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http://dx.doi.org/10.7465/jkdi.2013.24.1.161

The study of foreign exchange trading revenue model using decision tree and gradient boosting  

Jung, Ji Hyeon (Department of Statistics, Duksung Women's University)
Min, Dae Kee (Department of Information & Statistics, Duksung Women's University)
Publication Information
Journal of the Korean Data and Information Science Society / v.24, no.1, 2013 , pp. 161-170 More about this Journal
Abstract
The FX (Foreign Exchange) is a form of exchange for the global decentralized trading of international currencies. The simple sense of Forex is simultaneous purchase and sale of the currency or the exchange of one country's currency for other countries'. We can find the consistent rules of trading by comparing the gradient boosting method and the decision trees methods. Methods such as time series analysis used for the prediction of financial markets have advantage of the long-term forecasting model. On the other hand, it is difficult to reflect the rapidly changing price fluctuations in the short term. Therefore, in this study, gradient boosting method and decision tree method are applied to analyze the short-term data in order to make the rules for the revenue structure of the FX market and evaluated the stability and the prediction of the model.
Keywords
Decision tree; ensemble; FX margin; gradient boosting;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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1 Bauer, E. and Kohavi, R. (1999). An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Machine Learning, 55, 1-38.
2 Drucker, H. and Cortes, C. (1996). Boosting decision trees. Neural Information Processing, 8, 470-485.
3 Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. The Annals of Statistics, 29, 1189-1232.
4 Kim, A., Kim, J. H. and Kim. H. (2012). The guideline for choosing the right-size tree for boosting algorithm. Journal of the Korean Data & Information Science Society, 23, 949-959.   DOI   ScienceOn
5 Kim, Y. D., Kim, K. H. and Song, S. H. (2005). Comparison of boosting and SVM. Journal of the Korean Data & Information Science Society, 16, 999-1012.
6 Kohavi, R., Sommerfield, D. and Dougherty, J. (1996). Data mining using MLC. International Journal on Artificial Intelligence Tools, 6, 234-245.
7 Kim, S. H., Cho, D. H. and Seok, K. H. (2012). Study on the ensemble methods with kernel ridge regression. Journal of the Korean Data & Information Science Society, 23, 375-383.   DOI   ScienceOn