Prediction-based portfolio optimization model using neural networks

被引：96

作者：

Freitas, Fabio D. ^{[1
]}

De Souza, Alberto F. ^{[2
]}

de Almeida, Ailson R. ^{[3
]}

机构：

[1] Univ Fed Espirito Santo, Dept Engn Eletr, Programa Posgrad Engn Eletr, Secretaria Receita Fed Brasil RFB, BR-29010190 Vitoria, ES, Brazil

[2] Univ Fed Espirito Santo, Dept Informat, BR-29075910 Vitoria, ES, Brazil

[3] Univ Fed Espirito Santo, Dept Engn Eletr, BR-29075910 Vitoria, ES, Brazil

来源：

NEUROCOMPUTING | 2009年 / 72卷 / 10-12期

关键词：

Neural networks; Time series prediction; Portfolio optimization; SELECTION; RETURNS;

D O I：

10.1016/j.neucom.2008.08.019

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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. (C) 2009 Elsevier B.V. All rights reserved.

引用

页码：2155 / 2170

页数：16

共 54 条

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