Does Information Acquisition Alleviate Market Anomalies? Categorization Bias in Stock Splits - Kong, Dongmin; Lin, Chen; Liu, Shasha (Review of Finance, 2019)
Abstract Using a unique proprietary account-level trading dataset in China, we investigate how active information acquisition alleviates price-based return comovement, a typical anomaly in stock splits. We find that: 1) individual trading drives the comovement and the trading correlation between split stocks and the low-price portfolio increases significantly after splits; 2) individuals can learn the firm fundamentals through information acquisition, which effectively alleviates their categorized bias; and 3) the role of information acquisition is more significant in environments characterized by greater uncertainty. Our results are robust to different specifications and alternative measures. Taken together, this paper emphasizes the important role of information acquisition in alleviating behavioral bias and improving decision-making.