Intelligent stock portfolio management system

Abstract

For the selection of stock portfolios, the optimization models or expert systems are used separately. To take advantage of both approaches, the integration of optimization model and expert system is attempted. The proposed architecture - ISPMS (Intelligent Stock Portfolio Management System) - accomplishes the integration by interpreting the knowledge as a part of the formulation of optimization model. The other generic issues in ISPMS are the integration of personal preference with the expert knowledge and knowledge acquisition by machine learning. This paper thus describes the representation and the inference of an expert knowledge, the representation of personal preference and its integration with the expert knowledge, the interpretation of the knowledge and preference to associate with optimization model, and the solution algorithm of the modified optimization model accordingly.