Skip to main content
SpringerLink
Log in

A self-adaptive preference model based on dynamic feature analysis for interactive portfolio optimization

  • Original Article
  • Published:
International Journal of Machine Learning and Cybernetics Aims and scope Submit manuscript

Abstract

In financial markets, there are various assets to invest in. Recognizing an investor’s preferences is key to selecting a combination of assets that best serves his or her needs. Considering the mean–variance model for the portfolio optimization problem, this paper proposes an interactive multicriteria decision-making method and explores a self-adaptive preference model based on dynamic feature analysis (denoted RFFS-DT) to capture the decision maker (DM)’s complex preferences in the decision-making process. RFFS-DT recognizes the DM’s preference impact factor and constructs a preference model. To recognize the impact factors of the DM’s preferences, which could change during the decision-making process, three categories of possible features involved in three aspects of the mean–variance model are defined, and a feature selection method based on random forest is designed. Because the DM’s preference structure could be unknown a priori, a decision-tree-based preference model is built and updated adaptively according to the DM’s preference feedback and the selected features. The effectiveness of RFFS-DT for interactive multicriteria decision making is verified by a series of deliberately designed comparative experiments.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Markowitz H (1952) Portfolio selection. J Financ 7(1):77–91

    Google Scholar 

  2. Markowitz H (1959) Portfolio selection: efficient diversification of investments. John Wiley and Sons, New York

    Google Scholar 

  3. Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multi-objective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197

    Google Scholar 

  4. Zhang Q, Li H (2007) MOEA/D: a multi-objective evolutionary algorithm based on decomposition. IEEE Trans Evol Comput 11(6):712–731

    Google Scholar 

  5. Xin B, Chen L, Chen J, Ishibuchi H, Hirota K, Liu B (2018) Interactive multiobjective optimization: a review of the state-of-the-art. IEEE Access 6:41256–41279

    Google Scholar 

  6. Bonissone PP, Subbu R, Lizzi J (2009) Multi-criteria decision making: a framework for research and applications. IEEE Comput Intell Mag 4(3):48–61

    Google Scholar 

  7. Ozbey O, Karwan MH (2014) An interactive approach for multicriteria decision making using a Tchebycheff Utility function approximation. J Multi-Criteria Decis Anal 21(3–4):153–172

    Google Scholar 

  8. Ruiz AB, Saborido R, Luque M (2015) A preference-based evolutionary algorithm for multi-objective optimization: the weighting achievement scalarizing function genetic algorithm. J Global Optim 62(1):101–129

    MathSciNet  MATH  Google Scholar 

  9. Li L, Wang Y, Trautmann H, Jing N, Emmerich M (2018) Multi-objective evolutionary algorithms based on target region preferences. Swarm Evol Comput 40:196–215

    Google Scholar 

  10. Li L, Yevseyeva I, Basto-Fernandes V, Trautmann H, Jing N (2017) Building and using an ontology of preference-based multiobjective evolutionary algorithms. Int Conf Evol Multi-Criterion Optim 10173:406–421

    Google Scholar 

  11. Köksalan M, Wallenius J, Zionts S (2013) An early history of multiple criteria decision making. J Multi-Criteria Decis Anal 20(1–2):87–94

    Google Scholar 

  12. Phelps SP, Koksalan M (2003) An interactive evolutionary metaheuristic for multi-objective combinatorial optimization. Manag Sci 49(12):1726–1738

    MATH  Google Scholar 

  13. Branke J, Corrente S, Greco S, Słowi´nski R, Zielniewicz P (2016) Using choquet integral as preference model in interactive evolutionary multiobjective optimization. Eur J Oper Res 250(3):884–901

    MathSciNet  MATH  Google Scholar 

  14. Pedro LR, Takahashi RHC (2014) INSPM: an interactive evolutionary multi-objective algorithm with preference model. Inf Sci 268(1):202–219

    Google Scholar 

  15. Greco S, Ehrgott M, Figueira JR (2016) Multiple criteria decision analysis state of the art surveys. Springle, New York

    MATH  Google Scholar 

  16. Miettinen K, Mäkelä MM (2002) On scalarizing functions in multiobjective optimization. OR Spectr 24(2):193–213

    MathSciNet  MATH  Google Scholar 

  17. Deb K, Sundar J (2006) Reference point based multi-objective optimization using evolutionary algorithms. Proceedings of the 8th ACM annual conference on Genetic and evolutionary computation pp. 635–642

  18. Klamroth K, Miettinen K (2008) Integrating approximation and interactive decision making in multicriteria optimization. Oper Res 56(1):222–234

    MathSciNet  MATH  Google Scholar 

  19. Wang R, Purshouse RC, Fleming PJ (2013) Preference-inspired coevolutionary algorithms for many-objective optimization. IEEE Trans Evol Comput 17(4):474–494

