Buy-Side Participation and Information Production. in Earnings Conference Calls

Transcription

1 Buy-Side Participation and Information Production in Earnings Conference Calls Ling Cen Sudipto Dasgupta Vanitha Ragunathan University of Toronto HKUST University of Queensland November 2011 Abstract: We examine the incentives of buy-side analysts to participate in earnings conference calls, and how their participation and proactive involvement in the call affects information production during the call. Our results suggest that avoiding large losses on stocks in the buy-side portfolios is a major reason for their participation and involvement. Buy-side participation and early involvement lead to greater information production, but the effect exists only when the news around the earnings announcement event is negative. Sell-side analysts benefit from the information production they revise their forecasts more often and make more accurate forecasts when buy-side involvement is more; however, again, this effect is present only when the news is negative. Finally, when the news around the earnings announcement is positive, greater buy-side participation ameliorates the postannouncement drift, but when the news is negative, the drift is more pronounced when there is more buy-side involvement in the earnings conference call. Key Words: Buy-Side Analyst; Earnings Conference Calls; Information Production Cen is from the University of Toronto, Rotman School of Management, 105 St. George Street, Toronto, Ontario M5S 3E6 Canada; Dasgupta from the Hong Kong School of Science and Technology, Department of Finance, Clear Water Bay, Kowloon, Hong Kong; and Ragunathan from the University of Queensland, UQ Business School, St. Lucia QLD 4072, Australia. Cen: ling.cen@rotman.utoronto.edu; Dasgupta: dasgupta@ust.hk; Ragunathan: v.ragunathan@business.uq.edu.au. 1

2 Buy-Side Participation and Information Production in Earnings Conference Calls In contrast to the sell-side analyst position, the job of a buy-side analyst is much more about being right; benefiting the fund with high-alpha is crucial, as is avoiding major mistakes. In point of fact, avoiding the negative is often a key part of the buy-side analyst's job, and many analysts pursue their job from the mindset of figuring out what can go wrong with the idea. (Investopedia italics added.) 1. Introduction Analysts play a vital role in the dissemination of information in financial markets. Academic research, however, has focused almost exclusively on sell-side analysts (SSAs), that is, analysts employed by brokerage firms who generate research reports, and provide earnings forecasts and stock recommendations for their clients. 1 Buy-side analysts (BSAs), on the other hand, are employed by money managers and furnish research reports directly for the consumption of the asset managers of the firms that employ them. Unlike the SSA s output, buy-side research output is seldom publicly available. Consequently, we know very little about BSA s objectives, nor their role in information production. In this paper, we explore these twin issues pertaining to the BSAs by examining BSA s participation in earnings conference calls. Earnings calls are important information events. Not only do these calls (usually conducted to coincide with the release of quarterly or annual earnings 1 It has been argued that the most important function for SSAs is to provide information to the buy-side through discussions and facilitating access to management of the firms they cover. Their ability to attract investment banking business for their firms and generate trading volume for their firms in the stocks they cover is considered to be the most important determinants of their compensation. 2

3 announcements) represent events that provide management with opportunities to communicate and relay information to investors and analysts, they are usually concluded with a question-and-answer (henceforth, Q&A) session. In this Q&A session, company management answers questions by participants in the call, mostly comprising buy- and sell-side analysts. Mayew (2008) argues that the ability to ask questions is valuable to SSAs, because even though the management responses to the questions are public, these responses complement the private information possessed by the SSAs. However, management largely controls who gets to ask questions, and thus, can withhold the privilege from SSAs who are less favorable about the company. Mayew (2008) finds that SSAs who have more favorable outstanding stock recommendations are more likely to ask questions; moreover, downgrades by SSAs are associated with less access to management during earnings conference calls. In a related recent paper, Mayew, Sharp, and Venkatachalam (2011) find that SSAs participating in earnings conference calls make more accurate and timely forecasts, although the economic magnitudes are small. 2 BSAs also participate in earnings conference calls. While the number of BSAs participating is fewer than that of SSAs (28% versus 72% of call participants), they are almost as active in the Q&A sessions, (4.3 questions versus 5.4 questions), and ask slightly longer questions. The important difference between buy-side and sell-side participants, however, is that the former represent important stakeholders in the company. Instead of having to curry favor with management, it is more likely that it is management who wants to oblige. In other words, BSAs are less likely to be denied by management if they want to participate, and they are also in a position to intervene in the Q&A as and when they want. 2 However, they argue that rather than participating in the call per se increasing access to information, their result likely reflects the possibility that more informed SSAs participate in the call. In a study of pre-regulation Fair Disclosure conference calls, Bowen, Davis, and Matsumoto (2002) find that access to earnings conference calls increase SSA s forecast accuracy. 3

