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Chris Cole of Artemis Capital Management has created an incredibly cool film called “Volatility at World’s End: Two Decades of Movement in Markets” showing  a depiction of real stock market volatility using trading data from 1990 to 2011. It accompanied his speech at the 2012 Global Derivatives and Risk Management Conference in Barcelona, Spain.

Here’s Chris’s introduction:

“Nobody will deny there is roughness everywhere….” Benoit Mandelbrot

The movement of stock prices has been an obsession for generations of speculators and traders. On a higher level mathematicians believe that modern markets are an extension of the same fractal beauty found in nature. Visualized these stock markets may take the shape of a turbulent ocean with waves made of human hope, greed, and fear. Merging the world of high-finance and high-art Artemis Capital Management LLC is proud to present a creative visualization of stock market volatility over the last two decades. The video was first shown in conjunction with Christopher Cole’s speech at the 2012 Global Derivatives and Risk Management Conference in Barcelona, Spain.

For the value investor a cursory understanding of volatility can be an important component of market timing. Many value investors are aware of the VIX index that tracks 30 day volatility of the S&P 500 index. The film from Artemis goes one step further animating a series of theoretical VIX indices at different maturity levels extending from 21 days all the way to 1 year. The end effect is a vibrant volatility “wave” that shows when investors are most fearful or complacent in vivid motion. Artemis has produced an interesting piece of art and a multi-dimensional view into the sentiment of investors for over 20 years. When the volatility wave is violent, steep, or exploding investors are afraid and willing to pay more to protect their portfolio. The height of the wave represents the changing price of portfolio insurance far into the future.

And, without further ado, the film:

Head on over to his website for the research note that accompanies the film and other interesting research.

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Investors struggle to implement the Magic Formula strategy for behavioral reasons. They take a market beating model and proceed to underperform.

Greenblatt found that a compilation of all the “professionally managed” accounts earned 84.1 percent over two years against the S&P 500 (up 62.7 percent). A compilation of “self-managed” accounts over the same period showed a cumulative return of 59.4 percent, losing to the market by 20 percent, and to the machines by almost 25 percent. 

Joel Greenblatt has a series of recommendations that he describes as “a helpful list of things NOT to do!“:

1.  Self-managed investors avoided buying many of the biggest winners.

Wow? Well, the market prices certain businesses cheaply for reasons that are usually very well known. Whether you read the newspaper or follow the news in some other way, you’ll usually know what’s “wrong” with most stocks that appear at the top of the magic formula list. That’s part of the reason they’re available cheap in the first place! Most likely, the near future for a company might not look quite as bright as the recent past or there’s a great deal of uncertainty about the company for one reason or another. Buying stocks that appear cheap relative to trailing measures of cash flow or other measures (even if they’re still “good” businesses that earn high returns on capital), usually means you’re buying companies that are out of favor. These types of companies are systematically avoided by both individuals and institutional investors. Most people and especially professional managers want to make money now. A company that may face short term issues isn’t where most investors look for near term profits. Many self-managed investors just eliminate companies from the list that they just know from reading the newspaper face a near term problem or some uncertainty. But many of these companies turn out to be the biggest future winners.

2.  Many self-managed investors changed their game plan after the strategy underperformed for a period of time.

Many self-managed investors got discouraged after the magic formula strategy underperformed the market for a period of time and simply sold stocks without replacing them, held more cash, and/or stopped updating the strategy on a periodic basis. It’s hard to stick with a strategy that’s not working for a little while. The best performing mutual fund for the decade of the 2000’s actually earned over 18% per year over a decade where the popular market averages were essentially flat. However, because of the capital movements of investors who bailed out during periods after the fund had underperformed for awhile, the average investor (weighted by dollars invested) actually turned that 18% annual gain into an 11% LOSS per year during the same 10 year period.[2]

3.  Many self-managed investors changed their game plan after the market and their self-managed portfolio declined (regardless of whether the self-managed strategy was outperforming or underperforming a declining market).

This is a similar story to #2 above. Investors don’t like to lose money. Beating the market by losing less than the market isn’t that comforting. Many self-managed investors sold stocks without replacing them, held more cash, and/or stopped updating the strategy on a periodic basis after the markets and their portfolio declined for a period of time. It didn’t matter whether the strategy was outperforming or underperforming over this same period. Investors in that best performing mutual fund of the decade that I mentioned above likely withdrew money after the fund declined regardless of whether it was outperforming a declining market during that same period.

4.  Many self-managed investors bought more AFTER good periods of performance.

You get the idea. Most investors sell right AFTER bad performance and buy right AFTER good performance. This is a great way to lower long term investment returns.

 

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Yesterday I looked at James Montier’s 2006 paper The Little Note That Beats The Market and his view that investors would struggle to implement the Magic Formula strategy for behavioral reasons.

