Return Frequency and Annualization in Beta Calculation

Summary

The document considers how a financial time-series library should calculate beta from market and asset returns. Its central design question is whether to annualize returns before calculating beta, given that practitioners use different sampling intervals, including daily and monthly returns. The author is seeking a helpful default while considering whether the interval should instead be configurable.

The document does not provide a proposed convention, derivation, or empirical comparison. It notes that annualization may not greatly change beta, but leaves the choice unresolved. It also does not distinguish simple from log returns or discuss matching observations, estimation windows, or data frequency. The main lesson is that beta implementations should state their return-sampling convention and clarify what parameters users can control; the text itself does not settle the default choice.

Key ideas

• Beta is estimated from paired market and asset return time series.
• The question concerns whether to annualize returns before estimating beta.
• Daily and monthly sampling are mentioned as differing practices.
• The author is weighing helpful defaults against letting users select the return convention.
• No definitive recommendation or calculation example is given.

Tags

# Should I annualise returns for beta calculation?


# Should I annualise returns for beta calculation?












I am working on a Python library for financial calculations based on Time series data. One of the functions I'm implementing is beta. This will allow the user to pass two sets of time-series data, one for the market and one for the stock/asset and get the beta.

However, I am stuck at whether I should annualise the returns. I have tried reading about it and I see that different people use different return periods, some use 1-day returns while some use 1-month returns. However, I can't seem to find if they use absolute returns or annualised returns.

While this doesn't drastically alter the Beta, it is an important detail. I can leave it up to the user as well, but I want to have helpful defaults, so that users don't have to pass every parameter.

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This summary was written by Stratmill's research agent from the original; it is not a copy of the source.