IDEAS home Printed from https://ideas.repec.org/a/cys/ecocyb/v50y2017i3p127-142.html
   My bibliography  Save this article

Integrated Decision Support System for Portfolio Selection with Enhanced Behavioral Content

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: ftp://www.eadr.ro/RePEc/cys/ecocyb_pdf/ecocyb3_2017p127-142.pdf
    Download Restriction: no
    ---><---

    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. 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.
    3. 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..
    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.
    6. Borgonovo, Emanuele & Gatti, Stefano, 2013. "Risk analysis with contractual default. Does covenant breach matter?," European Journal of Operational Research, Elsevier, vol. 230(2), pages 431-443.
    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.
    9. Young, William E. & Trent, Robert H., 1969. "Geometric Mean Approximations of Individual Security and Portfolio Performance*," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 4(2), pages 179-199, June.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bi, Junna & Jin, Hanqing & Meng, Qingbin, 2018. "Behavioral mean-variance portfolio selection," European Journal of Operational Research, Elsevier, vol. 271(2), pages 644-663.
    2. Charles-Cadogan, G., 2018. "Losses loom larger than gains and reference dependent preferences in Bernoulli’s utility function," Journal of Economic Behavior & Organization, Elsevier, vol. 154(C), pages 220-237.
    3. Wei Liu & Shiji Song & Ying Qiao & Han Zhao, 2020. "Supply Chain Coordination with a Loss-Averse Retailer and Combined Contract," Mathematics, MDPI, vol. 8(4), pages 1-20, April.
    4. Fulga, Cristinca, 2016. "Portfolio optimization under loss aversion," European Journal of Operational Research, Elsevier, vol. 251(1), pages 310-322.
    5. Harris, Richard D. F. & Mazibas, Murat, 2022. "Portfolio optimization with behavioural preferences and investor memory," European Journal of Operational Research, Elsevier, vol. 296(1), pages 368-387.
    6. Song, Jingjing & Bi, Xiuchun & Li, Rong & Zhang, Shuguang, 2017. "Optimal consumption and portfolio selection problems under loss aversion with downside consumption constraints," Applied Mathematics and Computation, Elsevier, vol. 299(C), pages 80-94.
    7. Constantin Mellios & Anh Ngoc Lai, 2022. "Incentive Fees with a Moving Benchmark and Portfolio Selection under Loss Aversion," Post-Print hal-03708926, HAL.
    8. Chan, Chi Kin & Zhou, Yan & Wong, Kar Hung, 2019. "An equilibrium model of the supply chain network under multi-attribute behaviors analysis," European Journal of Operational Research, Elsevier, vol. 275(2), pages 514-535.
    9. Zsurkis, Gabriel & Nicolau, João & Rodrigues, Paulo M.M., 2024. "First passage times in portfolio optimization: A novel nonparametric approach," European Journal of Operational Research, Elsevier, vol. 312(3), pages 1074-1085.
    10. Amen Aissi Harzallah & Mouna Boujelbene Abbes, 2020. "The Impact of Financial Crises on the Asset Allocation: Classical Theory Versus Behavioral Theory," Journal of Interdisciplinary Economics, , vol. 32(2), pages 218-236, July.
    11. Bradley, Ian, 2003. "The representative bettor, bet size, and prospect theory," Economics Letters, Elsevier, vol. 78(3), pages 409-413, March.
    12. van den Bergh, J.C.J.M. & Botzen, W.J.W., 2015. "Monetary valuation of the social cost of CO2 emissions: A critical survey," Ecological Economics, Elsevier, vol. 114(C), pages 33-46.
    13. Shoji, Isao & Kanehiro, Sumei, 2016. "Disposition effect as a behavioral trading activity elicited by investors' different risk preferences," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 104-112.
    14. Jonathan Meng & Feng Fu, 2020. "Understanding Gambling Behavior and Risk Attitudes Using Cryptocurrency-based Casino Blockchain Data," Papers 2008.05653, arXiv.org, revised Aug 2020.
    15. Daniel Fonseca Costa & Francisval Carvalho & Bruno César Moreira & José Willer Prado, 2017. "Bibliometric analysis on the association between behavioral finance and decision making with cognitive biases such as overconfidence, anchoring effect and confirmation bias," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1775-1799, June.
    16. Boone, Jan & Sadrieh, Abdolkarim & van Ours, Jan C., 2009. "Experiments on unemployment benefit sanctions and job search behavior," European Economic Review, Elsevier, vol. 53(8), pages 937-951, November.
    17. Castro, Luciano de & Galvao, Antonio F. & Kim, Jeong Yeol & Montes-Rojas, Gabriel & Olmo, Jose, 2022. "Experiments on portfolio selection: A comparison between quantile preferences and expected utility decision models," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 97(C).
    18. Jos'e Cl'audio do Nascimento, 2019. "Behavioral Biases and Nonadditive Dynamics in Risk Taking: An Experimental Investigation," Papers 1908.01709, arXiv.org, revised Apr 2023.
    19. Caporin, Massimiliano & Costola, Michele & Jannin, Gregory & Maillet, Bertrand, 2018. "“On the (Ab)use of Omega?”," Journal of Empirical Finance, Elsevier, vol. 46(C), pages 11-33.
    20. Paolo Crosetto & Antonio Filippin, 2013. "The “bomb” risk elicitation task," Journal of Risk and Uncertainty, Springer, vol. 47(1), pages 31-65, August.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:cys:ecocyb:v:50:y:2017:i:3:p:127-142. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Corina Saman (email available below). General contact details of provider: https://edirc.repec.org/data/feasero.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.