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A Hybrid Fuzzy-SCOOT Algorithm to Optimize Possibilistic Mean Semi-absolute Deviation Model for Optimal Portfolio Selection

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Abstract

The uncertainty associated with the financial domain in modern portfolio selection problems can be overcome by using fuzzy set theory. The portfolio is modified in this paper using the possibility theory instead of the probability theory by formulating the risk return as fuzzy numbers, and we also take into account the V-shaped transaction costs. The possibilistic semi-absolute deviation portfolio selection technique is used to develop a portfolio selection framework by considering the investor demands and stock characteristics. The higher computational complexity associated with the possibilistic mean semi-absolute deviation portfolio model is reduced using the hybris salp swarm-based Coot algorithm (SCOOT). The main aim of the hybrid SCOOT algorithm is to reduce the risk and increase the expected return. The salp swarm algorithm is integrated with the coot algorithm to enhance the global search capability. The performance of the proposed approach is evaluated with the extensive experiments conducted on the Bombay Stock Exchange dataset. The results obtained show that the proposed methodology offers better performance when considering the transaction costs, and its performance is very high when compared to the conventional metaheuristic techniques.

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References

  1. Chen, W., Wang, Y., Mehlawat, M.K.: A hybrid FA–SA algorithm for fuzzy portfolio selection with transaction costs. Ann. Oper. Res. 269(1), 129–147 (2018)

    Article  MathSciNet  Google Scholar 

  2. Edirisinghe, C., Zhou, W.: Portfolio optimization using rank correlation. In: Wang, J. (ed.) Encyclopedia of Business Analytics and Optimization, pp. 1866–1879. IGI Global, Hershey (2014)

    Chapter  Google Scholar 

  3. Guo, S., Gu, J.W., Ching, W.K.: Adaptive online portfolio selection with transaction costs. Eur. J. Oper. Res. (2021). https://doi.org/10.1016/j.ejor.2021.03.023

    Article  MathSciNet  MATH  Google Scholar 

  4. Škrinjarić, T.: Fuzzy logic in portfolio selection: selected applications. In: Christiansen, B., Škrinjarić, T. (eds.) Handbook of Research on Applied AI for International Business and Marketing Applications, pp. 173–190. IGI Global, Hershey (2021)

    Chapter  Google Scholar 

  5. Tang, O., Musa, S.N.: Identifying risk issues and research advancements in supply chain risk management. Int. J. Prod. Econ. 133(1), 25–34 (2011)

    Article  Google Scholar 

  6. Chen, L.H., Huang, L.: Portfolio optimization of equity mutual funds with fuzzy return rates and risks. Expert Syst. Appl. 36(2), 3720–3727 (2009)

    Article  Google Scholar 

  7. Sundararaj, V.: Optimised denoising scheme via opposition-based self-adaptive learning PSO algorithm for wavelet-based ECG signal noise reduction. Int. J. Biomed. Eng. Technol. 31(4), 325 (2019)

    Article  Google Scholar 

  8. Sundararaj, V., Anoop, V., Dixit, P., Arjaria, A., Chourasia, U., Bhambri, P., Rejeesh, M.R., Sundararaj, R.: CCGPA-MPPT: Cauchy preferential crossover-based global pollination algorithm for MPPT in photovoltaic system. Prog. Photovolt. Res. Appl. 28(11), 1128–1145 (2020)

    Article  Google Scholar 

  9. Ravikumar, S., Kavitha, D.: CNN-OHGS: CNN-oppositional-based Henry gas solubility optimization model for autonomous vehicle control system. J. Field Robot. (2021). https://doi.org/10.1002/rob.22020

    Article  Google Scholar 

  10. Ravikumar, S., Kavitha, D.: IoT based home monitoring system with secure data storage by Keccak–Chaotic sequence in cloud server. J. Ambient Intell. Hum. Comput. (2020). https://doi.org/10.1007/s12652-020-02424-x

    Article  Google Scholar 

  11. Rejeesh, M.R.: Interest point based face recognition using adaptive neuro fuzzy inference system. Multimed. Tools Appl. 78(16), 22691–22710 (2019)

    Article  Google Scholar 

  12. Kavitha, D., Ravikumar, S.: IOT and context-aware learning-based optimal neural network model for real-time health monitoring. Trans. Emerging. Telecommun. Technol. 32(1), e4132 (2021)

    Google Scholar 

  13. Gowthul Alam, M.M., Baulkani, S.: Local and global characteristics-based kernel hybridization to increase optimal support vector machine performance for stock market prediction. Knowl. Inf. Syst. 60(2), 971–1000 (2019)

    Article  Google Scholar 

  14. Gowthul Alam, M.M., Baulkani, S.: Reformulated query-based document retrieval using optimised kernel fuzzy clustering algorithm. Int. J. Bus. Intell. Data Min. 12(3), 299 (2017)

