Sami Keskek

The Effects of Disclosure and Analyst Regulations on the Relevance of Analyst Characteristics for Explaining Analyst Forecast Accuracy: THE EFFECTS OF DISCLOSURE AND ANALYST REGULATIONS

We posit and find an effect of disclosure and analyst reporting regulations implemented from 2000 through 2003 (including Regulation Fair Disclosure, the Sarbanes‐Oxley Act and the Global Settlement Act) on the importance of analyst and forecast characteristics for analyst forecast accuracy. Following the enactment of these regulations, more experienced analysts and All‐Star analysts do not maintain their superior forecast accuracy, and analysts employed by large brokerage houses perform worse than other analysts. In addition, we find a decrease in the importance of analyst effort, the number of industries and firms followed, days elapsed since the last forecast, and forecast horizon. While the importance of bold upward forecast revisions does not change, bold downward revisions lose their relevance for forecast accuracy after 2003. Finally, we find an increase in the importance of prior forecast accuracy. We find that the importance of these characteristics varies with the precision of publicly available information. Specifically, the decrease in the importance of most analyst and forecast characteristics and the increase in the importance of prior forecast accuracy are greater when the precision of publicly available information is low. Overall, our results suggest that the positive effects of experience, effort, brokerage house size and All‐Star status on forecast accuracy in the pre‐regulation period were because of the information advantages that these analysts enjoyed (rather than their ability to generate private information). In contrast, our results suggest that prior forecast accuracy is related to analysts’ ability to generate private information.

Does Forecast Bias Affect Financial Analysts’ Market Influence?:

Prior studies find that analysts tend to bias their forecasts upward in poor information environments and downward in rich information environments, consistent with attempts to curry favor with management. We find that investors anticipate this behavior by reducing their response to upward forecasts in poor information environments and downward forecasts in rich information environments. Using Hugon and Muslu’s measure of analyst conservatism as an ex ante indicator of individual analysts’ forecast bias tendencies, we show that the stronger return response they find to conservative analysts’ forecast revisions is restricted to poor information environments, where optimistic analyst bias is prevalent. Our results suggest that analysts pay a price in market influence when their forecasts reinforce analysts’ typical forecast bias for the firm’s information environment. Conversely, analysts whose forecasts conflict with the typical bias for the firm are rewarded with larger than average return responses.

Investors’ Misweighting of Firm-level Information and the Market’s Expectations of Earnings

Because investors’ expectations of future earnings are unobservable, a large body of literature uses the firm-specific consensus analyst forecast to proxy for these expectations. However, prior research documents systematic differences between market expectations and analyst forecasts with respect to their propensities to incorporate some firm-level information. Using data from 1986 through 2012, we examine the extent to which market expectations differ from analyst forecasts by developing an alternative proxy for the market’s expectations of future earnings as reflected in stock returns (i.e., the implied market forecast). Based on predictable errors in the implied market forecast, we create a trading strategy that generates excess returns (of about 8% per year) which cannot be explained by predictable errors in the consensus analyst forecast. In addition, our strategy, which uses predictable errors in the implied market forecast, outperforms a strategy that uses predictable errors in the consensus analyst forecast, especially in recent years. Overall, our results reveal the extent to which market expectations differ from analyst forecasts and underscore the importance of developing alternative proxies for market expectations.

Investors’ Misweighting of Firm-level Information and Systematic Errors in Statistical Forecasts as Proxies for the Market’s Expectations of Earnings

Because investors’ expectations of future earnings are unobservable, a large body of literature uses the firm-specific consensus analyst forecast to proxy for these expectations. However, prior research documents systematic differences between market expectations and analyst forecasts with respect to their propensities to incorporate some firm-level information. Using data from 1986 through 2012, we examine the extent to which market expectations differ from analyst forecasts by developing an alternative proxy for the market’s expectations of future earnings as reflected in stock returns (i.e., the implied market forecast). Based on predictable errors in the implied market forecast, we create a trading strategy that generates excess returns (of about 8% per year) which cannot be explained by predictable errors in the consensus analyst forecast. In addition, our strategy, which uses predictable errors in the implied market forecast, outperforms a strategy that uses predictable errors in the consensus analyst forecast, especially in recent years. Overall, our results reveal the extent to which market expectations differ from analyst forecasts and underscore the importance of developing alternative proxies for market expectations.

