Modeling and Forecasting Financial Time Series, comparison of forecasting ability of neural networks, genetic programming and econometric methods
Ali Moltajaei Farid, Faiza Ather, Omer Sheikh, Umair Siddiqui · 2003
Financial time series like stock prices and exchange rate are non linear and non trivial in nature. The series is stochastic which makes it difficulty for modeling and prediction. Traditionally statistical methods like statistical clustering and regression analysis have been used for modeling the series. However most of models are more suitable for linear processes and their application to nonlinear series have generally shown less satisfactory results. Since the later half of the last decade, developments in the field of artificial intelligence and soft computing have made possible the use of neural networks for financial forecasting. Neural Networks, inspired from human neural system have the ability to approximate non linear functions. A further development in the field of AI has been the evolutionary regression or genetic programming method. Designing neural networks and genetic programming for robust financial prediction is a subject of on going research. This paper employs neural networks, genetic programming and regression based methods for modeling exchange rate series. Experimentation has been attempted with the input output set and the design of neural networks to achieve accurate modeling. This paper discusses the experimentation methods and the modeling techniques, which have been used, and compares the results that have been obtained from them. The results show that radial basis networks are the most suitable for forecasting the series and this model leads to very accurate prediction.