ناهنجاری های حسابداری و تجزیه و تحلیل اساسی: بررسی پیشرفت تحقیقات اخیر

We survey recent research in accounting anomalies and fundamental analysis. We use forecasting of future earnings and returns as our organizing framework and suggest a roadmap for research aiming to document the forecasting benefits of accounting information. We combine this with opinions from the academic and practitioner communities to critically evaluate key clusters of papers about accounting anomalies and fundamental analysis disseminated over the last decade. Finally, we provide a new analysis on how an ex ante and ex post treatment of risk and transaction costs affects the accrual and PEAD anomalies, and offer suggestions for future research.

In this paper, we review the literature on accounting anomalies and fundamental analysis. Given the existence of numerous excellent literature reviews of closely related topics, we have constructed our review to complement them. We focus on research studies that have publication or distribution dates after the year 2000, examine accounting-related anomalies and fundamental analysis geared toward forecasting future earnings and security returns, and examine empirical research methods. An underlying theme of our survey is that the information contained in general purpose financial statements can help investors make better portfolio allocation decisions. To this end, an investor can use information in these statements to forecast earnings for the reporting entity, estimate the risk of these earnings, and ultimately make an assessment of the intrinsic value of the firm that can be compared to observed market prices. We use this forecasting activity as our primary organizing principle for research on accounting anomalies and 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. The first part of our review tabulates the most highly cited research studies on accounting anomalies and fundamental analysis published or distributed after the year 2000. We then 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 accounting anomalies and fundamental analysis and how academic research has informed investment practice and to highlight some differences between that and the research conducted by investment professionals. The third part of our survey lays out a desired framework for research seeking to document the forecasting benefits of accounting information, which we then use to critically evaluate the relevant research disseminated over the last decade. In the fourth part, we present some empirical analysis on how an ex ante and ex post treatment of risk and transaction costs affects the well known accrual and PEAD anomalies. In the final part of our review, we offer suggestions for future research. Our survey focuses on empirical research covering accounting anomalies and fundamental analysis. However, empirical research is (or should be) informed by theory, because the interpretation of empirical analysis is impossible without theoretical guidance. While we do not review in detail papers already covered in prior surveys or those papers covered in concurrent Journal of Accounting and Economics survey papers (see, e.g., Beyer et al., forthcoming and Dechow et al., forthcoming), we do attempt to recognize linkages between them. Our survey, in some respects, reiterates a central theme from Kothari (2001). Specifically, academic research that seeks to explore relations between accounting attributes and future firm performance, particularly stock returns, should strive to keep market efficiency as a maintained null hypothesis. The mere finding of an association between an accounting attribute and future stock returns is not prima facie evidence of market inefficiency. As with the research reviewed in Kothari, we continue to find that researchers may be too quick to deviate from the maintained assumption of market efficiency. Furthermore, the documented deviations from market efficiency are many and varied, with little attempt to provide a framework linking them together so as to provide a compelling alternative hypothesis. We are believers in the potential for market inefficiencies; however we think that the hurdle for documenting these inefficiencies is non-trivial. Our survey also contains a citation analysis of recently 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 types of journals (see, e.g., Xie, 2001 and Richardson et al., 2005). We conduct a citation analysis on papers disseminated in the last decade and find four main clusters of overlapping citations common among these 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 post-earnings announcement drift (hereafter PEAD) and other forms of momentum], and Pricing Multiples and Value Anomaly. The Fundamental Analysis cluster cites a number of prior foundational papers including Abarbanell and Bushee, 1997 and Abarbanell and Bushee, 1998 and Feltham and Ohlson (1995). The citation foundation of the Accruals Anomaly cluster is from Sloan (1996). The Underreaction to Accounting Information cluster most often cites Bernard and Thomas, 1989 and Bernard and Thomas, 1990, Foster et al. (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 Fama and French, 1995. We categorize, evaluate, and discuss some of the main research advances after the year 2000 in each of the four research clusters. In addition, we identify what we believe to be essential components of “good” archival empirical research within the accounting anomalies and fundamental analysis umbrella. Those components are: (1) credible alternative hypotheses (relative to market efficiency) with sound theoretical foundations; (2) robust (in and out of sample) predictive power; (3) a sound treatment of risk; (4) a sound treatment of transaction costs (for research looking at future stock returns); (5) attempting to document additivity to pre-existing accounting attributes; and (6) incorporating non-price based tests to help strengthen inferences about risk versus mispricing. We then use these key ingredients to provide a structure for our survey. The questionnaire we distributed to investment professionals and to accounting academics indicate some interesting similarities and differences of opinion regarding the current state of research on accounting anomalies and fundamental analysis and where that literature should proceed. While our findings suggest that many of the conventions and techniques used in academic research differ from those in the investment community, both the practitioners and academics who completed our questionnaire placed high importance for future academic research on theoretically motivated empirical tests of investor behavior; empirical tests of asset pricing, risk, and factor models; empirical research on forecasting firm and industry fundamentals; and the empirical discovery and investigation of new “anomalies” or signals. We conduct our own empirical analyses to help illustrate some concepts and approaches to be considered and applied in future research studies. Specifically, based on the prominence of the accruals and PEAD anomalies in the recent literature and the practitioner interest in innovations related to empirical tests of investor behavior, asset pricing, and risk and factor models, we illustrate the time-series variation in the negative relations between future returns and both accruals and PEAD, and whether these relations are robust to a more comprehensive empirical treatment of risk and transaction costs, both from an ex ante and ex post perspective. Our empirical analysis shows that the negative relation between accruals and future stock returns is robust to a comprehensive treatment of risk and transaction costs, but that it has greatly attenuated in recent years (see also Green et al. (2009) for accruals anomaly only). For the relation between PEAD and future stock returns, we find that the relation is only marginally significant after accounting for transaction costs and that it has also greatly attenuated in recent years. In addition, 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 six major areas of opportunity. First, there is a significant opportunity for researchers to provide greater structure to the forecasting exercise. To date, very little research combines multiple accounting attributes to forecast future earnings or returns. Second, there is a lack of research that uses macroeconomic data to forecast future earnings, risk, and value. Third, current research does not fully exploit the wealth of information contained in general purpose financial reports. Fourth, there appear to be limitations to current forecasting techniques and opportunities to overcome these limitations. Fifth, 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. Sixth, we note that many capital market participants are using the same information sources to forecast future earnings and stock returns. This has lead to increased correlation in stock price movements, creating interesting market settings to explore how information is impounded into security prices.

