A review and empirical analysis of neural networks based exchange rate prediction

Financial time series data is very chaotic, noisy, fluctuating and nonlinear as different events have occurred in various time periods. Therefore, it is very challenging for researchers to develop the accurate predictive model. Prediction for Foreign Exchange rate is also a very crucial task for N days ahead prediction because of volatile nature of Foreign Exchange rate data. It is also become highly desirable due to it's role in financial and managerial decision making capacity of any country. A lot of efforts have been done by researchers over many years for the development of efficient models to improve the forecasting accuracy. As a result, various important time series forecasting models have been evolved in literature. From the literature survey we have analyzed that statistical techniques are not able to efficiently predict the Foreign Exchange rate. Hence, different machine learning techniques have been used by many researchers for accurate prediction. Over the years different types of neural network models such as multi - layer perceptron, radial basis function neural network, functional link artificial neural network and integrated model such as auto - regressive integrated moving average 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 the first part, we have reviewed a few selected models based on neural networks and statistical methods including fundamental and technical aspects of currency exchange rate prediction. In the second part, we have made a thorough and careful empirical study of the models reviewed in part one. Our study reveals that the daily currency exchange rates with multi - layer neural network having Bayesian learning technique produces more accurate results against the multi - layer neural network with back propagation learning technique. Similarly, integrated models of radial basis function neural network and functional link neural networks produce less amount of error in comparison to single radial basis neural networks and functional link neural network models. Additionally, we critically analyze an integrated work on statistical model such as auto-regressive integrated moving average model with neural networks and revealed that the integrated models produces better results than the individual models.