    Google Scholar 

  20. Goulart F, Campelo F (2016) Preference-guided evolutionary algorithms for many-objective optimization. Inf Sci 329:236–255

    Google Scholar 

  21. Miettinen K, Mäkelä MM (2006) Synchronous approach in interactive multi-objective optimization. Eur J Oper Res 170(3):909–922

    MATH  Google Scholar 

  22. Miettinen K, Mäkelä MM, Kaario K (2006) Experiments with classification-based scalarizing functions in interactive multi-objective optimization. Eur J Oper Res 175(2):931–947

    MATH  Google Scholar 

  23. Luque M, Miettinen K, Eskelinen P, Ruiz F (2009) Incorporating preference information in interactive reference point methods for multi-objective optimization. Omega 37(2):450–462

    Google Scholar 

  24. Vallerio M, Hufkens J, Van IJ, Logist F (2015) An interactive decision-support system for multi-objective optimization of nonlinear dynamic processes with uncertainty. Expert Syst Appl 42(21):7710–7731

    Google Scholar 

  25. Battiti R, Passerini A (2010) Brain-computer evolutionary multi-objective optimization: a genetic algorithm adapting to the decision maker. IEEE Trans Evol Comput 14(5):671–687

    Google Scholar 

  26. Larichev O (1992) Cognitive validity in design of decision aiding techniques. J Multi-Criteria Decis Anal 1(3):127–138

    MATH  Google Scholar 

  27. Pascoletti A, Serafini P (1984) Scalarizing vector optimization problems. J Optim Theory Appl 42(4):499–524

    MathSciNet  MATH  Google Scholar 

  28. Deb K, Sinha A, Korhonen PJ, Wallenius J (2010) An interactive evolutionary multi-objective optimization method based on progressively approximated value functions. IEEE Trans Evol Comput 14(5):723–739

    Google Scholar 

  29. Mukhlisullina D, Passerini A, Battiti R (2013) Learning to diversify in complex interactive multi-objective optimization. Proceedings of the 10th Metaheuristics International Conference, Singapore Management University pp. 230–239

  30. Sun M, Stam A, Steuer RE (2000) Interactive multiple objective programming using tchebycheff programs and artificial neural networks. Comput Oper Res 27(7):601–620

    MATH  Google Scholar 

  31. Sun M, Stam A, Steuer RE (1996) Solving multiple objective programming problems using feed-forward artificial neural networks: the interactive FFANN procedure. Manag Sci 42(6):835–849

    MATH  Google Scholar 

  32. Huang HZ, Tian Z, Zuo MJ (2005) Intelligent interactive multi-objective optimization method and its application to reliability optimization. IIE Trans 37(11):983–993

    Google Scholar 

  33. Zhang H, Llorca J, Davis CC et al (2012) Nature-inspired self-organization, control, and optimization in heterogeneous wireless networks. IEEE Trans Mob Comput 11(7):1207–1222

    Google Scholar 

  34. Zhang H, Cao X, Ho JKL et al (2017) Object-level video advertising: an optimization framework. IEEE Trans Industr Inf 13(2):520–531

    Google Scholar 

  35. Qamar N, Akhtar N, Younas I (2018) Comparative analysis of evolutionary algorithms for multi-objective travelling salesman problem. Int J Adv Comput Sci Appl 9(2):371–379

    Google Scholar 

  36. Masmoudi M, Abdelaziz FB (2018) Portfolio selection problem: a review of deterministic and stochastic multiple objective programming models. Ann Oper Res 267(1–2):335–352

    MathSciNet  MATH  Google Scholar 

  37. Källblad S (2017) Risk-and ambiguity-averse portfolio optimization with quasicon-cave utility functionals. Financ Stoch 21(2):397–425

    MATH  Google Scholar 

  38. Lwin KT (2015) Evolutionary approaches for portfolio optimization. Dissertation, University of Nottingham

  39. Gutjahr WJ, Pichler A (2016) Stochastic multi-objective optimization: a survey on non-scalarizing methods. Ann Oper Res 236(2):475–499

    MathSciNet  MATH  Google Scholar 

  40. Brucker P, Knust S (2012) Resource-constrained project scheduling in: complex scheduling. GOR-Publications

  41. Miettinen K, Ruiz F, Wierzbicki AP (2008) Introduction to multiobjective optimization: interactive approaches. In: Multiobjective Optimization, pp 27–57

  42. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

    MATH  Google Scholar 

  43. Biau G, Devroye L, Lugosi G (2008) Consistency of random forests and other averaging classifiers. J Mach Learn Res 9(1):2015–2033

    MathSciNet  MATH  Google Scholar 

  44. Mentch L, Hooker G (2014) Quantifying uncertainty in random forests via confidence intervals and hypothesis tests. J Mach Learn Res 17(1):841–881

