Earnings Revisions in SEC Filings From Prior Preliminary Announcements Journal of Accounting, Auditing & Finance 27(1) 3–31 Ó The Author(s) 2012 Reprints and permission: sagepub.com/journalsPermissions.nav DOI: 10.1177/0148558X11409006 http://jaaf.sagepub.com Dana Hollie1, Joshua Livnat2, and Benjamin Segal3 Abstract This article investigates earnings revisions that occur between preliminary earnings announcements and the immediate subsequent Securities and Exchange Commission (SEC) filings On average, the absolute value of the revision is 2.9% of the market value of equity where earnings were revised by more than US$100,000 The authors find that earnings revisions are more likely to occur for firms that are more complex, are more financially leveraged, have greater earnings volatility, have losses, and have switched auditors They find that investors react to the new information in the earnings revisions but find mixed evidence about whether the act of revision itself indicates lower earnings quality to investors The authors’ findings suggest that financial analysts, investors, and regulators alike should pay close attention not only to an earnings surprise at the preliminary earnings announcement date but also at the SEC filing date to determine whether a subsequent earnings surprise occurs Keywords market efficiency, asset pricing, earnings announcement, SEC filings, earnings revisions Regulators and prior research have shown that investors have suffered significant losses as market capitalizations have dropped by billions of dollars due to restatements of audited financial statements (Levitt, 2000; Palmrose, Richardson, & Scholz, 2004) Less discernable restatements occur between the preliminary earnings announcement and the immediately subsequent Securities and Exchange Commission (SEC) filing (henceforth called earnings revisions); however, very little is known in the academic literature about these revisions This study first examines the characteristics of firms most prone to earnings revisions and the type of income statement components that are most likely to be affected We then assess the market’s response to the additional earnings surprise (in the SEC filing) and the total earnings surprise (i.e., the preliminary earnings surprise plus the additional earnings surprise at the time of the SEC filing) to determine whether the market’s reactions to the mere act of earnings revision is negative, irrespective of its content Louisiana State University, Baton Rouge, LA, USA New York University, New York City, NY, USA Boulevard de Constance, Fontainbleau, France Corresponding Author: Dana Hollie, E J Ourso College of Business, Louisiana State University, Baton Rouge, LA 70803 Email: danahollie@lsu.edu Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Journal of Accounting, Auditing & Finance Typically, companies issue a press release to report preliminary earnings to the market 26 days after the fiscal quarter-end for the median company, with 1% of firms reporting in less than days.1 Subsequently, firms file a 10-Q/K Form with the SEC usually within the last days of the mandated filing period.2 A small but nontrivial portion of firms actually revises its earnings from the preliminary earnings announcement to the subsequent SEC filing for a variety of reasons For example, a firm’s auditor may require the filing of different earnings figures with the SEC than those previously released to investors Subsequent information revealed after the preliminary announcement may also cause firms to modify their earnings before the SEC filing Although an infrequent phenomenon (1.98% of our sample observations), some of these revisions are quite large On average, the absolute value of the revision is 2.9% of the market value of equity for our sample of 3,337 firm revisions where earnings were revised by more than US$100,000.3 More than 5% of the earnings revisions are greater in absolute value than 6.2% of the market value of equity at quarter-end (mean market value for sample firms is US$4.864 billion) Although these earnings revisions are economically significant at the time of their revision, unlike typical earnings restatements, virtually none of the earnings revisions are preceded by a formal announcement or a press release, nor are companies required to file an amendment such as 10-K/A or 10-Q/A Furthermore, in many cases, firms not even explain the reasons for the earnings revisions in their SEC filings Still, the phenomenon of earnings revisions is important for academic research and practitioners because it indicates cases of potential problems with earnings quality that have not previously been documented in the academic literature Furthermore, a study by Hollie, Livnat, and Segal (2005) shows that market participants seem to react adequately to the new earnings surprise contained in SEC filings of firms that made earnings revisions However, they not study, as we here, whether the total market reaction to both sources of earnings surprise indicate that market participants seem to treat firms with earnings revisions as if they have an inferior quality of earnings In our sample, approximately 64% of all significant earnings revisions are income decreasing, and a large portion of the revisions (about 50%) occur in the fourth fiscal quarter, consistent with both audit work at year-end and a longer period before SEC filings in which subsequent events may require an earnings revision We find that the most frequently revised components of net income are recurring items (e.g., sales; cost of sales; Selling, General, and Administration [SG&A]; and depreciation) and that earnings revisions are more likely to occur for firms that are more complex in nature, are more financially leveraged, have greater earnings volatility, have losses, and have switched auditors We also find that the likelihood of an earnings revision is inversely related to the persistence of earnings changes These variables are also shown in the literature to be associated with lower earnings quality and subsequent earnings restatements However, contrary to expectations, we not find that significant earnings revisions occur in highlitigation risk industries Most of the firms in our sample (about 68%) appear only once, indicating that most sample firms are probably not attempting to manage earnings information strategically across their preliminary earnings announcements and SEC filings in the long run.4 In regression results, we also find that market reactions to the total earnings surprise for firms that revise their earnings (i.e., the associations of abnormal returns from a day before the preliminary earnings announcement through a day after the SEC filing with the combined preliminary earnings surprise and SEC filing earnings surprise) not differ significantly from those of non-Revisers However, in matched-sample tests, we mostly find evidence consistent with weaker market reaction to total earnings of Revisers than those of Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Hollie et al non-Revisers Thus, we cannot unambiguously conclude that Revisers are associated with lower earnings quality Our empirical findings are particularly relevant to academics, financial analysts, regulators, and investors The results of this study are important to academic researchers because it focuses on cases of potential lower earnings quality, which have not been studied in the academic literature We find inconclusive results regarding whether firms with earnings revisions have lower earnings quality, as indicated by weaker market reactions to the total earnings surprise These cases may be of particular interest to the auditing profession, where the identification of firms with a higher audit risk is extremely valuable The evidence in this study also suggests that financial analysts, investors, and regulators alike should pay close attention not only to an earnings surprise at the preliminary earnings announcement date but also at the SEC filing date to determine whether a subsequent earnings surprise occurs The remainder of this article is organized as follows The section titled ‘‘Background, Hypotheses, and Research Design’’ reviews the related literature and outlines our hypotheses and methodology The section titled ‘‘Data and Results’’ describes the sample and presents the empirical findings The section titled ‘‘Summary and Conclusions’’ summarizes and concludes the article Background, Hypotheses, and Research Design Most firms disclose their preliminary earnings for the quarter or year through a press release, following it with an SEC filing several weeks later Easton and Zmijewski (1993) report a median lag between the balance sheet date and the preliminary earnings announcement of 28 days and a median SEC filing lag of 45 days for Forms 10-Q Our sample shows a similar pattern with a median preliminary earnings lag of about 25 to 26 days in recent years Some firms issue a press release to discuss earnings after their SEC filings (Stice, 1991), and others not issue any press release at all, relying on the information available in the SEC filings alone (Amir & Livnat, 2006) When firms issue their preliminary earnings release prior to the SEC filing, investors implicitly assume that these earnings will be identical to the SEC-filed earnings However, as this study shows, a nontrivial portion of firms file significantly different earnings figures with the SEC than the one previously provided in their preliminary earnings announcement.5 Consider the following two examples, which are highlighted in Appendix A R&B, Inc (NASDAQ: RBIN) reported net income of US$2.8 million in its second quarter preliminary earnings release on July 31, 2002, but revised it upward to US$4.115 million on August 8, 2002, in its SEC filing, an increase of almost 150% from its preliminary earnings An examination of news items regarding R&B, Inc (using LexisNexis) reveals that no press releases were issued between the preliminary earnings announcement and the SEC filing date Hence, no public information seems to have been available between the earnings announcement and the SEC filing date that would have indicated that an upward revision in