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High-Frequency Measures of Informed Trading and Corporate Announcements

Author

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  • Michael J. Brennan
  • Sahn-Wook Huh
  • Avanidhar Subrahmanyam

Abstract

We explore the dynamics of informed trading around corporate announcements of merger bids, dividend initiations, SEOs, and quarterly earnings by calculating daily posterior probabilities of informed buying and selling. We find evidence of informed trading before the announcements and a significant part of the news in announcements is impounded in stock prices before the announcements by pre-event informed trading. We also find evidence of informed trading after the announcements. Most strikingly, the probability of informed trading after merger bids predicts the probability of the bid being withdrawn or met with a competing bid. For other announcements, post-announcement informed-trading probabilities predict subsequent returns. Received September 26, 2016; editorial decision December 17, 2017 by Editor Andrew Karolyi.

Suggested Citation

  • Michael J. Brennan & Sahn-Wook Huh & Avanidhar Subrahmanyam, 2018. "High-Frequency Measures of Informed Trading and Corporate Announcements," The Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2326-2376.
    • Handle: RePEc:oup:rfinst:v:31:y:2018:i:6:p:2326-2376.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhy005
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    Cited by:

    1. Griffin, Jim & Oberoi, Jaideep & Oduro, Samuel D., 2021. "Estimating the probability of informed trading: A Bayesian approach," Journal of Banking & Finance, Elsevier, vol. 125(C).
    2. Chen, Tao, 2020. "Does news affect disagreement in global markets?," Journal of Business Research, Elsevier, vol. 109(C), pages 174-183.
    3. Van Ness, Bonnie & Van Ness, Robert & Yildiz, Serhat, 2021. "Private information in trades, R2, and large stock price movements," Journal of Banking & Finance, Elsevier, vol. 131(C).
    4. Adra, Samer & Barbopoulos, Leonidas G., 2023. "The informational consequences of good and bad mergers," Journal of Corporate Finance, Elsevier, vol. 78(C).
    5. Li, Zhuo & Wen, Fenghua & Huang, Zhijian James, 2023. "Asymmetric response to earnings news across different sentiment states: The role of cognitive dissonance," Journal of Corporate Finance, Elsevier, vol. 78(C).
    6. Rösch, Dominik M. & Subrahmanyam, Avanidhar & van Dijk, Mathijs A., 2022. "Investor short-termism and real investment," Journal of Financial Markets, Elsevier, vol. 59(PB).
    7. Cookson, J. Anthony & Moon, S. Katie & Noh, Joonki, 2020. "Imprecise and Informative: Lessons from Market Reactions to Imprecise Disclosure," SocArXiv akt2c, Center for Open Science.
    8. Dodd, Olga & Frijns, Bart & Indriawan, Ivan & Pascual, Roberto, 2023. "US cross-listing and domestic high-frequency trading: Evidence from Canadian stocks," Journal of Empirical Finance, Elsevier, vol. 72(C), pages 301-320.
    9. Ping-Chen Tsai & Chi-Ming Tsai, 2021. "Estimating the proportion of informed and speculative traders in financial markets: evidence from exchange rate," Journal of Economic Interaction and Coordination, Springer;Society for Economic Science with Heterogeneous Interacting Agents, vol. 16(3), pages 443-470, July.
    10. Marc Bohmann, 2020. "Price Discovery and Information Asymmetry in Equity and Commodity Futures Options Markets," PhD Thesis, Finance Discipline Group, UTS Business School, University of Technology, Sydney, number 1-2020.
    11. Ruwei Zhao & Xiong Xiong & Dehua Shen & Wei Zhang, 2019. "Investor Structure and Stock Price Crash Risk in a Continuous Double Auction Market: An Agent-Based Perspective," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 695-715, March.
    12. Emily Lin & Chu-Lan Michael Kao & Natasha Sonia Adityarini, 2021. "Data-driven tree structure for PIN models," Review of Quantitative Finance and Accounting, Springer, vol. 57(2), pages 411-427, August.
    13. Kurov, Alexander & Sancetta, Alessio & Wolfe, Marketa Halova, 2022. "Drift Begone! Release policies and preannouncement informed trading," Journal of International Money and Finance, Elsevier, vol. 128(C).
    14. Chen, Tao, 2021. "Informed trading and earnings announcement driven disagreement in global markets," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 43(C).
    15. Jurkatis, Simon, 2020. "Inferring trade directions in fast markets," Bank of England working papers 896, Bank of England.
    16. Jurkatis, Simon, 2022. "Inferring trade directions in fast markets," Journal of Financial Markets, Elsevier, vol. 58(C).
    17. Marie Dutordoir & Evangelos Vagenas‐Nanos & Patrick Verwijmeren & Betty Wu, 2021. "A rundown of merger target run‐ups," Financial Management, Financial Management Association International, vol. 50(2), pages 487-518, June.
    18. Duarte, Jefferson & Hu, Edwin & Young, Lance, 2020. "A comparison of some structural models of private information arrival," Journal of Financial Economics, Elsevier, vol. 135(3), pages 795-815.
    19. Phan, Hoàng-Long & Zurbruegg, Ralf & Brockman, Paul & Yu, Chia-Feng (Jeffrey), 2022. "Time-to-maturity and commodity futures return volatility: The role of time-varying asymmetric information," Journal of Commodity Markets, Elsevier, vol. 26(C).
    20. Bohmann, Marc & Michayluk, David & Patel, Vinay & Walsh, Kathleen, 2019. "Liquidity and earnings in event studies: Does data granularity matter?," Pacific-Basin Finance Journal, Elsevier, vol. 54(C), pages 118-131.

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