Financial Risk Management For Dummies. Aaron Brown
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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 island has an enduring romantic appeal. However unpleasant the reality would be, in imagination the island provides a blank canvas without all the complexities and accumulated environmental damages of modern life. But that imagination is false, and Defoe knew it deeply and instinctively, while Wyss apparently did not.
The world is highly evolved, and no blank canvases remain. Whatever projects you undertake, you need to think through the consequences of everything you’re changing. Even if you cannot trace direct cause-and-effect relations, you have to respect the possibility that even markets fight to survive.
A completely different understanding of randomness underlies the field of statistical thermodynamics.
One popular way to think about the stock market is as a random number generator. News comes out about each company every day, which pushes its stock price up or down. The movement of an index like the S&P 500 is just an aggregation of the moves of the 500 stocks that make it up. The day’s move is treated like a random variable. You try to guess its distribution by studying past moves and using other information. The risk to a stock investor is that she gets an extreme draw from the left tail of the distribution – that is, a big down move, as large or larger than the big down moves in history.
That’s a fine story and useful for answering some questions about stock market risk. But what if you invert the story and say that the way things work is that macro financial variables such as interest rates and gross domestic product growth and inflation, along with other large-scale financial forces like total investor risk appetite, tax policy and leverage rules, all combine to determine the appropriate move in the S&P 500. Instead of being a random variable, the S&P 500 move is determined by economic forces. Now no one understands all the forces and no one can measure them precisely, so no one knows what tomorrow’s S&P 500 move will be, but just because no one knows something doesn’t mean it’s random.
The macro-economic variables that affect the stock market as a whole don’t put much direct pressure on the prices of individual stocks, which are still driven mostly by company-specific news. But because the S&P 500 is just the sum of the 500 stocks that make it up, if it goes up 1 per cent, the average of the 500 stocks must also go up 1 per cent.
The randomness in the stock market is how the market-level move determined by macro-economic forces gets distributed down to move individual stocks. When I say the individual stock moves are random, I don’t mean that something like a lottery is in place to determine which stocks go up and how much. Individual stock prices are still determined mostly by company news and investor opinions. But suppose that on days when the S&P 500 goes up investors underreact to any bad news that comes out about companies and overreact to good news. If a big investor wants to sell a stock for some reason on a good market day, the sale has minimal price impact, but if a big investor (or a lot of little investors) decides to buy on a good day, the price of the stock will jump up.
For what it’s worth (and it’s probably not worth much), this is how the market feels to many participants – that macro forces determine a market mood and the market mood affects how investors react to individual pieces of news or changes in supply and demand. In this view, the stock market isn’t a clearinghouse for evaluating news and balancing supply and demand, it’s a mechanism for translating macro-economic forces into specific individual transactions in specific stocks.
Now the risk to a stock investor is completely different. It’s not the risk that the stock market as a whole will get a draw from the left tail of some distribution because there is no distribution. The person who invests in the stock market over long periods of time will earn a return based not on randomness but on how good the economy is. However, people who hold concentrated portfolios of only a few stocks, and especially people who hold levered positions and derivatives, face the risk that their particular positions will be randomly selected to do worse than the market as a whole.
For risk managers, the big issue isn’t normal day-to-day randomness, but the possibility that the stock market mechanism may break down. A breakdown may cause a crash unrelated to macro-economic forces, or a flash crash, or a bubble, or a liquidity crisis. These risks are the major ones for professional investors, and they’re significant risks even for long-term, diversified buy-and-hold investors, because a single major event can wipe out many years of normal returns. But these risks cannot be studied in a bottom-up random walk model.
