19 January 2014

Stockholm Syndrome [update]

Shiller gets to go to Stockholm and bloviate, while I'm stuck here in Arctic New England. Life ain't fair. At least, he's provided a precis of the proceedings. And, in the process, elevated my view of Left Wingnuts.

There are two major points: first, quants mostly get it wrong; and second, homo economicus is a myth. The first occurs because the second is true.
Does it make sense to suppose that economic decisions or market prices can be modeled in the precise way that mathematical economists have traditionally favored? Or is there some emotionality in all of us that defies such modeling?

Of course. The field was originally named 'Political Economics', and there's little in human existence that's more attached to emotion than politics. How else to explain Kansas? All those gun loving, God fearing folks voting for the corporate manipulators eager to keep them poor. Well, except for those few who get fat farm subsidizes. Not that such moolah is welfare, of course.
It is hard to sum up all this discussion, however, because of a basic problem: defining "rational."

Aye, matey, thar's the rub. A frequent quote from the housing bubble amounted to: "we all have to keep dancing as long as the band plays; all the others are dancing". Another way to put (from J.K. Galbraith, variously phrased) it: "financial genius is a rising market". Thus Li was able to foist a mechanistic formula on human behavior, which is to say, where they make the rules rather than Mother Nature. The ultimate basis of quants is that the rules are, more or less, stable and *not under the control of the analyst*. Such analysis works well with microarray data, for example. This is the crux of Shiller's point, too.

He closes:
The question is not simply whether people are rational. It's about how best to describe their complex behavior. A broader notion of irrationality may someday be reconciled with one of rationality, and account for actual human behavior. My bet is that real progress will come from outside economics -- from other social sciences, and even from information sciences and computer engineering.

In other words, don't assume that one can model human decisions using only "normal" data. If policy (those musical chairs during the housing bubble) trumps data whenever enough people decide that the policy is more fun, then data loses. The best that financial quants can hope to do is front run changes in money flows. Which is not to assert that data is useless in bubbles; those that noted the massive unsticking of house prices and incomes made money going up and coming down. But not so many. And that means Joe Sixpack with his PC and standard data and R doesn't stand much of a chance to beat the pros with their HPC machines and proprietary data. You're better off tracking Briefing.com, with an eye to spotting grey swans; watch for events.

I've spent the last few hours looking for more views on the subject, and found this one. Very interesting. Could have been written by me (well, in a dream, may be).

From page 20:
The very complexity of the mathematics used to measure and manage risk, moreover, made it increasingly difficult for top management and boards to assess and exercise judgement over the risks being taken. Mathematical sophistication ended up not containing risk, but providing false assurance that other prima facie indicators of increasing risk (e.g. rapid credit extension and balance sheet growth) could be safely ignored.

And, from page 24 (the punch line, in the gut):
New generations of students will have to use the tools and techniques of QRM [quantitative risk management] wisely in a world where the rules of the game will have been changed.

No comments: