Do Supplementary Sales Forecasts Increase the Credibility of Financial Analysts Earnings Forecasts?

Transcription

1 Do Supplementary Sales Forecasts Increase the Credibility of Financial Analysts Earnings Forecasts? Edmund C. Keung* Doctoral Candidate Olin School of Business, Washington University Comments welcome. First version: November 9, 2005 This version: January 10, 2006 Abstract: I examine whether the market reacts more strongly to earnings forecast revisions when financial analysts supplement their earnings forecasts with sales forecasts. I find that earnings forecast revisions supplemented with sales forecast revisions have a greater impact on security prices than stand-alone earnings forecast revisions. Supplemented earnings forecasts are more accurate and timely than stand-alone earnings forecasts, controlling for other individual analyst characteristics. Financial analysts who provide supplementary sales forecasts are more likely to be employed by smaller brokerage houses and less experienced. These results suggest that supplementary sales forecasts add credibility to earnings forecast revisions, and that financial analysts provide sales forecasts to convey their ability in earlier stages of their career. * Campus Box 1133, St. Louis, MO 63130; Tel ; Fax ; keungc@olin.wustl.edu. I thank Richard Frankel (Chair of my Dissertation Advisory Committee), Nick Dopuch, Ron King, Tzachi Zach, and workshop participants at Washington University for their helpful comments and suggestions. All errors are mine. I acknowledge the contribution of Thomson Financial for providing earnings per share forecast data, available through the Institutional Brokers Estimate System (I/B/E/S). These data are provided as part of a broad academic program to encourage earnings expectations research.

2 Do Supplementary Sales Forecasts Increase the Credibility of Financial Analysts Earnings Forecasts? I. INTRODUCTION In this paper I investigate two research questions: (1) How does the market react to individual analyst earnings forecast revisions when financial analysts provide supplementary sales forecast revisions? (2) What motivates financial analysts to provide supplementary sales forecasts? Specifically, I examine the association between supplementary sales forecast revisions and the market response to financial analysts earnings forecast revisions. I also examine the association between individual analyst characteristics and the likelihood that financial analysts supplement their earnings forecasts with concurrent sales forecast revisions. I define a sales forecast revision as supplementary if a financial analyst revises both her earnings and sales forecasts on the same date as reported in the Institutional Brokers Estimate System (I/B/E/S) database. Future sales are an important driver of firm value and a key variable to investors engaged in fundamental analysis. I argue that financial analysts increase the credibility of their earnings forecasts by providing supplementary sales forecasts because sales forecasts justify and explain earnings forecasts. First, financial analysts convey the causes of the earnings forecast revisions through sales forecasts, so that the investors can better evaluate the credibility of earnings forecasts with their knowledge of the business and other macroeconomic factors. 1 Second, sales forecasts are ex post verifiable since the realization of reported sales are observable at the year end. 2 Financial analysts are more likely to provide supplementary sales 1 For example, sales forecasts explain whether the revisions in earnings forecast are due to growth in revenue or cost control. Earnings growth supported by increase in revenue is more sustainable because growth in revenue reflects the success of the underlying product differentiation strategy (Porter 1980). Penman (2001, p.503) suggests that sales forecasts contain information on sales growth and firm s marketing plan, which is not contained in earnings forecasts. 2 In contrast to ex post verifiable forecasts, soft forecasts are those that are not specific enough to be compared with subsequent realizations (Hutton, Miller, and Skinner 2003). An example of a soft forecast would be qualitative 1

