The aim of the paper is the analysis of the sequential characteristics of the Athens Stock Exchange general index (ASE) using the time series metho-dology based on artificial intelligent techniques. The applied models include the Feed Forward Neural Network trained with the efficient Levenberg - Marquardt optimization algorithm, the Adaptive Neuro-Fuzzy Inference Sys-tem as well as traditional linear regression and ARIMA models for comparison. All these approaches are initially used for the short-term fore-casting of the series, providing an insight into the forecasting capabilities of each model. The analysis of the spectral characteristics of the series indicated the presence of strong persis-tence or alternatively that the models do not differ significantly from a random walk. This observation was also cemen-ted by the forecasting results of the developed models. The proposed approach is based on the application of low-pass digital filters on the series and the employment of the formerly mentioned models for the prediction of the created series. The filtered series contains a lower amount of noise and can be viewed as an alternative trend indication of the original series.
Keywords: stock prices, forecasting, neural networks, ANFIS, filters.