International Journal of Internet, Broadcasting and Communication

The Institute of Internet, Broadcasting and Communication (한국인터넷방송통신학회)
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Artificial neural network algorithm comparison for exchange rate prediction

  • Shin, Noo Ri (Graduate School of Smart Convergence Kwangwoon University) ;
  • Yun, Dai Yeol (Department of information and communication Engineering, Institute of Information Technology, Kwangwoon University) ;
  • Hwang, Chi-gon (Department of Computer Engineering, Institute of Information Technology, Kwangwoon University)
  • Received : 2020.06.11
  • Accepted : 2020.06.25
  • Published : 2020.08.31
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Abstract

At the end of 1997, the volatility of the exchange rate intensified as the nation's exchange rate system was converted into a free-floating exchange rate system. As a result, managing the exchange rate is becoming a very important task, and the need for forecasting the exchange rate is growing. The exchange rate prediction model using the existing exchange rate prediction method, statistical technique, cannot find a nonlinear pattern of the time series variable, and it is difficult to analyze the time series with the variability cluster phenomenon. And as the number of variables to be analyzed increases, the number of parameters to be estimated increases, and it is not easy to interpret the meaning of the estimated coefficients. Accordingly, the exchange rate prediction model using artificial neural network, rather than statistical technique, is presented. Using DNN, which is the basis of deep learning among artificial neural networks, and LSTM, a recurrent neural network model, the number of hidden layers, neurons, and activation function changes of each model found the optimal exchange rate prediction model. The study found that although there were model differences, LSTM models performed better than DNN models and performed best when the activation function was Tanh.

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References

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