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Comparing Neural Networks and ARMA Models in Artificial Stock Market
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SYSNO ASEP 0361537 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Ostatní články Title Comparing Neural Networks and ARMA Models in Artificial Stock Market Author(s) Krtek, Jiří (UTIA-B)
Vošvrda, Miloslav (UTIA-B) RIDNumber of authors 2 Source Title Bulletin of the Czech Econometric Society - ISSN 1212-074X
Roč. 18, č. 28 (2011), s. 53-65Number of pages 13 s. Language eng - English Country CZ - Czech Republic Keywords neural networks ; vector ARMA ; artificial market Subject RIV AH - Economics R&D Projects GD402/09/H045 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) Annotation Neural networks - feed-forward neural networks and Elman's simple recurrent neural networks - are compared with vector ARMA models - VAR and VARMA - in this paper. They are compared in anartifical stock market. One risk free and one risky asset are traded in the market. There are only trend followers in this model, which use the mentioned models for forecasting change of a price of the risky asset and the dividend. traded in the market Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2012
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