The Establishment and Research of Random Forest Quantitative Classification Mode

With the advancement of China's financial deepening reforms, research on the application of quantitative investment in the A-share market has gradually increased. Exploring the application of the Random Forest algorithm in stock price classification is of great significance for advancing the development of quantitative stock selection theories. This paper proposes an ensemble learning method based on Random Forest for the problem of quantitative classification. Multiple technical indicators of stock data are selected to construct feature vectors, with a prediction horizon of 5 days, and the target vector prediction results are categorized into 6 classes. By constructing multiple decision trees and incorporating feature importance evaluation, a Random Forest model corresponding to the stocks is established. Experimental results demonstrate that this model can reduce the risk of overfitting while improving classification accuracy. Compared with traditional classification algorithms, this model outperforms them in terms of accuracy, recall rate, and other metrics.