Since the beginning of the financial markets, boom and bust have happened. From the Dutch Tulip Mania of 1637, when a bulb was priced more than a house, other bubbles followed: The Mississippi Bubble, The South-sea Bubble and so on.
We could go on for an entire page, listing all the bubbles happened in the last centuries. You may think that at this point we learned many lessons from them. Did we? Of course, we did not. In fact, one common pattern of all those bubbles is “this time is different.”
Yet it wasn’t. In their brilliant paper entitled “This time is different” Carmen M. Reinhart Kenneth S. Rogoff show us how defaults and crises happening throughout the centuries taught us some valuable lessons, which very few seem to have grasped.
The catch is that those defaults often happened with some years or decades apart, with the consequence of inducing market players and policymakers that “this time was different” but when the next crisis struck they eventually turned out to be as severe if not more than the previous ones.
But if that is the case what did we get wrong?
We Got It All Wrong
The main assumption made by many economists and investors is that financial markets follow what is called a normal distribution, also called Gaussian curve. In short, this kind of distribution tells you that events that move too far from the average are rare.
A tool to know how far values are spread out from the average, is the standard deviation, also expressed with the Greek letter, sigma (σ). In other words, a value, which has a Sigma of five, is way more rare than a value that has a sigma of one.
The Gaussian curve though tells you that events of 5/10 sigma are so rare that you shouldn’t expect them to happen in million of years. But how is this possible if only in the last decades we saw financial market crashes – which were considered by most economists, in the magnitude of 10/20 sigma – happening time and time again?
The problem is that we got it all wrong. In fact, those events, which are deemed to be so improbable according to the Gaussian distribution, are not such if we make a different assumption: financial markets follow a different form of distribution, called Cauchy distribution. To understand this kind of distribution we will use an interesting analogy; that of a drunken squad shooting into a wall.
Financial Markets Are Like a Drunken Firing Squad
in the book “The Physics of Wall Street: A Brief History of Predicting the Unpredictable” James Owen Weatherall describes the Cauchy Distribution with the following analogy. Imagine a drunken squad about to shoot on the wall in front of them:
“If you make a notice of where each bullet hits the wall you can use this information to come up with a distribution that corresponds to the probability that any given bullet will hit any given part of the wall…The firing’s drunken squad bullets hit the middle part of the wall most of the time – more often, in fact that the normal distribution, would have predicted. But the bullets also hit very distant parts of the wall surprisingly often – much, much more often than the normal distribution would have predicted.“
In other words, this kind of distribution totally changes the probability of certain “rare events” to happen. In fact, according to David Hand, in his book “The Improbability Principle” a 5-sigma event that according to the Gaussian distribution has a 1 in 3.5 million years to happen, in the Cauchy distribution, it has a 1 in 16 probabilities to happen!
This means that not only market crashes are not rare events, but we should actually expect them to happen way more often that we usually have thought in the past!
Keeping this in mind you may want to be sure your retirement fund is not blindly exposed to financial markets.
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