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An empirical analysis of algorithmic trading around earnings announcements

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  • Frino, Alex
  • Prodromou, Tina
  • Wang, George H.K.
  • Westerholm, P. Joakim
  • Zheng, Hui

Abstract

This study examines the impact of corporate earnings announcements on trading activity and speed of price adjustment, analyzing algorithmic and non-algorithmic trades during the immediate period pre- and post-corporate earnings announcements. We confirm that algorithms react faster and more correctly to announcements than non-algorithmic traders. During the initial surge in trading activity in the first 90s after the announcement, algorithms time their trades better than non-algorithmic traders, hence algorithms tend to be profitable, while non-algorithmic traders make losing trades over the same time period. During the pre-announcement period, non-algorithmic volume imbalance leads algorithmic volume imbalance, however, in the post announcement period, the direction of the lead–lag association is exactly reversed. Our results suggest that as algorithms are the fastest traders, their trading accelerates the information incorporation process.

Suggested Citation

  • Frino, Alex & Prodromou, Tina & Wang, George H.K. & Westerholm, P. Joakim & Zheng, Hui, 2017. "An empirical analysis of algorithmic trading around earnings announcements," Pacific-Basin Finance Journal, Elsevier, vol. 45(C), pages 34-51.
    • Handle: RePEc:eee:pacfin:v:45:y:2017:i:c:p:34-51
    DOI: 10.1016/j.pacfin.2016.05.008
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    2. Salman Bahoo & Marco Cucculelli & Xhoana Goga & Jasmine Mondolo, 2024. "Artificial intelligence in Finance: a comprehensive review through bibliometric and content analysis," SN Business & Economics, Springer, vol. 4(2), pages 1-46, February.
    3. Carè, Rosella & Cumming, Douglas, 2024. "Technology and automation in financial trading: A bibliometric review," Research in International Business and Finance, Elsevier, vol. 71(C).
    4. Zhou, Hao & Elliott, Robert J. & Kalev, Petko S., 2019. "Information or noise: What does algorithmic trading incorporate into the stock prices?," International Review of Financial Analysis, Elsevier, vol. 63(C), pages 27-39.
    5. Zhou, Hao & Kalev, Petko S. & Frino, Alex, 2020. "Algorithmic trading in turbulent markets," Pacific-Basin Finance Journal, Elsevier, vol. 62(C).
    6. Nilabhra Bhattacharya & Bidisha Chakrabarty & Xu (Frank) Wang, 2020. "High-frequency traders and price informativeness during earnings announcements," Review of Accounting Studies, Springer, vol. 25(3), pages 1156-1199, September.
    7. Zhengxin Joseph Ye & Bjorn W. Schuller, 2020. "Capturing dynamics of post-earnings-announcement drift using genetic algorithm-optimised supervised learning," Papers 2009.03094, arXiv.org.
    8. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    9. Alex Frino & Michael Garcia & Zeyang Zhou, 2020. "Impact of algorithmic trading on speed of adjustment to new information: Evidence from interest rate derivatives," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 40(5), pages 749-760, May.
    10. Yan, Han & Liu, Bin & Zhu, Xingting & Wu, Yan, 2024. "Systemic risk monitoring model from the perspective of public information arrival," The North American Journal of Economics and Finance, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    Algorithmic trading; Earnings announcements; Market efficiency;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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