The Magic Formula is a logical value strategy, it works in backtest, and, most importantly, it seems to work in practice, as this chart from Formula Investing attests:

As Montier predicted, Joel Greenblatt has found that investors do in fact struggle to implement in the Magic Formula strategy in practice. In a great piece published earlier this year, Adding Your Two Cents May Cost You A Lot Over The Long-Term, Greenblatt examined the first two years of returns to Formula Investing’s US separately managed accounts:

Formula Investing provides two choices for retail clients to invest in U.S. stocks, either through what we call a “self-managed” account or through a “professionally managed” account. A self-managed account allows clients to make a number of their own choices about which top ranked stocks to buy or sell and when to make these trades. Professionally managed accounts follow a systematic process that buys and sells top ranked stocks with trades scheduled at predetermined intervals. During the two year period under study[1], both account types chose from the same list of top ranked stocks based on the formulas described in The Little Book that Beats the Market.

Greenblatt has conducted a great real-time behavioral investing experiment. Self-managed accounts have discretion over buy and sell decisions, while professionally managed accounts are automated. Both choose from the same list of stocks. So what happened?

[The] self-managed accounts, where clients could choose their own stocks from the pre-approved list and then follow (or not) our guidelines for trading the stocks at fixed intervals didn’t do too badly. A compilation of all self-managed accounts for the two year period showed a cumulative return of 59.4% after all expenses. Pretty darn good, right? Unfortunately, the S&P 500 during the same period was actually up 62.7%.

“Hmmm….that’s interesting”, you say (or I’ll say it for you, it works either way), “so how did the ‘professionally managed’ accounts do during the same period?” Well, a compilation of all the “professionally managed” accounts earned 84.1% after all expenses over the same two years, beating the “self managed” by almost 25% (and the S&P by well over 20%). For just a two year period, that’s a huge difference! It’s especially huge since both “self-managed” and “professionally managed” chose investments from the same list of stocks and supposedly followed the same basic game plan.

Let’s put it another way: on average the people who “self-managed” their accounts took a winning system and used their judgment to unintentionally eliminate all the outperformance and then some!

Just as Montier (and Greenblatt) predicted, investors struggle to implement the Magic Formula. Discretion over buy-and-sell decisions in aggregate can turn a model that generates a market beating return into a sub-par return. Extraordinary!

Greenblatt has to be admired for sharing this research with the world. Value investing is as misunderstood in the investment community at large as quantitative value investing is misunderstood in the value investing community. It takes a great deal of courage to point out the flaws (such as they are) in the implementation of a strategy, particularly when they are not known to those outside his firm. Given that Greenblatt has a great deal of money riding on the Magic Formula, he should be commended for conducting and sharing a superb bit of research.

I love his conclusion:

[The] best performing “self-managed” account didn’t actually do anything. What I mean is that after the initial account was opened, the client bought stocks from the list and never touched them again for the entire two year period. That strategy of doing NOTHING outperformed all other “self-managed” accounts. I don’t know if that’s good news, but I like the message it appears to send—simply, when it comes to long-term investing, doing “less” is often “more”. Well, good work if you can get it, anyway.

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In his 2006 paper, “The Little Note That Beats the Markets” James Montier backtested the Magic Formula and found that it supported the claim in the “Little Book That Beats The Market” that the Magic Formula does in fact beat the market:

The results certainly support the notions put forward in the Little Book. In all the regions, the Little Book strategy substantially outperformed the market, and with lower risk! The range of outperformance went from just over 3.5% in the US to an astounding 10% in Japan.

The results of our backtest suggest that Greenblatt’s strategy isn’t unique to the US. We tested the Little Book strategy on US, European, UK and Japanese markets between 1993 and 2005. The results are impressive. The Little Book strategy beat the market (an equally weighted stock index) by 3.6%, 8.8%, 7.3% and 10.8% in the various regions respectively. And in all cases with lower volatility than the market! The outperformance was even better against the cap weighted indices.

Regardless, Montier felt that investors would struggle to implement the strategy for behavioral reasons:

Greenblatt suggests two reasons why investors will struggle to follow the Little Book strategy. Both ring true with us from our meeting with investors over the years. The first is “investing by using a magic formula may take away some of the fun”. Following a quant model or even a set of rules takes a lot of the excitement out of stock investing. What would you do all day if you didn’t have to meet companies or sit down with the sell side?

As Keynes noted “The game of professional investment is intolerably boring and over- exacting to anyone who is entirely exempt from the gambling instinct; whilst he who has it must pay to this propensity the appropriate toll”.

Secondly, the Little Book strategy, and all value strategies for that matter, requires patience. And patience is in very short supply amongst investors in today’s markets. I’ve even come across fund managers whose performance is monitored on a daily basis – congratulations are to be extended to their management for their complete mastery of measuring noise! Everyone seems to want the holy grail of profits without any pain. Dream on. It doesn’t exist.

