Recurrent neural network for dynamic portfolio selection

Abstract

In this paper, the dynamic portfolio selection problem is considered. The Elman network is first designed to simulate the dynamic security behavior. Then, the dynamic covariance matrix is estimated by the cross-covariance matrices. Finally, the dynamic portfolio selection model is formulated. In addition, a numerical example is used to demonstrate the proposed method and compare with the vector autoregression (VAR) model. On the basis of the numerical example, we can conclude that the proposed method outperform to the VAR model and provide the accurate dynamic portfolio selection.