ARTIFICIAL INTELLIGENT BASED TIME SERIES FORECASTING OF STOCK PRICES USING DIGITAL FILTERS

A. Sfetsos and Costas Siriopoulos ()
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A. Sfetsos: Imperial College

Fuzzy Economic Review, 2002, vol. VII, issue 1, 29-44

Abstract: 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. (search for similar items in EconPapers)
JEL-codes: C53 G17 (search for similar items in EconPapers)
Date: 2002
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