Index tracking fund enhancement using evolving multi-criteria fuzzy decision models

An Index Tracking fund is designed to achieve similar investment performance to a market index by holding a portfolio of stocks in which each is weighted with consideration of its corresponding index weight. An ideal index tracking fund is exposed solely market risk. An enhanced index tracking fund should maintain a similar level of risk exposure through a high level of diversification (investing over a sufficiently wide base of assets) with a higher return on investment. In the approach suggested here, technical and fundamental valuation models found using an evolutionary fuzzy system provide enhancement by recommending under or over weighting some stocks in an index tracking portfolio.

[1]  Eric H. Sorensen,et al.  Quantitative Equity Portfolio Management: Modern Techniques and Applications , 2007 .

[2]  Dietmar Maringer.  Constrained Index Tracking under Loss Aversion Using Differential Evolution , 2008, Natural Computing in Computational Finance.

[3]  Jennifer Conrad,et al.  Profitability of Momentum Strategies: An Evaluation of Alternative Explanations , 2001 .

[4]  Josef Lakonishok,et al.  The Profitability of Momentum Strategies , 1999 .

[5]  E. Fama,et al.  COMPONENTS OF INVESTMENT PERFORMANCE , 1972 .

[6]  John E. Beasley,et al.  An evolutionary heuristic for the index tracking problem , 2003, Eur. J. Oper. Res..

[7]  E. Fama,et al.  Multifactor Explanations of Asset Pricing Anomalies , 1996 .

[8]  Ronald R. Yager,et al.  On ordered weighted averaging aggregation operators in multicriteria decisionmaking , 1988, IEEE Trans. Syst. Man Cybern..

[9]  Carlos A. Coello Coello,et al.  Applications of multi-objective evolutionary algorithms in economics and finance: A survey , 2007, 2007 IEEE Congress on Evolutionary Computation.

[10]  Antonia Azzini,et al.  Evolving neural networks for static single-position automated trading , 2008 .

[11]  Jan van den Berg,et al.  Optimized index tracking using a hybrid genetic algorithm , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[12]  Pei-Chann Chang,et al.  A TSK type fuzzy rule based system for stock price prediction , 2008, Expert Syst. Appl..

[13]  Robert G. Hagstrom,et al.  The Warren Buffett Way: Investment Strategies of the World's Greatest Investor , 1994 .

[14]  John B. Taylor,et al.  The Financial Crisis and the Policy Responses: An Empirical Analysis of What Went Wrong , 2009 .

[15]  Hisao Ishibuchi,et al.  Comparison of Heuristic Criteria for Fuzzy Rule Selection in Classification Problems , 2004, Fuzzy Optim. Decis. Mak..

[16]  Zbigniew Michalewicz,et al.  Computational Intelligence for Evolving Trading Rules , 2009, IEEE Transactions on Evolutionary Computation.

[17]  Andrew W. Alford,et al.  Quantitative Equity Portfolio Management , 2008 .

[18]  Katia Sycara,et al.  GP and the Predictive Power of Internet Message Traffic , 2002 .

[19]  Francisco Herrera,et al.  Ten years of genetic fuzzy systems: current framework and new trends , 2004, Fuzzy Sets Syst..

[20]  Sarat Kumar Patra,et al.  Fuzzy inference system-based noise prediction models for opencast mines , 2009 .

[21]  H. Rommelfanger.  Multicriteria decision making using fuzzy logic , 1998, 1998 Conference of the North American Fuzzy Information Processing Society - NAFIPS (Cat. No.98TH8353).

[22]  Mohammad Hossein Fazel Zarandi,et al.  A type-2 fuzzy rule-based expert system model for stock price analysis , 2009, Expert Syst. Appl..