Mean-variance-skewness portfolio optimization under uncertain environment using improved genetic algorithm

摘要

An indeterminacy economic environment includes uncertainty during adopting experts knowledge for the analysis of stock returns. The main goal in this paper is to discuss the problem of portfolio selection with uncertain environment; because, the experts alone has the ability to evaluate the security returns and not with the historical data. However, the uncertain variables considered have shown the stock returns. Uncertainty programming is used to formulate mean-variance skewness indicating the problem of portfolio selection in uncertain environment based on different decision criteria. At different conditions, some significant crisp equivalents are explained for the ease of solving models within the uncertainty theory framework. Furthermore, this paper has solved the new models included in general cases using a general method developed through designing a novel hybrid intelligent algorithm. Ultimately, the developed algorithm and models applications and performance was evidently proved using a numerical example.