Analyst Reputation Building via Sales Forecasting

Transcription

1 Analyst Reputation Building via Sales Forecasting Yonca Ertimur Duke University William J. Mayew Duke University* Stephen R. Stubben The University of North Carolina at Chapel Hill October 2007 *Corresponding Author Contact Information: Fuqua School of Business Duke University 1 Towerview Drive Durham, NC wmayew@duke.edu web: This paper was previously titled Analysts Incentives to Issue Revenue and Cash Flow Forecasts. We thank Shane Dikolli, William Franklin of Goldman Sachs, Brian Ossenbeck of Citigroup, Yun Zhang, and workshop participants at the University of Minnesota for helpful comments and suggestions. We thank Alok Kumar for providing the Institutional Investor All-Star data.

2 Analyst Reputation Building via Sales Forecasting Abstract We investigate whether sales forecasts play a signaling role for sell-side analysts. We posit that high ability analysts will choose to issue sales forecasts in order to more quickly reveal their type to the market. Relative to analysts issuing no sales forecasts, sales forecasting analysts allow the market to evaluate their predictions about how the mix of volume and margin will contribute to overall earnings of the firm. Consistent with reputational incentives to signal, we find that younger (All-Star) analysts are more (less) likely to issue sales forecasts, and that analysts are more likely to issue sales forecasts for a given firm when they issue forecasts for other firms in their portfolio. Analysts who issue sales forecast are more accurate earnings forecasters, and are almost half as likely to be fired in the subsequent year compared to analysts who do not issue sales forecasts. Furthermore, consistent with sales forecasts being a costly signal, we find that among analysts issuing sales forecasts, less accurate sales forecasters are more likely to be fired. These results suggest that sales forecasts are an important indicator of analyst ability. 1

3 1. Introduction Financial analysts reputations facilitate the selling of research reports and generation of trading commissions (Jackson, 2005), and impact their career outcomes (Hong and Kubik, 2003; Leone and Wu, 2007). Good reputations stem from an analyst s clients believing she has superior ability, which results in superior understanding of the firms she covers. This in turn generates valuable information on which analyst clients can base investment decisions. The literature has investigated how and when the market uses an analyst s past history of earnings accuracy to inform about reputation (Mikhail, Walther and Willis, 1997; Chen, Francis and Jiang, 2005a). However, in the absence of an extensive performance history, how does an analyst credibly signal to the market that she has high ability? This study investigates the potential role of sales forecasts as such a signaling mechanism for financial analysts. When an analyst forecasts sales in addition to earnings, she provides to the capital market an implicit disaggregation of earnings into sales and expense. We view disaggregated earnings forecasts as a means of credibly signaling the possession of better information about the firm (Hirst, Koonce and Venkataraman, 2007). Relative to aggregated earnings-only forecasts, disaggregated forecasts provide information to the market about the specific mix of volume and profit margin that the analyst believes will allow the firm to report a particular earnings number. Such disaggregation is a costly signaling mechanism because when earnings are eventually announced, the market obtains more information on which to evaluate the analyst s ability. Only high ability analysts should rationally be willing ex ante to offer the market a mechanism to more quickly reveal their type. 2

4 To test the signaling implication of sales forecasts that accompany earnings forecasts, we proceed in three steps. First, we model the analyst characteristics that explain the probability of issuing a sales forecast. Signaling theory implies that high ability analysts seeking to establish their reputations will issue sales forecasts in order to facilitate the revelation of their type to the market. Consistent with this prediction, we find that inexperienced analysts are more likely to issue sales forecasts. In contrast, Institutional Investor All-Star analysts are less likely to issue sales forecasts. Additionally, we find that analysts are more likely to issue a sales forecast for a given firm if they issue sales forecasts for other firms in their portfolio. This evidence suggests that analysts try to send a consistent signal to the market about their own ability. As a second step, we investigate whether disaggregation is a property of the earnings forecast that predicts higher accuracy in the earnings forecast. If sales forecasts signal analysts superior ability, the earnings forecasts issued by sales forecasting analysts should be more accurate. Indeed, we find that relative earnings forecast accuracy is higher for analysts who also provide a sales forecast, controlling for standard determinants of forecast accuracy. These results suggest that the issuance of a sales forecast with an earnings forecast is informative about which earnings forecasts will be more accurate. Finally, we assess whether analysts signaling choices are based on rational expectations by modeling analyst-firm separations. Our analysis of career outcomes reveals that analysts issuing disaggregated earnings forecasts are less likely to be fired in the subsequent year. This result holds after controlling for other earnings forecast properties known to influence job separation, including accuracy, boldness, and bias. Additional analysis confirms the costly nature of issuing a sales forecast. If there is a 3