SEC-filed earnings was forthcoming MEMC Electronic Materials, Inc (NYSE: WFR) issued its second quarter preliminary earnings release on August 10, 2001, reporting an US$88.1 million loss, followed by an SEC filing (August 13, 2001) reporting a US$355.3 million loss, reducing the previous earnings figure by approximately 400% just days later There are no other news reports in LexisNexis between the preliminary earnings announcement and the SEC filing date to suggest that a downward revision was forthcoming As these two examples illustrate, some earnings revisions between the preliminary earnings Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Journal of Accounting, Auditing & Finance release and the SEC filing date are upward revisions, whereas others are downward revisions and can be quite large in magnitude.6 Plausible explanations (not necessarily specific to the two examples above) for earnings revisions between the preliminary announcement and the SEC filing are the uncovering of issues not known at the preliminary earnings release date due to audit work or new auditors who are more likely to compel clients to revise previously issued earnings under the prior auditor, information that becomes known subsequent to the preliminary earnings announcement, or discovery of accounting errors before the filing date For example, several studies document the existence of accounting errors that are discovered by auditors and corrected before the public release of year-end financial statements.7 This is also consistent with about 50% of our firm-quarter observations existing in the fourth quarter, when a full audit is required (see Panel A of Table 2), and where the window between the preliminary earnings announcement and the SEC filing is typically longer, leading to greater opportunities for discovering material subsequent to information that requires earnings revisions Characteristics of Firms With Earnings Revisions Absent a theoretical model to guide the selection of potential variables that are associated with the likelihood of earnings revisions in SEC filings, we use variables that may indicate inferior earnings quality DeFond and Hung (2003) suggest earnings volatility and accruals magnitude as incentives to disseminate cash flow forecasts when earnings are of inferior quality Chen, DeFond, and Park (2002) claim that firms with losses and firms with greater earnings surprises are more likely to provide balance sheet information in their preliminary earnings to strengthen their weaker earnings quality We use these variables, as well as the persistence of earnings surprises, to show that firms with a lower quality of earnings are more likely to revise their earnings in the SEC filings Consistent with subsequent corrections during audit work (or review) between the preliminary earnings announcement and the SEC filing, we conjecture that the firm’s complexity (measured by the number of its operating segments), size, number of analysts following, and auditor changes are all positively associated with the likelihood of an earnings revision However, it may be argued that larger firms with a greater analyst following are managed more carefully and are less likely to have earnings revisions.8 We also expect that less profitable firms (lower return on assets) are more likely to have earnings revisions because managers in such firms have greater incentives to manage preliminary earnings and because lower profitability may indicate operational problems and potentially weaker accounting controls Studies of restatements in the literature (e.g., Richardson, Tuna, & Wu, 2002—annual restatements—and Livnat & Tan, 2004—quarterly restatements) assert that financial leverage is positively associated with the likelihood of restatements due to management’s desire to inflate earnings initially as a way to avoid debt restrictions The literature also asserts that growth firms are more likely to have earnings restatements because of their desire to show continued earnings growth and ability to beat analyst expectations Firms with high financial leverage may also be subject to additional scrutiny by auditors and creditors, leading to greater chances for earnings revisions Thus, we expect financial leverage to be positively associated with the likelihood of earnings revisions and the earnings-to-price ratio (an indicator of growth) to be negatively associated with the likelihood of earnings revisions Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Hollie et al Market Reactions to Preliminary Earnings Announcements and SEC Filing Dates Prior research shows that the market responds to earnings surprises included in preliminary earnings announcements and that stock prices incorporate this information instantaneously (see Kothari, 2001, and Lev, 1989, for summaries of these studies) However, there is substantially less agreement about the incremental information content in 10-Q/Ks beyond preliminary earnings announcements For example, Foster and Vickery (1978), as well as Wilson (1987), document that 10-Ks have information content beyond earnings announcements, whereas other studies suggest that the market fails to react to earnings information contained in SEC filings (e.g., Chung, Jacob, & Tang, 2003; Cready & Mynatt, 1991; Easton & Zmijewski, 1993; Foster, Jenkins, & Vickery, 1983, 1986; Stice, 1991) Easton and Zmijewski (1993) examine whether the 10-Q/K filing dates are associated with abnormal returns, using squared market model prediction errors to avoid any predictions about the direction of expected returns around the SEC filing dates Their results show abnormal market returns that are significantly different from zero around preliminary earnings announcements but show no significant difference for market reactions to SEC filings, except in those cases where only the 10-Q dates are known but no preliminary earnings announcement dates are available on the Quarterly Compustat File These results seem to imply that SEC filings contain no incremental information beyond preliminary earnings announcements Stice (1991) examines whether the information content of an earnings announcement can be affected by the method in which earnings are announced, concentrating on firms that file their 10-Q/Ks several days before the earnings announcement Stice finds that SEC filings are not fully reflected in prices until subsequent earnings announcements are made.9 Chung et al (2003) corroborate Stice’s findings and show that some firms in their sample behave as if they manage earnings Balsam, Bartov, and Marquardt (2002) investigate whether investors in firms that are suspected of engaging in earnings management are able to more rapidly incorporate the information about accruals available in 10-Q filings and whether institutional investors seem to respond even before the SEC filing dates They find evidence consistent with no investor reaction to the managed accruals around the preliminary earnings announcement (with event windows up to days later), with market reactions to discretionary accruals by firms with at least 40% institutional investors during the window spanning 10 days after the preliminary earnings announcement through days before the SEC filing date, and with market reactions to discretionary accruals in the window from a day prior to the SEC filing date through 15 days afterward for firms with fewer institutional investors Their interpretation is that institutional investors seem to find the information necessary to reverse accruals faster than other investors and prior to the SEC filing dates Qi, Wu, and Haw (2000) suggest that prior research’s inability to detect little, if any, information content around the SEC filing date may be due to the SEC paper filing system in place at the time of prior studies Their study compares SEC paper filings with SEC electronic filings to test whether the information content of 10-Ks has changed because of electronically available SEC filings In contrast to most of the prior research, Qi et al provide evidence that 10-K filings through the Electronic Data Gathering, Analysis, and Retrieval (EDGAR) system provide incremental information content that did not exist for the paper filings However, they study the years 1993 to 1995, in which the EDGAR system was still voluntary (becoming mandatory in May 1996) In addition, their study is limited to firms with available Association for Investment Management and Research analyst rankings Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Journal of Accounting, Auditing & Finance In a recent study covering the period 1996 to 2001, Griffin (2003) finds that SEC filings have abnormal market returns that are significantly different from zero, where abnormal returns represent the absolute value of excess returns around the filing date compared with the excess returns in a prior period He finds greater market reactions to 10-Q/Ks, to smaller firms, to firms with lower proportions of institutional holders, to firms that report on days with many filings by other firms, and to firms with delayed filings In addition, in multivariate results, Griffin finds evidence of stronger market reactions to filings made in recent periods and to delayed SEC filings Asthana, Balsam, and Sankaraguruswamy (2004) show that small trades increase in the 5-day period around the 10-K filing after EDGAR as compared with the pre-EDGAR period, but not large trades, implying that small investors are better able to use the information in SEC filings in the post-EDGAR period They also show that small investors seem to incorporate better the information content of the 10-Ks in the post-EDGAR period than before and provide evidence consistent with an erosion of the information advantage that larger traders have as compared with small traders in the post-EDGAR period Callen, Livnat, and Segal (2006) show that earnings news is important in explaining the volatility of unexpected returns around both the preliminary earnings announcements and the SEC filing dates, with the cash flows and accruals components of earnings significantly associated with the volatility of unexpected returns around the SEC filing dates Hollie et al (2005) show that firms