find that the extreme
accrual firms are small, risky, and have low profitability and
hence do not attract the attention of large institutional
investors. Although some active institutional
41
investors trade based on the accrual anomaly, the magnitude of
their trades are too small to arbitrage away the anomaly. As for
individual investors, given the
characteristics of these firms, transaction costs are too high.
Consistent with the findings in Lev and Nissim (2006), Ali, Chen,
Yao and Yu (2008) find that even the mutual funds that have the
largest exposure to low accrual stocks have limited exposure,
suggesting that few actively managed funds trade on the accrual
anomaly. The ones that do are smaller, less diversified, have
higher fund flow volatility and higher fund return volatility and
earn 2.83% abnormal returns annually. This
evidence suggest that that accrual anomaly is not arbitraged
away because it is too costly for individual and institutional
investors to trade based on it. In fact, Collins, Gong, and Hribar
(2003) find that firms with high institutional ownership and that
exceed a minimum level of holdings by active institutional
investors have accruals that are less mispriced. 4.3.4.2 Are
observed future stock returns due to risk or mispricing of
information in accruals? This question is at the crux of the debate
about any stock market anomaly, and the accruals anomaly is no
exception. As expected, the academic literature is quite divided on
this issue. On the one hand, using an intertemporal CAPM based
fourfactor model, Khan (2008) finds that the cross-sectional
variation in the stock returns of extreme accrual portfolios can be
largely explained by risk. On the other hand, Hirshleifer, Hou, and
Teoh (2006) conclude that their findings are more consistent with a
behavioral explanation than a risk-based explanation for the
accruals anomaly, as their analysis documents that it is the
accrual characteristic, not the accrual factor loading, that
predicts future stock returns. In section 5 of this review paper,
we revisit
42
the risk vs. mispricing question, and propose an innovative way
to tackle it using a more comprehensive treatment of risk. 4.3.5.
Robustness and generalizability of the accruals anomaly Although a
vast majority of the papers that examine the accruals anomaly find
that the original findings in Sloan (1996) are robust in different
samples, several recent papers more directly test the implications
of potential methodological concerns, sample characteristics, or
the choice of benchmark returns on the accruals anomaly. For
example, Kraft, Leone, and Wasley (2007) find that the accruals
anomaly is not robust to the addition of omitted variables to the
Mishkin test and that such omitted variables lead to incorrect
inferences about the pricing of accruals. Zach (2005) finds that
excluding observations related to mergers and acquisitions,
divestitures, NASDAQ-listed firms, and the use of size and
book-to-market adjusted returns instead of just size-adjusted
returns reduce the abnormal returns to the accrual anomaly, but the
majority of the returns are robust. A few papers examine whether
accruals anomaly is globally generalizable. The findings from these
studies indicate that accruals anomaly is somewhat mixed. LaFond
(2005), and Pincus, Rajgopal, and Venkatachalam (2007), document
that the accruals anomaly exists outside the U.S. Pincus et al.
(2007) find that it is more likely to occur in countries where
accrual accounting is used more, when there is lower shareholder
concentration, lower shareholder protection, and if the legal
system is of common-law origin. In contrast, Leippold and Lohre
(2008) document that the
global results are sensitive to methodological choices. Finally,
Hirshleifer, Hou, and Teoh (2008) examine whether the accruals
anomaly extends to aggregate returns and find that aggregate
accruals are positively43
associated with future market returns. However, consistent with
aggregate accruals containing information about changes in discount
rates (or firms managing earnings due to aggregate undervaluation),
Hirshleifer et al. find a negative contemporaneous association
between changes in aggregate accruals and market returns. Overall,
the body of literature that follows Sloan finds that the accrual
anomaly is robust in various samples, and that it is mainly
attributable to investors inability to incorporate the implications
of discretion in accruals for the persistence of earnings in their
forecasts of future earnings. 4.4. Underreaction to accounting
information, in particular the post-earnings announcement drift
(PEAD) anomaly The past 10 years has seen expanded research on the
first documented major accounting-based market anomaly known as
post-earnings announcement drift (PEAD) (see, for example, Bernard
and Thomas 1989). The main feature of the PEAD anomaly is that
investors appear to underreact to earnings news and a firms stock
price drifts in the direction of the earnings news after an
earnings announcement. Using our forecasting framework, we the PEAD
anomaly as follows: Investors attempt to forecast a firms future
free cash flows using innovations to current reported earnings to
generate these forecasts. Et[fcft+1]= f(oit) The anomalous returns
are generated because investors do not quickly impound in prices
the information contained in oit. Recent studies attempt to explain
why the anomaly occurs and why it persists. In addition, the
literature has expanded to consider underreaction to other
corporate information and the relation to momentum
44
in stock returns. We focus our review in this section on the
PEAD literature, but readers should note that our discussion
applies to other research studying the underreaction to other
sources of accounting information (e.g. option expense disclosures,
pension footnotes, etc.). We revisit this topic in section 6.
Again, our organizing framework emphasizes the forecasting
activities of equity investors. 4.2.1. Underreaction to earnings
information: What are the reasons for PEAD? Trading by
unsophisticated investors has been proposed as a reason for the
post-earnings announcement drift (PEAD). These unsophisticated
investors do not appear to realize the implications of current
earnings for forecasts of future earnings and stock returns. For
example, Bartov, Radhakrishnan, and Krisy (2000) find that
institutional ownership is negatively associated with the magnitude
of the abnormal returns after earnings announcements and that
transaction cost proxies do not have any explanatory power
incremental to institutional ownership. Bartov et al. conclude that
individual investors trading activities drive the anomaly.
Consistent with Bartov et al., Battalio and Mendenhall (2005) find
that investors executing small trades seem to respond to a less
sophisticated signal that does not fully impound the implication of
current earnings changes for future earnings, suggesting that small
investors underlie the PEAD. Similarly, Shantikumar (2004) also
finds that small traders are more likely to underreact to earnings
surprises relative to larger traders, although larger traders also
under-react. This evidence suggests that small (and likely less
informed) traders are a driver of the PEAD phenomenon. On the other
hand, Hirshleifer, Myers, Myers, and Teoh (2008) present contrary
findings that the returns to the PEAD strategy cannot be explained
by the trading activity of the individual investors.
45
Related to investor un-sophistication, limited investor
attention on the implication of current earnings for forecasts of
future earnings is also suggested as an explanation to PEAD by
Hirshleifer and Teoh (2006). Consistent with a broader definition
of limited investor attention, DellaVigna and Pollet (2006) find
that earnings announcements that take place on Fridays have more
drift than earnings announcements that occur on other weekdays.
Related, Liang (2003) finds that
investors overconfidence about their private information and the
reliability of earnings result in the underreaction to current
earnings innovations and a slow revision of investors forecasts of
future performance, which and in turn leads to PEAD. An example of
the possible factors that distort investors forecasts is
highlighted in Chordia and Shivakumar (2005). They document
evidence that inflation illusion hypothesis, i.e. that investors do
not incorporate the effect of inflation in their forecasts of
future earnings growth rates, provides a third explanation for the
PEAD. They find that the sensitivi