5 continuum of analyst ability types, it is unlikely that sales forecasting will yield complete separation and revelation of analyst types. We observe that among analysts who issue sales forecasts, less accurate sales forecasters are more likely to be fired. This implies sales forecasting is not costless once undertaken, making sales forecasting a credible signal. Collectively, our results improve our understanding how analysts communicate their ability to the market. We complement the literature documenting how the market learns about analyst ability (Chen, Francis and Jiang, 2005a). Our results also shed light on the incremental role of sales forecasting over other forecast characteristics, such as boldness and bias. Bold earnings forecasts are typically assumed to be a signal of superior private information (Hong, Kubik and Solomon, 2000; Clement and Tse, 2005). Our findings imply that analysts can incrementally signal their superior private information by disaggregating the earnings forecast. Our findings indicate that walkdown bias in earnings forecasts (Ke and Yu, 2006), along with the assumed increased access to management private information (Ke and Yu, 2006; Libby et al. 2007), does not subsume the effects of signaling ability via sales forecasting. Our results also have implications for the efficient allocation of investor resources and more efficient hiring decisions at brokerage houses. For investors, sales forecasts appear to be a salient signal that can help ex ante identify which earnings forecasts are more likely to be accurate. This addresses the call of Berger (2003), who questions what role sales forecasts might play in security valuation. These results also add to the large literature on the analyst and forecast characteristics that explain earnings forecast accuracy (Clement, 1999; Jacob, Lys and Neale, 1999; Ke and Yu, 2006). 4

6 Clement, Koonce and Lopez (2007) suggest that brokerage firms should try to hire analysts of high ability because such analysts make more accurate (and valuable) forecasts and because such analysts can use their ability to capitalize on task-specific experience better than analysts with low ability. Our results suggest that sales forecasting activity might serve as one mechanism to identify these high ability analysts, particularly when other information about the analysts ability, such as forecasting history, is not available. The paper proceeds as follows. Section 2 develops our hypotheses and Section 3 outlines our sample selection, variable measurement and research design. Section 4 provides descriptive evidence on the propensity of sales forecasting during our sample period, multivariate tests of our hypotheses and robustness checks, and Section 5 concludes. 2. Hypothesis Development Analysts have strong incentives to establish and maintain a good reputation. Stickel (1992) points to anecdotal evidence that reputation affects analysts compensation. Jackson (2005) shows that highly reputed analysts generate more trading volume for their employer, and evidence in Hong and Kubik (2003) and Leone and Wu (2007) suggests that reputable analysts are more likely to be promoted and less likely to be demoted or fired. Clients belief that an analyst has superior ability gives rise to good reputation. We define high ability analysts as those with innately low cost of effort. 1 We assume high ability analysts exploit their lower cost of effort to develop a better understanding of the firm and to assemble better information about the firm than low ability analysts. 2 Investors 1 Superior ability may result from learning by doing, innate talent or a combination of both (Clement et al., 2007; Leone and Wu, 2007). We abstract away from the source of the superior ability in this paper. 2 The notion that high ability translates into better private information is consistent with analytic models of reputation formation. For example, Prendergast and Stole (1996) define agents with high ability as those 5

7 will rationally pay more for the superior firm insights that a high ability analyst can provide. Thus, it is important for investors to be able to identify the high ability analysts. Similarly, it is important for high ability analysts to be able to signal their type to their clients and to the market. One mechanism through which the market can determine an analysts type is through a history of (superior) performance, e.g., earnings forecast accuracy. Mikhail, Walther and Willis (1997) show that analysts with a longer track record for following a firm issue more accurate earnings forecasts and that the market places more weight on these analysts forecasts than on the forecasts of novice analysts. Chen, Francis and Jiang (2005a) show that investors place greater weight on the accuracy of the analyst s forecast record as the length of that record increases, consistent with the idea that estimating the ability of an analyst from her past performance becomes more precise as the record gets longer. Prior literature, however, provides little insight on how an analyst without an extensive performance history credibly signals that she has high ability. We conjecture that sales forecasts are a credible signal such analysts can use to inform the market about their type. Hirst et al. (2007) document experimentally how disaggregated management earnings forecasts can improve investors perception of the credibility of the forecasts. The authors state a forecast of earnings supplemented by forecasts of individual line items should be considered a signal that management is particularly clear and confident about whether and how they will be able to achieve the earnings forecast (pg. 818). The same intuition can be applied to analyst earnings forecasts. When an analyst issues a sales forecast, she implicitly provides a disaggregation of her earnings forecast who receive more precise signals. The mechanism by which more precise signals are obtained is not discussed. Presumably low cost of effort allows analysts to spend more time accumulating and synthesizing signals, thereby generating a more precise view of a firm s prospects. 6

8 into sales and expenses. We posit that disaggregation in this case signals a better understanding of the firm s profit margins and top-line growth potential. Such information will be particularly helpful to the market in assessing the analyst in the absence of a (long) performance history. To illustrate, consider a firm that issues a management forecast at the beginning of the year calling for earnings of $4 for the year, consisting of $8 in sales and $4 in expenses (see Illustration A below). Then consider two analysts, A and B, who both issue disaggregated earnings forecasts during the year. Analyst A predicts sales of $10 and expenses of $5, while Analyst B predicts sales of $9 and expenses of $4. When the period ends, the firm reports sales of $10, expenses of $5, and earnings of $5. Illustration A: Beginning of Period Expectation based on Management Forecast End of Period Actual Reported Earnings Analyst A Forecast Analyst B Forecast Sales ($) Expenses ($) Earnings ($) Margin % 50% 50% 50% 55.6% Forecast Error ($) 0 0 Volume Error ($) 0-1 Margin Error 0.0% 5.6% Each analyst s earnings forecast is equally accurate, as they both correctly predicted actual earnings of $5. However, Analyst A better understood that the firm would obtain earnings via volume growth and constant margin percentages, while Analyst B felt the firm would obtain earnings of $5 via an increase in margins and lower volume growth. When the firm announces earnings, Analyst B is revealed to understand the firm less because of the underestimation (overestimation) of sales growth (margin percentage) relative to Analyst A. In contrast, if both analysts issued aggregated earnings-only 7