4 The above suggests that BSAs participation is motivated by different reasons than those of sell-side participants. Because the buy-side has positions in the company, the value of the positions is an important concern for the buy-side. Thus, one would expect that BSA participation would be responsive to information leakage, or price run-ups or run-downs, prior to the conference call. Second, conditional on participation, one would expect that when there is information leakage, the buy-side would get involved in the Q&A earlier, that is, will be more eager to ask questions, which management would be more likely to accommodate. Third, the buy-side would participate and get involved in the Q&A early if they expect that this would produce information that would not otherwise be forthcoming; thus, the informativeness of conference calls would improve with more buy-side participation, especially when the buy-side gets involved earlier in the Q&A. Fourth, SSA would also benefit from the greater information production associated with more buy-side participation and earlier involvement. In particular, if SSA s forecasts are normally overoptimistic or upward biased (as extensively documented in the literature), then one would expect a more positive association between forecasts revisions and forecast accuracy on the one hand and buy-side participation and earlier involvement in the Q&A on the other, when the news surrounding the earnings announcement is negative. 3 Finally, the extent of buy-side participation and earlier involvement in the Q&A should also have implications for the speed with which information revealed during the earnings announcement diffuses to the stock price; however, here, the implications are more nuanced, as we discuss below. Our results strongly support these expectations. We find that buy-side participation (the proportion of BSAs to total number of analyst participants in the call) increases in the absolute magnitude of cumulative abnormal returns from 10 trading days before to 2 trading days before the 3 For the rest of this paper, conference call events associated with positive (negative) abnormal returns in the (-1,+1) window are called positive news ( negative news ) calls, respectively. 4

5 call; we get similar results for our (inverse) measure of how early in the Q&A the BSAs, on average, get involved by asking questions. Interestingly, we find no such results for our measure of sell-side participation intensity, which is the number of SSAs that show up as a proportion of the total number in I/B/E/S that issue earnings forecasts on the firm. Next, we find that the absolute value of the cumulative abnormal returns around the earnings call event (which includes the earnings announcement event) increases with buy-side participation and our measure of how early the buy-side starts asking questions. This supports the hypothesis that buy-side involvement leads to greater information production. Again, we get no corresponding results for our measure of SSA participation, indicating that SSAs ask questions the answers to which complement their private information (consistent with Mayew (2008)), but may not immediately contribute to how informative the call event is, overall. We then show that greater buy-side involvement leads to more sell-side forecast revisions, larger magnitude of revisions, and smaller forecast errors on average. While we find similar results for our measure of sell-side participation for forecast revisions, we do not find that greater sell-side participation leads to greater accuracy. 4 Finally, we find that when the cumulative abnormal return around the call event is positive, the post-announcement drift is mitigated by greater buy-side participation. However, when the cumulative abnormal return is negative, we see the opposite: the drift is more pronounced with greater buy-side participation. There are several potential reasons why the buy-side, given its positions in a particular stock, would be interested in more information revelation during the earnings conference call. Given prior favorable (unfavorable) information which is not yet fully reflected in a stock, if a buy-side asset manager builds up a long (short) position in that stock, then it is in the interest of the corresponding BSAs to ask questions that would reveal information, which then is quickly reflected in the stock price. The asset manager then profits from her position in the stock. If this is the case, then we expect 4 Note that this refers to forecast revisions of all SSAs following the firm, and not just those who participate in the call. 5

6 similar buy-side incentives regardless of whether the news about the stock is positive or negative. Moreover, given that most of the BSA participants in our the conference call sample represent asset management companies that do not follow shorting strategies, we would expect the results in our sample to be driven by the subsample of conference calls that are associated with positive news surrounding the call and the earnings announcement. However, we find exactly the opposite results. Early involvement of BSAs in the Q&A, information production around the call event, SSA forecast revision likelihood and improvement in forecast accuracy, all are primarily driven by call events that are associated with negative news. This finding is consistent with the notion that BSAs are most concerned with avoiding large negative shocks to their holdings, as indicated in the quote from Investopedia in the opening vignette. Alerted by negative information spillover, BSAs show up in conference calls to ascertain whether they should unwind their positions quickly before things really go wrong with the company, and the stock price nosedives. We find two results that are suggestive of the roles played by BSAs when the news leading to the call event, or the news around the call event, is positive. First, we find that BSAs participate more when the prior stock price run-up is higher (that is, more positive); second, the post-announcement drift in stock price is mitigated by more buy-side participation when the news is positive. Note, however, that we do not find any evidence of greater information production associated with more BSA participation or early involvement in the Q&A for earnings call events associated with positive news. Thus, it does not appear as though the buy-side, armed with private information about the value of their holdings, participates with the intent of facilitating favourable information to be quickly revealed. Rather, these results suggest that when the information leakage indicates positive news, the BSA s role is more like that suggested by Mayew (2008), that is, asking questions that complement private information that they might already possess, rather than posing questions that generate public 6

