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Combining Multiple Criterion Systems for Improving Portfolio Performance

Author

Listed:
  • H. D. Vinod

    (Fordham University, Department of Economics)

  • D. F. Hsu

    (Fordham University, Department of Computer and Information Science)

  • Y. Tian

    (Fordham University, Department of Computer and Information Science)

Abstract

A central issue for managers or investors in portfolio management of assets is to select the assets to be included and to predict the value of the portfolio, given a variety of historical and concurrent information regarding each asset in the portfolio. There exist several criteria or models to predict asset returns, which in turn are sensitive to underlying probability distributions, their unknown parameters, whether it is a bull, bear or flat period subject to further uncertainty regarding switch times between bull and bear periods. It is possible to treat various portfolio-choice criteria as multiple criterion systems in the uncertain world of asset markets from historical market data. This paper develops the initial framework for the selection of assets using information fusion to combine these multiple criterion systems. These MCS' are combined, using the recently developed Combinatorial Fusion Analysis (CFA) to enhance the portfolio performance. We demonstrate with an example using US stock market data that combination of multiple criteria (or models) systems does indeed improve the portfolio performance.

Suggested Citation

  • H. D. Vinod & D. F. Hsu & Y. Tian, 2008. "Combining Multiple Criterion Systems for Improving Portfolio Performance," Fordham Economics Discussion Paper Series dp2008-07, Fordham University, Department of Economics.
    • Handle: RePEc:frd:wpaper:dp2008-07
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    File URL: https://archive.fordham.edu/ECONOMICS_RESEARCH/PAPERS/dp2008_07_vinod_hsu_tian.pdf
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    References listed on IDEAS

    as
    1. Vinod, H. D., 2004. "Ranking mutual funds using unconventional utility theory and stochastic dominance," Journal of Empirical Finance, Elsevier, vol. 11(3), pages 353-377, June.
    2. Kwong Bor Ng & Paul B Kantor, 2000. "Predicting the effectiveness of naïve data fusion on the basis of system characteristics," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 51(13), pages 1177-1189.
    Full references (including those not matched with items on IDEAS)

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    Keywords

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    JEL classification:

    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D81 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Criteria for Decision-Making under Risk and Uncertainty

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