Skip to main content

Advertisement

SpringerLink
Log in

Asset portfolio optimization using support vector machines and real-coded genetic algorithm

  • Published:
Journal of Global Optimization Aims and scope Submit manuscript

Abstract

This paper presents an integrated approach for portfolio selection in a multicriteria decision making framework. Firstly, we use Support Vector Machines for classifying financial assets in three pre-defined classes, based on their performance on some key financial criteria. Next, we employ Real-Coded Genetic Algorithm to solve a mathematical model of the multicriteria portfolio selection problem in the respective classes incorporating investor-preferences.

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

Similar content being viewed by others

References

  1. AitSahlia F., Pardalos P.M., Sheu Y-C.F.: Optimal execution of time-constrained portfolio transactions. In: Konthoghiorges, E.J., Rustem, B., Winker, P. (eds) Computational Methods in Financial Engineering, pp. 95–102. Springer, UK (2008)

    Chapter  Google Scholar 

  2. Arenas Parra M., Bilbao Terol A., Rodríguez Uría M.V.: A fuzzy goal programming approach to portfolio selection. Eur. J. Oper. Res. 133(2), 287–297 (2001)

    Article  Google Scholar 

  3. Boginski V., Butenko S., Pardalos P.M.: Mining market data: a network approach. Comput. Oper. Res. 33(11), 3171–3184 (2006)

    Article  Google Scholar 

  4. Burges C.J.C.: A tutorial on support vector machines for pattern recognition. Data. Min. Knowl. Discov. 2(2), 955–974 (1998)

    Article  Google Scholar 

  5. Chang, C.C., Lin, C.J.: LIBSVM:a library for support vector machines, Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm (2001)

  6. Chen J.S., Hou J.L., Wua S.M., Chang-Chien Y.W.: Constructing investment strategy portfolios by combination genetic algorithms. Exp. Syst. Appl. 36(2), 3824–3828 (2009)

    Article  Google Scholar 

  7. Colin A.M.: Genetic algorithms for financial modeling. In: Deboeck, G.J. (eds) Trading on the Edge: Neural, Genetic and Fuzzy Systems for Chaotic Financial Markets, pp. 148–173. John Wiley, New York (1994)

    Google Scholar 

  8. Cristianini N., Shawe-Taylor J.: An Introduction to Support Vector Machines. Cambridge University Press, Cambridge, UK (2000)

    Google Scholar 

  9. Davis L.: Handbook of Genetic Algorithms. Van Nostrand Reinhold, New York (1991)

    Google Scholar 

  10. Deboeck G.J.: Using GAs to optimize a trading system. In: Deboeck, G.J. (eds) Trading on the Edge: Neural, Genetic and Fuzzy Systems for Chaotic Financial Markets, pp. 174–188. Wiley, New York (1994)

    Google Scholar 

  11. Drucker H., Wu D., Vapnik V.N.: Support vector machines for spam categorization. IEEE Trans. Neural Network 10(5), 1048–1054 (1999)

    Article  Google Scholar 

  12. Ehrgott M., Klamroth K., Schwehm C.: An MCDM approach to portfolio optimization. Eur. J. Oper. Res. 155, 752–770 (2004)

    Article  Google Scholar 

  13. Fan, A., Palaniswami, M.: Stock Selection Using Support Vector Machines. In: Proceedings of International Joint Conference on Neural Networks 3, 1793–1798 (2001)

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

    Article  Google Scholar 

  15. Gupta P., Mehlawat M.K., Saxena A.: Asset portfolio optimization using fuzzy mathematical programming. Inform. Sci. 178, 1734–1755 (2008)

    Article  Google Scholar 

  16. Gupta P., Mehlawat M.K., Saxena A.: A hybrid approach to asset allocation with simultaneous consideration of suitability and optimality. Inform. Sci. 180, 2264–2285 (2010)

    Article  Google Scholar 

  17. Goldberg D.E.: Genetic Algorithms in Search Optimization and Machine Learning. Addison-Wesley, Reading, MA (1989)

    Google Scholar 

  18. Holland J.H.: Genetic algorithms. Sci. Am. 267, 66–72 (1992)

    Article  Google Scholar 

  19. Huang Z., Chen H., Hsu C.J., Chen W.H., Wu S.: Credit rating analysis with support vector machines and neural networks: A market comparative study. Decis. Support Syst. 37(4), 543–558 (2004)

    Article  Google Scholar 

  20. Hsu, C.W., Chang, C.C., Lin, C.J.: A practical guide to support vector classification. Available at http://www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf (2003)

  21. Joachims, T.: Text categorization with support vector machines : Learning with many relevant features. In: Proceedings of the ECML-98, 10 th European conference on machine learning, pp. 137– 142 (1998)

  22. Keerthi S.S., Lin C.J.: Asymptotic behaviors of support vector machines with Gaussian kernel. Neural Comput. 15(7), 1667–1689 (2003)

