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A behavioural model of investor sentiment in limit order markets

Author

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  • Carl Chiarella
  • Xue-Zhong He
  • Lei Shi
  • Lijian Wei

Abstract

By incorporating behavioural sentiment in a model of a limit order market, we show that behavioural sentiment not only helps to replicate most of the stylized facts in limit order markets simultaneously, but it also plays a unique role in explaining those stylized facts that cannot be explained by noise trading, such as fat tails in the return distribution, long memory in the trading volume, an increasing and non-linear relationship between trade imbalance and mid-price returns, as well as the diagonal effect, or event clustering, in order submission types. The results show that behavioural sentiment is an important driving force behind many of the well-documented stylized facts in limit order markets.

Suggested Citation

  • Carl Chiarella & Xue-Zhong He & Lei Shi & Lijian Wei, 2017. "A behavioural model of investor sentiment in limit order markets," Quantitative Finance, Taylor & Francis Journals, vol. 17(1), pages 71-86, January.
    • Handle: RePEc:taf:quantf:v:17:y:2017:i:1:p:71-86
    DOI: 10.1080/14697688.2016.1184756
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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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