Greenbackd is able to offer  a special discount – over 50% off! – on registrations for the NY Congress taking place September 8 & 9, 2014.

This year, seating will be strictly limited to 275, so we encourage Greenbackd readers to register now, before the Congress sells out.

Regular Price: $5,995

Special Offer – Over 50% off (Offer expires: June 24, 2014)

To access the special offer, go to Valueinvestingcongress.com/congress/register-now-partners/ and use discount code: GREENBACKD

Information about the 10th Annual New York Value Investing Congress

  • Date:  September 8 – 9, 2014

Confirmed speakers include:

  • Leon Cooperman, Omega Advisors
  • Alexander Roepers, Atlantic Investment Management
  • Carson Block, Muddy Waters Research
  • Whitney Tilson, Kase Capital
  • Sahm Adrangi, Kerrisdale Capital Management
  • David Hurwitz, SC Fundamental
  • Jeffrey Smith, Starboard Value
  • Michael Kao, Akanthos Capital Management
  • Guy Gottfried, Rational Investment Group
  • John Lewis, Osmium Partners
  • Tim Eriksen, Eriksen Capital Management
  • Cliff Remily, Northwest Priority Capital
  • With many more to come!



Greenbackd receives consideration for promoting this event.

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Last week  I looked at the implications of all-time-high valuations on returns in the S&P 500. This week I’ve examined the implications of high valuations on drawdown for stocks in the Russell 1000. I looked at the two most recent crashes–the 2000 to 2002 Dot Com Bust and the 2007 to 2009 Credit Crisis. I backtested the performance of the Russell 1000 Total Return (TR) and its value decile measured using the enterprise multiple (EBITDA / enterprise value — overview of the research on the enterprise multiple here). I lagged the fundamental data by 6 months (so portfolios formed 6/30 in year t use data from 12/31 in year t-1). All portfolios are equally weighted (the 100 stocks in the value decile hold 1 percent of the theoretical portfolio capital at inception and the ~1,000 stocks in the Russell 1000 portfolio hold 0.1 percent of portfolio capital at inception).

Here’s the Dot Com Bust:

Dot Com Bust

Performance of the Russell 1000 TR and Russell 1000 TR Value Decile During the Dot Com Bust (1999 to 2006)

The Dot Com Bust started in the Russell 1000 TR on September 1st, 2000. When it reached its low on October 9, 2002, two years and one month later, it had fallen 48.09 percent. It would not recover its losses from the September 1st, 2000 peak until October 12, 2006, more than six years and one month later.

By contrast, the value decile of the Russell 1000 fared much better. While it initially drew down from September 1st, 2000 more than 22 percent in sympathy with the market, it quickly recovered to make new highs by November that year. It would continue to make new highs until April 17, 2002, at which point it began falling, ending down 36.03 percent on October 9, 2002–the same day the market bottomed. The value decile recovered much faster than the Russell 1000 TR, regaining all its lost ground by September 19, 2003, one year and five months from its prior peak. 

It would not fare so well in the Credit Crisis (shown below):

Credit Crisis Drawdown

Performance of the Russell 1000 TR and the Russell 1000 TR Value Decile During the Credit Crisis (2007 to 2012)

The Credit Crisis actually began in the value decile of the Russell 1000 TR on June 4, 2007. The Russell 1000 itself would not start drawing down until a few months later on October 9, 2007. The value decile would find its low on November 21st, 2008 after falling for two years and four months. From peak to trough, the value decile lost 58.31 percent. It would not fully recover until March 30, 2011, almost three years and ten months from the start of the bust. The Russell 1000 bottomed March 9, 2009, after losing 55.41 percent. It would not make a new peak until March 15, 2012, more than three years after its low, and more than five years and four months its last peak.

