Optimizing Quantitative Intelligent Systems Investment Strategies and Asset Allocation with Machine Learning

Authors

  • Zhanyong Wu School of Economics and Management, Handan University, Handan 056005, China
  • Xiaohang Ma School of Economics and Management, Handan University, Handan 056005, China
  • Yanxue Li School of Economics and Management, Handan University, Handan 056005, China

Abstract

 

In the decision-making process of financial markets, machine learning techniques, especially support vector machines, have shown their potential to improve the efficiency and accuracy of investment strategies. This study explores the application of machine learning for the optimization of quantitative investment strategies and asset allocation by constructing and optimizing an SVM-based multi-factor stock selection model and asset allocation system. This study verifies the actual performance of the proposed SVM model in the financial market and its ability in terms of risk control and maximization of returns. The results show that SVM provides a higher rate of return and lower risk than traditional investment methods, which confirms its application value in modern financial strategies. This study provides a new perspective on, and technical support for, the field of quantitative investment and presents theoretical support for the further development and application of machine learning technology in the financial market.

Keywords: machine learning; support vector machine; finance; investment strategy; asset allocation

Cite As

Z. Wu, X. Ma, Y. Li, " Optimizing Quantitative Intelligent Systems Investment Strategies and Asset Allocation
with Machine Learning", Engineering Intelligent Systems, vol. 33 no. 4, pp. 365-375, 2025.

 

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Published

2025-07-01

Issue

Vol. 33 No. 4 (2025): International Journal of Engineering Intelligent Systems

Section

Article

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