9 forecasts, the market would not be able to make an inference regarding differential ability across these analysts at the release of earnings. Analysts seeking to signal high ability to the market would welcome the revelation of type at the earnings announcement date that comes with providing a disaggregated earnings forecast. On the contrary, low ability analysts would like to delay the revelation process by pooling with analysts of high type via an aggregated earnings forecast. This implies high ability analysts can signal their type by providing the market disaggregated earnings forecasts via the issuance of a sales forecast. If sales forecasting is in fact a signaling mechanism, analysts with higher (lower) incentives to signal ability should be more (less) likely to issue sales forecasts. Market participants have more opportunity to learn the abilities of experienced analysts by reviewing their earnings forecast and stock recommendation histories. However, less is known about newer, less experienced analysts. These analysts therefore have greater incentives to signal their type to investors, their current and potential future employers. H1a: Less experienced analysts are more likely to issue sales forecasts with their earnings forecasts. Analysts who have demonstrated superior ability to the market can earn distinction as an Institutional Investor All-Star analyst (Stickel 1992; Hong and Kubik, 2003; Leone and Wu, 2007). With established reputations, All-Star analysts have lower incentives to signal and should be less likely to issue a sales forecast: H1b: All-Star analysts are less likely to issue sales forecasts with their earnings forecasts. 8

10 If an analyst intends to signal her ability by disaggregating an earnings forecast, it should be the case that the analyst issues sales forecasts on other firms in her portfolio. Doing so sends a more consistent message to the market about the analyst s ability than does the absence of sales forecasting in the analyst s portfolio H1c: The likelihood of issuing a sales forecast on a given firm is increasing the frequency of sales forecasts for other firms in the analyst s portfolio. If high ability analysts signal their type via sales forecasting, one would expect their ex post output to be of higher quality than analysts who do not provide sales forecasts. Annual earnings forecasts represent an important and commonly monitored analyst output and more accurate earnings are considered higher quality in the financial and analyst labor markets (Stickel, 1992; Mikhail, Walther and Willis, 1999). This implies that earnings forecasts accompanied by sales forecasts should be more accurate than earnings not accompanied by sales forecasts. Stated formally: H2: Earnings forecasts from analysts issuing sales forecasts are more accurate than earnings forecasts where no sales forecast is issued. Ultimately, analysts signal their type to facilitate market revelation about their ability relative to other analysts. If this is indeed the case, we should expect to see relatively worse career outcomes for those analysts revealed to be of low type. This implies that analysts issuing sales forecasts in their portfolio should be less likely to be fired from their brokerage house relative to analysts who do not issue sales forecasts: H3a: Analysts issuing sales forecasts are less likely to be fired. While the sales forecasting signal is a mechanism to separate high and low ability analysts, it is likely not a powerful enough signal to facilitate complete revelation of 9

11 analyst ability among a continuum of analyst types. In such a semi-separating setting, we should still be able to observe the costly nature of the sales forecast signal if indeed the sales forecast signal is costly. This implies that, among the set of analysts issuing sales forecasts, the less accurate sales forecasters should be more likely to be fired. Stated formally: H3b: Among analysts issuing sales forecasts, less accurate sales forecasters are more likely to be fired. 3. Sample Selection, Variable Measurement and Research Design Panel A of Table 1 outlines our sample selection procedures. Our analyst forecast sample is derived from all annual sales and earnings forecasts available on I/B/E/S as of July 1 st 2007 made by identifiable analysts for U.S. firms, where the fiscal year end of the firm being forecasted falls between June 1995 and December Our sample period begins in June of 1995 because sales forecasts in I/B/E/S were sparsely populated prior to this date. This initial sampling yields 2,279,965 total forecasts, of which 72% (1,640,767) are for earnings and 28% (639,198) for sales. From these forecasts, we create unique analyst-firm-year observations that contain information on all sales and earnings forecasts made by analyst i following firm j in year t. We require that each analyst issue at least two earnings forecasts between two consecutive earnings announcement dates, with the first (second) coming during the first (second) half of this period. This requirement allows us to calculate walkdown bias in analyst earnings forecasts, which is an important explanatory variable in models of analyst earnings forecast accuracy and career outcomes (Ke and Yu, 2006). These procedures result in 240,178 analyst-firm-year observations. 10

