*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 İrem 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 Introduction Objective The editors of the Journal of Accounting and Economics gave us the assignment to review the literature on accounting anomalies and fundamental analysis Given the existence of numerous excellent prior literature surveys of closelyrelated topics such as market anomalies, market efficiency, fundamental analysis and behavioral finance, we have constructed our literature survey to complement and fillin-the-gaps left by related literature surveys These prior surveys include Barberis and Thaler (2003), Bauman (1996), Bernard (1989), Byrne and Brooks (2008), Damodaran (2005), Easton (2009a), Fama (1970), Fama (1991), Hirshleifer (2001), Keim and Ziemba (2000), Kothari (2001), Lee (2001), Schwert (2003), and Subrahmanyam (2007) To complement these literature surveys, we focus on research studies that: (i) have publication or distribution dates after the year 1999, (ii) examine accounting-related anomalies and fundamental analysis geared toward forecasting future earnings, cash flows and security returns, and (iii) focus on empirical research methodologies An underlying theme of our survey is that information contained in general purpose financial reports helps investors make better portfolio allocation decisions To this end, an investor can use information in general purpose financial reports to forecast free cash flows for the reporting entity, estimate the risk of these cash flows, and ultimately make an assessment of the intrinsic value of the firm which will be compared to observable market prices We view this forecasting activity as the fundamental organizing principle for research on accounting anomalies and fundamental analysis.1 While we recognize the co-existence of other accounting properties and objectives, we view forecasting as a powerful organizing concept for reviewing the recent literature on accounting anomalies and fundamental analysis As part of our review, we adopt a number of complementary approaches to identify, organize and capture recent advances in this literature The first part of our review tabulates a list of the most highly-cited research studies on accounting anomalies and fundamental analysis published or distributed since the year 2000 We also organize and categorize these highly-cited studies by identifying their common and overlapping citations to earlier papers in the literature The second part of our survey presents results from a questionnaire of investment professionals and accounting academics about their opinions on investment anomalies and fundamental analysis and how academic research has informed investment practice In the final part of our review, we offer suggestions for future research and draw on recent conceptual advances from both investment practice and academic research to demonstrate a more-encompassing definition and treatment of risk and transaction costs in empirical tests of equity market anomalies Specifically, we propose a benchmark empirical model and then apply it to a case study of the relation between accruals and future stock returns for a sample of U.S firms.2 The primary objective of our review is to produce a valuable research reference not only for academics and graduate students, but also for investment professionals In addition, the findings from our questionnaire of investment 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) 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 for an analysis of citations and impact of research studies published since the year 2000 professionals and academics highlight the spillovers from academic research to professional practice because, relative to other academic accounting research topics, academic research on anomalies and fundamental analysis has very direct applications and intellectual spillovers to actual practice Accounting anomalies and fundamental analysis also have direct intellectual connections to