Value Investing: Never Buy Expensive Stocks. Period.

Value Investing: Never Buy Expensive Stocks. Period.

July 1, 2014 Research Insights, Key Research, Value Investing Research
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(Last Updated On: October 17, 2015)

We did a recent internal simulation study on the performance of cheap and expensive stocks based on a variety of valuation metrics.

We looked at all our favorites from our Journal of Portfolio Management paper, “Analyzing Valuation Measures: A Performance Horse Race over the Past 40 Years:

  • EBIT/TEV
  • EBITDA/TEV
  • B/M
  • Gross Profits / TEV
  • FCF / TEV

This value investing research is part of a larger academic paper, but I did want to highlight one aspect of our study that we thought more practitioner-minded readers would find fascinating.

Note: We focus our results on EBIT/TEV because it is our preferred measure for identifying “cheapness.” The results for other valuation metrics are quantitatively similar.

How Does Our Simulation Work?

First, break stocks down into different valuation deciles from 1963 to 2013 based on EBIT/TEV (we only focus on US mid/large cap to avoid weird micro/small cap outlier effects).

  • For example, if there are 1000 stocks, stocks 1-100 go in the first decile; stocks 101-200 go in the second decile, etc.

Next, do 1000 simulations of random 30 stock portfolios drawn from the cheap stock decile or the expensive stock decile.

  • For example, simulation #1 draws 30 random stocks each month from the top and bottom decile from 1963 to 2013. This is the rough equivalent of saying, “we are going to have a monkey throw 30 darts,” every month during the 50 year period, to establish in each month separate 30 stock portfolios. Once our monkey has thrown his 30 darts in each month, we will then have 600 separate monthly portfolios (12 months * 50 years) and will have made 18,000 (30 stocks * 600 months) individual stock picks. This represents one simulation. We do 1000 simulations for the top decile and 1000 simulations for the lowest decile.

Calculate the performance statistics for each simulated strategy from 1963 to 2013.

Each simulated strategy represents the returns a value-investing monkey (cheap stock buyer) or growth-investing monkey (expensive stock buyer) would achieve over the full time period. We calculate compound annual growth rates (CAGR), standard deviation, and maximum drawdown.

Tabulate the performance statistics for all 1000 simulations.

What Do the Returns to Value and Growth Stocks Look Like?

First, let’s look at the distribution of CAGRs (compound annual growth rates). Notice that there isn’t even a POSSIBILITY of a 30 stock portfolio of expensive stocks beating a portfolio of 30 cheap stocks. This is actually amazing. Typically, when you run a simulation with 1000 runs, you get overlap in the “tails,” or extreme ends of the distribution.

Value Investing vs Growth Investing_1
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.

Clearly, buying expensive stocks is dangerous to one’s absolute returns.

What Do the Risks to Value and Growth Look Like?

Volatility

The results above show that cheap stocks beats expensive stocks. But let’s look at standard deviations of the portfolios from our dart-throwing monkeys.

Value Investing vs Growth Investing_2
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.

First, you’ll notice that standard deviations are tightly bound, even across 1000 simulations. The histograms show another remarkable finding. No matter how you cut it, holding baskets of expensive stocks means more volatility–at least historically.

Maximum Drawdowns

Finally, what about maximum drawdowns associated with cheap and expensive stocks?

Value Investing vs Growth Investing_3
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 above suggests that cheap stocks protect the downside better than expensive stocks. The big drawdowns for value come in the 2008 Financial Crisis, whereas for growth, the largest drawdowns can come from the Internet Bubble burst or the 2008 Financial Crisis, depending on the simulation run.

Important to note, all strategies involve some massive volatility and stomach churning losses. Equity investing is NOT FOR THE FAINT OF HEART!

There is simply no way to “avoid drawdowns” when investing in equity.

Conclusion

One can’t even simulate a scenario where a diversified portfolio of the best performing 30 expensive stocks can beat the worst performing portfolio of 30 cheap stocks.

Why do investors allocate to expensive, or “growth,” stocks?

Margin of safety is the only investing rule that matters.

Stay cheap and be sure to review our best attempt at building a quantitative value investing system.


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


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Please remember that past performance is not an indicator of future results. Please read our full disclosures. 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.


  • Doug

    Great work here. You guys are incredibly productive! One clarifying question. The annualized return for each portfolio was from month x year y of selection until 2013, correct? So, you might have one portfolio which you tracked from, say, January 1965 all the way to 2013, and another portfolio you tracked from June 1982 to 2013. I assume that the portfolios got smaller as time went on, as companies either were purchased or went bankrupt. How did you handle the reinvested funds for the companies that were bought out?

    Or, did you run one portfolio from 1963-2013, randomly rebalancing every month with 30 new randomly selected stocks from each universe?

    Also, while I agree with your fundamental principle of “buy cheap stocks,” your conclusion is a bit broad-brushed, as it ignores the very real momentum factor. While momentum DOES mean-revert over a long holding period, a monthly-rebalanced portfolio of “winners” (which contains a high number of “expensive” stocks) has outperformed a corresponding portfolio of “losers” (which contains a high number of “cheap” stocks) over a similar time period.

