Moneyball, & Where Should Confidence Come From?

{ Euclidean Q1 2012 Letter }

We think it is important that from time-to-time we provide you a view into why we are confident in our systematic approach to long-term investing. In this letter, we revisit this topic and illuminate key points using themes presented in a recent Hollywood movie.

Moneyball – The Opportunity

I suspect many of you have now seen the film Moneyball, starring Brad Pitt, or read Michael Lewis’ book on which the movie is based. The movie starts with scenes from Major League Baseball’s 2001 American League Divisional Series. The scene fades to black and this text dramatically appears:

Yankees: $114,457,768  vs.  A’s: $39,722,689

The two numbers were the teams’ player payrolls, with the New York Yankees outspending the Oakland A’s almost 3:1. The common wisdom in baseball was that if a team couldn’t compete on salaries, then it couldn’t compete on the playing field. Therefore, most people wrote off the A’s chances.

The charm of the Moneyball story is that, by thinking differently, the A’s and their General Manager, Billy Beane, were able to change the game of baseball. Thinking differently for the A’s involved looking at player statistics in new ways in order to find the blind spots in Baseball’s accepted wisdom. The A’s became the best at identifying players with potential that no one else noticed. By doing this, the A’s outperformed teams with much larger budgets, greatly exceeded others’ expectations, and forced everyone else in baseball to question long-standing, accepted truths regarding player selection.

We found the following quote to be inspiring:

“Using stats the way we read them, we’ll find value in players that nobody else can see. People are overlooked for a variety of biased reasons and perceived flaws: age; appearance; personality. Of the 20,000 notable players for us to consider, I believe there is a championship team of 25 people that we can afford because everyone else in Baseball undervalues them, like an island of misfit toys.”

- Spoken by A’s Assistant GM Peter Brand, portrayed in the movie by Jonah Hill.

We share this quote because, at our core, we believe in the value of questioning accepted wisdom. This is what Moneyball is all about. The A’s proved that there were ingrained and widespread misunderstandings across professional baseball’s executives about the very game they had been involved with their whole lives. These misunderstandings were severe enough that the A’s – by thinking differently – could be 3x as effective with their money as teams like the Yankees, and compete at baseball’s highest level.

The Moneyball story provides a timely analogy to what Euclidean aspires to do in rethinking long-term investing. We believe that in investing, as in baseball, there are many accepted but false “truths” regarding what leads to long-term success. By identifying these misconceived notions through the use of statistical computing, we believe there is an opportunity to manage a championship portfolio of 30 companies that everyone else mistakenly undervalues, like an island of misfit toys.

Moneyball – The Challenge # 1

With regard to Moneyball, there are two challenging aspects of the story that are useful to ponder when considering your own confidence in Euclidean’s future prospects. For the A’s, one of these challenges had a happy ending, and the other one, well not so much. We discuss the first one here as it relates to Euclidean’s 2011 performance. To appreciate and assess how the second challenge relates to our future, you will have to read to the end of the letter.

The first challenge highlighted in Moneyball has to do with the fact that, even when you operate with the odds on your side, those odds play out over time. Whether you are a .300 hitter or an investor adhering to sound principles, there will be periods when you will look better and worse than you really are.

1At the start of the 2002 season, the A’s were not playing well.    By the end of May, the team had a losing record, was 9 games out of first place, and Billy and Peter were summoned to discuss the situation with Steve Schott, the A’s owner. Here’s the dialogue.

Billy – “Look Steve, I believe in what we’re doing. I believe the record doesn’t accurately reflect the strength of the team and where we’re gonna be at the end of the season. Pete and I here feel very strongly that we stay on the track we’ve chosen.”

Peter – “Our sample size has just honestly been too small.”

Billy and Peter did not enjoy being 9 games behind and having to reaffirm to their owner why they believed. At times in 2011, as the market value of our portfolio depreciated, we felt the same way. It is just not fun to experience bad results. But, what did Peter mean by the sample size being too small? He meant that having a superior team does not mean you win every game or do well every month. He meant that a winning process puts the odds on your side and that, over the course of a season, the results will vindicate your commitment.

The A’s commitment to their winning process in the face of a difficult period ultimately paid off. The same team that was on the verge of last place through the first two months of the season went on to have the longest winning streak in baseball history (20 straight games) and finish tied with the Yankees for the best record in baseball. This is an amazing story, one good enough to become a Hollywood movie.

