Choosing Data Frequency for CAPM Beta Estimation

Summary

The document considers whether to annualize monthly returns before applying CAPM after estimating a stock’s beta from monthly observations against a market index. Its guidance is to choose the frequency at which the model will be used first, then estimate beta at that same frequency. This aligns the beta estimate with the return horizon of the intended application.

The answer notes that higher-frequency observations can produce a smaller beta when noise overwhelms the signal. It does not provide a conversion formula or empirical comparison, and its recommendation is presented as a general modeling judgment rather than a result demonstrated with data. The example concerns one stock and a limited monthly history, so it does not establish that matching frequencies is optimal in every setting. The underlying lesson is to make the beta estimation horizon consistent with the model’s intended use and to account for frequency-dependent noise.

Key ideas

• Choose the return frequency for the CAPM application before estimating beta.
• Estimate beta at the same frequency as the model’s intended use.
• Higher-frequency data may yield a smaller beta when noise dominates the signal.
• The document gives a general recommendation rather than a tested frequency-conversion rule.

Tags

# Should I convert monthly data into yearly for CAPM?


# Should I convert monthly data into yearly for CAPM?












I am trying to use the CAPM. I gathered monthly data on German government bonds and DAX40 (it's an index that contains top 40 German firm). Then based on only one company like Volkswagen monthly stock return in the last 60 months I calculated the $\beta$ by regressing the return of the stock on the market.

Should I convert these monthly calculated data into yearly and then apply in the CAPM formula, or it's not necessary? If so, what is the formula for it?

## Answer by Richard Hardy (score 1, accepted)

https://quant.stackexchange.com/a/78044

I would turn the question around: first determine what frequency you want to use your model on, then decide what frequency you will estimate the beta from. Beta estimated on higher-frequency data may be smaller, as noise may dominate the signal. If you want to use the model on data of a given frequency, I think it makes sense to use the same frequency for estimating the beta, too.

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