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Investor Sentiment in an Artificial Limit Order Market

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  • Lijian Wei
  • Lei Shi

Abstract

This paper examines the under/overreaction effect driven by sentiment belief in an artificial limit order market when agents are risk averse and arrive in the market with different time horizons. We employ agent-based modeling to build up an artificial stock market with order book and model a type of sentiment belief display over/underreaction by following a Bayesian learning scheme with a Markov regime switching between conservative bias and representative bias. Simulations show that when compared with classic noise belief without learning, sentiment belief gives rise to short-term intraday return predictability. In particular, under/overreaction trading strategies are profitable under sentiment beliefs, but not under noise belief. Moreover, we find that sentiment belief leads to significantly lower volatility, lower bid-ask spread, and larger order book depth near the best quotes but lower trading volume when compared with noise belief.

Suggested Citation

  • Lijian Wei & Lei Shi, 2020. "Investor Sentiment in an Artificial Limit Order Market," Complexity, Hindawi, vol. 2020, pages 1-10, June.
    • Handle: RePEc:hin:complx:8581793
    DOI: 10.1155/2020/8581793
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    References listed on IDEAS

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    1. Gao, Lei & Han, Yufeng & Zhengzi Li, Sophia & Zhou, Guofu, 2018. "Market intraday momentum," Journal of Financial Economics, Elsevier, vol. 129(2), pages 394-414.
    2. Zhang, Yaojie & Ma, Feng & Zhu, Bo, 2019. "Intraday momentum and stock return predictability: Evidence from China," Economic Modelling, Elsevier, vol. 76(C), pages 319-329.
    3. Klößner, Stefan & Becker, Martin & Friedmann, Ralph, 2012. "Modeling and measuring intraday overreaction of stock prices," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 1152-1163.
    4. De Long, J Bradford & Andrei Shleifer & Lawrence H. Summers & Robert J. Waldmann, 1990. "Noise Trader Risk in Financial Markets," Journal of Political Economy, University of Chicago Press, vol. 98(4), pages 703-738, August.
    5. Steven L. Heston & Robert A. Korajczyk & Ronnie Sadka, 2010. "Intraday Patterns in the Cross‐section of Stock Returns," Journal of Finance, American Finance Association, vol. 65(4), pages 1369-1407, August.
    6. Muzhao Jin & Fearghal Kearney & Youwei Li & Yung Chiang Yang, 2020. "Intraday time‐series momentum: Evidence from China," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(4), pages 632-650, April.
    7. Barberis, Nicholas & Shleifer, Andrei & Vishny, Robert, 1998. "A model of investor sentiment," Journal of Financial Economics, Elsevier, vol. 49(3), pages 307-343, September.
    8. 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.
    9. 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.
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