An empirical analysis of algorithmic trading around earnings announcements



Publication Details

Frino, A., Prodromou, T., Wang, G. H. K., Westerholm, P. & Zheng, H. (2017). An empirical analysis of algorithmic trading around earnings announcements. Pacific-Basin Finance Journal, 45 34-51.


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 90. s 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.

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