12 We then remove 21,714 observations where at least three analysts do not follow the firm, which enables us to calculate meaningful relative measures in our empirical tests. We remove 21,739 observations where control variables needed for the sales forecasting determinant model are not available. Finally, we remove 35,666 observations for firms with no sales forecasts. This requirement helps ensure that there is potentially some demand at the firm level for both volume and margin information. The collective results of these screens yield a sample of 161,059 analyst-firm-year observations that we use to investigate the determinants of sales forecasting. 3 To empirically test our hypotheses regarding the signaling role of sales forecasts, we begin by estimating the following logistic regression, with standard errors clustered by analyst: Pr(SAL i,j,t ) = β 0 + β 1 EXPERIENCE i,j,t +β 2 ALLSTAR i,j,t + β 3 SAL_COV i,j,t + β 4 LFR i,j,t + β 5 NEPS_R i,j,t + β 6 NFIRMS_R i,j,t + β 7 BROKSIZE_R i,j,t + YEAR FIXED EFFECTS + υ i,j,t (1) The dependent variable, SAL, is an indicator that equals one if the analyst issues at least one sales forecast during both the first half and second half of the year, and zero otherwise. 4 Our reputational signaling hypotheses imply that more experienced analysts and All-Star analysts are less likely to issue sales forecasts. We measure analyst experience, EXPERIENCE, as the number of years since the analyst s first forecast on 3 It can be argued that analysts at least implicitly forecast sales when forecasting earnings, suggesting that all analysts do privately generate a sales forecast. We examine the actual appearance of a sales forecast in I/B/E/S for at least two reasons. First, I/B/E/S has a large, electronic database of sales forecasts for a set of firms consistent with those receiving earnings forecasts. Tracking individual analyst reports would be impractical due to the volume of reports and the difficulty of obtaining every report. Second, even if there is not a one-to-one correspondence between the inclusion of a sales forecast in an analyst report and the inclusion of a sales forecast in I/B/E/S, an analyst s decision to report a sales forecast to I/B/E/S remains important because information in I/B/E/S can be used by a potential employer to evaluate an analyst s performance. 4 We require sales forecasts in both the first half and second half of the year to be consistent with our sample requirement that each analyst issue an earnings forecast in both the first half and second half of the year. Both sales and earnings forecasts are necessary for an analyst to provide information about volume and margins. 11

13 I/B/E/S. 5 All-Star status, ALLSTAR, equals one if the analyst made any of the Institutional Investor All-Star teams in during the year, and zero otherwise. We expect both β 1 and β 2 to be negative under H1a and H1b, respectively. We test H1c via SAL_COV, which equals the percentage of other firms in the analyst s portfolio for which she issues a sales forecast. If analysts have signaling motivations, then a more consistent signal to the market would occur if the analyst issues sales forecasts across the portfolio. H1c implies a positive coefficient on β 3. The remaining variables in equation (1) capture analyst activity and resource variables that can impact the development of an analysts knowledge about the firm. The precise definition of all variables is presented in Appendix 1. Analysts who tend to be leaders (Cooper et al. 2001) in forecasting the firm s earnings and who issue relatively more earnings forecasts during the year are potentially taking steps to inform the market of their knowledge of the firm that stems from their superior ability. As such, we expect LFR and NEPS_R to have positive coefficients. 6 We acknowledge that being a leader and an active forecaster of the firm s earnings could result from both superior analyst ability and other mechanisms that help lessen the amount of effort an analyst can dedicate to a given firm (such as brokerage resources and broker portfolio assignments). To the extent these variables capture analyst ability, our inclusion of these variables will make it more difficult to detect the effects we hypothesize. 5 Inferences are unchanged if we allow for potential nonlinearities in the relationship between sales forecasting and experience. Measuring analyst experience alternatively as the natural log of EXPERIENCE or including the square of EXPERIENCE in model (1) yields inferences consistent with those reported in the tables. 6 Chen et al. (2005b) characterize an equilibrium where high ability analysts signal their type by precommitting to a forecast strategy that results in high ability analysts issuing fewer forecasts for a given firm. This would imply a negative coefficient on NEPS_R. 12

14 We also include relative broker size as a control variable (BROKSIZE_R), but do not make a signed prediction. On one hand, larger brokerages can provide more resources to analysts, which in turn help them learn about the firm and develop insights that would underpin a sales forecast. On the other hand, larger brokerage houses are more prestigious and may tend to hire analysts who have already revealed themselves in some manner as high ability. These analysts would have less incentives to issue a sales forecast. Which effect dominates on average is an empirical matter. To provide further support for the signaling hypotheses we posit, we investigate whether ex post analyst work product quality and career outcomes are consistent with sales forecasting analysts attempting to signal their superior ability ex ante. We begin by modeling the quality of each analyst s last earnings forecast for the year. We only analyze the analyst s last forecast of the year so that we can measure whether the forecast contains walkdown bias, an indirect proxy for analyt s access to management information. 7 Ke and Yu (2006) show that walkdown bias is an important determinant of earnings accuracy. We measure accuracy and other analyst level determinants of accuracy on a relative basis to eliminate cross sectional firm characteristics that can impact the ability of the analyst to forecast earnings (Clement, 1999). Our model of forecast accuracy follows Ke and Yu (2006) and takes the following form, estimated via OLS with standard errors clustered by analyst: ACC_R i,j,t = β 0 + β 1 OP i,j,t + β 2 BOLD_R i,j,t + β 3 F_EXPER_R i,j,t + β 4 FOLLOW i,j,t + β 5 NFIRMS_R i,j,t +β 6 HORIZON_R i,j,t +β 7 BROKER_R i,j,t +β 8 ALLSTAR i,j,t + β 9 SAL i,j,t + YEAR FIXED EFFECTS + υ i,j,t (2) 7 Ke and Yu (2006) study the impact of walkdown bias in analyst s forecasts solely before the passage of Regulation FD. Research by Libby et al. (2007) reveals that after Regulation FD, analysts still believe in the benefits of issuing walkdown forecasts to access management information. 13