with significant earnings revisions in SEC filings experience significant market reactions to the new earnings surprise in the SEC filings They also show that market reactions to a dollar of earnings surprise in the preliminary earnings announcement are not statistically different from the market reactions to a dollar of earnings surprise in the SEC filing for their sample with material earnings revisions Their study, although important in highlighting that market participants are able to identify and react to the new earnings surprises in SEC filings, does not investigate whether the total market reaction to the total earnings surprise (preliminary and revision) is weaker due to the potential lower earnings quality Research Design Figure provides the time line of events in this study and highlights the range of periods over which abnormal returns are calculated This figure identifies the short windows around the preliminary earnings announcement and SEC filing dates, the long window between the preliminary earnings announcement and SEC filing, and the long window from day before the preliminary earnings announcement through day after the SEC filing date For some of our tests, we examine differences between two samples: a sample of firms with earnings revisions (Revisers) and a sample of control firms from the same Fama and French (1997) industry, closest in size (market value of equity) and in the same quarter (nonRevisers) For other tests, we use the entire population of non-Revisers We first assess the characteristics of firms that have earnings revisions between preliminary earnings announcements and SEC filings We use a logistic regression model to determine the likelihood that a firm is a Reviser REVISERit 5b0 1b1 EARNVOLit 1b2 PERSEit 1b3 DEBTit 1b4 AUCit 1b5 SEGNUMit 1b6 LOSSit 1b7 ROAit 1b8 EPit 1b9 ACCPROPit 1eit Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 ð1Þ Hollie et al where REVISER is a dummy variable equal to one if a firm-quarter has an earnings revision and zero otherwise The variables we use to discriminate Revisers and control firms are as follows: (a) Earnings volatility (EARNVOL) is the absolute value of the ratio of the standard deviation of Earnings per share (EPS)/Price over the most recent 12 months to the average EPS/Price over the same period It is winsorized to a maximum of 5; (b) Persistence of earnings changes (PERSE) is estimated as the first autocorrelation between scaled earnings surprises in the prior eight quarters The earnings surprise is earnings in the quarter minus earnings of the same quarter in the preceding year, scaled by market value at the beginning of the quarter; (c) Debt/Assets (DEBT) is estimated as short-term plus longterm debt divided by total assets at the end of the quarter; (d) Auditor change (AUC) is a dichotomous variable obtaining one if the firm’s auditor changed from the prior year (mergers of audit firms such as Coopers and Lybrand and Price Waterhouse are not counted as an auditor change); (e) The number of segments (SEGNUM) is from the Compustat Segment file and is a surrogate for operating complexity; (f) ROA is the ratio of earnings to total assets at quarter-end and proxies for firm profitability; (g) LOSS is a dummy variable obtaining one if earnings for the quarter are negative and zero otherwise; (h) EP is the current earnings to market value at quarter-end; and (i) ACCPROP is the proportion of accruals divided by sales over the prior four quarters Market Reactions to Earnings Revisions As portrayed in Figure 1, this study focuses on four buy-and-hold abnormal returns windows—the preliminary earnings announcement date (CARprelim), the SEC filing date (CARfile), the window between the preliminary earnings announcement and the SEC filing date (CARaf), and the window between a day before the preliminary earnings announcement and the day after the SEC filing date (CARtotal) The abnormal return is calculated as the raw buy-and-hold return from the Center for Research in Security Prices (CRSP) minus the buy-and-hold return on the portfolio of firms with the same size (the market value of CARtotal CARaf CARfile CARprelim –1 –1 +1 Preliminary Earnings Announcement Date +1 SEC Filing Date Figure Time line: Preliminary earnings announcements and SEC filings Note: CARprelim = the buy-and-hold abnormal return from Day 21 through Day 11, where Day is the preliminary earnings announcement date The abnormal return is the raw return minus the average return on a same size, book-to-market (B/M) portfolio (six portfolios), as provided by professor French; CARaf = the buy-and-hold abnormal return between preliminary earnings announcements and SEC filings It controls for information released to the stock market between the two time periods; CARfile = the buy-and-hold abnormal return from Day 21 through Day 11, where Day is the SEC filing date of 10-Q/10-K; CARtotal = the buy-and-hold abnormal return from day before the preliminary earnings announcement through day after the SEC filing date Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 10 Journal of Accounting, Auditing & Finance equity) and B/M ratio The daily returns (and cutoff points) on the size and B/M portfolios are obtained from Professor Kenneth French’s data library, based on classification of the population into six (two size and three B/M) portfolios.10 We omit all observations with abnormal returns in the top and bottom 0.5% of the population We then examine the stock returns around the preliminary earnings announcements to provide initial evidence on the effect of earnings surprises in preliminary earnings announcements Consistent with prior research (e.g., Kothari, 2001; Lev, 1989; Penman, 1987), we expect a positive and significant relation between earnings announcements and stock market returns This analysis is intended only to show the consistency of our sample with the results in prior studies For this test, we examine the relation between the first earnings surprise (FSURP) and CARprelim using the following regression: CARprelim it 5b0 1b1 FSURPit 1eit ð2Þ where CARprelim is the 3-day (21 to 11) buy-and-hold abnormal returns centered on the preliminary earnings announcement date (Date 0) and FSURP is the preliminary (or first) earnings surprise, calculated as preliminary Compustat earnings at Quarter t minus Compustat earnings at Quarter t 4, scaled by market value of equity at the end of Quarter t We expect a positive coefficient on b1, which represents the earnings response coefficient (ERC) for the preliminary earnings surprise We then examine the stock market reaction to the additional earnings surprise in SEC filings We measure the additional earnings surprise (ASURP) as the SEC-filed earnings (As-First-Reported [AFR] Compustat earnings at Quarter t) minus the preliminary Compustat earnings, scaled by market value at quarter-end In a similar manner to CARprelim, we define CARfile as the 3-day (21 to 11) buy-and-hold abnormal returns centered on the SEC filing date (Date 0) To control for additional news possibly obtained by market participants between the preliminary earnings announcement and the SEC filing date, we include CARaf CARaf is the buy-and-hold abnormal return from days after the preliminary earnings announcement through days before the SEC filing date It is assumed that changes in stock prices capture all news during this event period Accordingly, we investigate the relationship between ASURP (additional surprise), FSURP, CARaf, and CARfile using the following regression model: CARfile it 5b0 1b1 FSURPit 1b2 ASURPit 1b3 CARaf it 1eit ð3Þ where ASURP is defined above Our equation for total earnings surprise (TSURP) for nonRevisers and Revisers is provided below Because non-Revisers not have an additional earnings surprise, ASURP, by definition, will always be equal to zero for the non-Reviser control group of firms For Revisers and for non-Revisers: Total earnings surprise5FSURP ðfirst surpriseÞ1ASURP ðadditional surpriseÞ 5½Preliminary Compustat earnings at Quarter t minus AFR Compustat earnings at Quarter t À 4; scaled by market value of equity at the end of Quarter tŠ1½AFR Compustat Earnings in the SEC filing À Preliminary Compustat earningsŠ Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Hollie et al 11 The coefficient b1 captures the market reaction at the time of the SEC filing to the already-known initial earnings surprise in preliminary earnings We expect b1 to be insignificantly different from zero if stock prices have already fully impounded the earnings information during the preliminary earnings announcement window The coefficient on b2 represents the market reaction to the additional earnings surprise contained in the SEC filing beyond earnings reported in the preliminary earnings announcement It is expected to be positive and statistically different from zero if the SEC filings are noticed by investors who react to the additional earnings surprise, as is documented in Hollie et al (2005) We have no expectation about the sign or magnitude of the coefficient on b3 as CARaf is a control variable for information released to the market between the two event dates This analysis is intended to show that our sample produces similar results to those of Hollie et al Next, we estimate the market reactions to the total earnings surprises of Revisers as compared with those of control firms through a system of simultaneous equations Essentially, this method consists of the following steps: a first stage regression to get fitted values for the probability that the firm is a Reviser and a second stage to run the regressions using these fitted values The two endogenous variables are therefore REVISER and CARtotal The exogenous variables (i.e., ROA, DEBT, SEGNUM, EARNVOL, PERSE, AUC, and LOSS), identified in the logistic analysis previously, are used as instrumental variables to estimate whether Reviser equals ‘‘1’’ or ‘‘0.’’ In a similar manner to CARfile, we define CARtotal as the buy-and-hold abnormal returns from day before the earnings announcement date through day after the SEC filing date CARtotal it 5b0 1b1 REVISERit 1b2 TSURPit 1b3 REVISERit 3TSURPit 1eit ð4Þ CARtotal it 5b0 1b1 REVISERit 1b2 TSURPit 1b3 REVISERit 3TSURPit 1b4 ROAit 1b5 DEBTit 1b6 SEGNUMit 1b7 EARNVOLit 1b8 PERSEit 1b9 AUCit 1b10 LOSSit 1eit ð5Þ where TSURP is the sum of ASURP and FSURP as defined above We assign each firmquarter into 10 deciles according to their TSURP We then substitute the deciles rank for TSURP, divide it by and subtract 0.5, as is often done in the postearnings-announcement drift literature The coefficient on TSURP measures the return on a hedge portfolio that holds long (short) positions in the top (bottom) decile of earnings surprises.11 To the extent that the market views an earnings revision in a negative light, likely due to weaknesses in accounting or control systems or perceived lower earnings quality, we expect the coefficient on REVISERit TSURPit (b3) to be negative and statistically different from zero This would indicate that a dollar of earnings surprise in the case of Revisers contributes less to the market reaction than a dollar of earnings surprise of non-Revisers We further test whether the total market reactions to the total earnings surprises for Revisers are different from those of the matched control firms by comparing the returns of Revisers and matched control firms in the same quarter The matching is done by the total surprise decile and size (market value of equity); total surprise decile and industry; total surprise decile, size, and industry; and total surprise decile, size, and number of segments We also match the firm to itself in another quarter with no earnings revision but when the total earnings surprise was in the same decile rank If the market reacts negatively to the act of earnings revisions, we should observe lower total market reactions for Revisers than for the matched control firms Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 130 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 0.651** (80.02) 0.023** (2.84) 0.027** (3.24) (0.00) 20.019* (22.04) (0.00) 20.024** (22.71) 20.164** (220.48) 20.009 (21.43) 0.032 25,739 0.650** (76.76) 0.022** (2.71) 0.023** (2.54) 0.008 (1.07) 20.020* (22.01) 0.002 (0.23) 20.024** (22.69) 20.164** (220.61) 20.009 (21.43) 0.032 25,739 Coefficient estimate (t-statistics) (2) 0.025** (3.14) 20.008 (20.86) 0.000 (0.00) 20.011 (21.13) 0.004 (0.42) 20.023** (22.55) 20.163** (220.21) 20.010 (21.49) 0.032 25,739 0.660** (79.14) (3)a 0.039** (3.38) 0.045** (3.45) 0.008 (0.67) 20.007 (20.55) 20.006 (20.47) 20.015 (21.26) 20.167** (213.82) 20.005 (20.49) 0.056 11,459 0.567** (38.15) 0.112** (10.90) (4) 0.023** (2.76) 0.047** (4.43) 0.040** (3.42) 0.015 (1.50) 20.017 (21.39) 20.006 (20.58) 20.022* (21.97) 20.158** (214.90) 20.001 (20.08) 0.0387 14,890 0.611** (46.75) (5) 0.569** (33.85) 0.102** (9.52) 0.013 (1.36) 0.040** (3.19) 0.040** (2.90) 0.010 (0.80) 20.005 (20.33) 20.008 (20.60) 20.019 (21.50) 20.171** (213.02) 20.002 (20.17) 0.0553 9,839 (6) Note: The dependent variable is the relative cash flow forecast accuracy (ACC) See the Appendix for definitions of the variables T-statistics are based on analyst-cluster adjusted standard errors a In Model 3, firm-specific experience (EXPF) and general experience (EXPG) are measured by the number of years of either earnings or cash flow forecasts available in the I/B/E/S so that EXPF and EXPG account for the earnings forecasting experience as well as the cash flow forecasting experience *Significant at the 5% level (one-tailed test) **Significant at the 1% level (one-tailed test) Intercept (?) ACCL ( ) ACCEPSL ( ) FREQ ( ) EXPGa ( ) EXPFa ( ) NFIRM (2) NIND (2) BSIZE ( ) HORZ (2) DAYS (2) Adjusted R2 No of observations Variables (1) Table Determinants of the Cash Flow Forecast Accuracy Pae and Yoon 131 forecasting earnings but meager experience in forecasting cash flows If forecasting cash flows is similar to forecasting earnings, it remains possible that the overall forecasting experience of analysts matters more than their cash flow–specific forecasting experience In the I/B/E/S, cash flow forecasts are available from 1993, whereas earnings forecasts are available from as early as 1983 Given the relatively recent coverage of cash flow forecasts in the I/B/E/S, forecasting experience, as measured by the number of years of cash flow forecasts available in the I/B/E/S, may be misleading To address this concern, we consider earnings forecasting experience in addition to cash flow forecasting experience In Model 3, we measure the forecasting experience of an analyst by the number of years that the analyst has been issuing either earnings or cash flow forecasts; that is, we measure both general and firm-specific experiences (EXPG and EXPF) by the number of years of either earnings or cash flow forecasts available in the I/B/E/S The regression results for Model indicate that the overall forecasting experience of analysts, including their earnings forecasting experience, does not adequately explain differences in cash flow forecast accuracy Moreover, when we include both cash flow–specific forecasting experience and overall forecasting experience, only the former is associated with the predicted sign; the variables regarding the overall forecasting experience are associated with the opposite sign (untabulated) Thus, compared with overall forecasting experience, cash flow– specific forecasting experience better explains cash flow forecast accuracy Impact of Past Cash Flow Accuracy on Current Cash Flow Forecasting Performance The literature on earnings forecast accuracy finds that past forecast accuracy is as important as analyst characteristics in terms of explaining forecast accuracy (Brown, 2001) To determine whether this is also the case with cash flow forecast accuracy, we augment Regression with lagged cash flow forecast accuracy (ACCL) ACCijt b0 b1 ACCLijt b2 FREQijt b3 EXPGit b4 EXPFijt b5 NFIRMit b6 NINDit b7 BSIZEit b8 HORZijt b9 DAYSijt eijt ; ð2Þ where ACCLijt is the standardized cash flow forecast accuracy in the preceding year Model of Table presents the estimation results of Regression on a restricted sample of 11,459 firm-year-analyst observations (2,126 firm-year pairs) for companies in which at least two analysts issued cash flow forecasts in the preceding year.10 Consistent with previous findings on earnings forecast accuracy, past cash flow forecasting performance is a significant determinant of current cash flow forecasting performance The coefficient on past cash flow forecast accuracy (ACCL) is significantly positive at 1% level The inclusion of past cash flow forecast accuracy improves the adjusted R2 to 056 The corresponding adjusted R2 of the regression, excluding ACCL with regard to the restricted sample, is 041 (untabulated) However, the usefulness of past forecast accuracy does not apply to analysts who issue cash flow forecasts for the first time or to those who did not issue cash flow forecasts in the preceding year In the full sample, past cash flow forecast accuracy information is unavailable for more than half of the analysts In such case, analyst and forecast characteristics would be particularly pertinent The estimation results for Model in Table demonstrate that many analyst and forecast characteristic variables remain significant even after controlling for past cash flow forecast accuracy, suggesting that investors can identify more accurate cash flow forecasters by taking into account analyst and forecast characteristics as well as past forecasting performance Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 132 Journal of Accounting, Auditing & Finance Relationship Between Earnings Forecast and Cash Flow Forecast Accuracy Many analysts provide earnings forecasts along with cash flow forecasts In our sample, approximately 90% of the analysts provide earnings forecasts in addition to cash flow forecasts, whereas 10% of them provide only cash flow forecasts In this section, we examine the relationship between earnings forecasts accuracy and cash flow forecast accuracy The preceding regression results indicate that factors influencing earnings forecast accuracy, as documented in the literature, are also relevant to cash flow forecast accuracy Ceteris paribus, we expect that more accurate earnings forecasters also predict cash flows more accurately Consistent with this prediction, when analysts provide both earnings and cash flow forecasts, we find that more accurate earnings forecasters predict cash flows more accurately than less accurate earnings forecasters (untabulated) However, information on current earnings forecast accuracy remains unknown until the earnings are announced later by firms Thus, we utilize the preceding year’s earnings forecast accuracy rather than the unavailable current earnings forecast accuracy Previous research supports the use of the preceding year’s earnings forecast accuracy as a determinant of the current earnings forecast accuracy (Brown, 2001) We estimate Regression on a restricted sample of 14,890 firm-year-analyst observations for companies in which at least two analysts issued earnings forecasts in the preceding year ACCijt b0 b1 ACCEPSLijt b2 FREQijt b3 EXPGit b4 EXPFijt b5 NFIRMit b6 NINDit b7 BSIZEit b8 HORZijt b9 DAYSijt eijt ; ð3Þ where ACCEPSLijt is the standardized earnings forecast accuracy in the preceding year In Model of Table 2, the coefficient on ACCEPSL is significantly positive at 1% level, indicating that cash flow forecast accuracy is higher for analysts who predicted earnings more accurately in the preceding year than for those who predicted earnings less accurately However, little improvement is noted in the overall explanatory power of current cash flow forecast accuracy over the regression without past earnings forecast accuracy The adjusted R2 is 039, which is no better than 038 when we exclude ACCEPSL from the regression (untabulated) Subsequently, we include both preceding cash flow forecast accuracy (ACCL) and preceding earnings forecast accuracy (ACCEPSL) to determine which past forecast accuracy information is more useful in explaining current cash flow forecast accuracy ACCijt b0 b1 ACCLijt b2 ACCEPSLijt b3 FREQijt b4 EXPGit b5 EXPFijt b6 NFIRMit b7 NINDit b8 BSIZEit ð4Þ b9 HORZijt b10 DAYSijt eijt : We estimate Regression on a restricted sample of 9,839 