To study whether information affects post-event performance of stocks experiencing large price changes, the author uses analysts’ reports that issue recommendations as a proxy for the presence of information. He finds that events unaccompanied by analyst recommendations experience reversals, whereas events accompanied by analyst recommendations exhibit momentum. Portfolios created to exploit such strategies generate large abnormal returns.
The author studies how information affects post-event performance of stocks that are experiencing large price changes. He uses analysts’ reports that issue recommendations as a proxy for the presence of information. Empirical tests show that no-information price events (i.e., events unaccompanied by analyst recommendations) experience reversals, whereas information-based events (i.e., events accompanied by analyst recommendations) exhibit momentum. When investors design portfolios to exploit such strategies, they are able to generate large abnormal returns.
How Is This Research Useful to Practitioners?
The author aims to determine how the presence of information affects post-event performance of stocks that are experiencing large price changes. He finds that price moves unaccompanied by analyst recommendations experience reversals, whereas price moves accompanied by analyst recommendations exhibit momentum. Portfolios created to exploit such strategies generate abnormal returns of 35%. The size of this abnormal return suggests that implementation costs would have to be substantial to eliminate this profitable investment opportunity.
The author also discovers several other findings useful to practitioners. First, price moves in the same direction as analyst recommendations exhibit momentum, whereas price moves in the opposite direction of analyst recommendations exhibit reversals. Second, investors underreact to new information about fundamentals. Third, a given positive (negative) price move predicts a higher (lower) earnings surprise if it is accompanied by analysts’ reports. Fourth, analysts possess at least some level of skill that is not dependent on access to privileged information sources.
How Did the Author Conduct This Research?
The author begins by separating the sample of stocks into those that experience major price shocks and those that do not. A major price shock is defined as having a daily abnormal return greater than 10% or less than –10%, in which the abnormal return is defined as the actual stock return less the predicted stock return estimated using the Fama–French three-factor plus momentum model. Parameter estimates of the model for the 255 days prior to the event window are also included. The sample period is 1995–2009 and includes only stocks in Thomson Reuters I/B/E/S with at least five analysts’ reports with recommendations published over the previous 12 months. Daily stock return data are from CRSP, annual accounting data are from the CRSP/Compustat Merged Database, earnings announcement dates are from Compustat, and NYSE size breakpoints are from Kenneth French’s website.
The author uses regression and portfolio analysis to test how event-day abnormal returns affect abnormal returns over holding periods between 5 and 40 trading days after he controls for various factors (e.g., size and trading volume). The author finds no-information price events experience reversals, whereas information-based events exhibit momentum. The portfolio analysis indicates whether this potential anomaly leads to a profitable trading strategy. The author creates the following four portfolios: negative return with no information, negative return with information, positive return with no information, and positive return with information. The results for a trading strategy that exploits both reversals after no-information price events and drift after information-based price events yield an annualized abnormal return of 35%.
Although the author finds that the primary results are robust for alternative models (e.g., raw returns and market-adjusted returns) and alternative thresholds, some additional factors were not included in the analysis. As he acknowledges, this study ignores other factors that affect large price moves, such as private information, investor sentiment, and liquidity shocks. Further research is necessary to determine the impact of these factors.
Although the results are interesting, it would be worthwhile, as newer data arrive, to test whether the substantial abnormal returns generated by portfolios exploiting the author’s findings persist. In addition, another interesting takeaway is that the results indicate, in many different ways, how analysts, the content of their reports, and their recommendations provide value to the investment community. The results of this study should help dispel some of the criticisms imposed on analysts, especially by the popular press during the most recent crisis.