15 The dependent variable, ACC_R, is the relative forecast accuracy of analyst i s final forecast for firm j in year t. ACC_R is constructed by differencing the absolute forecast error of analyst i against the average forecast error for the final forecast of all analysts following firm j in year t, scaled by this same average forecast error and multiplied by negative one. Larger values of ACC_R indicate relatively more accurate earnings forecasts. The independent variable of interest is SAL, the indicator variable for sales forecasting previously defined. H2 predicts a positive coefficient on SAL if in fact sales forecasting helps signal superior analyst ability. We also include standard control variables prior literature has found to be important determinants of annual earnings forecast accuracy (Ke and Yu, 2006; Clement and Tse 2003; Clement 1999). These include earnings forecast characteristics of walkdown bias (OP), boldness (BOLD_R), and timeliness (HORIZON_R). We control for analyst characteristics including the relative experience the analyst has following the firm (F_EXPER_R), the relative number of firms the analyst follows (NFIRMS_R), whether the analyst was an All-Star (ALLSTAR) and the relative size of the brokerage house the analyst works for (BROKER_R). Finally, we include the natural log of the number of analysts following the firm (FOLLOW) to help control for measurement error in our relative analyst measures due to heterogeneity in analyst following across firms. Analysts provide value to the financial markets in ways other than forecasting annual earnings. The total value analysts provide to the market is unobservable, but can be inferred from their future employment. We adopt the approach in Ke and Yu (2006) and 14

16 Hong and Kubik (2003) and model analyst separations from their employer using the following analyst-year logistic regression model with standard errors clustered by analyst: Pr(A_FIRE i,t+1 ) = β 0 + β 1 A_ACC_R i,t + β 2 A_OP i,t + β 3 A_BOLD_R i,t + β 4 A_EXP i,t + β 5 A_ALLSTAR i,t +β 6 A_SAL i,t + YEAR FIXED EFFECTS + υ i,j,t (3) The dependent variable, A_FIRE, is an indicator variable that equals one if the analyst moves downward to a brokerage employing less than 25 analysts or is not listed in I/B/E/S during the subsequent year, and zero otherwise. The independent variable of interest, A_SAL, is an indicator variable that equals one if SAL equals one for any firm in the analyst s portfolio, and zero otherwise. H3a predicts that the coefficient on A_SAL will be negative if analysts who provide disaggregated earnings forecasts via sales forecasts are less likely to be fired. The remaining independent variables include average relative earnings accuracy forecast across the analyst s portfolio (A_ACC_R), the average walkdown bias in the analyst s portfolio (A_OP) and the average boldness across the analyst s portfolio (A_BOLD_R). We also include an indicator for whether the analyst is an All-Star (A_ALLSTAR) and the natural log of EXPERIENCE (A_EXP). If sales forecasting alone is not a powerful enough signal to completely reveal analyst types, we should be able to observe the costly nature of sales forecasting among those analysts issuing sales forecasts. To do so, we augment equation (3) by including relative sales forecast accuracy, A_SAL_ACC_R, across the analyst s portfolio: Pr(A_FIRE i,t+1 ) = β 0 + β 1 A_ACC_R i,t + β 2 A_OP i,t + β 3 A_BOLD_R i,t + β 4 A_EXP i,t + β 5 A_ALLSTAR i,t +β 6 A_SAL_ACC_R i,t + YEAR FIXED EFFECTS + υ i,j,t (3a) H3b predicts that the coefficient on A_SAL_ACC_R will be negative, implying that among analysts that issue sales forecasts, more accurate sales forecasters are less likely to be fired. 15

17 4. Results 4.1 Descriptive Statistics Before proceeding to a discussion of the variables and observations utilized in our empirical tests, we first investigate sales forecasting activity in the entire I/B/E/S database during our sample period. Table 1 of Panel A shows that earnings forecasting is more prevalent than sales forecasting over the sample period. In particular, of all 2,279,965 annual forecasts in I/B/E/S over the sample period, only 639,198 (28%) are annual sales forecasts. Panel B provides time series descriptive evidence on the overall sales forecasting activity during our sample period. The first two columns in Table 1 Panel B document sales forecasting activity at the analyst level in each year of the sample period. Sales forecasting by analysts has increased dramatically over time, with only 0.5% (16/3,223) analysts issuing at least one sales forecast in their portfolio of followed firms in 1995, compared with 85.0% (3,782/4,451) by It is difficult to ascertain whether sales information has been provided more frequently by analysts over time or whether I/B/E/S has increased its collection efforts for this variable in recent years. Our conversations with an I/B/E/S representative suggested that the increase in sales forecasts over time was initiated by analysts rather than I/B/E/S. Nevertheless, we include year fixed effects in all of our models to control for temporal effects. Columns three and four in Panel B of Table 1 document analyst forecasting activity for each unique analyst-firm combination in each sample year. The number of unique analyst-firm combinations where at least one sales forecast is issued for the firm has been growing over the sample period, from 18 in 1995 to 29,191 by However, even in 16

