Skip to main content
SpringerLink
Log in

Applying Data Processing Method for Relationship Discovery in the Stock Market

  • Conference paper
  • First Online:
Recent Developments in Data Science and Business Analytics

Part of the book series: Springer Proceedings in Business and Economics ((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.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover 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

References

  1. Wang, Y. F. (2003). Mining stock price using fuzzy rough set system. Expert Systems with Applications, 24, 13–23.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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.

    Article  Google Scholar 

  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. 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. 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.

    Article  Google Scholar 

  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.

    Article  Google Scholar 

  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. 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. Kohonen, T. (1998). The self-organizing map. Neurocomputing, 21(1), 1–6.

    Article  Google Scholar 

  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.

    Chapter  Google Scholar 

  13. Shneiderman, B., & Wattenberg, M. (2001). Ordered treemap layouts. IEEE Symposium on Information Visualization : Proceedings, INFOVIS, 2001, 2–7.

    Google Scholar 

  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.

    Article  Google Scholar 

  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. 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 

Download references

Author information

Authors and Affiliations

  1. Western Sydney University, Sydney, NSW, Australia

    Mouataz Zreika

  2. University of Technology Sydney, Sydney, NSW, Australia

    Jie Hua

  3. South China University of Technology, Guangzhou, China

    Guohua Wang

Authors
  1. Mouataz Zreika
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jie Hua
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Guohua Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jie Hua .

Editor information

Editors and Affiliations

  1. Department of Business Systems and Analytics, La Salle University, Philadelphia, Pennsylvania, USA

    Madjid Tavana

  2. Department of Computer Science and Engineering, SOA University, Bhubaneswar, Odisha, India

    Srikanta Patnaik

Rights and permissions

Reprints and permissions

Copyright information

© 2018 Springer International Publishing AG, part of Springer Nature

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zreika, M., Hua, J., Wang, G. (2018). Applying Data Processing Method for Relationship Discovery in the Stock Market. In: Tavana, M., Patnaik, S. (eds) Recent Developments in Data Science and Business Analytics. Springer Proceedings in Business and Economics. Springer, Cham. https://doi.org/10.1007/978-3-319-72745-5_27

Download citation

Publish with us

Policies and ethics

Search

Navigation