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Posts Tagged ‘Value investing’

The Superinvestors of Graham-and-Doddsville is a well-known article (see the original Hermes article here.pdf) by Warren Buffett defending value investing against the efficient market hypothesis. The article is an edited transcript of a talk Buffett gave at Columbia University in 1984 commemorating the fiftieth anniversary of Security Analysis, written by Benjamin Graham and David L. Dodd.

In a 2006 talk, “Journey Into the Whirlwind: Graham-and-Doddsville Revisited,” Louis Lowenstein*, then a professor at the Columbia Law School, compared the performance of a group of “true-blue, walk-the-walk value investors” (the “Goldfarb Ten”) and “a group of large cap growth funds” (the “Group of Fifteen”).

Here are Lowenstein’s findings:

For the five years ended this past August 31, the Group of Fifteen experienced on average negative returns of 8.89% per year, vs. a negative 2.71% for the S&P 500.4 The group of ten value funds I had studied in the “Searching for Rational Investors” article had been suggested by Bob Goldfarb of the Sequoia Fund.5 Over those same five years, the Goldfarb Ten enjoyed positive average annual returns of 9.83%. This audience is no doubt quick with numbers, but let me help. Those fifteen large growth funds underperformed the Goldfarb Ten during those five years by an average of over 18 percentage points per year. Hey, pretty soon you have real money. Only one of the fifteen had even modestly positive returns. Now if you go back ten years, a period that includes the bubble, the Group of Fifteen did better, averaging a positive 8.13% per year.Even for that ten year period, however, they underperformed the value group, on average, by more than 5% per year.6 With a good tailwind, those large cap funds were not great – underperforming the index by almost 2% per year – and in stormy weather their boats leaked badly.

Lowenstein takes a close look at one of the Group of Fifteen (a growth fund):

The first was the Massachusetts Investors Growth Stock Fund, chosen because of its long history. Founded in 1932, as the Massachusetts Investors Second Fund, it was, like its older sibling, Massachusetts Investors Trust, truly a mutual fund, in the sense that it was managed internally, supplemented by an advisory board of six prominent Boston businessmen.7 In 1969, when management was shifted to an external company, now known as MFS Investment Management, the total expense ratio was a modest 0.32%.

I am confident that the founders of the Massachusetts Investors Trust would no longer recognize their second fund, which has become a caricature of the “do something” culture. The expense ratio, though still below its peer group, has tripled. But it’s the turbulent pace of trading that would have puzzled and distressed them. At year-end 1999, having turned the portfolio over 174%, the manager said they had moved away from “stable growth companies” such as supermarket and financial companies, and into tech and leisure stocks, singling out in the year- end report Cisco and Sun Microsystems – each selling at the time at about 100 X earnings – for their “reasonable stock valuation.” The following year, while citing a bottom-up, “value sensitive approach,” the fund’s turnover soared to 261%. And in 2001, with the fund continuing to remark on its “fundamental . . .bottom-up investment process,” turnover reached the stratospheric level of 305%. It is difficult to conceive how, even in 2003, well after the market as a whole had stabilized, the managers of this $10 billion portfolio had sold $28 billion of stock and then reinvested that $28 billion in other stocks.

For the five years ended in 2003, turnover in the fund averaged 250%. All that senseless trading took a toll. For the five years ended this past August, average annual returns were a negative 9-1/2%. Over the past ten years, which included the glory days of the New Economy, the fund did better, almost matching the index, though still trailing our value funds by 4% a year. Net assets which had been a modest $1.9 billion at Don Phillips’ kickoff date in 1997, and had risen to $17 billion in 2000, are now about $8 billion.

If you’re feeling some sympathy for the passengers in this financial vehicle, hold on. Investors – and I’m using the term loosely – in the Mass. Inv. Growth Stock Fund were for several years running spinning their holdings in and out of the fund at rates approximating the total assets of the fund. In 2001, for example, investors cashed out of $17-1/2 billion in Class A shares, and bought $16 billion in new shares, leaving the fund at year end with net assets of about $14 billion. Having attracted, not investors, but speculators trying to catch the next new thing, management got the shareholders they deserved.

And the value investors?

Having updated my data through August of this year, I am happy to report that the Goldfarb Ten still look true blue – actually better than at year-end 2003. The portfolio turnover rates have dropped on average to 16% – translation, an average holding period of six years. Honey, what did you do today? Nothing, dear.The average cash holding is 14% of the portfolio, and five of the funds are closed to new investors.f Currently, however, two of the still open funds, Mutual Beacon and Clipper, are losing their managers. The company managing the Clipper Fund has been sold twice over and Jim Gipson and two colleagues recently announced they’re moving on. At Mutual Beacon, which is part of the Franklin Templeton family, David Winters has left to create a mutual fund, ah yes, the Wintergreen Fund. It will be interesting to see whether Mutual Beacon and Clipper will maintain their discipline.

