I'm reading this paper Zura Kakushadze: 4-Factor Model for Overnight Returns https://arxiv.org/pdf/1410.5513.pdf and I am slightly confused about the methodology of the regressions.
It says it uses Fama MacBeth to use the residuals for a mean reversion strategy. Now, I am also somewhat confused about Fama MacBeth as well.
From what I understand, first you run cross sectional regressions to get your betas, then you run a time series regression to get the risk premiums.
So, if we had say four factors, and let's say we had 10 years of daily data (2,520 days) and let's say we had 5,000 stocks. So if my understanding is clear, first we would use the cross sectional regression to estimate 4*5,000=20,000 betas? And then you get the risk premia, which you would have a value for each day for each factor (2,520 per factor)?
Given I understand that correctly (PLEASE tell me if I am mistaken), I am confused what this paper is doing. It looks like it calculates the betas directly. But it's saying it is running the cross sectional regressions to get the factor returns. Is that the same as the risk premium?
Any clarification would be much appreciated. I am trying to replicate this but am having major confusion.