PEAD.txt: Using AI and Machine Learning to Profit from Corporate Earnings Calls
In this video, I discuss a recent research paper written by Vitaly Meursault and his co-authors on Post Earnings Announcement Drift, or PEAD. The authors show that one potentially can use Machine Learning and AI to extract "positive" and "negative" earnings surprises from a company's earnings calls, and then use that information to trade profitably around earnings announcement.
The paper is titled "PEAD.txt: Post Earnings Announcement Drift Using Text". Full copy of the paper available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3778798
You can learn more about the authors here:
1. Vitaly Meursault: https://www.vmeursault.com/
2. Pierre Jinghong Liang: https://www.cmu.edu/tepper/faculty-and-research/faculty-by-area/profiles/liang-pierre.html
3. Bryan Routledge: https://www.cmu.edu/tepper/faculty-and-research/faculty-by-area/profiles/routledge-bryan.html
4. Madeline Marco Scanlon: https://mscanlon19.github.io/
In this video, I discuss a recent research paper written by Vitaly Meursault and his co-authors on Post Earnings Announcement Drift, or PEAD. The authors show that one potentially can use Machine Learning and AI to extract “positive” and “negative” earnings surprises from a company’s earnings calls, and then use that information to trade profitably around earnings announcement.
The paper is titled “PEAD.txt: Post Earnings Announcement Drift Using Text”. Full copy of the paper available here: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3778798
You can learn more about the authors here:
1. Vitaly Meursault: https://www.vmeursault.com/
2. Pierre Jinghong Liang: https://www.cmu.edu/tepper/faculty-and-research/faculty-by-area/profiles/liang-pierre.html
3. Bryan Routledge: https://www.cmu.edu/tepper/faculty-and-research/faculty-by-area/profiles/routledge-bryan.html
4. Madeline Marco Scanlon: https://mscanlon19.github.io/
