Posterior probability model for stock return prediction based on analyst's recommendation behavior

Existing studies on stock return forecasting mainly formulate the issue in a numeric analysis framework. Various kinds of time series models and optimization methods are applied. In this paper, we explore a new prediction approach based on the behavior of analysts' recommendations. By combining each recommendation and stock return, a posterior probability model associated with an analyst's recommendation is built based on Bayesian inference. It provides an estimation of stock return distribution for next several days after recommendation, and thus serves as a novel predictor from point of view of behavioral finance. Based on the empirical studies on China stock market, we demonstrate the superior forecasting performance over traditional methods. The model's maximum accuracy can be reached between 84.3% and 94.2%. The average accuracy falls between 58.6% and 60.3%, while it is just from 43.5% to 56.2% or lower by traditional prediction methods. We also find that most of the analysts can produce recommendations which fitness lies between 0.5 and 0.6 at the successive recommendation time. The finding is in accordance with early conclusion which indicates that stock analysts tend to maintain their reputation when they issue recommendations. The consistency also confirms the effectiveness of the proposed method.