18 later years there remains substantial variation in whether an analyst will issue even one sales forecast for a given firm. For example, in 2006, 22.5% (8,485/37,676) of analysts issued no sales forecasts on the firms they covered, suggesting analysts are making some choice as to whether they forecast sales or not. This 22.5% cannot simply be attributed to firms where no demand exists for sales forecasts. As shown in columns five and six, 95.6% (3,934/4,115) of firms followed in 2006 have at least one analyst issuing at least one sales forecast during the year. This implies that while there is not much cross sectional variation in sales forecasting across firms, there is cross sectional variation in whether an individual analyst following a firm decides to issue a sales forecast. In our empirical tests, we remove firms for which there is no analyst issuing a sales forecast to help mitigate the impact of firm characteristics on an analyst s choice to issue a sales forecast. We also include firm characteristics explicitly in robustness checks. Our hypotheses are based on analyst choices to disclose or not disclose a sales forecast. However, discussions with sell side analysts reveal that sometimes this can be a brokerage choice. To observe the extent to which sales forecasting is mandated at the brokerage level and not the analyst level, we analyze analyst sales forecasting at the brokerage level each year. In Panel C of Table 1, we measure for each brokerage house the percentage of unique analyst-firm combinations that issued at least one sales forecast during the year. If brokerage houses have a policy mandating all analysts issue sales forecasts, we should observe the percentage of analysts issuing sales forecasts on each of their followed firms to approach 100%. In each year we calculate the number of brokerages in various percentage-of-salesforecasting bins. We find that the density in the <5% bin is decreasing over time and the 17

19 density of brokerage houses with greater than 95% analyst sales forecasting activity is growing over time. By 2006, there were 339 unique brokerages in I/B/E/S. Of these 339, 44 (176) brokerage houses had less than 5% (greater than 95%) of their unique analystfirm combinations issue at least one sales forecast during the year. This suggests that some brokerage houses may mandate the issuance of sales forecasts. In our empirical tests, we ensure our results are robust to removing analysts who are employed by brokerages who appear to have policies that either mandate or prohibit the issuance of a sales forecasts. 8 Table 2 provides descriptive statistics for the variables of interest in each of our three analysis samples. Panel A of Table 2 describes the Sales Forecasting Sample variables. On average, 39.5% of analyst-firm-year observations contain sales forecasts. The average analyst has been employed for 6.6 years, with interquartiles ranging from 2 to 10 years. This substantial variation in analyst experience should facilitate high powered tests of our hypotheses. All-Star analysts comprise 13.5% of the analyst-firm-year observations and 38.5% of an analysts portfolio excluding the current firm contains sales forecasts. Panel B of Table 2 provides descriptive evidence on the variables comprising the Forecast Accuracy Sample. The dependent and continuous independent variables are measured relative to other analysts following the firm, which by definition makes their average values zero. Similar to the Sales Forecasting Sample, there are 39.5% of 8 To understand the forecasting activity in recent years at the largest employers, we investigated the percentage-of-sales forecasting bins for the 10 largest brokerage houses in Merrill Lynch was in the <5% bin, Bear Stearns and J.P. Morgan were in the 60-80% bin, Citigroup, Credit Suisse, Deutsche Bank and Lehman Brothers were in the 80-95% bin, and Goldman Sachs, UBS and Morgan Stanley were in the >=95% bin. So, even among the largest brokerage houses there appears to be variation in whether it is a broker policy to issue a sales forecast or not. 18

20 observations that contain sales forecasts and 13.5% of observations come from All-Star analysts. Finally, in Panel C of Table 2, we describe the Career Outcomes Sample variables. In this sample, we create analyst-year observations by aggregating the values of each variable over each analyst s portfolio each year. Of interest for our empirical tests, on average 5.1% of analysts are fired in the subsequent year, and similar to the previously discussed samples, 40.1% of analysts are sales forecasters. 4.2 Determinants of Sales Forecasts We first focus on the determinants of the analyst decision to issue a sales forecast for a given firm as a function of their signaling incentives, while controlling for other mechanisms that can lower an analyst s cost of effort and proxies for analyst activity that may signal ability via a mechanism other that sales forecasting. We begin by estimating equation (1) for the 161,059 observations in the Sales Forecasting Sample. Column A of Table 3 reports the estimation of equation (1) and reveals the model fits the data well. The Pseudo R 2 is 63% and the model correctly classifies about 91% of observations. Consistent with H1a, H1b and H1c, the results reveal that analysts with stronger signaling incentives are more likely to provide sales forecasts for a given firm. The coefficients of EXPERIENCE and ALLSTAR are negative and highly significant, implying that older analysts and analysts with an All-Star ranking are less likely to issue sales forecasts. This is consistent with our expectations that analysts with established reputations are less likely to issue sales forecasts. In addition, the coefficient of SAL_COV is positive and significant, suggesting that the more sales forecasting an analyst does across the other firms in her portfolio, the more likely she is to issue a sales forecast for a given 19

