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Stock portfolio optimization based on factor analysis and second-order memetic differential evolution algorithm

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Abstract

Portfolio optimization will apply the concept of diversification across asset classes, which means investing in a wide variety of asset types and classes for a risk-mitigation strategy. Portfolio optimization is a way to maximize net gains in a portfolio while minimizing risk. A portfolio means investing in a wide variety of asset types and classes for a risk-mitigation strategy by the investor. In this paper, factor analysis and cluster algorithm are used to screen stocks and an improved differential evolution algorithm for solving portfolio optimization model is proposed. By comprehensively analyzing the stock data with factor analysis and k-means clustering algorithm, it has found that important factors have important effect on stock price movement, and finally 10 stocks are selected with investment value. Besides, a Mean-Conditional Value at Risk (CVaR) model is constructed, which takes into account both the cost function and the diversification constraint. Finally, a second-order memetic differential evolution (SOMDE) algorithm is presented for solving the proposed model. The experiments show that the proposed SOMDE algorithm is valid for solving the Mean-CVaR model and that factor analysis for stock selection can benefit portfolio with higher return and less risk greatly.

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References

  1. Markowitz HM (1952) Portfolio selection. J Finance 7(1):77

    Google Scholar 

  2. Best MJ, Grauer RR (1991) Sensitivity analysis for mean-variance portfolio problems. Manag Sci 37(8):980–989

    Article  Google Scholar 

  3. Zhou XY, Li D (2000) Continuous-time mean-variance portfolio selection: a stochastic LQ framework. Appl Math Optim 42(1):19–33

    Article  MathSciNet  Google Scholar 

  4. Cura T (2009) Particle swarm optimization approach to portfolio optimization. Nonlinear Anal Real World Appl 10(4):2396–2406

    Article  MathSciNet  Google Scholar 

  5. Lwin KT, Qu R, MacCarthy BL (2017) Mean-VaR portfolio optimization: a nonparametric approach. Eur J Oper Res 260(2):751–766

    Article  MathSciNet  Google Scholar 

  6. Czaplewski L, Bax R, Clokie M et al (2016) Alternatives to antibiotics-a pipeline portfolio review. Lancet Infect Dis 16(2):239–251

    Article  CAS  PubMed  Google Scholar 

  7. Brandt M W (2010). Portfolio choice problems. Handbook of financial econometrics: tools and techniques. North-Holland. pp 269–336

  8. Devereux MB, Sutherland A (2010) Country portfolio dynamics. J Econ Dyn Control 34(7):1325–1342

    Article  MathSciNet  Google Scholar 

  9. Huberman G, Kandel S (1987) Mean-variance spanninge. J Finance 42(4):873–888

    MathSciNet  Google Scholar 

  10. Markowitz H (2014) Mean-variance approximations to expected utility. Eur J Oper Res 234(2):346–355

    Article  MathSciNet  Google Scholar 

  11. Jorion P (1996) Measuring the risk in value at risk. Financ Anal J 52(6):47–56

    Article  Google Scholar 

  12. Duffie D, Pan J (1997) An overview of value at risk. J Deriv 4(3):7–49

    Article  Google Scholar 

  13. Rockafellar RT, Uryasev S (2000) Optimization of conditional value-at-risk. J Risk 2:21–42

    Article  Google Scholar 

  14. Rockafellar RT, Uryasev S (2002) Conditional value-at-risk for general loss distributions. J Bank Finance 26(7):1443–1471

    Article  Google Scholar 

  15. Alexander GJ, Baptista AM (2004) A comparison of VaR and CVaR constraints on portfolio selection with the mean-variance model. Manag Sci 50(9):1261–1273

    Article  Google Scholar 

  16. Hong LJ, Hu Z, Liu G (2014) Monte Carlo methods for value-at-risk and conditional value-at-risk: a review. ACM Trans Model Comput Simulat (TOMACS) 24(4):1–37

    MathSciNet  Google Scholar 

  17. Boudt K, Carl P, Peterson BG (2012) Asset allocation with conditional value-at-risk budgets. J Risk 15(3):39–68

    Article  Google Scholar 

  18. Cui X, Gao J, Shi Y et al (2019) Time-consistent and self-coordination strategies for multi-period mean-conditional value-at-risk portfolio selection. Eur J Oper Res 276(2):781–789

    Article  MathSciNet  Google Scholar 

  19. Chen Y, Zhao X, Yuan J (2022) Swarm intelligence algorithms for portfolio optimization problems: overview and recent advances. Mobile Inform Syst 15:2022