observations for companies in which at least two analysts issued both cash flow and earnings forecasts in the preceding year The results are shown in Model of Table The coefficient on preceding cash flow forecast accuracy (ACCL) remains significant, but the coefficient on preceding earnings forecast accuracy (ACCEPSL) is no longer significant Past cash flow forecast accuracy subsumes the effect of past earnings forecast accuracy in explaining current cash flow forecast accuracy Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Pae and Yoon 133 These results indicate that information on past cash flow forecasts is more useful than past earnings forecast accuracy in explaining current cash flow forecast accuracy In Model of Table 2, we also demonstrate that cash flow forecasting experience is more important than earnings forecasting experience Taken together, cash flow–specific forecasting information is more useful than earnings forecasting information in explaining cash flow forecast accuracy Sensitivity Analysis Prior literature on earnings forecasts documents several factors that affect earnings forecast accuracy In this section, we examine how these factors impact cash flow forecast accuracy First, we partition the sample into 17,351 bold forecasts and 8,388 herding forecasts to examine the impact of forecast boldness on cash flow forecast accuracy.11 We classify the cash flow forecast of a given analyst as a bold or high-innovation forecast if the forecast is above both the preceding forecast of the analyst and the consensus forecast or below both; otherwise, we designate the forecast as a herding or low-innovation forecast (Clement & Tse, 2003, 2005; Gleason & Lee, 2003)  BOLDijt if (Fijt FijtÀ1 and Fijt CONFjt ) or (Fijt FijtÀ1 and Fijt CONFjt ); 0, otherwise: where Fijt is the cash flow forecast of analyst i for firm j in year t, and CONFjt is the mean consensus forecast based on the most recent forecasts by each brokerage house for firm j as of the day before the forecast date We estimate the following regression to assess the impact of bold forecasts on cash flow forecast accuracy ACCijt b0 b1 BOLDijt b2 FREQijt b3 EXPGit b4 EXPFijt b5 NFIRMit b6 NINDit b7 BSIZEit b8 HORZijt b9 DAYSijt eijt : ð5Þ Column of Table shows that analysts who issue bold forecasts predict cash flows more accurately than those who issue herding forecasts The coefficient on BOLD is significantly positive at 1% level This result is consistent with the assertion that analysts who issue bold cash flow forecasts have better private information than those who issue herding cash flow forecasts (Clement & Tse, 2003, 2005) The regression results for these subsamples are similar to those for the full sample in Model of Table 2, with some exceptions First, for analysts who issue bold forecasts, the coefficient on the number of firms (NFIRM) is of the predicted sign, but is insignificant at the conventional level, and the coefficient on the days elapsed since the last forecast (DAYS) is significant at 1% level Second, for analysts who issue herding forecasts, forecasting frequency (FREQ) is insignificant By definition, herding forecasts are more likely to mimic existing forecasts and thus contain less private information than other forecasts It appears that herding forecasters cannot improve their forecast accuracy by increasing their forecasting frequency To summarize, bold analysts predict cash flows more accurately than herding analysts Our results are consistent with findings of prior research on earnings forecast accuracy (Clement & Tse, 2005; Hong & Kubik, 2003; Hong, Kubik, & Solomon, 2000) Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 134 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 0.651** (77.06) Intercept (?) 0.625** (68.49) 0.660** (72.31) 0.629** (45.14) BOLD ( ) 0.037** (6.89) NEGCF (?) FREQ ( ) 0.022** (2.76) 0.024** (2.73) 0.017 (1.27) EXPG ( ) 0.024** (2.62) 0.019* (1.92) 0.032* (2.27) EXPF ( ) 0.008 (1.01) 0.008 (0.92) 0.007 (0.58) NFIRM (2) 20.020* (22.02) 20.013 (21.17) 20.036* (22.32) NIND (2) 0.001 (0.16) 20.003 (20.35) 0.012 (0.89) BSIZE ( ) 20.024** (22.67) 20.020* (22.05) 20.032* (22.22) HORZ (2) 20.165** (220.70) 20.159** (217.42) 20.179** (212.57) DAYS (2) 20.010 (21.43) 20.017* (22.12) 0.006 (0.55) 0.034 0.031 0.036 Adjusted R2 No of 25,739 17,351 8,388 observations 0.582** (9.33) Negative CF (6) Full sample (7) Pre-FD period (8) Post-FD Period (9) Note: The dependent variable is the relative cash flow forecast accuracy (ACC) See the Appendix for definitions of the variables T-statistics are based on analyst-cluster adjusted standard errors *Significant at the 5% level (one-tailed test) **Significant at the 1% level (one-tailed test) 0.651** (77.43) Positive CF (5) Regulation fair disclosure (FD), coefficient estimate (t-statistics) 0.626** (68.67) 0.613** (39.98) 0.636** (54.81) 0.037** (7.01) 0.053** (5.73) 0.034** (5.34) 20.081** (22.99) 20.084** (23.14) 0.002 (0.05) 20.098** (23.00) 0.022** (2.75) 0.021** (2.56) 0.064 (1.23) 0.022** (2.81) 0.024* (1.91) 0.023* (2.11) 0.023** (2.54) 0.020* (2.17) 0.184** (2.99) 0.023** (2.62) 0.019 (1.56) 0.025* (1.91) 0.008 (1.03) 0.009 (1.24) 20.033 (20.61) 0.007 (0.97) 0.009 (0.85) 0.011 (1.02) 20.019* (21.95) 20.015 (21.59) 20.181** (23.07) 20.019* (21.96) 20.017 (21.12) 20.023* (21.65) 0.002 (0.19) 0.002 (0.24) 20.033 (20.66) 0.001 (0.11) 20.008 (20.64) 0.011 (0.91) 20.024** (22.65) 20.022** (22.47) 20.097* (21.68) 20.023** (22.63) 20.019 (21.43) 20.027** (22.34) 20.164** (220.60) 20.166** (220.80) 20.069 (21.38) 20.164** (220.69) 20.169** (212.55) 20.170** (216.81) 20.009 (21.38) 20.009 (21.32) 20.013 (20.26) 20.009 (21.39) 20.008 (20.71) 20.009 (21.06) 0.034 0.033 0.043 0.0352 0.035 0.038 25,739 25,290 449 25,739 8,002 16,335 Full sample (4) Full sample (1) Sign of cash flows (CF), coefficient estimate (t-statistics) Bold forecasts (2) Herding forecasts (3) Variables Forecast boldness, coefficient estimate (t-statistics) Table Sensitivity Analyses—Boldness, Negative Cash Flows, and Regulation Fair Disclosure Pae and Yoon 135 Second, we partition the sample into positive and negative forecast subsamples Negative cash flow forecasts are significantly less frequent than positive cash flow forecasts Only 449 firm-year-analysts issue negative cash flow forecasts, corresponding to less than 2% of the 25,739 total cash flow forecasts We estimate the following regression ACCijt b0 b1 NEGCFijt b2 FREQijt b3 EXPGit b4 EXPFijt b5 NFIRMit b6 NINDit b7 BSIZEit b8 HORZijt b9 DAYSijt eijt ; ð6Þ where NEGCFijt is an indicator variable equal to one if analyst i issues a negative cash flow forecast for firm j in year t, and zero if otherwise Column of Table shows that cash flow forecast accuracy is lower for analysts who issue negative cash flow forecasts than for those who issue positive cash flow forecasts Columns and of Table contain the regression results for the positive versus negative cash flow subsamples The regression results for analysts who issue nonnegative cash flow forecasts are similar to those for the full sample shown in Table However, for analysts who issue negative cash flow forecasts, forecast frequency (FREQ) and forecast horizon (HORZ) are no longer significant To summarize, analysts are very cautious when they issue negative cash flow forecasts because negative operating cash flow forecasts are less frequent and are more suggestive of serious financial difficulties than negative earnings forecasts However, we not find any undue influence of negative cash flow forecasts in our main results Our finding that cash flow forecast accuracy is lower for negative cash flow forecasts is in line with the finding that earnings forecast accuracy is lower for negative earnings forecasts (Gu & Wu, 2003) Finally, we partition the sample into preregulation fair disclosure (pre-Reg FD) and post-Reg FD periods We exclude observations in year 2000 There are 8,002 observations in the pre-Reg FD period and 16,335 observations in the post-Reg FD period The U.S Securities and Exchange Commission implemented Reg FD in October 2000 This regulation prohibits companies from selectively disclosing value-relevant information to their favored analysts We expect that the impact of analyst and forecast characteristics on forecast accuracy is stronger in the post-Reg FD period than in the pre-Reg FD period Column of Table presents the regression results for the full sample, including the BOLD and NEGCF dummies Consistent with the results shown for Models and in Table 3, the coefficient on BOLD is positive and that on NEGCF is negative Column of Table presents the regression results for the pre-Reg FD period The coefficients on NEGCF and NFIRM are no longer significant In comparison, the regression results for the post-Reg FD period in Column are qualitatively similar to those found for the full sample in column Analyst and forecast characteristics are significant determinants of cash flow forecast accuracy in the post-Reg FD period Our results for cash flow forecast accuracy is consistent with those for earnings forecasts in that there are differences in the factors affecting forecast accuracy in the pre- and post-Reg FD periods (Findlay & Mathew, 2006; Mohanram & Sunder, 2006) Are Cash Flow Forecasts Naı¨ve Extensions of Earnings Forecasts? Analysts provide cash flow forecasts to satisfy the informational needs of investors However, there are concerns over the accuracy of cash flow forecasts relative to earnings forecasts Givoly et al (2009) argued that cash flow forecasts are naı¨ve extensions of Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 136 Journal of Accounting, Auditing & Finance earnings forecasts, providing little incremental information beyond earnings forecasts If cash flow forecasts are naı¨ve extensions of earnings forecasts, the factors determining the earnings forecast accuracy will also determine the cash flow forecast accuracy In that case, there would be no benefits of examining the determinants of cash flow forecast accuracy In this section, we conduct several analyses to demonstrate that cash flow forecasts matter in the presence of earnings forecasts In particular, we show that, unlike consensus