Models are not RealityWhen we model the world we simplify it, because we have to: to model the real-world accurately we’d need to have all the atoms in the universe and a few more. So, the globe of the world revolving gently on my desk afore me, is a highly simplified representation of the planet most of us live on, leaving out what many of us would consider to be most of the important details – stuff like people, beer, football, lively teenage daughters and the latest episode of House. You fill in your own details.
Quants, the purveyors of quantitative investment analysis, also model the world, although in a much more complex, mathematical way. Yet despite the sophistication and elegance of the mathematics they too make simplifications because they have to, and these matter to us all because they’ve brought the world to the point of financial meltdown thrice within a few years.
Models are Metaphors
What quants bring to investment analysis is a set of metaphors about the way the world actually works, rather than any kind of accurate description of what’s really happening. In fact the world is run by such approximations – models of average life expectancy, mean time to failure of aerospace components, the probability of Michael Jackson’s chimp contesting his will – and, in general, these inaccuracies haven’t mattered much to humanity en-masse. However, we’re now in a world dominated by mass computing power and intricately interconnected such that small perturbations in one area can have dramatic ramifications elsewhere.
From the 1980’s, when Portfolio Insurance turned out to be no such thing, to the 1990’s, when Long Term Capital Management became a glorious oxymoron, through to the 2000’s, when the securitised mortgage market turned into a series of financial time bombs in a greater game of pass the ticking parcel, the quantitative models that mathematically illiterate managements have relied on have given no warning of what was about to occur. Yet these models were created by some of the cleverest minds in the world and supported by some of the most serious back-testing that has ever been carried out.
Yet still they failed.
People Are Uncorrelated, Until They Aren’t
What the models failed to capture was that humans don’t behave in simple, predictable and uncorrelated ways. It’s impossible to overstate the importance of the way these models cope with correlation of peoples’ psychology. To sum it up: they don’t. Let me know if that’s too complex an analysis for the mathematical masters of the universe.
Anyone who’s ever been to a nightclub, a football game or even a very loud party will know that there are situations where we don’t act as individuals, buzzing about doing our own thing. These are occasions when we all suddenly stop being individuals and start doing the same thing – usually involving large quantities of drugs and some very bad singing. Although these sorts of events are specifically designed to trigger this behaviour – which is probably a deep evolutionary adaptation to sponsor group behaviour, useful when it comes to running down tasty antelope and dealing with giant, carnivorous sabre toothed beavers – it can also happen in other situations. Most stockmarket booms and busts are generated by similar group effects.
In general, people behave in an uncorrelated fashion right up until the point they don’t. Then we all suddenly do the same thing together. We stop taking flights in the wake of 9/11, we stop letting our children play in the streets because of a single, heavily reported abduction in another country and we start selling our shares because everyone else is. Fear is an awfully big motivator.
If Something Can’t Continue, It Won’t
Quantitative models don’t handle this sudden polarisation of human behaviour very well. Every so often something surprising happens that causes us all to scurry into the nearest hole and the models promptly fall over, usually accompanied by some whizz-kid earnestly explaining that this was a one in a million year event and that it was just bad luck it happened after three years and can he please have his bonus anyway?
Think about the booms and busts in stockmarkets compared to the relatively stable periods in between. In the stable times it’s possible to roughly model the way that people are going to invest, on average, making certain assumptions about the companies comprising the markets and general economic conditions. While these assumptions hold so do the models and this can go on for a long time, long enough to convince people that stability is the norm.
Only stability is not the norm. When the markets enter one of their periodic manic-depressive phases these general models break down – people start to cluster together in fear or greed and do the same thing. Quantitative models work on the basis that the stable times will last forever, but the reality is that they don’t and when they end they do so in highly unexpected and unpredictable ways. Worse still, if any model becomes too popular it will start to influence the real world, to swing the pendulum one way. The trouble with pendulums is that eventually they swing back.
Credit Rating Madness
We can trace the collapse of the banks over the past couple of years to the excessive risks they were taking in holding Collateralised Debt Obligations on mortgage securities, the idea being that if you took a lot of very safe mortgage debt and bundled it up with a bit of really unsafe mortgage debt you’d end up with a safe investment. Whereas, in fact, you ended up with a lot of really unsafe lending to people who were never going to be able to repay their mortgages.
Yet at the time the models investment analysts were relying on simply didn’t identify these risks. Indeed the models of the credit rating agencies were explicitly changed to take into account the quantitative models showing that such securities weren’t risky.
The sheer nuttiness of the credit rating agencies changing their risk models purely because a quantitative model existed that indicated that the risk of these securities collapsing like dominoes in the event of isolated defaults was remote is still hard to believe. It’s not that the models didn’t indicate exactly that. Nor is it that the mathematics behind the models was particularly stupid. Nor was it that the analysts who dreamt up the models were doing anything obviously wrong.
No, it’s none of those things. It’s the fact that a bunch of smart people can possibly believe that any computer model can accurately reflect the real risks in a world dominated by stubborn, irrational, fearful humankind. You’ve got to ask – did none of the overpaid executives running the world’s financial corporations and regulators actually stop to wonder whether someone might have, just possibly, failed to predict everything that might happen in the real world? Did none of them look at the collapse of LTCM and wonder?
We Need Bridge Builders, Not Quants
Well, oddly enough, some of them did. Back in 2006 when the CEO of Citigroup was still dancing in the last chance sub-prime disco the risk management team at Goldman Sachs got together and solemnly inspected their VaR models which told them that risk levels were still low. Then they inspected their brains – and bailed out. Whether that was luck or judgement is still to be decided.
The great Victorian engineers built bridges that endure to this day because they couldn’t exactly model the risks. They built with a margin of safety not with a bonus on margins in mind. Remember this because the quants are not dead, they’re out there yet. They will rise again.
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