How does this relate to Euclidean? After a few strong years, we had a difficult year. Enduring it well required a belief that we have a winning process that will yield superior results over time. As the story of how we will fare going forward has yet to be written, let’s turn now to why we believe.

Where Our Belief Comes From

In 2009’s annual letter, on the back of strong returns, we felt it was important to focus our investors away from short-term performance and back to the logic of our long-term investment process. We emphasized that the right investor for Euclidean does not put much weight on one or two years of performance as, to us, perhaps 5 investment years equates to a single baseball season. While it is nice to win individual games and it is not fun to endure losing streaks, what matters to us is that, over an ‘investing season’, we do very well.

Given this, we emphasized that the reliable clues to Euclidean’s long-term performance come not from our short-term results but from the nature of our investment approach. We provided a section to explain where our confidence came from, which we have built upon and evolved here.

*     *     * 

To believe in our (or any) investment process, three equally important conditions must be met:

  1. First, there must be strong historic evidence that the investment process worked in the past.
  2. Second, it must be shown that an understandable cause-and-effect relationship explains how the historic results were achieved.
  3. Third, there must be a reason to believe this relationship will exist in the future.

Let us now discuss why we believe that Euclidean’s approach, unlike (as you will soon see) Moneyball’s 2002 Oakland A’s, satisfies all three conditions necessary for confidence in future success.

Condition 1 – Historic Evidence Our Process Has Worked In The Past

With 3.5+ years under our belts, we believe our track record is the primary evidence worth considering. It should be noted, however, that as we considered launching Euclidean in 2008, we satisfied this first condition by simulating (or back-testing) how well a fund managed the same way we manage ours today would have performed if it had been started as far back in history as the data will allow us to go.

We differed from most quantitative funds in our desire to simulate performance over long periods of time. We were just as interested in how well our fund might have performed in 1975 as we were of potential performance in 2005. We wanted a large sample size because we knew that if we found something that worked very well for a short period of time (like a growth strategy might have worked in the 1990’s or a ‘fat-tail’ strategy might have worked during the financial crisis), there would be a good chance we would have found a temporary trend. Therefore, it always seemed imperative that we should seek not the best performing strategy for any given time but the most timeless investment methods possible.

In this simulation, from January 1973 – August 2008, when the S&P 500’s annualized total return (that is, the S&P index return with dividends reinvested) was just over 10%, we saw the opportunity to deliver investors net, compounded returns in the high teens. To get a sense of the character and variability of these returns, we also measured the percentage of overlapping twelve month periods that the simulated performance might have outperformed the S&P 500’s total return. We called the 12-month consistency figure the “win-rate” and, for our simulated returns, this number was about 70%.

The two numbers – compounded annual return and win-rate – represented the first component of how we gained confidence in our approach, but they were by no means an estimate or prediction of how the fund might perform in reality. The power of the simulation was solely its ability to tell us whether our approach had any potential for doing well.

Actual Performance

It is interesting now to reflect, after managing a fund through the very volatile past several years, how we thought about the simulated results. It was easy for us to get excited about the potential success and also easy for us to ignore some of future challenges evident in that data. That is, it is easy to get excited when you imagine outperforming 70% of the time and to ignore the flip-side, which implies 30% of the time your results will lag and certain investors may wonder if you’ve lost your touch!

With retrospect, however, we feel that our first 3.5+ years provide a strong satisfaction of Condition 1.

Consider that, with regard to evaluating individual companies as potential investments, we committed to simply adhere to our sense of the most reliable lessons we were able to derive from the record of the past ~40 years. We launched our fund on August 7th 2008, when the S&P 500 Index stood just below 1,300. Our launch was only six weeks prior to Lehman Brothers’ fall and the dramatic, subsequent escalation of the recent financial crisis. Many market veterans view this period as having no precedent in the post-Great- Depression era and certainly not in the 40-years of history from which our models ‘learned’.

Yet an investor today, who entered the fund at its inception, would have grown his Euclidean investment dollars at a net, compounded rate of 14.8% per year (and, relevant to taxable investors, almost all in the form of long-term capital gains) in the context of the S&P 500 delivering a total return of 4.7% per year. Although 3.5 years is not yet a full ‘investing season’, we feel it is noteworthy that our approach has done well in an environment that has many differences from what transpired during the past several decades.