earnings forecasts, the cash flow forecasts of individual analysts are neither garbling nor naı¨ve extensions of earnings forecasts Recently, several articles have presented empirical evidence supporting the usefulness of cash flow forecasts For example, McInnis and Collins (2009) argued that the provision of cash flow forecasts deters opportunistic accrual management by enhancing the transparency of accruals Consistent with the monitoring role of cash flow forecasts in the financial reporting process, companies with cash flow forecasts engage in less earnings management and exhibit better quality accruals than companies without cash flow forecasts (McInnis & Collins, 2009) In line with this finding, Call (2008) finds that the provision of cash flow forecasts improves the ability of current operating cash flows to predict future operating cash flows, and attenuates the accrual anomaly or the mispricing of operating cash flows in the capital market Other studies have examined the benefit of cash flow forecasts at the level of individual analysts Using the forecast data of individual analysts, Pae et al (2007) and Call et al (2009) find that earnings forecasts are more accurate when they are accompanied by cash flow forecasts than when only earnings forecasts are issued Given the apparent inconsistency in the literature on cash flow forecasts, we perform several tests to shed light on the issue of the usefulness of cash flow forecasts in the presence of earnings forecasts First, we examine whether cash flow forecasts are trivial extrapolations of earnings forecasts or are products of additional information gathering and analysis Following Givoly et al (2009), we construct ‘‘naı¨ve’’ cash flow forecasts by adding depreciation and amortization expenses to earnings forecasts We construct naı¨ve cash flow forecasts at the analyst level, whereas Givoly et al construct firm-level naı¨ve forecasts based on consensus forecasts In other words, we assess the level of sophistication of individual forecasts rather than that of average or consensus forecasts If analysts are naı¨ve, cash flow forecasts are similar to naı¨ve cash flow forecasts In such a case, we expect no significant difference between forecast errors based on cash flow forecasts and those based on naı¨ve cash flow forecasts Meanwhile, if analysts are sophisticated, they incorporate into their cash flow forecasts other relevant information, such as changes in working capital and deferred taxes, as DeFond and Hung (2003) and Call et al (2009) argue In such case, there is a significant difference between forecast errors based on cash flow forecasts and those based on naı¨ve cash flow forecasts Table presents test results on differences in the means and medians of forecast errors between cash flow forecasts and naı¨ve cash flow forecasts constructed by adding actual depreciation and amortization expenses to concurrently forecasted earnings.12 The forecast errors are deflated by the beginning-of-year share price We find that cash flow forecasts are more accurate than naı¨ve cash flow forecasts The mean (median) forecast error for cash flow forecasts is 0.023 (0.010), whereas the corresponding statistic for naı¨ve cash flow forecasts is 0.043 (0.019) Inconsistent with Givoly et al (2009), this result indicates that cash flow forecasts issued by analysts are likely to be sophisticated As explained earlier, a key difference between our analysis and that of Givoly et al is that our analysis is based Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Pae and Yoon 137 Table Performance of Analysts’ Cash Flow Forecasts Versus Naı¨ve Cash Flow Forecasts Absolute forecast errora Type of forecast Full sample M Median Accurate CF groupb M Median Inaccurate CF groupb M Median Analysts’ cash flow forecasts 0.023 0.010 0.0126 0.0033 0.0456 0.0252 0.043 0.019 0.0350 0.0146 0.0682 0.0165 Naı¨ve cash flow forecastsc Difference 20.021** 20.009** 20.0224** 20.0113** 20.0225** 0.0087** No of observations 9,646 2,411 2,411 a The absolute forecast error is the absolute value of the difference between the forecasted and actual cash flows The absolute forecast errors are deflated by the beginning-of-year share price b The accurate (inaccurate) CF group includes cash flow forecasts in the top (bottom) quartile with respect to the relative cash flow forecast accuracy in each firm-year c The naı¨ve cash flow forecasts are constructed by adding depreciation and amortization expenses to earnings forecasts **Significant at the 1% level on analyst-level data, whereas Givoly et al use firm-level data We believe that the question of whether cash flow forecasts are sophisticated is better addressed by examining the forecasts of individual analysts rather than by investigating aggregate firm-level data, such as consensus forecasts There are several other differences between our sample and Givoly et al.’s sample: (a) We use actual I/B/E/S cash flow figures, whereas Givoly et al use COMPUSTAT cash flow figures; (b) we restrict our analysis to forecasts issued in the first 11 months of the fiscal period, whereas Givoly et al use the last forecasts before earnings announcements; and (c) we require that at least two analysts cover our sample companies The results we document here may differ from those in Givoly et al because of the aforementioned reasons But we are not differentiating between the various explanations Thus, our results on the sophistication of analysts’ cash flow forecast should be interpreted with caution We further partition our sample into three groups: (a) a group of accurate cash flow forecasters, (b) a group of average cash flow forecasters, and (c) a group of inaccurate cash flow forecasters The accurate cash flow group includes analysts in the top quartile with respect to the relative cash flow forecast accuracy for each firm-year The inaccurate cash flow group includes analysts in the bottom quartile The average group includes analysts in the two middle quartiles We repeat the same analysis separately for the accurate and the inaccurate cash flow groups Regardless of ex post cash flow forecast accuracy, we find that cash flow forecasts are significantly different from naı¨ve cash flow forecasts, suggesting that at the individual-analyst level, we cannot easily replicate cash flow forecasts simply by adding depreciation and amortization expenses to earnings forecasts We compare the efficiency of cash flow forecasts with that of earnings forecasts We run the following regression of current forecast errors on the preceding year’s forecast errors for cash flow and earnings forecasts, respectively: FEijt b0 b1 FEijtÀ1; ð7Þ We estimate Regression to examine differences in forecast efficiency between the three different cash flow forecast accuracy groups Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 138 Journal of Accounting, Auditing & Finance Table Forecast Efficiency of Cash Flow Forecasts and Earnings Forecasts (1) (2) Variables Cash flow FE Earnings FE Intercept 0.009** (6.97) 0.004** (6.16) 0.533** (6.99) 0.396** (2.44) Cash flow FE 0.009** 0.003** (12.83) (6.80) 20.003** 20.001 (22.63) (21.03) 0.013** 0.002** (4.52) (2.50) 0.331** 0.577** (6.20) (4.42) 20.143* 0.046 (22.14) (0.11) 0.322** 20.273 (2.58) (21.30) 0.474 0.201 38.07 ( \ 0.01) ACCD INACCD FEt21 (A) ACCD FEt21 (B) INACCD FEt21 Adjusted R2 Wald statistic for the equality of (A) (p value) Wald statistic for the equality of (A B) (p value) No of observations 0.362 0.177 68.35 ( \ 0.01) 130.23 ( \ 0.01) NA 4,513 Earnings FE 4,513 4,513 4,513 Note: The dependent variable is either the cash flow forecast error or the earnings forecast error The forecast error (FE) is deflated by the beginning-of-year share price FEt21 is the lagged FE The sample is partitioned into quartiles based on the relative cash flow forecast accuracy ACCD equals one when the relative cash flow forecast accuracy is in the top quartile, zero otherwise INACCD equals one when the relative cash flow forecast accuracy is in the bottom quartile, zero otherwise T-statistics are based on analyst-cluster adjusted standard errors *Significant at the 5% level **Significant at the 1% level FEijt b0 b1 ACCDijt b2 INACCDijt b3 FEijtÀ1 b4 ACCD FEijtÀ1 b5 INACCD FEijtÀ1 eijt : ð8Þ Givoly et al (2009) report that both cash flow and earnings forecasts exhibit significant serial correlations, that is, the coefficient on FEijt21 in Regression is significantly different from zero, and the serial correlation between successive cash flow forecast errors is larger than that between successive earnings forecast errors Accordingly, this suggests that compared with earnings forecasts, cash flow forecasts are less efficient in incorporating past forecast errors Consistent with the finding of Givoly et al., Column of Table shows that the serial correlation of cash flow forecast errors (.533) is greater than that of earnings forecast errors (.396), implying that cash flow forecasts are on average less efficient than earnings forecasts If cash flow forecasts are ‘‘naı¨ve’’ extensions of earnings forecasts, then cash flow forecast accuracy is determined mainly by earnings forecast accuracy and no incremental information beyond earnings forecasts is derived Thus, partitioning by cash flow forecast accuracy should not matter Under the view that cash flow forecasts are naı¨ve extensions of earnings forecasts, cash flow forecasts are less efficient than earnings forecasts, regardless of their levels of accuracy However, we find that the degree of cash flow forecast inefficiency depends on the level of cash flow forecast accuracy Column of Table shows that cash flow forecasts are more efficient than earnings forecasts when analysts accurately predict cash flows For example, the Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Pae and Yoon 139 serial correlation of cash flow forecast errors for the average cash flow forecast group (0.331) is smaller than that of earnings forecast errors (0.577) For the accurate cash flow forecast group, the serial correlation of cash flow forecast errors (0.188 = 0.331 – 0.143) is much smaller than that of earnings forecast errors (0.603 = 0.577 0.046) The superiority of earnings forecasts is