Evaluation of Artificial Neural Networks in Foreign Exchange Forecasting
American Journal of Theoretical and Applied Statistics
Volume 2, Issue 4, July 2013, Pages: 94-101
Received: Jun. 17, 2013; Published: Jul. 10, 2013
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Akintunde Mutairu Oyewale, Department of Statistics, University of Botswana, Botswana
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This study investigates the modeling, description and forecasting of exchange rates of four countries (Great Britain Pound, Japanese Yen, Nigerian Naira and Batswana Pula) using Artificial Neural Network, the objective of this paper is to use ANN to predict the trend of these four currencies. ANN was used in training and learning processes and thereafter the forecast performance was evaluated or measured making use of various loss functions such as root mean square error (RMSE), mean absolute error (MAE), mean absolute error (MAE), mean absolute precision error (MAPE) and Theill inequality coefficient (TIC). The loss functions used are good indicator of measuring the forecast performance of these series, the series with the lowest function gave a best forecast performance. Results show that the ANN is a very effective tool for exchange rate forecasting. Classical statistical methods are unable to efficiently handle the prediction of financial time series due to non-linearity, non-stationarity and high degree of noise. Advanced intelligence techniques have been used in many financial markets to forecast future development of different capital markets. Artificial neural network is a well tested method for financial markets analysis.
Artificial Neural Networks, Foreign Exchange, Loss Functions, Training and Learning Processes
To cite this article
Akintunde Mutairu Oyewale, Evaluation of Artificial Neural Networks in Foreign Exchange Forecasting, American Journal of Theoretical and Applied Statistics. Vol. 2, No. 4, 2013, pp. 94-101. doi: 10.11648/j.ajtas.20130204.11
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