7 information. A more informed buy-side then helps the information get quickly incorporated into stock prices through their trades. 5 In an early survey of buy-side professionals following the revelations of sell-side conflicts of interest in 2001, Boni and Womack (2002) document that 70% of the respondents believe that SSAs are pressurized by their buy-side clients not to downgrade stocks in their portfolio at least some of the time. Clearly, buy-side sensitivity to a decline in the value of their stocks can create pressure on SSAs, in the same way that company management of the firm being covered by the analyst can create pressure. However, our results on conference calls also demonstrate a bright side of the buy-sell divide. When it is important for the buy-side to have timely access to information, buy-side participation and questioning in conference calls lead to more information production, from which the SSAs also benefit. The rest of this paper is organized as follows. Section 2 presents a brief literature review. Section 3 discusses the data sources and the summary statistics of our sample. Section 4 presents the empirical results, and Section 5 concludes the paper. 2. Literature Review Conference calls act as one forum that managers use to convey information to market participants. Even in the pre-regulation FD setting when conference calls were only open to a select group of analysts, they had a positive impact on analyst accuracy (Bowen, Davis, and Matsumoto, 2002). The importance of being able to participate in a call has not declined in importance in a post Reg FD world even though anyone is able to listen in on conference calls. For instance, Mayew, 5 For earnings call events associated with negative news, more BSA participation adds to the drift. This is consistent with papers (see, for example, Zhang (2008)) finding that the speed with which SSAs revise their forecasts affects the speed of diffusion and the post-earnings announcement drift. Since more forecast revisions occur with more BSA involvement after negative news earnings call events, and these revisions do not occur immediately (perhaps because of behavioral factors as discussed in Clement (1999), Daniel et al. (1998) or Hong and Stein (1999)), the drift is more pronounced in this case. 7

8 Sharp, and Venkatachalam (2011) provide evidence of improvement in accuracy for analysts participating in a call. However, they are unable to rule out that these analysts have superior private information about the firm. Despite the mixed evidence in recent studies, analysts who have fallen out of favour with management and had to drop coverage as they have no longer had access to management (e.g., not participate in conference calls) claim that the ability to be able to question management in such a forum is a valuable opportunity for analysts to form a better picture of the firm s financial position. The form of discrimination that Mayew (2008) studies concerns analysts being barred from asking questions in earnings calls. He finds that analysts are more likely to be excluded when they have either issued an unfavourable recommendation or have downgraded the firm. Greater openness does not necessarily result in reduced information asymmetry. As Hollander, Pronk and Roelofsen (2010) show, managers do refuse to answer what they consider sensitive questions despite the threat of being punished by the market. The literature on buy-side analysts is sparse in large part due to the unavailability of recommendations that are generated for internal consumption only. A dated comparison of buy-side versus sell-side analysts shows that they tend to be fewer in number relative to sell-side, follow firms across more industries (10 vs. 3) and are given the task of covering more firms (73 vs. 20). The two groups also differ in how they source information. Unlike the sell-side that relies on soft information and cultivates ties with managers, the buy-side focuses on trading strategies based on fundamental analysis using widely available financial information. 6 Indirect evidence on the use of buy-side recommendations is either obtained from surveys of fund managers (Cheng, Liu, and Qian, 2006) or from trading patterns of a sole money manager 6 E.g., Williams,Moyes and Park (1996). 8

9 (Groysberg, Healy, and Chapman, 2008; Frey and Herbst, 2010). Using a comparison of sell-side recommendations and buy-side data from a single money manager, Groysberget al. (2008) argue that the higher retention rates of low-quality analysts explains the higher forecast errors and optimism of buy-side forecasts. In contrast, Chen et al. (2006) and Frey and Herbst (2010) document the reliance of fund managers on buy-side research. For example, the later shows buy-side recommendations are more likely to trigger a trade in the same direction by fund managers and the analysis of the profitability of such an approach suggests that fund performance also increases. 3. Data 3.1. Data Sources We source our sample of conference call transcripts from Streetevents, a ThomsonReuters database. This database has transcripts for earnings conference calls, investment bank hosted analyst conferences and corporate events such as merger announcements, the release of sales numbers etc. Given the sparse coverage and inconsistent formatting of files in the early years of this database (Mayew, 2008; Li, Minnis, Nagar and Rajan, 2009), we download transcripts for the period from January 2003 to December Our initial sample comprises of all transcripts of earnings conference calls for U.S. firms that are available on StreetEvents. The transcripts we download are Microsoft Word documents with standardized formats. The first page has information on the company hosting the call, the time, date and reason for the call. The second page of the transcript has the names of all participants with corporate participants listed first. They are followed by the conference call participants that catalogues the name and affiliation of analysts, journalists from public media and other outside participants. The names of these outside 9

10 participants are listed in the order in which they get to ask questions. The third part of the transcript comprises of the prepared statements read by the corporate participants, usually the Chief Executive Officer and/or the Chief Financial Officer. The call is then opened up to questions from outside participants. Earnings conference calls on average last 32 minutes, if we assume that participants talk in an average speed of 150 words per minute. Using a Word macro, we extract the following information from each transcript, including ticker, date of conference call, the reason for the call, the names of call participants and their affiliations. In addition, we obtain several measures of participation for each individual including a count of the number of times a participant s name appears in the transcript, number of words spoken by each participant and the number of questions asked by outside participants. The output is then manually checked for any missing information pertaining to the identification of the external participant. Such errors are generated during the transcription process and are flagged either with the term Unidentified Participant or with the affiliation of the analyst missing. In such cases, we find the piece of missing information by reading the relevant transcript since it is usually contained in the Question and Answer section of the transcript. The last stage in putting our sample together is the matching the names of brokerage firms that analyst participant represents to those that provide forecast to I/B/E/S. To this end, we use the most recent version of the I/B/E/S brokerage translation file to hand-match brokerage names in both datasets. 7 An analyst who provides earnings forecasts to I/B/E/S is classified as a SSA, while one that does not is classified as a BSA. Buy-side analysts in our sample are almost always from asset management firms. To ensure that a SSA is not misclassified as buy-side, we undertake the additional step of confirming that the name and affiliation of analyst participant in the transcript 7 The I/B/E/S translation file links the brokerage (analyst) name to the broker (analyst) code in the detail history file. This file is no longer provided to researchers. 10

