Personalised Modelling for Multiple Time-Series Data Prediction: A Preliminary Investigation in Asia Pacific Stock Market Indexes Movement

  • Harya Widiputra
  • Russel Pears
  • Nikola Kasabov
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5506)


The behaviour of multiple stock markets can be described within the framework of complex dynamic systems (CDS). Using a global model with the Kalman Filter we are able to extract the dynamic interaction network (DIN) of these markets. The model was shown to successfully capture interactions between stock markets in the long term. In this study we investigate the effectiveness of two different personalised modelling approaches to multiple stock market prediction. Preliminary results from this study show that the personalised modelling approach when applied to the rate of change of the stock market index is better able to capture recurring trends that tend to occur with stock market data.


Stock Market Stock Index Stock Market Index Personalise Modelling Index Future Market 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Antoniou, A., Pescetto, G., Violaris, A.: Modelling international price relationships and interdependencies between the stock index and stock index future markets of three EU countries: A Multivariate Analysis. Journal of Business Finance and Accounting 30, 645–667 (2003)CrossRefGoogle Scholar
  2. 2.
    Caporale, G.M., Serguieva, A., Wu, H.: A mixed-game agent-based model for simulating financial contagion. In: Proceedings of the 2008 Congress on Evolutionary Computation, pp. 3420–3425. IEEE Press, Los Alamitos (2008)Google Scholar
  3. 3.
    Chan, Z., Kasabov, N., Collins, L.: A two-stage methodology for gene regulatory network extraction from time-course gene expression data. Expert System with Applications 30, 59–63 (2006)CrossRefGoogle Scholar
  4. 4.
    Chiang, T.C., Doong, S.: Empirical analysis of stock returns and volatility: Evidence from seven Asian stock markets based on TAR-GARCH model. Review of Quantitative Finance and Accounting 17, 301–318 (2001)CrossRefGoogle Scholar
  5. 5.
    Collins, D., Biekpe, N.: Contagion and interdependence in African stock markets. The South African Journal of Economics 71(1), 181–194 (2003)CrossRefGoogle Scholar
  6. 6.
    D’haeseleer, P., Liang, S., Somogyi, R.: Gene expression data analysis and modelling. In: Proceedings of the Pacific Symposium on Biocomputing, Hawaii (1999)Google Scholar
  7. 7.
    Goldfeld, S., Quandt, R.: A Markov model for switching regressions. Journal of Econometrics 1(1), 3–16 (1973)CrossRefzbMATHGoogle Scholar
  8. 8.
    Kasabov, N.: Evolving connectionist systems: Methods and applications in bioinformatics, Brain Study and Intelligent Machines. Springer, Heidelberg (2002)zbMATHGoogle Scholar
  9. 9.
    Kasabov, N., Chan, Z., Jain, V., Sidorov, I., Dimitrov, D.: Gene regulatory network discovery from time-series gene expression data – A computational intelligence approach. In: Pal, N.R., Kasabov, N., Mudi, R.K., Pal, S., Parui, S.K. (eds.) ICONIP 2004. LNCS, vol. 3316, pp. 1344–1353. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. 10.
    Kasabov, N.: Global, local and personalised modelling and pattern discovery in bioinformatics: An integrated approach. Pattern Recognition Letters 28, 673–685 (2007a)CrossRefGoogle Scholar
  11. 11.
    Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer, Heidelberg (2007b)zbMATHGoogle Scholar
  12. 12.
    Masih, A., Masih, R.: Dynamic modelling of stock market interdependencies: An empirical investigation of Australia and the Asian NICs, Working Papers, 98–18, 1323–9244, University of Western Australia (1998)Google Scholar
  13. 13.
    Welch, G., Bishop, G.: An Introduction to the Kalman Filter, Computer Science Working Papers TR95-041, University of North Carolina at Chapel Hill (2006)Google Scholar
  14. 14.
    Serguieva, A., Kalganova, T., Khan, T.: An intelligent system for risk classification of stock investment projects. Journal of Applied Systems Studies 4(2), 236–261 (2003)Google Scholar
  15. 15.
    Serguieva, A., Khan, T.: Knowledge representation in risk analysis. Business and Management Working Papers. Brunel University, pp. 1–21 (March 2004)Google Scholar
  16. 16.
    Serguieva, A., Wu, H.: Computational intelligence in financial contagion analysis. In: Seventh International Conference on Complex Systems, Boston, Massachusetts (2007); InterJournal on Complex Systems 2229, 1–12 (2008)Google Scholar
  17. 17.
    Song, Q., Kasabov, N.: Dynamic evolving neuro-fuzzy inference system (DENFIS): On-line learning and application for time-series prediction. IEEE Transactions of Fuzzy Systems 10, 144–154 (2002)CrossRefGoogle Scholar
  18. 18.
    Vapnik, V.N.: Statistical Learning Theory. Wiley Inter-Science, Chichester (1998)zbMATHGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Harya Widiputra
    • 1
  • Russel Pears
    • 1
  • Nikola Kasabov
    • 1
  1. 1.Knowledge Engineering and Discovery Research InstituteAuckland University of TechnologyAucklandNew Zealand

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