An adaptive neuro-fuzzy system for stock portfolio analysis

Authors:
Meysam Alizadeh;Roy Rada;Fariborz Jolai;Elnaz Fotoohi
Affiliations:
Department of Information Systems, University of Maryland Baltimore County, Baltimore, MD 21250;Department of Information Systems, University of Maryland Baltimore County, Baltimore, MD 21250;Department of Industrial Engineering, University College of Engineering, University of Tehran, Tehran, Iran;Department of Industrial Engineering, Amirkabir University of Technology, Tehran, Iran
Venue:
International Journal of Intelligent Systems
Year:
2011

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Abstract

We propose an adaptive neuro-fuzzy inference system (ANFIS) for stock portfolio return prediction. Previous work has shown that portfolio optimization can be improved by using predicted stock earnings rather than historical earnings. We show that predicted portfolio returns can be improved by using ANFIS and taking as input a variety of technical and fundamental attributes about various indices of the stock market. To generate membership functions, we use a robust noise rejection-clustering algorithm. The neuro-fuzzy model is tested on portfolios constituted from the Tehran Stock Exchange. In our experiments, the proposed method performs better in predicting the portfolio return than the classical Markowitz portfolio optimization method, a multiple regression, a neural network, and the Sugeno–Yasukawa method. © 2010 Wiley Periodicals, Inc. © 2011 Wiley Periodicals, Inc.