the efficient markets and behavioral finance literatures in financial economics Given these linkages, we now briefly summarize the coverage of prior related literature surveys in accounting and finance Coverage of previous literature surveys Literature reviews of the academic literature on efficient markets have origins going back to Fama (1970) Given that financial market anomalies and market efficiency are two sides of single intellectual debate, prior surveys attempt to capture the tensions in this debate and give insights about the extent to which markets are informationally efficient (see, for example Kothari, 2001 and Lee, 2001) Surveys that summarize the literature in the 1980‟s and 1990‟s include Keim and Ziemba (2000), Hirshleifer (2001), Barberis and Thaler (2003), and Schwert (2003) More recent surveys that focus on papers in the finance literature include Subrahmanyam (2007), and Byrne and Brooks (2008) These surveys cover issues related to market efficiency, technical, fundamental and event-driven anomalies, and the now maturing field of behavioral finance Papers that review the literature on accounting-based anomalies and fundamental analysis include Bauman‟s (1996) survey of the fundamental analysis literature up to the mid-1990‟s and Kothari‟s (2001) broad survey of capital markets research in accounting (with a related discussion by Lee, 2001) While exhaustive at the time, Kothari (2001) and Lee (2001) cover the literature only up to the year 2000 Recent surveys by Damodaran (2005) and Ohlson (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, papers from The Accounting Review, and 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 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 papers published in either the Journal of Financial Economics or the Review of Financial Studies Only 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 not cover accounting anomalies or fundamental analysis Therefore, one of the goals of our survey is to “fill in” some of the gaps of prior literature surveys and capture research innovations since the year 2000 What we don’t cover Our survey focuses on empirical research on accounting anomalies and fundamental analysis However, empirical research is (or should be) informed by theory, since interpretation of empirical analysis is impossible without theoretical guidance As we stated above, we 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 not discuss in detail research papers in these areas, although we 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) 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, 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 0.1 0 10 13 14 15 16 17 20 21 22 23 24 27 28 29 30 31 -0.1 Cumulative Return -0.2 -0.3 -0.4 -0.5 -0.6 -0.7 -0.8 -0.9 Day in August 2007 Figure 3: Daily returns to characteristic portfolios built on dNOA for a set of large capitalization US equity securities for the month of August 2007 108 Figure 4: Graphical representation of linkages between board director members in Australia (ASX 200 companies) 109 Figure 5: Graphical representation of linkages between Japanese firms as of 2008 based on cross-equity ownership 110 Table Summary of Results of Questionaire/Survey of Investment Professionals’ and Academics’ Opinions on Academic Research on Fundamental Analysis and Equity Market Anomalies The samples consist of (i) 201 practitioner responses to the questionnaire, and (ii) 63 academic responses to the questionnaire The academic response numbers for each question is listed below each sub-table The differences across the sample mean for each answer is calculated using a chi-square test of populations of unequal size and unequal variance The p-values are adjusted using Cochran-Cox‟s approximation of the degrees of freedom for the unmatched samples Q1: Which risk model is most appropriate for risk calibration of an equity trading strategy? Practitioner Opinions 35% 24% Academic Opinions 7% ** 22% CAPM with size & industry adjustments