Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/54993 
Erscheinungsjahr: 
2011
Schriftenreihe/Nr.: 
BERG Working Paper Series on Government and Growth No. 82
Verlag: 
Bamberg University, Bamberg Economic Research Group on Government and Growth (BERG), Bamberg
Zusammenfassung: 
The unpredictability of returns counts as a stylized fact of financial markets. To reproduce this fact, modelers usually implement noise terms - a method with several downsides. Above all, systematic patterns are not eliminated but merely blurred. The present article introduces a model in which systematic patterns are removed endogenously. This is achieved in a reality-oriented way: Intelligent traders are able to identify patterns and exploit them. To identify and predict patterns, a very simple artificial neural network is used. As neural network mimic the cognitive processes of the human brain, this method might be regarded as a quite accurate way of how traders identify patterns and forecast prices in reality. The simulation experiments show that the artificial traders exploit patterns effectively and thereby remove them, which ultimately leads to the unpredictability of prices. Further results relate to the influence of pattern exploiters on market efficiency.
Schlagwörter: 
financial markets
autocorrelations
artificial intelligence
agent-based modeling
JEL: 
C45
G14
G17
ISBN: 
978-3-931052-92-8
Dokumentart: 
Working Paper

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