An empirical analysis of algorithmic trading around earnings announcements

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

Pacific-Basin Finance Journal, 2017, vol. 45, issue C, 34-51

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.

Keywords: Algorithmic trading; Earnings announcements; Market efficiency (search for similar items in EconPapers)
JEL-codes: G12 G14 (search for similar items in EconPapers)
Date: 2017
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Citations: View citations in EconPapers (10)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:pacfin:v:45:y:2017:i:c:p:34-51

DOI: 10.1016/j.pacfin.2016.05.008

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