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A New Fuzzy DSS/ES for Stock Portfolio Selection using Technical and Fundamental Approaches in Parallel

Authors: H. Zarei, M. H. Fazel Zarandi, M. Karbasian


A Decision Support System/Expert System for stock portfolio selection presented where at first step, both technical and fundamental data used to estimate technical and fundamental return and risk (1st phase); Then, the estimated values are aggregated with the investor preferences (2nd phase) to produce convenient stock portfolio. In the 1st phase, there are two expert systems, each of which is responsible for technical or fundamental estimation. In the technical expert system, for each stock, twenty seven candidates are identified and with using rough sets-based clustering method (RC) the effective variables have been selected. Next, for each stock two fuzzy rulebases are developed with fuzzy C-Mean method and Takai-Sugeno- Kang (TSK) approach; one for return estimation and the other for risk. Thereafter, the parameters of the rule-bases are tuned with backpropagation method. In parallel, for fundamental expert systems, fuzzy rule-bases have been identified in the form of “IF-THEN" rules through brainstorming with the stock market experts and the input data have been derived from financial statements; as a result two fuzzy rule-bases have been generated for all the stocks, one for return and the other for risk. In the 2nd phase, user preferences represented by four criteria and are obtained by questionnaire. Using an expert system, four estimated values of return and risk have been aggregated with the respective values of user preference. At last, a fuzzy rule base having four rules, treats these values and produce a ranking score for each stock which will lead to a satisfactory portfolio for the user. The stocks of six manufacturing companies and the period of 2003-2006 selected for data gathering.

Keywords: Stock Portfolio Selection, Fuzzy Rule-Base ExpertSystems, Financial Decision Support Systems, Technical Analysis, Fundamental Analysis.

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[1] W. Breen, "Specific versus General Models of Portfolio Selection" Oxford Economic Papers, November 1968, New Series ed., pp. 361- 368.
[2] F. J. Travers, Investment Manager Analysis: A Comprehensive Guide to Portfolio Selection, Monitoring, and Optimization. John Wiley & Sons, Inc., 2004.
[3] H. Markowitz, "Portfolio Selection." The Journal of Finance (Cowles Foundation), vol. VII, no. 1, pp. 77-91, March 1952.
[4] J. Dong, H. S. Du, S. Wang, K. Chen, X. Deng. "A framework of Web-based Decision Support Systems for portfolio selection with OLAP and PVM." Decision Support Systems, no. 37, pp. 367- 376, 2004.
[5] H. Markowitz, Portfolio Selection, Efficient diversification of Investment. New York: John Wiley & Sons, Inc., 1959.
[6] W. F. Sharp, "A simplified model for portfolio analysis" Management Science, pp. 277-293, January 1963.
[7] G. M. Frankfurter, E. P. Herbert, J. P. Seagle, "Performance of the Sharpe Portfolio Selection Model: A Comparison." The Journal of Financial and Quantitative Analysis (University of Washington School of Business administration) 11, no. 2, pp. 195-204, June 1976.
[8] W. F. Sharpe, "Portfolio Analysis" The Journal of Financial and Quantitative Analysis (University of Washington School of Business Administration), vol. 2, no. 2, pp. 76-84, June 1976.
[9] L. Stevens, Essential Technical Analysis, Tools and Techniques to Spot Market Trends. John Wiley & Sons, Inc., 2002.
[10] P. Chang, L. Chen-Hao, "A TSK type fuzzy rule based system for stock price prediction." Expert Systems with Applications, no. 34, pp. 135-144, 2008.
[11] E. Helfert, Financial Analysis: Tools and Techniques, a Guide for Managers, McGraw-Hill, 2001.
[12] K. G. Palepu, P. M. Healy, V. L. Bernard. Business analysis & valuation: using financial statements: text & cases. Thomson Learning, 2004.
[13] A. Groppelli, E. Nikbakht, Finance. 4th edition, Barron-s Educational Series Inc., 2000.
[14] M. Sugeno, T. Yasukawa, "A Fuzzy-Logic-Based Approach to Qualitative Modeling" lEEE Transactions on Fuzzy Systems, pp. 7-31, February 1993.
[15] J.-S. R. Jang, "ANFIS: Adaptive-Network-based Fuzzy Inference", IEEE Transactions on Systems, Man, and Cybernetics, vol.27, no. 3, pp. 665-685, May 1993