Why Fama–French Regressions Usually Use Monthly Returns

Summary

The document asks why researchers often regress stock returns on Fama–French factors monthly instead of using annual observations, despite the availability of annual factor data. The answer notes that the library also provides monthly or daily data for some model versions, then explains practical advantages of monthly frequency for time-series regressions.

Monthly observations provide more data points over the same history, allow analysis of assets with shorter records, and support more frequent updates to estimated factor exposures. Derived quantities such as alpha can also be refreshed more often, helping reflect changing market conditions. The discussion is a concise explanation of data frequency tradeoffs rather than an empirical comparison of monthly and annual regressions. It does not address issues such as return aggregation, serial dependence, or whether a particular research question calls for a different sampling frequency.

Key ideas

• Monthly returns yield more observations than annual returns over the same historical span.
• Higher-frequency data can make it possible to include assets with shorter return histories.
• Monthly regressions allow factor exposures and estimated alpha to be refreshed more frequently.
• The preferred frequency depends on the modeling purpose and available data.

Tags

# Fama-French Annual Returns Regression?


# Fama-French Annual Returns Regression?












I see that the Fama-French library offers annual factors for their models, but everyone seems to exclusively use monthly returns of stocks in their regressions involving Fama-French factors. I am very fresh to this so excuse me if I'm asking a severely dumb question, but why doesn't anyone use annual returns over say 20 years. Is it simply because the regression probably wouldn't have much data to go on?

## Answer by Alexandre Oliveira (score 1)

https://quant.stackexchange.com/a/48738

Eric, please, check again their website. It is possible to find monthly or daily returns for some model versions in this website.

In general, there are some advantages of using monthly returns:

a. You don't need very long-term data series as we have much more data points for running time-series regressions;

b. It is possible to cover new assets with shorter returns history;

c. Stock's Sensitivities (betas or exposures) may be updated every month, then adjusting faster for company characteristics changes;

d. Any other model derived data (like estimated stock's alpha) will have new a data point every month, better adjusting for current market conditions.

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