only observed for the inaccurate cash flow forecast group The overall inefficiency of cash flow forecasts as compared with earnings forecasts in Model of Table seems to be driven by the inaccurate cash flow forecast group Our test of the relative forecast efficiency between cash flows and earnings corroborates the result in Table that cash flow forecasts are unlikely to be naive extensions of earnings forecasts Finally, we examine whether the potential benefit of cash flow forecasts is greater when cash flow forecasts are more accurate than when they are less accurate Prior studies on the effect of cash flow forecasts on earnings forecast accuracy (e.g., Call et al., 2009; Pae et al., 2007) have focused largely on the presence of cash flow forecasts We extend the existing literature on the benefit of cash flow forecasts by considering differences in the levels of cash flow forecast accuracy.13 We estimate the following regressions of relative earnings forecast accuracy (ACCE) on analyst characteristic variables We use superscript E to indicate that variables are measured with respect to earnings, and we use superscript cash flow in ACCCF to denote that relative forecast accuracy is measured with respect to cash flow forecasts ACC E ijt b0 b1 CFDE ijt b2 FREQE ijt b3 EXPGE it b4 EXPF E ijt b5 NFIRM E it b6 NINDE it b7 BSIZEE it b8 HORZ E ijt b9 DAYS E ijt ð9Þ eijt : ACC E ijt b0 b1 ACC CF ijt b2 FREQE ijt b3 EXPGE it b4 EXPF E ijt b5 NFIRM E it b6 NINDE it b7 BSIZEE it b8 HORZ E ijt b9 DAYS E ijt ð10Þ eijt : Regression contains an indicator variable, CFD, which is equal to one when earnings forecasts are accompanied by cash flow forecasts We predict a positive coefficient on CFD because earnings forecasts are more accurate when they are accompanied by cash flow forecasts than when only earnings forecasts are issued (Call et al., 2009; Pae et al., 2007) Regression 10 contains the continuous, relative cash flow forecast accuracy (ACCCF) instead of the indicator variable for the presence of cash flow forecasts (CFD) We argue that the beneficial impact of cash flow forecasts on earnings forecast accuracy, if any, is greater for more accurate cash flow forecasts than for less accurate cash flow forecasts, and predict a positive coefficient on ACCCF Table reports the estimation results of the relative earnings forecast accuracy regressions on the presence of cash flow forecasts (CFD) or the relative cash flow forecast accuracy (ACCCF) after we control for analyst and forecast characteristics measured with respect to earnings forecasts We estimate Regression on an earnings forecast sample of 387,134 analyst-firm-year observations The size of the earnings forecast sample is much larger than that of the cash flow forecast sample because earnings forecasts are more prevalent than cash flow forecasts in the I/B/E/S Column of Table reports the estimation results of Regression 9, whereas column reports the estimation results of the earnings forecast regression without CFD The Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 140 Journal of Accounting, Auditing & Finance Table The Effect of the Presence of Cash Flow Forecasts and the Cash Flow Forecast Accuracy on the Earnings Forecast Accuracy (1) Variables Intercept (?) CFD (1) ACCCF (1) FREQE (1) EXPGE (1) EXPFE (1) NFIRME (2) (2) NINDE BSIZEE (1) HORZE (2) DAYSE (2) Adjusted R2 No of observations Coefficient estimate (2) t value Coefficient estimate (3) t value 0.715** (357.27) 0.713** 0.019** (355.35) (6.08) 0.054** 20.004 0.003 20.008** 20.027** 20.011** 20.271** 20.012** 0.090 387,134 (27.31) (21.45) (1.39) (23.04) (211.20) (24.37) (2131.12) (27.32) 0.053** 20.004 0.003 20.009** 20.027** 20.011** 20.271** 20.012** 0.090 387,134 (27.29) (21.40) (1.42) (23.23) (211.04) (24.45) (2131.13) (27.22) Coefficient estimate t value 0.664** (74.51) 0.114** 0.057** 20.025** 0.014* 20.011 20.022** 0.011 20.263** 20.025** 0.115 26,108 (17.60) (7.06) (22.56) (1.97) (21.02) (22.68) (1.36) (230.79) (24.54) Note: The dependent variable is the relative earnings forecast accuracy (ACCE) CFD is an indicator variable that equals one if the earnings forecasts are accompanied by cash flow forecasts, zero otherwise The superscript, E (CF), represents variables that are measured with respect to earnings (cash flow) forecasts See the Appendix for definitions of the variables T-statistics are based on analyst-cluster adjusted standard errors *Significant at the 5% level (one-tailed test) **Significant at the 1% level (one-tailed test) coefficient on CFD (0.019) is significantly positive at 1% level, suggesting that earnings forecasts are on average more accurate when they are accompanied by cash flow forecasts This result is consistent with the findings of Pae et al (2007) and Call et al (2009) We estimate Regression 10 on a restricted sample of 26,108 observations in which analysts provide both earnings and cash flow forecasts.14 Our regression specification differs from those of Pae et al (2007) and Call et al (2009) in that we restrict our analysis to analysts who provide both cash flow and earnings forecasts, and examine the effect of cash flow forecast accuracy on earnings forecast accuracy instead of the effect of the mere presence of cash flow forecasts Column of Table shows that the coefficient on ACCCF (0.014) is significantly positive at 1% level, suggesting that the beneficial impact of cash flow forecasts on earnings forecast accuracy increases as cash flow forecast accuracy increases However, our result should be interpreted with a caveat: Cash flow forecast accuracy is associated with earnings forecast accuracy, but this does not necessarily mean that accurate cash flow forecasts increase earnings forecast accuracy Our research design only ascertains an association not the direction of causality Taken together, our analyses based on forecasts issued by individual analysts indicate that cash flow forecasts are unlikely to be ‘‘naı¨ve’’ extensions of associated earnings forecasts Thus, cash flow forecasts are useful to investors even in the presence of earnings forecasts Summary and Conclusion Among investors, the demand for cash flow information has increased significantly in recent years, due in part to earnings quality concerns precipitated by the Enron accounting Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Pae and Yoon 141 scandal Companies with incentives to inflate their reported earnings are more likely to manipulate earnings by exercising their reporting discretion over accruals, rather than by taking real actions, such as cutting down cash expenditures on R&D and advertising, which influence corporate competitiveness In relation to these companies, cash flow information should prove useful in understanding the quality of reported earnings, as well as in determining the true financial conditions and operating performance of companies, because cash flows are less susceptible to manipulation because of their greater visibility and scrutiny (McInnis & Collins, 2009) Prior research has determined that analysts are more likely to issue cash flow forecasts, in addition to earnings, for companies with bigger accruals and those in financial distress (DeFond & Hung, 2003) We attempt to determine why individual analysts differ in terms of their cash flow forecasting performance Consistent with previous findings, earnings forecast accuracy, analyst and forecast characteristics, and past cash flow forecasting performance are useful in explaining differences in the cash flow forecast accuracy of individual analysts We find that compared with earnings forecasting experience and past earnings forecast accuracy, cash flow–specific forecasting experience and past cash flow forecast accuracy better explain current cash flow forecast accuracy This suggests that forecasting cash flows requires skills and expertise different from those needed in forecasting earnings We also observe that cash flow forecast accuracy is higher for analysts who issue bold cash flow forecasts or predict positive cash flows than for those who issue herding cash flow forecasts or predict negative cash flows In addition, we provide empirical evidence proving that cash flow forecasts are unlikely to be ‘‘naı¨ve’’ extensions of earnings forecasts Investors may find the results of this study useful in identifying more accurate cash flow forecasters Appendix Definition of Variables ACCijt: Cash flow forecast accuracy of analyst i for firm j in year t, measured by the negative of the absolute cash flow forecast error (AFEijt) AFEijt is the absolute value of the difference between the actual and forecasted cash flows per share ACCLijt: Cash flow forecast accuracy of analyst i for firm j in year t21 ACCEPSLijt: Earnings forecast accuracy of analyst i for firm j in year t21 FREQijt: The number of cash flow forecasts issued by analyst i for firm j in year t EXPGit: General cash flow forecasting experience of analyst i as of year t, measured by the number of years that analyst i has issued cash flow forecasts for any firm EXPFijt: Firm-specific cash flow forecasting experience of analyst i as of year t, measured by the number of years that analyst i has issued cash flow forecasts for firm j NFIRMit: The number of firms that analyst i follows in year t NINDit: The number of industries that analyst i follows in year t, measured by the number of Institutional Brokers’ Estimate System (I/B/E/S) SIC codes BSIZEit: The size of the brokerage house employing analyst i in year t, measured by the number of analysts employed by the brokerage house HORZijt: The number of days from the forecast date to the fiscal year-end date DAYSijt: Days elapsed since the last forecast by any analyst, calculated as the number of days between analyst i’s cash flow forecast date for firm j in year t and the most recent cash flow forecast date for firm j by any analyst Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 142 Journal of Accounting, Auditing & Finance BOLDijt: Indicator variable equal to one if analyst i’s cash flow forecast for firm j in year t is bold; otherwise, it is set to zero We classify a cash flow forecast as a bold forecast if the forecast is above both the preceding forecast