Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
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)
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)
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)
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)
Collins, D., Biekpe, N.: Contagion and interdependence in African stock markets. The South African Journal of Economics 71(1), 181–194 (2003)
D’haeseleer, P., Liang, S., Somogyi, R.: Gene expression data analysis and modelling. In: Proceedings of the Pacific Symposium on Biocomputing, Hawaii (1999)
Goldfeld, S., Quandt, R.: A Markov model for switching regressions. Journal of Econometrics 1(1), 3–16 (1973)
Kasabov, N.: Evolving connectionist systems: Methods and applications in bioinformatics, Brain Study and Intelligent Machines. Springer, Heidelberg (2002)
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)
Kasabov, N.: Global, local and personalised modelling and pattern discovery in bioinformatics: An integrated approach. Pattern Recognition Letters 28, 673–685 (2007a)
Kasabov, N.: Evolving Connectionist Systems: The Knowledge Engineering Approach. Springer, Heidelberg (2007b)
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)
Welch, G., Bishop, G.: An Introduction to the Kalman Filter, Computer Science Working Papers TR95-041, University of North Carolina at Chapel Hill (2006)
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)
Serguieva, A., Khan, T.: Knowledge representation in risk analysis. Business and Management Working Papers. Brunel University, pp. 1–21 (March 2004)
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)
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)
Vapnik, V.N.: Statistical Learning Theory. Wiley Inter-Science, Chichester (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Widiputra, H., Pears, R., Kasabov, N. (2009). Personalised Modelling for Multiple Time-Series Data Prediction: A Preliminary Investigation in Asia Pacific Stock Market Indexes Movement. In: Köppen, M., Kasabov, N., Coghill, G. (eds) Advances in Neuro-Information Processing. ICONIP 2008. Lecture Notes in Computer Science, vol 5506. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02490-0_150
Download citation
DOI: https://doi.org/10.1007/978-3-642-02490-0_150
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02489-4
Online ISBN: 978-3-642-02490-0
eBook Packages: Computer ScienceComputer Science (R0)