Dark Trading and Post-Earnings-Announcement Drift

• 成果类型: Article
• 署名作者: Thomas, Jacob; Zhang, Frank; Zhu, Wei
• 署名单位: Yale University; University of Illinois System; University of Illinois Urbana-Champaign
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2020.3828
• 发表日期: 2021
• 页码: 7785-7811
• 关键词: Dark trading; post-earnings-announcement drift; arbitrage costs; Price efficiency

摘要

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.