Optimal profit-making strategies in stock market with algorithmic trading

Abstract

Machine learning (ML) techniques are being increasingly applied to financial markets for analyzing trends and predicting stock prices. In this study, we compared the price prediction and profit-making performance of various ML algorithms embedded into stock trading strategies. The dataset comprised daily data from the CSI 300 Index of the China stock market spanning approximately 17 years (2006-2023). We incorporated investor sentiment indicators and relevant financial elements as features. Our trained models included support vector machines (SVMs), logistic regression, and random forest. The results show that the SVM model outperforms the others, achieving an impressive 60.52% excess return in backtesting. Furthermore, our research compared standard prediction models (such as LASSO and LSTM) with the proposed approach, providing valuable insights for users selecting ML algorithms in quantitative trading strategies. Ultimately, this work serves as a foundation for informed algorithm choice in future financial applications.