SpringerLink
Log in

Portfolio Rebalancing Model Utilizing Support Vector Machine for Optimal Asset Allocation

  • Research Article-Systems Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Achieving an optimal asset allocation strategy is critical for investors aiming to maximize returns while managing risk in a dynamic financial market. This research presents a portfolio rebalancing model that leverages the power of Support Vector Machine (SVM) algorithms to optimize asset allocation decisions and enhance portfolio performance. For this purpose, initially, the assets are classified based on the SVM technique to identify the most favorable assets. Secondly, a widely accepted mean–variance optimization technique is employed by this method to balance risk and return objectives, accommodating diverse transaction costs, time horizons, and other realistic conditions, thus facilitating the selection of assets and their respective weights for both initial investment and the rebalancing of the existing portfolio. Further, to assess the effectiveness of the portfolio rebalancing model incorporating SVM, comprehensive historical testing and performance analyses are conducted using historical data from the Indian Stock Market. This evaluation is contrasted with outcomes derived from other prevalent machine learning techniques such as K-Nearest Neighbors, Naive Bayes, Random Forest, and Decision Tree. By comparing the results from different methodologies, the study highlights the superior efficacy of the SVM-based portfolio rebalancing model in achieving optimal asset allocation in a dynamic financial landscape.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
EUR 32.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or Ebook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (Canada)

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Algorithm 1
Algorithm 2
Algorithm 3
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20

Similar content being viewed by others

Data Availability

This research has not used any data.

References

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

    Google Scholar 

  2. Sharpe, W.F.: Mean-absolute-deviation characteristic lines for securities and portfolios. Manag. Sci. 18(2), 1 (1971)

    Article  Google Scholar 

  3. Speranza, M.G.: Linear programming models for portfolio optimization. Finance 14, 107–123 (1993)

    Google Scholar 

  4. Li, Z.; Yao, J.; Li, D.: Behavior patterns of investment strategies under Roy’s safety-first principle. Q. Rev. Econ. Finance 50(2), 167–179 (2010)

    Article  Google Scholar 

  5. Qin, Z.: Mean–variance model for portfolio optimization problem in the simultaneous presence of random and uncertain returns. Eur. J. Oper. Res. 245(2), 480–488 (2015)

    Article  MathSciNet  Google Scholar 

  6. Hung, K.; Yang, C.; Zhao, Y.; Lee, K.-H.: Risk return relationship in the portfolio selection models. Theor. Econ. Lett. 8(3), 358–366 (2018)

    Article  Google Scholar 

  7. Abensur, E.O.; Moreira, D.F.; De Faria, A.C.R.: Geometric Brownian motion: an alternative to high-frequency trading for small investors. Indep. J. Manag. Prod. 11(3), 1434–1453 (2020)

    Article  Google Scholar 

  8. Kumar, P.; Rani, B.S.; B., Bhurjee, A.: Multi-objective portfolio selection problem using admissible order vector space. In: AIP Conference Proceedings, vol. 2516. AIP Publishing (2022)

  9. Liu, S.; Wang, B.; Li, H.; Chen, C.; Wang, Z.: Continual portfolio selection in dynamic environments via incremental reinforcement learning. Int. J. Mach. Learn. Cybern. 14(1), 269–279 (2023)

    Article  CAS  Google Scholar 

  10. Best, M.J.; Hlouskova, J.: Portfolio selection and transactions costs. Comput. Optim. Appl. 24, 95–116 (2003)

    Article  MathSciNet  Google Scholar 

  11. Best, M.J.; Hlouskova, J.: An algorithm for portfolio optimization with transaction costs. Manag. Sci. 51(11), 1676–1688 (2005)

    Article  Google Scholar 

  12. Best, M.J.; Hlouskova, J.: An algorithm for portfolio optimization with variable transaction costs, part 1: theory. J. Optim. Theory Appl. 135(3), 563–581 (2007)

    Article  MathSciNet  Google Scholar 

  13. Best, M.J.; Hlouskova, J.: An algorithm for portfolio optimization with variable transaction costs, part 2: computational analysis. J. Optim. Theory Appl. 135(3), 531–547 (2007)

    Article  MathSciNet  Google Scholar 

  14. Patel, N.R.; Subrahmanyam, M.G.: A simple algorithm for optimal portfolio selection with fixed transaction costs. Manag. Sci. 28(3), 303–314 (1982)

    Article  MathSciNet  Google Scholar 

  15. Guastaroba, G.; Mansini, R.; Speranza, M.G.: Models and simulations for portfolio rebalancing. Comput. Econ. 33, 237–262 (2009)

    Article  Google Scholar 

  16. Fang, Y.; Lai, K.K.; Wang, S.-Y.: Portfolio rebalancing model with transaction costs based on fuzzy decision theory. Eur. J. Oper. Res. 175(2), 879–893 (2006)

