Abstract：

We examine the information asymmetry between local and nonlocal investors with a large dataset of stock message board postings. We document that abnormal relative postings of a firm, that is, unusual changes in the volume of postings from local versus nonlocal investors, capture locals' information advantage. This measure positively predicts firms' short-term stock returns as well as those of peer firms in the same city. Sentiment analysis shows that posting activities primarily reflect good news, potentially due to social transmission bias and short-sales constraints. We identify the information driving return predictability through content-based analysis. Abnormal relative postings also lead analysts' forecast revisions. Overall, investors' interactions on social media contain valuable geography-based private information.

Local information advantage and stock returns: Evidence from social media

Contemporary Accounting Research：Volume41, Issue2

http://doi.org/10.1111/1911-3846.12935

Code available at http://github.com/feng-li/local-information-advantage/

Keywords：local information advantage；return predictability；sentiment analysis；social media；topical analysis

About the author:Dr. Feng Li is an Associate Professor in the Department of Statistics and Mathematics at Guanghua School of Management in Peking University . His research interests include Bayesian Statistics, Econometrics and Forecasting, and Distributed Learning. Dr. Feng Li develops highly scalable algorithms and software for solving real business problems. Dr. Li has led and contributed to impactful research projects and grants. He is currently funded by the National Natural Science Foundation of China (NSFC, 2025+) for research on video time series forecasting. His prior projects include a study on hierarchical economic forecasting from a global modeling perspective, supported by the National Social Science Fund of China (2022–2024).