Dark Trading and Post-Earnings-Announcement Drift

Jacob Thomas (), Frank Zhang () and Wei Zhu ()
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Jacob Thomas: Yale School of Management, Yale University, New Haven, Connecticut 06520
Frank Zhang: Yale School of Management, Yale University, New Haven, Connecticut 06520
Wei Zhu: Gies College of Business, University of Illinois at Urbana-Champaign, Champaign, Illinois 61820

Management Science, 2021, vol. 67, issue 12, 7785-7811

Abstract: Both theory and evidence are mixed regarding the impact on prices of trading on “dark” venues partially exempt from National Market System requirements. Theory predicts that price discovery improves as dark venues siphon noisy uninformed trades, but increased adverse selection reduces liquidity. Empirical studies, which focus on intraday inefficiency, also find contradictory results. We extend that literature to investigate the impact of dark trading on a long-standing inefficiency based on under-reaction to quarterly earnings. We study a randomized controlled trial created by the “trade-at” rule of the Securities and Exchange Commission’s Tick Size Pilot Program that exogenously shocks dark trading. We supplement that with ordinary least squares and two-stage least squares regressions on a more representative Compustat/Center for Research in Security Prices sample. All our results suggest that under-reaction increases with dark trading, consistent with reduced liquidity limiting arbitrage. We contribute to the literature on dark trading and inefficient processing of accounting disclosures, highlighting the role of advances in trading technology.

Keywords: dark trading; post-earnings-announcement drift; arbitrage costs; price efficiency (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (1)

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