Supervised support vector machine in predicting foreign exchange trading

Автор: Thuy Nguyen Thi Thu, Vuong Dang Xuan

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 9 vol.10, 2018 года.

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Trends of currency rates can be predicted with supporting from supervised machine learning in the transaction systems such as support vector machine (SVM). By assumption of binary classification problems, the SVM can predict foreign exchange transaction as uptrend or downtrend. The prediction is performed basing on collected historical data. Alternative SVM models have been used to vote the best one, which is deployed detail in Expert Advisor (Robotics). This is to show that support vector machine models might help investors to automatically make transaction decisions of Bid/Ask in Foreign Exchange Market. For comparison, the transactions without using SVM model also are performed. The results of experimental transactions show the advantages of using SVM model compared to the transactions without using SVM model.


Foreign Exchange Market, Exchange Rates, Machine Learning, Support Vector Machine (SVM), Prediction

Короткий адрес:

IDR: 15016526   |   DOI: 10.5815/ijisa.2018.09.06

Список литературы Supervised support vector machine in predicting foreign exchange trading

  • A.a. Baasher, and W.F. Mohamed, “FOREX Trend Classification using Machine Learning Techniques”, Arab Academy for Science and Technology, 2010, pp. 41-47.
  • B. J. Almeida, R. F. Neves, N. Horta, “Combining Support Vector Machine with Genetic Algorithms to optimize investments in Forex markets with high leverage”, Applied Soft Computing, Vol. 64, 2018, pp. 596–613.
  • C. F. Tsai, and Y.C. Hsiao, “Combining multiple feature selection methods for stock prediction: Union, intersection, and multi-intersection approaches”, Decision Support Systems, 50(1), 2010, pp. 258 – 269.
  • C. Scholkopf, J.C. Burges, and A. J. Smola, “Advances in Kernel Methods”, MIT Press, 1999.
  • C.L. Huang, and C.Y. Tsai, “A hybrid SOFM-SVR with a filter based feature selection for stock market forecasting”, Expert Systems with Applications, 36(2, Part 1), 2010, pp: 1529 – 1539.
  • D. m. Nemeş, & A. Butoi, ” Data Mining on Romanian Stock Market Using Neural Networks for Price Prediction”, Informatica Economică, vol. 17, No. 3, 2013, pp. 125-136.
  • F.E.H. Tay, L. Cao, “Application of support vector machines in Financial time series forecasting”, Omega 29, 2001, pp. 309–317.
  • G.E.P. Box, and G. M. Jenkins, “Time Series Analysis: Forecasting and Control”, Holden- Day, San Francosco, CA. 1970.
  • G.J. Deboeck, “Trading on the Edge: Neural, Genetic and Fuzzy Systems for Chaotic Financial Markets”, New York Wiley, 1994.
  • H. Drucker, D. Wu, V.N. Vapnik, “Support vector machines for spam categorization”, IEEE Trans. Neural Networks 10 (5),1999, pp.1048–1054.
  • H. R.Erfanian, M. Hajimohammadi, M. J. Abdi, “Using the Euler-Maruyama Method for Finding a Solution to Stochastic Financial Problems” I.J. Intelligent Systems and Applications, 2016, No. 6, pp.48-55.
  • H.A.R. Akkar, F. B. Ali Jasim, “Intelligent Training Algorithm for Artificial Neural Network EEG Classifications”, I.J. Intelligent Systems and Applications, 2018, No.5, pp. 33-41.
  • I.H. Witten, E. Frank, MA. Hall. “Data mining: practical machine learning tools and techniques”. Morgan Kaufmann Publishers, 2011.
  • K. Kim, “Financial time series forecasting using Support Vector Machines”, Neurocomputing 55, 2003, pp. 307- 319.
  • K. Veropoulos, C. Campbell, & N. Cristianini, “Controlling the Sensitivity of Support Vector Machines”. In Proceedings of the International Joint Conference on Artificial Intelligence, 1999. (IJCAI99).
  • K.R. Müller, A. Smola, G. Rätsch, B. Schölkopf, J. Kohlmorgen, and V. Vapnik,.”Predicting time series with support vector machines", In proceedings of the IEEE workshop on neural networks for signal processing 7, 1997, pp.511 – 519.
  • L. Liu, and W. Wang, “Exchange Rates Forecastiing with Least Squares SVM”, International Conference on Computer Science and Software Engineering, 2008.
  • M. Punniyamoorthy, & J.J. Thoppan, “ANN-GA based model for stock market surveillance”, Journal of Financial Crime, vol. 20, No. 1, 2013, pp. 52-66.
  • M. Reboredo, J.J. Rubio,” Nonlinearity in forecasting of high-frequency stock returns”, Computational Economics, 40(3), 2012, pp. 245– 264.
  • J. C. Platt, “Sequential Minimal Optimization: A Fast Algorithm for Training Support Vector Machines”, Microsoft Research Technical Report MSR-TR-98-14,1998.
  • P.Thakar, A. Mehta, “A Unified Model of Clustering and Classification to Improve Students’ Employability Prediction”, I.J. Intelligent Systems and Applications, 2017, No.9, pp. 10-18.
  • S. B. Kotsiantis, “Supervised Machine Learning: A Review of Classification Techniques”, Informatica: 31, 2007, pp. 249-268.
  • S. Galeshchuk, “Neural networks performance in exchange rate prediction”, Neurocomputing, 2016, No. 172, pp. 446–452.
  • S. Mukherjee, E.Osuna, F. Giroso, “Nonlinear prediction of chaotic time series using SVM”, In proceedings of the IEEE workshop on neural networks for signal processing 7, 1997, pp. 511 – 519.
  • T. Scaria, T. Christopher, “Microarray Gene Retrieval System Based on LFDA and SVM”, I.J. Intelligent Systems and Applications, 2018, No.1, pp. 9-15.
  • V. Vapnik, “The Nature of Statistical Learning Theory”, Springer Verlag, 1995.
  • W. H. Kuhn, W.A. Tucker, "Nonlinear programming", Proceedings of 2nd Berkeley Symposium. Berkeley: University of California Press, 1951, pp. 481–492.
  • W. Huang, Y. Nakamori, and S. Y. Wang, “Forecasting foreign exchange rates with artificial neural networks, a review", International Journal of Information Technology & Decision Making. Vol 3, No 1, 2004, pp. 145-165.
  • W. Shen, X. Guo, C. Wu, and D. Wu, “Forecasting stock indices using radial basis function neural networks optimized by artificial fish swarm algorithm”, Knowledge-Based Systems, 24(3), 2011, pp. 378–385.
  • Metatrader 4, (2016). Website:
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