Condition 2 – An Underlying Cause Explaining How The Historic Results Were Achieved

Euclidean’s first 3.5 years represents a short period of real world results. Those results and the simulation data do not, on their own, provide a logical foundation for believing that Euclidean is likely to do well in the future. This is where the second condition of articulating a plausible cause-and-effect relationship comes in. We need to be able to explain why our process has worked in the past in order to explore whether our process is likely to work in the future.

When we did our original research and took an unbiased look at the historical record, we did not know what we would find. We could have found relationships that were difficult to explain and to believe. But, we did not find inexplicable relationships.

We found instead that the historical record was unambiguous in communicating that:

  1. Prices decrease when investors become pessimistic, for a wide variety of reasons, about a company’s future earning power.
  2. The extent of this fear often proves to have been overdone for companies with high returns on capital, conservative balance sheets, and consistent operating histories.
  3. In those instances when people realize that a company’s earnings are not in fact deteriorating, or are not deteriorating as much as expected, the prices of the company’s shares often recover.

Finding these things did not surprise us. They can be explained by the behavioral biases that are inside all of us as human beings. For example, it is well documented that people fear losses more than they value gains. Perhaps this is why investors often become fearful, willing sellers when stock prices are declining. It is also well documented that when people make decisions, they have a tendency to give more weight to recent experiences than to experiences from the more distant past. This may be why investors have a tendency to discard long-standing principles when they fail to yield short-term results. There are many other examples, which perhaps we will devote a future letter to discussing in more detail.

These biases are important as they get to the cause-effect relationship we found persisting across the past 40-years. The cause Euclidean depends on is excess fear created simply by humans being human, and the effects are compelling investment opportunities. This relationship is at the core of how Euclidean’s models identify investments, and it provides a plausible explanation for our past success.

Condition 3 – The Cause-And-Effect Relationship Is Likely To Exist In The Future And, Moneyball Challenge #2

Even promising results that can be explained by an underlying cause-and-effect relationship can fail to persist in the future. This can happen for a few reasons.

First, the conditions in which you operate might sufficiently change as to make prior relationships break down. As an example, imagine you found a pattern in the late 1990s that by investing in fast-growing technology companies with no profits, you could make a lot of money. The cause was investor enthusiasm for the ‘new economy’, and the result was rising prices for Internet stocks. That pattern might have served you well for a few months into the year 2000, but then the world very quickly changed. From that point forward (until, perhaps, very recently), you would have lost a great deal of money investing in these kinds of companies.

Second, competitors may arbitrage away the opportunity for gain. This is what happened with the A’s. The part of the Moneyball story that is only vaguely alluded to at the end of the movie is that the A’s became a mediocre team again within 5 years of their amazing success in 2002. The reason is that other teams caught on to what Billy Beane had discovered. They learned how to use statistics to break down their own ingrained misunderstandings about how to evaluate players. Once other teams were interested in and able to find the same hidden gems, it became more difficult for Billy Beane to acquire great players on the cheap.

These reasons are why our third and final condition is so important. To believe in Euclidean’s future prospects, you need to believe that the cause-and-effect relationship underlying our simulated performance and successful first years in the business will continue to exist in the future.

Here are three reasons why we believe it will:

  1. There is currently a great deal of energy being focused in the areas of indexing, ETF's, and high- frequency trading, all of which make investment decisions with limited regard for company 

    fundamentals. As more capital is devoted to these areas, the market becomes more likely to create disconnects between the price and long-term, economic value of a company’s shares. These developments are favorable to Euclidean’s long-term prospects. 

  2. Many investors struggle with the concept of Time. We believe sound investment approaches for capitalizing on the mispricings of individual companies prove their worth over years. They do not provide constant, reassuring feedback that you are on the right course (remember, we expect to lag at least 30% of trailing 12-month periods). This makes adhering to value-minded principles very difficult. Indeed, market participants are becoming more and more short-term in their orientation. This is evidenced by the average holding period of individual shares falling dramatically over the past few decades and continuing to fall today. If others’ short-termism continues to intensify, we will have less – not more – competition for the opportunities we seek.

