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Archive for May, 2012

Equal Weight and Fundamental Indexing Beats The Market

Posted in About, Behavioral economics, Quantitative investment, Stocks, Strategy, Value Investment, tagged , , , on May 16, 2012| 4 Comments »

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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Value-Weighted Indexing: The Problem with Active Management

Posted in Behavioral economics, Stocks, Value Investment, tagged , on May 15, 2012| 4 Comments »

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.”

Let’s examine each of these points in some more depth.

Most investors, pro’s included, can’t beat the index.

The most famous argument against active management (at least by mutual funds) is by John Bogle, made before the Senate Subcommittee on Financial Management, the Budget, and International Security on November 3, 2003. Bogle’s testimony was on the then market-timing scandal, but he used the opportunity to speak more broadly on the investment industry.

Bogle argued that the average mutual fund should earn the market’s return less costs, but investors earn even less because they try to time the market:

What has been described as “a pathological mutation” in corporate America has transformed traditional owners capitalism into modern-day managers capitalism. In mutual fund America, the conflict of interest between fund managers and fund owners is an echo, if not an amplification, of that unfortunate, indeed “morally unacceptable”5 transformation. The blessing of our industry’s market-timing scandal—the good for our investors blown by that ill wind—is that it has focused the spotlight on that conflict, and on its even more scandalous manifestations: the level of fund costs, the building of assets of individual funds to levels at which they can no longer differentiate themselves, and the focus on selling funds that make money for managers while far too often losing money—and lots of it—for investors.

The net results of these conflicts of interest is readily measurable by comparing the long-term returns achieved by mutual funds, and by mutual fund shareholders, with the returns earned in the stock market itself. During the period 1984-2002, the U.S. stock market, as measured by the S&P 500 Index, provided an annual rate of return of 12.2%. The return on average mutual fund was 9.3%.6 The reason for that lag is not very complicated: As the trained, experienced investment professionals employed by the industry’s managers compete with one another to pick the best stocks, their results average out. Thus, the average mutual fund should earn the market’s return—before costs. Since all-in fund costs can be estimated at something like 3% per year, the annual lag of 2.9% in after-cost return seems simply to confirm that eminently reasonable hypothesis.

But during that same period, according to a study of mutual fund data provided by mutual fund data collector Dalbar, the average fund shareholder earned a return just 2.6% a year. How could that be? How solid is that number? Can that methodology be justified? I’d like to conclude by examining those issues, for the returns that fund managers actually deliver to fund shareholders serves as the definitive test of whether the fund investor is getting a fair shake.

This makes sense. Large mutual funds are the market, so on average earn returns that equate to the market average less costs. While it’s not directly on point, the huge penalty for timing and selection errors is worth exploring further.

Timing and selection penalties

Timing and selection penalties eat a huge proportion of the return. These costs are the result of investors investing in funds after good performance, and withdrawing from funds after poor performance:

It is reasonable to expect the average mutual fund investor to earn a return that falls well short of the return of the average fund. After all, we know that investors have paid a large timing penalty in their decisions, investing little in equity funds early in the period and huge amounts as the market bubble reached its maximum. During 1984-1988, when the S&P Index was below 300, investors purchased an average of just $11 billion per year of equity funds. They added another $105 billion per year when the Index was still below 1100. But after it topped the 1100 mark in 1998, they added to their holdings at an $218 billion(!) annual rate. Then, during the three quarters before the recent rally, with the Index below 900, equity fund investors actually withdrew $80 billion. Clearly, this perverse market sensitivity ill-served fund investors.

The Dalbar study calculates the returns on these cash flows as if they had been invested in the Standard & Poor’s 500 Index, and it is that simple calculation that produces the 2.6% annual investor return. Of course, it is not entirely fair to compare the return on those periodic investments over the years with initial lump-sum investments in the S&P 500 Stock Index and in the average fund. The gap between those returns and the returns earned by investors, then, is somewhat overstated. More appropriate would be a comparison of regular periodic investments in the market with the irregular (and counterproductive) periodic investments made by fund investors, which would reduce both the market return and the fund return with which the 2.6% return has been compared.

But if the gap is overstated, so is the 2.6% return figure itself. For investors did notselect the S&P 500 Index, as the Dalbar study implies. What they selected was an average fund that lagged the S&P Index by 2.9% per year. So they paid not only a timing penalty, but a selection penalty. Looked at superficially, then, the 2.6% return earned by investors should have been minus 0.3%.

