A week ago Paul Krugman, the Nobel Prize-winning economist argued in the New York Times magazine that economists and the modern love of mathematical macroeconomics contributed mightily to the credit crunch and recession (aka the GFC).

“As I see it, the economics profession went astray because economists, as a group, mistook beauty, clad in impressive-looking mathematics, for truth,” Krugman said.

A week later in the same paper this story appeared with no sense of irony or recognition of what he’d had written:

“In the aftermath of the great meltdown of 2008, Wall Street’s quants have been cast as the financial engineers of profit-driven innovation run amok. They, after all, invented the exotic securities that proved so troublesome.”

But the real failure, according to finance experts and economists, was in the quants’ mathematical models of risk that suggested the arcane stuff was safe.

The risk models proved myopic, they say, because they were too simple-minded. They focused mainly on figures such as the expected returns and the default risk of financial instruments. What they didn’t take into account was human behavior, specifically the potential for widespread panic. When lots of investors got too scared to buy or sell, markets seized up and the models failed.

That failure suggests new frontiers for financial engineering and risk management, including trying to model the mechanics of panic and the patterns of human behavior.

“What wasn’t recognised was the importance of a different species of risk — liquidity risk,” said Stephen Figlewski, a professor of finance at the Leonard N. Stern School of Business at New York University. “When trust in counterparties is lost, and markets freeze up so there are no prices,” he said, it “really showed how different the real world was from our models.”

In the future, experts say, models need to be opened up to accommodate more variables and more dimensions of uncertainty.

So to try and track human behaviour, American economists, mathematicians, behaviouralists and others desperate for a new high-paying gig, are going to devise new and better computer models to tell everyone what everyone is doing.

There was a recognition of the problems in the old models, but not in the concept itself.

Now that sounds suspiciously like macroeconomics, what Professor Krugman nicely skewered for trusting too much in models and theory and not trying to understand human behaviour a bit more.

“… the central cause of the profession’s failure was the desire for an all-encompassing, intellectually elegant approach that also gave economists a chance to show off their mathematical prowess,” he wrote a week ago.

“Unfortunately, this romanticised and sanitised vision of the economy led most economists to ignore all the things that can go wrong. They turned a blind eye to the limitations of human rationality that often lead to bubbles and busts; to the problems of institutions that run amok; to the imperfections of markets — especially financial markets — that can cause the economy’s operating system to undergo sudden, unpredictable crashes; and to the dangers created when regulators don’t believe in regulation.”

Now the same people claim they can improve the ability of traders to handle crisis — such as the one from which we are emerging — that they helped give us.

The failures of 2007 and 2008 where black-box trading mechanisms blew up as liquidity disappeared and the highly leveraged deals tore banks and other companies apart have morphed into so-called “edge trading” (a modern form of front running, connived at by new stock markets) and newer forms of financial exotica now being looked at to protect us from repeating collapses of the likes of Bear Stearns, Lehman Brothers and a host of hedge funds, Citigroup, AIG, HRE in Germany, Northern Rock in Britain and Allco and Babcock and Brown in Australia.

Now, according to this mostly uncritical report in The New York Times, computer programs once labelled as being “unfathomable” and therefore “black boxes” are now described as “too simple minded”.

Perhaps the author should have looked deeper: In an article in the Financial Times on August 14, 2007, about the problems Goldman Sachs found with its “black boxes” which basically blew up less than a week after the global liquidity freeze started on August 9-10.

Goldman is the most high-profile victim of the recent upset in quantitative strategies: the fund management style that uses sophisticated computer models to spot opportunities to buy and sell securities, sometimes exploiting very small price differences. The funds then use leverage in order to amplify their potential returns.

These funds, which are typically sold to institutional investors and wealthy individuals, have proved profitable in recent years. Last week, however, things began to go drastically wrong in the sub-set of quantitative funds that specialise in equities.

For reasons that are still unclear, shares began to move in ways that were the opposite of those predicted by computer models. These moves triggered selling by the funds as they attempted to cover their losses and meet margin calls from banks. This in turn exacerbated the share price movements.

“We were seeing things that were 25-standard deviation moves, several days in a row,” said David Viniar, Goldman’s chief financial officer. “There have been issues in some of the other quantitative spaces. But nothing like what we saw last week.”

Repeated large moves in share prices are supposed not to happen to the extent that the did in August 2007 and again a year ago: the maximum for the Goldman Sachs models was two standard deviations, which were said to be the most any extreme movement in markets could be at any one time.

Does anyone seriously think that simply redrafting or updating computer models, black boxes, etc, will provide protection against a new set of factors that could unhinge markets?

Models are built on past evidence to predict future movements.

Having given the appearance of not having understood the Krugman article from the week before, the story and the paper’s editors don’t realise that the most basic lesson from the crunch insofar as economic models are concerned is that once you model it, you are doomed to be destroyed by it because there will always be some variable that you don’t give enough weight to (or give too much weight to) and that imbalance gradually skews the model.

Remember the models for securitised (home) mortgage-backed securities (CDOs) were based on house prices rising (not falling) and that they were based on low correlation when in fact once home prices started falling, they spread across all of the US and that in turn infected other asset classes, damaging investor confidence, drying up liquidity and magnifying the already high levels of leverage, till everything blew apart on August 9-10, 2007, (which is when it all officially started, not a year ago when Lehman Brothers collapsed).

The point of Krugman’s article was that falling in love with an approach (or investment strategy) can blind you to changing realities: the one line summary should be “don’t put all your faith in models and markets”; be sceptical and open minded.