11 is the one providing earnings forecasts in I/B/E/S. This check is crucial in cases when investment banks that provide sell-side services also have asset management divisions. Given the nature of this study, we require that the dates of earning conference calls in our sample must be matched with the dates of earnings announcements from the I/B/E/S actual file. In addition, we require that stock trading information and corporate accounting information of firms holding earnings conference calls on the dates of earnings announcements must be available from CRSP and COMPUSTAT. These screening criteria finally yield a sample of earnings conference calls between 2003 and Summary Statistics Table 1 presents summary statistics on call participation by BSAs and SSAs on the firms that use StreetEvents to disseminate earnings conference calls. Our summary statistics suggest the following features of our sample. First, the average number of buy-side representatives that ask questions is 1.29 compared to 4.93 for SSAs. This mainly reflects the relative proportions of active BSAs and SSAs in the financial industry. Second, on average, sixty per cent of SSAs who actively cover the stock appear in the earnings conference calls, suggesting the importance of these events to SSAs. Third, the mean and median book value of total assets of firms in our sample are 4907 million and 619 million. These suggest that firms in our sample are larger than average firms in the COMPUSTAT sample. The large difference between the mean and median of book value of total assets indicates that this variable is positively skewed. Therefore, we use the natural logarithm of the book value of total assets as a proxy for firm size in our regression analysis. In addition, we also provide the summary statistics of cumulative abnormal returns (CARs) around the earnings conference calls in Table 1. The computation of CARs follows the standard approach in the event study literature. The estimation window is (t-250, t-40), which is a period from 11

12 250 trading days before to 40 trading days before the earnings conference calls (also the earnings announcements). We estimate the parameters using the market model within the estimation window and we use estimated parameters to compute the abnormal returns and cumulative abnormal returns in three test windows: (t-10, t-2), (t-1, t+1) and (t+2, t+10). These three test windows represent the pre-announcement period, during-announcement period and post-announcement period, respectively. As shown in Table 1, the CARs for three test windows exhibit two distinct features. First, the means of CARs for all three test windows are very close to zero. This is expected since there were no positive or negative shocks in aggregate in the financial market between 2003 and Second, there exists a huge dispersion of CARs in the cross section. For example, standard deviations of CARs for all three test windows are higher than 7%, suggesting that earnings announcements and earnings conference calls are indeed important information events. 4. Empirical Analysis 4.1. Differences between BSAs and SSAs: An Overview In Table 2, we present the differences between BSAs and SSAs in earnings conference calls. When BSAs attend the calls, they are in the minority and only account for 28% of outside participants. Furthermore, while they ask fewer questions than their sell-side counterparts, the number of words spoken by them is higher. For example, while the average number of questions asked by BSAs (4.30) is lower than that of SSAs (5.43), the number of words per question for BSAs (32.45) is slightly higher than that of SSAs (31.84). Focusing on calls in which at least one BSA participate, we find that they are on average asking questions later than SSAs. We capture the order of questions by the variable, Sequence, which is 12

13 defined as the order of questions scaled by the total number of questions in the earnings conference call. By construction, Sequence is bounded between 0 and 1. A lower Sequence indicates that the question is asked in the earlier part of the conference calls. Our results in Table 2 suggest that the average Sequence is higher for BSAs than SSAs (0.73 vs. 0.49). Further evidence of the BSAs being relegated to the end of the call is the percentage of cases where BSAs get to ask the first question; a random allocation amongst the participants would imply that a BSA would get to ask the first question 28% of the time. We find that this happens in only 11% of all calls, and the BSAs are also over-represented in the percentage of the last question that they get to ask. All the differences mentioned above are statistically significant at 1% level. We select two measures, the percentage of BSAs among all analysts participating in the earning conferences calls (Buy-side Pct) and the Sequence of questions from BSAs (Buy-side Sequence) 8, to proxy for the BSA participation in our main specifications. The choice is dictated by several considerations. First, as discussed above, these two measures capture distinctive differences between BSAs and SSAs. Second, these two measures exhibit a high degree of variations in the cross section. For example, the standard deviations of Buy-side Pct (0.32) is higher than its mean (0.28). Finally, these two measures capture two complementary aspects of buy-side participation in earnings conference calls: Buy-side Pct captures the importance of buy-side presence, and the Buy-side Sequence captures the timing of the buy-side involvement, which indicates BSAs eagerness to ask questions and set the tone of questions in the earnings conference calls. It is important to point out that Buy-side Pct is positively related to the buy-side participation and Buy-side Sequence is negatively related to the buyside participation. 8 While Buy-side Sequence is used in our analysis, we require that at least one BSA participate the earnings conference call. 13

