INFORMATICA International Journal Main Page Editorial Board Abstracting/Indexing Instructions to Authors Subscription Information Contents Author Index Papers in Production		INFORMATICA, 2002, Vol. 13, No. 4, 465-484 © Institute of Mathematics and Informatics, ISSN 0868-4952 Research of Neural Network Methods for Compound Stock Exchange Indices Analysis Darius PLIKYNASa, Leonas SIMANAUSKASb, Sigitas BUDAc aDepartment of Theoretical Economics, Vilnius University Sauletekio 9, 2040 Vilnius, Lithuania E-mail: d.plikynas@delfi.lt bDepartment of Economical Informatics, Vilnius University Sauletekio 9, 2040 Vilnius, Lithuania E-mail: leonas.simanauskas@ef.vu.lt cInstitute 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 To preview Lithuanian abstract see full article text To preview full article text in PDF format click here You could obtain free Acrobat Reader from Adobe
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