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Newton’s Financial Crisis – The Limits of Quantification
“To the man with only a hammer, all problems are almost like a nail”
The blame for the current financial market crisis lies, in my opinion, with one man. Isaac Newton.
The observant among you may spot a possible flaw in this theory. The great English scientist has been dead for two hundred and eighty-two years. But let’s not let such an insignificant detail put us off, the genesis of the credit crunch can clearly be traced back to the man whose balls dangle so freely on the desk of many a stressed-out executive.
Newton’s theory of gravity was a milestone in the history of science. Not only was he able to describe the mechanics of celestial motion from a study in plague-ridden rural Oxfordshire by observing a falling apple. It was pretty spectacular, as was his accidental invention of calculus and the fact that he was so busy sticking needles in his eye and trying to get gold out of bits of coal, lead and mouse droppings that he missed the original proof of his theory and he had to do it. being bribed by Edmond Halley (he of the comet) to write another one.
No, the most amazing thing was that he described his theory using mathematics. Today this seems perfectly natural – no theory in physics has much credibility until the mathematical models that describe it can be demonstrated – but in Newton’s time it was revolutionary (in both senses of the word). Modeling his theory using mathematics, he was able to develop something that not only described the observed movements of the heavens, but was also able to predict them, and in doing so introduced the whole gamut of modern science.
This had a couple of pretty immediate effects that we can still see the impact of today. First, doing science quickly became equivalent to doing math. Before Newton, physics was descriptive and dominated by amateurs. After Newton, a class of professional scientists began to develop for whom mathematical modeling was the key to understanding the universe. This change happened very quickly: Robert Hooke, the hunchbacked polymath who was Newton’s contemporary (and who is a particular hero of mine) spent the rest of his life claiming that Newton had stolen his idea to develop the theory gravitational Indeed, there is some evidence that Hooke gave Newton a key insight into centripetal motion, but Hooke’s ideas were purely descriptive. It was Newton’s unique mathematical approach that won him acclaim, a key place in history, and set the scene for the rise of mathematical modeling.
What this move towards mathematics meant, however, was that science moved away from ordinary people, and we increasingly had to rely on scientists themselves to check their own work. Fortunately, most scientists have egos the size of the US budget deficit, so if they ever agree on something, there’s a pretty high degree of probability that it’s pretty much right. Anyway, the peer review process that governs modern science has grown to provide a basis for accepting new work, and the norm for the rest of us to take experts at their word has grown along with it.
The second impact of Newton’s new approach was to establish physics as the gold standard to which other disciplines should aspire. Predictive, mathematical science was for a long time held in much higher regard than descriptive subjects such as sociology and psychology and became a fairly natural progression for other subjects to try to develop similar processes. So much so that “science” is now roughly equated with “prediction”. If you can’t predict you’re not a scientist, that’s the general idea.
Unfortunately, these two characteristics: the appreciation of predictive sciences and the general population’s ignorance of the underlying mathematics that underpin them have conspired to make many unpleasant things happen without much debate. Some of them led to eugenics and gas chambers and others to IQ tests and mass sterilization. And still others led to the efficient market hypothesis, quantitative investment analysis, and the wholesale destruction of the modern financial system.
The efficient market hypothesis is one of the great problems of modern investment analysis. What it says is that markets are price efficient, all the time. So all known information is already in the price of stocks or bonds or whatever, which means that unless you know something that’s not in the public domain, you can’t, on average, profit from the negotiation The corollary to this is that you can develop mathematical models to describe – and predict – market movements.
Charlie Munger, the octogenarian billionaire vice chairman of Berkshire Hathaway has a name for the efficient market hypothesis. He calls her “crazy.”
For one thing, it’s perfectly obvious that markets are pretty efficient most of the time. The purpose of a market is to find a price at which an instrument clears, where buyers and sellers meet. However, the key is in that “most of the time” critic. Occasionally, things go spectacularly wrong: think the South Sea Bubble, the Wall Street Crash, and the dotcom craze. There are many other examples. Unfortunately, “most of the time” doesn’t model very well in math. There are things like “this model will only go wrong once in a million years”. With this he does, usually more or less immediately.
What we can see here is investment analysis that is modeled in physics dressed in precision parts. Unfortunately, this is illusory: just because you can model something doesn’t mean it’s right. Unfortunately, it seems that the leaders of the world’s largest financial corporations are just as wide-eyed as the rest of us when it comes to implicitly believing people armed with a white coat, weird hair, and a supercomputer. Although given the accuracy of some of the models used to predict the valuation of subprime guaranteed mortgages, one is left with the sneaking suspicion that this was actually the work of a couple of geeks in a basement armed with a sheet of Excel calculation. Anyway, the problem with this goggle acceptance of these quantitative approaches is that, unlike science, there is no peer review. The insane had taken over the asylum.
Someone who invests solely on the basis of the efficient market hypothesis and mathematical models is like the man with the hammer: when faced with a problem that is not a nail, they hammer it anyway. Munger describes this as the pursuit of spurious precision: the inappropriate application of hard science models to domains where it is simply not appropriate.
Isaac Newton ended up running the Royal Mint, but like many of today’s financial guardians he had no idea about investing because, at bottom, it’s not math that rules the markets, it’s human behavior. He lost a lot of money in the South Sea Bubble bust of 1720 and learned the hard way that while you can take the humanity out of models, you can’t take it out of the markets. His lament rings as true today as it did three centuries ago:
“I can calculate the movements of the heavenly bodies, but not the folly of people.”
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