Investor Sentiment and Stock Option Vesting Terms

Using the details of vesting terms, we document that stock options granted in high sentiment periods tend to have shorter vesting period/duration, and are more likely to vest completely or have a significantly larger fraction vested within one year of the grant date relative to low sentiment periods. Further, we show that the sentiment effect on vesting terms is more pronounced among managers whose newly granted compensation comprises primarily stock options, and less pronounced for firms with strong monitoring mechanisms (i.e., more independent boards and higher institutional ownership). In addition, we explore the cross-sectional variation in sentiment-driven mispricing, and find that greater analyst coverage attenuates the impact of investor sentiment on vesting terms. Finally, we find that firms providing short vesting schedules during high sentiment periods indeed have significantly lower stock returns in the post-vesting period. Taken together, our evidence suggests that managers recognize prevailing sentiment, and demand stock options with short vesting terms to take advantage of mispriced stock prices and reduce the exposure of their wealth to the risk imposed by sentiment-driven mispricing.

Analysts' Forecasts, Market Expectations, and the Misweighting of Firm-Level Information

Because investors’ expectations of future earnings are unobservable, a large body of literature uses the firm-specific consensus analyst forecast to proxy for these expectations. However, prior research documents systematic differences between market expectations and analyst forecasts with respect to their propensities to incorporate some firm-level information. Using data from 1986 through 2012, we examine the extent to which market expectations differ from analyst forecasts by developing an alternative proxy for the market’s expectations of future earnings as reflected in stock returns (i.e., the implied market forecast). Based on predictable errors in the implied market forecast, we create a trading strategy that generates excess returns (of about 8% per year) which cannot be explained by predictable errors in the consensus analyst forecast. In addition, our strategy, which uses predictable errors in the implied market forecast, outperforms a strategy that uses predictable errors in the consensus analyst forecast, especially in recent years. Overall, our results reveal the extent to which market expectations differ from analyst forecasts and underscore the importance of developing alternative proxies for market expectations.

Did Analysts Contribute to the Disappearance of the Accrual Anomaly

We use the recent disappearance of the accrual anomaly to investigate analysts’ contribution to improved information processing by investors. Prior research finds that investors and analysts made accrual-related pricing and forecast errors, respectively, in the anomaly period. As sophisticated information intermediaries, analysts could have corrected their accrual-related forecast bias and thus provided a mechanism for investors to correct accrual-related mispricing. Using all-star status and the analyst’s provision of a cash flow forecast as proxies for expertise, we find that both expert and non-expert analysts continue to issue forecasts with predictable accrual-related bias after the disappearance of the accrual anomaly. Furthermore, the accrual anomaly is similar for firms followed by analysts and for non-followed firms, and disappears at the same time for both. Thus, investors began to correctly incorporate accruals information in security prices even though analysts continued issuing earnings forecasts that have predictable accrual-related bias. Our results conflict with the widely-held notion that analysts are sophisticated information intermediaries who improve market efficiency.

The Effects of Analysts’ Access to Management’s Private Information and the Precision of Publicly Available Information on Analyst Forecast Accuracy

We posit and find that the importance of analyst and forecast characteristics for analyst forecast accuracy varies with analysts’ access to management’s private information and with the precision of publicly available information. In particular, more experienced analysts and All-Star analysts do not maintain their superior forecast accuracy and analysts employed by large brokerage houses perform even worse than other analysts following the enactment of Regulation Fair Disclosure (Reg FD). In addition, we find a decrease in the importance of effort, the number of industries and firms followed, days elapsed since the last forecast, forecast horizon, and forecast boldness post-Reg FD. The decrease in the importance of most of these characteristics is greater when the precision of publicly available information is low. Our results suggest that the positive effects of experience, effort, brokerage house size, All-Star status for forecast accuracy pre-Reg FD were due to the information advantage these analysts enjoyed, rather than their ability to generate private information. In contrast, following the enactment of Reg FD, the importance of prior forecast accuracy increases and this increase is even greater when the precision of publicly available information is low. This suggests that prior forecast accuracy is related to analysts’ ability to generate private information. Because prior evidence suggests that investors consider analyst and forecast characteristics when they evaluate the relevance of analyst forecasts, our findings can help investors to better assess and use the information in analyst forecasts.