Information Acquisition and Expected Returns: Evidence from EDGAR Search Traffic
日期: 2018-05-10

Abstract

This paper examines expected return information embedded in investors' information acquisition activity. Using a novel dataset containing investors' access of company filings through the SEC's EDGAR system, we reverse engineer investors' expectations of future payoffs and show that the abnormal number of IPs searching for firms’ financial statements strongly predicts future returns. The return predictability stems from investors allocating more effort to firms with improving fundamentals and following exogenous shock to underpricing. A long-short portfolio based on our measure of information acquisition activity generates a monthly abnormal return of 80 basis points that is not reversed in the long-run. The return predictability is stronger for firms with larger and lengthier financial filings that are more costly to process. Collectively, these findings support theories of endogenous information acquisition that costly information acquisition reveals the value of information.

Bio

Professor Weikai Li is an assistant professor at the Lee Kong Chian School of Business, Singapore Management University. He received his Ph.D. in Finance from Hong Kong University of Science and Technology (HKUST) and joined Singapore Management University in 2017. Weikai’s research interests include empirical asset pricing, behavioral finance, informed trading, institutional investors, international financial markets and information economics. He has published several papers in academic finance journals such as Journal of Financial and Quantitative Analysis, Finance Research Letters and Journal of Econometrics. He has won the 2017 Asian Finance Association Best Paper Award.

Information Acquisition and Expected Returns: Evidence from EDGAR Search Traffic