Multi-factor Stock Selection Model Based on Adaboost

Ru Zhang, Tong Cao


In this paper, we established multi-factor stock selection model based on Adaboost by using Adaboost to integrate the custom week classifier model, and Shanghai and Shenzhen 300 stocks are taken as the research object. During the stock retest, the first is make a comparative test between Adaboost multi-factor stock selection model and the traditional multi-factor model, among them, the factor large class isn’t considered in the multi-factor stock selection model. And the results of two contrast experiment showed that the multi-factor stock selection model based on Adaboost has stronger profitability and less risk than the traditional multi-factor model.

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Copyright (c) 2018 Ru Zhang, Tong Cao

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Business and Economic Research  ISSN 2162-4860

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