Stochastic Portfolio Selection Problem with Reliability Criteria

الملخص EN

Portfolio selection focuses on allocating the capital to a set of securities such that the profit or the risks can be optimized.

Due to the uncertainty of the real-world life, the return parameters always take uncertain information in the realistic environments because of the scarcity of the a priori knowledge or uncertain disturbances.

This paper particularly considers a portfolio selection process in the stochastic environment, where the return parameters are characterized by sample-based correlated random variables.

To decrease the decision risks, three evaluation criteria are proposed to generate the reliable portfolio selection plans, including max-min reliability criterion, percentile reliability criterion, and expected disutility criterion.

The equivalent linear (mixed integer) programming models are also deduced for different evaluation strategies.

A genetic algorithm with a polishing strategy is designed to search for the approximate optimal solutions of the proposed models.

Finally, a series of numerical experiments are implemented to demonstrate the effectiveness and performance of the proposed approaches.