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Noise trading and the price formation process

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  • Berkman, Henk
  • Koch, Paul D.

Abstract

This study proposes the dispersion in daily net initiated order flow across brokers as a proxy for the level of noise trading in a stock, and applies this proxy to test some basic implications of market microstructure theory. We use data from the Australian Stock Exchange, a computerized limit order market where price, quantity, and broker identity for each incoming order are shown on broker screens. We find daily movements in our noise measure are positively associated with trading volume and market depth, and negatively related to the bid-ask spread. We find monthly movements in our noise measure are negatively associated with the probability of informed trading, and positively correlated with the arrival rate of uninformed traders. We also find the sensitivity of stock prices to net initiated order flow decreases in the level of noise trading. In addition we find that, after controlling for noise trading, the sensitivity of stock prices to net initiated order flow is significantly greater on Mondays. These empirical results consistently support the implications of various models of market microstructure, suggesting that our proxy provides useful information as a daily measure of noise trading.

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  • Berkman, Henk & Koch, Paul D., 2008. "Noise trading and the price formation process," Journal of Empirical Finance, Elsevier, vol. 15(2), pages 232-250, March.
    • Handle: RePEc:eee:empfin:v:15:y:2008:i:2:p:232-250
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    Cited by:

    1. Rannou, Yves, 2019. "Limit order books, uninformed traders and commodity derivatives: Insights from the European carbon futures," Economic Modelling, Elsevier, vol. 81(C), pages 387-410.
    2. Zhang, Chris H. & Frijns, Bart, 2019. "Noise trading and informational efficiency," EconStor Preprints 198037, ZBW - Leibniz Information Centre for Economics.
    3. Doojin Ryu & Robert I. Webb & Jinyoung Yu, 2023. "Who pays the liquidity cost? Central bank announcements and adverse selection," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 43(7), pages 904-924, July.
    4. Dean Hanlon & Sean Pinder, 2013. "Capital gains tax, supply-driven trading and ownership structure: direct evidence of the lock-in effect," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 53(2), pages 419-439, June.
    5. Agudelo, Diego A. & Giraldo, Santiago & Villarraga, Edwin, 2015. "Does PIN measure information? Informed trading effects on returns and liquidity in six emerging markets," International Review of Economics & Finance, Elsevier, vol. 39(C), pages 149-161.
    6. Zhang, Chris H. & Kalev, Petko S., 2021. "How noise trading affects informational efficiency: Evidence from an order-driven market," Pacific-Basin Finance Journal, Elsevier, vol. 68(C).
    7. Hua, Wei & Wei, Peihwang, 2017. "National culture, population age, and other country factors in volume–price volatility relationship," Global Finance Journal, Elsevier, vol. 32(C), pages 83-96.
    8. Albert Wang, F., 2010. "Informed arbitrage with speculative noise trading," Journal of Banking & Finance, Elsevier, vol. 34(2), pages 304-313, February.

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