    Google Scholar 

  15. Nisha, S., Madheswari, A.N.: Secured authentication for internet voting in corporate companies to prevent phishing attacks. Int. J. Emerg. Technol. Comput. Sci. Electron. 22(1), 45–49 (2016)

    Google Scholar 

  16. Sundararaj, V., Muthukumar, S., Kumar, R.S.: An optimal cluster formation based energy efficient dynamic scheduling hybrid MAC protocol for heavy traffic load in wireless sensor networks. Comput. Secur. 77, 277–288 (2018)

    Article  Google Scholar 

  17. Vinu, S.: An efficient threshold prediction scheme for wavelet based ECG signal noise reduction using variable step size firefly algorithm. Int. J. Intell. Eng. Syst. 9(3), 117–126 (2016)

    Google Scholar 

  18. Marling, H., Emanuelsson, S.: The Markowitz portfolio theory. November 25, 2012

  19. Francis, J.C., Kim, D.: Modern Portfolio Theory: Foundations, Analysis, and New Developments, p. 795. Wiley, Hoboken (2013)

    Google Scholar 

  20. Ali, M.R., Ma, W.X.: Detection of new multi-wave solutions in an unbounded domain. Mod. Phys. Lett. 33(34), 1950425 (2019)

    Article  MathSciNet  Google Scholar 

  21. Baleanu, D., Sadat, R., Ali, M.R.: The method of lines for solution of the carbon nanotubes engine oil nanofluid over an unsteady rotating disk. Eur. Phys. J. Plus 135(10), 1–13 (2020)

    Article  Google Scholar 

  22. Ali, M.R., Hadhoud, A.R., Ma, W.X.: Evolutionary numerical approach for solving nonlinear singular periodic boundary value problems. J. Intell. Fuzzy Syst. (2020). https://doi.org/10.3233/JIFS-201045

    Article  Google Scholar 

  23. Ali, M.R., Hadhoud, A.R.: Hybrid orthonormal Bernstein and block-pulse functions wavelet scheme for solving the 2D Bratu problem. Results Phys. 12, 525–530 (2019)

    Article  Google Scholar 

  24. Ma, W.X., Ali, M.R., Sadat, R.: Analytical solutions for nonlinear dispersive physical model. Complexity (2020). https://doi.org/10.1155/2020/3714832

    Article  MATH  Google Scholar 

  25. Ali, M.R., Sadat, R.: Lie symmetry analysis, new group invariant for the (3 + 1)-dimensional and variable coefficients for liquids with gas bubbles models. Chin. J. Phys. 71, 539–547 (2021)

    Article  MathSciNet  Google Scholar 

  26. Ali, M.R., Sadat, R.: Construction of lump and optical solitons solutions for (3 + 1) model for the propagation of nonlinear dispersive waves in inhomogeneous media. Opt. Quan. Electron. 53(5), 1–13 (2021)

    Google Scholar 

  27. Sabir, Z., Ali, M.R., Raja, M.A.Z., Shoaib, M., Núñez, R.A.S., Sadat, R.: Computational intelligence approach using Levenberg–Marquardt backpropagation neural networks to solve the fourth-order nonlinear system of Emden–Fowler model. Eng. Comput. (2021). https://doi.org/10.1007/s00366-021-01427-2

    Article  Google Scholar 

  28. Ayub, A., Sabir, Z., Altamirano, G.C., Sadat, R., Ali, M.R.: Characteristics of melting heat transport of blood with time-dependent cross-nanofluid model using Keller–Box and BVP4C method. Eng. Comput. (2021). https://doi.org/10.1007/s00366-021-01406-7

    Article  Google Scholar 

  29. Chen, L., Gao, R., Bian, Y., Di, H.: Elliptic entropy of uncertain random variables with application to portfolio selection. Soft Comput. 25(3), 1925–1939 (2021)

    Article  Google Scholar 

  30. Guo, S., Ching, W.K.: High-order Markov-switching portfolio selection with capital gain tax. Expert Syst. Appl. 165, 113915 (2021)

    Article  Google Scholar 

  31. Moghadam, A., M.R., Mansouri, T., Sheykhizadeh, M.: Markowitz-based cardinality constrained portfolio selection using Asexual Reproduction Optimization (ARO). arXiv Preprint. https://arxiv.org/abs/2101.03312 (2021)

  32. Yaman, I., Dalkılıç, T.E.: A hybrid approach to cardinality constraint portfolio selection problem based on nonlinear neural network and genetic algorithm. Expert Syst. Appl. 169, 114517 (2021)

    Article  Google Scholar 

  33. Mohammed, H.J.: The optimal project selection in portfolio management using fuzzy multi-criteria decision-making methodology. J. Sustain. Finan. Invest. (2021). https://doi.org/10.1080/20430795.2021.1886551

