An Example of Machine Learning-Based Multifactor Dynamic Quantitative Stock Picking Models

Abstract

This study utilizes the XGBoost algorithm in the field of machine learning to conduct quantitative stock picking research for CSI 300 stocks. The article firstly outlines the importance and practical application background of quantitative stock selection, and then discusses in depth the basic principle of XGBoost algorithm and its application method in quantitative stock selection. By collecting historical data of CSI 300 stocks and after data preprocessing, this study constructs a multi-factor stock prediction model based on XGBoost and conducts relevant backtesting. Comparative experiments show that the XGBoost algorithm exhibits good effectiveness and demonstrates the unique advantages and characteristics of its stock selection strategy. The conclusion of the study shows that the XGBoost-based stock selection strategy has potential application value in the stock market and can provide investors with accurate and efficient stock selection reference.