Interesting Tactical Asset Allocation Tool: Value portfolios

/Interesting Tactical Asset Allocation Tool: Value portfolios

Interesting Tactical Asset Allocation Tool: Value portfolios

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(Last Updated On: August 18, 2017)

Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns


The Fama-French three factor model is ubiquitous in modern finance. Returns are modeled as a linear combination of a market factor, a size factor and a book-to-market equity ratio (or “value”) factor. The success of this approach, since its introduction in 1992, has resulted in widespread adoption and a large body of related academic literature. The risk factors exhibit serial correlation at a monthly timeframe. This property is strongest in the value factor, perhaps due to its association with global funding liquidity risk. Using thirty years of Fama-French portfolio data, I show that autocorrelation of the value factor may be exploited to efficiently allocate capital into segments of the US stock market. The strategy outperforms the underlying portfolios on an absolute and risk adjusted basis. Annual returns are 5% greater than the components and Sharpe Ratio is increased by 86%. The results are robust to different time periods and varying composition of underlying portfolios. Finally, I show that implementation costs are much smaller than the excess return and that the strategy is accessible to the individual investor.

Alpha Highlight: 

This paper uses the monthly autocorrelation property of the HML factor as a signal to construct a dynamic strategy that switches capital from value to either growth or momentum portfolios. The author proposes that a positive (negative) HML factor in the current month predicts value will outperform (underperform) growth/momentum next month. All the data are from Kenneth French library. The author uses 6 portfolios formed on size and book-to-market from 1984 to 2014 (30 years), and portfolios are value weighted.  Here is how the strategy works:

  1. If the previous monthly return and the sign of HML factor are both positive, then risk in Value portfolios;
  2. If the previous monthly return is positive, but the sign of HML factor is negative, then switch 100% of capital from Value to Growth/Momentum portfolios;
  3. If the previous monthly return is negative, then risk off to risk-free.
  • The paper construct switching strategies on both Value/Growth and Value/Momentum.

Key Findings: 

  • Value/Growth switching strategy

2014-08-15 12_50_09-Exploiting factor autocorrelation to improve risk adjusted returns.pdf - Adobe RThe results shows that Switch strategy has a 3.4% greater return than the average of the base FF portfolios. Sharpe Ratio increases from 0.7 to 1.1.

  • Value/Momentum switching strategy

2014-08-15 12_54_41-Exploiting factor autocorrelation to improve risk adjusted returns.pdf - Adobe R 2014-08-15 12_56_49-Exploiting factor autocorrelation to improve risk adjusted returns.pdf - Adobe RThe results shows that Switch strategy has a 5% greater return than the average of the base FF portfolios. Sharpe Ratio increases by 86% over the value portfolio. Also, the Maximum Drawdown (DD) is dramatically reduced from 62.8% to 18.3%. Interesting result. We’ll do an in-house backtest and report the results in a few weeks…

  • The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).
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  • This site provides NO information on our value ETFs or our momentum ETFs. Please refer to this site.

About the Author:

After serving as a Captain in the United States Marine Corps, Dr. Gray earned a PhD, and worked as a finance professor at Drexel University. Dr. Gray’s interest in bridging the research gap between academia and industry led him to found Alpha Architect, an asset management that delivers affordable active exposures for tax-sensitive investors. Dr. Gray has published four books and a number of academic articles. Wes is a regular contributor to multiple industry outlets, to include the following: Wall Street Journal, Forbes,, and the CFA Institute. Dr. Gray earned an MBA and a PhD in finance from the University of Chicago and graduated magna cum laude with a BS from The Wharton School of the University of Pennsylvania.
  • Jan Vrot

    This is easy to test except that
    -I cant replicate the CAGR returns for the MOM and GRO portfolio’s
    -Also I am not sure which HML to use, the one calculated by FF (the difference between 30th and 70th decile) or the HML of the small cap BM portfolio?
    Other than these technicalities, which can easily be resolved if the spreadsheets were available, it seems to work.
    While you are at it how about a lesson on autocorrelation. I know how it works but I am not sure what is considered to be a good score when looking at stock market data. Thanks

  • Mike

    Wesley, is this somehow related to the findings in the “Devil in HML’s details” paper? HML = a mix of (mostly) Value and (a bit) Momentum due to the time lag in accounting data or something, if I remember correctly.

  • Wray Grigorakis

    A little uncertain:

    FF’s “6 portfolios Formed on Size and Book-to-Market (2 x 3)” orders the data in six columns of Small (low-mid-high) and Big (low-mid-high) values. I believe the data is returns in basis points–right?!
    Given the below data from the three most recent months, can Wesley or anybody else show how the paper’s calculations would look like?!
    201404 -6.35 -3.08 -2.48 0.22 0.85 -0.41
    201405 -0.59 1.15 -0.26 2.51 2.03 1.39
    201406 7.39 4.63 4.53 1.77 2.45 3.38

  • The data off of the FF site need to be divided by 100 to get them back to ‘normal’.
    We’ll post our spreadsheet in the next post and you can investigate how to calculate

  • Devil in HML details is about updating the price component in the B/M ratio. Academics usually use a 12/31 price to represent a 6/30 sort, giving a 6 month lag in information.

    We wrote up the details here:

  • We’ll post the spreadsheet when we finalize the follow up post.

    Stock markets are generally considered to be “unpredictable” so even small levels of auto-correlation can be interesting…

  • jlivermore

    Wesley, thank you for featuring my paper on your site. The results should be easy to replicate but if you need my spreadsheet let me know (contact through ssrn).

  • Lorenzo

    5 bp commission and slippage does not seem realistic.

  • Steve

    Read this interesting and thoughtful paper a few days back. I tested it on Fama French back to 1927 – but I used val/mom in the top tercile (30%) of the “large” or above median universe. Value weighted returns.

    I found that the switch worked, but not the further ‘auto-correlation’ overlay on the strategy itself (it degraded results).


    Also – after doing it by hand (which I found, as I have before, makes some kind of difference when you get ‘close’ to the data)….I agree with the author; this is for ETF switching. Stock pickers (like me)…I believe ought to look elsewhere.