IDEAS home Printed from https://ideas.repec.org/a/spr/opsear/v62y2025i1d10.1007_s12597-024-00776-y.html
   My bibliography  Save this article

A hybrid approach based on multi-criteria decision making and data-driven optimization in solving portfolio selection problem

Author

Listed:
  • Meysam Doaei

    (Islamic Azad University)

  • Kazem Dehnad

    (Islamic Azad University)

  • Mahdi Dehnad

    (Khatam University)

Abstract

In this paper, a two-phase approach based on multi-criteria decision making and multi-objective optimization models is proposed to select portfolio optimally. In the first phase, potential companies for investment are selected initially by considering the criteria extracted from the literature review. In the second phase, a multi-objective optimization model is proposed to optimize the investment in selected companies according to risk and return objectives. In order to deal with uncertainty conditions, a data-driven approach is used, which is one of the newest applied methods in this field. According to the obtained results, it is observed that cash adequacy ratio with score 0.1604 is the most important criterion and operating profit with score 0.004 is the least important one. In the alternative prioritization section, it is concluded that Shraz, Shavan, Shenft and Vanft companies have a high priority for investment. In solving the mathematical model under certain conditions, it is observed that the Pareto members 152, 154 and 193 have the smallest distance from the ideal solution (0.0121) and therefore each of them can be used as the final solution. In solving the problem under uncertain conditions, numerical scenarios resulting from changes in the prioritization of companies based on the coefficient v is used in the VIKOR model. Based on the results, it is observed that the impact of different scenarios on corporate investment is not negligible and consequently investors need to pay attention to this fact.

