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A Combined AHP-PROMETHEE Approach for Portfolio Performance Comparison

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  • Mirza Sikalo

    (School of Economics and Business, University of Sarajevo, Trg oslobodjenja—Alija Izetbegovic 1, 71000 Sarajevo, Bosnia and Herzegovina)

  • Almira Arnaut-Berilo

    (School of Economics and Business, University of Sarajevo, Trg oslobodjenja—Alija Izetbegovic 1, 71000 Sarajevo, Bosnia and Herzegovina)

  • Adela Delalic

    (School of Economics and Business, University of Sarajevo, Trg oslobodjenja—Alija Izetbegovic 1, 71000 Sarajevo, Bosnia and Herzegovina)

Abstract

Comparing portfolio performance is complex due to the fact that each model is dominant in its own risk space. Since there is no single dominant performance measure, the research problem is how to incorporate several different measures into a performance evaluation model that allows portfolios to be ranked. In this regard, the objective of this study was to develop a new comprehensive method for comparing portfolio performance based on multiple-criteria decision-making (MCDM). This paper proposes an integrated approach for stock market decision making that combines the Analytic Hierarchy Process (AHP) and the Preference Ranking Organization Method for Enrichment Evaluations (PROMETHEE), which allow hierarchical evaluation of a finite number of alternatives according to different criteria. This hybrid approach is especially advantageous, utilizing the strengths of both individual methods. AHP enables the decomposition of a complex problem into its constituent parts and the determination of weights for criteria, while the PROMETHEE method allows the investor to determine the preference function, complete ranking, and analysis of the robustness of the results. For the MCDM model in this study, different dimensions of performance measures are considered criteria: return measures, risk measures, stability measures, and predictability measures. The methodology has been applied in comparing real portfolios selected on the basis of different risk measures. For this purpose, weekly return data were used for a sample of stocks that are components of the STOXX Europe 600 Index for the period 2000–2020. In addition, a sensitivity analysis is performed to investigate the strength of the results of this method. It suggests that the simultaneous consideration of different performance measures and the investor’s attitude towards the importance of these measures are notably important in the portfolio efficiency estimation process.

Suggested Citation

  • Mirza Sikalo & Almira Arnaut-Berilo & Adela Delalic, 2023. "A Combined AHP-PROMETHEE Approach for Portfolio Performance Comparison," IJFS, MDPI, vol. 11(1), pages 1-15, March.
    • Handle: RePEc:gam:jijfss:v:11:y:2023:i:1:p:46-:d:1095631
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    References listed on IDEAS

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    1. Albadvi, Amir & Chaharsooghi, S. Kamal & Esfahanipour, Akbar, 2007. "Decision making in stock trading: An application of PROMETHEE," European Journal of Operational Research, Elsevier, vol. 177(2), pages 673-683, March.
    2. Peter Byrne & Stephen Lee, 2004. "Different Risk Measures: Different Portfolio Compositions?," Real Estate & Planning Working Papers rep-wp2004-03, Henley Business School, University of Reading.
    3. Thomas L. Saaty, 1994. "How to Make a Decision: The Analytic Hierarchy Process," Interfaces, INFORMS, vol. 24(6), pages 19-43, December.
    4. Saaty, Thomas L., 1990. "How to make a decision: The analytic hierarchy process," European Journal of Operational Research, Elsevier, vol. 48(1), pages 9-26, September.
    5. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2021. "Robust Portfolio Selection Problems: A Comprehensive Review," Papers 2103.13806, arXiv.org, revised Jan 2022.
    6. Righi, Marcelo Brutti & Borenstein, Denis, 2018. "A simulation comparison of risk measures for portfolio optimization," Finance Research Letters, Elsevier, vol. 24(C), pages 105-112.
    7. Peter Byrne & Stephen Lee, 2004. "Different Risk Measures: Different Portfolio Compositions?," ERES eres2004_516, European Real Estate Society (ERES).
    8. Alireza Ghahtarani & Ahmed Saif & Alireza Ghasemi, 2022. "Robust portfolio selection problems: a comprehensive review," Operational Research, Springer, vol. 22(4), pages 3203-3264, September.
    9. Ahmed Imran Hunjra & Suha Mahmoud Alawi & Sisira Colombage & Uroosa Sahito & Mahnoor Hanif, 2020. "Portfolio Construction by Using Different Risk Models: A Comparison among Diverse Economic Scenarios," Risks, MDPI, vol. 8(4), pages 1-23, November.
    10. Mirza Sikalo & Almira Arnaut-Berilo & Azra Zaimovic, 2022. "Efficient Asset Allocation: Application of Game Theory-Based Model for Superior Performance," IJFS, MDPI, vol. 10(1), pages 1-15, March.
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