Fama-French 3-factor model (Market, Size, Book Value/Market Value) Multifactor model 12% 4% ** Other model 11% 15% CAPM 10% 4% * Fama-French 3-factor model plus other factors 5% 33% ** CAPM with size adjustments 4% 15% ** * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (61 academic responses) Q2: Which risk model(s) have you used in the last 12 months for risk calibration of an equity trading strategy? CAPM with size & industry adjustments Fama-French 3-factor model (Market, Size, Book Value/Market Value) Multifactor model Other model CAPM Fama-French 3-factor model plus other factors CAPM with size adjustments Practitioner Opinions 29% 10% 16% 8% 23% 4% 11% Q3: What effect you think the current financial market will have on the use and/or demand for each of the following? (Practitioner – BLACK / Academic – RED UNDERLINE) Increase No Decrease use/demand effect use/demand Practitioner demand for new academic research 69% 22% 9% on fundamental analysis/anomalies 55% * 37% * 8% Risk models used in investment management 68% 17% 14% (general) 68% 24% 8% Techniques used in fundamental analysis and 55% 31% 14% quant fund management 62% 34% 4% ** Risk models used in investment management 58% 19% 23% (quant funds specifically) 60% 29% 21% PhDs in quant fund management 26% 38% 37% 27% 49% 24% * * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (63 academic responses) Q4: For the following equity trading strategies, please indicate how successful each has been over the past decade (Practitioner – BLACK / Academic – RED UNDERLINE) Successful Neutral Unsuccessful 61% 28% 11% 22% ** 65% ** 13% Value strategies (for example, book value 56% 28% 16% multiples) 52% 35% 13% Growth strategies (for example, earnings 57% 25% 18% growth) 22% ** 52% ** 26% Return momentum 47% 35% 18% 70% ** 26% 4% ** Misreaction to earnings announcements or 40% 41% 18% management forecasts 52% 44% 4% ** Accounting quality (for example, accruals 41% 37% 23% anomaly) 74%** 22% ** 4% ** Misreaction to analyst forecasts 29% 45% 26% 35% 70% ** 5% ** * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (57 academic responses) Earnings or cash flow momentum 112 Q5: For the following equity trading strategies, how frequently each will be used over the next years (Practitioner – BLACK / Academic – RED UNDERLINE) Frequently Infrequently Never 70% 27% 3% 46% ** 54% ** 0% * Value strategies (for example, book value 58% 39% 3% multiples) 83% ** 13% ** 4% Growth strategies (for example, earnings 53% 39% 7% growth) 30% ** 61% ** 9% Return momentum 48% 48% 4% 74% ** 22% ** 4% Misreaction to earnings announcements or 37% 59% 3% management forecasts 71% ** 29% ** 0% * Accounting quality (for example, accruals 37% 57% 5% anomaly) 71% ** 29% ** 0% ** Misreaction to analyst forecasts 32% 59% 9% 50% ** 46% * 4% * * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (55 academic responses) Earnings or cash flow momentum Q6: Over the last 12 months, how often have you used the following valuation techniques in your work? (Practitioner – BLACK / Academic – RED UNDERLINE) Frequently Infrequently 74% 23% 54% ** 33% 52% 41% 38% * 50% 53% 39% 25% ** 29% 59% 28% 58% 38% 26% 43% 21% 50% 16% 46% 71% ** 17% ** 26% 25% 23% 41% * 25% 22% 29% 38% ** Never Earning multiples 3% 13% ** Book value multiples 7% 12% Cash flow multiples 8% 46% ** Discounted free cash flow model 14% 4% ** Discounted dividend model 31% 29% Residual income (Economic Profit) model 38% 12% ** Other multiples 50% 36% * Other valuation models 52% 33% ** * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (60 academic responses) 113 Q7: How frequently you read or reference the following academic and practitioner research for your work? (Practitioner – BLACK / Academic – RED UNDERLINE) Regularly Sometimes Never 10% 44% 46% 83% ** 14% ** 3% ** Journal of Financial and Quantitative Analysis 5% 32% 63% 8% 67% ** 25% ** Journal of Financial Economics 6% 26% 68% 72% ** 28% 0% ** Review of Financial Studies 6% 23% 71% 51% ** 45% ** 4% ** Journal