3 forecasts when they are more informed because investors can use the accuracy of sales forecasts to gauge their performance. 3 My first set of tests examines whether supplementary sales forecasts add credibility to earnings forecast revisions. I define forecast credibility as the extent to which investors believe the forecast and measure forecast credibility using the forecast response coefficient to an earnings forecast revision. Specifically I examine whether investors react more strongly to earnings forecast revisions supplemented with sales forecast revisions (hereafter supplemented revisions ) than to revisions not supplemented with sales forecast revisions (hereafter stand-alone revisions ). I find supplemented earnings forecast revisions have a greater impact on contemporaneous security prices than stand-alone earnings forecast revisions, controlling for the independent information in sales forecast revisions. The difference in return responses suggests that investors view supplemented earnings forecast revisions as more credible. My second set of tests examines the association between individual analyst characteristics and supplementary sales forecasts. I find that supplemented earnings forecasts are more accurate and timely, controlling for other individual analyst characteristics. A financial analyst is more likely to issue a supplementary sales forecast when she follows a larger number of firms and industries, when she is from a smaller brokerage house, and when she has less experience. These results suggest financial analysts provide sales forecasts to convey their ability in earlier stages of their career when their perceived earning forecasts accuracy is low. My study contributes to the existing literature in two ways. First, I extend the literature on discussion on a firm s prospect by a financial analyst in an analyst report. 3 Hutton, Miller, and Skinner (2003) find that good news management earnings forecasts are informative only when supplemented with verifiable forward-looking statements. Baginski, Hassell, and Kimbrough (2004) find that explanations for management earnings forecasts are associated with a greater price reaction to management forecasts, controlling for magnitude of unexpected earnings. Wasley and Wu (2005) find that managers issue cash flow forecasts to signal good news in cash flows and to lend credibility to good news in earnings. Taken together, these findings suggest that financial analysts add credibility to their earnings forecasts by providing verifiable supplementary information when they revise their earnings forecasts. 2

4 the market reaction to individual analyst forecast revisions by examining sales forecasts, a relatively new form of forecast disseminated by analyst tracking services that has not been studied. I document the market reaction to supplementary sales forecasts and find that supplementary sales forecasts both convey independent information to the market and add credibility to earnings forecasts. Second, I extend prior research on individual analyst characteristics by examining whether they are associated with financial analysts decision to issue supplementary sales forecasts. My evidence suggests the decision to provide sales forecasts is driven by financial analysts incentive to build their reputation in earlier stages of their career. The presence of supplementary sales forecasts, conditional on other individual analyst characteristic, might be used to identify more accurate and timely earnings forecasts. The remainder of the paper is organized as follows. Section 2 discusses prior literature and develops hypotheses. Section 3 describes sample selection criteria and provides sample descriptive statistics. Sections 4 and 5 present the empirical results for tests of market reaction and tests of determinants of supplementary sales forecasts respectively. Section 6 concludes. II. RELATED LITERATURE AND HYPOTHESES DEVELOPMENT This study extends two streams of literature. The first is the literature on the market reaction to individual analyst forecast revisions. The second is the literature on the association between individual analyst characteristics and the properties of earnings forecasts. Prior research documents the market response to individual analyst earnings forecast revisions (Lys and Sohn 1990; Stickel 1991; Park and Stice 2000). While individual analyst earnings forecasts are informative, earnings forecasts is only one of the many signals about firms prospects provided by financial analysts. Francis and Soffer (1997) find that earnings forecasts and stock recommendations are incrementally informative to one another and that 3

5 investors place greater reliance on earnings forecast revisions when stock recommendations are favorable. Asquith, Mikhail, and Au (2005) examine the market reaction to analyst reports and find that the market reaction is associated with both magnitude of revision in earnings forecast and the strength of an analyst's arguments in the analyst report. 4 Although Asquith, Mikhail, and Au (2005) show that supplementary information in analyst reports provides incremental information to the market, they do not distinguish whether the supplementary information provided by financial analysts conveys information independent of earnings forecast revisions or whether such information adds credibility to earnings forecasts, resulting in strong market reactions. Because sales forecasts justify earnings forecasts and are ex post verifiable, I hypothesize that they increase the credibility of earnings forecasts. Thus, my first hypothesis (in alternative form): H1: Supplementary sales forecasts add credibility to earnings forecast revisions, controlling for the information independent of earnings forecast revisions. Prior literature finds individual analyst earnings forecast accuracy increases with prior accuracy, broker size, forecast frequency, and firm experience, and decreases with days elapsed since the last forecast, forecast horizon, number of companies and number of industries the financial analyst follows (Mikhail, Walther, and Willis 1997; Clement 1999; Jacob, Lys, and Neale 1999; Brown 2001; Clement and Tse 2003). 5 Because difference in forecast accuracy is 4 Asquith, Mikhail, and Au (2005) measure the level of analyst justification by aggregating the number of positive remarks less the number of negative remarks related to 14 specific criteria about the firm in the analyst report: revenue growth, earnings growth, new product introductions, new projects, cost efficiencies, expectations met, mergers and acquisitions, repurchase programs, industry climate, management, international operations, leverage, competition, and risk. 5 Mikhail, Walther, and Willis (1997) document a decline in the absolute value of analyst quarterly forecast errors as firm-specific experience increases. Clement (1999) finds that forecast accuracy is positively associated with analysts' experience and employer size but negatively associated with the number of firms and industries followed by the analyst. Jacob, Lys, and Neale (1999) report that analysts' aptitude and brokerage house characteristics are associated with forecast accuracy. Brown (2001) finds that past performance predicts forecast accuracy. Clement and Tse (2003) find that forecast frequency is as important as past accuracy in explaining future accuracy. 4