Suggested Citation

  • Meysam Doaei & Kazem Dehnad & Mahdi Dehnad, 2025. "A hybrid approach based on multi-criteria decision making and data-driven optimization in solving portfolio selection problem," OPSEARCH, Springer;Operational Research Society of India, vol. 62(1), pages 1-36, March.
  • Handle: RePEc:spr:opsear:v:62:y:2025:i:1:d:10.1007_s12597-024-00776-y
    DOI: 10.1007/s12597-024-00776-y
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s12597-024-00776-y
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s12597-024-00776-y?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Lei Ruan, 2018. "Research on Sustainable Development of the Stock Market Based on VIX Index," Sustainability, MDPI, vol. 10(11), pages 1-12, November.
    2. Francesco Cesarone & Andrea Scozzari & Fabio Tardella, 2020. "An optimization–diversification approach to portfolio selection," Journal of Global Optimization, Springer, vol. 76(2), pages 245-265, February.
    3. Mostafaei Darmian, Sobhan & Doaei , Meysam, 2022. "Optimization of Stock Portfolio Selection in Iran Capital Market Using Meta-heuristic Algorithms," Quarterly Journal of Applied Theories of Economics, Faculty of Economics, Management and Business, University of Tabriz, vol. 8(4), pages 253-284, March.
    4. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03380580, HAL.
    5. Mavrotas, George & Florios, Kostas, 2013. "An improved version of the augmented epsilon-constraint method (AUGMECON2) for finding the exact Pareto set in Multi-Objective Integer Programming problems," MPRA Paper 105034, University Library of Munich, Germany.
    6. Cui, Yu & Zhang, Yamin & Guo, Jingjing & Hu, Hao & Meng, Hua, 2019. "Top management team knowledge heterogeneity, ownership structure and financial performance: Evidence from Chinese IT listed companies," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 14-21.
    7. Yoshino, Naoyuki & Taghizadeh-Hesary, Farhad & Otsuka, Miyu, 2021. "Covid-19 and Optimal Portfolio Selection for Investment in Sustainable Development Goals," Finance Research Letters, Elsevier, vol. 38(C).
    8. Nada Mselmi & Amine Lahiani & Taher Hamza, 2017. "Financial distress prediction: The case of French small and medium-sized firms," Post-Print hal-03529325, HAL.
    9. Mohammad Fattahi, 2020. "A data-driven approach for supply chain network design under uncertainty with consideration of social concerns," Annals of Operations Research, Springer, vol. 288(1), pages 265-284, May.
    10. Jianjian Wang & Feng He & Xin Shi, 2019. "Numerical solution of a general interval quadratic programming model for portfolio selection," PLOS ONE, Public Library of Science, vol. 14(3), pages 1-16, March.
    11. Masoud Rahiminezhad Galankashi & Farimah Mokhatab Rafiei & Maryam Ghezelbash, 2020. "Portfolio selection: a fuzzy-ANP approach," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 6(1), pages 1-34, December.
    12. Rezaei, Jafar, 2016. "Best-worst multi-criteria decision-making method: Some properties and a linear model," Omega, Elsevier, vol. 64(C), pages 126-130.
    13. Mselmi, Nada & Lahiani, Amine & Hamza, Taher, 2017. "Financial distress prediction: The case of French small and medium-sized firms," International Review of Financial Analysis, Elsevier, vol. 50(C), pages 67-80.
    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. Mselmi, Nada & Hamza, Taher & Lahiani, Amine & Shahbaz, Muhammad, 2019. "Pricing corporate financial distress: Empirical evidence from the French stock market," Journal of International Money and Finance, Elsevier, vol. 96(C), pages 13-27.
    2. Seiler, Volker & Fanenbruck, Katharina Maria, 2021. "Acceptance of digital investment solutions: The case of robo advisory in Germany," Research in International Business and Finance, Elsevier, vol. 58(C).
    3. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    4. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    5. Vladislav V. Afanasev & Yulia A. Tarasova, 2022. "Default Prediction for Housing and Utilities Management Firms Using Non-Financial Data," Finansovyj žhurnal — Financial Journal, Financial Research Institute, Moscow 125375, Russia, issue 6, pages 91-110, December.
    6. Alexandra Horobet & Stefania Cristina Curea & Alexandra Smedoiu Popoviciu & Cosmin-Alin Botoroga & Lucian Belascu & Dan Gabriel Dumitrescu, 2021. "Solvency Risk and Corporate Performance: A Case Study on European Retailers," JRFM, MDPI, vol. 14(11), pages 1-34, November.
    7. ElBannan, Mona A., 2021. "On the prediction of financial distress in emerging markets: What matters more? Empirical evidence from Arab spring countries," Emerging Markets Review, Elsevier, vol. 47(C).
    8. Bravo-Urquiza, Francisco & Moreno-Ureba, Elena, 2021. "Does compliance with corporate governance codes help to mitigate financial distress?," Research in International Business and Finance, Elsevier, vol. 55(C).
    9. Sanjay Sehgal & Ritesh Kumar Mishra & Ajay Jaisawal, 2021. "A search for macroeconomic determinants of corporate financial distress," Indian Economic Review, Springer, vol. 56(2), pages 435-461, December.
    10. Carmona, Pedro & Dwekat, Aladdin & Mardawi, Zeena, 2022. "No more black boxes! Explaining the predictions of a machine learning XGBoost classifier algorithm in business failure," Research in International Business and Finance, Elsevier, vol. 61(C).
    11. Christophe Schalck & Meryem Yankol-Schalck, 2021. "Predicting French SME failures: new evidence from machine learning techniques," Applied Economics, Taylor & Francis Journals, vol. 53(51), pages 5948-5963, November.
    12. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    13. Pham, Tho & Talavera, Oleksandr & Wood, Geoffrey & Yin, Shuxing, 2022. "Quality of working environment and corporate financial distress," Finance Research Letters, Elsevier, vol. 46(PB).
    14. Khoja, Layla & Chipulu, Maxwell & Jayasekera, Ranadeva, 2019. "Analysis of financial distress cross countries: Using macroeconomic, industrial indicators and accounting data," International Review of Financial Analysis, Elsevier, vol. 66(C).
    15. Oliver Lukason & María-del-Mar Camacho-Miñano, 2019. "Bankruptcy Risk, Its Financial Determinants and Reporting Delays: Do Managers Have Anything to Hide?," Risks, MDPI, vol. 7(3), pages 1-15, July.
    16. Daniel Ogachi & Richard Ndege & Peter Gaturu & Zeman Zoltan, 2020. "Corporate Bankruptcy Prediction Model, a Special Focus on Listed Companies in Kenya," JRFM, MDPI, vol. 13(3), pages 1-14, March.
    17. Fernández-Gámez, Manuel Ángel & Soria, Juan Antonio Campos & Santos, José António C. & Alaminos, David, 2020. "European country heterogeneity in financial distress prediction: An empirical analysis with macroeconomic and regulatory factors," Economic Modelling, Elsevier, vol. 88(C), pages 398-407.
    18. Jessica Thacker & Debdatta Saha, 2025. "Financial Performance and Corporate Distress: Searching for Common Factors for Firms in the Indian Registered Manufacturing Sector," Computational Economics, Springer;Society for Computational Economics, vol. 65(6), pages 3841-3883, June.
    19. Gradojevic, Nikola & Kukolj, Dragan & Adcock, Robert & Djakovic, Vladimir, 2023. "Forecasting Bitcoin with technical analysis: A not-so-random forest?," International Journal of Forecasting, Elsevier, vol. 39(1), pages 1-17.
    20. Wei Liu & Yoshihisa Suzuki & Shuyi Du, 2025. "Ensemble learning algorithms based on easyensemble sampling for financial distress prediction," Annals of Operations Research, Springer, vol. 346(3), pages 2141-2172, March.

    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:spr:opsear:v:62:y:2025:i:1:d:10.1007_s12597-024-00776-y. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.