Asset shortlisting via fuzzy support vector machines for portfolio optimization model with second-order stochastic dominance criterion

Authors

  • Simrandeep Kaur * University School of Basicand Applied Sciences, Guru Gobind Singh Indraprastha University, Dwarka, Sector16-C, Delhi, 110078, Delhi, India. https://orcid.org/0009-0005-1666-2476
  • Ruchika Sehgal University School of Automationand Robotics, Guru Gobind Singh Indraprastha University, Surajmal Vihar, Delhi, 110092, Delhi, India.
  • Arti Singh University School of Automationand Robotics, Guru Gobind Singh Indraprastha University, Surajmal Vihar, Delhi, 110092, Delhi, India.
  • Abha Aggarwal University School of Basicand Applied Sciences, Guru Gobind Singh Indraprastha University, Dwarka, Sector16-C, Delhi, 110078, Delhi, India.

https://doi.org/10.48313/uda.vi.87

Abstract

The emergence and underdevelopment of machine learning (ML) methods to effectively process large-scale finance data and make accurate predictions encourage their application to traditional Portfolio Optimization (PO) models. This paper formulates a two-stage hybrid model for portfolio optimization. The first stage involves (i) Fuzzy Support Vector Machines (Fuzzy SVM) shortlisting of assets, which is an effective approach to deal with uncertainty and nonlinearity in finance data. Stage (ii) optimizes the chosen assets under Second-Order Stochastic Dominance (SSD) constraints in the second stage to address the pitfalls of the classical Markowitz model for maximizing investor satisfaction and utility. The new framework is tested on eight international equity markets, such as Bovespa 90 (Brazil), DAX 40 (Germany), Dow Jones Industrial Average (U.S.A.), EURO 50 (Europe), FTSE 100 (U.K.), Hang Seng (Hong Kong), Nifty 100 (India), and Nikkei 400 (Japan), and compared with models having no asset shortlisting and using traditional SVM-based selection. Empirical results indicate that Fuzzy SVM–SSD performs better consistently compared to the benchmarks on various measures of performance, such as average returns, Value at Risk (VaR), Conditional Value at Risk (CVaR), and minimum return. Also, better reward-to-risk performance is witnessed by virtue of higher STARR, Sharpe, and Sortino ratios. On the whole, the findings emphasize the significance of smart asset shortlisting and show the usefulness of combining fuzzy learning processes with risk-sensitive portfolio optimization for better financial decision-making.

Keywords:

Portfolio Optimization, Stochastic Dominance, Fuzzy Support Vector Machine, Membership function, Shortlisting Assets, Classification, Prediction

Author Biographies

  • Simrandeep Kaur, University School of Basicand Applied Sciences, Guru Gobind Singh Indraprastha University, Dwarka, Sector16-C, Delhi, 110078, Delhi, India.

    I'm an Assistant Professor at EMPI Business School, where I teach deep learning Research in Business Analytics Specialization to PGDM students.

  • Ruchika Sehgal, University School of Automationand Robotics, Guru Gobind Singh Indraprastha University, Surajmal Vihar, Delhi, 110092, Delhi, India.

    She is an Assistant Professor of Mathematics at IP University

  • Arti Singh, University School of Automationand Robotics, Guru Gobind Singh Indraprastha University, Surajmal Vihar, Delhi, 110092, Delhi, India.

    She is an Assistant Professor of Mathematics at IP University.

  • Abha Aggarwal, University School of Basicand Applied Sciences, Guru Gobind Singh Indraprastha University, Dwarka, Sector16-C, Delhi, 110078, Delhi, India.

    She is a Professor of Mathematics at IP University.