6 associated with observable analyst and brokerage firm characteristics, I examine whether supplementary sales forecasts could be an ex ante signal to the market that the financial analysts are better informed and that their earnings forecasts are more accurate. On one hand, if financial analysts provide sales forecasts because they are more informed and want to convey their ability, the presence of supplementary sales forecasts should be associated with more accurate earnings forecasts, controlling for other determinants of forecast accuracy. On the other hand, if supplementary sales forecasts are issued by uninformed analysts but are perceived by the market to be useful, supplementary sales forecasts would not add to earnings forecast accuracy. Thus, my second hypothesis: H2: Earnings forecasts supplemented with sales forecast revisions are more accurate than stand-alone earnings forecasts, controlling for other individual analyst characteristics. Besides accuracy, timeliness is another important dimension of analyst performance. Financial analysts are rewarded for the amount of trading commissions they generate for their brokerage houses. 6 Financial analysts have an incentive to release timely information so that the market would trade on their analyst reports (Schipper 1991; Jackson 2005). Financial analysts trade off accuracy and timeliness when they issue earnings forecasts. Financial analysts that issues timelier earnings forecasts have a greater impact on stock prices when they revise their earnings forecasts (Cooper, Day, and Lewis 2001). Clement and Tse (2003) find that investors respond more strongly to forecasts issued earlier in the fiscal year even though those forecasts are less accurate than later forecasts. I argue that the more timely an earnings forecast revision is, the more likely it is supplemented with sales forecast revision. This leads me to my third 6 This is known as the soft dollar practice, where institutional investors intentionally direct a portion of their trades through the brokerage houses whose financial analysts have provided the trade-generating research. Since brokerage firms profits depend directly on commission revenues, analyst compensation is tied to the amount of commissions they generate from their research (Irvine 2001). 5

7 hypothesis: H3: Earnings forecasts supplemented with sales forecast revisions are more timely than stand-alone earnings forecasts, controlling for other individual analyst characteristics. Perceived forecast accuracy increases with analyst reputation (Stickel 1992; Sinha, Brown, and Das 1997; Park and Stice 2000). Hayes (1998) shows that trading volume in the forecast stock increases in the deviation of the individual analyst earnings forecast from investors prior expectation and the perceived accuracy of the forecast. Clement and Tse (2003) find the market reaction is stronger when individual analyst characteristics predict that the earnings forecast revisions are more likely to be accurate. Chen, Francis, and Jiang (2005) show that market reaction to an individual analyst forecast revision is increasing in the product of the accuracy and the length of a financial analyst forecast record. According to their findings, the market using a Bayesian learning model to infer the ability of financial analysts, where the market reacts more strongly to individual analyst forecast revisions as the more able financial analysts accumulate a record of forecasting performance. 7 The market learns about the ability of financial analysts and updates its beliefs about their perceived accuracy through their performance and action over time. I argue that the more able analysts build up their reputation more quickly by providing supplementary sales forecasts. As more information about a more able financial analyst is revealed, the market is more certain about her ability and reacts more strongly, resulting in higher trading volume (Abarbanell, Lanen, and Verrecchia 1995). Specifically, I hypothesize that less experienced financial analysts and those employed by smaller brokerage houses are more likely to issue supplementary sales 7 In contrast to a Bayesian learning model, a static learning model predicts that investors responses condition only on the average prior accuracy of the analyst, where the length of past forecast record plays no role per se. 6