of Banking and Finance 6% 23% 71% 0% ** 32% 68% Journal of Accounting and Economics 3% 16% 81% 88% ** 8% 4% ** Contemporary Accounting Research 0% 18% 82% 48% ** 48% ** 4% ** The Accounting Review 1% 14% 85% 92% ** 8% 0% ** Journal of Accounting Research 1% 11% 88% 92% ** 8% 0% ** CFA Magazine 48% 44% 8% 0% ** 32% 68% ** Financial Analysts Journal 49% 37% 14% 24% ** 60% ** 16% CFA Institute Conference Proceedings 28% 46% 26% Quarterly 0% ** 17% ** 83% ** Journal of Portfolio Management 12% 34% 54% 4% ** 44% 52% Journal of Investment Management 6% 31% 63% 0% ** 11% ** 89% ** Journal of Investing 3% 28% 69% 0% * 16% ** 84% * Journal of Fixed Income 2% 22% 76% 0% 8% ** 92% ** Journal of Applied Corporate Finance 2% 18% 80% 4% 72% ** 24% ** European Financial Management 1% 10% 89% 0% 7% 93% * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (63 academic responses) Journal of Finance 114 Q8: How frequently you read or reference the following new, unpublished academic research for your work? (Practitioner – BLACK / Academic – RED UNDERLINE) Regularly Sometimes Never Papers posted on specific university department 7% 39% 54% or business school websites 20% ** 52% 28% ** Papers posted on specific faculty member or 6% 38% 56% researcher websites 21% ** 71% ** 8% ** Papers posted on "Social Sciences Research 9% 18% 73% Network" (http://www.ssrn.com) 96% ** 6% ** 0% ** Papers posted on "EconPapers" 4% 12% 84% (http://econpapers.repec.org) 16% ** 41% ** 43% ** * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (63 academic responses) Q9: How important is it that future academic research on fundamental analysis and anomalies focus on the following? (Practitioner – BLACK / Academic – RED UNDERLINE) Important Neutral Not Important Empirical tests of investor behavior 62% 22% 16% 92% ** 8% ** 0% ** Empirical research on forecasting firm and 59% 25% 16% industry fundamentals 75% * 15% 10% Empirical tests of asset pricing, risk, and factor 62% 20% 18% models 52% 44% ** 4% ** Empirical discovery and investigation of new 55% 25% 19% "anomalies" or signals 55% 36% 9% ** Theoretical models of investor behavior 51% 29% 20% 68% * 28% 4% ** Empirical implementation of trading strategies 51% 26% 23% 63% 28% 9% ** Theoretical asset pricing, risk, and factor models 44% 31% 25% 56% 37% 7% ** Theoretical models of trading strategies 27% 43% 30% 48% ** 48% 4% ** * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (59 academic responses) 115 Q10: Overall, academic research studies about anomalies/trading strategies have appropriate emphasis on: (Practitioner – BLACK / Academic – RED) Too Much Appropriate Emphasis Emphasis Not Enough Emphasis Theoretical foundations of a strategy? 21% 61% 18% 4%** 40% ** 56% ** Empirical tests of a strategy? 11% 64% 25% 24% ** 40% ** 36% Possible (alternative) risk-based explanations? 7% 54% 38% 24% ** 40% * 36% Potential market impact of executing a 10% 44% 46% strategy? 4% 28% * 68% ** Economic/psych origins of an anomaly that 8% 43% 50% leads to a strategy? 3% * 65% ** 32% * Real world transactions & trading costs for a 9% 41% 50% strategy? 5% 36% 59% Applicability of strategy to other markets 4% 45% 51% (countries/types of markets)? 12% ** 47% 41% * and ** indicate difference in means across practitioner and academic sample answers are significant at 5% and 1% levels, respectively (59 academic responses) 116 Table 2: Returns to Accrual Anomaly Through Time Panel A: Monthly returns (ignoring transaction costs) 1973-2008 1970s 1980s 1990s 2000s 1973-1999 #Average 0.0126 0.0163 0.0128 0.0151 0.0067 0.0146 Std Dev 0.0328 0.0330 0.0339 0.0327 0.0310 0.0331 7.98 4.51 4.14 5.05 2.23 7.90 1.33 1.72 1.31 1.60 0.75 1.52 Test statistic H0: Average = Annualized Sharpe Ratio Panel B: Monthly returns (including transaction costs) 1973-2008 1970s 1980s 1990s 2000s 1973-2000 Average 0.0139 0.0170 0.0128 0.0177 0.0086 0.0157 Std Dev 0.0314 0.0319 0.0337 0.0306 0.0286 0.0321 9.21 4.84 4.16 6.35 3.12 8.79 1.54 1.84 1.32 2.01 1.05 1.69 Test statistic