Affiliations: Department of Theoretical Economics, Vilnius University, Saulėtekio 9, 2040 Vilnius, Lithuania. E-mail: d.plikynas@delfi.lt | Department of Economical Informatics, Vilnius University, Saulėtekio 9, 2040 Vilnius, Lithuania. E-mail: leonas.simanauskas@ef.vu.lt | Institute of Mathematics and Informatics, Akademijos 4, LT-2021 Vilnius, Lithuania. E-mail: s.buda@it.lt
Abstract: The presented article is about a research using artificial neural network (ANN) methods for compound (technical and fundamental) analysis and prognosis of Lithuania's National Stock Exchange (LNSE) indices LITIN, LITIN-A and LITIN-VVP. We employed initial pre-processing (analysis for entropy and correlation) for filtering out model input variables (LNSE indices, macroeconomic indicators, Stock Exchange indices of other countries such as the USA – Dow Jones and S&P, EU – Eurex, Russia – RTS). Investigations for the best approximation and forecasting capabilities were performed using different backpropagation ANN learning algorithms, configurations, iteration numbers, data form-factors, etc. A wide spectrum of different results has shown a high sensitivity to ANN parameters. ANN autoregressive, autoregressive causative and causative trend model performances were compared in the approximation and forecasting by a linear discriminant analysis.
Keywords: neural networks, artificial intelligence, forecasting, time series
DOI: 10.3233/INF-2002-13407
Journal: Informatica, vol. 13, no. 4, pp. 465-484, 2002
