Dispersion, Coverage, and the Rational Investor: Using Analysts’ Earnings Forecasts to Predict Stock Returns

Researchers have often used intrinsic properties of stocks such as earnings per share ratios and dividend yields to predict stocks’ future performances. In their paper Differences of Opinion, Diether et al. (2002) propose a new, non-intrinsic predictive variable based on analyst earnings forecasts. Using their methods, we validate the claim that greater differences of opinion in analyst predictions of future stock returns tracks poorer future returns of a stock and that small stocks and poorly performing stocks are especially affected. Going further, we also found that the dispersion effect was greatest for mid-level analyst coverage and that market performance was negatively related to the dispersion effect. Also, by considering more realistic and rational investor behavior, we are able to more confidently reject the hypothesis that dispersion effects can be explained by Fama-French factors. However, inclusion of more realistic rational investor behavior led to different portfolio behavior, and decreased explanatory power of dispersion, suggesting that the dispersion effect is not as robust as previously thought. Introduction Over the years, there have been many attempts to predict the future performance of stocks. Early researchers tested the use of the historical performance of stocks as indicators for future returns. Currently, stocks’ intrinsic properties, such as interest rates, dividend yields (dividends over stock price), book-to-market ratios (the book value of a stock over the market value of the stock), and earnings-price ratios have begun to be examined as predictive factors. Karl B. Diether et al. (2002) extend the list of predictive variables beyond a stock’s intrinsic properties by introducing a new variable called dispersion, measuring the difference in opinion regarding the future returns of that stock. It is assumed that the difference in opinion on the earnings forecasts made by analysts reflect the difference in opinion on the earnings forecasts made by investors overall. The logic behind the use of dispersion as a predictive variable by Karl B. Diether et al. (2002) is as follows: the best indicator of a stock’s price is the average opinion (that is, average prediction of future returns) of all investors since this ultimately determines the market value of the stock. If there is high dispersion, than there are many investors who believe the stock have high value, and also many who believe it has low value. Those who believe it has a high value, the optimists, invest in the stock regardless of price because they believe the price will continue to grow. On the other end of the spectrum, because so many people believe the stock will do poorly, there will be an expected increase in demand for short-sales; thus driving short-sale costs higher. Therefore, due to the higher short-sale costs we expect pessimists to be less likely to short the stock. Since pessimists are kept out of the market by these high short-sale costs, the price of the stock is artificially inflated relative to the average opinion of all investors. We expect that the higher the dispersion, the more artificially high the price will be and the worse actual future performance will be. Thus, ∗Zhao and Suo are at Harvard College. This paper replicates the results of Differences of Opinion and the Cross Section of Stock Returns by Karl B. Diether, Christopher J. Malloy, and Anna Scherbina. The authors would like to thank Dave Kane, Jeff Enos, Steve Wollkind, Gary King, Holger L. Kern, Justin R. Grimmer, Meghan Dolan, Tom Laakso, and Andy Stokes. Data on analysts’ earnings forecasts was provided by I/B/E/S Inc. through the Wharton Research Data Services database.