H0: Average = Annualized Sharpe Ratio 117 Table - Continued Panel C: Regression of accrual anomaly decline (ignoring transaction costs) RdNOAt = α + βTIMETIMEt + βTIME_SQTIME2t + εt α βTIME βTIME_SQ Coefficient 0.0176 -0.0023 T-statistic 5.59 -1.84 Coefficient 0.0121 0.0053 -0.0018 T-statistic 2.56 1.05 Adjusted R2 -1.56 0.0056 0.0089 Panel D: Regression of accrual anomaly decline (including transaction costs) RdNOAt = α + βTIMETIMEt + βTIME_SQTIME2t + εt α βTIME βTIME_SQ Coefficient 0.0177 -0.0018 T-statistic 5.85 -1.44 Coefficient 0.0120 0.0062 -0.0018 T-statistic 2.64 1.27 Adjusted R2 -1.68 0.0025 0.0067 Portfolios are formed from the 1,000 US largest securities (as measured by market capitalization) from February 1973 to December 2008 Portfolios are rebalanced monthly to achieve a target annualized risk of 10 percent Portfolios are fully invested with individual positions limited to be no more than 5% of the total portfolio 118 Table 3: Ex Post Return Analysis of Accrual Anomaly Panel A: Explaining returns to accruals with ‘known’ characteristics’ (ignoring transaction costs) RdNOAt = α + βMKTRMKTt + βSIZERSIZEt + βB/PRB/Pt + βMOMRMOMt + εt α βMKT βSIZE βB/P βMOM Coefficient 0.0125 0.0056 T-statistic 7.48 0.16 Coefficient 0.0112 0.0047 -0.0377 0.1129 0.1210 T-statistic 6.53 0.14 -0.82 2.50 3.12 Adjusted R2 -0.0023 0.0269 Panel B: Explaining returns to accruals with ‘known’ characteristics’ (including transaction costs) RdNOAt = α + βMKTRMKTt + βSIZERSIZEt + βB/PRB/Pt + βMOMRMOMt + εt α βMKT βSIZE βB/P βMOM Coefficient 0.0141 -0.0082 T-statistic 8.78 -0.25 Coefficient 0.0119 -0.0143 -0.0830 0.1690 0.1223 T-statistic 7.14 -0.45 -1.91 3.73 3.22 Adjusted R2 -0.0022 0.0513 Panel C: Fama-Macbeth characteristic regressions XRETi,t+k = α + β∆NOAi,t+ εi,t+k Number of months ahead k=1 k=3 k=6 k=9 k=12 β 0.0126 0.0119 0.0094 0.0066 0.0068 FamaMacbeth 7.88 7.33 5.53 4.09 4.30 T-Stat 119 RNOA is the monthly return to the ∆NOA characteristic portfolio ∆NOA is measured as in section 5.1 RMOM is the monthly return to the MOM characteristic portfolio MOM is measured as the total equity return over the previous 12 months ignoring the most recent month RB/P is the monthly return to the B/P characteristic portfolio B/P is measured as the ratio of common equity to market capitalization RSIZE is the monthly return to the SIZE characteristic portfolio SIZE is measured as the average of the log of total assets and the log of market capitalization as per the BARRA USE3 risk model All of the characteristic portfolios are formed with a 10% annualized risk target XRET is the excess return It is the regression residual from the first stage of the full risk model estimation Specifically, total returns are projected onto a set of 13 characteristics and 55 industries The residual from this weighted least squares regression is the excess return 120 Table 4: Ex Post Return Analysis (Fama-French) of Accrual Anomaly Panel A: Characteristic portfolio returns (ignoring transaction costs) RdNOAt = α + βMKTRMKTt + βSMBSMBt + βHMLHMLt + εt α βMKT βSMB βHML Adjusted R2 Coefficient 0.0126 0.0056 0.0050 -0.0004 -0.0069 T-statistic 7.72 0.15 0.10 -0.01 Panel B: Characteristic portfolio returns (including transaction costs) RdNOAt = α + βMKTRMKTt + βSMBSMBt + βHMLHMLt + εt α βMKT βSMB βHML Adjusted R2 Coefficient 0.0136 0.0150 0.0017 0.0703 -0.0032 T-statistic 8.71 0.41 0.03 1.26 Panel C: Equal weighted zero-cost investment (extreme deciles) RdNOAt = α + βMKTRMKTt + βSMBSMBt + βHMLHMLt + εt α βMKT βSMB βHML Adjusted R2 Coefficient 0.0149 0.1615 0.0864 0.1187 0.0012 T-statistic 3.52 1.62 0.64 0.78 Panel D: Value weighted zero-cost investment (extreme deciles) RdNOAt = α + βMKTRMKTt + βSMBSMBt + βHMLHMLt + εt α βMKT βSMB βHML Adjusted R2 Coefficient 0.0079 0.0277 -0.0648 0.0422 -0.0032 T-statistic 3.92 0.58 -1.01 0.58 121 ...*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. .. 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... 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