Value strategies work over the long run, but not necessarily in the short term. There can be prolonged periods of underperformance. It is these periods of underperformance that ensure that not everyone becomes a value investor (coupled with a hubristic belief in their own abilities to pick stocks).

As Greenblatt notes “Imagine diligently watching those stocks each day as they do worse than the market averages over the course of many months or even years… The magic formula portfolio fared poorly relative to the market average in 5 out of every 12 months tested. For full-year periods… failed to beat the market averages once every four years”.

The chart below shows the proportion of years within Montier’s sample where the Magic Formula failed to beat the market  in each of the respective regions.

Europe and the UK show surprisingly few years of historic market underperformance. Montier says investors should “bear in mind the lessons from the US and Japan, where underperformance has been seen on a considerably more frequent basis:”

It is this periodic underperformance that really helps ensure the survival of such strategies. As long as investors continue to be overconfident in their abilities to consistently pick winners, and myopic enough that even a year of underperformance is enough to send them running, then strategies such as the Little Book are likely to continue to do well over the long run. Thankfully for those of us with faith in such models, the traits just described seem to be immutable characteristics of most people. As Warren Buffet said “Investing is simple but not easy”.

Montier has long promoted the theme that the reason value investors underperform value models is due to behavioral errors and cognitive biases. For example, in his excellent  2006 research report Painting By Numbers: An Ode To Quant Montier attributes most of the underperformance to overconfidence:

We all think that we know better than simple models. The key to the quant model’s performance is that it has a known error rate while our error rates are unknown.

The most common response to these findings is to argue that surely a fund manager should be able to use quant as an input, with the flexibility to override the model when required. However, as mentioned above, the evidence suggests that quant models tend to act as a ceiling rather than a floor for our behaviour. Additionally there is plenty of evidence to suggest that we tend to overweight our own opinions and experiences against statistical evidence.

Greenblatt has conducted a study on exactly this point. More tomorrow.

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Since Joel Greenblatt’s introduction of the Magic Formula in the 2006 book “The Little Book That Beats The Market,” researchers have conducted a number of studies on the strategy and found it to be a market beater, both domestically and abroad.

Greenblatt claims returns in the order of 30.8 percent per year against a market average of 12.3 percent, and S&P500 return of 12.4 percent per year:

In Does Joel Greenblatt’s Magic Formula Investing Have Any Alpha? Meena Krishnamsetty finds that the Magic Formula generates annual alpha 4.5 percent:

It doesn’t beat the index funds by 18% per year and generate Warren Buffett like returns, but the excess return is still more than 5% per year. This is better than Eugene Fama’s DFA Small Cap Value Fund. It is also better than Lakonishok’s LSV Value Equity Fund.

Wes Gray’s Empirical Finance Blog struggles to repeat the study:

[We] can’t replicate the results under a variety of methods.

We’ve hacked and slashed the data, dealt with survivor bias, point-in-time bias, erroneous data, and all the other standard techniques used in academic empirical asset pricing analysis–still no dice.

In the preliminary results presented below, we analyze a stock universe consisting of large-caps (defined as being larger than 80 percentile on the NYSE in a given year). We test a portfolio that is annually rebalanced on June 30th, equal-weight invested across 30 stocks on July 1st, and held until June 30th of the following year.

Wes finds “serious outperformance” but “nowhere near the 31% CAGR outlined in the book.

Wes thinks that the outperformance of the Magic Formula is due to small cap stocks, which he tests in a second post “Magic Formula and Small Caps–The Missing Link?

Here are Wes’s results:

[While] the MF returns are definitely higher when you allow for smaller stocks, the results still do not earn anywhere near 31% CAGR.

Some closer observations of our results versus the results from the book:

For major “up” years, it seems that our backtest of the magic formula are very similar (especially from a statistical standpoint where the portfolios only have 30 names): 1991, 1995, 1997, 1999, 2001, and 2003.

The BIG difference is during down years: 1990, 1994, 2000, and 2002. For some reason, our backtest shows results which are roughly in line with the R2K (Russell 2000), but the MF results from the book present compelling upside returns during market downturns–so somehow the book results have negative beta during market blowouts? Weird to say the least…

James Montier, in a 2006 paper, “The Little Note That Beats the Markets” says that it works globally:

The results of our backtest suggest that Greenblatt’s strategy isn’t unique to the US. We tested the Little Book strategy on US, European, UK and Japanese markets between 1993 and 2005. The results are impressive. The Little Book strategy beat the market (an equally weighted stock index) by 3.6%, 8.8%, 7.3% and 10.8% in the various regions respectively. And in all cases with lower volatility than the market! The outperformance was even better against the cap weighted indices.

So the Magic Formula generates alpha, and beats the market globally, but not by as much as Greenblatt found originally, and much of the outperformance may be due to small cap stocks.

