Optimisation of allocated portfolio using multi-objective stochastic programming

Decision making about portfolio selection problem under uncertainty is very important and critical, so it is rational and necessary to use ideas of a DM who is proficient in stochastic problems. For example, the Goal Attainment Programming (GAP) is one of the methods in priori category that compared to the Goal Programming (GP) method has several advantages. At now, several techniques have been introduced to solve stochastic programming problems and most popular technique is the Chance Constrained Programming (CCP). In this paper, by combining CCP approach and GAP method a new model called Chance Constrained Goal Attainment Programming (CCGAP) is proposed which can be used to optimise multi-objective stochastic problems. This proposed model is illustrated by a problem of portfolio selection from the market of Iran stock exchange.