Independent Academic Research

Limited Attention and Market Pricing of Earnings in a High Frequency World

Bidisha Chakrabarty, Saint Louis University
Pamela C. Moulton, Cornell University
Xu (Frank) Wang, Saint Louis University

Updated April 2020

Wall Street Horizon Abstract

In this study Chakrabarty et al examine the effect of limited attention on stock prices in modern financial markets, where the majority of trades are conducted by machines using pre-programmed algorithms. Given that machines are not expected to suffer from limited attention or distraction, the authors ask how limited attention affects stock price reactions to earnings news in today’s computer-driven markets. The paper finds that high-frequency trading improves short-term and long-term price efficiency around low attention earnings surprises. Specifically using multiple attention proxies, their research shows that price inefficiencies lower by 65% to 100% when HFTs trade following low-attention earnings announcements.

Note:  While the authors of this research made extensive use of Wall Street Horizon's corporate events data, Wall Street Horizon does not sponsor academic research; all papers are conducted independently by the researchers and their teams at their respective organizations.

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