The shelter offered by the value decile in the Dot Com Bust was missing in the Credit Crisis. If anything, the Credit Crisis hit the value decile harder than the market. It started drawing down four months before the rest of the Russell 1000, and it fell further–58.31 percent versus the Russell 1000′s 55.41 percent. One reason is the relative valuations of the value deciles in the Dot Com Bust and the Credit Crisis. In the early 2000s, value stocks were unusually cheap, with the median of 100 stocks in the value decile of the Russell 1000 yielding (on an EBITDA/EV basis) 17.5 percent in June 1999, 19.5 percent in June 2000, 16.9 percent in June 2001, and 17.2 percent in June 2002. By contrast, the median stock in the Russell 1000 TR yielded 9.0 percent in June 1999, 10.7 percent in June 2000, 10.4 percent in June 2001, and 9.8 percent in June 2002. In the Credit Crisis, the median stock in the value decile yielded 16.75 percent in June 2007, more expensive than at any time in the early 2000s, while the median stock in the market yielded 9.2 percent. The greater yield in the value decile, and the larger spread between the value decile and the market in the Dot Com Bust manifested in a smaller drawdown and a shorter recovery period for the value decile. The slightly smaller yields, and tighter spread in the Credit Crisis led to a larger drawdown, although the value decile did recover much faster than the market, three years and ten months for the value decile versus five years and four months for the market.

Valuation doesn’t tell the whole story, but it’s an important component in the performance of portfolios during stock market crashes. High market valuations are one of the factors that precipitate crashes. Better value portfolios of stocks  sometimes offer a little more protection than overvalued stocks, as they did in the Dot Com bust, but that is unusual. In the ordinary course, correlations go to 1 and everything sells off at the same rate, as it did in the Credit Crisis. Presently, the median stock in the Russell 1000 TR yields just 9.2 percent, while the median stock in the value decile yields almost 16.8 percent. Both are expensive.

Click here if you’d like to read more on Quantitative Value, or connect with me on LinkedIn.


Last week I ran a post about the median stock trading at an all-time high valuation that included this chart from “Millennial Investor” Patrick O’Shaughnessy showing historical EBITDA yields for all stocks in the universe greater than $200 million market capitalization from the period 1971 to date:

Historical EBITDA Yields Millennial v2


I backtested the returns for all three portfolios during the most extreme period in the data (from 1999 to date marked in the red square above). I limited my universe to the stocks in the S&P 500 and lagged the fundamental data by 6 months (so portfolios formed 6/30 in year t use data from 12/31 in year t-1). All portfolios are equally weighted (the 50 stocks in each for the Cheap and Expensive portfolios hold 2 percent of the theoretical portfolio capital at inception and the 500 stocks in the Market portfolio hold 0.2 percent of portfolio capital at inception). Here’s the return chart for the three portfolios:

S&P 500 EBITDA Portfolio Returns

(c) Eyquem Investment Management LLC

Here are the return statistics for each of the three portfolios (the cells are conditionally formatted such that green indicates a low yield or a high return, both “good” things, and red indicates a high yield or a low return (both “bad” things):

Value Statistics S&P 500 Deciles

(c) Eyquem Investment Management LLC

There are a few things to note in the chart above.

  • First, the median stock is the most expensive it has been (in the data) at an EBITDA yield of 8.2 percent (here’s my overview of the research on the enterprise multiple — the inverse of the EBITDA yield). The previous peak was 9.2 percent in 2007, and before that 9.7 percent in 1999. We see something similar in the Cheap portfolio too. It is the much more expensive than average, and is exceeded only by 2007 (15.1 percent), and 2002 (14.7 percent–the all-time high).
  • Second, it’s no accident that the worst returns are associated with the lowest EBITDA yields. If we compare the performance of the portfolios we can see that the Expensive portfolio always has the lowest yields and has consistently underperformed the Market and Cheap portfolios (with the only exception being the last year of the dot com bubble in 1999). The Cheap portfolio always has the highest yields and has consistently outperformed the Market and Expensive portfolios (again, with the only exception being the last year of the dot com bubble in 1999).
  • If we look within portfolios and compare one year to the next, we can see that, though the relationship isn’t perfect, there too low yields produce low returns. (The relationship persists perhaps because it isn’t perfect.) The worst returns don’t necessarily occur in the year of overvaluation, but they follow closely. For example, the worst yearly performances for the Market occurred in 2008 (-31.1 percent), 2007 (-11 percent) and 2001 (-6.6 percent) and 1999 (-1.9 percent), and those dates roughly conform with the lowest yields.
  • Finally, the worst years for the Cheap portfolios were in 2008 (-32.1 percent), 1999 (15.7 percent), 2011 (-8.4 percent), 2002 (-5.4 percent), and 2007 (-5.3 percent), and those dates also roughly conform with the dates that the portfolios held stocks with the lowest yields.