In this survey, we highlight recent advances in accounting anomalies and fundamental analysis. A key theme to our survey is that information contained in general purpose financial reports helps facilitate better decision making about the allocation of scarce economic resources from an investor’s perspective. We focus our attention on research whose primary aim is to forecast earnings and stock returns. In conclusion, we offer a few philosophical comments about the entire literature we review. The debate about market efficiency, which underpins a large portion of the literature on the use of accounting information for security pricing, has moderated substantially over the years. Grossman and Stiglitz (1980) make the pertinent observation that security prices need to become efficient and this can only happen if capital market participants actively trade on useful information driving security prices toward a ‘true’ price. In this sense the capital market is adaptive in its efficiency (see e.g., Lee, 2001 and Lo, 2004 on market efficiency from an evolutionary perspective). The analysis that we provide in Section 5 speaks to this issue directly: markets adapt over time and what was once mispriced now appears to be correctly priced. This field of research is coming full circle: by documenting robust relations between a given accounting attribute and future stock returns; the research process helps improve market efficiency. Much prior research has attempted to ‘explain away’ anomalous return patterns by showing that the association between a given attribute and future stock returns is concentrated in a subset of firms. These subsets tend to contain smaller, less liquid securities where transaction costs and/or idiosyncratic risk are greater (e.g., Fama and French, 2008). This is a useful and informative literature, but there is a risk of misinterpreting the results. Market (in)efficiency is clearly related to these partitioning variables. When securities are more liquid and information is more readily available, we would expect security prices to be more efficient. Thus, finding that a relation between an attribute and future stock returns is concentrated in a subset of less liquid securities is not prima facie evidence in support of market efficiency. It may well still be possible to successfully exploit an anomaly in this subset of securities, and, in Grossman-Stiglitz terms, receive an adequate compensation for that effort, but of course, only to the extent that the additional transaction costs incurred from trading in smaller, less liquid securities is compensated by higher levels of returns. With the wealth of information available to investors today, and the computational power available for empirical archival analysis, there is a risk of information overload, which could exacerbate frictions in the market and impede the price discovery process. There has been considerable research on information over-load and the impact that this could have on the price discovery process (see e.g., Daniel, Hirshleifer and Teoh (2002), and Libby et al. (2002) for surveys of this literature). Going forward, we expect the quantity of information, as well as the ease with which researchers can access it (i.e., machine readability), to increase. The joint impact of increased information, and an increasing ease for investors to process and manipulate this information, on the ability of investors to forecast earnings and stock returns will continue to be a productive research field. Our ability to conduct archival analysis has increased through time both in terms of research design and in terms of computing power to manipulate large data sets. This increased ability creates a significant ‘inefficiency bias’ in research that uses long time series of data. The further one goes back in time to examine associations between an attribute and future stock returns, the greater this risk. In some sense, it is relatively ‘easy’ to find an ‘anomaly’ in the 1960s, 1970s, and even in the 1980s. It is much harder to do so in the last ten years. Part of this is attributable to advances in computing ability, but part is surely attributable to the increase in capital invested on the basis of many of the insights discussed in this review. A common thread that we weaved throughout our survey is the ability of accounting information to help forecast future earnings and future stock returns. This is a necessary condition for the usefulness of the research effort (i.e., are we able to improve our forecasts?). It is a separate question as to whether security prices reflect that information on a timely basis. For example, the question of whether an attribute is associated with future stock returns is the focus of much of the literature that we have surveyed. We would like to re-emphasize that this understates the potential usefulness of this field of research. Given the multiple other users of general purpose financial reports (e.g., customers, suppliers, competitors, management, etc.) that make financial decisions, analysis to improve forecasting models of future earnings is invaluable regardless of the answer to the risk vs. mispricing debate. Finally, we believe that the accounting anomaly and fundamental analysis literatures have provided considerable influence to academics, standard setters, and practitioners by demonstrating the usefulness of accounting information to forecast future earnings and stock returns. This is an area that is at the core of the accounting profession. We are hopeful that future research will benefit from the suggestions offered in this paper and capitalize on the wealth of accounting information that is available for forecasting future earnings and stock returns. We would like to remind researchers of the opportunities to be gained from improved forecasts of future earnings. It is still the case that perfect foresight of future earnings would generate a very profitable investment strategy. So, while it may be the case that forecasting has become an increasingly competitive activity, the rewards from undertaking this activity are potentially still substantial.