The Magic Formula and EBIT/TEV

Last week I took a look at the Loughran Wellman and Gray Vogel papers that found the enterprise multiple,  EBITDA/enterprise value, to be the best performing price ratio. A footnote in the Gray and Vogel paper says that they conducted the same research substituting EBIT for EBITDA and found “nearly identical results,” which is perhaps a little surprising but not inconceivable because they are so similar.

EBIT/TEV is one of two components in the Magic Formula (the other being ROC). I have long believed that the quality metric (ROC) adds little to the performance of the value metric (EBIT/EV), and that much of the success of the Magic Formula is due to its use of the enterprise multiple. James Montier seems to agree. In 2006, Montier backtested the strategy and its components in the US, Europe ex UK, UK and Japan:

The universe utilised was a combination of the FTSE and MSCI indices. This gave us the largest sample of data. We analysed the data from 1993 until the end of 2005. All returns and prices were measured in dollars. Utilities and Financials were both excluded from the test, for reasons that will become obvious very shortly. We only rebalance yearly.

Here are the results of Montier’s backtest of the Magic Formula:

And here’re the results for EBIT/TEV over the same period:

Huh? EBIT/TEV alone outperforms the Magic Formula everywhere but Japan?

Montier says that return on capital seems to bring little to the party in the UK and the USA:

In all the regions except Japan, the returns are higher from simply using a pure [EBIT/TEV] filter than they are from using the Little Book strategy. In the US and the UK, the gains from a pure [EBIT/TEV] strategy are very sizeable. In Europe, a pure [EBIT/TEV] strategy doesn’t alter the results from the Little Book strategy very much, but it is more volatile than the Little Book strategy. In Japan, the returns are lower than the Little Book strategy, but so is the relative volatility.

Montier suggests that one reason for favoring the Magic Formula over “pure” EBIT/TEV is career defence. The backtest covers an unusual period in the markets when expensive stocks outperformed for an extended period of time.

The charts below suggest a reason why one might want to have some form of quality input into the basic value screen. The first chart shows the top and bottom ranked deciles by EBIT/EV for the US (although other countries tell a similar story). It clearly shows the impact of the bubble. For a number of years, during the bubble, stocks that were simply cheap were shunned as we all know.


However, the chart below shows the top and bottom deciles using the combined Little Book strategy again for the US. The bubble is again visible, but the ROC component of the screen prevented the massive underperformance that was seen with the pure value strategy. Of course, the resulting returns are lower, but a fund manager following this strategy is unlikely to have lost his job.

In the second chart, note that it took eight years for the value decile to catch up to the glamour decile. They were tough times for value investors.

Conclusion

The Magic Formula beats the market, and generates real alpha. It might not beat the market by as much as Greenblatt found originally, and much of the outperformance is due to small cap stocks, but it’s a useful strategy. Better performance may be found in the use of pure EBIT/EV, but investors employing such a strategy could have very long periods of lean years.

Buy my book The Acquirer’s Multiple: How the Billionaire Contrarians of Deep Value Beat the Market from on Kindlepaperback, and Audible.

Here’s your book for the fall if you’re on global Wall Street. Tobias Carlisle has hit a home run deep over left field. It’s an incredibly smart, dense, 213 pages on how to not lose money in the market. It’s your Autumn smart read. –Tom Keene, Bloomberg’s Editor-At-Large, Bloomberg Surveillance, September 9, 2014.

Click here if you’d like to read more on The Acquirer’s Multiple, or connect with me on Twitter, LinkedIn or Facebook. Check out the best deep value stocks in the largest 1000 names for free on the deep value stock screener at The Acquirer’s Multiple®.

 

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Wes Gray’s Turnkey Analyst has a guest post from Paul Sepulveda in which Paul  asks if it’s possible to improve net net returns by removing stocks with the highest risk of going to zero (the real losers).

Paul has an interesting approach:

My goal was to chop off the left tail of the distribution of returns. Piotroski uses his F-Score to achieve a similar goal among a universe of firms with low P/B (i.e., “value” firms). After collecting the data on recent net-net “cigar-butts”, I quickly realized something: about half of my list consisted of Chinese reverse-merger companies! These firms definitely had a decent shot of going to zero after shareholders realized Bernie Madoff was the CEO and Arthur Anderson was performing the audit work. I separated these companies from the remaining universe. For completeness, I also recorded market caps and Piotroski scores to create alternative net-net universes I could study.

Here are his results:

Paul has only six months of data, but the experiment is ongoing. He has some other interesting observations. See the rest of the post here.

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In their March 2012 paper, “Analyzing Valuation Measures: A Performance Horse-Race over the past 40 Years,” Wes Gray and Jack Vogel asked, “Do long-term, normalized price ratios outperform single-year price ratios?

Benjamin Graham promoted the use of long-term, “normalized” price ratios over single-year price ratios. Graham suggested in Security Analysis that “[earnings in P/E] should cover a period of not less than five years, and preferably seven to ten years.

