*Title Page/Author Identifier Page/Abstract

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

Scott Richardson Barclays Global Investors Scott.Richardson@barclaysglobal.com rem Tuna London Business School ituna@london.edu Peter Wysocki University of Miami School of Business Administration pwysocki@bus.miami.edu

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

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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.

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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

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(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

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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

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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).

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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,

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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

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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.

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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.

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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.

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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.

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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

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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.

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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

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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

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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.

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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).

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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

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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).

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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.

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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.

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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

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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

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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

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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

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(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.

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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

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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).

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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.

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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

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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

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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

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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.

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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