Abstract
In this paper, I investigate the relationship between the verbal complexity of annual earnings announcement conference calls and the Post-Earnings Announcement Drift. I determine the degree of linguistic complexity in 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 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.
Publication Statement
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Recommended Citation
Youngs, Johnathan
(2020)
"Linguistic Complexity and the Post-Earnings Announcement Drift,"
DU Undergraduate Research Journal Archive: Vol. 1:
Iss.
2, Article 7.
Available at:
https://digitalcommons.du.edu/duurj/vol1/iss2/7
Included in
Applied Linguistics Commons, Business and Corporate Communications Commons, Economics Commons