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.
Highlights
Based on the computer technology, quantitative investment provides a meaningful direction for investment decision-making by using reasonable algorithms
This paper carries out kernel function test and parameter optimization for the kernel support vector machine system, and 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
Summary
Based on the computer technology, quantitative investment provides a meaningful direction for investment decision-making by using reasonable algorithms. Huang (2017) combined the support vector machine with the traditional Fama-Fench three-factor model to construct A new stock selection strategy. From what has been discussed above, the existing research mostly focused on the use of machine learning algorithm to optimize the traditional time series prediction model and optimizing the traditional factor to choose a single strategy, more empirical analysis of the strategy combination is less. Based on the test results, a portfolio model of stock selection strategy applied to CSI 300 and CSI 500 will be constructed. Through comparative analysis of two different portfolio strategies, market-neutral strategy and stock equity strategy, we further build a more profitable and more robust multi-factor stock selection model, providing new ideas for the application and development of machine learning methods in the financial investment field
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