Incorporating Markov decision process on genetic algorithms to formulate trading strategies for stock markets

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• 作者：Ying-Hua Chang ; ^{yhchang@mail.tku.edu.tw} ; Ming-Sheng Lee ^{kevin.lee@mail.im.tku.edu.tw}
• 关键词：Markov decision processes ; Genetic algorithms ; Stock selection ; Market timing ; Capital allocation ; Portfolio optimization
• 刊名：Applied Soft Computing
• 出版年：2017
• 出版时间：March 2017
• 年：2017
• 卷：52
• 期：Complete
• 页码：1143-1153
• 全文大小：2302 K
• 卷排序：52

文摘

The paper proposed a novel application for incorporating Markov decision process on genetic algorithms to develop stock trading strategies. This predicts the results of applying the Markov decision process with real-time computational power to help investors formulate correct timing (portfolio adjustment) and trading strategies (buy or sell). This study thus uses the excellent genetic algorithm parallel space searching ability to provide investors with the optimal stock selection strategy and capital allocation, and combines them with both constructs to solve the portfolio problem and improve return on investment for investors. This research can solve stock selection, market timing and capital allocation at the same time for investors when investing in the stock market.