    Article  Google Scholar 

  34. Lampariello, L., Neumann, C., Ricci, J.M., Sagratella, S., Stein, O.: Equilibrium selection for multi-portfolio optimization. Eur. J. Oper. Res. (2021). https://doi.org/10.1016/j.frl.2021.102019

    Article  MathSciNet  MATH  Google Scholar 

  35. Joo, Y.C., Park, S.Y.: Optimal portfolio selection using a simple double-shrinkage selection rule. Finan. Res. Lett. 43, 102019 (2021)

    Article  Google Scholar 

  36. Amin, G.R., Hajjami, M.: Improving DEA cross-efficiency optimization in portfolio selection. Expert Syst. Appl. 168, 114280 (2021)

    Article  Google Scholar 

  37. Akbaş, S., Dalkiliç, T.E.: A hybrid algorithm for portfolio selection: an application on the Dow Jones Index (DJI). J. Comput. Appl. Math. 398, 113678 (2021)

    Article  MathSciNet  Google Scholar 

  38. Zolfaghari, S., Mousavi, S.M.: A novel mathematical programming model for multi-mode project portfolio selection and scheduling with flexible resources and due dates under interval-valued fuzzy random uncertainty. Expert Syst. Appl. 182, 115207 (2021)

    Article  Google Scholar 

  39. Tavana, M., Khosrojerdi, G., Mina, H., Rahman, A.: A hybrid mathematical programming model for optimal project portfolio selection using fuzzy inference system and analytic hierarchy process. Eval. Progam Plan. 77, 101703 (2019)

    Article  Google Scholar 

  40. Zhang, Y., Liu, W., Yang, X.: An automatic trading system for fuzzy portfolio optimization problem with sell orders. Expert Syst. Appl. 187, 115822 (2022)

    Article  Google Scholar 

  41. Bai, Z., Liu, H., Wong, W.K.: On the Markowitz mean–variance analysis of self-financing portfolios. Risk Decis. Anal. 1(1), 35–42 (2009)

    Article  Google Scholar 

  42. Steinbach, M.C.: Markowitz revisited: mean-variance models in financial portfolio analysis. SIAM Rev. 43(1), 31–85 (2001)

    Article  MathSciNet  Google Scholar 

  43. Yue, W., Wang, Y., Peng, Z.: A multi-objective evolutionary algorithm for fuzzy mean-variance-entropy portfolio models with transaction cost and liquidity. J. Comput. 29(4), 39–56 (2018)

    Google Scholar 

  44. Wang, S., Zhu, S.: On fuzzy portfolio selection problems. Fuzzy Optim. Decis. Mak. 1(4), 361–377 (2002)

    Article  MathSciNet  Google Scholar 

  45. Konno, H., Yamazaki, H.: Mean-absolute deviation portfolio optimization model and its applications to Tokyo stock market. Manag. Sci. 37(5), 519–531 (1991)

    Article  Google Scholar 

  46. Yue, W., Wang, Y., Xuan, H.: Fuzzy multi-objective portfolio model based on semi-variance–semi-absolute deviation risk measures. Soft Comput. 23(17), 8159–8179 (2019)

    Article  Google Scholar 

  47. Naruei, I., Keynia, F.: A new optimization method based on coot bird natural life model. Expert Syst. Appl. 183, 115352 (2021)

    Article  Google Scholar 

  48. Çelik, E., Öztürk, N., Arya, Y.: Advancement of the search process of salp swarm algorithm for global optimization problems. Expert Syst. Appl. 182, 115292 (2021)

    Article  Google Scholar 

  49. Sefiane, S., Benbouziane, M.: Portfolio selection using genetic algorithm. J. Appl. Finance Banking 2(4), 143–154 (2012)

    Google Scholar 

  50. Golmakani, H.R., Fazel, M.: Constrained portfolio selection using particle swarm optimization. Expert Syst. Appl. 38(7), 8327–8335 (2011)

    Article  Google Scholar 

  51. Khan, A.Q., Ikram, S., Mehtab, M.: Testing weak form market efficiency of Indian capital market: a case of national stock exchange (NSE) and Bombay stock exchange (BSE). Afr. J. Mark. Manag. 3(6), 115–127 (2011)

    Google Scholar 

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  1. Department of Mathematics, Maulana Azad National Institute of Technology, Bhopal, India

    Jagdish Kumar Pahade & Manoj Jha

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  1. Jagdish Kumar Pahade
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  2. Manoj Jha
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Correspondence to Jagdish Kumar Pahade.

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Pahade, J.K., Jha, M. A Hybrid Fuzzy-SCOOT Algorithm to Optimize Possibilistic Mean Semi-absolute Deviation Model for Optimal Portfolio Selection. Int. J. Fuzzy Syst. 24, 1958–1973 (2022). https://doi.org/10.1007/s40815-022-01251-w

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  • DOI: https://doi.org/10.1007/s40815-022-01251-w

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