Stock Selection Strategy Based on Random Forest and Support Vector Machine

2020 ◽  
Author(s):  
Yin Sun
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Selection Strategy ◽  
Support Vector ◽  
Stock Selection

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

2018 ◽  
Vol 10 (5) ◽  
pp. 9 ◽  
Author(s):  
Ru Zhang ◽  
Zi-ang Lin ◽  
Shaozhen Chen ◽  
Zhixuan Lin ◽  
Xingwei Liang
Keyword(s):  
Support Vector Machine ◽  
Predictive Power ◽  
Factor Model ◽  
Selection Strategy ◽  
Support Vector ◽  
Stock Selection ◽  
Financial Investment ◽  
Model Based ◽  
Kernel Support Vector Machine ◽  
Selection Of

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.

scholarly journals Adaboost-SVM Multi-Factor Stock Selection Model Based on Adaboost Enhancement

2018 ◽  
Vol 7 (5) ◽  
pp. 9
Author(s):  
Ru Zhang ◽  
Zi-ang Lin ◽  
Shaozhen Chen ◽  
Min Zhao ◽  
Mingjie Yuan
Keyword(s):  
Support Vector Machine ◽  
Selection Model ◽  
Selection Strategy ◽  
Support Vector ◽  
Linear Support Vector Machine ◽  
Financial Industry ◽  
Stock Selection ◽  
Financial Investment ◽  
Original Algorithm ◽  
Model Based

In recent years, the applications of machine learning techniques to perfect traditional financial investment models has gained a widespread attention from the academic circle and the financial industry. This paper takes CSI300 stocks as the object of the research, uses Adaboost to enhance the classification ability of original linear support vector machine, and combines all major factors to build Adaboost-SVM multi-factor stock selection model based on Adaboost enhancement. In the backtesting analysis, the stock selection strategy of original linear support vector machine was compared with the Adaboost-SVM multi-factor stock selection strategy based on Adaboost enhancement. The result shows that the Adaboost-SVM multi-factor stock selection strategy based on Adaboost enhancement possesses stronger profitability and smaller income fluctuation than the original algorithm model.

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