Worse, what fund investors selected was not the average fund. Rather they invested most of their money, not only at the wrong time, but in the wrong funds. The selection penalty is reflected by the eagerness of investors as a group to jump into the “new economy” funds, and in the three years of the boom phase, place some $460 billion in those speculative funds, and pull $100 billion out of old-economy value funds—choices which clearly slashed investor returns.

I can imagine how difficult the investment decision is for mutual fund investors. How else does an investor in a mutual fund differentiate between similar funds other than by using historical return? I wouldn’t select a fund with a poor return. I’d put my money into the better one. Which is what everyone does, and why the average return sucks so bad. How bad? Bogle has calculated it below.

Dollar-weighted returns

The calculation of dollar-weighted returns speaks to the cost of timing and selection penalties:

Now let me give you some dollars-and-cents examples of how pouring money into the hot performers and hot sector funds of the era created a truly astonishing gap between (time-weighted) per-share fund returns and (dollar-weighted) returns that reflect what the funds actually earned for their owners. So let’s examine the astonishing gap between those two figures during the recent stock market boom and subsequent bust.

Consider first the “hot” funds of the day—the twenty funds which turned in the largest gains during the market upsurge. These funds had a compound return of 51% per year(!) in 1996-1999, only to suffer a compound annual loss of –32% during the subsequent three years. For the full period, they earned a net annualized return of 1.5%, and a cumulative gain of 9.2%. Not all that bad! Yet the investors in those funds, pouring tens of billions of dollars of their money in after the performance gains began, earned an annual return of minus 12.2%, losing fully 54% of their money during the period.

Now consider sector funds, specific arenas in which investors can (foolishly, as it turns out) make their bets. The computer, telecommunications, and technology sectors were the favorites of the day, but only until they collapsed. The average annual returns of 53% earned in the bull market by a group of the largest sector funds were followed by returns of minus 31% a year in the bear market, a net annual return of 3% and a cumulative gain of 19.2%. Again, not too bad. Yet sector fund investors, similar to the hot fund investors I described earlier, poured billions of dollars in the funds as they soared, and their annual return averaged –12.1%, a cumulative loss of 54% of their capital, too.

While the six-year annual returns for these funds were hardly horrible, both groups did lag the 4.3% annual return of the stock market, as measured by the largest S&P 500 Index Fund, which provided a 29% cumulative gain. But the investors in that index fund, taking no selection risk, minimized the stock market’s influence on their timing and earned a positive 2.4% return, building their capital by 15% during the challenging period. Index investor +15%; sector fund and hot fund investor –54%. Gap: 69 percentage points. It’s a stunning contrast.

Bogle’s conclusion says it all: Index investor +15%; sector fund and hot fund investor –54%. Gap: 69 percentage points. It’s a stunning contrast.

Tomorrow, why fundamental indexing beats the market.

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Joel Greenblatt’s solution to value investors’ behavioral errors

Posted in About, Behavioral economics, Quantitative investment, Stocks, Strategy, tagged , , on May 14, 2012| 2 Comments »

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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Volatility at World’s End: Two Decades of Movement in Markets

Posted in Austrian Economics, Stocks, tagged , on May 11, 2012| Leave a Comment »

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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How to beat the market: Joel Greenblatt’s List of Four Things Not To Do

Posted in Stocks, Strategy, tagged , on May 10, 2012| 2 Comments »

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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Joel Greenblatt on how and why investors struggle to follow the Magic Formula

Posted in Stocks, tagged , , , on May 9, 2012| 10 Comments »

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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James Montier on why investors struggle to follow the Magic Formula: Cognitive Biases and Behavioral Errors

Posted in About, Behavioral economics, Enterprise Multiple, Enterprise Value, Stocks, Strategy, tagged , , , on May 8, 2012| Leave a Comment »

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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How to beat The Little Book That Beats The Market: An analysis of the Magic Formula

Posted in About, Behavioral economics, Enterprise Multiple, Stocks, Strategy, Warren Buffett, tagged , , , , , , , on May 7, 2012| 17 Comments »

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 Kindle, paperback, 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.

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Recent net net performance: Parsing on F_SCORE and Size

Posted in Net Current Asset Value, Stocks, tagged , on May 4, 2012| Leave a Comment »

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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Should the business cycle affect the choice of price ratio? Asset-based measures versus earnings and cash flow-based measures

Posted in About, Enterprise Multiple, Strategy, Value Investment, tagged , , , on May 3, 2012| 1 Comment »

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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