Robert Shiller has also advocated for long-term price ratios because “annual earnings are noisy as a measure of fundamental value.” A study in the UK by Anderson and Brooks [2006] found that a long-term average (eight-years) of earnings increased the value premium (i.e. the spread in returns between value and growth stocks) by 6 percent over one-year earnings.

Gray and Vogel test a range of year averages for all the price ratios from yesterday’s post. The results are presented below. Equal-weight first:

Market capitalization-weight:

Commentary

We can make several observations about the long-term averages. First, there is no evidence that any long-term average is consistently better than any other, measured either on the raw performance to the value decile, or by the value premium created. This is true for both equal-weight portfolios and market capitalization-weighted portfolios, which we would expect. For example, in the equal-weight table, the E/M value portfolio generates its best return using a 4-year average, but the spread is biggest using the 3-year average. Compare this with EBITDA/TEV, which generates its best return using a single-year ratio, and its biggest spread using a 3-year average, or FCF/TEV, which generates both its best return and biggest spread with a single-year average. There is no consistency, or pattern to the results that we can detect. If anything, the results appear random to me, which leads me to conclude that there’s no evidence that long-term averages outperform single-year price ratios.

We can make other, perhaps more positive observations. For example, in the equal-weight panel, the enterprise multiple is consistently the best performing price ratio across most averages (although it seems to get headed by GP/TEV near the 7-year and 8-year averages). It also generates the biggest value premium across all long-term averages.  It’s also a stand-out performer in the market capitalization-weighted panel, delivering the second best returns to GP/TEV, but generating a bigger value premium than GP/TEV about half the time.

The final observation that we can make is that the value portfolio consistently outperforms the “growth” or expensive portfolio. For every price ratio, and over every long-term average, the better returns were found in the value portfolio. Value works.

Conclusion

While long-term average price ratios have been promoted by giants of the investment world like Graham and Shiller as being better than single-year ratios, there exists scant evidence that this is true. A single UK study found a significant premium for long-term average price ratios, but Gray and Vogel’s results do not support the findings of that study. There is no evidence in Gray and Vogel’s results that any long-term average is better than any other, or better than a single-year price ratio. One heartening observation is that, however we slice it, value outperforms glamour. Whichever price ratio we choose to examine, over any long-term average, value is the better bet.

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Friends, Romans, countrymen, lend me your ears;
I come to bury Caesar, not to praise him.

Having just anointed the enterprise multiple as king yesterday, I’m prepared to bury it in a shallow grave today if I can get a little more performance. Fickle.

In their very recent paper, “Analyzing Valuation Measures: A Performance Horse-Race over the past 40 Years,” Wes Gray and Jack Vogel asked, “Which valuation metric has historically performed the best?

Gray and Vogel examine a range of price ratios over the period 1971 to 2010:

  • Earnings to Market Capitalization (E/M)
  • Earnings before interest and taxes and depreciation and amortization to total
  • enterprise value (EBITDA/TEV)
  • Free cash flow to total enterprise value (FCF/TEV)
  • Gross profits to total enterprise value (GP/TEV)
  • Book to market (B/M)
  • Forward Earnings Estimates to Market Capitalization (FE/M)

They find that the enterprise multiple is the best performing price ratio:

The returns to an annually rebalanced equal-weight portfolio of high EBITDA/TEV stocks, earn 17.66% a year, with a 2.91% annual 3-factor alpha (stocks below the 10% NYSE market equity breakpoint are eliminated). This compares favorably to a practitioner favorite, E/M (i.e., inverted Price-to-earnings, or P/E). Cheap E/M stocks earn 15.23% a year, but show no evidence of alpha after controlling for market, size, and value exposures. The academic favorite, book-to-market (B/M), tells a similar story as E/M and earns 15.03% for the cheapest stocks, but with no alpha. FE/M is the worst performing metric by a wide margin, suggesting that investors shy away from using analyst earnings estimates to make investment decisions.

The also find that the enterprise multiple generates the biggest value premium:

We find other interesting facts about valuation metrics. When we analyze the spread in returns between the cheapest and most expensive stocks, given a specific valuation measure, we again find that EBITDA/TEV is the most effective measure. The lowest quintile returns based on EBITDA/TEV return 7.97% a year versus the 17.66% for the cheapest stocks—a spread of 9.69%. This compares very favorably to the spread created by E/M, which is only 5.82% (9.41% for the expensive quintile and 15.23% for the cheap quintile).

Here are the results for all the price ratios (click to make it bigger):

Which price ratio outperforms the enterprise multiple? None. Vivat rex.

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Which price ratio best identifies undervalued stocks? It’s a fraught question, dependent on various factors including the time period tested, and the market capitalization and industries under consideration, but I believe a consensus is emerging.

