Prediction-based portfolio optimization model using neural networks

Authors:
Fabio D. Freitas;Alberto F. De Souza;Ailson R. de Almeida
Affiliations:
Secretaria da Receita Federal do Brasil-RFB, Programa de Pós-Graduação em Engenharia Elétrica, Departamento de Engenharia Elétrica, Universidade Federal do Espírito S ...;Departamento de Informática, Universidade Federal do Espírito Santo, Av. Fernando Ferrari 514, 29075-910 Vitória ES, Brazil;Departamento de Engenharia Elétrica, Universidade Federal do Espírito Santo, Av. Fernando Ferrari 514, 29075-910 Vitória ES, Brazil
Venue:
Neurocomputing
Year:
2009

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Abstract

This work presents a new prediction-based portfolio optimization model that can capture short-term investment opportunities. We used neural network predictors to predict stocks' returns and derived a risk measure, based on the prediction errors, that have the same statistical foundation of the mean-variance model. The efficient diversification effects hold thanks to the selection of predictors with low and complementary pairwise error profiles. We employed a large set of experiments with real data from the Brazilian stock market to examine our portfolio optimization model, which included the evaluation of the Normality of the prediction errors. Our results showed that it is possible to obtain Normal prediction errors with non-Normal time series of stock returns and that the prediction-based portfolio optimization model took advantage of short-term opportunities, outperforming the mean-variance model and beating the market index.