Designing a Multi-objective Stochastic programming model for portfolio selection

In traditional portfolio selection model coefficients often are certain and deterministic, but in real world these coefficients are probabilistic. So decision maker cannot estimate them exactly. Financial optimization is one of the most attractive areas in decision under uncertainty. In the portfolio selection problem the Decision Maker considers simultaneously conflicting objectives such as rate of return, liquidity, Dividend and risk. Multi-objective programming techniques such as goal programming and compromise programming are used to choose the portfolio best satisfying the Decision Maker’s aspirations and preferences; additionally Multi Criteria Decision Making (MCDM)Techniques for dealing with portfolio selection have been used. In this article, we assume that the parameters associated with the objectives are random and normally distributed. We propose a chance constrained compromise programming model is based on compromise programming and chance constrained programming models as a deterministic transformation to multi-objective stochastic programming portfolio model. To determine the share of industry investment planning MCDM were used. The result of the planning model for portfolio selection in Tehran Stock Exchange is shown.