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Integrated Decision Support System for Portfolio Selection with Enhanced Behavioral Content

Author

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  • Cristinca FULGA

    (Department of Applied Mathematics The Bucharest University of Economic Studies)

Abstract

The portfolio selection problem is a crucial problem that every investor at individual or institutional level has to deal with. There is a vast amount of literature about systems designed to support portfolio management decisions with a large diversity in focus and approach. However, even if it is well known that all decisions depend on decision maker’s preferences, the preferences are not represented satisfactorily in most systems. In this paper, we propose a decision support system design for portfolio selection that relies on an optimization model with enhanced behavioural content based on the stochastic programming paradigm. The proposed system is capable of supporting loss averse investors in the complex task of selecting portfolios that are simultaneously optimal from the reward-risk viewpoint and suitable for investor’s specific loss aversion profile.

Suggested Citation

  • Cristinca FULGA, 2017. "Integrated Decision Support System for Portfolio Selection with Enhanced Behavioral Content," ECONOMIC COMPUTATION AND ECONOMIC CYBERNETICS STUDIES AND RESEARCH, Faculty of Economic Cybernetics, Statistics and Informatics, vol. 51(3), pages 127-142.
  • Handle: RePEc:cys:ecocyb:v:50:y:2017:i:3:p:127-142
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    References listed on IDEAS

    as
    1. Xidonas, Panagiotis & Mavrotas, George & Zopounidis, Constantin & Psarras, John, 2011. "IPSSIS: An integrated multicriteria decision support system for equity portfolio construction and selection," European Journal of Operational Research, Elsevier, vol. 210(2), pages 398-409, April.
    2. Daniel Kahneman & Amos Tversky, 2013. "Prospect Theory: An Analysis of Decision Under Risk," World Scientific Book Chapters, in: Leonard C MacLean & William T Ziemba (ed.), HANDBOOK OF THE FUNDAMENTALS OF FINANCIAL DECISION MAKING Part I, chapter 6, pages 99-127, World Scientific Publishing Co. Pte. Ltd..
    3. Tversky, Amos & Kahneman, Daniel, 1992. "Advances in Prospect Theory: Cumulative Representation of Uncertainty," Journal of Risk and Uncertainty, Springer, vol. 5(4), pages 297-323, October.
    4. Markowitz, Harry, 2014. "Mean–variance approximations to expected utility," European Journal of Operational Research, Elsevier, vol. 234(2), pages 346-355.
    5. Fulga, Cristinca, 2016. "Portfolio optimization with disutility-based risk measure," European Journal of Operational Research, Elsevier, vol. 251(2), pages 541-553.
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    7. Fulga, Cristinca, 2016. "Portfolio optimization under loss aversion," European Journal of Operational Research, Elsevier, vol. 251(1), pages 310-322.
    8. Siskos, Y. & Spyridakos, A. & Yannacopoulos, D., 1999. "Using artificial intelligence and visual techniques into preference disaggregation analysis: The MIIDAS system," European Journal of Operational Research, Elsevier, vol. 113(2), pages 281-299, March.
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    More about this item

    Keywords

    Decision support system; loss aversion; risk measure; utility function; portfolio optimization.;
    All these keywords.

    JEL classification:

    • C02 - Mathematical and Quantitative Methods - - General - - - Mathematical Economics
    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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