I have a fairly large dataset of CRSP firms with their daily closing prices from 2006 till 2017. I need to filter the data so that I have one daily observation per unique firm. I've identified the firms that have multiple share classes (A, B, C etc.). They are 78 firms with daily data from 2006 till 2017, which is about 190.800 observations.

I can't really figure out the best way to filter for the primary share class so that I can drop the excess observations? Does anyone have experience with this to offer some help?

I have access to the closing price, share code, share class, permco, permno and shares outstanding. I thought at least one of these variables would be a feasible way to identify a firms primary share class (the one most traded / accessible to the masses) but since not all firm's set up their share class in the same way, it's proving to be difficult.

Any insights? Below is a sample of my data. Stata Data Example

  • $\begingroup$ Restricting your universe to shrcd = 11 gives you some progress. You could try something somewhat fancier by examining what is most traded in the past and designate that as primary share class for subsequent periods? $\endgroup$ Jul 30 '18 at 16:47
  • $\begingroup$ Hi Matthew, thank you for the idea. This might work if I can figure out to code it, will get back to this post when I figure it out. $\endgroup$
    – S Wijn
    Jul 31 '18 at 17:47
  • $\begingroup$ My guess would be that however you do it (even choosing one at random), won't change your analysis substantively. $\endgroup$ Jul 31 '18 at 17:49

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