    Article  Google Scholar 

  23. Khemchandani R., Jayadeva Chandra S.: Regularized least squares fuzzy support vector regression for financial time series forecasting. Exp. Syst. Appl. 36, 132–138 (2009)

    Article  Google Scholar 

  24. Kingdom, J., Feldman, K.: Genetic algorithms for bankruptcy prediction. Search Space Research Report No. 01-95, Search Space Ltd. London (1995)

  25. Konno H., Yamazaki H.: Mean-absolute deviation portfolio optimization model and its applications to the Tokyo Stock Market. Manag. Sci. 37, 519–531 (1991)

    Article  Google Scholar 

  26. Lai, K.K., Yu, L., Wang, S., Zhou, C.: A double-stage genetic optimization algorithm for portfolio selection. In: Proceedings of the 13th international conference on neural information processing: LNCS 4234, pp. 928–937 (2006)

  27. Lee Y.C.: Application of support vector machines to corporate credit rating prediction. Exp. Syst. Appl. 33(1), 67–74 (2007)

    Article  Google Scholar 

  28. Lin C.M., Gen M.: An effective decision-based genetic algorithm approach to multiobjective portfolio optimization problem. Appl. Math. Sci. 1(5), 201–210 (2007)

    Google Scholar 

  29. Lin, H.T., Lin, C.J.: A study on sigmoid kernels for SVM and the training of non-PSD kernels by SMO-type methods. Technical report, Department of Computer Science, National Taiwan University (2003)

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

    Google Scholar 

  31. Mukuddem-Petersen J., Mulaudzi M.P., Petersen M.A., Schoeman I.M.: Optimal mortgage loan securitization and the subprime crisis. Optim. Lett. 4(1), 97–115 (2010)

    Article  Google Scholar 

  32. Packard N.: A genetic learning algorithm for the analysis of complex data. Complex Syst. 4, 543–572 (1990)

    Google Scholar 

  33. Pardalos P.M., Sandström M., Zopounidis C.: On the use of optimization models for portfolio selection: a review and some computational results. Comput. Econ. 7, 227–244 (1994)

    Article  Google Scholar 

  34. Pardalos P.M., Tsitsiringos V.: Financial Engineering, Supply Chain and E-commerce. Kluwer, UK (2002)

    Google Scholar 

  35. Pontil M., Verri A.: Support vector machines for 3D object recognition. IEEE Trans. Pattern Anal. Mach. Intell. 20(6), 637–646 (1998)

    Article  Google Scholar 

  36. Rutan, E.: Experiments with optimal stock screens. In: Proceedings of the 3 rd International Conference on Artificial Intelligence Applications on Wall Street, pp. 269–273 (1993)

  37. Saaty T.L.: Fundamentals of Decision Making and Priority Theory with the AHP, 2nd edn. RWS Publications, Pittsburg, PA (2000)

    Google Scholar 

  38. Shin K.S., Lee T.S., Kim H.J.: An application of support vector machines in a bankruptcy prediction model. Exp. Syst. Appl. 28, 127–135 (2005)

    Article  Google Scholar 

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

    Google Scholar 

  40. Tay F.E.H., Cao L.: Application of support vector machines in financial time series forecasting. Omega 29(4), 309–317 (2001)

    Article  Google Scholar 

  41. Vapnik V.N.: The Nature of Statistical Learning Theory. Springer, New York (1995)

    Google Scholar 

  42. Viaene S., Derrig R.A., Baesens B., Dedene G.: A comparison of state-of-the-art classification techniques for expert automobile insurance claim fraud detection. J. Risk Insur. 69(3), 373–421 (2002)

    Article  Google Scholar 

  43. Walker R., Haasdijk E., Gerrets M.: Credit evaluation using a genetic algorithm. In: Coonatilake, S., Treleaven, P. (eds) Intelligent Systems for Finance and Business, pp. 39–59. Wiley, Chichester (1995)

    Google Scholar 

  44. Yu, G.X., Ostrouchov, G., Geist, A., Samatova, N.F.: An SVM based algorithm for identification of photosynthesis-specific genome features. In: Proceedings of the Second IEEE Computer Society Bioinformatics Conference, pp. 235–243 (2003)

Download references

Author information

Authors and Affiliations

  1. Department of Operational Research, University of Delhi, Delhi, India

    Pankaj Gupta, Mukesh Kumar Mehlawat & Garima Mittal

  2. Flat No.-01, Kamayani Kunj, Plot No. 69, Indraprastha Extension, Delhi, 110092, India

    Pankaj Gupta

Authors
  1. Pankaj Gupta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mukesh Kumar Mehlawat
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Garima Mittal
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Pankaj Gupta.

Rights and permissions

Reprints and permissions

About this article

Cite this article

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). https://doi.org/10.1007/s10898-011-9692-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10898-011-9692-3

Keywords

Search

Navigation