How to Be a Great Investor, Part Four: Compare Effectively

This article is the fourth in a ten-part series loosely based on Michael J. Mauboussin’s white paper “Thirty Years: Reflections on the Ten Attributes of Great Investors.” See “Part One: Be Numerate,” “Part Two: Understand Value,” and “Part Three: Properly Assess Strategy” for previous installments. And please keep in mind that although I quote Mauboussin a great deal, I am departing from his ideas on occasion.

Part I: Fundamentals Versus Expectations

Mauboussin begins:

Investors compare all day: stocks versus bonds, active versus passive, value versus growth, stock A versus stock B, and now versus later. Humans are quick to compare but not very good at it. Perhaps the most important comparison an investor must make, and one that distinguishes average from great investors, is between fundamentals and expectations. Fundamentals capture a sense of a company’s future financial performance. . . . Expectations reflect the financial performance implied by the stock price. Making money in markets requires having a point of view that is different than what the current price suggests. Michael Steinhardt called this a “variant perception.” Most investors fail to distinguish between fundamentals and expectations. . . . But great investors always distinguish between the two.

Let’s pretend that you had to choose between investing in Intel (INTC) and Advanced Micro Devices (AMD).

Intel’s ROE is 27% and its margins are massive. It has low debt, plenty of cash, pays a nice and growing dividend, and has solid growth in both sales and EPS.

AMD, on the other hand, has an ROE of 14% and its margins are pretty poor. Its sales and EPS growth have been in a steep decline lately. Its debt-to-EBITDA ratio is close to 3, which is pretty high; it doesn’t look like it’ll be paying any dividends in the foreseeable future.

Let’s look a little deeper. Both Intel and AMD are spending plenty of money on R&D, which is good. AMD has huge projected EPS growth for the next quarter while Intel is projected to have negative growth. AMD has significantly better asset turnover, another point in its favor; on the other hand, it has the highest share turnover of any company in the S&P 500, which is a point against it. AMD’s cash conversion cycle is growing too fast for my liking; on the other hand, its net operating assets are only 40% of its total assets, which is a good thing. Lastly, AMD’s cash-flow accruals are appallingly high, while Intel’s are quite low.

What are the market’s expectations for these two firms? As of market close on 9/4, Intel is selling at an 18% discount to its 52-week high, while AMD is only 13% from its high. AMD’s price has risen 10% in the last year; Intel’s has risen 2%. Intel’s enterprise value is $239 billion, which matches my estimate of its intrinsic value based on its free cash flow and EBITDA, $246 billion. AMD’s enterprise value is $35 billion, which is more than five times higher than my estimate of its intrinsic value, $5.8 billion. Institutions are, overall, selling shares in Intel and buying shares in AMD. Clearly, the market has significantly higher expectations for AMD than for Intel.

The reason is clear. With its 80% to 90% market share, Intel seems like a dinosaur, with nowhere to go but down, while AMD is on the cutting edge, producing brilliant CPUs that outperform Intel’s. It has just inked several valuable partnership deals, while Intel has been labeled a “chronic underperformer.”

The average investor will look at these signs and put their money into AMD. The great investor will see that there’s a huge divergence between fundamentals and expectations, and will keep their money out of AMD and in solid, safe, boring, inexpensive firms like Intel.

Why? AMD certainly seems to have better prospects than Intel. Cutting-edge technology stocks can really pay off. If this were a race, AMD seems to have much better odds.

Mauboussin continues:

One vivid analogy is pari-mutuel betting. Horse racing is a good example. The amount bet on a horse gets reflected in the horse’s odds, or probability, of winning the race. The goal is not to figure out which horse will win but rather which horse has odds that are mispriced relative to how it will likely run the race.

(I’ve written about this gambling analogy here.) AMD may have better odds, but they’re mispriced. Even if AMD performs spectacularly well over the next few years, its risk of failing to meet expectations is far higher than that of Intel.

Basically, the expectations, based on their market price, for these two companies are that one will remain roughly the size it currently is and that the other will grow to be five or six times its current size. Now which, I ask you, is a safer bet?

Part II: Instinctive Versus Reasonable Comparisons

Mauboussin writes,

Humans tend to think by analogy, which can create some cognitive trouble. One issue is that a single analogy, or even a handful of analogies, may fail to reflect a full reference class of relevant cases. For example, rather than asking whether this turnaround is similar to a prior turnaround, it is useful to ask for the base rate of success for all turnarounds. Psychologists have shown that properly integrating the outcomes from an appropriate reference class improves the quality of forecasts.

