to do it – fire her).
This is the big gulf between risk management and most conventional quantitative modelling. If you see people casually assuming something is random and compiling statistics or casually assuming something is predictable and making calculations, you’re not looking at risk managers. Risk managers are sure that they can exploit the wisps of pattern in other people’s randomness and the wisps of noise in other people’s signals. You see them obsessively cleaning data that everyone else thinks are both irrelevant and already clean enough for all practical purposes. At the same time, the risk managers are ignoring what everyone else thinks are the important data.
Getting Scientific with Risk
It’s a little embarrassing philosophically that neither of the two main concepts of randomness actually exists. Dice rolls are determined by physics, not chance. We just pretend they’re random. And, although experts know less about the human mind than about simple physics, you can be confident that people do not have a consistent set of subjective beliefs about any possible eventuality. So Bayesian probabilities don’t really exist either. (See “Betting with Bayes” earlier in this chapter for an explanation of Baynesianism.)
However, in the 350 years since mathematical investigation of probability began, science has uncovered some important kinds of randomness that actually exist in nature. These models have been much more important to the development of risk management than traditional probability and statistics.
Darwinian evolution is defined as random variation and natural selection. It was the random part that was revolutionary when Darwin published On the Origin of Species in 1859. The idea of random selection is what distinguished Darwin’s ideas from earlier theories of evolution and is what upset many religious people at the time.
The main difference between the randomness exploited by evolution and the randomness manufactured in a casino or used to model the uncertainty of an individual is that the mechanism of randomness is created and regulated by evolution. I’m not going to go into the complex theoretical and mathematical meaning of that, but I can illustrate it with three examples.
Stealing from a tiger
Consider the question of what the stock market will do tomorrow. A frequentist pretends that the result will be the draw from some probability distribution, and tries to guess the characteristics of that distribution. She knows that the actual outcome will be the result of a complex interaction of economic news and traders’ reactions, but she considered that too complicated to model in detail. To a Bayesian, the question is, ‘What do I think are the possible moves the market might make and what probability do I assign them?’ The frequentist treats the market like a roulette wheel and tries to guess what numbers will come up with what frequency. The Bayesian treats it as something she’s uncertain about and tries to quantify that uncertainty.
Both attitudes are unwise for someone managing risk. They fail to give the market the respect it deserves. Suppose instead that you think about the stock market as a highly evolved entity. In order to survive, it evolves defences against people guessing what it would do. If people make accurate guesses they can extract money, which comes from other participants who eventually leave the market. The market’s defences don’t have to be perfect – they can allow some people to make some money – but the defences have to be extremely good given the number of smart people devoting great resources to beating the market.
✔ Encourage people to bring information to it
✔ Attract both issuers of securities and investors in securities
✔ Direct economic activity in reasonable ways
If the market fails in any of these tasks, it won’t survive. Of course, many financial markets have failed over the years.
If you think of the market as a roulette wheel, you think that all you have to do is predict its next number with a bit better than random accuracy. If you think that the market is a highly evolved entity threatened by any profits you extract, you think you have to snatch a piece of meat from a tiger. One of the formative events in the career of a risk manager is getting mauled by the market. I don’t mean losing money because you’re wrong – that’s justice, not a mauling. I mean getting blown up despite being right because you didn’t see the market’s defences.
A Bayesian approach disrespects the market in another way: it treats the market as something that can be understood, albeit with some uncertainty. You won’t get the meat by understanding the tiger and negotiating. What you want is inconsistent with the tiger’s survival. That’s what you have to understand.
Shorting the big one
Michael Lewis’ book The Big Short (WW Norton and Company) is an entertaining account of traders who managed to get rich during the 2007–2008 financial crisis by betting against subprime mortgages. If you don’t work on Wall Street, you probably think the hard parts of that are figuring out the right bet to make, and getting the money to back your opinions. But as the book shows, those two things were minor hurdles compared to figuring out how to place the bets and then to collect the winnings. Lots of people got the bet right and lost all their money anyway. In addition, all the successful bettors in Lewis’ book had to survive multiple crises, none of which had anything to do with the economics of their bet and any one of which may have gone the other way.
You can look at each of problem one at a time and ascribe it to a tricky detail of the market or regulation, or some shady practice by dealers or an attack by people on the other side of the bet. Of course, if you want to be a successful trader, you have to discover all the tricks that can be used to extract your profits when you win, so this analysing each factor makes sense. However, in another sense it misses the point. These people were all trying to take money out of the market. The market has evolved ways to make that difficult. Not all these market defences can be traced to rational actions by individuals; many of them are consequences of group behaviour.
On one extreme are certain academic thinkers who treat the market as if it doesn’t care what they own. At the other extreme are superstitious traders who believe that the market is always out to get them. For risk managers, the traders’ perspective is closer to right attitude. There’s an old military adage, ‘Prepare for your enemy’s capabilities, not his intentions.’ Sound financial risk management prepares for anything the market is capable of doing, not just what the market should do, or what you expect it to do, or what makes sense.
Getting shipwrecked
Most people are familiar with the stories of Robinson Crusoe and the Swiss Family Robinson about people who had to find a way to survive in a completely new environment. These stories offer an excellent contrast between treating risk as something that powers evolution versus risk as something manufactured in a casino or resulting from subjective uncertainty.
Daniel Defoe’s realistic Crusoe is thoroughly aware that he is thrusting himself into a foreign ecosystem that he must respect in order to survive. Mostly that means he must adapt himself, and while changing things on the island where he’s shipwrecked, he must make small changes and think the consequences through thoroughly before acting.
In contrast, Johann Wyss’s Robinson family sets energetically to the task of recreating the Swiss environment they came from on the tropical island they land on. In the novel, they’re completely successful. In real life, their strategy would have been a disaster.
The idea of being shipwrecked on a desert
