Quantitative Value Research: A Summary of Various Value Metrics!

Quantitative Value Research: A Summary of Various Value Metrics!

October 27, 2014 Research Insights, Value Investing Research
Print Friendly
(Last Updated On: January 18, 2017)

Analyzing Valuation Measures: A Performance Horse-Race Over the Past 40 Years

Core Idea:

Paper asks a simple question: “Which valuation metric has historically performed the best?” Who are the participants in this horse-race?

  1. Earnings to Market Capitalization (E/M).
  2. EBITDA to Total Enterprise Value (EBITDA/TEV).
  3. Free Cash Flow to Total Enterprise Value (FCF/TEV).
  4. Gross-Profits to Total Enterprise Value (GP/TEV).
  5. Book Value of Equity to Market Capitalization (B/M)

For clarification, Total Enterprise Value (TEV) is defined as:

  • TEV = Market Capitalization + Debt + Preferred Stock Value – Cash and Short-term Investments.

While investors rely on a wide range of metrics for establishing a firm’s value, there isn’t much research devoted to comparing how effective such metrics have performed over time. Are all valuation metrics created equal? Or are there reasons to prefer one over another? Indeed, Eugene Fama and Ken French went so far as to claim, “…the average returns spreads produced by different [valuation] ratios are similar…and, in statistical terms, [are] indistinguishable from one another.” This is a pretty sweeping claim, and as such, deserves closer scrutiny. Does this claim stand up to empirical scrutiny? The paper finds evidence that this claim is not necessarily true, and that there are reasons to believe all valuation metrics are not, after all, created equal.


value research
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

The evidence suggests that EBITDA/TEV has outperformed other valuation metrics tested (E/M, FCF/TEV, GP/TEV, B/M) during the period of 1971 to 2010. Examining the equal-weight portfolios, this measure generates the largest spread between value and growth stocks, and generates the largest 3-factor alpha for the value stock portfolio.

More Evidence:

Gray and Carlisle (2012) did a comprehensive comparisons of various value metrics in their book – Quantitative Value. The results in the book are consistent with Gray and Vogel (2012).

  • The value portfolios created from the EBIT/TEV generates a CAGR of 14.55% over 1964 to 2011. The closest competitor is EBITDA/TEV at 13.72%. These two metrics also generate the biggest value premia.
2014-10-03 17_52_42-0Value Reseach Recap.pptx - Microsoft PowerPoint (Product Activation Failed)
The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged, do not reflect management or trading fees, and one cannot invest directly in an index. Additional information regarding the construction of these results is available upon request.

So next time someone says to you, “Hey, when evaluating a stock, you should really use the price/cash flow ratio, and and the price-to-book ratio,” the next question should be: “Really? Why?”

Note: This site provides no information on our value investing ETFs or our momentum investing ETFs. Please refer to this site.

Join thousands of other readers and subscribe to our blog.

Please remember that past performance is not an indicator of future results. Please read our full disclaimer. 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. This material has been provided to you solely for information and educational purposes and does not constitute an offer or solicitation of an offer or any advice or recommendation to purchase any securities or other financial instruments and may not be construed as such. The factual information set forth herein has been obtained or derived from sources believed by the author and Alpha Architect to be reliable but it is not necessarily all-inclusive and is not guaranteed as to its accuracy and is not to be regarded as a representation or warranty, express or implied, as to the information’s accuracy or completeness, nor should the attached information serve as the basis of any investment decision. No part of this material may be reproduced in any form, or referred to in any other publication, without express written permission from Alpha Architect.

Definitions of common statistics used in our analysis are available here (towards the bottom)

About the Author

Wesley R. Gray, Ph.D.

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, ETF.com, 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.

  • DavidR

    Does this mean that CEOs who make increases in FCF their top priority are misdirected? Should they be maximizing EBIT or EBITDA instead? Why might this be so, from a theoretical perspective, since FCF maximization is so often considered the standard?

  • Steve

    Playing Devil’s advocate, as I seem to be wont to do; in an attempted answer at your question that finishes this post (though I’m not sure I agree with myself – just posing the question)…

    – What about composites (or using various single factors) giving you a guaranteed average of the factors, going forward? Choosing one factor based on performing best in the past…does that correlate with performing best over the next 10, 30, 70 years?

    – EV not being very applicable to financials?

    – What about Damodaran’s comment that PE / PB are probably more appropriate for individual investors (as they are concerned with the leveraged returns of a business?)

  • Hi David,
    For firms, maximizing the discounted cash flow is still a primary goal. I think what the results imply is that, historically, investors have overlooked enterprise multiples as a way to forecast future stock prices. In other words, the information embedded in FCF/TEV is already priced in the marketplace, whereas information in enterprise multiples has been underappreciated. Another example: We’ve done studies that show that basic quality metrics like EBIT/ROC actually perform poorly as a predictor of future returns. But does this does mean that firms should try and generate low EBIT/ROC? Of course not. What this means is that investors already price information embedded in the EBIT/ROC signal, on average.

    Does that make sense?

  • Hi Steve,

    The point of this research piece is to highlight which valuation metric has been the most effective as predicting future returns–at least historically.

    There are 100’s of stories out there suggesting that investors should do X in this situation and Y in the other situation. The problem with many of these stories is there is no evidence suggesting they are true. I am a believer in evidence-based decision making, not story-based decision making. Especially in situations where there are many theories about what should work, but not a lot of testing on what ACTUALLY works.

    Now, the danger of an evidence-based approach is understanding how to disentangle signal from noise–perhaps all valuation metrics are the same from a statistical standpoint. In other words, the differences in valuation metrics can be explained entirely by random chance. I am open to this interpretation of the results.

    Anyway, all your comments are valid hypothesis that would need to be tested with data to ascertain if they are fact or fiction. Thanks for sharing.

  • Steve

    Your second last paragraph is put brilliantly. I don’t want it to be so, I’m human – so I prefer certainty. I can’t help but wonder if it’s a bit like quality metrics…look at enough of them and something will stand out. Now, value is not quite the same as ‘quality’ – in the sense that I think we all agree that, “value works” and is beyond practical doubt. But the *differences* between the valuation methods…could possibly be due to random chance.

    I remember in Dreman’s book (his 90’s edition) seeing the differences between PE/PB/PC as insignifcant – the point was, “value works.”

    Jim O’Shaughnessy once hailed the P/S as the king of all value metrics then tested (3rd edition)…ironically by the 4th edition not only had a different ratio overtaken the P/S; the P/S had such a bad time of it between editions as to drag its entire historical outperformance down (PE was now the best, notwithstanding the newer enterprise multiple).

    All that to say: I think your research piece adds to “value works” hypotheses. Works well, I would add. I think the research MIGHT show that EV/EBIT or EBITDA is the best valuation metric. I want it to be true; as I too, like the way it makes sense for it to be the best one, in that it seems closest to a business valutation.
    But accuracy doesn’t always equal profits.
    In other words…would I be in the least surprised if Price to Book (or even dividend yield) turned out to be the best metric over the next 20-40 years (an investing lifetime?) Nope; I honestly wouldn’t.

    Thanks for discussing this Wes!

  • DavidR

    Fascinating. Definitely does. The reminder in this is that value investing is a behavioral phenomenon, and as such it’s not an absolutely fixed target.

    Thanks for engaging in your comments.

  • Yes, I think if you can understand the underlying behavior behind an anomalous result and also can identify why large swaths of capital are not pursuing that opportunity with a vengeance, you have an interesting “anomaly” that probably isn’t a data-mining artifact…