Impact of Quantitative Trading Strategies on Stock Investment Performance Under AI Empowerment

DOI

10.2991/978-94-6239-801-6_41How to use a DOI?

Keywords

Quantitative Trading; Random Forest; Trading Strategy; Investment Performance

Abstract

Against the complex and nonlinear characteristics of financial markets, traditional quantitative analysis has limitations in capturing stock price fluctuation patterns. Based on machine learning and quantitative trading, this paper constructs a research framework covering data processing, feature engineering, model construction and strategy back testing. Taking the CSI 300 Index as the research object, this paper uses the traditional dual moving average strategy as the benchmark and introduces the random forest algorithm to optimize trading signals. Subsequently, under a unified back testing framework, a comparative analysis is conducted on the investment performance of the baseline strategy and the AI-optimized strategy, evaluating the effectiveness of the strategies from both return capability and risk control dimensions. The findings indicate that, compared to traditional moving average strategies, the AI strategy optimized with the random forest model demonstrates superior performance in cumulative returns, annualized returns, and Sharpe ratio, while also showing a significant reduction in maximum draw-down and a more stable net value curve. This study proves that integrating machine learning with traditional quantitative strategies can improve the validity of trading signals and optimize risk-return characteristics, providing empirical reference for the application of AI in quantitative investment.

Copyright

© 2026 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Yusha Wang
PY  - 2026
DA  - 2026/10/23
TI  - Impact of Quantitative Trading Strategies on Stock Investment Performance Under AI Empowerment
BT  - Proceedings of the 2026 2nd International Conference on Financial Innovation and Marketing Management (FIMM 2026)
PB  - Atlantis Press
SP  - 374
EP  - 382
SN  - 2352-5428
UR  - https://doi.org/10.2991/978-94-6239-801-6_41
DO  - 10.2991/978-94-6239-801-6_41
ID  - Wang2026
ER  -