There’s no magic to value investment. Low yields produce low returns. High yields produce high returns. The relationship isn’t perfect. Outlier years like 1999, and 2011 will occur occasionally, but, on average, you’re better served buying Cheap stocks, and remaining cautious during periods when the median stock in the market offers a historically low yield, like right now.

Click here if you’d like to read more on Quantitative Value, or connect with me on LinkedIn.

Great charts in a piece by “Millennial Investor” Patrick O’Shaughnessy called The Five Year Market Metamorphosis. O’Shaughnessy says that the market is much more expensive now than it was at the bottom in 2009. He cites the following chart, which divides the universe of stocks with a market capitalization >$200 million into three buckets–Cheap, Expensive and Market Median–on the basis of the “EBITDA yield” (1/enterprise multiple).

O’Shaughnessy’s chart shows that the Median stock is at an all-time low EBITDA yield, meaning it’s at an all-time high valuation. As we’ve seen previously (Worst value opportunity set in 25 years, and A Market of Stocks? Distribution of S&P 500 P/E Multiples Tightest In 25 Years), market-level overvaluation of this magnitude has typically led to highly attenuated returns over the ensuing decade.

Optimists often point to the outperformance of value stocks in the early 2000s, which seemed to buck the trend of the overall market. O’Shaughnessy’s chart shows why value outperformed in 2000, but not in 2007. In 2000, Cheap stocks offered enormous 20+ percent EBITDA yields. The Market Median stock, by contrast, offered only around 10 percent, slightly less expensive than it is now. Expensive stocks, on historically low EBITDA yields  (high valuations), offered only a little over 1 percent.

Fast forward to 2007. The EBITDA yield on the Market Median stock was comparable to its yield in 2000 (and its yield now), but Cheap stocks were close to all-time low yields (all-time high valuations). That’s why there was nowhere to hide in 2007. Value stocks didn’t offer any more protection than the market did because there wasn’t much value in the “Value.”

The Market Median stock is now more overvalued than it has ever been (or at least in data going back to 1971). The bad news now is that, while the Cheap stocks aren’t quite as expensive as they were in 2007, they’re close.

Click here if you’d like to read more on Quantitative Value, or connect with me on LinkedIn.



Cliff Asness, founder of AQR, seems to be doing the rounds lately. Forbes has a great interview with him called Efficient Market? Baloney, Says Famed Value And Momentum Strategist Cliff Asness.

Here’s Asness talking about the outperformance of value:

Forbes: Isn’t it all about probabilities? You can’t predict the future, but do you feel you can find patterns that generally hold up?

Asness: It’s all about probabilities. And I love that you put it that way. I don’t think it’s different necessarily for non-quantitative firms. We just might acknowledge it a little more explicitly. But I’m in a business where if 52% of the day I’m right, I’m doing pretty well over the long term. That’s not so easy to live with on a daily basis. I like to say, when I say a strategy works, I kind of mean six or seven out of 10 years. A little more than half the days. If your car worked like this you’d fire your mechanic. But we are playing the odds. Some famous findings, cheap stocks, defined simply, price-to-book, cash flow, sales.

You can try to do better, but define it simply. Beat expensive stocks. They beat them on average with a small margin and you want to own a ton of them and be underweight or short a ton of the expensive ones because something like this for one stock means almost nothing.

On active versus passive investment, and the central paradox of efficient markets:

Forbes: How do you defend your approach? Not in the specifics, but the whole thing on indexing. We all know Malkiel and others, Charlie Ellis, will tell you that if you have the discipline to stick to an index, low fees. Your fees are relatively high. Wouldn’t you just be better off saving all that brain power and just riding the wave?

Asness: Sure. Off the bat I’ll tell you my two investing heroes, and there are a lot of good ones to choose from, are Jack Bogle and my dissertation advisor, Gene Fama. So I can’t sit here and put down indexing too much. And in fact when I’m asked, “What advice would you give individuals who are not going to dedicate themselves to this and what not,” I tell them, “The market might not be perfectly efficient, but for most people acting as if it is,” and this is not the only way to get to an index fund, but it’s one route to get to it, it’s certainly one route that implies an index fund. I tell them to do that.

