Optimal Portfolio Allocation based on two Novel Risk Measures and Genetic Algorithm

Abstract

The problem of optimal portfolio selection has attracted a great attention in the finance and optimization field. The future stock price should be predicted in an acceptable precision, and a suitable model and criterion for risk and the expected return of the stock portfolio should be proposed in order to solve the optimization problem. In this paper, two new criterions for the risk of stock price prediction has been presented, of which the first one is based on the interval predictions which vary with time and proportional to the uncertainty of stock price data, while the second one is a constant risk term that is proportional to the prediction error variances of the neural networks. A novel cost function has been presented to simultaneously consider the expected returns and risks. Genetic algorithm has been used to solve this optimization problem. Finally, 18 shares of the Tehran Stock Exchange have been considered to evaluate the performance of the proposed risk criterions. Two proposed risk criteria, by the conditional value at risk (CVaR) associated with the same stock. The problem of stock portfolio optimization has been solved for all three situations, and the PI-based risk criteria yielded a better return