Advertisements
Feeds:
Posts
Comments

Posts Tagged ‘Joel Greenblatt’

Yesterday I took a look at the different ways of structuring an index suggested by Joel Greenblatt.

Greenblatt finds that an equal-weight portfolio far outperforms a market capitalization weight portfolio.

And for good reason. Greenblatt says that market cap weighted indexes suffer from a systematic flaw – they increase the amount they own of a particular company as that company’s stock price increases.  So they systematically invest too much in stocks when they are overpriced and too little in stocks when they are priced at bargain levels. 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). 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.

There is some good research on the structuring of indices. In a Janaury 2012 paper Why Does an Equal-Weighted Portfolio Outperform Value- and Price-Weighted Portfolios? Yuliya Plyakha, Raman Uppal and Grigory Vilkov examine the performance of equal-, value-, and price-weighted portfolios of stocks in the major U.S. equity indices over the last four decades (note that here “value” weight is used in the academic sense, meaning “market capitalization weight”).

The researchers find find that the equal-weighted portfolio with monthly rebalancing outperforms the value- and price-weighted portfolios in terms of total mean return, four factor alpha, Sharpe ratio, and certainty-equivalent return, even though the equal-weighted portfolio has greater portfolio risk. (It’s interesting that they find the equal-weighted index possesses alpha. I think that says more about the calculation of alpha than it does about the equal-weight index, but I digress.)

They find that total return of the equal-weighted portfolio exceeds that of the value- and price-weighted because the equal-weighted portfolio has both a higher return for bearing systematic risk and a higher alpha measured using the four-factor model. The higher systematic return of the equal-weighted portfolio arises from its higher exposure to the market, size, and value factors.

They seem to agree with Greenblatt when they find that the higher alpha of the equal-weighted portfolio arises from the monthly rebalancing required to maintain equal weights, which is a “contrarian strategy that exploits reversal and idiosyncratic volatility of the stock returns; thus, alpha depends only on the monthly rebalancing and not on the choice of initial weights.”

[We demonstrate that the source of this extra alpha of the equal-weighted portfolio is the “contrarian” rebalancing each month that is required to maintain equal weights, which exploits the “reversal” in stock prices that has been identified in the literature (see, for instance, Jegadeesh (1990) and Jegadeesh and Titman (1993, 2002)).

To demonstrate our claim, we consider two experiments, which are in opposite directions. In the first experiment, we reduce the frequency for rebalancing the equal-weighted portfolio from 1 month, to 6 months and then to 12 months. If our claim is correct, then as we reduce the rebalancing frequency, we should see the alpha of the equal-weighted portfolio decrease toward the level of the alpha of the value- and price-weighted portfolios, which do not entail any rebalancing.

In the second experiment, we reverse the process and artificially fix the weights of the value- and price-weighted portfolios to give them the contrarian flavor of the equal-weighted portfolio. For instance, consider the case where the rebalancing frequency is t = 12 months. Then each month we change the weights of the value- and price-weighted portfolios so that they are the same as the initial weights at t = 0. Only after 12 months have elapsed, do we set the weights to be the true value and price weights. Then, again for the next 12 months, we keep the weights of the value- and price-weighted portfolios constant so that they are equal to the weights for these portfolios at the 12-month date. Only after another 12 months have elapsed do we set the weights to be the true value and price-weighted weights at t = 24 months. We undertake this experiment for rebalancing frequencies of 6 and 12 months. If our claim is correct, then as we keep fixed the weights of the value- and price-weighted portfolios for 6 months and 12 months, the alphas of these two portfolios should increase toward the alpha of the equal-weighted portfolio.

The results of both experiments confirm our hypothesis that it is the monthly rebalancing of the equal-weighted portfolio that generates the alpha for this strategy. Table 4 shows that as we reduce the rebalancing frequency of the equal-weighted portfolio from the base case of 1 month to 6 months and then to 12 months, the per annum alpha of the equal-weighted portfolio drops from 175 basis points to 117 basis points and then to 80 basis points.Once the rebalancing frequency of the equal-weighted portfolio is 12 months, the difference in the alpha of the equal-weighted portfolio and that of the value- and price-weighted portfolios is no longer statistically significant (the p-value for the difference in alpha of the equal- and value-weighted portfolios is 0.96 and for the difference of the equal- and price-weighted portfolios is 0.98).

Similarly, for the second experiment we see from Table 5 that once we hold constant the weights of the value- and price-weighted portfolios for 12 months and rebalance the weights only after 12 months, the differences in alphas for the equal-weighted portfolio relative to the value- and price-weighted portfolios is statistically insignificant (with the p-values being 0.65 and 0.30).

An important insight from these experiments is that the higher alpha of the equal-weighted portfolio arises, not from the choice of equal weights, but from the monthly rebalancing to maintain equal weights, which is implicitly a contrarian strategy that exploits reversal that is present at the monthly frequency. Thus, alpha depends on only the rebalancing strategy and not on the choice of initial weights.

Table 4 (Click to embiggen)

Table 5 (click to embiggen)


And two charts showing size and book-to-market measures:

Conclusion

Equal-weighting is a contrarian strategy that exploits the “reversal” in stock prices and eliminates some of the errors in market capitalization-weighted indices.

The monthly rebalancing of the equal-weighted portfolio generates the alpha for this strategy. As we reduce the rebalancing frequency of the equal-weighted portfolio from the base case of 1 month to 6 months and then to 12 months, the per annum alpha of the equal-weighted portfolio drops from 175 basis points to 117 basis points and then to 80 basis points.

For me, the most important part of the study is the finding that “The nonparametric monotonicity relation test indicates that the differences in the total return of the equal-weighted portfolio and the value- and price-weighted portfolios is monotonically related to size, price, liquidity and idiosyncratic volatility.” (Kidding, I’ve got no idea what that means.)

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®.

Advertisements

Read Full Post »

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.

Read Full Post »

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.

Read Full Post »

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.

Read Full Post »

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.

 

Read Full Post »

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.

Read Full Post »

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.

Read Full Post »

« Newer Posts - Older Posts »

%d bloggers like this: