Double Surprise into Higher Future Returns

Post-earnings-announcement drift is the well-documented ability of earnings surprises to predict future stock returns. Despite nearly four decades of research, little has been written about the importance of how earnings surprise is actually measured. We compare the magnitude of the drift when historical time-series data are used to estimate earnings surprise with the magnitude when analyst forecasts are used. We show that the drift is significantly larger when analyst forecasts are used. Furthermore, we show that using the two models together does a better job of predicting future stock returns than using either model alone.One of the most puzzling characteristics of the stock market is that earnings surprises seem to predict future stock return performance. That is, when companies announce quarterly earnings that exceed market expectations, on average, the stocks of those companies exhibit higher-than-normal return performance for weeks, even months, following the earnings announcement. The opposite is true for stocks of companies whose earnings fall short of market expectations; they tend to perform poorly. This well-documented phenomenon is normally referred to as either “post-earnings-announcement drift” or “the SUE (standardized unexpected earnings) effect.” The drift was first noticed almost 40 years ago, but a constant stream of research from 1968 to the present has confirmed the anomaly.A question that has been largely ignored in the drift literature is: What is the best way to measure the earnings surprise? If the drift represents a slow market reaction to the information in earnings announcements, the way in which that information is assessed may be vital to the magnitude of the drift. Most prior SUE studies estimated the earnings surprise as the difference between reported EPS and a time-series earnings forecast (usually deflated by price or past earnings variability). But another prominent measure of earnings surprise is the difference between reported earnings and financial analysts’ forecasts of earnings.Research has shown that analyst forecast errors are a better measure of earnings surprise than time-series errors—at least in terms of initial stock market reaction. This finding makes sense because analysts have access to a broader and more timely set of information than simply the pattern of past earnings. Although analyst forecast errors may be superior measures of surprise, research has also shown that this measure does not completely subsume time-series errors in explaining the immediate stock price reaction to earnings.Time-series errors may capture a component of the earnings surprise that is not caught by analyst forecast errors because of some analyst bias. For example, analysts may be hesitant to make low earnings forecasts for several reasons. One is the fear of alienating company managers and risking the analyst’s ability to obtain information from the company in the future.Another reason that the information in time-series errors may not be subsumed by those of analysts is that time-series errors calculated from Compustat data rely on earnings that reflect GAAP whereas analyst tracking services, such as Thomson Corporation’s I/B/E/S, tend to use “Street” earnings figures that exclude some expenses required by GAAP.Whatever the reasons, neither model subsumes the other as a measure of earnings surprise. Therefore, we estimated the magnitude of the drift by using a time-series model, by using analyst forecasts, and by combining the two.We show that for companies followed by professional security analysts, using analyst forecast errors to define earnings surprise leads to greater predictability of future stock returns than does using time-series forecast errors. Return predictability can be further enhanced by combining the two measures of drift. These findings proved to be robust to a range of specifications.Although analyst forecasts are not available for all companies, those companies for which they are available tend to be more liquid than other companies. Therefore, investors will generally find it easier and less expensive to exploit any mispricings among these stocks than among other stocks.This study can be beneficial for practitioners and academics alike. Professional investors can use the results to improve their selection of stocks. Instead of using the earnings surprise based on either analyst forecasts or time-series forecasts, they can, by using both measures, focus on a restricted set that has extreme surprises. For academics, the findings have implications that may be useful in understanding how stock market participants process information and how that information is incorporated in stock prices.