Applying Data Processing Method for Relationship Discovery in the Stock Market

Conference paper
Part of the Springer Proceedings in Business and Economics book series (SPBE)

Abstract

Decision making in the stock market is often made based on current events and the historical data analysis. In addition, related stock trends may affect investors’ future decisions. To extract such relationship between stocks, a proposed methodology applies data processing techniques on raw data collected from the Australian Stock Market, to provide investors another angle of view, comes with initiative potential connections analysis between listed corporations, which is based on pure mathematics computing.

Keywords

Data processing The stock market Relationship analysis Time-series chart 

References

  1. 1.
    Wang, Y. F. (2003). Mining stock price using fuzzy rough set system. Expert Systems with Applications, 24, 13–23.CrossRefGoogle Scholar
  2. 2.
    Matías, J. M., & Reboredo, J. C. (2012). Forecasting performance of nonlinear models for intraday stock returns. Journal of Forecasting, 31, 172–188.  https://doi.org/10.1002/for.1218.CrossRefGoogle Scholar
  3. 3.
    Joseph, J., & Indratmo, I. (2013) Visualizing stock market data with self-organizing map. North America: Florida Artificial Intelligence Research Society Conference.Google Scholar
  4. 4.
    Asadi, S., Hadavandi, E., Mehmanpazir, F., & Nakhostin, M. M. (2012). Hybridization of evolutionary Levenberg-Marquardt neural networks and data pre-processing for stock market prediction. Knowledge-Based Systems, 35, 245–258.CrossRefGoogle Scholar
  5. 5.
    Al-Radaideh, Q., Assaf, A., & Alnagi, E. (2013). Predicting stock prices using data mining techniques. International Arab Conference on Information Technology (ACIT’2013).Google Scholar
  6. 6.
    Sekar, P. S., Kannan, K. S., Sathik, M. M., & Arumugam, P. (2010). Financial stock market forecast using data mining techniques. Proceedings of The International MultiConference of Engineers and Computer Sciece, I, 5.Google Scholar
  7. 7.
    Tsang, P. M., et al. (2007). Design and implementation of NN5 for Hong Kong stock price forecasting. Engineering Applications of Artificial Intelligence, 20(4), 453–461.CrossRefGoogle Scholar
  8. 8.
    Huang, C.-Y., & Lin, P. K. P. (2014). Application of integrated data mining techniques in stock market forecasting. Cogent Economics & Finance, 2(1), 1–18.CrossRefGoogle Scholar
  9. 9.
    Jungmeister, W. A., & Turo, D. (1992). Adapting treemaps to stock portfolio visualization. Tech. Rep. CS-TR-2996, Computer Science Department, University of Maryland, College Park, MD.Google Scholar
  10. 10.
    Csallner, C., Handte, M., Lehmann, O., & Stasko, J. (2003). Fundexplorer: Supporting the diversification of mutual fund portfolios using context treemaps. In Information Visualization, 2003. IEEE Symposium on (pp. 203–208). IEEE, INFOVIS.Google Scholar
  11. 11.
    Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1), 1–6.CrossRefGoogle Scholar
  12. 12.
    Wattenberg, M. (1999). Visualizing the stock market. In CHI’99 extended abstracts on Human factors in computing systems (pp. 188–189). New York, NY: ACM.CrossRefGoogle Scholar
  13. 13.
    Shneiderman, B., & Wattenberg, M. (2001). Ordered treemap layouts. IEEE Symposium on Information Visualization : Proceedings, INFOVIS, 2001, 2–7.Google Scholar
  14. 14.
    Bederson, B. B., Shneiderman, B., & Wattenberg, M. (2002). Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Transactions on Graphics, 21(4), 833–854.CrossRefGoogle Scholar
  15. 15.
    Dwyer, T., & Eades, P. (2002). Visualising a fund manager flow graph with columns and worms. In Information Visualisation, Proceedings. sixth international conference on (pp. 147–152). IEEE.Google Scholar
  16. 16.
    Šimunić, K. (2003). Visualization of stock market charts. In Proceedings International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision (WSCG), 2003.Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Western Sydney UniversitySydneyAustralia
  2. 2.University of Technology SydneySydneyAustralia
  3. 3.South China University of TechnologyGuangzhouChina

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