A case study on using neural networks to perform technical forecasting of forex
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Jingtao Yao
, 
, a and Chew Lim Tanb [Author vitae]a Department of Information Systems, Massey University, Palmerston North, New Zealand
b School of Computing, National University of Singapore, Singapore 119260, Singapore
Received 15 November 1997;
accepted 12 April 2000.
Available online 21 August 2000.
Abstract
This paper reports empirical evidence that a neural network model is applicable to the prediction of foreign exchange rates. Time series data and technical indicators, such as moving average, are fed to neural networks to capture the underlying “rules” of the movement in currency exchange rates. The exchange rates between American Dollar and five other major currencies, Japanese Yen, Deutsch Mark, British Pound, Swiss Franc and Australian Dollar are forecast by the trained neural networks. The traditional rescaled range analysis is used to test the “efficiency” of each market before using historical data to train the neural networks. The results presented here show that without the use of extensive market data or knowledge, useful prediction can be made and significant paper profits can be achieved for out-of-sample data with simple technical indicators. A further research on exchange rates between Swiss Franc and American Dollar is also conducted. However, the experiments show that with efficient market it is not easy to make profits using technical indicators or time series input neural networks. This article also discusses several issues on the frequency of sampling, choice of network architecture, forecasting periods, and measures for evaluating the model's predictive power. After presenting the experimental results, a discussion on future research concludes the paper.Author Keywords: Foreign exchange rate; Neural network; Forecasting; Time series
Fig. 1. Purely time-delay model prediction on weekly exchange rates, Nov 1993–July 1995.
Fig. 2. Indicator model prediction on weekly exchange rates, Nov 1993–July 1995.
Fig. 3. Each models profits compared with benchmarks. The vertical axis is the annualized profit in percentage, the horizontal axis is the names of different time periods.
Table 1. Weekly foreign exchange rates statistics: number of observations: 510; period: May 1984–July 1995 
Table 2. Hurst exponent and correlation for the experimented five currencies
Table 3. The out of sample forecasting results for neural network models (delay method) for weekly foreign exchange dataa
Table 4. The testing results for neural network models using indicators for weekly foreign exchange dataa
Table 5. The result of using ARIMA (AUD(101) stands for the ARIMA result of AUD using ARIMA(1, 0, 1) model and the same rules are applied to other currencies)
Table 6. The technical details of chosen architectures and their forecasting potentialsa
Table 7. Benchmark results for different time period
Table 8. Analysis of results for different modelsa
Corresponding author; email:
j.t.yao@massey.ac.nz
Vitae
Yao:
J. Yao is a Senior Lecturer in the Department Information Systems, College of Business, Massey University. (J.T.Yao@massey.ac.nz) Jingtao Yao was a Teaching Assistant in the Department of Information Systems, School of Computing, National University of Singapore. His research interests include financial forecasting, neural networks, software engineering, and business process reengineering. His research has been published in international refereed journals and conference proceedings. He obtained a B.Eng. (1983), a M.Sc. (1988) in Computer Science from Xi'an Jiaotong University, and a Ph.D. (1999) in Information Systems from the National University of Singapore.
Tan:
Chew Lim Tan is an Associate Professor in the Department of Computer Science, School of Computing, National University of Singapore. His research interests are expert systems, neural networks, computer vision and natural language processing. He obtained a B.Sc. (Hons) degree in Physics in 1971 from the University of Singapore, an M.Sc. degree in Radiation Studies in 1973 from the University of Surrey in UK, and a Ph.D. degree in Computer Science in 1986 from the University of Virginia, USA.
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