A Sideways Look At ... Economic Models

Economic Models on the Psy-Fi Blog

Almost inevitably economic models keep on appearing on this blog, an annoying but inevitable occurrence. These models underpin so much of what happens in finance that it’s impossible to ignore them. In fact they’re now so important that the models themselves can change markets, although usually only because they've screwed everything up again.

All models are simplifications of the real-world – a model that described everything would have to be bigger than the universe, so until we can figure out how to start consuming other dimensions we’ll have to make do with approximations. Of course, when we do break out of this cosmos we’ll end up with a bunch of do-gooders complaining about the effects of dimensional change as vast chunks of the space-time continuum break off and strand the pan-dimensional equivalent of the polar bear.

Anyway, here’s a brief summary of the Psy-Fi Blog’s thoughts on economic models ...

Newton’s Financial Crisis: The Limits of Quantification

It all started nearly three centuries ago when a bewigged philosopher-scholar named Issac Newton went and invented calculus in order to model planetary motion. Soon if you weren’t using maths to do your modelling other physicists were calling you a sissy. It wasn’t long before generating mathematical models became the gold standard for everyone and that’s where the trouble started. The next thing you know we have the Efficient Markets Hypothesis:

“The Efficient Market Hypothesis is one of the great blights of modern investment analysis. What it says is that markets price efficiently, 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 by trading. The corollary of this is that you can develop mathematical models to describe – and predict – market movements.

Charlie Munger, the octogenarian billionaire Vice-Chair of Berkshire Hathaway has a name for the Efficient Market Hypothesis. He calls it “bonkers”.” >> Read More

Markowitz’s Portfolio Theory and the Efficient Frontier

In investment circles math was, for a very long time, something done by rocket scientists. In the wake of the Second World War the climb and climb of the stockmarket led many to think that there was something inevitable about the perpetual rise of stocks. Then the seventies hit, the Arabs decided that the oil in their countries was, well, theirs and the markets went into a dizzying tailspin.

Belatedly recognising the existence of risk, and casting about for some way of managing it, fund managers happened upon some twenty year old research which equated risk with volatility and meant that everything could be boiled down to a few simple numbers. The Efficient Market Hypothesis and models that go with it were born.

“What Markowitz did was put a number on risk to allow it to be managed. The first danger for investors is in not understanding the importance of Portfolio Theory for risk management of stockmarket investments. The second is in believing that it can explain everything. People don’t get programmed linearly – we come with randomness built in, not as an optional extra. Thank goodness.” >> Read More

Of course, no one understood the downside of what rapidly turned into a quest for spurious precision.

Alpha and Beta – Beware Gift Bearing Greeks

Markowitz’s ideas led eventually to something called the Capital Asset Pricing Model (CAPM). CAPM assumes that returns from a market lie on a Bell Curve or, in the jargon, are distributed normally. Sometimes you get very bad returns, sometimes you get very good returns but mainly you get something in the middle. Only this turns out to be dead wrong.

“The markets can behave normally for long periods of time but when they go wrong it can be spectacular. Long Term Capital Management (LTCM) a hedge fund run by Nobel laureates found this out to their cost in 1998 when their normally distributed model collapsed when they were unable to sell assets at any price due to the collapse in the Russian bond market. Only concerted government intervention prevented a massive financial crisis.” >> Read More

The search for spurious mathematical precision was to lead to all sorts of problems.

Holes in Black Scholes

The LTCM Noble Laureates had made the basic assumption that the world they knew was the only world that there was to know and constructed their models accordingly. Unfortunately a human lifetime isn’t time enough to get to know even a fraction of the possibilities. What’s odd, not to say worrying, is that the inefficient Black-Scholes option pricing model that underpinned LTCM (which itself depends on the distribution of returns on a Bell Curve, aka the Gaussian distribution) is still in use today.

“Peer under the covers of Black-Scholes and you find our old friend, the Gaussian distribution, assuming that extreme events are impossible instead of just rather unlikely. The unlikely happens all the time in markets, usually because of human behavioural biases which kick in at extreme moments and lead to sustained overshoots in valuations and liquidity.” >> Read More

Of course, any hope that the lessons of the past would be learned by the financiers of the future was forlorn.

Risky Bankers Need Swiss Cheese Not VaR

Underpinning many of the risk models being used by financial institutions is something called Value at Risk (VaR) which attempts to measure the likelihood of an unlikely event under everyday conditions. Unfortunately, like many models, it’s open to abuse if the people overseeing it don’t know what they’re doing or are too distracted by large bonuses to bother. Guess what?

“To summarise a vast range of problems in simple terms, the people running the banks, the credit rating agencies and the regulatory bodies didn’t have a clue about the limitations of the risk management models they were all using. They were all looking at the same data and using the same models. And all drawing the same conclusions. Which were wrong. >> Read More

Which eventually led to the almost inevitable problems the world started reluctantly facing up to in late 2007.

Quibbles with Quants

The rise of all of these quantitative models, based on the spurious precision accompanying analogies with Isaac Newton’s models of gravitation, have resulted in continual market failures culminating in the crash of 2007-2008, which was by far the most spectacular implosion of math based financial models yet.

“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. ... 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?” >> Read More

The Death of Homo economicus

Dig hard enough into these models and you’ll uncover the idea of the perfectly rational human being, weighing up decisions in the light of perfect information. Yet the brain is, at best, an imperfect rationalising machine making all sorts of shortcuts in an increasingly desperate attempt to make sense of our information saturated world. In 1979 Kahneman and Tversky came up with a different model, behavioural finance, based on human psychology which suggest that ...

“...investors – were more risk adverse when it came to protecting a profit than they were in trying to recover a loss.

So, in effect, if something went wrong with a stock they were holding the theory stated that they would be more likely to sell it if they were in profit than if they were making a loss. This is, indeed, illogical since it’s the same company with the same prospects. If investors were truly rational they would decide whether to sell or not based on the current information – stock history is irrelevant to whether a stock is currently a good investment or not. Yet the evidence suggests that this decision is, in fact, heavily biased by their personal history and, therefore, that the decision is not really a rational one.” >> Read More

Exit homo economicus, leaving a mathematical vacuum just dying to be filled.

The Special Theory of Behavioural Economics

The death of Homo economicus is required by behavioural finance, which looks at how intelligent human beings behave irrationally for the most rational of reasons. Increasingly it looks like the Efficient Markets Hypothesis is just what happens when we don’t all have some particular bee in our collective bonnets.

“A hypothesis, then, is that behavioural biases effect investors all of the time but while there’s a reasonable balance between different types of investors in the market any deviation in valuation is corrected, leading to a market that exhibits the hallmarks of a standard efficient model. However, this is only correct at the gross level – look under the covers and you’ll find a whole bunch of behavioural biases twitching away but doing so fairly randomly, cancelling each other out.” >> Read More

To Infinity and Beyond

If rational man is dead and the rational models they go with him are similarly extinct you’d hope that these adventures in stupidity are over. Unfortunately the quest for spurious precision through mathematical models that can be programmed to make institutions easy money isn’t likely to end any time soon. Whether any of these models can successfully integrate human behaviour is questionable but, on the other hand, perhaps we should hope that they don’t.

At least we can be reasonably sure that people will continue to do exactly what these models expect until they don't. That's the thing about people, we're unpredictable. Which, fortunately, is still about the only predictable thing about us.