Comparing Neural Networks and ARMA Models in Artificial Stock Market

Neural networks - feed-forward neural networks and Elman's simplerecurrent neural networks - are compared with vector ARMA models- VAR and VARMA - in this paper. They are compared in anartificial stock market. One risk free and one risky asset aretraded in the market. There are only trend followers in thismodel, which use the mentioned models for forecasting change of a price of the risky asset and the dividend.

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Year of publication:	2011
Authors:	Krtek, Jiri ; Vošvrda, Miloslav
Published in:	Bulletin of the Czech Econometric Society. - Česká ekonometrická společnost - CES. - Vol. 18.2011
Publisher:	Česká ekonometrická společnost - CES
Subject:	neural networks \| vector ARMA \| artificial market

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Extent:	text/html
Type of publication:	Article
Classification:	C23 - Models with Panel Data ; C45 - Neural Networks and Related Topics ; C53 - Forecasting and Other Model Applications ; G17 - Financial Forecasting
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10009371041