The academic favorite remains book value-to-market capitalization (the inverse of price-to-book value). Fama and French maintain that it makes no difference which “price-to-a-fundamental” is employed, but if forced to choose favor book-to-market. In the Fama/French Forum on Dimensional Fund Advisor’s website they give it a tepid thumbs up despite the evidence that it’s not so great:

Data from Ken French’s website shows that sorting stocks on E/P or CF/P data produces a bigger spread than BtM over the last 55 years. Wouldn’t it make sense to use these other factors in addition to BtM to distinguish value from growth stocks? EFF/KRF: A stock’s price is just the present value of its expected future dividends, with the expected dividends discounted with the expected stock return (roughly speaking). A higher expected return implies a lower price. We always emphasize that different price ratios are just different ways to scale a stock’s price with a fundamental, to extract the information in the cross-section of stock prices about expected returns. One fundamental (book value, earnings, or cashflow) is pretty much as good as another for this job, and the average return spreads produced by different ratios are similar to and, in statistical terms, indistinguishable from one another. We like BtM because the book value in the numerator is more stable over time than earnings or cashflow, which is important for keeping turnover down in a value portfolio. Nevertheless, there are problems in all accounting variables and book value is no exception, so supplementing BtM with other ratios can in principal improve the information about expected returns. We periodically test this proposition, so far without much success.

There are a variety of papers on the utility of book value that I’ve beaten to death on Greenbackd. I used to think it was the duck’s knees because that was what all the early research seemed to say (See, for example, Roger Ibbotson’s “Decile Portfolios of the New York Stock Exchange, 1967 – 1984,” Werner F.M. DeBondt and Richard H. Thaler’s “Further Evidence on Investor Overreaction and Stock Market Seasonality”). Josef Lakonishok, Andrei Shleifer, and Robert Vishny’s Contrarian Investment, Extrapolation and Risk, which was updated by The Brandes Institute as Value vs Glamour: A Global Phenomenon reopened the debate, suggesting that price-to-earnings and price-to-cash flow might add something to price-to-book.

A number of more recent papers have moved away from book-to-market, and towards the enterprise multiple ((equity value + debt + preferred stock – cash)/ (EBITDA)). As far as I am aware, Tim Loughran and Jay W. Wellman got in first with their 2009 paper “The Enterprise Multiple Factor and the Value Premium,” which was a great unpublished paper, but became in 2010 a slightly less great published paper, “New Evidence on the Relation Between the Enterprise Multiple and Average Stock Returns,” suitable only for academics and masochists (but I repeat myself). The abstract to the 2009 paper (missing from the 2010 paper) cuts right to the chase:

Following the work of Fama and French (1992, 1993), there has been wide-spread usage of book-to-market as a factor to explain stock return patterns. In this paper, we highlight serious flaws with the use of book-to-market and offer a replacement factor for it. The Enterprise Multiple, calculated as (equity value + debt value + preferred stock – cash)/ EBITDA, is better than book-to-market in cross-sectional monthly regressions over 1963-2008. In the top three size quintiles (accounting for about 94% of total market value), EM is a highly significant measure of relative value, whereas book-to-market is insignificant.

The abstract says everything you need to know: Book-to-market is widely used (by academics), but it has serious flaws. The enterprise multiple is more predictive over a long period (1963 to 2008), and it’s much more predictive in big market capitalization stocks where book-to-market is essentially useless.

What serious flaws?

The big problem with book-to-market is that so much of the return is attributable to nano-cap stocks and “the January effect”:

Loughran (1997) examines the data used by Fama and French (1992) and finds that the results are driven by a January seasonal and the returns on microcap growth stocks. For the largest size quintile, accounting for about three-quarters of total market cap, Loughran finds that BE/ME has no significant explanatory power over 1963-1995. Furthermore, for the top three size quintiles, accounting for about 94% of total market cap, size and BE/ME are insignificant once January returns are removed. Fama and French (2006) confirm Loughran’s result over the post- 1963 period. Thus, for nearly the entire market value of largest stock market (the US) over the most important time period (post-1963), the value premium does not exist.

That last sentence bears repeating: For nearly the entire market value of largest stock market (the US) over the most important time period (post-1963), the value premium does not exist, which means that book-to-market is not predictive in stocks other than the smallest 6 percent by market cap. What about book-to-market in the stocks in that smallest 6 percent? It might not work there either:

Keim (1983) shows that the January effect is primarily limited to the first trading days in January. These returns are heavily influenced by December tax-loss selling and bid-ask bounce in low-priced stocks. Since many fund managers are restricted in their ability to buy small stocks due to ownership concentration restrictions and are prohibited from buying low-prices stocks due to their speculative nature, it is unlikely that the value premium can be exploited.

More scalable

The enterprise multiple succeeds where book-to-market fails.