Speaking of discipline, you may remember that after Buffett published “The Superinvestors,” someone calculated that while they were indeed superinvestors, on average they had trailed the market one year in three.20 Tom Russo, of the Semper Vic Partners fund, took a similar look at the Goldfarb Ten and found, for example, that four of them had each underperformed the S&P 500 for four consecutive years, 1996-1999, and in some cases by huge amounts. For the full ten years, of course, that underperformance was sharply reversed, and then some. Value investing thus requires not just patient managers but also patient investors, those with the temperament as well as intelligence to feel comfortable even when sorely out of step with the crowd. If you’re fretting that the CBOE Market Volatility Index may be signaling fear this week, value investing is not for you.

* Louis was father to Roger Lowenstein of Buffett: The Making of an American Capitalist.

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A recent study by Wes Gray and Jack Vogel, Dissecting Shareholder Yield, makes the stunning claim that dividend yield doesn’t predict future returns, but more complete measures of shareholder yield might hold some promise. Gray and Vogel say that, “regardless of the yield metric chosen, the predictive power of separating stocks into high and low yield portfolios has lost considerable power in the last twenty years.”

This seems to be part of a trend away from dividends and towards share repurchases, presumably for tax reasons:

Our work is related to previous research on payout yield as a predictor of future returns. Grullen and Michaely [2002] find that firms have substituted away from dividends towards share repurchases. Boudoukh et al (2007) construct two measures of payout yields (Dividends plus repurchases, as well as Dividends plus net repurchases). They find that these payout measures have more predictive ability than the dividend yield. We contribute to the literature by examining an additional variable to our payout yield, namely net debt pay down. Net debt pay down was first proposed by Priest and McClelland (2007), but is not rigorously analyzed. As a preview of our results, we find that the addition of net debt pay down helps performance, but is not a panacea. Similar to all yield metrics, results in the latter half of the sample (1992-2011) are not as strong as those in the first half of the sample (1972-1991).

Gray and Vogel examine four yield measures:

  • Dividends (DIV)
  • Dividends plus repurchases (PAY1)
  • Dividends plus net repurchases (repurchases minus equity issuance) (PAY2)
  • Dividends plus net repurchases plus net debt paydown (SH_YD)

Here’s their table of returns:

They find as follows:

We perform a similar study as Patel et al. on all our yield metrics, but focus on the dividend yield (DIV) and our complete shareholder yield metric (SH_YD) to assess the “high yield, low payout” outperformance hypothesis. We confirm the basic conclusion from Patel et al. that low payout firms outperform high payout firms across all yield quintiles. For example, in the top DIV quintile, high DIV firms earn 12.16% CAGR from 1972-2011, however, low payout firms earn 13.43%, and high payout firms earn 12.15%. After risk adjusting the results with the 3-factor model we find no evidence of outperformance for any DIV portfolio. In Table V we assess a variety of additional risk/reward characteristics. There is no clear evidence that splitting high DIV yield firms into low and high payout adds risk-adjusted value relative to the standard high DIV yield strategy. For example, max drawdowns suggest that high DIV, low payout strategies are actually riskier than high DIV, high payout strategies (64.35% drawdown compared to 58.27%). However, Sharpe and Sortino ratios are marginally higher for high DIV, low payout strategies relative to high DIV, high payout strategies.

When we examine high SH_YD stocks, we come to a similar conclusion: there is no conclusive evidence that separating stocks on payout percentage within a given yield category can systematically add value to an investment strategy.

In summary, we confirm that separating yield quintiles into low and high payout bins has worked historically on a raw returns basis for DIV. Nonetheless, an investigation of the strategy on a risk-adjusted basis and across different yield metrics and samples suggest there is no evidence that a high yield low payout strategy can help an investor predict stocks. If anything, the evidence suggests that investors should potentially investigate strategies that focus on low SH_YD low payout strategies. The alphas for these stocks are -6.30% for the Top 2000 sample and -5.33% for the S&P 500 sample; the additional risk/reward ratios in Table V also show terrible performance for the low SH_YD low payout strategies.

And the table showing the reduction in performance over time:

Gray and Vogel make three key points in their conclusion:

1. More complete yield measures improve performance.

2. All yield measures are becoming less effective over time.

3. Attempting to improve yield measures by separating on payout percentages is not a reliable tool to enhance investment returns.

