# How to Be a Great Investor, Part One: Be Numerate

In 2016, the analyst, teacher, writer, and researcher Michael J. Mauboussin published a white paper called “Thirty Years: Reflections on the Ten Attributes of Great Investors.” In it, he outlines “the top ten attributes” that he believes “great fundamental investors share.”

I am writing a series of ten articles on the ten attributes he singles out. This is the first.

When I read it, I had to take a step back. Although I’d never read much of Mauboussin’s work before (much to my shame), my thoughts had been traveling very much along the same lines as his. I now see that I’m not exactly the first investor to compare stock market investing to parimutuel betting on horses, to take just one example out of many. Mauboussin has much more experience than I have, so the fact that we agree on so much makes me feel less alone in the investing world.

**Be Numerate (and Understand Accounting).**

When we invest in a company, we can consider a lot of things: how smart the CEO is, how well the company seems to manage its employees, what news sources are telling us about the stock, an evaluation of the product. But the most solid evidence for a company’s prospects lies in its financial statements.

The trick to meaningful analysis is to figure out which numbers to combine with others to arrive at a consequential assessment. I use a computerized ranking system on Portfolio123 to analyze companies, and in doing so I look at about 35 different line items—and not just the latest figures, but figures from the last eight or twelve quarterly statements. Using various combinations of these numbers, I can assess to some degree how well a company is doing and how likely it is to beat expectations.

It’s vital to properly understand these numbers and ratios. If you have no idea how a company’s book value can be negative, or if you don’t understand the distinction between R&D expenses and capital expenditures, or if you don’t know why you’re supposed to use market cap when considering net income but enterprise value when considering operating income, or if you think that a company’s cost of debt can exceed its cost of equity, your assessment of a company’s potential is likely to be flawed.

And it’s equally vital not to ignore any major aspect of a company’s financial statements. There are simply too many red flags. Is the company’s short-term debt, accruals, or net operating assets too large? Is its free cash flow consistently negative? Is its growth unsustainable? Some of these may *not *be warning signs, depending on what industry that company is in (financial and utility companies, for example, can thrive with negative free cash flow).

Other numbers are also essential, and the most essential are the stock’s price and the volume traded. You may also benefit from taking into account the bid-ask spread, some historical prices and volume levels, how long it has been since the last earnings announcement, analyst estimates and recommendations and how they’ve changed, short interest, and so on.

But numeracy doesn’t mean only dealing with the facts and figures about individual investments. It also means using mathematical skills to design a strategy that has the best chances of beating the market, and being mathematically savvy enough to realize when researchers are misusing mathematics.

Take, for example, a recent presentation published by Marcos López de Prado (a principal at AQR) entitled “The 7 Reasons Most Econometric Investments Fail.” In it, López de Prado convincingly shows that econometric models (most of them based on regression analysis) “rely on strong assumptions that are not satisfied by financial phenomena.”

Almost all econometrics is based on linear regression. In his book Advances in Financial Machine Learning, López de Prado compares this to pre-Kepler astronomy, which was based on circular orbits. Financial functions are, to put it plainly, *not linear*. The risk-reward relationship, for example, is far better described by a fourth-order polynomial than a straight line. To analyze non-linear functions using multiple linear regression models will result in terribly flawed investment tools.

Imagine throwing out the entire canon of econometrics, including the efficient market hypothesis, the capital asset pricing model, the ideas of alpha and beta, the Sharpe ratio, risk factor analysis, and so on. (I could write a very long article pointing out the numerous errors, mostly mathematical in nature, that the creators of all those ideas made; if you’re curious, these two articles are a good start.) After all, of the great investors of the past and present, very few of them have had any use for these metrics or have subscribed to these theories.

Are there better mathematical tools for analyzing the performance of a strategy, or for helping us to decide what strategy to follow?

I believe that there are. You can use correlations, for example, without using ordinary least squares methods—Kendall’s *tau* is a great tool for analysis—and you can take into account that data can be strongly codependent without being correlated if the relationship is nonlinear. You can examine the entirety of a returns distribution without ever using regression by looking at omega ratios, and arrive at risk measures that are unaffected by flawed analysis methods. You can look at probabilities, one of the most useful of all mathematical tools. You can rank investments without using any econometrics. You can even find and assess factors without impractical long-short strategies and significantly flawed p-tests.

(I have no evidence that Mauboussin would agree with me on the use of correlations or omega ratios; he seems to studiously avoid the subject of backtesting. I hope I can show that these can be derived from laws of probability, which Mauboussin views as essential; after all, the omega ratio is little more than a measure of what Mauboussin calls “slugging percentage”: “what matters is how much money you make when you are right versus how much money you lose when you are wrong.” But Mauboussin may well argue that all backtests are to some degree rigged.)

Numeracy enables you to question established orthodoxy, whether that’s the price that the market assigns to a stock or a theory whose reasoning is flawed. And that is perhaps its most valuable function.

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