Having said that, there are a lot of ways to get here, but there’s a central paradox to efficient markets. Efficient markets says you can’t beat an index, the price contains all the information. For a long time we’ve known, academics have known, that somebody has to be gathering that information. The old conundrum: What if everyone indexed? Prices would be wildly inefficient.

So I do take what ends up being an arrogant view, and I admit it, that on average people don’t beat the index. Here are the mistakes they make. Here are the risk premiums you can pick up. I do think it’s somewhat profitable, and we want to be some of the people helping make the markets efficient and we think you get paid for that. So that is both how I reconcile it and how I sleep at night.

But if an individual came to me and said, “What should I do?” I don’t say, “Pour your money into my fund,” because, for one thing, they don’t know that much and if we have a bad year they won’t stick with it. I tell them, “Study the history a little bit, just a little bit, and put it in the most aggressive mix of stocks and bonds that you wouldn’t have thrown up and left in the past. And go to Jack Bogle to get it.”

On quantitative value as practiced by AQR:

Forbes: Quickly go over what you call the four styles of investing, starting with value.

Asness: …

And that’s kind of the holy grail of investing. To find various investments, of course, and I know you know this, that go up over time — there’s no substitute for going up — but that go up at different times.

To us, the academic literature has produced a ton. If you want to be a cynic, they’ve produced too many. A lot of smart people with even smarter computers will turn out a lot of past results and we have hundreds of different effects. The blank effect. The silliest ones are things like the Super Bowl, the sunspot effect.

But when someone searches every piece of data and it’s not that silly, it involves an accounting variable, even if ultimately it’s silly when you drill down, it’s harder to dismiss. What we did was kind of almost a self examination of going through and saying, “Of all these things we’ve been reading about for years, many of which we’ve been implementing, if we had to bet the ranch,” now we’re quant, so we never bet the single ranch on anything but over the long-term, if we had to really say, “What are the biggies that we would be most confident in?” They have to have worked for the long term. Data still counts. They need to have great intuition. They have to have worked out of sample, that thing I was talking about before, after they’ve been discovered. If they were discovered in 1970, how’d they do after that? A telltale sign of something that was dredged out of the data is discovered in 1970. Wonderful for the first 70 years of the century and terrible for the last 30 years.

And it has to be implementable. Meaning, there are some things that academics and others look at that when you try with real world transactions cost in a real world portfolio, you find, “Gee, gross, I made a lot of money but net Wall Street made a lot of money. I didn’t make a lot of money.”

Came down to four. Value. Cheap beats expensive. Famous is in U.S. stocks. For me it’s very related to Graham and Dodd value. It’s the same intuition. But where most Graham and Dodd investors will use it to pick a handful of stocks we’ll say the thousand cheapest on our favorite measures will beat the thousand most expensive. We’re betting on a concept more than a specific firm, but looking for the same ideas.

I still think of value as the hero of the story. You’re a manager long cheap and short expensive. I’m the momentum heretic. I’m long good momentum, short bad momentum. A good year for you is usually not my best year. Think about it. It works in the math but also in spirit. When value’s being rewarded you would not think it’s a particularly good time for momentum.

If there’s any magic to the finding, and I’m still amazed by it, is while we hedge each other a bit, more than a bit, both of us make money if we follow it with discipline over time.

Read Forbes’ Efficient Market? Baloney, Says Famed Value And Momentum Strategist Cliff Asness.

Click here to read earlier articles on Asness, AQR’s Value Strategies In Practice or On The Great Shiller PE Controversy: Are Cyclically-Adjusted Earnings Below The Long-Term Trend?.

Click here if you’d like to read more on Quantitative Value, or connect with me on LinkedIn.

