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Using Fama–French Size and Value Betas to Tilt an Index Portfolio

Article Quant Q&A · Author: alybel
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Summary

The note describes a factor-investing approach that begins with a diversified, market-cap-weighted index portfolio. Practitioners can adjust stock weights according to their estimated exposures to the Fama–French size and value factors, increasing weights in stocks with stronger desired exposures and reducing weights in those with weaker exposures. The goal is to retain broad diversification and approximate index behavior while deliberately tilting the portfolio toward selected factors.

The explanation says these portfolios may remain long-only, with market exposure near that of the index and limited tracking error, though the exact weighting method is not specified. It characterizes factor tilting as a way to seek improved long-run performance, not a guaranteed result. The evidence is descriptive rather than empirical: it gives no performance study or construction formula, and it cautions that widespread adoption raises questions about whether the approach will continue to reward investors.

Key ideas

  • Factor tilting can start from a market-cap-weighted index portfolio.
  • Stock weights can be adjusted to increase exposure to desired size and value factors.
  • A factor-tilted portfolio can remain diversified and long-only while tracking its benchmark relatively closely.
  • Factor exposure is sought for potential long-run benefits, which are not assured.

Tags

Factor investingPortfolio constructionEquities
Full text
# How is the Fama French 3 factor model used for portfolio construction?


# How is the Fama French 3 factor model used for portfolio construction?












In which ways is the Fama French 3 factor model used by practitioners to construct portfolios?

I understand that the betas can be calculated for a portfolio of stocks or for single stocks. Are the betas then being used to select stocks or to determine short / long exposures? Or am I getting it all wrong?

Thanks for your answer.

## Answer by Alex C (score 3, accepted)

https://quant.stackexchange.com/a/46246

The main way this is used in portfolio construction is that you start with an index fund portfolio, i.e. with weights that are proportional to market caps. An index fund will usually have a market beta of 1 and SMB and HML betas that are fairly small (close to zero). You then try to enhance the SMB and HML betas of the portfolio, by increasing (though some mathematical formula) the weights of stocks with high SMB and HML beta and reduce the weights of stocks with low SMB and HML beta.

In this way you arrive at a portfolio that is still well diversified, has no short positions and tracks the index fairly closely (with market beta of 1 and modest Tracking Error) but has the desired "overloading" of HML and SML. People who do this believe that this extra exposure will be beneficial for long run performance.

Probably the earliest firm to do this was Dimensional Fund Advisors, but nowadays many more firms do it (and not only with SMB and HML as factors, but others as well) and it has generally become known as Factor Investing. As always when many people do something in investing, there are questions as to whether it will continue to be a good idea.

Shown in full with attribution under the source's licence. Licence: CC BY-SA 4.0 (Stack Exchange)

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