Tuesday, August 23, 2011

Complexity is a Cash Cow (but not for you)

“Fortuna's wheel had turned on humanity, crushing its collarbone, smashing its skull, twisting its torso, puncturing its pelvis, sorrowing its soul. Having once been so high, humanity fell so low. What had once been dedicated to the soul was now dedicated to the sale.” – from John Kennedy Toole’s A Confederacy of Dunces

Frank Partnoy, in his recent Financial Times commentary, makes the bold point that while “[most] for-profit companies are run for the benefit of shareholders … banks have been run more for the benefit of employees.”

Partnoy doesn’t delve too deeply into the basis for his claim, but he may well be alluding to the fact that traders were being financially rewarded for executing trades that brought short-term profits at the expense of long-term pain.

We have all heard about the Abacus case, where the bank was accused of siding with one client at the expense of others. (Goldman settled with the SEC for $550mm). In other cases it is argued that banks actually positioned themselves in direct opposition to their clients. Needless to say it doesn’t augur well from a long-term, shareholder value perspective for a bank to be adverse to its clients. Either the bank will suffer or its client will suffer.

From a corporate governance perspective one might argue that senior management failed to the extent its traders were not being compensated based on the long-term quality of their decisions, but rather on their short-term profits. In such a scenario, the traders would not have been incentivized, or forced, to consider the long-term benefits of strong client relationships. They would simply want to execute high margin, million dollar trades.

And hence the layering on of complexity, and the disappearance of transparency.


Complex, opaque, private trades afford broker-dealing banks numerous short-term money-making opportunities.

First up, the lack of asset transparency (inability to see through to the asset’s support) and trading transparency (inability, due to the private nature of certain markets, to follow the money or the trading levels) makes it easier for banks to get away with manufacturing prices to their advantage, or taking advantage of comparatively unsophisticated (trusting) clients.

Jim Grant (founder of Grant’s Interest Rate Observer) posited in a recent Bloomberg interview that the world we live in “is a world of fake prices and of manipulated prices.” For liquid, traded securities like municipal bonds or US Treasuries, it is understandably quite difficult to massage the numbers; but for lesser-traded, or illiquid, assets price discovery can be cumbersome if not impossible, making price manipulation all the more feasible.

In Michael Lewis’ The Big Short, Scion Capital’s Michael Burry warns that “[whatever] the banks’ net position was would determine the mark,” and that “I don’t think they were looking to the market for their marks. I think they were looking to their needs.”

The lack of transparency, too, is entirely convenient to banks in the know: it creates numerous opportunities to profit at the expense of those with less information. We call this imbalance an "informational asymmetry." It may be very difficult to sell Apple stock at an above-market price to even the least sophisticated of investors: they can readily tell that the security ought to be valued lower. But when the security is complex and privately traded, and when the comparatively unsophisticated investors do not have the market know-how or savvy to model the deals, it can be much easier for a bank to "pull one over" on them. The Fed ponders the severity of this very advantage in its aptly titled report "Could Asymmetric Information Alone Have Caused the Collapse of Private-Label Securitization?"

Complexity also undermines the potential for investigative journalism (they cannot get access to the data or make a complex deal sound too interesting) and, more importantly, the ability for regulators to oversee the markets they regulate. The IMF in 2006 warned that “[while] structured credit products provide a wealth of market information, there remains a paucity of data available for public authorities to more quantitatively assess the degree of risk reduction among banks and to monitor where credit risk has gone.”

Investors would do well to acknowledge the incongruent incentives banks may have to add their complexity to their products. But as buyers, complex deals can be difficult – and expensive – to analyze, and cumbersome if not impossible to trade (out of) during times of heightened volatility.

Investors can push back when offered complex deals that don’t meet their interests – and they can strive to ensure that their rights to high quality information and transparent disclosures are upheld.

Complexity allows for high margin trades that elicit high profits, but sometimes on terms that are not commercially reasonable. And in times of high volatility, they tend to be accompanied by high bid-offer spreads. As always, it’s buyer beware.

Wednesday, August 10, 2011

The Downgrade Cometh

The concept of conflicted opinions is dear to us. When a conflict is more than a conflict it has the power to put the conflicted party in a false position; the conflict doesn’t act simply as a distracting factor which may throw doubt upon the opinions rendered – but it brings with it an uncanny ability to intrude on the precision of the supporting analysis, and the delicacy with which it is handled.

