People use those type of models (such as the fama-french model) to evaluate their portfolio.
Literally, you run a regression of a stock/portfolio agains the FF factor model to understand if your portfolio beats known risk factors (i.e. whether its $\alpha$ is positive).
If the $\alpha$ is negative you are better off taking the money out of your ...
Yes, the second step of the Fama MacBeth procedure requires you to run a cross-sectional regression of the monthly returns of each stock against their betas for each month. This regression gives you a return for each factor for each period. The average factor return is the risk premium for the factor - see Rationale of Fama Macbeth procedure for a good ...
I think your best shot is to share with us your 3,000 stocks. How far can that be from FF sample?
As a quick check I took the 25 book-to-market portfolios and the Fama-French 3 factor model and run the standard fama macbeth regressions.
First stage results:
Only 6 alphas are statistically significant from zero (which is good news for the model).
At this point I don't really get any further, as I am unsure about
which "cross section" is being talked about here. Since I have created
25 portfolios, I can only have all in all 25 values in the cross
section, right? Isn't that far too little for a sufficient regression?
Or do I have to run new time series regressions for each company ...