References

  1. [1] Markowitz, H. (1952). Portfolio selection. The Journal of Finance, 7(1), 77–91. https://doi.org/10.1111/j.1540-6261.1952.tb01525.x
  2. [2] Yitzhaki, S. (1982). Stochastic dominance, mean variance, and Gini’s mean difference. The American Economic Review, 72(1), 178–185. https://www.jstor.org/stable/1808571
  3. [3] Konno, H., & Yamazaki, H. (1991). Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. Management Science, 37(5), 519–531. https://doi.org/10.1287/mnsc.37.5.519
  4. [4] Rockafellar, R. T., & Uryasev, S. (2000). Optimization of conditional value-at-risk. Journal of Risk, 2(3), 21–41. https://doi.org/10.21314/JOR.2000.038
  5. [5] Sharpe, W. F. (1971). Mean-absolute-deviation characteristic lines for securities and portfolios. Management Science, 18(2), B1–B13. https://doi.org/10.1287/mnsc.18.2.B1
  6. [6] Young, M. R. (1998). A minimax portfolio selection rule with linear programming solution. Management Science, 44(5), 673–683. https://doi.org/10.1287/mnsc.44.5.673
  7. [7] Linsmeier, T. J., & Pearson, N. D. (2000). Value at risk. Financial Analysts Journal, 56(2), 47–67. https://doi.org/10.2469/faj.v56.n2.2343
  8. [8] Sharpe, W. F. (1966). Mutual fund performance. The Journal of Business, 39(1), 119–138. https://doi.org/10.1086/294846
  9. [9] Keating, C., & Shadwick, W. F. (2002). A universal performance measure. Journal of Performance Measurement, 6(3), 59–84. https://www.finance-finance.net/articles/Keating-Shadwick2002.pdf
  10. [10] Stoyanov, S. V., Rachev, S. T., & Fabozzi, F. J. (2007). Optimal financial portfolios. Applied Mathematical Finance, 14(5), 401–436. https://doi.org/10.1080/13504860701413984
  11. [11] Boyd, S., et al. (2017). Multi-period trading via convex optimization. Foundations and Trends® in Optimization, 3(1), 1–76. https://doi.org/10.1561/2400000003
  12. [12] Ogryczak, W., & Ruszczyński, A. (2001). On consistency of stochastic dominance and mean–semideviation models. Mathematical Programming, 89, 217–232. https://doi.org/10.1007/PL00011391
  13. [13] Dentcheva, D., & Ruszczyński, A. (2006). Portfolio optimization with stochastic dominance constraints. Journal of Banking & Finance, 30(2), 433–451. https://doi.org/10.1016/j.jbankfin.2005.02.009
  14. [14] Roman, D., Mitra, G., & Zverovich, V. (2013). Enhanced indexation based on second-order stochastic dominance. European Journal of Operational Research, 228(1), 273–281. https://doi.org/10.1016/j.ejor.2012.11.046
  15. [15] Sharma, A., Agrawal, S., & Mehra, A. (2017). Enhanced indexing for risk averse investors using relaxed second order stochastic dominance. Optimization and Engineering, 18(2), 407–442. https://doi.org/10.1007/s11081-016-9324-8
  16. [16] Sehgal, R., & Mehra, A. (2020). Robust portfolio optimization with second order stochastic dominance constraints. Computers & Industrial Engineering, 144, 106396. https://doi.org/10.1016/j.cie.2020.106396
  17. [17] Peng, C., & Delage, E. (2022). Data-driven optimization with distributionally robust second order stochastic dominance constraints. Operations Research. https://doi.org/10.1287/opre.2022.2368
  18. [18] Post, T., & Kopa, M. (2017). Portfolio choice based on third-degree stochastic dominance. Management Science, 63(10), 3381–3392. https://doi.org/10.1287/mnsc.2016.2516
  19. [19] Paiva, F. D., et al. (2019). Decision-making for financial trading: A fusion approach of machine learning and portfolio selection. Expert Systems with Applications, 115, 635–655. https://doi.org/10.1016/j.eswa.2018.08.017
  20. [20] Wang, W., et al. (2020). Portfolio formation with preselection using deep learning from long-term financial data. Expert Systems with Applications, 143, 113042. https://doi.org/10.1016/j.eswa.2019.113042
  21. [21] Solares, E., et al. (2022). A comprehensive decision support system for stock investment decisions. Expert Systems with Applications, 210, 118485. https://doi.org/10.1016/j.eswa.2022.118485
  22. [22] Ma, Y., Han, R., & Wang, W. (2020). Prediction-based portfolio optimization models using deep neural networks. IEEE Access, 8, 115393–115405. https://doi.org/10.1109/ACCESS.2020.3004284