14 The pattern that the BSAs typically ask questions later than the SSAs is consistent with Mayew (2008), i.e., firms prefer favorable and optimistic SSAs to ask questions in the earlier part of the earnings conference calls. However, as noted above, here is significant variation in the timing of BSA involvement in the Q&A. The literature, focused on SSAs, does not address the issue of why BSAs sometimes jump in early Pre-announcement Abnormal Returns and Buy-side Participation of Earnings Conference Calls Since anyone with access to the internet can listen in on a conference call, we are interested in the factors that might trigger a request from the BSAs to participate in a call. If the BSAs are sensitive to new information incorporated into stock prices, or they are directly involved in the trading that leads to the stock price changes before the earnings conference calls, we expect that a larger magnitude of pre-announcement CARs will lead to a more active participation of BSAs in the earnings conference call. Since both dependent variables of our interest in this test, the percentage of BSAs among all analysts participating in the earning conferences calls (Buy-side Pct) and the Sequence of questions from BSAs (Buy-side Sequence), are bounded between 0 and 1, we carry out Tobit regressions to test our hypothesis and the results are reported in Table 3. The key independent variable is CAR (t-10, t-2), the absolute CAR between 10 trading days before to 2 trading days before the earnings announcements. The coefficients of CAR (t-10, t-2) on Buy-side Pctand Buy-side Sequence for the full sample, as reported in the Column (1) and Column (2) of Table 3, are (statistically significant at 1% level) and (statistically significant at 10%). This result suggests that larger absolute pre-announcement CARs lead to more active and earlier participation of BSAs in the earning conference calls. This result, however, does not shed light on whether the BSA participation is driven by their sensitivity to new information incorporated into the stock price, or their own trading activities 14

15 before the earnings announcements. However, these two arguments do generate different empirical predictions. More specifically, the former predicts an asymmetric relationship between CAR (t-10, t-2) and the buy-side sequence of questions conditional on whether the earnings shock is positive or negative. Since most buy-side institutions favoured by firms in the earnings conference calls hold long positions, BSAs can free-ride on the positive shocks but may lose their jobs if the shock is negative. Therefore, BSAs should be more sensitive to negative news than positive ones. On the other hand, we expect a symmetric relationship for the latter argument. If the buy-side institutions have established positions before the announcements and their BSAs use the earnings conference calls to spread information and realize returns, we should observe a negative relationship between CAR (t-10, t-2) and the buy-side sequence of questions irrespective of whether the CARs are positive or negative. Our results in Column (3) - (6) suggest that, although the positive relationship between CAR (t-10, t-2) and Buy-side Pct holds for both positive and negative abnormal returns prior to the call, the BSAs only ask early questions when negative information is incorporated into the stock prices before the earnings announcement. This evidence is therefore more consistent with the view that early BSA involvement in the Q&A is motivated by a desire to assess to what extent their buy-side asset managers are exposed to a precipitous decline in the price of a stock in their portfolio Buy-side Participation of Earnings Conference Calls and the Earnings Announcement Effect Our next test is to address the information production role of BSAs during the earnings conference calls. To achieve this end, we examine the correlation between the buy-side participation of earnings conference calls and the absolute CARs for a period one trading day before to one trading day after the earnings announcements, i.e., CAR(t-1, t+1). Since we have shown in Table 2 that the buy-side participation can be affected by pre-announcement CARs, we have to rule out 15

16 the possibility that the relationship between buy-side participation and the earnings announcement effect is driven by the pre-announcement CARs, which can be a common factor affecting both. Therefore, we explicitly control for CAR(t-10, t-2) in our test specification. Further, since corporate earnings of different companies usually comove as the entire economy fluctuates and the corporate earnings are highly auto-correlated, we control for the firm-fixed effect as well as yearfixed effect in all test specifications reported in Table 4. Our results suggest that, in the full sample, both Buy-side Pct and Buy-side Sequence are significantly correlated with the magnitude of earnings announcement effect. For example, an increase (decrease) of one standard deviation in Buy-side Pct (Buy-side Sequence) will be associated with an increase of the magnitude of CAR(t-1, t+1) by 0.26 (0.17) percentage points, while the mean of CAR(t-1, t+1) is -0.14%. Further, we test the interactive effect between Buy-side Pct and Buy-side Sequenceon the magnitude of announcement effect. We find that the correlation between Buy-side Pct and CAR(t-1, t+1) is much stronger if the BSAs ask early questions in the earnings conference calls. For example, if the Buy-side Sequence is very small (i.e., there are a lot of analysts in the earnings conference calls and a buy-side analyst asks the first question), an increase of one standard deviation in Buy-side Pct will be associated with an increase of the magnitude of CAR(t-1, t+1) by 0.80 percentage points. Consistent with our findings reported in Table 2, we find that the positive relationship between buy-side participation and the magnitude of earning announcement effect comes from the subsample where CAR(t-1, t+1) is negative. This suggests that BSAs play a more important role under negative earnings shocks. Intuitively, buy-side institutions with long positions will be eager to send their analysts to the earnings conference calls and participate more proactively in the Q&A if they are concerned about corporate earnings. However, if they anticipate potential positive shocks to 16