    Article  Google Scholar 

  17. Lim, Q.Y.E.; Cao, Q.; Quek, C.: Dynamic portfolio rebalancing through reinforcement learning. Neural Comput. Appl. 34(9), 7125–7139 (2022)

    Article  Google Scholar 

  18. Woodside-Oriakhi, M.; Lucas, C.; Beasley, J.E.: Portfolio rebalancing with an investment horizon and transaction costs. Omega 41(2), 406–420 (2013)

    Article  Google Scholar 

  19. Kumar, P.; Panda, G.; Gupta, U.: Portfolio rebalancing model with transaction costs using interval optimization. Opsearch 52, 827–860 (2015)

    Article  MathSciNet  Google Scholar 

  20. Horn, M.; Oehler, A.: Automated portfolio rebalancing: automatic erosion of investment performance? J. Asset Manag. 21, 489–505 (2020)

    Article  Google Scholar 

  21. Emamat, M.S.M.M.; Mota, C.M.D.M.; Mehregan, M.R.; Sadeghi Moghadam, M.R.; Nemery, P.: Using electre-tri and flowsort methods in a stock portfolio selection context. Financial Innov. 8(1), 1–35 (2022)

    Article  Google Scholar 

  22. Zhang, W.; Li, B.; Liew, A.W.-C.; Roca, E.; Singh, T.: Predicting the returns of the us real estate investment trust market: evidence from the group method of data handling neural network. Financial Innov. 9(1), 98 (2023)

  23. Gupta, P.; Mehlawat, M.K.; Mittal, G.: Asset portfolio optimization using support vector machines and real-coded genetic algorithm. J. Glob. Optim. 53, 297–315 (2012)

    Article  MathSciNet  Google Scholar 

  24. Gupta, P.; Mehlawat, M.K.; Inuiguchi, M.; Chandra, S.; Gupta, P.; Mehlawat, M.K.; Inuiguchi, M.; Chandra, S.: Multi-criteria portfolio optimization using support vector machines and genetic algorithms. In: Fuzzy Portfolio Optimization: Advances in Hybrid Multi-criteria Methodologies, pp. 283–309 (2014)

  25. Ma, Y.; Han, R.; Wang, W.: Prediction-based portfolio optimization models using deep neural networks. Ieee Access 8, 115393–115405 (2020)

    Article  Google Scholar 

  26. Chen, W.; Zhang, H.; Mehlawat, M.K.; Jia, L.: Mean–variance portfolio optimization using machine learning-based stock price prediction. Appl. Soft Comput. 100, 106943 (2021)

    Article  Google Scholar 

  27. Chaweewanchon, A.; Chaysiri, R.: Markowitz mean–variance portfolio optimization with predictive stock selection using machine learning. Int. J. Financial Stud. 10(3), 64 (2022)

    Article  Google Scholar 

  28. Behera, J.; Pasayat, A.K.; Behera, H.; Kumar, P.: Prediction based mean-value-at-risk portfolio optimization using machine learning regression algorithms for multi-national stock markets. Eng. Appl. Artif. Intell. 120, 105843 (2023)

    Article  Google Scholar 

  29. Faridi, S.; Madanchi Zaj, M.; Daneshvar, A.; Shahverdiani, S.; Rahnamay Roodposhti, F.: Portfolio rebalancing based on a combined method of ensemble machine learning and genetic algorithm. J. Financial Report. Account. 21(1), 105–125 (2023)

    Article  Google Scholar 

Download references

Acknowledgements

The authors express their sincere appreciation to the referee for their rigorous review and valuable insights, which significantly improved the quality and clarity of the manuscript. The referee’s expertise and thoughtful feedback have been instrumental in enhancing the overall scholarly impact of our work.

Funding

This research has no funding by any organization or individual.

Author information

Authors and Affiliations

  1. Department of Mathematics, College of Engineering and Technology, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, 603203, India

    B. R. B. Sahu & P. Kumar

  2. DoMSC, National Institute of Technology Hamirpur, Hamirpur, HP, 177005, India

    P. Kumar

Authors
  1. B. R. B. Sahu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. P. Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to P. Kumar.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Appendix

Appendix

See Tables 8, 9, 10, 11, 12, 13, and 14.

Table 8 Return and variance of 50 assets
Full size table
Table 9 Parameters for Portfolio rebalancing optimization problem
Full size table
Table 10 SVM degree 2 \(+1\) class selected assets for different time horizons
Full size table
Table 11 DT \(+1\) class selected assets for different time horizons
Full size table
Table 12 KNN \(+1\) class selected assets for different time horizons
Full size table
Table 13 NB \(+1\) class selected assets for different time horizons
Full size table
Table 14 RF \(+1\) class selected assets for different time horizons
Full size table

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sahu, B.R.B., Kumar, P. Portfolio Rebalancing Model Utilizing Support Vector Machine for Optimal Asset Allocation. Arab J Sci Eng (2024). https://doi.org/10.1007/s13369-024-08850-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s13369-024-08850-9

Keywords

Search

Navigation