In the top three size quintiles, accounting for about 94% of total market value, EM is a highly significant measure of relative value, whereas BE/ME is insignificant and size is only weakly significant. EM is also highly significant after controlling for the January seasonal and removing low-priced (<$5) stocks. Robustness checks indicate that EM is also better to Tobin’s Q as a determinant of stock returns.

And maybe the best line in the  paper:

Our results are an improvement over the existing literature because, rather than being driven by obscure artifacts of the data, namely the stocks in the bottom 6% of market cap and the January effect, our results apply to virtually the entire universe of US stocks. In other words, our results may actually be relevant to both Wall Street and academics.

Why does the enterprise multiple work?

The enterprise multiple is a popular measure, and for other good reasons besides its performance. First, the enterprise multiple uses enterprise value. A stock’s enterprise value provides more information about its true cost than its market capitalization because it includes information about the stock’s balance sheet, including its debt, cash and preferred stock (and in some variations minorities and net payables-to-receivables). Such things are significant to acquirers of the business in its entirety, which, after all, is the way that value investors should think about each stock. Market capitalization can be misleading. Just because a stock is cheap on a book value basis does not mean that it’s cheap 0nce its debt load is factored into the valuation. Loughran and Wellman, quoting Damodaran (whose recent paper I covered here last week), write:

Damodaran shows in an unpublished study of 550 equity research reports that EM, along with Price/Earnings and Price/Sales, were the most common relative valuation multiples used. He states, “In the past two decades, this multiple (EM) has acquired a number of adherents among analysts for a number of reasons.” The reasons Damodaran cites for EM’s increasing popularity also point to the potential superiority of EM over book-to-market. One reason is that EM can be compared more easily across firms with differing leverage. We can see this when comparing the corresponding inputs of EM and BE/ME. The numerator of EM, Enterprise Value, can be compared to the market value of equity. EV can be viewed as a theoretical takeover price of a firm. After a takeover, the acquirer assumes the debt of the firm, but gains use of the firm’s cash and cash equivalents. Including debt is important here. To take an example, in 2005, General Motors had a market cap of $17 billion, but debt of $287 billion. Using market value of equity as a measure of size, General Motors is a mid-sized firm. Yet on the basis of Enterprise Value, GM is a huge company. Market value of equity by itself is unlikely to fully capture the effect GM’s debt has on its returns. More generally, it is reasonable to think that changing firm debt levels may affect returns in a way not fully captured by market value of equity. Bhojraj and Lee (2002) confirm this, finding that EV is superior to market value of common equity, particularly when firms are differentially levered.

The enterprise multiple’s ardor for cash and abhorrence for debt matches my own, hence why I like it so much. In practice, that tendency can be a double-edged sword. It digs up lots of little cash boxes with a legacy business attached like an appendix (think Daily Journal Corporation (NASDAQ:DJCO) or Rimage Corporation (NASDAQ:RIMG)). Such stocks tend to have limited upside. On the flip side, they also have happily virtually no downside. In this way they are vastly superior to the highly leveraged pigs favored by book-to-market, which tends to serve up heavily leveraged slivers of somewhat discounted equity, and leaves you to figure out whether it can bear the debt load. Get it wrong and you’ll be learning the intricacies of the bankruptcy process with nothing to show for it at the end. When it comes time to pull the trigger, I generally find it easier to do it with a cheap enterprise multiple than a cheap price-to-book value ratio.

The earnings variable: EBITDA

There’s a second good reason to like the enterprise multiple: the earnings variable. EBITDA contains more information than straight earnings, and so should give a more full view of where the accounting profits flow:

The denominator of EM is operating income before depreciation while net income (less dividends) flows into BE. The use of EBITDA provides several advantages that BE lacks. Damodaran notes that differences in depreciation methods across companies will affect net income and hence BE, but not EBITDA. Also, the McKinsey valuation text notes that operating income is not affected by nonoperating gains or losses. As a result, operating income before depreciation can be viewed as a more accurate and less manipulable measure of profitability, allowing it to be used to compare firms within as well as across industries. Critics of EBITDA point out that it is not a substitute for cash flow; however, EV in the numerator does account for cash.

The enterprise multiple includes debt as well as equity, contains a clearer measure of operating profit and captures changes in cash from period to period. The enterprise multiple is a more complete measure of relative value than book-to-market. It also performs better:

Performance of the enterprise multiple versus book-to-market

From CXOAdvisory:

  • EM generates an annual value premium of 5.8% per year over the entire sample period (compared to 4.8% for B/M during 1926-2004).
  • EM captures more premium than B/M for all five quintiles of firm size and is much less dependent on small stocks for its overall premium (see chart below).
  • In the top three quintiles of firm size (accounting for about 94% of total market capitalization), EM is a highly significant measure of relative value, while B/M is not.
  • EM remains highly significant after controlling for the January effect and after removing low-priced (<$5) stocks.
  • EM outperforms Tobin’s q as a predictor of stock returns.
  • Evidence from the UK and Japan confirms that EM is a highly significant measure of relative value.

