starting on PEAD (post earnings announcement drift) analysis


I decided to start on my PEAD project, which is to semi-replicate the analysis in the research paper “The Extreme Future Stock Returns Following I/B/E/S Earnings Surprises”. One of the things the researchers do in that paper is construct a multiple regression of 1 year, 2 year, and 3 year returns on earnings surprise %, beta, market cap, momentum, accruals, and a few other explanatory variables. They find that the coefficients for earnings surprise % and accruals are the most significant, followed by market cap, beta, and momentum I believe.

The plan is to construct a multiple regression similar to theirs: for now it is a regression of future intermediate term returns (1 month? 6 months? 1 year? haven’t decided yet) on surprise, historical volatility, and momentum. Earnings estimates will be obtained from IBES, price data from CRSP (I am grateful to be a Wharton student…). Replicating the research done in a research paper really forces you to understand it and actually allows you to see areas for improvement…

It’s funny how the stars kind of aligned on this one. For my statistics class we have to do a final project using something we’ve learned: multiple regression is a big one. I’m running a small fund with a few friends; one of them introduced me to PEAD and wanted to learn more about it as a potential strategy. I proceed to read research on it and find out that in one paper the researchers use a multiple regression model. So now I’m doing this PEAD exploration as a stat class final project, as preliminary research to a potential investment strategy, and as a way to learn and practice R. Talk about killing multiple birds with one stone.

This is way over my self-imposed word count…

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