Information acquisition and expected returns: Evidence from EDGAR search traffic - LI, Frank Weikai (Institutional Knowledge at Singapore Management University, 2022)
This paper examines expected return information embedded in investors' information acquisition activity. Using a novel dataset containing investors' access of company filings through SEC's EDGAR system, we reverse engineer their expectations over future payoffs and show that the abnormal number of IPs searching for firms' financial statements strongly predict future returns. The return predictability stems from investors allocating more effort to firms with improving fundamentals and following exogeneous shock to underpricing. A long-short portfolio based on our measure of information acquisition activity generate monthly abnormal return of 80 basis points and does not reverse over the long-run.. In addition, the return predictability is stronger among firms with larger and lengthy financial filings that are more costly to process. Collectively, these findings support theoretical predictions that costly information acquisition reveals the value of information.