Otimização de Portfólio Futuro Baseado em Aprendizagem Profunda e Algoritmo Evolutivo Multiobjetivo
Abstract
O mercado de ações é importante para o crescimento econômico, pois fornece a oportunidade para investidores formar patrimonio e fornece recursos para o crescimento de organizaçoes. Porém, o comportamento dinâmico do mercado é um desafio tanto para humanos quanto para algoritmos. Assim, este trabalho propoe um modelo regressivo usando uma rede neural profunda (LSTM) para encontrar padroes nos dados históricos de ações e tentar predizer os valores esperados para o futuro. Em seguida, faz-se a otimizaçao o portfólio utilizando um algoritmo evolutivo multiobjetivo chamado MOEA/D. Os dados utilizados nos testes sao dados históricos de dois indices conhecidos: o S&P 500 e o Dow Jones. Os resultados indicam que o modelo de prediçao associado ao MOEA/D pode sugerir portfolios com retorno entre 14% e 22% no indice S&P 500, e entre 4% e 10% no indice Dow Jones dependendo do risco que o investidor esta disposto a assumir.
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