science

The central finding is this: people who win large amounts are just as likely to end up bankrupt as people who win small amounts. People who win a large amount, $50,000 to $150,000, have a lower bankruptcy rate immediately after winning but a higher bankruptcy rate a few years later so the 5-year bankruptcy rate for the big winners is no lower than for the small winners.

Amazingly, by the time the big winners do go bankrupt their assets and debts are not significantly different from those of the small winners. The big winners who ended up bankrupt could have paid off all of their debts but chose not to.

Here’s the link.

Yikes. Read a conservative-type person this and prime them into ‘far’ mode by linking this to income transfers, entitlement spending and welfare receipts. I dare you.

What’s the sympathetic view? Professional athletes that are too trusting and get taken advantage of? Mike Tyson?

A bit harder to pull off.

That’s my metaphor for describing economic growth theory.

So what’s the problem? Not enough reliable data.

Let’s say there are millions of interdependent variables that contribute to the economic growth (and measurement of even these is often royally screwed up).  But you have only hundreds of observations in which to evaluate whether a particular combination of circumstances worked. Is that science? No way.

And yet, all this whining is no substitute for plausible answers is it. We want to know how to grow!

So bring on the smorgasboard of cognitive bias.

Easterly and Roberts focus on a few of these biases, for instance leadership bias (ie looking for heroes bias), which is one I particularly dislike. They link this to the technocrat’s fantasy: “imagine you were the sole adviser of an absolute dictator. What good you could do with that power!”

The bottom line from E&R’s discussion is that autocracies produce a greater variance of growth outcomes. One interesting observation was that variance is not just between leadership regimes, but within the same regime. So yesterday’s stars are tomorrow’s dogs when the growth ‘miracle’ inevitably falters.

And it’s not really a surprise: what does a dictator care about most? Staying in power (the minute he leaves he’s either going to the Hague or the hangman). Is economic growth the best way of doing this? No, doing favors for the military is the best way of doing this.

Anyway, the entire field is a technocratic aphrodisiac. You can tell how exciting the prospect of having “the answer” is when the titans of policy commentary get super upset with their lack of influence.

But limiting an individuals’ influence is the whole point. If you’ve really invented a telescope, everyone will start using it on their own.

That is a paraphrasing of the title of an article in an issue of CFA magazine I found around my apartment. It caught my attention. Particularly because I’m inclined to think anything that makes that sort of claim is either BS or dangerously misleading.

So here’s the mechanism to the ‘omega-score’:

1. Correlate returns data to ‘operational issues’ (“These include criminal, civil, and regulatory issues.”) disclosed in a regulatory filing *some* hedge funds have to make.
2. Establish that funds with these issues perform poorly. No surprise. They use something called ‘canonical correlation‘, which assigns weights to a set of variables (‘operational issues’) and tests their weighted effect on returns. Without reading the paper, it’s tough know what the ‘omega-score’ is, but it’s probably some kind of representation of the correlation.
3. Now find some data that is publicly disclosed by all hedge funds. These are the new variables.
4. Test various correlations that match the profile of the #2 variables. This is how we set some kind of statistical equivalence between the variables we know have an effect on the returns (from #1) and those we suppose have an effect (the ones that are always disclosed).

That ‘canonical correlation’ thing is a clever technique that I don’t know much about. One thing to watch out for is that we’re talking about establishing a statistical artifact that has no direct intuitive basis.

In other words, there’s a difference between saying “hey, I think that these two or three variables will have an effect on returns” and saying “hey, these variables might be related to these other variables that probably have an effect on returns”. Gets messy.

And the focus is a bit confusing. What we’re really doing is correlating ‘operational issues’ with variables that heretofore had not been associated with that kind of nastiness. If I’m right that that’s what we’re doing, I’d have two questions:

• Why do we need to do this for each fund individually? If variable x is associated with bad behavior at one fund, wouldn’t it be similarly associated at another?
• Aren’t the findings of the correlation study interesting in of themselves? If you can get better at figuring out which funds are in legal trouble, why risk building a statistical white elephant trying to link it to returns results?

Anyway, really interesting stuff. This kind of study demonstrates the power of even modest bits of information if they’re consistently and universally disclosed.