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Intraday trading patterns in an intelligent autonomous agent-based stock market

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  • Kluger, Brian D.
  • McBride, Mark E.

Abstract

Abstract Market microstructure studies of intraday trading patterns have established that there is a regular pattern of high volumes near both the open and close of the trading day. O'Hara (1995) points out the many difficulties in specifying all the necessary elements of a strategic model for determining and attaining an equilibrium describing intraday patterns. We develop an autonomous agent-based market microstructure simulation with both informed agents and uninformed liquidity-motivated agents. Both types of agents can learn when to trade, but are zero-intelligence on all other behavior. We do not impose an equilibrium concept but instead look for emergent behavior. Our results demonstrate that trading patterns can arise in such a model as a result of interactions between informed and uninformed agents even though the agents are non-strategic and not fully rational. As long as there is rudimentary social or individual learning, uninformed liquidity-motivated agents can coordinate to avoid trading with informed agents and suffering adverse selection losses. The extent and pattern of coordination between uninformed agents depends on the learning specification, the percentage of informed agents and the degree of cooperation/competition among the informed agents.

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Bibliographic Info

Article provided by Elsevier in its journal Journal of Economic Behavior & Organization.

Volume (Year): 79 (2011)
Issue (Month): 3 (August)
Pages: 226-245

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Handle: RePEc:eee:jeborg:v:79:y:2011:i:3:p:226-245

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Web page: http://www.elsevier.com/locate/jebo

Related research

Keywords: Agent-based artificial stock markets Intraday trading patterns;

References

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Citations

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Cited by:
  1. Pyo, Dong-Jin, 2014. "A Multi-Factor Model of Heterogeneous Traders in a Dynamic Stock Market," Staff General Research Papers 37358, Iowa State University, Department of Economics.
  2. Doris Neuberger & Roger Rissi, 2014. "Macroprudential Banking Regulation: Does One Size Fit All?," Journal of Banking and Financial Economics, University of Warsaw, Faculty of Management, vol. 1(1), pages 5-28, May.
  3. Chen, Shu-Heng, 2012. "Varieties of agents in agent-based computational economics: A historical and an interdisciplinary perspective," Journal of Economic Dynamics and Control, Elsevier, vol. 36(1), pages 1-25.
  4. Lijian Wei & Wei Zhang & Xue-Zhong He & Yongjie Zhang, 2013. "Learning and Information Dissemination in Limit Order Markets," Research Paper Series 333, Quantitative Finance Research Centre, University of Technology, Sydney.
  5. Carl Chiarella & Xue-Zhong He & Lijian Wei, 2013. "Learning and Evolution of Trading Strategies in Limit Order Markets," Research Paper Series 335, Quantitative Finance Research Centre, University of Technology, Sydney.

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