Читать книгу Safe Haven - Mark Spitznagel - Страница 14

We need a sound, deductive framework for understanding why and how risk mitigation can lead to higher compound growth rates. Is it even possible? How can we expect risk mitigation to be cost‐effective? What's the mechanism behind it? And how might we recognize this ability if or when we see it?

There are so many forces swirling around in investing and markets; any attempts at explaining risk mitigation based on that swirling data will likely get us into trouble. So we will need to figure out, deductively, the mechanism that gives rise to our hypothesis. It won't be enough just to show that a strategy raises wealth and, poof!, it is cost‐effective risk mitigation. We need to understand the forces at work to make that happen.

The best deductive tool at our disposal will be the same deductive tool used from prehistory right up to today to discover and comprehend the general science of probability and the formal idea of risk.

Archaeological excavations have uncovered collections of talus bones, or ankle bones of goats and sheep, dating back to 5,000 BCE. These four‐sided bones were the oldest of all gambling devices. Our more familiar six‐sided “bones” started showing up by 3,000 BCE. Visibly, dice have been embedded in civilization's history as the generators of fate, then chance, and then even skill (in the early precursors of modern backgammon). They were ubiquitous throughout both the depth and breadth of that history and are a part of our collective unconscious.

But it took ages, and the need to better understand wagers on games, for dice to become the intuitive pedagogical tools of deductive inference that would set in motion a theory of probability. As early as the fourth‐century BCE, Aristotle casually pointed out that, while it is easy to make a couple of lucky rolls of a die, with 10,000 repeated trials, divine intervention be damned, the luck of the die evens out: “The probable is that which for the most part happens.” Imagine, this was revolutionary stuff! But the ancient Greeks and Romans never really got it—they never even bothered to make sure their dice faces were symmetric. If everything was fate anyway, what difference did it make? Julius Caesar's famous line “the die has been cast” was not a statement about probability. (For all the wisdom that we ascribe to the ancients, if you had the ability to go back in time, you would totally clean up gambling against them.)

It wasn't until much later, by the seventeenth century, that Galileo and then Blaise Pascal and Pierre de Fermat became gambling advisor‐mercenaries to various noblemen. For instance, the Chevalier de Méré needed advice on his costly observation that betting with even odds on the appearance of a 6 in four rolls of a single die was profitable in the long run, whereas betting with the same odds on a double‐6 in 24 rolls of two dice was not. (In 1952, the famed New York City gambler “Fat the Butch” rediscovered this same deductive fact in his own rather costly hypothesis test.)

At that point, probability was all about deductive reasoning, starting with the known properties of the generator (a die) and then reasoning forward with expectations about its particular outcomes. Repeatability was implicitly a necessary condition to probabilistic inference. This was the frequentist perspective, where the very meaning of probability was the frequency of occurrences over many trials. It is the logic of the gambler with an edge, the logic of the casino. Probability was truly coming of age as a neat trick to allow mathematicians to pick off degenerate gamblers. These were the original quants—money on the line has a way of sparking innovation. (Heck, it took having an options position for me to ever start really thinking about math.)

Of course, we have always understood risk and its mitigation in our bones; that's how we made it this far, after all. But along with advancements in our understanding of probability grew a gradual formalization and sophistication of risk mitigation. And we can think of the growth of that formality first and foremost as the growth of innovations in insurance—which itself would facilitate an explosion in risk taking and innovations. Insurance is an ancient idea, and a key part of the very progress of our civilization. It began as solidarity, as risks were shared—spreading throughout small villages, for instance, as commitments to mutually self‐insure and share the replacement costs of homes within the community. This aggregation of individual risks created a frequentist's perspective where there otherwise was none, effectively expanding an individual's sample size from 1 to the size of their community.

Fast forward to the twentieth century, and an epic nerd skirmish would erupt over this perspective. The newly founded Bayesians and Popper's propensity theory—where probability instead meant “degrees of belief” or “tendencies,” respectively—went head‐to‐head with the simpleton frequentists. And it doesn't really matter who was right. All that matters is which perspective is being used. When your sample size is small, and worse yet unique and unrepeatable, no matter your subjective probabilities, there is so much noise in your sample you can hardly know anything anyway. Your N equals 1. You are a punter, hoping for good luck or good fate. But if your success or failure relies on many outcomes, over many rolls of the dice, then you naturally care about the properties over many rolls of the dice. Your N is large. You are the house exploiting the “house edge” through repetition, to quash the randomness. And the house doesn't gamble. You are, as the poker theorist David Sklansky wrote, “at war with luck.”