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The London Value Investor Conference, which is Moderated by Richard Oldfield and David Shapiro, will take place on Thursday 22nd May at the Queen Elizabeth II Conference Centre in Westminster, London. The Conference will feature well known investors such as Mason Hawkins, Don Yacktman, Mason Morfit and Jon Moulton. As an introduction this conference, please find a video of Michael Price’s 42 minute presentation from May 2013 below:


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Institutional Investor has a great piece from Clifford Asness and John Liew called The Great Divide over Market Efficiency on the efficient markets debate. Most interesting for me was their discussion on the launch of AQR and the “value” strategies it employs:

Starting in the mid-1980s, researchers began investigating simple value strategies. That’s not to say value investing was invented at that time. We fear the ghosts of Benjamin Graham and David Dodd too much to ever imply that. This was when researchers began formal, modern academic studies of these ideas. What they found was that Graham and Dodd had been on to something. Stocks with lower price multiples tended to produce higher average returns than stocks with higher price multiples. As a result, the simplest diversified value strategies seemed to work. Importantly, they worked after accounting for the effects of CAPM (that is, for the same beta, cheaper stocks still seemed to have higher expected returns than more expensive stocks). The statistical evidence was strong and clearly rejected the joint hypothesis of market efficiency and CAPM.

We started our careers in the early 1990s, when as a young team in the asset management group at Goldman, Sachs & Co. we were asked to develop a set of quantitative trading models. Why they let a small group of 20-somethings trade these things we’ll never know, but we’re thankful that they did. Being newly minted University of Chicago Ph.D.s and students of Gene Fama and Ken French, the natural thing for us to do was develop models in which one of the key inputs was value. …

Asness Long Short Value v2

Above is a graph of the cumulative returns to something called HML (a creation of Fama and French’s). HML stands for “high minus low.” It’s a trading strategy that goes long a diversified portfolio of cheap U.S. stocks (as measured by their high book-to-price ratios) and goes short a portfolio of expensive U.S. stocks (measured by their low book-to-price ratios). The work of Fama and French shows that cheap stocks tend to outperform expensive stocks and therefore that HML produces positive returns over time (again, completely unexplained by the venerable CAPM). The graph above shows this over about 85 years.

If you notice the circled part, that’s when we started our careers. Standing at that time (before the big dip you see rather prominently), we found both the intuition and the 65 years of data behind this strategy pretty convincing. Obviously, it wasn’t perfect, but if you were a long-term investor, here was a simple strategy that produced positive average returns that weren’t correlated to the stock market. Who wouldn’t want some of this in their portfolio?

Fortunately for us, the first few years of our live experience with HML’s performance were decent, and that helped us establish a nice track record managing both Goldman’s proprietary capital, which we began with, and the capital of some of our early outside investors. This start also laid the groundwork for us to team up with a fellow Goldman colleague, David Kabiller, and set up our firm, AQR Capital Management.

As fate would have it, we launched our first AQR fund in August 1998. You may remember that as an uneventful little month containing the Russian debt crisis, a huge stock market drop and the beginning of the rapid end of hedge fund firm Long-Term Capital Management. It turned out that those really weren’t problems for us (that month we did fine; we truly were fully hedged long-short, which saved our bacon), but when this scary episode was over, the tech bubble began to inflate.

We were long cheap stocks and short expensive stocks, right in front of the worst period for value strategies since the Great Depression. Imagine a brand-new business getting that kind of result right from the get-go. Not long cheap stocks alone, which simply languished, but long cheap and short expensive! We remember a lot of long-only value managers whining at the time that they weren’t making money while all the crazy stocks soared. They didn’t know how easy they had it. At the nadir of our performance, a typical comment from our clients after hearing our case was something along the lines of “I hear what you guys are saying, and I agree: These prices seem crazy. But you guys have to understand, I report to a board, and if this keeps going on, it doesn’t matter what I think, I’m going to have to fire you.” Fortunately for us, value strategies turned around, but few know the limits of arbitrage like we do (there are some who are probably tied with us).

On the question of market efficiency, years as practitioners have put Asness and Liew somewhere between Fama and Shiller:

We usually end up thinking the market is more efficient than do Shiller and most practitioners — especially, active stock pickers, whose livelihoods depend on a strong belief in inefficiency. As novelist Upton Sinclair, presumably not a fan of efficient markets, said, “It is difficult to get a man to understand something, when his salary depends upon his not understanding it!” However, we also likely think the market is less efficient than does Fama.

After backtesting countless “value” and fundamental strategies for our book Quantitative Value I found myself in the same boat. There exist some strategies that, over the long term, lead to a consistent, small margin over market, but fewer work than most believe, and our own efforts to cherry pick the model inevitably lead to underperformance.

Click here to read The Great Divide over Market Efficiency and here if you’d like to read more on Quantitative Value, or connect with me on LinkedIn.


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