mathbabe

From my experience, there are only a few basic kinds of trading models encountered on Wall Street.  These are:

• chasing dumb money, which I’ve described already,
• asymmetrical information, which I want to talk about today,
• market-making,
• providing “insurance”,
• seasonality, which I’ve touched on, and
• taking advantage of macroeconomic misalignment (think Soros’s pound trade)

The concept of asymmetrical information is incredibly simple:  I know more than you so I can make a more informed assessment of the value of some underlying contract.  This could mean I know inside information about a company and trade before the announcement (illegal but common), or that I know the likelihood of bankruptcy for a company is higher than the market seems to think, or that the underlying mortgages of a packaged security are likely to default.

I could go on, and probably will in another post, but I’d like to make a very basic point, which is this: a lot of money is made every day via asymmetrical information, and in particular there’s a major motivation to obfuscate data in order to create asymmetry.  One of the missions of this blog is to uncover and expose major, unreasonable examples of obfuscated information that I know about.

At this point it’s critical to differentiate between two things which typically get confused by non-nerds.  Namely, the difference between a technical but thorough explanation and true information obfuscation.  A technical explanation, if thorough, can be worked through eventually by someone with enough expertise, or someone who is motivated enough to get that expertise, whereas true information obfuscation just doesn’t provide enough details to really know anything.

The worst is when you are given pretty specific technical information, but which only explains half of the story.  This leads to an imprecise false sense of security, which I suspect underlies most of the very large mistakes we’ve seen in finance in the last few years.

For example, let’s talk about the bank stress tests in the United States in 2009. They were conducted in two distinct phases. In the first, a bunch of economists were asked to write down two scenarios.  The first was kind of a prediction of how 2009 and 2010 would play out, and the second was a more negative scenario.  Okay so far, even though economists aren’t all that pessimistic as people (more on this on another post).  The scenarios were averaged in some way and then publicly posted.  The good news is, if you thought the scenarios were unrealistic, you’d at least know how to complain about them.  The bad news is that they are pretty vague, only really specifying the GDP growth and the unemployment rate.

In the second phase, the banks were allowed to predict the impact of those two scenarios on their portfolios using their own internal models, which were not made public.  Here’s the white paper if you don’t believe me. So, in the name of asymmetrical information, why is this a problem?  Here are a few reasons:

• Banks had bad internal risk models
• Banks had clear motivation to mark their portfolios to their advantage
• The fact that their methods weren’t made public gives them ample cover to do whatever they wanted

There are two reasons I say that banks had bad internal risk models. The first reason is the one you know about already- they evidently bought a whole bunch of toxic securities leading up to 2008 and seemed to have no idea about the risks. But moreover, my personal experience working in the risk field is that banks used external risk modeling companies as a rubber stamp, essentially to placate those worrywarts who insisted on obsessing about risks. To be more precise without getting anyone into trouble, it was commonplace for banks to not notice when a model at a risk software company had very basic problems and would spit out nonsensical numbers.  It was almost as if you couldn’t trust the banks to look at their risk numbers at all.  This isn’t true of every bank at all times, but as a general rule when models had major problems it was hedge funds, not banks, who would bring attention to those problems.  Moreover, the banks did not seem to have internal risk modeling across their desks.  In other words, a trading desk which trades a certain kind of instrument may have some risk monitoring in place (mostly to bound the amount of trading of that type), but when it comes to understanding systemic risk across instrument types, the external risk companies were the source.

It is obvious that banks were motivated to mark their portfolios to their advantage.  The ultimate result of bank stress tests were possible additional capital requirements, which they clearly wanted to avoid.  This temptation meant it would benefit them to make every assumption of their risk model liberal to their cause.

Finally, they didn’t expose their methods- not even to explain in general terms how they dealt with, say, interest rate risks across instrument types.  This meant that only the Fed people involved got to decide how honest the banks were.  This is the opposite of what is needed in this situation.  There is no reasonable need to keep these methodologies secret from the general public, since it is we who are on the hook if their methods are flawed, as we have seen.

Here’s where I admit that it’s actually really hard to come up with good methodologies to measure impact of vague GDP growth and unemployment estimates. But that admission is only going to add to my rant, because my overall point is that the instruments themselves have been created to make that hard.  They are examples, especially tranched mortgage-backed securities but others as well, of intentional obfuscation for the sake of creating asymmetrical information. Instead of living in a world where banks who own things like this are allowed to measure them at their whim, and benefit from that obfuscation, we need to create a system where they are penalized for having illiquid or complex instruments.

And here’s where I admit that I’m not an expert on all of these instruments – some would say I don’t have the right to talk about how they should be assessed. Yet again, I choose to use that fact to add to my rant: if, after working for four years in finance as a quant at a hedge fund and then a researcher and account manager at a risk company, I can’t have an opinion about how to assess risk, then the system is too freaking complicated.