A new multi-period investment strategies method based on evolutionary algorithms
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This work introduces a new algorithmic trading method based on evolutionary algorithms and portfolio theory. The limitations of traditional portfolio theory are overcome using a multi-period definition of the problem. The model allows the inclusion of dynamic restrictions like transaction costs, portfolio unbalance, and inflation. A Monte Carlo method is proposed to handle these types of restrictions. The investment strategies method is introduced to make trading decisions based on the investor’s preference and the current state of the market. Preference is determined using heuristics instead of theoretical utility functions. The method was tested using real data from the Mexican market. The method was compared against buy-and-holds and single-period portfolios for metrics like the maximum loss, expected return, risk, the Sharpe’s ratio, and others. The results indicate investment strategies perform trading with less risk than other methods. Single-period methods attained the lowest performance in the experiments due to their high transaction costs. The conclusion was investment decisions that are improved when information providing from many different sources is considered. Also, profitable decisions are the result of a careful balance between action (transaction) and inaction (buy-and-hold).
KeywordsEvolutionary algorithms Portfolio optimization Algorithmic trading Multi-objective
The authors are with the Research Group with Strategic Focus on Intelligent Systems of the National School of Engineering and Sciences at the Tecnológico de Monterrey and gratefully acknowledge its support. The first author thanks the Consejo Nacional de Ciencia y Tecnología (CONACyT) for its financial support through the PNPC program.
Compliance with ethical standards
Conflict of interest
The authors declare they have no conflict of interest.
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