The “value premium” is the difference in returns to a portfolio of glamour stocks (i.e., the most expensive decile) when compared to a portfolio of value stocks (i.e., the cheapest decile) ranked on a given price ratio (in this case, the enterprise multiple and book-to-market). The bigger the value premium, the better a given price ratio sorts stocks into winners and losers. It’s a more robust test than simply measuring the performance of the cheapest stocks. Not only do we want to limit our sins of commission (i.e., buying losers), we want to limit our sins of omission (i.e., not buying winners). 

Here are the value premia by market capitalization (from CXOAdvisory again): Ring the bell. The enterprise multiple kicks book-to-market’s ass up and down in every weight class, but most convincingly in the biggest stocks.

Strategies using the enterprise multiple

The enterprise multiple forms the basis for several strategies. It is the price ratio limb of Joel Greenblatt’s Magic Formula. It also forms the basis for the Darwin’s Darlings strategy that I love (see Hunting Endangered Species). The Darwin’s Darlings strategy sought to front-run the LBO firms in the early 2000s, hence the enterprise multiple was the logical tool, and highly effective.

Conclusion

This post was motivated by the series last week on Aswath Damodaran’s paper ”Value Investing: Investing for Grown Ups?” in which he asks, “If value investing works, why do value investors underperform?Loughran and Wellman also asked why, if Fama and French (2006) find a value premium (measured by book-to-market) of 4.8% per year over 1926-2004, mutual fund managers couldn’t capture it:

Fund managers perennially underperform growth indices like the Standard and Poor’s 500 Index and value fund managers do not outperform growth fund managers. Either the value premium does not actually exist, or it does not exist in a way that can be exploited by fund managers and other investors.

Loughran and Wellman find that for nearly the entire market value of largest stock market (the US) over the most important time period (post-1963), the value premium does not exist, which means that book-to-market is not predictive in stocks other than the smallest 6 percent by market cap (and even there the returns are suspect). The enterprise multiple succeeds where book-to-market fails. In the top three size quintiles, accounting for about 94% of total market value, the enterprise multiple is a highly predictive measure, while book-to-market is insignificant. The enterprise multiple also works after controlling for the January seasonal effect and after removing low priced (<$5) stocks. The enterprise multiple is king. Long live the enterprise multiple.

Buy my book The Acquirer’s Multiple: How the Billionaire Contrarians of Deep Value Beat the Market from on Kindlepaperback, and Audible.

Here’s your book for the fall if you’re on global Wall Street. Tobias Carlisle has hit a home run deep over left field. It’s an incredibly smart, dense, 213 pages on how to not lose money in the market. It’s your Autumn smart read. –Tom Keene, Bloomberg’s Editor-At-Large, Bloomberg Surveillance, September 9, 2014.

Click here if you’d like to read more on The Acquirer’s Multiple, or connect with me on Twitter, LinkedIn or Facebook. Check out the best deep value stocks in the largest 1000 names for free on the deep value stock screener at The Acquirer’s Multiple®.

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Aswath Damodaran, in his excellent paper “Value Investing: Investing for Grown Ups?”, asks whether spending time researching a company’s fundamentals (“active” investing) generates a higher return for investors than a comparable value-based index (“passive” investing)?

Says Damodaran:

Of all of the investment philosophies, value investing comes with the most impressive research backing from both academica and practitioners. The excess returns earned by stocks that fit value criteria (low multiples of earnings and book value, high dividends) and the success of some high-profile value investors (such as Warren Buffett) draws investors into the active value investing fold.

But does spending time researching a company’s fundamentals generate higher returns for investors than a passive index?  Does active value investing pay off?

A simple test of the returns to the active component of value investing is to look at the returns earned by active value investors, relative to a passive value investment option, and compare these excess returns with those generated by active growth investors, relative to a passive growth investment alternative. In figure 17, we compute the excess returns generated for all US mutual funds, classifed into value, blend and growth categories, relative to index funds for each category. Thus, the value mutual funds are compared to index fund of just value stocks (low price to book and low price to earnings stocks) and the growth mutual funds to a growth index fund (high price to book and high price earnings stocks).

Shocker! Active value investing mutual fund managers would be better off buying the index.

The results are not good for value investing. The only funds that beat their index counterparts are growth funds, and they do so in all three market cap classes. Active value investing funds generally do the worst of any group of funds and particularly so with large market cap companies.

Damodaran has a great conclusion:

If you are an individual value investors, you can attribute this poor performance to the pressures that mutual funds managers operate under, to deliver results quickly, an expectation that may be at odds with classic value investing. That may be the case, but it points to the need for discipline and consistency in value investing and to the very real fact that beating the market is always difficult to do, even for a good value investor.

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