Advertisement

Decision Support System for Foreign Exchange Markets

  • Róbert Magyar
  • František BabičEmail author
  • Ján Paralič
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 263)

Abstract

Selection of the right decision strategy is a crucial factor to success in the foreign exchange market. This article presents an innovative approach how to support related decision steps by means of suitable data mining methods applied on collected data from the market. The motivation is a trading under the best conditions, i.e. with the highest chance to be successful. To meet this requirement, we designed and implemented a decision support system (DSS) for trading on the foreign exchange market which uses a possibility to speculate on this market and in line with extracted rules, economic news and outputs of the technical analysis recommend the future trading direction. We extracted the rules from the historical Forex data with the C5.0 and CART algorithms for decision trees generation. The best achieved accuracy was 56.03% that is typical for this type of data. We used the best rules to design a dynamic trading strategy, which we experimentally verified as profitable.

Keywords

Forex Data mining Decision support system Technical analysis 

Notes

Acknowledgments

The work presented in this paper was partially supported by the Slovak Grant Agency of the Ministry of Education and Academy of Science of the Slovak Republic under grant No. 1/0493/16 and by the Cultural and Educational Grant Agency of the Slovak Republic under grant No. 025TUKE-4/2015.

References

  1. 1.
    Bank of international settlements: Trennial Central Bank Survey (2013)Google Scholar
  2. 2.
    Bollinger, J.A.: Bollinger on Bollinger Bands, 1st edn. McGraw-Hill Education, New York (2001)Google Scholar
  3. 3.
    Breiman, L., Friedman, J.H., Olshen, R., Stone, C.J.: Classification and Regression Tree. Chapman & Hall/CRC Press, Boca Raton (1984)Google Scholar
  4. 4.
    Castiglione, F.: Forecasting price increments using an artificial neural network. Complex Dyn. Econ. 3(1), 45–56 (2001)Google Scholar
  5. 5.
    Dymova, L., Sevastjanov, P., Kaczmarek, K.: A Forex trading expert system based on a new approach to the rule-base evidential reasoning. Expert Syst. Appl. 51(C), 1–13 (2016)CrossRefGoogle Scholar
  6. 6.
    Eiamkanitchat, N., Moontui, T.: Decision support for the stocks trading using MLP and data mining techniques. In: Kim, K.J., Joukov, N. (eds.) Information Science and Applications (ICISA) 2016. LNEE, vol. 376, pp. 1223–1233. Springer, Heidelberg (2016). doi: 10.1007/978-981-10-0557-2_116 CrossRefGoogle Scholar
  7. 7.
    Kathy, L.: Day Trading and Swing Trading the Currency Market, 2nd edn. Wiley, New Jersey (2009)Google Scholar
  8. 8.
    Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer-Verlag, New York (2013)CrossRefGoogle Scholar
  9. 9.
    Lai, K.,K., Yu, L., Wang, S.: A neural network and web-based decision support system for Forex forecasting and trading. In: Shi, Y., Xu, W., Chen, Z. (eds.) CASDMKM 2004. LNCS (LNAI), vol. 3327, pp. 243–253. Springer, Heidelberg (2005). doi: 10.1007/978-3-540-30537-8_27 CrossRefGoogle Scholar
  10. 10.
    Larsen, F.: Automatic stock market trading based on Technical Analysis. Master thesis (2006)Google Scholar
  11. 11.
    Mehta, J.R., Menghini, M.D., Sarafconn, D.A.: Automated foreign exchange trading system. An Interactive Qualifying Project Report (2011)Google Scholar
  12. 12.
    Patil, N., Rekha, L., Vidya, C.: Comparison of C5.0 & CART classification algorithms using pruning technique. Int. J. Eng. Res. Technol. (IJERT) 1(4), 1–5 (2012)Google Scholar
  13. 13.
    Pham, H.V., Cao, T., Nakaoka, I., Cooper, E.W., Kamei, K.: A proposal of hybrid Kansei-som model for stock market investment. Int. J. Innov. Comput. Inf. Control 7(5), 2863–2880 (2011)Google Scholar
  14. 14.
    Peachavanish, R.: Stock selection and trading based on cluster analysis of trend and momentum indicators. In: Proceedings of the International MultiConference of Engineers and Computer Scientists 2016, vol. 1, IMECS 2016, Hong Kong, pp. 317–321 (2016)Google Scholar
  15. 15.
    Peramunetilleke, D., Wong, R.K.: Currency exchange rate forecasting from news headlines. In: Proceedings of the 13th Australasian Database Conference, pp. 131–139, Australia (2002)Google Scholar
  16. 16.
    Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)Google Scholar
  17. 17.
    Wirth, R., Hipp, J.: CRISP-DM: towards a standard process model for data mining. In: Proceedings of the Fourth International Conference on the Practical Application of Knowledge Discovery and Data Mining, pp. 29–39 (2000)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Róbert Magyar
    • 1
  • František Babič
    • 1
    Email author
  • Ján Paralič
    • 1
  1. 1.Department of Cybernetics and Artificial Intelligence, Faculty of Electrical Engineering and InformaticsTechnical University of KošiceKošiceSlovakia

Personalised recommendations