We have written before of potential conflicts inherent in the ratings model; today our guest author Marc Joffe brings the conflicts question closer to home in the context of S&P’s much debated US downgrade. While at PF2 we’re agnostic on the value added by the downgrade at this late stage, we are happy to host his views to encourage meaningful debate around the importance and implementation of sovereign ratings – and we welcome your comments.

The Downgrade Cometh

By Marc Joffe*

S&P took the right action last Friday. The timing may have been unfortunate. And while Treasury’s announcement of a calculation error was embarrassing, the fact is that $2 trillion is little more than a rounding error in relation to the $211 trillion overall fiscal gap calculated by Lawrence Kotlikoff. But rather than crucifying S&P, the media should be asking other rating agencies what numerical analysis they have done – if any – to justify their decision to maintain status quo.

The persistence of the US AAA rating in the face of high debt ratios and political paralysis was becoming an embarrassment for the rating industry. The ratings disconnect reminded one of the Enron saga, or the subprime misratings, which were sorely lacking in any intellectual basis. Let’s hope that the S&P downgrade begins the long march back to credibility.

Faulty ratings should be viewed in a much larger context of biased equity research during the internet bubble (Who is Jack Grubman?), Arthur Anderson’s failure to effectively audit Enron and more recently the phony appraisals and poor underwriting practices that triggered the foreclosure crisis.

Credit evaluation (whether by lenders or rating agencies), equity analysis, auditing and appraising are intimately related. All four of these disciplines affect the flow of capital, and professionals working in these fields are torn between cutting corners and ignoring professional protocol when conducting their analysis. Those of us who work in these professions face a stark choice: either (1) submit to management, public or client pressure to do a lousy job, or (2) perform thorough, unimpeded analyses that produces the most objective answer. If too many take the first option, funds are mismanaged, investors lose money and savings are curtailed. After all, if you can’t trust financial statements or credit analyses, what confidence can one have in her investment? A society in which the first choice dominates rapidly degenerates from a transparent advanced economy to a corrupt banana republic. This is the risk faced by the US today.

I see in the S&P Sovereign Group’s rating announcement that they had the courage to say what many of us already knew, and were willing to face public criticism when no money was on the line in the name of professional integrity. S&P’s action should remind all of us who assess debt, assets and financial reports to summon up the courage to speak the truth on a regular basis. The short term consequences may be difficult, but the benefits of an appropriate analysis include a clear conscience and a good night’s sleep. If all of us maintain our standards, we can create stronger financial markets and a healthier society.

* Marc Joffe (joffemd@yahoo.com) is a consultant in the credit assessment field. He previously worked as a Senior Director at Moody’s Analytics. This article reflects his personal opinion of sovereign rating practice. Although previously employed by Moody’s Analytics, the author no longer works at Moody’s and, when he did work there, his area of professional responsibility was software development and data collection. He had no professional experience as a ratings analyst, and no knowledge of Moody’s ratings practices beyond what is in the public record.

Tuesday, August 2, 2011

A New Approach to Sovereign Ratings

The debt ceiling debacle has rejuvenated the discussion about the adequacy and effectiveness of sovereign credit ratings, as they're currently being provided.

We remain agnostic on the value added of a US downgrade today – does anybody really benefit from a downgrade at this stage? The objective of a downgrade watch notification, or a downgrade itself, is to encourage governmental concern, which precipitates action. We already have international concern. A downgrade at this stage seems only to cause further turmoil.

We are additionally skeptical of the adequacy of sovereign ratings being provided today: we wonder at the raters' ability to determine what they refer to as “willingness to pay.” Are they any better than anybody else at estimating this highly subjective measure? And if so, why is it they cannot provide to users the separate outcomes of each of these two components of the credit rating - the ability to pay and the willingness?** (Note also distinguished researcher Arturo Cifuentes’ timely suggestion of a third component: permission to pay.)

Sadly it seems a crisis has to occur before we resolve flawed systems. Since we believe sovereign ratings ought to be more consistently applied and more transparent, if worthwhile at all, we are happy to host guest author Marc Joffe's posts. We hope his views continue to encourage meaningful debate around the importance and implementation of sovereign ratings, and we welcome your comments.