21 firm. This evidence is consistent with analysts making signaling decisions at the portfolio level, which would be viewed as a more consistent signal of ability in the market. 9 The results for control variables also corroborate our expectations. Analysts who follow the firm more vigilantly are more likely to be sales forecasters as evidenced by the positive and significant coefficients on LFR and NEPS_R, respectively. The coefficient on NFIRMS_R is significantly negative, consistent with our expectation that analysts who follow more firms can devote less time and effort to understand any specific firm. Finally, the coefficient on BROKSIZE_R is negative and significant, suggesting that analysts at relatively smaller brokerage houses are more likely to issue sales forecasts. This result is more consistent with the effect of larger brokerages tending to hire better quality analysts who do not need to signal their type as opposed to the effect of larger brokerages providing more resources that allow generation of firm knowledge to underpin a sales forecast. Determinants of Sales Forecasts Robustness Checks Access to Management. Prior research suggests that conference call participation is an important way in which an individual analyst learns about the firm (Libby et al., 2007; Mayew 2007). To control for the potential that an analyst s relationship with management is the mechanism that lowers the analysts cost of effort and not analyst ability, we reestimate equation (1) by including the additional control variable ONCALL. 10 ONCALL indicates whether an analyst was allowed to participate on any of the firm s quarterly 9 Annual estimation of equation (1) shows that SAL_COV is positive and significant each year, EXPERIENCE is negative and significant in nine of the sample years and insignificant in the remaining years, and ALLSTAR is negative and significant in five of the sample years and insignificant in the remaining years. 10 ONCALL is only defined for the subset of firms with quarterly conference call transcripts available on the Thomson Financial StreetEvents database between the inception of the database in June 2001 and March To identify which analysts participated on the conference call, I/B/E/S translation files were used to match names and brokerages listed on the conference call transcripts with the related unique I/B/E/S identification codes. I/B/E/S has discontinued the issuance of translation files to researchers, which prevents us from coding of ONCALL through the end of our sample period. 20

22 conference calls during the year. Column B of Table 3 reveals that, after controlling for ONCALL, the signaling variables continue to exhibit signs and magnitudes similar to the estimation in Column A of Table 3. Additionally, as predicted, the coefficient of ONCALL is positive and significant, suggesting that management access is an important source of information for the analyst. The only notable difference from the results for the full sample is that the coefficient of LFR becomes insignificant. This may not be surprising if access to management also allows an analyst to become a leader. Brokerage Effects. It is possible that equation (1), while controlling for relative broker size, does not take into account the possibility that the decision to issue sales forecasts may be a brokerage house level decision, not an analyst level decision. We use two approaches to test the robustness of our results to this possibility. First, we include broker fixed effects in the sales forecast determinants model. Second, we exclude observations for all analysts working for brokerages who appear to have a policy of either issuing or not issuing sales forecasts. We define policy brokerages in a given year as those brokerage houses where less than 5% or more than 95% of the unique analyst-firm combinations contain at least one sales forecast. We present these results in Table 4. Column A in Table 4 first re-estimates the equation (1) logit model using ordinary least squares. We do so because, as we begin including brokerage house fixed effects, the large number of brokerage house indicator variables does not allow convergence in the logit maximum likelihood estimation. Therefore, Column A in Table 4 represents the linear probability model estimation, which is comparable to Column A in Table 3 and serves as a benchmark for assessing the remaining columns in Table 4. 21

23 In Column B of Table 4, we include broker fixed effects. The results on the signaling variables EXPERIENCE and SAL_COV remain significant and of similar magnitude to Column A. The coefficient on ALLSTAR is negative but statistically insignificant. This may not be surprising if certain brokerages tend to hire the majority of All-Star analysts. Column C removes analyst observations where it appears the brokerage has a policy of issuing or not issuing sales forecasts. The coefficients on each signaling variable are statistically significant, and of a sign and magnitude consistent with Column A. This suggests that broker policy choices do not appear to confound our inferences. Firm Effects. To ensure that we are observing analysts who follow firms where demand exists for disaggregated earnings information, we condition our sample to include only those firms where at least one analyst issues a sales forecast. However, there can still be variation in demand for volume and margin information. For example, firm characteristics may make earnings a relatively uninformative measure of firm performance thereby yielding a disaggregation of earnings not particularly useful. For growth firms, loss-making firms and young firms, the market may be more concerned with top line sales growth than with margin information. 11, In such settings, demand may be higher for sales forecasts as sales become the preferred performance metric instead of earnings. 12 Additionally, sales metrics alone may be of interest in certain industries, such as retail. Since analysts tend to follow many firms in a small number of industries, the positive coefficient on SAL_COV could be driven by analysts covering firms in industries where demand for sales forecasts is relatively high. In untabulated results, we re-estimate 11 Ertimur, Livnat and Martikainen (2003) report that sales forecasts are more likely to exist for growth firms than for value firms. 12 This argument implies that for some firms, there would be little demand for earnings forecasts. We condition the sample to require that each observation have an earnings forecast, which helps remove the potential for demand effects to contaminate our inferences. 22