  23. [23] Nanda, S. R., Mahanty, B., & Tiwari, M. K. (2010). Clustering Indian stock market data for portfolio management. Expert Systems with Applications, 37(12), 8793–8798. https://doi.org/10.1016/j.eswa.2010.06.026
  24. [24] Bjerring, T. T., Ross, O., & Weissensteiner, A. (2017). Feature selection for portfolio optimization. Annals of Operations Research, 256, 21–40. https://doi.org/10.1007/s10479-016-2296-1
  25. [25] Tayalı, S. T. (2020). A novel backtesting methodology for clustering in mean–variance portfolio optimization. Knowledge-Based Systems, 209, 106454. https://doi.org/10.1016/j.knosys.2020.106454
  26. [26] Gupta, P., et al. (2014). Multi-criteria portfolio optimization using support vector machines and genetic algorithms. In Fuzzy Portfolio Optimization: Advances in Hybrid Multi-criteria Methodologies (pp. 283–309). Springer. https://doi.org/10.1007/978-81-322-1598-5_11
  27. [27] Ma, Y., Han, R., & Wang, W. (2021). Portfolio optimization with return prediction using deep learning and machine learning. Expert Systems with Applications, 165, 113973. https://doi.org/10.1016/j.eswa.2020.113973
  28. [28] Kaur, S., Singh, A., & Aggarwal, A. (2024). Mean-variance optimal portfolio selection integrated with support vector and fuzzy support vector machines. Journal of Fuzzy Extension and Applications. https://doi.org/10.22105/jfea.2024.453926.1453
  29. [29] Kaur, S. (2025). From sentiment to strategy: Machine learning in emotion-based asset allocation. https://scholar.google.com/
  30. [30] Kaur, S., Singh, A., & Aggarwal, A. (2025). Optimal portfolio construction with fuzzy least square support vector machines and conditional value-at-risk: A risk-adjusted approach. International Journal of System Assurance Engineering and Management. https://doi.org/10.1007/s13198-025-03020-y
  31. [31] Kaur, S., Singh, A., & Aggarwal, A. (2025). A novel fuzzy multi-class support vector machine: An application to asset selection and portfolio optimization. Computational Economics, 1–46. https://doi.org/10.1007/s10614-025-10709-3
  32. [32] Lin, C.-F., & Wang, S.-D. (2002). Fuzzy support vector machines. IEEE Transactions on neural networks, 13(2), 464–471. https://doi.org/10.1109/72.991432
  33. [33] Bradley, P. S., & Mangasarian, O. L. (1998). Feature selection via concave minimization and support vector machines. In Proceedings of the Fifteenth International Conference on Machine Learning (pp. 82–90). https://dl.acm.org/doi/10.5555/645527.657132
  34. [34] Murphy, J. J. (1999). Technical analysis of the financial markets: A comprehensive guide to trading methods and applications. New York Institute of Finance. https://books.google.com/books?id=5zhhQgAACAAJ
  35. [35] Wilder, J. W. (1978). New concepts in technical trading systems. Trend Research. https://books.google.com/books?id=WesJAQAAMAAJ
  36. [36] Williams, L. R. (1986). The secret of selecting stocks for immediate and substantial gains. Windsor Books. https://openlibrary.org/books/OL2713271M/The_secret_of_selecting_stocks_for_immediate_and_substantial_gains
  37. [37] Sehgal, R., & Mehra, A. (2022). Applying regression techniques in designing optimal trade execution strategy for an asset. Optimization, 71(3), 463–484. https://doi.org/10.1080/02331934.2020.1772792
  38. [38] Sharpe, W. F. (1998). The Sharpe ratio. In Streetwise The best of the journal of portfolio management (pp. 169–185). Princeton University Press. https://web.stanford.edu/~wfsharpe/art/sr/SR.htm

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Published

2026-03-25

Issue

Vol. 3 No. 1 (2026): Uncertainty Discourse and Applications

Section

Articles

How to Cite

Kaur, S., Sehgal, R., Singh, A., & Aggarwal, A. (2026). Asset shortlisting via fuzzy support vector machines for portfolio optimization model with second-order stochastic dominance criterion. Uncertainty Discourse and Applications, 3(1), 89-106. https://doi.org/10.48313/uda.vi.87

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