INTEGRATING ARBITRAGE PRICING THEORY AND ARTIFICIAL NEURAL NETWORKS TO SUPPORT PORTFOLIO MANAGEMENT

The paper presents an innovative approach that integrates the arbitrag e pricing theory (APT) and artificial neural networks (ANN) to support portfolio management. The integrated approach takes advantage of the synergy between APT and ANN in extracting risk factors, predicting the trend of individual risk factor, generating candidate portfolios, and choosing the optimal portfolio. It uses quadratic programming for ide ntifying surrogate portfolios in APT and ANN to predict factor returns . Empirical results indicate that the integrated method beats the benc hmark and outperforms the traditional method that uses the ARIMA model .