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

Ru Zhang, Zi-ang Lin, Shaozhen Chen, Min Zhao, Mingjie Yuan

Abstract


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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DOI: https://doi.org/10.5539/ijsp.v7n5p9

License URL: http://creativecommons.org/licenses/by/4.0

International Journal of Statistics and Probability   ISSN 1927-7032(Print)   ISSN 1927-7040(Online)

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