Accounting Anomalies and Fundamental Analysis: A Review of Recent Research Advances doc

*Title Page/Author Identifier Page/Abstract

Accounting Anomalies and Fundamental Analysis:
A Review of Recent Research Advances*

Scott Richardson
Barclays Global Investors

İrem Tuna
London Business School

Peter Wysocki
University of Miami School of Business Administration


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. Introduction
Objective
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 investment
1

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 1980‟s and 1990‟s 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 Bauman‟s (1996) survey of the
fundamental analysis literature up to the mid-1990‟s and Kothari‟s (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 don’t 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 four
10


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), and
11


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 Harzing‟s “Publish or Perish” program,
downloadable at 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 effect”
3

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. To
14


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-Cox‟s
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 teacher‟s valid e-mail address as generated from the Google search criteria, and
(b) the ranking of the FSA teacher‟s 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 are
19


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 firm‟s 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 firm‟s 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 /ρt ]

(1)

where ρ 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 firm‟s 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 period‟s free cash flows or operating income are
likely to be determined by this period‟s 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}

(4)

Et[oit+1] = f{oit , ∆noat , noat-1, OTHERt}

(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 firm‟s securities, and the
12

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}

(4-G)

Et[fcft+1] = G{OICt , ∆NOACt, NOACt-1, OTHERt}

(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)