Integrating arbitrage pricing theory and artificial neural networks to support portfolio management

摘要

The paper presents an innovative approach that integrates the arbitrage 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 identifying surrogate portfolios in APT and ANN to predict factor returns. Empirical results indicate that the integrated method beats the benchmark and outperforms the traditional method that uses the ARIMA model.