  3. As previously explained, the opportunities that Euclidean depends on – good businesses selling for bargain prices – are created by a wide variety of human biases. These biases make it such that, during times when a business has a temporary setback or when conventional wisdom is that difficult times are ahead for a particular industry, investors can get very afraid. When fear and pessimism prevail, good companies are sometimes offered at very low prices.

This last point, we believe, may be the most timeless one. Human nature is a powerful force, and its irrational qualities have shaped centuries of financial history.

For the opportunities we seek to be fully arbitraged away, humanity’s irrational tendencies would need to be held at bay, and the world would have to be very different from today. No more investor focus on daily market swings. No more emotion influencing how companies are valued. Just a market with much less trading activity, comprised of highly disciplined investors evaluating companies as potential long-term investments based on their intrinsic business value.

There is little evidence that such a shift is occurring. Euclidean’s look-through earnings on the next page seem to illustrate this point. Still, we are surprised how persistent Euclidean’s opportunity seems to be. There is, after all, a class of investors with whom our process shares much in common (value investors) who have delivered strong results over time and who have been transparent about their source of their success (buying companies at discounts to their fundamental business value). Yet, value investors – both in the traditional sense and in the Euclidean sense – remain a small minority. Very few investors seem interested or able to maintain the long-term focus and discipline required of value strategies.

Therefore, unlike the A’s, we like our chances in the future. Technology innovation will surprise us, industry structures will change, and regulations will evolve, but the underlying cause of our success – human fear, greed, loss aversion, recency bias, desire for the comfort of the herd, etc. – does not seem likely to change or be arbitraged away very soon.


Please let us know if you have any thoughts or comments on this letter. We look forward to connecting with you in the weeks ahead.

Best Regards,

John & Mike

We share these numbers because they are easy-to-communicate measures that show the results of our systematic process for buying shares in historically sound companies when their earnings are on sale. [1] [2]

It is important to note that Euclidean uses similar concepts but different measures to assess individual companies as potential investments. Our models look at certain metrics over longer periods and seek to understand their volatility and rate of growth. Our process also makes a series of adjustments to company financial statements that our research has found to more accurately assess results, makes complex trade- offs between measures, and so on. These numbers should, however, give you a sense of what you own as a Euclidean Investor. In general, higher numbers for these measures are more attractive. The key measures are:

  1. Earnings Yield – This measures how inexpensive a company is in relation to its demonstrated ability to generate cash for its owners. A company with twice the earnings yield as another is half as expensive; therefore, all else being equal, we seek companies with very high Earnings Yields. Earnings Yield reflects a company’s past four-year average earnings before interest and tax divided by its current enterprise value (enterprise value = market value + debt – cash).
  2. Return on Capital – This measures how well a company has historically generated cash for its owners in relation to how much capital has been invested (equity and long-term debt) into the business. At its highest level, this measure reflects two important things. First, it is an indicator of whether a company’s business is efficient at deploying capital in a way that generates additional income for its shareholders. Second, it indicates whether management has good discipline in deciding what to do with the cash it generates. For example, all else being equal, companies that overpay for acquisitions, or retain more capital than they can productively deploy, will show lower returns on capital than businesses that do the opposite. Return on Capital reflects a company’s four- year average earnings before interest and tax, divided by its current equity + long-term debt.
  3. Equity / Assets – This measures how much of a company’s assets can be claimed by its common shareholders versus being claimed by others. High numbers here imply that the company owns a large portion of its figurative ‘house’ and, all else being equal, indicates a better readiness to weather tough times.
  4. Revenue Growth Rate – This is the annualized rate a company has grown over the past four years.

[1] All Euclidean measures are formed by summing the values of Euclidean’s pro-rata share of each portfolio company’s financials.  That is, if Euclidean owns 1% of a company’s shares, it first calculates 1% of that company’s market value, revenue, debt, assets, earnings, and so on.  Then, it sums those numbers with its pro-rata share of all other portfolio companies.  This provides the total revenue, assets, earnings, etc. across the portfolio that are used to calculate the portfolio’s aggregate measures presented here. 

[2] The S&P 500 measures are calculated in a similar way as described above.  The market values, revenue, debt, assets, earnings, etc. for each company in the S&P 500 are added together.  Those aggregate numbers are then used to calculate the metrics above.  For example, the earnings yield of the S&P 500 is calculated as the total average 4-year earnings before interest and taxes across all 500 companies divided by those companies’ collective enterprise values (all 500 companies’ market values + cash – debt).