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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

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.

Suggested Citation

  • Witte, Björn-Christopher, 2011. "Removing systematic patterns in returns in a financial market model by artificially intelligent traders," BERG Working Paper Series 82, Bamberg University, Bamberg Economic Research Group.
    • Handle: RePEc:zbw:bamber:82
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    4. Schmitt, Noemi & Westerhoff, Frank, 2018. "Evolutionary Competition And Profit Taxes: Market Stability Versus Tax Burden," Macroeconomic Dynamics, Cambridge University Press, vol. 22(8), pages 2007-2031, December.
    5. Noemi Schmitt & Frank Westerhoff, 2017. "Herding behaviour and volatility clustering in financial markets," Quantitative Finance, Taylor & Francis Journals, vol. 17(8), pages 1187-1203, August.
    6. 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.
    7. Schmitt, Noemi & Tuinstra, Jan & Westerhoff, Frank, 2017. "Side effects of nonlinear profit taxes in an evolutionary market entry model: Abrupt changes, coexisting attractors and hysteresis problems," Journal of Economic Behavior & Organization, Elsevier, vol. 135(C), pages 15-38.
    8. Fatoke Dato, Mafaizath A., 2015. "Impact of income shock on children’s schooling and labor in a West African country," MPRA Paper 64317, University Library of Munich, Germany.
    9. Fatoke-Dato, Mafaïzath A., 2015. "Impact of an educational demand-and-supply policy on girls' education in West Africa: Heterogeneity in income, school environment and ethnicity," BERG Working Paper Series 101, Bamberg University, Bamberg Economic Research Group.
    10. González-Díaz, Julio & Herold, Florian & Domínguez, Diego, 2016. "Strategic sequential voting," BERG Working Paper Series 113, Bamberg University, Bamberg Economic Research Group.
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    More about this item

    Keywords

    financial markets; autocorrelations; artificial intelligence; agent-based modeling;
    All these keywords.

    JEL classification:

    • C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation

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