Research on information fusion of security analysts’ stock recommendations based on two-dimensional D-S evidence theory

Abstract

Security analysts play a vital role as an information intermediary in the stock market. Their stock recommendations are important references for investors. The efficiency of investment decision-making could be improved by judging the reliability of stock recommendations based on analyst characteristics and fusing the recommendations. We propose an information fusion method for security analysts’ stock recommendations based on two-dimensional Dempster-Shafer (D-S) evidence theory, which comprehensively considers the external and internal characteristics of analysts. The characteristics of analysts are used to measure the reliability of the stock recommendations and modify the evidence, then the D-S fusion rule is used for evidence fusion. Compared with the forecast results of statistical methods and machine learning methods, the two-dimensional D-S evidence theory model we proposed has a higher forecast accuracy, which effectively improves the information efficiency of the stock market and helps investors to make decisions efficiently and scientifically.