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

DOI:

https://doi.org/10.14419/jacst.v4i1.4194

Published:

2015-02-22

Keywords:

CART Classification Algorithm, EURJPY Exchange Rate, Forecasting, Foreign Exchange Market, Hidden Markov Model, Indicators, Neural Network.

Abstract

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.

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