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Complexity
Article . 2003 . Peer-reviewed
License: Wiley Online Library User Agreement
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Article . 2020
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Heterogeneous trading agents

Authors: Luc Neuberg; Koen Bertels;

Heterogeneous trading agents

Abstract

AbstractIn this article, we present a multiagent system (MAS) simulation of a financial market and investigate the requirements to obtain realistic data. The model consists of autonomous, interactive agents that buy stock on a financial market. Transaction decisions are based on a number of individual and collective elements, the former being risk aversion and a set of decision rules reflecting their anticipation of the future evolution of prices and dividends and the latter the information arriving on the market influencing the decision making process of each trader. We specifically look at this process and the following observations hold: The market behavior is determined by the information arriving at the market and agent heterogeneity is required in order to obtain the right statistical properties of the price and return time series. The observed results are not sensitive to changes in the parameter values. © 2003 Wiley Periodicals, Inc.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
7
Average
Top 10%
Average
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