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


  • 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





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.


[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

[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.

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