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dc.contributor.authorWitte, Björn-Christopheren_US
dc.date.accessioned2012-01-27en_US
dc.date.accessioned2012-02-03T13:07:37Z-
dc.date.available2012-02-03T13:07:37Z-
dc.date.issued2011en_US
dc.identifier.isbn978-3-931052-92-8en_US
dc.identifier.urihttp://hdl.handle.net/10419/54993-
dc.description.abstractThe 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.en_US
dc.language.isoengen_US
dc.publisherBERG Bambergen_US
dc.relation.ispartofseriesBERG working paper series on government and growth 82en_US
dc.subject.jelC45en_US
dc.subject.jelG14en_US
dc.subject.jelG17en_US
dc.subject.ddc330en_US
dc.subject.keywordfinancial marketsen_US
dc.subject.keywordautocorrelationsen_US
dc.subject.keywordartificial intelligenceen_US
dc.subject.keywordagent-based modelingen_US
dc.titleRemoving systematic patterns in returns in a financial market model by artificially intelligent tradersen_US
dc.typeWorking Paperen_US
dc.identifier.ppn684357208en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen_US
dc.identifier.repecRePEc:zbw:bamber:82-
Appears in Collections:BERG Working Paper Series, Universität Bamberg

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