17 earnings, they will be more than happy to ride on the trend and wait for voluntary disclosure from the corporate side The Impact of Buy-side Participation on Sell-side Forecast Revisions after the Earnings Conference Calls Our results in Table 4 suggest an information production role of BSAs in the earnings conference calls. Since the majority of outsider participants in the earnings conference calls are SSAs, a natural following-up question to ask is whether SSAs learn from BSAs during the earnings conference calls. To answer this question, we examine the impact of buy-side participation in the earnings conference calls on the earnings forecast revisions of SSAs in the post-announcement period (t+2, t+10), i.e., from 2 trading days after to 10 trading days after the earnings announcements. In particular, we only count the first revision of each SSA within (t+2, t+10) to avoid complication from multiple revisions. Further, in order to make a meaningful comparison of earnings forecasts before and after the earnings announcements, we require that earnings forecasts before and after the earnings announcements must be corresponding to the same fiscal period. Therefore, we carry out this test based on one-year-ahead (F1) forecasts of SSAs around the quarterly earnings announcements only. We adopt three dependent variables to capture the forecast revisions from the SSAs: Pct Sellside Revised is the percentage of sell-side analysts that make revisions during this period, among all active sell-side analysts covering this firm; Forecast Revisions is the average level of absolute magnitude of forecast revisions made by sell-side analysts during this period; Forecast Errors is the change of absolute forecast errors generated by sell-side forecast revisions during this period. Forecast Revisions and Forecast Errors are standardized by the price levels before the earnings conference calls, to make them comparable across different firms. It is worth mentioning 17

18 that, in this test, we include all active SSAs listed in I/B/E/S, irrespective to whether they have participated the earnings conference calls or not. Summary statistics in Table 1 suggest that, on average, 75% of SSAs covering the firm revise their earnings forecasts after earnings conference calls. The forecast revisions after the earnings announcements, on average, reduce the forecast errors, indicating that new information is incorporated into the forecasts of SSAs. In examining whether SSAs learn from BSA participation, we have to address the possibility that the former do not learn from management responses to BSA questions directly. Instead, they learn from the price effect triggered by the questions from BSAs in the earnings conference calls. To rule out this possibility, we control for CAR (t-1, t+1) in our regression. Similar to our previous tests, we control for firm-fixed effect and year-fixed effect in all specifications reported in Table 5. Results in Table 5 suggest that SSAs indeed learn from BSAsin the earnings conference calls, especially under the negative earnings shocks. The economic magnitude is meaningful. For example, while the CAR (t-1, t+1) is negative, an increase of one standard deviation in Buy-side Pct will lead to 3.38% increase in Pct Sell-side Revised, 19.32% increase in Forecast Revisions and 23.72% decrease in Forecast Errors relative to their mean levels. We find a similar but slightly weaker effect based on Buy-side Sequence. Overall, our evidence supports the notion that the SSAs learn from BSAs through earnings conference calls, especially during negative earnings shocks. More important, such learning process leads to prompt forecast revisions and reduce forecast errors of SSAs Buy-side Participation in Earnings Conference Calls and the Earnings Announcement Drift Since the seminar work of Ball and Brown (1968), the post-earnings-announcement drift, or PEAD (also named the SUE effect) has been extensively studied in the finance and accounting 18

19 literature. As summarized by Bernard & Thomas (1989) and Bernard & Thomas (1990), the documented PEAD suggests a strong tendency for a stock s cumulative abnormal returns to drift in the direction of an earnings surprise for several weeks following an earnings announcement. One of the main explanations for PEAD in the existing literature is that investors underreact to information disclosed in earnings announcements (see Bernard (1993) for an excellent survey of these studies). In previous sections, we saw that, for calls associated with negative earnings shocks, i) BSAs are sensitive to new information incorporated into the stock prices before the earnings announcements; ii) BSAs play an important information production role in the earnings conference calls at the earnings announcements; iii) forecast revisions of SSAs are significantly affected by the buy-side participation in the earnings conference calls. These results suggest that, under negative earnings shocks, BSAs produce new public information to which other market participants may underreact. Therefore, we expect that, when there is a bad news about corporate earnings, the PEAD effect is likely to be stronger when there is a higher level of buy-side participation in the earnings conference calls. We examine this conjecture in test reported in Table 6. To test the PEAD effect conditional on the intensity of buy-side participation in earnings conference calls, we have to first identify positive and negative earnings shocks. We present our results based on two cut-offs, i.e., the earnings shocks are defined if the magnitude of CAR(t-1, t+1) is larger than 3% in the first case and 5% in the second case. Not surprisingly, a higher cut-off leads to a smaller sample and a stronger result. Consistent with our conjecture, we find that when the earnings shocks are negative, the coefficient of the interactive term between CAR(t-1, t+1) and Buy-side Pct is positive and statistically significant at 1% level. This suggests that the buy-side participation in the earnings conference calls generate additional amount of new information that cannot be incorporated into stock prices quickly, 19