24 equation (1) to control for these factors by including the firm s market to book ratio, an indicator variable for negative net income, the age of the firm, and fixed effects at the twodigit SIC code level. Inferences on each of the three signaling variables remain unchanged. The sign, magnitude and statistical significance are virtually identical to those reported in Table 3. Further, the control variables do appear to behave as predicted, with higher sales forecasting probabilities for loss firms, young firms and growth firms. 4.3 Forecast Accuracy Having established in the previous section that sales forecasting activity appears to be consistent with signaling incentives, we investigate whether analyst products stemming from analysts who signal via sales forecasts are of higher quality. In particular, we expect the high ability of the signaling analysts to manifest in the accuracy of their annual earnings forecasts. In Table 5 we test whether the final annual forecast issued by the analyst for the year is more accurate, on average, for sales forecasting analysts relative to other analysts. 13 The first column in Table 5 presents the results. The coefficient for the variable of interest, SAL, is positive and significant indicating that sales forecasters issue more accurate earnings forecasts. 14 This result is consistent with H2 and our conjecture that analysts who issue sales forecasts have superior ability. The results for other variables are generally consistent with prior literature. Specifically, analysts who walk down their forecasts during the year (from an initial optimistic forecast to a final pessimistic forecast), 13 Consistent with Ke and Yu (2006) we model the accuracy of the last forecast of each analyst so that we can measure and control for walkdown bias in the analyst forecast. Since analyst forecasts issued closer to the earnings announcement date are known to be more accurate, measuring the last forecast reduces the variability in forecast accuracy, thereby reducing the power of our hypothesis tests. 14 Estimating model (2) each year yields positive and significant coefficients in 1999, 2000, 2002, 2003 and The remaining year results are not statistically significant. 23

25 analysts with relatively more firm specific experience and analysts working at relatively larger brokerage houses tend to be more accurate. Bold forecasts made early in the year tend to be less accurate, as Ke and Yu (2006) report. Accuracy also decreases as the forecast horizon lengthens. All-Star analysts are more accurate, but not statistically so. Finally, we find analysts following relatively fewer firms are less accurate, inconsistent with the extant literature. The result is marginally significant however, with a p-value of Career Outcomes Our final tests relate to analysts career outcomes in the spirit of Hong and Kubik (2003) and Ke and Yu (2006). We investigate whether sales forecasters are less likely to be fired than other analysts, after controlling for determinants of career outcomes identified in the prior literature. Our estimation of equation (3) is presented in Table 6. Consistent with H3a, we find that analysts that issue sales forecasts are less likely to be fired. The coefficient of A_SAL i,t in column A of Table 6 Panel A is negative and statistically significant. 15 We find other properties of earnings maintain their known relationship with analyst-brokerage separations found in prior literature. In particular, more accurate analysts are less likely to be fired (A_ACC_R = , p<0.001). This implies the effects of issuing sales forecasts do not simply reduce the probability of firing via the accuracy of earnings forecasts. Analysts issuing biased walkdown earnings forecasts in their portfolio are less likely to be fired, consistent with Ke & Yu (2006), as are analysts issuing bold forecasts. As expected, more experienced analysts are less likely 15 Estimating model (3) annually yields a negative and statistically significant coefficient on A_SAL in each year. 24

26 to be fired. Finally, the All-Star coefficient is negative, although not statistically significant. To assess the economic magnitude of this result, we measure the predicted probabilities generated from equation (3) for sales forecasters and non-salesforecasters each year while setting all continuous variables at their mean values, and setting A_ALLSTAR equal to zero. Panel B of Table 6 shows that in each sample year, the probability of firing for non sales forecasting analysts is approximately double that of sales forecasting analysts. For example, in 1996 (2006) the probability of firing for non-sales forecasting analysts was 2.8% (5.9%) while the probability for sales forecasting analysts was only 1.5% (3.3%) Overall, our results with respect to career outcomes are consistent with high ability analysts rationally expecting the sales forecasting signal to separate themselves from other analysts. To further validate the sales forecasting as a credible costly signal of analyst type, we investigate the effects of relative sales accuracy across the analyst s portfolio. We do so by conditioning the sample on only those analysts who issue sales forecasts and then add relative sales accuracy, A_SAL_ACC_R to equation (3). The estimation of this augmentation of model (3) is presented in column B of Table 6. Consistent with H3b, we find that relative sales accuracy is negatively and statistically associated with the probability of being fired (A_SAL_ACC_R = , p<0.001). This implies that there is a cost to being relatively inaccurate on sales forecasts even among analysts providing sales forecasts. This result is incremental to the effects of overall relative earnings accuracy. The results in Table 6 imply the costly nature of sales forecasting. If sales forecasting is costly, are the less accurate sales forecasters worse off compared to those 25