Please use this identifier to cite or link to this item:
Title: A case study on using neural networks to perform technical forecasting of forex
Authors: Yao, J.
Tan, C.L. 
Keywords: Forecasting
Foreign exchange rate
Neural network
Time series
Issue Date: 2000
Citation: Yao, J., Tan, C.L. (2000). A case study on using neural networks to perform technical forecasting of forex. Neurocomputing 34 : 79-98. ScholarBank@NUS Repository.
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. (C) 2000 Elsevier Science B.V. All rights reserved.
Source Title: Neurocomputing
ISSN: 09252312
DOI: 10.1016/S0925-2312(00)00300-3
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.


checked on Mar 23, 2020


checked on Mar 23, 2020

Page view(s)

checked on Mar 22, 2020

Google ScholarTM



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.