Decision-Making Model to Portfolio Selection Using Analytic Hierarchy Process (AHP) with Expert Knowledge

On the premise of ensuring profits, how to give a relatively dispersed portfolio selection result reasonably and rapidly is a challenging problem in both theory and practice. Although the use of optimization models to make decision has been shown to be an essential approach towards portfolio selection, there still has an acute need for developing a knowledge-based expert model for portfolio selection so that this model can achieve better performance in reliability and real time, especially in leading more distributed investments. In this paper, a knowledge-based expert model is proposed for portfolio selection with the aid of analytic hierarchy process (AHP) and fuzzy sets. In the proposed model, the expert knowledge which can reflect the investment attitude and experience of different investors is mainly integrated into the criterion layer and represented by a reciprocal matrix, and the scheme layer is abstracted to a strictly consistent matrix by comparing and analyzing the state characteristics of investment objects. In order to characterize the state characteristics of investment objects under fuzzy environment, their corresponding time series data are quantified as fuzzy variables in advance. Experiments involving synthetic and real-world data demonstrate that the proposed model produces better performance than other typical portfolio selection models and gives more distributed investments.