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.
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Bank of international settlements: Trennial Central Bank Survey (2013)
Bollinger, J.A.: Bollinger on Bollinger Bands, 1st edn. McGraw-Hill Education, New York (2001)
Breiman, L., Friedman, J.H., Olshen, R., Stone, C.J.: Classification and Regression Tree. Chapman & Hall/CRC Press, Boca Raton (1984)
Castiglione, F.: Forecasting price increments using an artificial neural network. Complex Dyn. Econ. 3(1), 45–56 (2001)
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)
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
Kathy, L.: Day Trading and Swing Trading the Currency Market, 2nd edn. Wiley, New Jersey (2009)
Kuhn, M., Johnson, K.: Applied Predictive Modeling. Springer-Verlag, New York (2013)
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
Larsen, F.: Automatic stock market trading based on Technical Analysis. Master thesis (2006)
Mehta, J.R., Menghini, M.D., Sarafconn, D.A.: Automated foreign exchange trading system. An Interactive Qualifying Project Report (2011)
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)
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)
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)
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)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann Publishers Inc., San Francisco (1993)
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)
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.
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Magyar, R., Babič, F., Paralič, J. (2017). Decision Support System for Foreign Exchange Markets. In: Abramowicz, W., Alt, R., Franczyk, B. (eds) Business Information Systems Workshops. BIS 2016. Lecture Notes in Business Information Processing, vol 263. Springer, Cham. https://doi.org/10.1007/978-3-319-52464-1_9
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DOI: https://doi.org/10.1007/978-3-319-52464-1_9
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