Otimização de Portfólio Futuro Baseado em Aprendizagem Profunda e Algoritmo Evolutivo Multiobjetivo
Abstract
The stock market is essential to economic growth since it provides investors the opportunity of rising heritage and provides financial resources to companies. However, its dynamic behavior is a challenge for both humans and algorithms. Thus, this work proposes a regression model using a deep neural network (LSTM) to discover patterns in historical stock data in order to predict future stock values. Afterwards, we optimize a portfolio using a multiobjective evolutionary algorithm named MOEA/D. This work used data from two famous indexes: S&P500 and Dow Jones. Results indicate that our model associated with the MOEA/D can suggest portfolios returning between 14% and 22% in the SP500 index. Also, between 4% and 10% in the Dow Jones. Both according to the risk that an investor is willing to face.
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References
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