Multi-factor Stock Selection Model Based on Kernel Support Vector Machine

Ru Zhang, Zi-ang Lin, Shaozhen Chen, Zhixuan Lin and Xingwei Liang

Journal of Mathematics Research, 2018, vol. 10, issue 5, 9-18

Abstract: In recent years, the combination of machine learning method and traditional financial investment field has become a hotspot in academic and industry. This paper takes CSI 300 and CSI 500 stocks as the research objects. First, this paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and then predict and optimize the combination of market-neutral stock selection strategy and stock right strategy. The results of the experiment show that the multi-factor model based on SVM has a strong predictive power for the selection of stock, and it has a difference in the predictive power of different nuclear functions.

Keywords: multi-factor model; support vector machine; quantitative investment (search for similar items in EconPapers)
Date: 2018
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Citations: View citations in EconPapers (4)

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