    Despite my quibbles, value definitely makes sense, but empirically and intuitively, and is more tax-efficient due to the longer hold periods vs. momentum.

  • Doug, all these portfolios run from 1963 to 2013.
    Dividends, buyouts, tenders, etc are already incorporated into the monthly returns.

    We are doing a similar study on momentum as well. The simulations burn a lot of computer time. Will post on those results. I assume they will be similar. I agree that momentum is equal–and probably better–than value from an empirical standpoint.

  • gmacd18

    What happens when you apply a momentum strategy to ONLY glamour stocks? What about value-momentum vs. glamour-momentum?

  • This idea, as well as many others, will be included in the paper we are writing on the subject. We’ll post to ssrn soon

  • Michael Milburn

    Interesting post (and I’m enjoying the articles on this site). fwiw, I’ve been looking at momentum (hobbyist), and based on a rough sampling from 2000 to 2013 of high momentum stocks it wouldn’t surprise me if many of strongest momentum stocks don’t even have trailing earnings at time of their momentum (or very little earnings). I’m interested to see what the study says, but I wouldn’t be surprised if high PE (or no PE stocks) with momentum perform well.

  • Michael, we are testing momentum distributions as well. Just takes time to grind the data and run the simulations…

  • Michael Milburn

    Wesley, the variation of momentum I was looking at required a stock to be underperforming and then surge to become overperforming within a window of time. Most of these predictive momentum effects seem to happen within 9 months or so from what I can tell. But in the sample and thresholds I was looking at about 20% of stocks had little to no earnings, and as a group these very high to no PE stocks were the strongest performers in my sample. My methods are archaic (manually looking up historical PE at rough periods in time) because I don’t have appropriate databases to do it properly, but it was enough to make me question my tendencies. I am wanting to blend these powerful momentum effects w/ fundamental analysis, but it made me question my natural inclination of crossing momentum w/ value investing traits. Papers and research seems to say using value characteristic w/ momentum is the way to go, but in this admittedly small sample (134 buys over about 7 yrs 2007-2014) the low PE stocks momentum stocks didn’t perform very well compared to the high to no PE stocks. My PE database doesn’t go back farther than 07 so was unable to check prior to that. I need to build out a better database to do this properly though.

  • Michael, congrats on doing your own work and grinding through the data. Love the initiative!

    Lot to digest here, but do want to comment on your point about blending value and momentum. My perspective is that it is better to identify a best of breed value philosophy and a best of breed momentum philosophy and have them stand on their own 2 feet. You could blend the 2 and essentially get to the same endstate (6 of one, half a dozen of the other), however, I like the “clean” aspect of being pure on value and pure on momentum because it makes attribution analysis much more transparent. It gets tricky trying to identify–after the fact–what was driving performance. I generally try to limit brain damage in my life and this is just one way to do it. I’m sure its not optimal, but everyone has to suffer from behavioral bias–including me!

  • Michael Milburn

    Wesley, I appreciate the comments and perspective. I really do. It’s difficult to find discussion of this on the web. I appreciate the univariate approach to isolating momentum and how strong the effect is. It’s difficult to not want to take these momentum approaches and then try to segment them further though. These strategies can kick off a very high number of trade opportunities across variations of momentum I’ve looked at, and while just requiring “more” momentum seems effective at increasing risk adjusted return, there’s a big part of me that wants to anchor the approach in fundamentals if I can. I have run the same models against several databases, and I admit momentum doesn’t seem to perform better when just using higher quality companies, but it’s difficult to give up on the idea that fundamentals should matter. But maybe the better companies are better understood and are more predictable and might not be as susceptible to momentum? I actually have a database I call “the dependables” which have stocks that have consistent growth in book value, earnings, solid historical ROE, ROC – and while momentum works w/ that database, it doesn’t work as well as just running pure momentum on the universe. (there’s all sorts of built-in bias in a dbase like that – but the fact that even with predictability and desirability built into the stocks of the database – they still don’t convey additional advantage over just running momentum vs. S&P500 or similar extended smaller company dbase. )

    Momentum does seem to work a bit differently in small cos vs. large companies (I think?), I have ideas but it’s all conjecture as to why for me right now.

    One particular question of interest is what _really_ underlies why momentum exists? I want to think it exists for reasons that reflect reality – that it takes time for information to disseminate and for people to understand new reality; and for behavioral reasons (people tend to sell winners early) or “set points”; but it bothers me that it might also happily exist more as a Minsky moment type effect, or self-reinforcing feedback loop. I guess I’m inclined to think there’s a reason stocks are moving – but try to be aware that at some point other “less real” factors can come into play – like when I get a buy signal based on a momentum model. 🙂

  • Michael, I’m off to NYC tomorrow, so can’t chat long here.

