Forecasting about EURJPY exchange rate using hidden Markova model and CART classification algorithm

  • Authors

    • Abdorrahman Haeri School of Industrial Engineering, Iran University of Science & Technology, Tehran, Iran
    • Seyed Morteza Hatefi Faculty of Engineering, Shahrekord University, Rahbar Boulevard, P.O. Box 115, Shahrekord, Iran
    • Kamran Rezaie School of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran
    2015-02-22
    https://doi.org/10.14419/jacst.v4i1.4194
  • CART Classification Algorithm, EURJPY Exchange Rate, Forecasting, Foreign Exchange Market, Hidden Markov Model, Indicators, Neural Network.

  • The goal of this paper is forecasting direction (increase or decrease) of EURJPY exchange rate in a day. For this purpose five major indicators are used. The indicators are exponential moving average (EMA), stochastic oscillator (KD), moving average convergence divergence (MACD), relative strength index (RSI) and Williams %R (WMS %R). Then a hybrid approach using hidden Markov models and CART classification algorithms is developed. Proposed approach is used for forecasting direcation (increase or decrease) of Euro-Yen exchange rates in a day. Also the approach is used for forecasting differnece between intial and maximum exchange rates in a day. As well as it is used for forecasting differnece between intial and minimum exchange rates in a day. Reslut of proposed method is compared with CART and neural network. Comparison shows that the forecasting with proposed method has higher accuracy.

  • References

    1. [1] Jingtao Yao and Chew Lim Tan, “A case study on using neural networks to perform technical forecasting of Forexâ€
      Neurocomputing, Vol. 34, No. 1-4, (2000), pp. 79-98. http://dx.doi.org/10.1016/S0925-2312(00)00300-3.

      [2] A. Muriel, “Short-term predictions in Forex tradingâ€, Physica A: Statistical Mechanics and its Applications, Vol. 344, No. 1-2, (2004), pp. 190-193.

      [3] Fang-Mei Tseng, Gwo-Hshiung Tzeng, Hsiao-Cheng Yu and Benjamin J. C. Yuan, “Fuzzy ARIMA model for forecasting the foreign exchange marketâ€, Fuzzy Sets and Systems, Vol. 118, No. 1, (2001), pp. 9-19. http://dx.doi.org/10.1016/S0165-0114(98)00286-3.

      [4] Chakradhara Panda and V. Narasimhan, “Forecasting exchange rate better with artificial neural networkâ€, Journal of Policy Modeling, Vol. 29, No. 2, (2007), pp. 227-236. http://dx.doi.org/10.1016/j.jpolmod.2006.01.005.

      [5] Vincent C.S. Lee and Hsiao Tshung Wong, “A multivariate neuro-fuzzy system for foreign currency risk management decision makingâ€, Neurocomputing, Vol. 70, No. 4-6, (2007), pp. 942-951. http://dx.doi.org/10.1016/j.neucom.2006.10.025.

      [6] Roberto Baviera, Michele Pasquini, Maurizio Serva, Davide Vergni and Angelo Vulpiani, “Antipersistent Markov behavior in foreign exchange marketsâ€, Physica A: Statistical Mechanics and its Applications, Vol. 312, No. 3-4, (2002), pp. 565-576.

      [7] Craig Ellis, Patrick Wilson, “A stochastic approach to modeling the USD/AUD exchange rateâ€, International Journal of Managerial Finance,Vol. 1,No. 1, (2005), pp.36-48. http://dx.doi.org/10.1108/17439130510584874.

      [8] Kuntara Pukthuanthong,Lee R. Thomas III,Carlos Bazan, “Random walk currency futures profits revisitedâ€, International Journal of Managerial Finance, Vol. 3, No.3, (2007), pp. 263-286. http://dx.doi.org/10.1108/17439130710756916.

      [9] Gan, Woon-Seng, Ng, Kah-Hwa, “Multivariate Forex forecasting using artificial neural networksâ€, Proceedings of the IEEE International Conference on Neural Networks, Vol. 2, (1995), pp: 1018-1022. http://dx.doi.org/10.1109/ICNN.1995.487560.

      [10] Oh, K.J., Kim, T.Y., Lee, H.Y., Lee, H., “Using neural networks to support early warning system for financial crisis forecastingâ€, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Vol. 3809, (2005), pp. 284-296.

      [11] Hau, H., Rey, H., “Exchange rates, equity prices, and capital flowsâ€, Review of Financial Studies, Vol. 19, No. 1, (2006), pp. 273-317. http://dx.doi.org/10.1093/rfs/hhj008.

      [12] Ahmad, S.M., El Gayar, N., Abd Elazim, H.Y., “A fuzzy engine model for financial market predictionâ€, WSEAS Transactions on Information Science and Applications, Vol. 4, No. 2, (2007), pp. 362-368.

      [13] Mei-Chih Chen · Chang-Li Lin · An-Pin Chen, “Constructing a dynamic stock portfolio decision-making assistance model: using the Taiwan 50 Index constituents as an exampleâ€, Soft Computing, Vol. 11, (2007), pp. 1149–1156. http://dx.doi.org/10.1007/s00500-007-0158-y.

      [14] Shik, T.C., Chong, T.T.-L., “A comparison of MA and RSI returns with exchange rate interventionâ€, Applied Economics Letters, Vol. 14, No. 5, (2007), pp. 371-383. http://dx.doi.org/10.1080/13504850500426269.

      [15] Chong, T.T.-L., Ng, W.-K., “Technical analysis and the London stock exchange: Testing the MACD and RSI rules using the FT30â€, Applied Economics Letters, Vol. 15, No. 14, (2008), pp. 1111-1114. http://dx.doi.org/10.1080/13504850600993598.

  • Downloads

  • How to Cite

    Haeri, A., Hatefi, S. M., & Rezaie, K. (2015). Forecasting about EURJPY exchange rate using hidden Markova model and CART classification algorithm. Journal of Advanced Computer Science & Technology, 4(1), 84-89. https://doi.org/10.14419/jacst.v4i1.4194