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Joel Greenblatt’s rationale for a value-weighted index can be paraphrased as follows:

  • Most investors, pro’s included, can’t beat the index. Therefore, buying an index fund is better than messing it up yourself or getting an active manager to mess it up for you.
  • If you’re going to buy an index, you might as well buy the best one. An index based on the market capitalization-weighted S&P500 will be handily beaten by an equal-weighted index, which will be handily beaten by a fundamentally weighted index, which is in turn handily beaten by a “value-weighted index,” which is what Greenblatt calls his “Magic Formula-weighted index.”

Yesterday we examined the first point. Today let’s examine the second.

Market Capitalization Weight < Equal Weight < Fundamental Weight < “Value Weight” (Greenblatt’s Magic Formula Weight)

I think this chart is compelling:

It shows the CAGRs for a variety of indices over the 20 years to December 31, 2010. The first thing to note is that the equal weight index – represented by the &P500 Equal Weight TR – has a huge advantage over the market capitalization weighted S&P500 TR. Greenblatt says:

Over time, traditional market-cap weighted indexes such as the S&P 500 and the Russell 1000 have been shown to outperform most active managers. However, market cap weighted indexes suffer from a systematic flaw. The problem is that market-cap weighted indexes increase the amount they own of a particular company as that company’s stock price increases. As a company’s stock falls, its market capitalization falls and a market cap-weighted index will automatically own less of that company. However, over the short term, stock prices can often be affected by emotion. A market index that bases its investment weights solely on market capitalization (and therefore market price) will systematically invest too much in stocks when they are overpriced and too little in stocks when they are priced at bargain levels. (In the internet bubble, for example, as internet stocks went up in price, market cap-weighted indexes became too heavily concentrated in this overpriced sector and too underweighted in the stocks of established companies in less exciting industries.) This systematic flaw appears to cost market-cap weighted indexes approximately 2% per year in return over long periods.

The equal weight index corrects this systematic flaw to a degree (the small correction is still worth 2.7 percent per year in additional performance). Greenblatt describes it as randomizing the errors made by the market capitalization weighted index:

One way to avoid the problem of buying too much of overpriced stocks and too little of bargain stocks in a market-cap weighted index is to create an index that weights each stock in the index equally. An equally-weighted index will still own too much of overpriced stocks and too little of bargain-priced stocks, but in other cases, it will own more of bargain stocks and less of overpriced stocks. Since stocks in the index aren’t affected by price, errors will be random and average out over time. For this reason, equally weighted indexes should add back the approximately 2% per year lost to the inefficiencies of market-cap weighting.

While the errors are randomized in the equal weight index, they are still systematic – it still owns too much of the expensive stocks and too little of the cheap ones. Fundamental weighting corrects this error (again to a small degree). Fundamentally-weighted indexes weight companies based on their economic size using price ratios such as sales, book value, cash flow and dividends. The surprising thing is that this change is worth only 0.4 percent per year over equal weighting (still 3.1 percent per year over market capitalization weighting).

Similar to equally-weighted indexes, company weights are not affected by market price and therefore pricing errors are also random. By correcting for the systematic errors caused by weighting solely by market-cap, as tested over the last 40+ years, fundamentally-weighted indexes can also add back the approximately 2% lost each year due to the inefficiencies of market-cap weighting (with the last 20 years adding back even more!).

The Magic Formula / “value” weighted index has a huge advantage over fundamental weighting (+3.9 percent per year), and is a massive improvement over the market capitalization index (+7 percent per year). Greenblatt describes it as follows:

On the other hand, value-weighted indexes seek not only to avoid the losses due to the inefficiencies of market-cap weighting, but to add performance by buying more of stocks when they are available at bargain prices. Value-weighted indexes are continually rebalanced to weight most heavily those stocks that are priced at the largest discount to various measures of value. Over time, these indexes can significantly outperform active managers, market cap-weighted indexes, equally-weighted indexes, and fundamentally-weighted indexes.

I like Greenblatt’s approach. I’ve got two small criticisms:

1. I’m not sure that his Magic Formula weighting is genuine “value” weighting. Contrast Greenblatt’s approach with Dylan Grice’s “Intrinsic Value to Price” or “IVP” approach, which is a modified residual income approach, the details of which I’ll discuss in a later post. Grice’s IVP is a true intrinsic value calculation. He explains his approach in a way reminiscent of Buffett’s approach:

[How] is intrinsic value estimated? To answer, think first about how much you should pay for a going concern. The simplest such example would be that of a bank account containing $100, earning 5% per year interest. This asset is highly liquid. It also provides a stable income. And if I reinvest that income forever, it provides stable growth too. What’s it worth?

Let’s assume my desired return is 5%. The bank account is worth only its book value of $100 (the annual interest payment of $5 divided by my desired return of 5%). It may be liquid, stable and even growing, but since it’s not generating any value over and above my required return, it deserves no premium to book value.