8 forecasts when their perceived forecast accuracy is low. Thus the following hypotheses: H4a: Financial analysts that have less experience are more likely to provide supplementary sales forecasts, controlling for other individual analyst characteristics. H4b: Financial analysts employed by smaller brokerage houses are more likely to provide supplementary sales forecasts, controlling for other individual analyst characteristics. III. SAMPLE SELECTION AND DESCRIPTIVE STATISTICS 3.1 Sample Selection I obtain data on analyst forecasts and individual analyst characteristics from the Institutional Brokers Estimate System (I/B/E/S) database, financial statement data and earnings announcement dates from COMPUSTAT, and stock returns data from the Center for Research on Security Prices (CRSP) database. I restrict my analysis to annual forecasts to avoid the complication of seasonality. Firms in the initial sample are required to have daily security return data available in the CRSP database. I define earnings (sales) forecast revision as the newly revised earnings (sales) forecast less the analyst s own prior earnings (sales) forecast. 8 A sales forecast revision is defined as supplementary when a financial analyst revises both her earnings and sales forecasts on the same date as reported in the I/B/E/S detail file. I measure the information content of a supplementary sales forecast using three-day size-adjusted buy-and-hold return around the revision date. Size-adjusted return is the compounded buy-and-hold return of the firm minus the compounded return for an equal-weighted portfolio of firms in the same NYSE/AMEX/NASDAQ size decile. To allow for variation in individual analyst characteristics at the time of the revisions to 8 Prior studies (e.g., Stickel 1990; Stickel 1992; Park and Stice 2000; Gleason and Lee 2003) show that an analyst s own prior forecast is a better benchmark than the consensus forecast for measuring the amount of surprise in an individual forecast revision. My results are robust to the choice of benchmark. 7

9 explain the decision to issue supplementary sales forecasts, I perform the empirical analysis at the forecast revision level. 9 I remove revisions around earnings announcements (-1 to +1) because the price response to the news in the earnings announcements might confound measures of price response to earnings forecast revisions. I exclude forecast revisions made after the last day of the fiscal period. Following Gleason and Lee (2003), in order to mitigate inflated test statistics due to sample dependence induced by clustered revisions, I create a non-overlapping sample by including revisions that have no other revisions occurring within a three-day window around the revisions. In other words, when more than one revision occurs in a three-day event window, I randomly select one forecast. The non-overlapping sample contains 239,339 earnings forecast revisions from 1998 through Descriptive statistics For each individual earnings forecast revision, I compute the number of trading days between the revision date and the nearest earnings announcement date. I use a 63-day event window around quarterly earnings announcements because there are on average 63 trading days in a fiscal quarter (Ivkovic and Jegadeesh 2004). 11 Table 1 reports the frequency distribution of one-year-ahead earnings forecast revisions over six different horizons of ten successive trading days and for days -1, 0 and +1 separately, where day 0 is the quarterly earnings announcement date. 9 A related stream of literature examines the determinants of cash flow forecasts. Defond and Hung (2003) find that financial analysts tend to issue cash flow forecasts for firms where accounting, operating and financing characteristics suggest that cash flows are useful in interpreting earnings and assessing firm viability. While Defond and Hung (2003) document what type of firms financial analysts are more likely to provide cash flow forecasts for, I examine what type of financial analysts are more likely to provide supplementary sales forecasts. 10 I start with the year 1998 because it is the first year with a significant number of annual sales forecasts. 11 Following Ivkovic and Jegadeesh (2004), when there are more than 63 trading days between successive earnings announcements, I exclude any revisions that occur more than 31 trading days from the previous earnings announcement date and more than 31 days before the next earnings announcement date. When there are fewer than 63 trading days between successive earnings announcements, I assign an event date with respect to the next earnings announcement date. 8

