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I want to calculate annual excess returns on portfolios using monthly (total) returns for a CAPM (for the assets in the portfolio as well as for the benchmark), in order to have more information on the correlations, more precise betas.

Is it standard practice to adjust (slightly) for shorter months having somewhat less information on the correlations? Shall I weight by the number of days or only trading days before return dates?

Full disclosure: This breaks down my longer question into specifics. Please bear with me. From: annual excess returns from CAPM on monthly total returns

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  • $\begingroup$ The question seems to be asking about what is "standard practice", which is open-ended and subjective. $\endgroup$
    – madilyn
    Jul 19, 2013 at 5:16
  • $\begingroup$ @kristine "Standard practice" is not subjective. We routinely give advice about how to handle corporate actions, cleanse data, etc. $\endgroup$
    – chrisaycock
    Jul 19, 2013 at 16:22
  • $\begingroup$ Is the general problem that monthly data are irregularly sampled in terms of trading days? $\endgroup$
    – deprecated
    Jun 5, 2014 at 13:43

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Normalize for trading days if possible.

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I have never seen such an adjustment. While monthly data are irregularly sampled in time (in every way...calendar days, trading days, seconds, etc), that irregularity is likely to be a smaller effect than your choice of data frequency (monthly, weekly, daily data).

That said, your question is intriguing because in other fields they do have to deal with irregularly sampled time series data and methods (apparently) exist. See this paper for example. This is way outside of my field so I can't vouch for the paper but the issue is interesting.

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