This focus on an asset’s earnings power and, in particular, the ability of assets to earn returns in excess of desired returns is the essence of my intrinsic valuation, which is based on Steven Penman’s residual income model.1 The basic idea is that if a company is not earning a return in excess of our desired return, that company, like the bank account example above, deserves no premium to book value.

And it seems to work:

Grice actually calculates IVP while Greenblatt does not. Does that actually matter? Probably not. Even if it’s not what I think the average person understands real “value” weighting to be, Greenblatt’s approach seems to work. Why quibble over semantics?

2. As I’ve discussed before, Greenblatt’s Magic Formula return owes a great deal to his selection of EBIT/TEV as the price limb of his model. EBIT/TEV has been very well performed historically. If we were to substitute EBIT/TEV for the P/B, P/E, price-to-dividends, P/S, P/whatever, we’d have seen slightly better performance than the Magic Formula provided, but you might have been out of the game somewhere between 1997 to 2001.

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Last week 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, a view borne out by Greenblatt’s own research. This is not a criticism of the strategy, which is tractable and implementable, but an observation on how pernicious our cognitive biases are.

Greenblatt found that a compilation of all the “professionally managed” – read “systematic, automatic (hydromatic)” – accounts earned 84.1 percent over two years against the S&P 500 (up 62.7 percent). A compilation of “self-managed” accounts (the humans) 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. So the humans took this unmessupable system and messed it up. As predicted by Montier and Greenblatt.

Ugh.

Greenblatt, perhaps dismayed at the fact that he dragged the horses all the way to the water to find they still wouldn’t drink, has a new idea: value-weighted indexing (not to be confused with the academic term for market capitalization-weighting, which is, confusingly, also called value weighting).

I know from speaking to some of you that this is not a particularly popular idea, but I like it. Here’s Greenblatt’s rationale, paraphrased:

  • Most investors, pro’s included, can’t beat the index. Therefore, buying an index fund is better than messing it up yourself or getting an active manager to mess it up for you.
  • If you’re going to buy an index, you might as well buy the best one. An index based on the market capitalization-weighted S&P500 will be handily beaten by an equal-weighted index, which will be handily beaten by a fundamentally weighted index, which is in turn handily beaten by a “value-weighted index,” which is what Greenblatt calls his “Magic Formula-weighted index.”

I like the logic. I also think the data on the last point are persuasive. In chart form, the data on that last point look like this:

The value weighted index knocked out a CAGR of 16.1 percent per year over the last 20 years. Not bad.

Greenblatt explains his rationale in some depth in his latest book The Big Secret. The book has taken some heavy criticism on Amazon – average review is 3.2 out of 5 as of now – most of which I think is unwarranted (for example, “Like many others here, I do not exactly understand the reason for this book’s existence.”).

I’m going to take a close look at the value-weighted index this week.

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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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In their March 2012 paper, “Analyzing Valuation Measures: A Performance Horse-Race over the past 40 Years,” Wes Gray and Jack Vogel asked whether the business cycle should affect our choice of price ratio:

For example, cash-focused measures, such as free-cash-flow, might perform better during economic downturns than accounting-focused measures like earnings. Or perhaps a more asset-based measure, like book value, will outperform when the economy is more manufacturing-based (‘70s and ‘80s), and struggle when the economy is more human capital and services oriented (therefore making asset-based measures less relevant).

Gray and Vogel analyze the returns of different price ratios over economic expansions and contractions defined by the National Bureau of Economic Research.

Economic Expansions and Contractions 

(Click to enlarge)

The first panel presents the returns for different price ratios during economic expansions. Gray and Vogel observe:

B/M enjoys periods of relative out-performance in the early ‘70s, early ‘80s, and in late 2009. The B/M performance pattern lends weak evidence to the hypothesis that balance-sheet-based value measures perform better than income or cash-flow statement value metrics when the economy generates more returns from tangible assets (e.g., property, plant, and equipment) relative to intangible assets (e.g., human capital, R&D, and brand equity). Overall, there is no strong evidence that a particular valuation metrics systematically outperform all other metrics during expanding economic periods.

The second panel presents the returns for different price ratios during economic contractions. Gray and Vogel observe:

[The] results in Panel B suggest there is no clear evidence that a particular value strategy systematically outperforms all other strategies in contracting economic periods. For example, during the July 1981 to November 1982 and March 2001 to November 2001 contractions GP/TEV shows strong outperformance, but this same metric has the worst performance in the December 2007 to June 2009 recession.

Conclusion

Gray and Vogel conclude:

[There] is little evidence that a particular value strategy outperforms all other metrics during economic contractions and expansions. However, there is clear evidence that value strategies as a whole do outperform passive benchmarks in good times and in bad. The one exception to this rule is during the April 1975 to June 1981 business cycle, a time when a passive small-cap equity portfolio performed exceptionally well.

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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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