10 Earnings forecast revisions are concentrated on days 0 and +1, suggesting that they are driven by public information released during quarterly earnings announcements. Moreover, supplemented earnings forecast revisions are more common on days 0 and +1. For example, 23% of earnings forecast revisions on day +1 are supplemented with sales forecast revisions, compared to 15% for all revisions over time. If financial analysts are skilled at interpreting publicly available information, their expertise should be most apparent right after the release of financial statements (Ivkovic and Jeegadesh 2004). This suggests that interpretation of public information triggers supplementary sales forecast revisions when financial analysts process the information released during quarterly earnings announcements. [Insert Table 1 here] Table 2 reports descriptive statistics on supplemented and stand-alone earnings forecast revisions. On average, financial analysts revise their earnings forecasts downward. This is consistent with management s incentives to guide analysts forecasts downward (Matsumoto 2002). The average magnitude of earnings forecast revision ( ) is smaller for supplemented earnings forecast revision than for stand-alone earnings forecast revision ( ). However, supplemented earnings forecast revisions result in stronger market reactions (-0.43%) than stand-alone revisions do (-0.21%). Upward earnings forecast revisions are more common for supplemented revisions (43.64%) than for stand-alone revisions (40.64%). When an earnings forecasts revision is supplemented with a revision in sales forecast, the market is more likely to react in the same direction as the earnings forecast revision. For example, 58.46% of supplemented earnings forecast revisions are associated with abnormal returns in the same direction as the earnings forecast revisions, compared to 55.71% for stand-alone earnings 9

11 forecast revisions. Taken as a whole, the univariate results show that the market reacts more strongly to earnings forecast revisions supplemented with sales forecast revisions. However, these results do not distinguish whether supplementary sales forecasts convey information independent of earnings forecast revisions or add credibility to earnings forecasts. I examine this issue in my regression analysis. [Insert Table 2 here] IV. TESTS OF MARKET REACTIONS TO SUPPLEMENTARY SALES FORECASTS 4.1 Research Design In this section I test whether supplementary sale forecast revisions add credibility to earnings forecast revisions (H1). I define forecast credibility as the extent to which investors believe the forecast and measure forecast credibility using the forecast response coefficient to the forecast revision. To examine whether investors react more strongly to supplemented earnings forecast revisions, I estimate the following regression (subscripts omitted): BHAR3 = β 0 + β 1 E_REV + β 2 E_REV * SUPPL + β 3 S_REV + ε (1) where BHAR3 E_REV SUPPL = three-day size-adjusted buy-and-hold return (-1 to +1) surrounding an annual earnings forecast revision. Size-adjusted buy-and-hold return is the raw buy-and-hold return of the firm minus the mean buy-and-hold return of an equal-weighted portfolio of firms in the same NYSE/AMEX/NASDAQ size decile over the holding period; = revision in annual earnings forecast relative to the analyst s own prior forecast deflated by stock price two days before revision; = 1 if the revision in earnings forecast is supplemented with revision 10

12 S_REV in sales forecast, or = 0 if otherwise; and = revision in annual sales forecast relative to the analyst s own prior forecast deflated by stock price two days before revision. In equation (1), I include the interaction between earnings forecast revision and a dummy variable indicating the presence of supplementary sales forecast. The coefficient on the interaction term (β 2 ) captures the incremental effect on the forecast revision response coefficient by supplementary sales forecast. I expect that supplementary sales forecasts increase the credibility of the earnings forecast revisions (β 2 > 0). I include revision in sales forecast in the regression to control for the information in supplementary sale forecast revisions independent of earnings forecast revisions (Baginski, Hassell, and Kimbrough 2004). Since investors react more strongly to permanent changes in earnings than to transitory ones (Collins and Kothari 1989), to the extent that sales forecast revisions imply more persistent earnings changes, I expect that stronger market reactions will follow (β 3 > 0) Regression results on association between contemporaneous market reaction and supplementary sales forecast revision Table 3 reports the results of the regression analysis to examine the impact of supplementary sales forecast revisions on stock prices. In Model 1a the coefficient on earnings forecast revisions (β 1 ) is positive and significant (0.2189, p-value < ). This confirms the positive relationship between announcement returns and earnings forecast revisions documented in prior literature (Lys and Sohn 1990; Stickel 1991; Park and Stice 2000). The coefficient on sales forecast revision (β 3 ) is positive and significant (0.0017, p-value < ), controlling for revision in earnings forecast. Sales forecast revisions are incrementally informative to the market, 12 Revisions in sales forecasts provide information about future earnings because aggregate earnings mask the information on the persistence of different revenue and expense items by weighting them equally in the calculation of net income (Lambert 2004). 11