of the analyst and the consensus forecast or below both, and otherwise designate the forecast as a herding forecast NEGCFijt: Indicator variable equal to one if analyst i’s cash flow forecast for firm j in year t is negative; otherwise, it is set to zero Acknowledgments The authors are grateful for the useful comments from and discussions with Wooseok Choi, Seok Woo Jeong, and Choong-Yuel Yoo Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/ or publication of this article Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by a Korea University Grant Notes Givoly, Hayn, and Lehavy (2009) report that the percentage of firms with cash flow forecasts increased from 2.5% in 1993 to 57.2% in 2005 Consistent with the increase in the proportion of firms with cash flow forecasts, the proportion of analysts who provide cash flow forecasts in addition to earnings forecasts increased from 3.7% in 1993 to 53.5% in 2005 (Pae, Wang, & Yoo, 2007) In the I/B/E/S U.S detail database, earnings per share (EPS) forecasts comprise more than 40% of all forecasts When we include the net income, pretax income, operating income, EBIT, and EBITDA, the proportion of earnings-related forecasts increase to more than 70% In contrast, sales and cash flow forecasts account for 12.61% and 4.05%, respectively Analysts provide cash flow forecasts for about 52% of firms with management cash flow forecasts About 7% of firms feature management cash flow forecasts when analysts provide cash flow forecasts for the same firms In only 1.2% of firms, managements issue cash flow forecasts for firms without analyst cash flow forecasts, and analysts provide cash flow forecasts for about 15% of firms without management cash flow forecasts (Wasley & Wu, 2006) Cash flow forecasts associated with analyst code are deleted because the characteristics of these analysts cannot be determined We lose about 49% of cash flow forecast observations because of the lack of reported cash flow values in the I/B/E/S over the forecast period from 1993 to 2006 If we include year 2007, the percentage of cash flow forecasts without actual cash flow figures increases to 54% We collect forecast and actual data from the March 2008 production version of I/B/E/S, in which most of the 2007 cash flow actual figures are unavailable If there is no variation in a variable, the variable is set to zero For example, if all the analysts following a firm have the same number of years of general forecasting experience, the range of EXPG is zero To avoid the zero-denominator problem in standardization, the (standardized) EXPG is set to zero If we include both earnings and cash flow forecasting experience, analysts have an average of 6.31 years of general experience and 2.94 years of firm-specific experience (untabulated) Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 Pae and Yoon 143 As noted in Clement and Tse (2005), brokerage houses appear several times in the sample 10 11 12 13 14 because they employ multiple analysts When each brokerage house is counted once in the sample each year, the median brokerage house size is 22 In general, extant studies on earnings forecast accuracy (e.g., Clement & Tse, 2003, 2005) report a positive association between earnings forecast accuracy and brokerage house size It is surprising that cash flow forecast accuracy is lower for analysts who are affiliated with large brokerage houses The use of a brokerage house dummy, which is equal to one for a brokerage house whose size is bigger than the median of the brokerage house size, yields similar results To check whether the relationship between forecast accuracy and brokerage house size differs between cash flows and earnings, we perform the same analysis for earnings forecast accuracy We find that the coefficient on brokerage house size is positive but insignificant (untabulated) Our sample period is relatively recent (1994–2007) and we require that analysts issue cash flow forecasts in addition to earnings forecasts; hence, it is premature to infer a negative relationship between forecast accuracy and brokerage house size In the full sample, about 46% of the analysts issued cash flow forecasts in the preceding year (11,964 out of 25,739) The restriction to at least two analysts with cash flow forecasts in the preceding year further reduces the sample by 505 observations About two thirds of the forecasts are classified as bold forecasts The proportion of bold forecasts in our sample is slightly lower than the figure of 73.3% in Clement and Tse (2005) The use of the preceding year’s depreciation and amortization expenses yields similar results After controlling for earnings forecast accuracy, Call, Chen, and Tong (2009) find that brokerage houses are less likely to fire accurate cash flow forecasters However, they not examine whether there are differences in earnings forecast accuracy between more accurate cash flow forecasters and less accurate cash flow forecasters There is a subtle difference in the cash flow sample size presented in Tables and The analyst and forecast control variables in Table are measured with respect to earnings forecasts, whereas in Table 2, they are measured with respect to cash flow forecasts References Ali, A (1994) The incremental information content of earnings, working capital from operations, and cash flows Journal of Accounting Research, 32, 61-74 Block, B (1999) A study of financial analysts: Practice and theory Financial Analysts Journal, 55, 86-95 Brown, L (2001) How important is past analyst forecast accuracy? 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Journal of Accounting Research, 44, 387-429 Wilson, G (1986) The relative information content of accruals and cash flows: Combined evidence at the Earnings Announcement and Annual Report Release Date Journal of Accounting Research, 24, 165-200 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2012 [...]... 13 0 14 2 14 7 15 5 12 5 18 1 223 18 5 17 7 205 80 2 ,13 2 5 14 30 41 44 78 77 94 12 3 71 77 59 12 2 10 7 12 2 16 4 66 1, 294 14 21 66 98 13 9 12 8 12 7 11 2 95 16 0 13 1 227 232 17 9 15 0 11 3 51 2,043 5 10 47 44 66 90 83 99 11 4 91 90 10 5 14 9 11 2 14 8 202 82 1, 537 14 25 49 95 11 7 11 6 12 1 10 7 10 4 14 0 11 8 18 1 205 17 4 12 4 75 35 1, 800 Downloaded from jaf.sagepub.com at Taylor's University on December 2, 2 012 18 Journal of Accounting,. .. 0 012 0 .13 07 \.00 01 20.0728 3566 0.2499 0343 0.2300 0060 4, 913 16 9.04 \.00 01 60 .1 Full sample Parameter estimate Full sample Parameter estimate 24.5 711 \.00 01 21. 8394 \.00 01 0.2478 011 4 0 .16 31 \.00 01 0.0700 \.00 01 20 .14 11 016 7 0 .15 37 0660 0 .13 04 0269 21. 6833 \.00 01 0. 011 4 8295 11 6 ,14 3 4 71. 63 \.00 01 57.2 24.5330 \.00 01 23.2767 \.00 01 0.5674 \.00 01 0 .17 45 \.00 01 0.0522 \.00 01 20 .17 08 0020 0 .17 94 0 210 ... 218 2 31 208 286 354 286 272 277 11 7 3,337 19 35 96 13 9 18 3 206 204 206 218 2 31 208 286 354 286 272 277 11 7 3,337 38 70 19 2 278 366 412 408 412 436 462 416 572 708 572 544 554 234 6,674 0 11 18 33 33 40 36 43 48 33 25 34 49 47 40 31 40 5 61 0 4 11 24 32 38 45 34 42 41 28 25 51 44 35 43 28 525 0 10 21 22 42 36 35 37 29 40 40 32 54 37 45 41 35 556 19 10 46 60 76 92 88 92 99 11 7 11 5 19 5 200 15 8 15 2 16 2 14 ... Accounting, Auditing & Finance Table 2 (continued) _ Panel C: Frequency of Revisions per Reviser-Only Sample Revisions Firms 1 2 3 4 5 6 7 8 9 10 11 12 13 15 16 19 23 26 40 1, 348 360 13 8 62 25 12 4 8 5 6 3 1 1 1 2 1 1 1 1 % 68.08 18 .18 6.97 3 .13 1. 26 0. 61 0.20 0.40 0.25 0.30 0 .15 0.05 0.05 0.05 0 .10 0.05 0.05 0.05 0.05 Cumulative n 1, 348 1, 708 1, 846 1, 908 1, 933 1, 945 1, 949... 66.096 1. 000 20.042 0 0 0 20.057 20. 018 20.089 20.055 20 .16 2 20 .10 9 0.425 20. 010 0.0 71 20 .11 7 20 .13 8 0.0 61 0 218 .677 1. 000 20.006 1. 000 0 1. 000 20. 011 20.004 20.038 20.024 20.076 20.050 0.947 0.0080 0 .12 3 0.236 0 .11 5 0. 215 2.527 5.000 0. 018 0.030 0.223 0.423 0.563 0.764 0. 412 0.644 0.378 0. 515 0 0 826.0 41 3,442.30 11 ,16 4 .13 2.000 4.000 5.000 0.004 0. 014 0.029 5.000 10 .000 16 .000 1. 000 1. 000 1. 000 1. 000 1. 000... (clustered by firm) 0052 \.00 01 2.2038 \.00 01 0476 \.00 01 2.0039 5879 2.0804 \.00 01 012 4 \.00 01 0039 \.00 01 0 010 \.00 01 2.00 31 0044 0039 018 0 0032 00 61 0007 014 6 2.008 00 01 0478 \.00 01 0 013 8452 0 016 0608 2.006 02 31 0422 \.00 01 2.0082 313 9 0289 013 3 2.0 014 4589 0 010 \.00 01 2.0000 9220 0 016 0993 2.0030 0 412 2. 012 7 \.00 01 \.00 01 00422 015 32 \.00 01 015 8 \.00 01 018 6 9,703 7,275 12 0,646 Note: The dependent... Differences t statistics p 3,249 20. 011 1 20.0032 20.0079 23.35 0008 1, 1 71 20. 010 3 20.0039 20.0065 21. 30 19 34 3 ,18 0 20. 010 7 2 ,17 2 20. 011 8 20.0007 20.0024 20. 010 0 20.0094 24.06 22.79 \.00 01 0053 2,820 20. 011 1 0.00 01 20. 011 1 24 .16 \.00 01 Notes: 1 The table presents excess total stock returns for a sample of companies with earnings revisions and a matched control sample of companies The table also presents... DEBT 1 SEGNUM 1 EARNVOL 1 PERSE 2 AUC ? LOSS 1 E/P ? ACCPROP 1 No of observations Likelihood ratio p value Percentage concordant Matched pair sample Parameter estimate Matched pair sample Parameter estimate 20.5405 \.00 01 22.20 21 0035 0.34 01 019 5 0.0695 00 01 0 .14 02 \.00 01 20 .12 59 13 47 0.2 613 0402 0.2505 0050 20.5763 0972 20.0705 4 013 4,327 18 1.52 \.00 01 61. 0 20.4988 \.00 01 22.8365 \.00 01 0.4 311 0020... 14 1, 695 Panel B: Upward and Downward Earnings Revisions and Initial Earnings Surprise Sample firms Year 19 93 19 94 19 95 19 96 19 97 19 98 19 99 2000 20 01 2002 2003 2004 2005 2006 2007 2008 2009 All Control firms Sample firms Upward revisions Downward revisions Positive FSURP Negative FSURP Positive FSURP Negative FSURP 10 20 51 58 73 84 74 64 71 76 83 10 5 13 1 10 1 95 72 37 1, 205 9 15 45 81 110 12 2 13 0 14 2... CARtotal CARaf n 3,2 31 3,337 2,552 2,527 3 ,15 7 3 ,16 5 2,928 3,337 2,768 3,337 3,337 3,337 3,337 3,337 3,337 3,337 3,337 3,337 3,337 M SD 1. 658 1. 589 20.028 0 .16 4 0.227 0. 419 0.206 0.434 0 .13 0 0.376 0.244 0.209 0.075 0.264 4,864.54 16 ,9 51. 71 2.662 1. 813 20.006 71 0.072 6.664 6.504 0.277 0.448 0.797 0.402 20.020 0.228 20. 017 0 .11 1 20.005 0.075 20.003 0.047 20. 011 0 .12 8 20.003 0.094 10 th 25th 50th 75th 90th