Forecasting of currency exchange rates using ANN: a case study

In today's global economy, accuracy in forecasting the foreign exchange rate or at least predicting the trend correctly is of crucial importance for any future investment. The use of computational intelligence based techniques for forecasting has been proved extremely successful in recent times. In this paper, we developed and investigated three artificial neural network (ANN) based forecasting model using standard backpropagation (SBP), scaled conjugate gradient (SCG) and backpropagation with Baysian regularization (BPR) for Australian foreign exchange to predict six different currencies against Australian dollar. Five moving average technical indicators are used to build the models. These models were evaluated on five performance metrics and a comparison was made with traditional ARIMA model. All the ANN based models outperform ARIMA model. It is found that SCG based model performs best when measured on the two most commonly used metrics and shows competitive results when compared with BPR based model on other three metrics. Experimental results demonstrate that ANN based model can closely forecast the forex market.

[1]  Paul A. Fishwick,et al.  Feedforward Neural Nets as Models for Time Series Forecasting , 1993, INFORMS J. Comput..

[2]  HillTim,et al.  Neural Network Models for Time Series Forecasts , 1996 .

[3]  Jae Kyu Lee,et al.  Performance of Neural Networks in Managerial Forecasting , 1993 .

[4]  Martin T. Hagan,et al.  Neural network design , 1995 .

[5]  Milton S. Boyd,et al.  Designing a neural network for forecasting financial and economic time series , 1996, Neurocomputing.

[6]  Martin Fodslette Møller,et al.  A scaled conjugate gradient algorithm for fast supervised learning , 1993, Neural Networks.

[7]  Jingtao Yao,et al.  A case study on using neural networks to perform technical forecasting of forex , 2000, Neurocomputing.

[8]  Guido Deboeck,et al.  Trading on the Edge: Neural, Genetic, and Fuzzy Systems for Chaotic Financial Markets , 1994 .

[9]  H. Brian Hwarng,et al.  A simple neural network for ARMA(p,q) time series , 2001 .

[10]  Jung-Hua Wang,et al.  Stock market trend prediction using ARIMA-based neural networks , 1996, Proceedings of International Conference on Neural Networks (ICNN'96).

[11]  Michael Y. Hu,et al.  Neural network forecasting of the British pound/US dol-lar exchange rate , 1998 .

[12]  M. Møller.  A Scaled Conjugate Gradient Algorithm for Fast Supervised Learning , 1990 .

[13]  B. LeBaron.  Technical Trading Rule Profitability and Foreign Exchange Intervention , 1996 .

[14]  Barbara D. Klein,et al.  Data quality in neural network models: effect of error rate and magnitude of error on predictive accuracy , 1999 .

[15]  Amir F. Atiya,et al.  Introduction to financial forecasting , 1996, Applied Intelligence.

[16]  C. Tan,et al.  Option price forecasting using neural networks , 2000 .

[17]  P. Young,et al.  Time series analysis, forecasting and control , 1972, IEEE Transactions on Automatic Control.

[18]  Philipp Slusallek,et al.  Introduction to real-time ray tracing , 2005, SIGGRAPH Courses.

[19]  David J. C. MacKay,et al.  Bayesian Interpolation , 1992, Neural Computation.

[20]  William Remus,et al.  Neural Network Models for Time Series Forecasts , 1996 .

[21]  Shouhong Wang,et al.  An insight into the standard back-propagation neural network model for regression analysis , 1998 .

[22]  Francis Eng Hock Tay,et al.  Financial Forecasting Using Support Vector Machines , 2001, Neural Computing & Applications.

[23]  Gwilym M. Jenkins,et al.  Time series analysis, forecasting and control , 1971 .