    Google Scholar 

  20. Chen Y, Ye L, Li R et al (2023) A multi-period constrained multi-objective evolutionary algorithm with orthogonal learning for solving the complex carbon neutral stock portfolio optimization model. J Syst Sci Complexity 36(2):686–715

    Article  MathSciNet  Google Scholar 

  21. Chen Y, Zhao X, Hao J (2023) A novel MOPSO-SODE algorithm for solving three-objective SR-ES-TR portfolio optimization problem. Expert Syst Appl 14:120742

    Article  Google Scholar 

  22. Ertenlice O, Kalayci CB (2018) A survey of swarm intelligence for portfolio optimization: algorithms and applications. Swarm Evol Comput 39:36–52

    Article  Google Scholar 

  23. Zhu H, Wang Y, Wang K et al (2011) Particle swarm optimization (PSO) for the constrained portfolio optimization problem. Expert Syst Appl 38(8):10161–10169

    Article  Google Scholar 

  24. Chen W, Zhang WG (2010) The admissible portfolio selection problem with transaction costs and an improved PSO algorithm. Physica A 389(10):2070–2076

    Article  ADS  Google Scholar 

  25. Chang TJ, Yang SC, Chang KJ (2009) Portfolio optimization problems in different risk measures using genetic algorithm. Expert Syst Appl 36(7):10529–10537

    Article  Google Scholar 

  26. Das S, Mullick SS, Suganthan PN (2016) Recent advances in differential evolution–an updated survey. Swarm Evolut Computat 27:1–30

    Article  Google Scholar 

  27. Zhao X, Feng S, Hao J et al (2021) Neighborhood opposition-based differential evolution with Gaussian perturbation. Soft Comput 25(1):27–46

    Article  Google Scholar 

  28. Krink T, Paterlini S (2011) Multiobjective optimization using differential evolution for real-world portfolio optimization. CMS 8(1):157–179

    Article  MathSciNet  Google Scholar 

  29. Zhao X, Xu G, Liu D et al (2017) Second-order DE algorithm. CAAI Trans Intell Technol 2(2):80–92

    Article  Google Scholar 

  30. Noman N, Iba H (2008) Accelerating differential evolution using an adaptive local search. IEEE Trans Evol Comput 12(1):107–125

    Article  Google Scholar 

  31. Li R, Zhao X, Zuo X et al (2021) Memetic algorithm with non-smooth penalty for capacitated arc routing problem. Knowl-Based Syst 220:106957

    Article  Google Scholar 

  32. Rummel RJ (1988) Applied factor analysis. Northwestern University Press

  33. Valadkhani A, Chancharat S, Harvie C (2008) A factor analysis of international portfolio diversification. Stud Econ Finance. 25:165–174

    Article  Google Scholar 

  34. Bholowalia P, Kumar A (2014) A clustering technique based on elbow method and K-Means in WSN. Int J Comput Appl 105:9

    Google Scholar 

  35. Chung KL (1967) Markov chains. Springer-Verlag, New York

    Book  Google Scholar 

  36. Lobo MS, Fazel M, Boyd S (2007) Portfolio optimization with linear and fixed transaction costs. Ann Oper Res 152(1):341–365

    Article  MathSciNet  Google Scholar 

  37. Fang CD, Wei ZX, Zhang MY (2015) The portfolio model with typical transaction cost based on CVaR. J Guangxi Univ 40:1611

    Google Scholar 

  38. Hoskisson RE, Hitt MA, Johnson RA et al (1993) Construct validity of an objective (entropy) categorical measure of diversification strategy. Strateg Manag J 14(3):215–235

    Article  Google Scholar 

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (61973042) and Beijing Natural Science Foundation (1202020). We will express our awfully thanks to the Swarm Intelligence Research Team of Beijing University of Posts and Telecommunications.

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Authors and Affiliations

Authors

Contributions

Han Ning: Conceptualization, Methodology, Software, Validation, Writing Original Draft, Writing - Review & Editing, Data Curation Yinnan Chen: Methodology, Resources, Investigation, Writing - Review & Editing Lingjuan Ye: Methodology, Validation, Formal analysis, Review & Editing Xinchao Zhao: Conceptualization, Writing - Original Draft, Writing - Review & Editing, Project administration, Funding acquisition

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Correspondence to Lingjuan Ye.

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Appendix

Appendix

See Table 

Table 16 Stock code and original indicator complete data

16

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Han, N., Chen, Y., Ye, L. et al. Stock portfolio optimization based on factor analysis and second-order memetic differential evolution algorithm. Memetic Comp. 16, 29–44 (2024). https://doi.org/10.1007/s12293-024-00405-7

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