Asset Price Dynamics with Limited Attention

• 成果类型: Article
• 署名作者: Hendershott, Terrence; Menkveld, Albert J.; Praz, Remy; Seasholes, Mark
• 署名单位: University of California System; University of California Berkeley; Vrije Universiteit Amsterdam; Arizona State University; Arizona State University-Tempe
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhab045
• 发表日期: 2022
• 页码: 962
• 关键词: TRADING VOLUME; optimal inattention; large numbers; stock-market; INFORMATION; liquidity; inventories; LAW

摘要

We identify long-lived pricing errors through a model in which inattentive investors arrive stochastically to trade. The model's parameters are structurally estimated using daily NYSE market-maker inventories, retail order flows, and prices. The estimated model fits empirical variances, autocorrelations, and cross-autocorrelations among our three data series from daily to monthly frequencies. Pricing errors for the typical NYSE stock have a standard deviation of 3.2 percentage points and a half-life of 6.2 weeks. These pricing errors account for 9.4%, 7.0%, and 4.5% of the respective daily, monthly, and quarterly idiosyncratic return variances.