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

This research combines Markov decision process and genetic algorithms to propose a new analytical framework and develop a decision support system for devising stock trading strategies. This investigation uses the prediction characteristics and real-time analysis capabilities of the Markov decision process to make timing decisions. The stock selection and capital allocation employ string encoding to express different investment strategies for genetic algorithms. The parallel search capabilities of genetic algorithms are applied to identify the best investment strategy. Additionally, when investors lack sufficient money and stock, the architecture of this study can complete the transaction via credit transactions. The experiments confirm that the model presented in this research can yield higher rewards than other benchmarks.