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Removing systematic patterns in returns in a financial market model by artificially intelligent traders

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  • Witte, Björn-Christopher
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    Abstract

    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. --

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    Bibliographic Info

    Paper provided by Bamberg University, Bamberg Economic Research Group in its series BERG Working Paper Series with number 82.

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    Date of creation: 2011
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    Handle: RePEc:zbw:bamber:82

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    Fax: 0951/8632550
    Web page: http://www.uni-bamberg.de/vwl/forschung/berg/
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    Related research

    Keywords: financial markets; autocorrelations; artificial intelligence; agent-based modeling;

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    1. Manzan, Sebastiano & Westerhoff, Frank, 2005. "Representativeness of news and exchange rate dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 29(4), pages 677-689, April.
    2. Giulia Iori, 1999. "A microsimulation of traders activity in the stock market: the role of heterogeneity, agents' interactions and trade frictions," Finance 9905005, EconWPA.
    3. Simone Alfarano & Thomas Lux & Friedrich Wagner, 2005. "Estimation of Agent-Based Models: The Case of an Asymmetric Herding Model," Computational Economics, Society for Computational Economics, vol. 26(1), pages 19-49, August.
    4. Arifovic, Jasmina & Gencay, Ramazan, 2000. "Statistical properties of genetic learning in a model of exchange rate," Journal of Economic Dynamics and Control, Elsevier, vol. 24(5-7), pages 981-1005, June.
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    Cited by:
    1. Franke, Reiner & Westerhoff, Frank, 2011. "Why a simple herding model may generate the stylized facts of daily returns: Explanation and estimation," BERG Working Paper Series 83, Bamberg University, Bamberg Economic Research Group.
    2. Seregi, János & Lelovics, Zsuzsanna & Balogh, László, 2012. "The social welfare function of forests in the light of the theory of public goods," BERG Working Paper Series 87, Bamberg University, Bamberg Economic Research Group.

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