13 which is consistent with the findings from prior research that sales is informative about stock returns and conveys information about the persistence of earnings (Ertimur, Livnat, and Martikainen 2003; Ghosh, Gu, and Jain 2005). In Model 1b the coefficient on the interaction term between earnings forecast revision and a dummy variable indicating the presence of supplementary sales forecasts (β 2 ) is positive (0.2158) and significant (p-value < ). This shows that the market reacts more strongly to supplemented earnings forecast revisions (0.4347) than to stand-alone earnings forecast revisions (0.2189), controlling for the independent information in sales forecast revision. My results are robust to restricting my sample to earnings forecast revisions from firm-years with at least one supplemented earnings forecast revision and another stand-alone earnings forecasts revision. This suggests that my results are not driven by variation in firm characteristics. I investigate whether the effect of supplementary sales forecast revision is conditional on the direction of earnings forecast revision. Each earnings forecast revision is classified as an upward or a downward revision based on whether the revised forecast is above or below the prior forecast. Results in Model 2 shows that the market reacts more strongly to upward earnings forecast revisions (0.5322) than to downward revisions (0.1771). However, supplementary sales forecast revisions greatly increase the market response to downward earnings forecast revisions (0.2235) than to upward earnings forecast (revisions ). The market reaction to the independent information in downward sales forecast revisions is weak and insignificant after controlling for earnings forecast revision. [Insert Table 3 here] 12

14 V. TESTS OF THE ASSOCIATION BETWEEN SUPPLEMENTARY SALES FORECASTS AND INDIVIDUAL ANALYST CHARACTERISTICS 5.1 Research design In this section I test whether supplemented earnings forecasts are more accurate and timely and examine the determinants of supplementary sales forecasts. To control for time- and firm-specific effects, I measure individual analyst characteristics of a financial analyst relative to the characteristics of other analysts following the same firm in the same year. I scale the individual analyst characteristic to lie between 0 and 1 by computing the difference between the observed value of the characteristic and the minimum value of the characteristic for the analysts following firm j in year t, divided by the range of the characteristic for the analysts following firm j in year t (Clement and Tse 2005; Herrmann and Thomas 2005). The scaled individual analyst characteristic for a revision issued by analyst i for firm j in year t is given by: SCALED _ CHAR ijt CHARijt min jt ( CHAR) = max ( CHAR) min ( CHAR) jt jt (2) where max jt (CHAR) and min jt (CHAR) are the maximum and the minimum values of the individual analyst characteristic for the analysts following firm j in year t. This scaling preserves the relative distances among the individual analyst characteristics for firm j in year t and facilitates comparisons of regression coefficients across firms and over time. I randomly select one revision for an analyst if there are multiple revisions by the same analyst in a firm-year, so that my sample would not be over-represented by multiple revisions from the same analyst. I also delete observations from firm-years with only one analyst issuing earnings forecasts. The sample is reduced to 132,146 earnings forecast revisions My results are robust to restricting the sample to earnings forecasts from firm-years with at least one 13

15 To examine the association between supplementary sales forecasts and forecast accuracy, I model forecast accuracy as a function of forecast frequency, forecast horizon, days elapsed since the last forecast, prior earnings forecast accuracy, broker size, analyst experience, the numbers of companies and industries the analyst follows, the magnitude of earnings forecast revision, and the level of earnings forecast innovation (Clement 1999; Jacob, Lys, and Neale 1999; Brown 2001; Clement and Tse 2003; Gleason and Lee 2003). I run the following regression: FE ijt = β 0 + β 1 DAYS ijt + β 2 HORIZON ijt + β 3 FREQ ijt + β 4 LAG_FE ijt + β 5 NEW ijt where + β 6 BROKER ijt + β 7 FIRM_EXP ijt + β 8 GEN_EXP ijt + β 9 COMPANY ijt + β 10 INDUSTRY ijt + β 11 E_INNO ijt + β 12 E_REV ijt + β 13 SUPPL ijt + ε ijt (3) FE ijt = forecast error for analyst i who follow firm j in year t minus the minimum forecast error for the analysts following firm j in year t, scaled by the range of forecast error for the analysts following firm j in year t. Definition of the independent variables is in the appendix. The test variable, SUPPL, is a dummy variable indicating the presence of supplementary sales forecast. SUPPL equals 1 if the revision in earnings forecast is supplemented with sales forecast revision, or equals 0 if otherwise. I expect that forecast accuracy increases with prior accuracy (LAG_FE), broker size (BROKER), forecast frequency (FREQ), analyst experience (FIRM_EXP and GEN_EXP), and the level of revision innovation (E_INNO), and decreases with days elapsed since the last forecast (DAYS), forecast horizon (HORIZON), the numbers of companies (COMPANY) and industries (INDUSTRY) the analyst follows. If earnings forecasts supplemented with sales forecast revisions are more accurate, the coefficient on SUPPL (β 13 ) would be positive (H2). supplemented earnings forecast revisions and another stand-alone earnings forecasts revision, or to analysts providing at least one supplemented earnings forecast revision and another stand-alone earnings forecasts revision. 14

