Linguistic Complexity and the Post-Earnings Announcement Drift

Abstract

In this paper, I investigate the relationship between the relative complexity of the annual earnings announcement conference call and the Post-Earnings Announcement Drift. I measure the linguistic complexity and the length of transcripts of conference calls of large public companies in the S&P 500 using the Fog Index from computational linguistics. Consistent with my hypotheses, I find that both the timeliness and magnitude of the market’s reaction to qualitative information in annual conference calls exhibit some evidence of a price drift. This research may be relevant to analysts, investors, managers, and regulators that wish to standardize how information within earnings conference calls is presented.