Hi all,
I've got a large dataset with monthly stock returns of 350 companies, which I want to correct for risk using Fama-French's method (or Carhart, for that matter). I know how to do regressions, but what would happen is that I run a regression per company, leaving me with the alpha and the betas in the regression report. I am interested in the alpha, but I don't want the overall intercept of my regression, but the out/underperformance PER MONTH.
So my data looks somewhat like this. I've got a big matrix with as said 350 companies, every column is company. Every row represents a month (360 in total), so every cell is the stock return for that month for that company. I also have the four Carhart factors per month (market return, small-minus-big, high-minus-low, and momentum). Depending on a stock's beta exposure to these factors, this forms the benchmark unique for every stock. All under/overperformance wrt this benchmark, per month, is the risk corrected return per stock.
But like I say, just running regressions will leave me with one alpha per stock, instead of the outperformance per month per stock. Any ideas how I could do this?
Lots of thanks! You'd save my life!
How to correct raw stock returns using Fama-French?
Moderators: EViews Gareth, EViews Jason, EViews Moderator, EViews Matt
Re: How to correct raw stock returns using Fama-French?
It is not possible to obtain alpha for each month if you have only monthly observations. What you can do is obtain daily observations, then run the regression for each month separately, and obtain monthly alpha this way.
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