The SEC v. Goldman Sachs

The civil suit filed by the Securities and Exchange Commission against Goldman Sachs revolves around a combination of three patterns we have seen recently:

(1) the ability of external parties to adversely influence the portfolios that support securitized vehicles (see for example the Magnetar trade or the TPG trade);

(2) the harmful effects of the negative basis trade (according to the New York Times article, seven of Goldman’s Abacus deals were wrapped by AIG); and consequently

(3) the capacity for ratings to be “gamed.”

Ultimately, the complexity and opacity of the structured finance product make it susceptible to abuse by poorly incentivized parties. Securitization is abounding with conflicts of interest while informational asymmetries, resulting in various forms of moral hazard, proliferate.

But none of these problems would exist if ratings were always perfect: the negative basis trade would never have existed and the Abacus deal’s reportedly poorly-selected portfolio would never have received the ratings it was able to achieve.

And so we must deal with the very serious business of underwriters “gaming” the ratings system, which jeopardizes both the accuracy and the integrity of ratings and the ratings process.

But first we need to understand the observer effect.

The Observer Effect

In the social sciences any framework or methodology is subject to what is called the observer effect. The observer effect deals broadly with the process by which subjects alter their behavioral patterns on becoming aware of a test’s objectives.

Consequently, behavior induced by the methodology can be different from that on which the historical data was based. The subjects’ response varies based on the incentives of the subjects and the potential complexity of the underlying product.

The problems for ratings become most apparent when the incentives of the subjects (e.g. mortgage bankers) vary significantly from those of the creators of the methodology (i.e. rating agencies). The assumption, or hope, that models and careful study of the historical data can overcome the misalignment of incentives has been shown to be a fallacy: poorly incentivized market participants were able to create products and scenarios never contemplated by historical analysis.

How? Suppose we have a duopoly of rating agencies X and Y. Rating agency X decides that according to its internal calculations, it will more heavily scrutinize a particular quantitative factor – the borrower’s FICO score – when considering the quality of a mortgage for the purposes of its ratings methodology. Rating agency Y, however, discloses that its methodology predominantly hinges on the loan-to-value (LTV) ratio of each mortgage.

As an originator of mortgage loans (or structurer of RMBS securities) if you have a package of loans possessing high FICO scores, capitalistic tendencies will encourage you to approach rating agency X for your rating.

This is known as ratings arbitrage, or “gaming the system.” A more realistic scenario may be that once rating agency X publicly discloses that it considers FICO to be the key driver of mortgage performance, mortgage originators or structurers, will seek to put together a bunch of comparatively cheap mortgages representing high FICO score borrowers but with very poor other qualities and bundle those together in an RMBS to be rated by rating agency X. Originators might specifically target high-FICO individuals, knowing they’ll be able to off-load these mortgages by way of an RMBS to be rated by rating agency X, almost irrespective of the other qualities pertaining to the borrower (e.g. salary) or the mortgage itself (e.g. documentation level). While FICO score may have originally been a key driver of mortgage performance, all else equal, these high-FICO pools now significantly underperform.

Thus, due to what we call customization — or active adverse selection – rating agency X’s RMBS analysis turns out to have been inaccurate or compromised. (The “problem” of customization is not limited to the selection of the portfolio, but may include adverse selection of the securitized vehicle itself, or counterparties to the structure. Like the product itself, the problem is multidimensional.)

If the rating agencies continue to publicly disclose their procedures they will need to be increasingly adaptive and accurate in our computationally-intensive market, lest their rating models be otherwise gamed. They will have to be nimble and swift, like investment banks: they will need to be everything they currently are not.

The regulatory drive towards creating multiple rating agencies presumes that increased competition encourages higher standards. Niels Bohr noted that “the opposite of a great truth is also true.” In this situation, perhaps the flip-side is a greater truth: that as far as creating rating agencies goes, less is more.

Ratings competition was a key driver in the decline in standards, with rating agencies competing on ratings for market share: higher ratings translate into increased market share, which is crucial for publicly-traded companies.

The more models and options available, the easier it is to arbitrage the system, finding the least conservative rating agency to rate each particular pool of assets.