16 To measure the timeliness of an earnings forecast revision I calculate the leader-follower ratio (LFR) following Cooper, Day and Lewis (2001). For each forecast, the preceding five forecasts and the subsequent five forecasts by other analysts are identified. 14 Let t 0i (t 1i ) be the number of days by which forecast i either precedes (follows) an earnings forecast revision. The cumulative lead-time for the revision is and the cumulative follow-time is T 0 = Σ i t 0i T 1 = Σ i t 1i The timeliness of the revision is given by the leader-follower ratio at the forecast revision level, which is the cumulative lead-time divided by the cumulative follow-time LFR = T 0 / T 1 (4) The leader-follower ratio compares the expected release times of forecasts by other analysts preceding and following each forecast revision. Because a timelier forecast should be issued before other less timely forecasts, the leader-follower ratio for a timelier forecast would be greater than one. The leader-follower ratio is percentile ranked from 0 to 1 within each firm-year to control for skewness and to facilitate both cross-sectional and time-series comparisons. I examine the association between supplementary sales forecasts and forecast timeliness by running the following regression: LFR ijt = β 0 + β 1 HORIZON ijt + β 2 FREQ ijt + β 3 LAG_FE ijt + β 4 NEW ijt where + β 5 BROKER ijt + β 6 FIRM_EXP ijt + β 7 GEN_EXP ijt + β 8 COMPANY ijt + β 9 INDUSTRY ijt + β 10 E_INNO ijt + β 11 E_REV ijt + β 12 SUPPL ijt + ε ijt (5) 14 My results are robust to using the preceding two forecasts and the subsequent two forecasts by other analysts in calculating the leader-follower ratio. 15

17 LFR ijt = percentile rank of the leader-follower ratio, which is the ratio of the cumulative number of days by which the preceding forecasts lead the forecast revision made by analyst i for firm j in year t to the cumulative number of days by which the subsequent forecasts follow the forecast revision. I expect that forecast timeliness increases with broker size (BROKER), forecast frequency (FREQ), analyst experience (FIRM_EXP and GEN_EXP), and the level of revision innovation (E_INNO), and decreases with the numbers of companies (COMPANY) and industries the analyst follows (INDUSTRY). If earnings forecasts supplemented with sales forecasts are more timely, the coefficient on SUPPL (β 13 ) should be positive (H3). Finally, I examine the determinants of supplementary sales forecasts using the following logit model: SUPPL ijt = β 0 + β 1 DAYS ijt + β 2 HORIZON ijt + β 3 FREQ ijt + β 4 LAG_FE ijt + β 5 NEW ijt + β 6 BROKER ijt +β 7 FIRM_EXP ijt + β 8 GEN_EXP ijt + β 9 COMPANY ijt + β 10 INDUSTRY ijt + β 11 E_INNO ijt + β 12 E_REV ijt +ε ijt (6) To the extent that the individual analyst characteristics are related to financial analysts incentive to convey their ability, individual analyst characteristics would be associated with the presence of supplementary sales forecasts. Specifically, I expect that a financial analyst is more likely to issue a supplementary sales forecast when she has less experience (H4a) and when she is from a smaller brokerage house (H4b). 5.2 Univariate analysis of the difference between supplemented and stand-alone earnings forecast revisions I compare the means of forecast error, leader-follower ratio, and other individual analyst characteristics for supplemented and stand-alone earnings forecast revisions in Panel A of Table 16

