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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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    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. 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.
    3. Gao-Feng Gu & Xiong Xiong & Hai-Chuan Xu & Wei Zhang & Yongjie Zhang & Wei Chen & Wei-Xing Zhou, 2021. "An empirical behavioral order-driven model with price limit rules," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 7(1), pages 1-24, December.
    4. 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.
    5. Lijian Wei & Lei Shi, 2020. "Investor Sentiment in an Artificial Limit Order Market," Complexity, Hindawi, vol. 2020, pages 1-10, June.
    6. Roberto Dieci & Xue-Zhong He, 2018. "Heterogeneous Agent Models in Finance," Research Paper Series 389, Quantitative Finance Research Centre, University of Technology, Sydney.
    7. Gaoshan Wang & Guangjin Yu & Xiaohong Shen, 2020. "The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach," Complexity, Hindawi, vol. 2020, pages 1-11, December.
    8. Lin Liu, 2022. "Economic Uncertainty and Exchange Market Pressure: Evidence From China," SAGE Open, , vol. 12(1), pages 21582440211, January.
    9. 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.
    10. 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 Kiel).

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