Attention: How High-Frequency Trading Improves Price Efficiency Following Earnings Announcements

How does limited attention affect stock prices following earnings announcements in today’s computer-driven financial markets? We examine the effects of limited attention using a dataset that separately identifies trades made by high-frequency traders (HFTs, or computers) versus non-high-frequency traders (human decision-makers). Using six attention proxies, we find pricing inefficiencies lower by 64% to 100% when HFTs trade following low-attention earnings announcements. An event study of an exogenous shock to algorithmic trading suggests that computerized trading causally reduces low-attention effects. Price efficiency improvements are more closely tied to HFT liquidity demand than supply, consistent with HFTs improving efficiency by processing and aggressively trading on the information in low-attention announcements.