Skip to main content

A Hybrid Model for Financial Portfolio Optimization Based on LS-SVM and a Clustering Algorithm

  • Conference paper
  • First Online:
ICT Innovations 2019. Big Data Processing and Mining (ICT Innovations 2019)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1110))

Included in the following conference series:

  • 676 Accesses

Abstract

An investment decision is one of the most important financial decisions. With the aim of optimizing investment in securities from the aspect of return and risk, investors usually diversify their portfolio securities. This paper presents a hybrid model for portfolio optimization, which consist of two steps. The first step predicts future returns on the shares, and the second step, by applying hierarchical clustering algorithm, identifies various groups of shares. The test results indicate that the suggested model is suitable for optimization of a financial portfolio as a hybrid model based on selected shares, which if included in the portfolio, enable the diversification of risk.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

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

    Google Scholar 

  2. Fernández, A., Gómez, S.: Portfolio selection using neural networks. Comput. Oper. Res. 34(4), 1177–1191 (2007)

    Article  Google Scholar 

  3. Ko, P.C., Lin, P.C.: Resource allocation neural network in portfolio selection. Expert Syst. Appl. 35(1–2), 330–337 (2008)

    Article  Google Scholar 

  4. Oh, K.J., Kim, T.Y., Min, S.: Using genetic algorithm to support portfolio optimization for index fund management. Expert Syst. Appl. 28(2), 371–379 (2005)

    Article  Google Scholar 

  5. Chang, T.J., Yang, S.C., Chang, K.J.: Portfolio optimization problems in different risk measures using genetic algorithm. Expert Syst. Appl. 36(7), 10529–10537 (2009)

    Article  Google Scholar 

  6. Nanda, S.R., Mahanty, B., Tiwari, M.K.: Clustering Indian stock market data for portfolio management. Expert Syst. Appl. 37(12), 8793–8798 (2010)

    Article  Google Scholar 

  7. Tola, V., Lillo, F., Gallegati, M., Mantegna, R.: Cluster analysis for portfolio optimization. J. Econ. Dyn. Control 32(1), 235–258 (2008)

    Article  MathSciNet  Google Scholar 

  8. Aghabozorgi, S., The, Y.W.: Stock market co-movement assessment using a three-phase clustering method. Expert Syst. Appl. 41(4), 1301–1314 (2014)

    Article  Google Scholar 

  9. Marković, I.P., Stanković, J.M., Stanković, J.Z., Stojanović, M.B.: Financial portfolio optimization using clustering algorithms. In: 54th International Scientific Conference on Information, Communication and Energy Systems and Technologies – ICEST, 27–29 June 2019 (2019, in press)

    Google Scholar 

  10. Basalto, N., Bellotti, R., De Carlo, F., Facchi, P., Pascazio, S.: Clustering stock market companies via chaotic map synchronization. Physica A 345(1–2), 196–206 (2005)

    Article  Google Scholar 

  11. De Luca, G., Zuccolotto, P.: A tail dependence-based dissimilarity measure for financial time series clustering. Adv. Data Anal. Classif. 5(4), 323–340 (2011)

    Article  MathSciNet  Google Scholar 

  12. Durante, F., Pappadà, R., Torelli, N.: Clustering of financial time series in risky scenarios. Adv. Data Anal. Classif. 8(4), 359–376 (2014)

    Article  MathSciNet  Google Scholar 

  13. Cheong, D., Kim, Y.M., Byun, H.W., Oh, K.J., Kim, T.Y.: Using genetic algorithm to support clustering-based portfolio optimization by investor information. Appl. Soft Comput. 61, 593–602 (2017)

    Article  Google Scholar 

  14. Stojanović, M.B., Božić, M.M., Stanković, M.M., Stajić, Z.P.: A methodology for training set instance selection using mutual information in time series prediction. Neurocomputing 141, 236–245 (2014)

    Article  Google Scholar 

  15. Herrera, L.J., Pomares, H., Rojas, I., Guillen, A., Prieto, A., Valenzuela, O.: Recursive prediction for long term time series forecasting using advanced models. Neurocomputing 70, 2870–2880 (2007)

    Article  Google Scholar 

  16. Sorjamaa, A., Reyhani, J., Hao, N., Ji, Y., Lendasse, A.: Methodology for long-term prediction of time series. Neurocomputing 70(16–18), 2861–2869 (2007)

    Article  Google Scholar 

  17. Suykens, J.A.K., Van Gestel, T., De Brabanter, J., De Moor, B., Vandewalle, J.: Least Squares Support Vector Machines. World Scientific, Singapore (2002)

    Book  Google Scholar 

  18. Arlot, S., Celisse, A.: A survey of cross-validation procedures for model selection. Stat. Surv. 4, 40–79 (2010)

    Article  MathSciNet  Google Scholar 

  19. Fu, T.: A review on time series data mining. Eng. Appl. Artif. Intell. 24(1), 164–181 (2011). https://doi.org/10.1016/j.engappai.2010.09.007

    Article  Google Scholar 

  20. Berndt, D.J., Clifford, J.: Using dynamic time warping to find patterns in time series. In: KDD Workshop, vol. 10, no. 16, pp. 359–370 (1994)

    Google Scholar 

  21. Kruskal, J.B., Liberman, M.: The symmetric time-warping problem: from continuous to discrete. In: Kruskal, J.B., Sankoff, D. (eds.) Time Warps, String Edits, and Macromolecules: The Theory and Practice of Sequence Comparison, pp. 125–161. CSLI Publications, Stanford (1999)

    Google Scholar 

  22. Gavrishckaka, V.V., Banerjee, S.: Support vector machine as an efficient framework for stock market volatility forecasting. CMS 3(2), 147–160 (2006)

    Article  MathSciNet  Google Scholar 

  23. Gavrishckaka, V.V., Ganguli, B.S.: Volatility forecasting from multiscale and high-dimensional market data. Neurocomputing 55(1–2), 285–305 (2003)

    Article  Google Scholar 

  24. Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3(1), 1–27 (1974)

    Article  MathSciNet  Google Scholar 

  25. Keating, C., Shadwick, W.F.: A universal performance measure. J. Perform. Meas. 6(3), 59–84 (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jovica M. Stanković .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2019 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Marković, I.P., Stanković, J.Z., Stojanović, M.B., Stanković, J.M. (2019). A Hybrid Model for Financial Portfolio Optimization Based on LS-SVM and a Clustering Algorithm. In: Gievska, S., Madjarov, G. (eds) ICT Innovations 2019. Big Data Processing and Mining. ICT Innovations 2019. Communications in Computer and Information Science, vol 1110. Springer, Cham. https://doi.org/10.1007/978-3-030-33110-8_15

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-33110-8_15

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33109-2

  • Online ISBN: 978-3-030-33110-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics