Abstract:In view of the shortcomings of traditional forecasting models such as easy overfitting, missing data sensitivity and large computation, a random forest algorithm was used to study the stock price change forecast, utilizing its benefits of double randomness excellent data processing performances. First, the correlation index analysis was carried out to select the initial index system once and twice. Next, the important parameters of the random forest were optimized, and then an importance estimation method was adopted to improve the accuracy of the training model. Through the comparison between different index systems, the correctness of the experimental process was verified. Finally, comparing the empirical results of different modeling methods, it is shown that the random forest model is superior to the binary logistic regression model and has higher prediction accuracy.