Consistent expectations equilibria and learning in a stock market

Sögner, Leopold and Mitlöhner, Johann (1999) Consistent expectations equilibria and learning in a stock market. Working Papers SFB "Adaptive Information Systems and Modelling in Economics and Management Science", 30. SFB Adaptive Information Systems and Modelling in Economics and Management Science, WU Vienna University of Economics and Business, Vienna.


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In this article we investigate the question whether the highly demanding informative requirements of rational expectations models are necessary to derive equilibria within capital market models. In the analysis agents are only provided with publicly available information such as prices and dividends. Nevertheless, we require that agents should behave like econometricians. Additionally, we skip the assumption of rational expectations models that agents know the implied law of motion of the system. By these assumptions, the stock market can be considered as a Sorger-Hommes consistent expectations model. In this article, we show the existence of consistent expectations equilibria with myopic agents, where the only CEE is the rational expectations equilibrium. In the simulation part we demonstrate how the steady state CEE can be derived by means of sample autocorrelation learning. Thus, we are able to derive a stock market equilibrium with less demanding requirements, where this equilibrium is equal to the rational expectations equilibrium. (author's abstract)

Item Type: Paper
Keywords: artificial markets / consistent expectations / learning
Classification Codes: JEL_D83, JEL_D84, JEL_G10
Divisions: Departments > Informationsverarbeitung u Prozessmanag. > Produktionsmanagement > Taudes
Departments > Finance, Accounting and Statistics > Statistics and Mathematics
Departments > Marketing > Service Marketing und Tourismus
Departments > Informationsverarbeitung u Prozessmanag. > Informationswirtschaft
Depositing User: Repository Administrator
Date Deposited: 04 Mar 2002 11:57
Last Modified: 22 Oct 2019 00:41


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