18 4. Most of the individual analyst characteristics differ significantly across supplemented and stand-alone earnings forecast revisions under both two-sample t-test and Wilcoxon rank-sum test. 15 The means for days elapsed since the last forecast (DAYS), forecast frequency (FREQ), number of firms followed (COMPANY), number of industries followed (INDUSTRY) and leader-follower ratio (LDR) are higher for supplemented earnings forecast revisions, whereas the means for forecast error (FE), forecast horizons (HORIZON), broker size (BROKER), firm experience (FRIM_EXP) and general experience (GEN_EXP) are lower for supplemented earnings forecast revisions. Consistent with my hypotheses (H2 and H3), supplemented earnings forecast revisions are more accurate and timely than stand-alone earnings forecast revisions. The differences in means also provide univariate evidence that financial analysts who provide supplementary sales forecasts are more likely to be less experienced (H4a) and employed by smaller brokerage houses (H4b). I report Pearson correlations among individual analyst characteristics in Panel B of Table 4. The presence of supplementary sales forecast revision is positively correlated with forecast accuracy and timeliness. Forecast accuracy is negatively correlated with days since the last forecast, forecast horizon, the number of companies and the number of industries the analyst follows. Prior accuracy is not significantly correlated to accuracy of earnings forecast in the current year. One possible reason is that prior research measures forecast accuracy using the last earnings forecast issued by a financial analyst, but I randomly choose one earnings forecast during the fiscal year. If a more able financial analyst issues earnings forecasts earlier in the year at the cost of lower accuracy, the accuracy of her earlier forecasts in the current year might not be related to the accuracy of her last forecast in the prior year. Forecast timeliness is positively 15 Parametric two-sample t-test tests the null hypothesis that the mean difference of two populations is zero, assuming that the observations are drawn from normally distributed populations with equal variances. Non-parametric Wilcoxon rank sum test compares the locations of two populations to determine if one population is shifted with respect to the other. 17

19 correlated with days since the last forecast, forecast frequency, broker size, analyst experience, but is negatively correlated with the number of companies and the number of industries the financial analyst follows. Taken as a whole, results from Panel A and Panel B suggest that supplemented earnings forecasts are more accurate and timely than stand-alone earnings forecasts. [Insert Table 4 here] 5.3 The association between earnings forecast accuracy and supplementary sales forecast I report regression results for the association between earnings forecast accuracy and supplementary sales forecast in Table 5. To mitigate the effects of potentially inflated t-statistics in the pooled regression, I run individual annual regressions for each year and report the mean coefficients from the annual regressions, along with the p-values for the Fama-MacBeth t-statistics. Fama-MacBeth t-statistic is the mean of the coefficients divided by the standard error of the coefficients (Fama and MacBeth 1973; Bernard 1987). Because the Fama-MacBeth t-statistics are based on the coefficients from the annual regressions, they are unaffected by potential sample dependence in the cross-section. In Model 1, the coefficient on the supplementary sales forecast dummy variable (SUPPL) is negative. This supports my hypothesis (H2) that earnings forecasts with supplementary sales forecast revisions are more accurate, controlling for other individual analyst characteristics. Consistent with prior literature, I also find forecast accuracy increases with general experience and decreases with days lapsed since the last forecast, forecast horizon and forecasts frequency. High-innovation earnings forecasts are on average more accurate than low-innovation earnings forecasts. 18

20 In Model 2, the dummy variable NEW equals 1 if the analyst issuing the forecast first appears in the I/B/E/S detail file in year t, or equals 0 if otherwise. The results suggest that earnings forecasts with supplementary sales forecast revisions are more accurate. As a robustness check I repeat my analysis using the last forecast revision by an analyst in each firm-year (Sinha, Brown, and Das 1997; Clement and Tse 2003; Herrmann and Thomas 2005). The results are qualitatively the same and are not tabulated. [Insert Table 5 here] 5.4 The association between earnings forecast timeliness and supplementary sales forecast Table 6 presents regression results for the association between earnings forecast timeliness and supplementary sales forecast. I report the mean coefficients from the annual regressions, along with p-values for the Fama-MacBeth t-statistics. Model 1 and Model 2 show that the coefficients on the supplementary sales forecast dummy variables (SUPPL) are significantly positive. This finding supports my hypothesis (H3) that supplemented earnings forecast revisions are more timely, controlling for other individual analyst characteristics. Forecast timeliness increases with forecast frequency, prior accuracy, broker size, general experience, and level of forecast innovation, but decreases with forecast horizon, and number of industry followed. The signs of the coefficients on individual analyst forecast characteristics suggest that the more informed analysts are more likely to provide timely earnings forecasts. [Insert Table 6 here] 19