Prediction of currency exchange rate becomes highly desirable due to its greater role in financial and managerial decision making process. The fluctuations in exchange rate affect the economy of a country. Hence, over the years different types of neural network models along with statistical models are developed to predict the currency exchange rates of different countries with varying parameters. In this paper, we divide our effort into two parts. In first part, we have reviewed a few selected models of neural networks and statistics including fundamental and technical aspects of currency exchange rate prediction. Additionally, a thorough and careful experimental result analysis has been conducted on the models reviewed in part one. A committee machine has been proposed in part two to address the shortcomings of both neural networks and statistical models in the context of exchange rate prediction. Our study reveals that the currency exchange rates with multi-layer neural networks having Bayesian learning predictive accuracy is better than multi-layer neural networks with back-propagation learning. However, in the case of higher-order neural network multi-stage radial basis function network is predicting better than single stage radial basis function network. In the case of statistical models, it is drawn that under the umbrella of root mean square error measure, random walk is predicting better than other models of this category, whereas variance based model predicts better than rest of the models grouped under normalized mean square error measure. On the other hand, the integrated model is performing better than its counterpart like models with stand-alone mode. Moreover, our newly proposed committee machine is drawing a clear line over all the models while predicting exchange rate of GBP/USD.
|Number of pages||13|
|Journal||Journal of King Saud University - Computer and Information Sciences|
|Publication status||Published - 2020 Nov|
Bibliographical notePublisher Copyright:
© 2018 The Authors
All Science Journal Classification (ASJC) codes
- Computer Science(all)