How to Improve Post-Earnings Announcement Drift with NLP Analysis

9 Pages Posted: 20 Oct 2022

Date Written: October 17, 2022

Abstract

Post–earnings-announcement drift (abbr. PEAD) is a well-researched phenomenon that describes the tendency for a stock’s cumulative abnormal returns to drift in the direction of an earnings surprise for some time (several weeks or even several months) following an earnings announcement. There have been many explanations for the existence of this phenomenon. One of the most widely accepted explanations for the effect is that investors under-react to the earnings announcements. Although we already addressed such an effect in some of our previous articles and strategies, we now present a handy method of improving the PEAD by using linguistic analysis of earnings call transcripts.

Keywords: alternative data, earnings announcement, equity long short, own-research, trading earnings

Suggested Citation

Dujava, Cyril and Kalús, Filip and Vojtko, Radovan, How to Improve Post-Earnings Announcement Drift with NLP Analysis (October 17, 2022). Available at SSRN: https://ssrn.com/abstract=4251574 or http://dx.doi.org/10.2139/ssrn.4251574

Cyril Dujava (Contact Author)

Quantpedia ( email )

Dulovo namestie 14
Bratislava, 85110
Slovakia

Filip Kalús

Quantpedia ( email )

Dulovo namestie 14
Bratislava, 85110
Slovakia

Radovan Vojtko

Quantpedia.com ( email )

Dulovo namestie 14
Bratislava, 85110
Slovakia

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