20 which leads to the information diffusion and henceforth stock price drift within a few weeks after the earnings announcements. This pattern is robust under both cut-offs. On the other hand, we find that, when the earnings shocks are positive, the coefficient of the interactive term between CAR(t-1, t+1) and Buy-side Pct is negative. The magnitude of the coefficient for the interactive term is sufficiently large to negate the overall positive relationship between CAR(t-1, t+1) and CAR(t+2, t+10). This suggests that, when the earnings shocks are positive, buy-side participation in the earnings conference calls may not generate new public information. However, similar to Mayew s (2008) argument for sell-side analysts, the buy-side may simply be asking questions that complement their private signals. If this is true, the new information will be incorporated into stock price quickly, which leads to a weak PEAD effect or even a price reversal Robustness Checks: Sell-side Participation of Earnings Conference Calls We have not included a proxy for the sell-side participation in the earnings conference calls in our specifications for a simple reason: if we define sell-side participation measures in a similar way as the buy-side participation measures, they are perfectly correlated and cannot be included in the same regression. For example, if Sell-side Pct is defined as the percentage of sell-side analysts among all analysts participating in the earnings conference call, Buy-side Pct and Sell-side Pct would add up to 1. However, there are good reasons to control for sell-side participation in the earnings conference calls. For example, it is possible that BSAs and SSAs have uncorrelated private information ex ante. Therefore, both BSAs and SSAs may produce information and they may learn from each other during the earnings conference calls. Moreover, it is important to benchmark the buy-side results against those for the sell-side to pin down our arguments about the role of the buyside in conference calls. 20

21 To avoid multicollinearity issue while controlling for the sell-side participation in our tests, we define the sell-side participation measure, Sell-side PctCov, by using the total number of active sellside analysts listed in I/B/E/S as the scalar. Not surprisingly, Buy-side Pct and Sell-side PctCov are still negatively correlated. However, the correlation is reasonably low (around -30%). We repeat all tests in our study after adding Sell-side PctCov into control variables. We show that 1) sell-side participation in the earnings conference calls are not sensitive to the preannouncement abnormal returns; 2) while the buy-side participation is controlled, the sell-side participation in the earnings conference calls is not correlated with the magnitude of earnings announcement effect; 3) a higher level of sell-side participation in the earnings conference calls indeed leads to a larger number and magnitude of forecast revisions from SSAs after the earnings announcements; however, it does not help reduce the forecast errors; 4) the sell-side participation in the earnings conference calls does not affect the PEAD. All results above suggest that the BSAs have a unique information production role in the earnings conference calls, which cannot be subsumed by the sell-side participants. 5. Conclusion Earnings conference calls are important information events. Previous research documents that sell-side analysts acquire valuable information in these calls; however, a darker side is that company management uses the threat of cutting off sell-side analysts privilege of asking questions via these calls to obtain favourable recommendations on the stocks of their companies. In this paper, we focus on buy-side analysts. We find that the buy-side also uses the calls to gather and verify information; however, this tendency is strongest when they are concerned about poor outcomes for stocks in the buy-side portfolio. Nonetheless, buy-side involvement generates additional public information, and in particular, benefits the sell-side. While buy-side pressure on the sell-side analysts 21

22 not to downgrade stocks in the buy-side portfolios have previously been identified as a potential channel that creates biased recommendations, conference calls provide a forum where buy-side concern generates more information and enables the sell-side to make more informed recommendations. 22

23 Reference Ball, R., and P. Brown, 1968, An Empirical Evaluation of Accounting Income Numbers, Journal of Accounting Research 6, Bernard, V.L., J. K. Thomas, 1989, Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium? Journal of Accounting Research 27, Bernard, V.L., J. K. Thomas, 1990, Evidence that Stock Prices Do Not Fully Reflect the Implications of Current Earnings for Future Earnings, Journal of Accounting and Economics 13, Bernard, V. L., 1993, Stock Price Reactions to Earnings Announcements: A Summary of Recent Anomalous Evidence and Possible Explanations, In R. Thaler (ed.), Advances in Behavioral Finance, New York: Russell Sage Foundation, Boni L., and K. Womack, 2002, Solving the Sell-Side Research Problem: Insights from Buy-Side Professionals, Working Paper, University of New Mexico and University of Toronto. Bowen, R. M., A. K. Davis, and D. A. Matsumoto, 2002, Do Conference Calls Affect Analysts' Forecasts? The Accounting Review 77, Bushee, B., D. Matsumoto, and G. Miller, 2004, Managerial and Investor Response to DisclosureRegulation: The Case of Reg FD and Conference Calls, The Accounting Review 79, Cheng, Y., M. H. Liu and J. Qian, 2006, Buy-Side Analysts, Sell-Side Analysts, and Investment Decisions of Money Managers, Journal of Financial and Quantitative Analysis 41,

