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A Behavioural Model of Investor Sentiment in Limit Order Markets

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Abstract

This paper examines the effect of behavioral sentiment in a limit order market when agents are risk averse and arrive in the market with different time horizons. The order submission rules with respect to order type and size are determined by maximizing the expected utility of agents with heterogeneous beliefs on the fundamental price and investment horizon. We show that behavioral sentiment has a double-edge impact on market quality: it improves market liquidity by reducing bid-ask spread and market volatility but increasing trading volume; however, it reduces pricing efficiency by increasing the price deviation from the fundamental value. Consistent with empirical observations, the model is able to replicate a number of stylized facts and limit book phenomena, including insignificant autocorrelations in returns, but significant and decaying autocorrelations in the absolute returns, the bid-ask spread and the trading volume, hump shaped order books, a concave relationship between trade imbalance and average mid-price returns, and event clustering in order submissions. More important, the behavioral sentiment plays a very important role in explaining the positive autocorrelation in trading volume and also event clustering.

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  • Carl Chiarella & Xue-Zhong He & Lei Shi & Lijian Wei, 2014. "A Behavioural Model of Investor Sentiment in Limit Order Markets," Research Paper Series 342, Quantitative Finance Research Centre, University of Technology, Sydney.
  • Handle: RePEc:uts:rpaper:342
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    1. Chiarella, Carl & Iori, Giulia, 2009. "The impact of heterogeneous trading rules on the limit order book and order flows," Journal of Economic Dynamics and Control, Elsevier, vol. 33(3), pages 525-537.
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    6. Juhani T. Linnainmaa, 2010. "Do Limit Orders Alter Inferences about Investor Performance and Behavior?," Journal of Finance, American Finance Association, vol. 65(4), pages 1473-1506, August.
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    10. Covrig, Vicentiu & Ng, Lilian, 2004. "Volume autocorrelation, information, and investor trading," Journal of Banking & Finance, Elsevier, vol. 28(9), pages 2155-2174, September.
    11. Xavier Gabaix & Parameswaran Gopikrishnan & Vasiliki Plerou & H. Eugene Stanley, 2006. "Institutional Investors and Stock Market Volatility," The Quarterly Journal of Economics, Oxford University Press, vol. 121(2), pages 461-504.
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    13. repec:hrv:faseco:30747159 is not listed on IDEAS
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    Cited by:

    1. Danilo Liuzzi & Paolo Pellizzari & Marco Tolotti, 2019. "Fast traders and slow price adjustments: an artificial market with strategic interaction and transaction costs," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 14(3), pages 643-662, September.
    2. Gao-Feng Gu & Xiong Xiong & Hai-Chuan Xu & Wei Zhang & Yong-Jie Zhang & Wei Chen & Wei-Xing Zhou, 2017. "An empirical behavioural order-driven model with price limit rules," Papers 1704.04354, arXiv.org.
    3. Chiarella, Carl & He, Xue-Zhong & Wei, Lijian, 2015. "Learning, information processing and order submission in limit order markets," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 245-268.
    4. Michiel Leur & Mikhail Anufriev, 2018. "Timing under individual evolutionary learning in a continuous double auction," Journal of Evolutionary Economics, Springer, vol. 28(3), pages 609-631, August.
    5. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    6. Zhou, Liyun & Yang, Chunpeng, 2019. "Stochastic investor sentiment, crowdedness and deviation of asset prices from fundamentals," Economic Modelling, Elsevier, vol. 79(C), pages 130-140.
    7. Schasfoort, Joeri & Stockermans, Christopher, 2017. "Fundamentals unknown: Momentum, mean-reversion and price-to-earnings trading in an artificial stock market," Economics Discussion Papers 2017-63, Kiel Institute for the World Economy (IfW).

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