    MathSciNet  MATH  Google Scholar 

  45. Verikas A, Gelzinis A, Bacauskiene M (2011) Mining data with random forests: a survey and results of new tests. Pattern Recogn 44(2):330–349

    Google Scholar 

  46. Marill T, Green D (1963) On the effectiveness of receptors in recognition systems. IEEE Trans Inf Theory 9(1):11–17

    Google Scholar 

  47. Grabczewski K (2014) Meta-learning in decision tree induction. Springer International Publishing, Berlin

    Google Scholar 

  48. Safavin SR, Landgrebe D (1991) A survey of decision tree classifier methodology. IEEE Trans Syst Man Cybern 21(3):660–674

    MathSciNet  Google Scholar 

  49. Zhang H, Singer BH (2010) Recursive partitioning and applications. Springer, New York

    MATH  Google Scholar 

  50. Breiman L, Friedman J, Olshen R, Stone C (1984) Classification and regression trees. Chapman & Hall, New York

    MATH  Google Scholar 

  51. Wang R, Kwong S, Wang XZ, Jiang QS (2015) Segment based decision tree induction with continuous valued attributes. IEEE Trans Cybern 45(7):1262–1275

    Google Scholar 

  52. Bosman PAN, Thierens D (2003) The balance between proximity and diversity in multi-objective evolutionary algorithms. IEEE Trans Evol Comput 7(2):174–188

    Google Scholar 

  53. Fowler JW, Gel ES, Koksalan MM, Korhonen P, Marquis JL, Wallenius J (2010) Interactive evolutionary multi-objective optimization for quasi-concave preference functions. Eur J Oper Res 206(2):417–425

    MATH  Google Scholar 

  54. Sinha A, Korhonen P, Wallenius J, Deb K (2010) An interactive evolutionary multi-objective optimization method based on polyhedral cones. Learn Intell Optim 6073(3):318–332

    Google Scholar 

  55. Koksalan M, Karahan I (2010) An interactive territory defining evolutionary algorithm: iTDEA. IEEE Trans Evol Comput 14(5):702–722

    Google Scholar 

  56. Goulart F, Campelo F (2016) Preference-guided evolutionary algorithms for many-objective optimization. Inf Sci 329:236–255

    Google Scholar 

  57. Saaty TL, Vargas LG (2017) Models, methods, concepts & applications of the analytic hierarchy process. International 7(2):159–172

    Google Scholar 

  58. Wang R, Wang XZ, Kwong S, Xu C (2017) Incorporating diversity and informativeness in multiple-instance active learning. IEEE Trans Fuzzy Syst 25(6):1460–1475

    Google Scholar 

  59. Du B, Wang ZM, Zhang LF, Zhang LP, Liu W, Shen JL, Tao DC (2015) Exploring representativeness and informativeness for active learning. IEEE Trans Cybern 47(1):1–13

    Google Scholar 

  60. Wang R, Chow CY, Kwong S (2016) Ambiguity based multiclass active learning. IEEE Trans Fuzzy Syst 24(1):242–248

    Google Scholar 

  61. Samat A, Gamba P, Liu SC (2018) Fuzzy multiclass active learning for hyperspectral image classification. IET Image Proc 12(7):1–7

    Google Scholar 

  62. Wang XZ, Wang R, Xu C (2018) Discovering the relationship between generalization and uncertainty by incorporating complexity of classification. IEEE Trans Cybern 48(2):703–715

    MathSciNet  Google Scholar 

  63. Wang XZ, Xing HJ, Li Y, Hua Q, Dong CR, Pedrycz W (2015) A study on relationship between generalization abilities and fuzziness of base classifiers in ensemble learning. IEEE Trans Fuzzy Syst 23(5):1638–1854

    Google Scholar 

Download references

Acknowledgments

The authors thank the referee for his or her comments on the manuscript. This work was supported in part by the NSFS under Grant No. ZR2016FM27and by the NSFC under Grant No. 61702139.

Author information

Authors and Affiliations

  1. School of Economics and Management, Harbin Institute of Technology, No. 2 West Wenhua Road, Weihai, 264209, China

    Shicheng Hu, Fang Li & Song Wang

  2. School of Computer Science and Technology, Harbin Institute of Technology, No. 2 West Wenhua Road, Weihai, 264209, China

    Yang Liu

Authors
  1. Shicheng Hu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Fang Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Song Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shicheng Hu.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix

Appendix

See Table 9.

Table 9 The 18 selected stocks and their returns
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hu, S., Li, F., Liu, Y. et al. A self-adaptive preference model based on dynamic feature analysis for interactive portfolio optimization. Int. J. Mach. Learn. & Cyber. 11, 1253–1266 (2020). https://doi.org/10.1007/s13042-019-01036-y

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13042-019-01036-y

Keywords

Search

Navigation