24 Clement, M.B., 1999, Analyst Forecast Accuracy: Do Ability, Resources and Portfolio Complexity Matter? Journal of Accounting and Economics 27, Daniel, K., D. Hirshleifer, and A. Subrahmanyam, 1998, Investor Psychology and Security Market Under- and Over-Reactions, Journal of Finance 53, Frey, S., and P. Herbst, 2011, The Influence of Buy-side Analysts on Mutual Fund Trading, Working Paper, University of Stirling and Leibniz University Hannover. Groysberg, B., P. Healy and D. Maber, 2008, What Drives Sell-Side Analyst Compensation at High- StatusBanks?Working paper, Harvard Business School. Hollander, S., M. Pronk and E. Roelofsen, 2010, Does Silence Speak? An Empirical Analysis of Disclosure Choices during Conference Calls, Journal of Accounting Research 48, Hong, H., and J. Stein, 1999, A Unified Theory of Underreaction, Momentum Trading, and Overreaction in Asset Markets, Journal of Finance 54, Li, F., M.Minnis, V. Nagar and M. V. Rajan, 2009, Formal and Real Authority in Organizations: An Empirical Assessment, Working Paper, University of Michigan and University of Chicago. Mayew, W., 2008, Evidence of Management Discrimination among Analysts during Earnings ConferenceCalls, Journal of Accounting Research 46, Mayew, W, N.Y. Shape, and M. Venkatachalam, 2011, Using Earnings Conference Calls to Identify Analysts with Superior Private Information, Working paper, Duke University. Williams, P. A., G. Moyes and K. Park, 1996, The Relative Importance of Factors Affecting Earnings Forecast Revisions for the Buy-Side and Sell-Side Analyst, Accounting Horizons 10,

25 Zhang, Y., 2008, Analyst Responsiveness and the Post-Earnings-Announcement Drift, Journal of Accounting and Economics 46,

26 Table 1 Summary Statistics Our sample includes all earnings conference calls held on the quarterly or annual earnings announcement dates between 2003 and Summary statistics of key variables in this paper are reported in this table. Num Buy-side (Sell-side) Analysts is the number of buy-side (sell-side) analysts in anearnings conference call; Buy-side Pct is the percentage of buy-side analysts among all analysts participating in the earnings conference call;buyside Sequence isthe sequence of questions asked by the buy-side analysts, defined by the order of questions scaled by the total number of questions in the earnings conference call; Sell-side PctCov is the percentage of sell-side analysts participating the earnings conference among all sell-side analysts actively covering this stock. CAR(t-x, t+y) is the market-model-based cumulative abnormal returns in a period from x trading days before to y trading days after the day of the earnings announcement. Pct Sell-side Revised is the percentage of sell-side analysts that make revisions during this period, among all active sell-side analysts covering this firm; Forecast Revisions is the average level of absolute magnitude of forecast revisions made by sell-side analysts during this period; Forecast Errors is the change in absolute forecast errors generated by sell-side forecast revisions during this period.log(total asset) is the natural logarithm of book value of total assets; Bookto-market is the book to market ratio of equity; Leverage is the book value of total debt scaled by the total assets. Variables Mean Median Std. Dev. P10 P90 Num Buy-side Analysts Buy-side Pct Buy-side Sequence Num. Sell-side Analysts Sell-side PctCov CAR(t-10,t-2) -0.19% -0.26% 7.06% -8.04% 7.44% CAR(t-1,t+1) -0.14% -0.04% 8.28% -9.57% 9.06% CAR(t+2, t+10) -0.45% -0.45% 7.07% -8.40% 7.31% Pct Sell-side Revised 74.72% 77.78% 21.76% 43.75% % Forecast Revisions 0.79% 0.31% 1.41% 0.07% 1.87% Forecast Errors -0.41% -0.13% 1.17% -1.30% 0.22% Total Assets Book-to-Market Leverage The summary statistics for Buy-side Sequence is based on a subsample with at least one buy-side analyst participating in the earnings conference call. 26

27 Table 2 Difference between Buy-side and Sell-side Analystsin Earnings Conference Calls Our sample includes all earnings conference calls held on the quarterly or annual earnings announcement dates between 2003 and We compare the following characteristics between the buy-side and sell-side analysts: Pct in the Call (%) is the average percentage of buy-side or sell-side analysts in earnings conference calls; Num of Questions is the average number of questions asked by one buy-side or sell-side analyst; Num of Words Per Questions is the average number of words in one question asked by the buy-side or sell-side analysts; Sequence is the average scaled sequence of questions from buy-side or sell-side analysts (scaled sequence= order of analysts who raise questions/ total number of analysts raising questions in the earnings conference call); First Question is the percentage of earnings conference calls where a buy-side or sell-side analyst raises the first question; LastQuestion is the percentage of earnings conference calls where a buy-side or sell-side analyst raises the last question. t-statistics corresponding to differences of these characteristics between the buy-side and the sell-side analysts across all earnings conference calls are reported in the last column. Buy-side Sell-side Difference t-stat Pct in the Call (%) Num of Questions Num of Words Per Question Sequence First Question (%) Last Question (%) The differences computed for First Question and Last Question are not between the buy-side and the sell-side analysts; rather, they are the differences between buy-side s First Question (Last Question) and buy-side s Pct in the Call. 27