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Using Industry Fixed Effects with Fama-French Factor Regressions

Article Quant Q&A · Author: finance_renegade

Summary

The document considers whether a panel model with industry fixed effects is suitable for estimating the Fama-French five-factor alphas of many industry portfolios. The factors can be common across industries and vary over time; constructing separate factor series for each industry is not presented as necessary. A fixed-effects specification can estimate a distinct intercept for each industry, but it constrains factor loadings to be the same across industries.

The response advises against that shared-slope restriction when the goal is to assess each industry's alpha and factor exposures. It recommends running a separate factor regression for each industry and collecting the resulting alphas. The discussion also confirms that fixed-effects models can include multiple predictors, so the number of factors is not itself a limitation. The recommendation is brief and does not address inference adjustments, multiple testing across industries, or how to compare estimated alphas statistically; those issues would matter in a research application.

Key ideas

  • Common time-varying Fama-French factors can be used across industry portfolios.
  • Industry fixed effects allow intercepts to vary while imposing common factor slopes.
  • Shared slopes may be unsuitable when industries are expected to have different factor exposures.
  • Separate regressions by industry provide industry-specific alphas and factor loadings.
  • Fixed-effects regressions can include multiple explanatory factors.

Tags

Full text
# Using the Fama-French 5 factor model in Panel Data


# Using the Fama-French 5 factor model in Panel Data












I have a question regarding the use of the FF5 Factors in an industry-fixed effects model. In order to clearify my question I'll post an example of my dataset

Note that this is just an example, the values for the FF5 factors and the Return(Y) are just random numbers.And there are a total of 70 industries, spanning across 15 years.

What I am trying to do: Let's for example take industry A; I am trying to see whether the industry-portfolio A has a significant alpha, or if it's returns can be attributed to any of the FF5 factors. This can, of course, be done by a simple FF5 regression, but the problem is that I have over 70 industries to check. So my thesis supervisor recommended that I use an industry-fixed effect model

So, my question are:

(1) Since the portfolio returns (Return(Y)) are collected on an indistry-level, do the FF5 factors have to be specific for that industry as well? The FF5 factors will, of course, vary over time, but as of now they are the same across all industries (A,B,C,...)

(2) And, as I understand, doing a industry-fixed effect model will force the Betas to have the same slope across all industries, but the intercept (Alpha) will vary. Since the alpha is what I'm looking for, this seems to be a nice approach, but I am worried that the Beta coefficients will be insignificant

(3) And thirdly, is it possible to run industry-fixed effect models like this with multiple predictor variables (FF5 factors)? My statistics teacher (and the textbook) only show these fixed-effects models with one predictor.

I hope my question is understandable. I highly appreciate any form of help. Thank you all in advance

## Answer by phdstudent (score 1)

https://quant.stackexchange.com/a/79214

- You can run that panel regression you don't have to use industry specific FF5 factors. That would actually make no sense at all (unless you want to dwell into weak factors, which does not seem to be the case).

- You can indeed run a panel regression with industry fixed effects. This will give you an estimate of alpha for each industry indeed. But as you mention, the betas won't change, which again makes little sense. You are better off running 70 regressions (one for each industry) and save the alphas. It's a simple loop in stata or python or matlab.

- Yes. But again that's not what you should be doing. You should be running the regressions one by one.

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