Research on Quantitative Investment Strategies Based on Artificial Intelligence

Artificial Intelligence (AI) has been developing rapidly in recent years, and the application of AI in various fields has gradually emerged. This paper focuses on exploring the cross-fertilization of AI with the Chinese stock market, and the study adopts 7 types of factors, totaling 29 factor indicators, covering multiple types of value, valuation, leverage, financial quality, growth, technology, and risk factors, etc. Through data preprocessing, feature engineering, and other steps on the factors, and then combined with 8 machine learning algorithms to construct corresponding quantitative trading strategies on CSI 300 constituent stocks. By comparing the results of various models, it is found that the trading strategies constructed by machine learning algorithms can obtain significant excess returns in the Chinese market. Except for Gaussian Park Bayes, the other seven model strategy returns beat the benchmark returns, among which XGBoost performs the best, achieving a 20.10% return and the smallest retraction. This paper has a positive impact on advancing the research on the intersection of machine learning and quantitative investment.