Emotional Learning Approach for Multi Objective Investment Management

A. Abbaspour, C. Lucas, and B.N. Araabi (Iran)


Emotional Learning, InvestmentManagement, Decision Making, Portfolio Selection,Neurofuzzy Model


Neural networks and Neurofuzzy models have been successfully used in decision making. Several learning methods have been introduced to train the Neurofuzzy decision makers, such as ANFIS, ASMOD and FUREGA. Many of these methods, constructed over Takagi Sugeno fuzzy inference system, are characterized by high generalization. In this paper, the emotional learning method, which has been used in control applications to provide multiple objectives, is proposed to train neurofuzzy decision making. The emotional learning based fuzzy inference system (ELFIS) has the advantage of low computational complexity in comparison with other multi-objective optimization methods. Appropriate emotional signal is composed for portfolio selection. In this article, the expected return of stocks and the risk of portfolio (measured by semi-variance, variance and etc) are considered as the criteria for portfolio selection.

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