Most people would say (or at least their actions imply) that they are the house in their investing. And 93% of people also say that they are above average drivers. They aren't, in either case.

“At war with luck”—using our “skills to minimize luck as much as possible”—does indeed apply to risk mitigation. That is precisely how this war is fought in investing. In my case, it is no less than everything I do as an investor. So it is fitting that we begin to understand risk mitigation by deductively stripping it down to its bare bones, literally.

This is one of the most valuable things I have learned from Nassim Taleb, his valid warnings against the ludic fallacy notwithstanding (where “the narrow world of games and dice” have so little in common with the untamed risk of the real world): that playing around with and meditating on simplified Monte Carlo simulations, or “alternative histories,” is the best way to figure things out.

After all, according to Popper, science is “the art of systematic over‐simplification.”

Beyond epistemological rigor, the biggest advantage I gain from building my safe haven hypothesis deductively and piecemeal using games of dice is transparency. You are going to see some things about safe haven investing that will seem to defy common sense. Couple that with the fact that there are frighteningly many ways that the investment industry regularly smokes people with complicated and unfalsifiable (and thus pseudoscientific) theories and cherry‐picked market data, and you can see my concern about a healthy skepticism. “Trust, but verify”: When something doesn't smell right, go back to the beginning, to our simple and transparent deductive dice illustrations. Roll your own and play along at home. There's no place to hide there.

As Feynman famously said:

In general, we look for a new law by the following process. First, we guess it. Then, we compute the consequences of the guess to see what, if this is right, it would imply. And then we compare the computation results directly with observations to see if it works. If it disagrees with experiment, it's wrong. In that simple statement is the key to science. It doesn't make any difference how beautiful your guess is, it doesn't make any difference how smart you are who made the guess, or what his name is—if it disagrees with experiment, it's wrong. That's all there is to it.

This will be my analytical framework for this book.

In Part One, we start with “what comes first” (the a priori), with an intuitive construction and examination of those fundamental safe haven mechanisms, with the help of our deductive dice. “First, we guess it.” Then in Part Two, with “what comes after” (the a posteriori), we start to formulate testable safe haven hypotheses based on those mechanisms—hypotheses about how we might expect them to work. We will conduct clinical trials or experiments on different idealized safe havens (what I call cartoons)—to “compute the consequences of the guess.” Then, we can compare those results “directly with observations to see if it works,” by running those same experiments on the diverse range of real‐world safe havens themselves. Our aim is to try to falsify, in a meaningful and rigorous way, the hypothesis that safe havens as a group—and various safe havens in particular—can raise wealth by lowering risk. This is not a foregone conclusion; after all, it is considered a crackpot idea.

Through this methodology, you will hopefully understand what works in risk mitigation, what does not, and why. And this understanding will protect you more than any individual safe haven ever could. It will guide you toward our goal as investors.

If you think about it, cost‐effective risk mitigation—or raising compound growth rates and thus wealth through lower risk—is really our comprehensive goal as investors. It is the true essence of investment management. In and of itself, it is the specific meta‐purpose or meaning we pursue when we deploy capital—what we hunt for relentlessly, our buried treasure.

Yes, there really is a buried treasure for investors, one that solves our monumental problem by showing that the great dilemma of risk—the ostensible tradeoff between higher returns and lower risk—is actually a false choice. But this treasure wasn't so much hidden away by pirates of lore as it was cloaked behind the flawed apparatus of modern finance, shrouded behind its veil of rigor. As a result, it appears as a myth, an idealized and elusive goal. But that's only because investors have been looking too narrowly and in the wrong places for it. We need a more holistic approach; we also need a treasure map to know where to dig.

But just because that buried treasure exists doesn't mean we will ever find it. The greatest value—more than in the treasure itself—will be in what we gain from the hunt.