The price of Apple (AAPL), after adjusting for dividends and splits, has gone up by over 8,000% in the last twenty years. And how many times have you heard someone say of a company, “This could be the next Apple!” That is a simple analogy, easy to grasp.

What people don’t say is that twenty years ago there were stocks quite similar to Apple. The proper response when someone says “This could be the next Apple!” is “Yes! And this could be the next Gateway!” or “Yes! And this could be the next Lexmark!” or “Yes! And this could be the next ATI Technologies!” Stretch it a little and this could also be the next Comverse, or Altera, or Unisys, or BMC Software, or Teradyne! All of these companies showed some strong similarities to Apple in 1999.

As Mauboussin writes, “What you are looking for is what you see.” Choosing companies to compare a potential investment with is a daunting task. If you do so on a purely intuitive level, you’re bound to choose companies you’re familiar with rather than companies that sank without a trace. If you try to use a more data-driven approach, using screens to narrow down your options, you’ll come up with a better class of comparisons, but only barely. Every company is truly unique.

Moreover, this method can lead to another trap. Mauboussin writes,

A final challenge is considering causality from the point of view of attributes versus circumstances. Attributes are features that allow for categorization. For instance, animals with wings and feathers can fly. Circumstances capture causal mechanisms. Since the physics of lift causes flight, animals or objects that can create lift will fly, including most birds and airplanes, and those that can’t create lift won’t fly. To learn from history, you need to understand causality. We commonly limit our comparisons to attributes and hence miss essential insights.

What caused Apple’s price to rise over 8,000%? Clearly it was not the fact that its market cap in 1999 was $10 billion or that its P/E was 18 or that it was in the technology sector. Was it the fact that it had a visionary at its helm? In that case, let’s take a quick look at people who were called “visionary” in 1999 and see where they and their companies ended up.

  • Marc Collins-Rector was the visionary founder of Digital Entertainment Network, one of the first Internet media content delivery companies, a precursor of Netflix. The company was valued at $58.5 billion in 1999, and was slated for a $75 billion IPO. Collins-Rector and his co-founders were accused of sexual assault and the company went bankrupt in 2000.
  • Nobuyuki Idei was the visionary CEO of Sony; if you had bought shares in Sony in 1999, they would now be worth 2% more than what you paid for them.
  • Mark Weiser was the visionary chief technology officer of Xerox, and the father of “ubiquitous computing.” But then he died at the age of 46. If you had bought shares in Xerox in 1999, you would have lost 68% of your money by now.
  • Robert B. Shapiro was the visionary CEO of Monsanto. In 2000, Monsanto was merged with Pharmacia and Upjohn, and Shapiro stepped down in 2001. If you had invested in Monsanto once it was spun off in 2002, you would have made a boatload of money, but by that time Shapiro was no longer working for them.

Clearly, following visionary CEOs won’t make you rich.

What kinds of comparisons will, then? Data-driven ones, perhaps.

The main thing is not to get lost in analogies. Rather than looking for what made Apple’s price rise 8,000%, one might better ask what factors will at least somewhat consistently induce a future rise in a stock’s price. And there is data that will answer that question.

To stick with comparisons, let’s say you’re faced with the choice of investing in two companies, one with low accruals and the other with high accruals, but they’re otherwise pretty similar. If you look at the data, companies in the Russell 3000 with the lowest accruals have gained, on average, 7.04% in any given six-month period over the last twenty years (I ascertained this fact using a screen on Portfolio123 that chose companies that ranked among the lowest 25% on two very different and largely uncorrelated methods of measuring accruals); companies with the highest accruals have gained, on average, 3.09% (which is lower than the average six-month gain of the S&P 500). Now are low accruals simply an attribute of a stock or a cause of the price gain? There are some very good arguments to be made for the latter position.

And that’s how factor-based investing works. We look for factors that seem to result in price increases. We then compare thousands of companies and choose those that rank the best on each factor or on all of them combined. We should not only compare each company to all others, but perform industry-specific comparisons too. We can focus on causal factors and let attributional factors take a back seat.

This goes back to Mauboussin’s initial point in this part: “rather than asking whether this turnaround is similar to a prior turnaround, it is useful to ask for the base rate of success for all turnarounds.” I just did that for low accruals, and if I had a data-driven definition of a turnaround, I could get such a base rate.

Using data, backtesting it, discovering properties and relationships, looking for causal factors – that is the best way to compare effectively.

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