    You’ve obviously thought hard about this stuff. I am constantly humbled when a long held belief I have is absolutely destroyed by cold hard facts. Humiliating, but after getting battered and bruised and losing all confidence in my ability to do anything useful with my lizard brain I’ve reached a nirvana in my investing life where I finally saw the light–I’m completely full of BS, but so is everyone else. We’re all equally bad at understanding each zig and zag in the stock market. And the great irony is that by understanding that nobody really knows anything about where the zigs and zags are going, you end up knowing a lot about where the zigs and zags are going because behavior is somewhat predictable. Value anomalies are built on a foundation of behavioral bias, but so are momentum strategies.

    An old roommate of mine at the University of Chicago decided that pursuing a PhD was silly and instead became a big shot trader at a big bank. Well, he’s retired now counting his money and I’m still working all day and every day.

    I asked my old roommate how he did it. “What’s the secret sauce, man?”

    He summed it up with a few words:

    “Rising prices attract buyers; falling prices attract sellers.”

    Without even getting into a single bit of behavioral finance research, that very simple statement sums up a large swath of academic research on the subject of momentum and WHY those systems work, on average, over long periods of time. Throw in disposition effects, limited attention, ambiguity, overconfidence, and a whole slew of behavioral problems and momentum all the sudden becomes a fairly “obvious” area to explore for market mispricing.

    Now, all that said, who the heck really knows. Data-mining is always a viable alternative hypothesis and face ripping 60-70%+ drawdowns suggest that momentum isn’t some free lunch for the masses.

    We’re going to try and figure it out, however. My team and I are working on another book–Quantitative Momentum–which is a deep dive summary of momentum research as it currently stands, an attempt to understand the key behavioral drivers behind the anomaly, and–of course–trying to construct the most effective way to exploit the effect in the marketplace. A lot of brain damage for sure, and Jack Bogle’s SP 500 Index fund gets sweeter and sweeter by the day, but usually when you’re tempted to give up, its signals you might be on to something interesting…

  • Michael Milburn

    I understand where you’re coming from. I used Buffettology approaches w/ some success for a long time but it seems they don’t work as well now – at least the methods that worked for me many years ago don’t work as well now. I was really persuaded by the Joel Greenblatt quant approach but have suffered a share of gut punches causing me to reassess other opportunities. Count me as “surprised” at how well the momentum stuff works, and comparatively it’s so simple. I didn’t even know it was called momentum when I started working on the programming, so it took me a while when doing searches online to figure out the right search terms.

    Good luck w/ the book – but not too much good luck. Every good idea that becomes common knowledge goes away! Then, like you mention, you’re back to Bogle’s index funds.

  • Funny, Buffettology was one of my first exposures to the value investment philosophy (Intelligent Investor was the first).

    We are confident in the robustness of behavioral bias and the limits of arbitrage in the marketplace so we have little issue with sharing a lot of our ideas. Value investing is the ultimate example. Graham has been discussing simple value strategies since the 1930s until his last days.

    Guess what…

    Following the Graham model hasn’t lost an ounce of mojo since he’s passed.

    Why?

    1) The behavioral biases that drive value returns are still buried in the monkey brain…
    2) The principal/agent problems in asset management, which prevent institutions from following high tracking error, concentrated, noise-trader-filled value approaches, prevents massive amounts of capital from arbitraging value opportunities away.

  • I’ve done some testing in this area and noticed some alpha decay the last few years. Did you find that to be the case?

  • Do you have the results posted? Would love to see. What years?

  • Last few years have been flat. I’ve noticed a lot of value oriented quant strategies struggling lately. It could be a cycle or the factors are too well known now. Unfortunately, I can’t publish my results. I was just wondering if you found the numbers to be front loaded as well.

  • Just got numbers of French site for past 14+years.
    Value beats Growth by over 10% a year using the most generic method out there.

    Moving to the past 7 years the spread gets smaller.

    Moving to the past 3 years the spread is decent.

    Basically what this says to me is that over short periods anything can happen and from 2007 to 2011 value had a bad run. I’m not sure one can conclude that value investing is dead, but who knows…

  • I’m not suggesting value investing is dead. Just some alpha decay. I don’t think it can ever die. Once you factor in real world trading cost the numbers above are even smaller. Anyway, I enjoy reading your blog. Keep up the good work.

  • That makes a ton of sense. I’m sure they all decay. Markets are uber efficient.

  • Steve

    This is my approach. Asness absolutely convinced me of it, though there have been a couple of papers on it prior. After much mulling about it – I decided a “mix” (i.e. keep them separate) approach is better (for me at least) than a “blend” (i.e. a combined approach).

    Now, a blend does work. You only need to read O’Shaughnessy’s 3rd and 4th edition to see that. But the thing that many ‘blenders’ seem to miss…is the greater capture of the lower correlation from mixing. i.e. Mixing rather than blending keeps a good result, whilst giving you extra bang for buck by lowering the volatility.

    In even simpler terms…there are times that momentum is no good and value is (and vice verca). You miss out on some of that when you blend.