A New Approach to Sovereign Ratings

By Marc Joffe*

A couple of my friends in the Moody’s diaspora have argued that rating agencies should not assign sovereign ratings due to difficulties in managing conflicts. I disagree for a couple of reasons. First, to the best of my knowledge, sovereign ratings have performed fairly well over the past few decades. While rating changes could have been faster, I have not seen evidence of systematic bias – except perhaps in the maintenance of the home country AAA rating.

Second, sovereign ratings provide a type of government accountability not unlike that offered by an independent press. For this reason, I am actually sympathetic to the idea that ratings warrant First Amendment protection. The best sovereign rating analysis is politically aware without being politically opinionated. As S&P and Moody’s have repeatedly stated during the current debt ceiling debate, they have no view about which fiscal measures should be employed to adjust the nation’s fiscal trajectory, they just believe that inaction or minimal action is no longer consistent with the highest rating.

Although the prevailing sovereign rating methodology may not actually display bias, it is certainly being subjected to accusations of bias, especially in Europe. Also, the present qualitative approach necessitates delays in rating changes unless the rating group is heavily staffed. In last week’s Congressional testimony, S&P President Deven Sharma said that his agency’s sovereign group consists of roughly 100 analysts rating 126 countries – less than 1 full time equivalent per issuer.

Thus, the biggest opportunity for improvement in sovereign rating performance is the application of transparent, quantitative modeling technology fueled by frequently updated data for exogenous variables. Model driven ratings can be calculated daily, weekly or monthly and then reviewed by analysts prior to release. And models don’t worry about being criticized for a lack of patriotism or for affecting interest rates.

Quantitative credit assessment techniques have been successfully applied to consumers, public firms and private firms. Bloomberg and Morningstar have already implemented ratings based on Merton-style public firm models first popularized by KMV Corporation, while RapidRatings uses financial statement data to automatically rate both public and private firms.[1]

Sovereign risk modeling is newer and less developed. The most interesting work I have seen in this area has come from Nouriel Roubini and colleagues, from Dale Gray and colleagues, and from Kamakura Corporation (full disclosure: Kamakura is a client and also has a public firm model). These efforts are generally focused on emerging market issuers, since that is where just about all recent default observations are available. Data for advanced economy sovereign defaults (or, more precisely, defaults by nations that went on to become advanced economies) is older, hard to gather and may require restatement to be meaningful in modern terms. Reinhart and Rogoff have assembled this data for their book and related IMF papers, lowering the data collection barrier.

I propose a different modeling approach for advanced economies that focuses on their primary risk factor: the impact of population aging on social insurance spending. The approach leverages the wealth of budget, economic and demographic data and forecasts available for these countries. While the remainder of the discussion focuses on the US, it should be equally applicable to major European economies.

In the US, the Congressional Budget Office and other government agencies (including OMB and GAO), periodically issue long term budget forecasts. Typical lengths of these forecasts are 10 and 75 years. For Treasury investors, the longest relevant time horizon is 30 years, which also happens to be the length of the longest term macroeconomic forecasts provided by IHS Global Insight and Moody’s Analytics.

The CBO forecasts include projected Debt-to-GDP ratios. It is also possible to derive Interest Expense to Revenue ratios from the CBO outlooks. This latter ratio may be more predictive of default than Debt-to-GDP because it embeds information about the government’s ability to tax economic activity and the level of its Treasury interest rates. (This ratio effectively ignores principal repayment schedules – an exclusion that could be justified by the assumption of continued liquidity and absence of rollover risk for advanced economy sovereign debt.)

Consequently, the CBO data provides expectations for key ratios at the maturity date of long term Treasuries. These expectations are based on policy and macroeconomic forecasts. If different policy and macroeconomic parameters are used, different projected ratios can be generated. If we provide a range of possible scenarios to a Monte Carlo simulation engine, we could generate a distribution of ratio outcomes.

Next, we can use historical data to identify ratios that are associated with default. A nice property of interest expense to government revenue is that, with rare and extremely idiosyncratic exceptions, it is always in the range of zero (absolute certainty of non-default) to 100% (absolute certainty of default). This relationship prevails across all countries and through time. For purposes of this discussion, let’s assume that an interest expense to revenue ratio of 30% is determined to be a reasonable default point. This would mean that a government would be unwilling to pay interest beyond this threshold, because the political pain associated with further crowding out other kinds of spending exceeds that stemming from a default. Admittedly limited post-Reconstruction experience in the US suggests that the critical value of this ratio, i.e. the default point, is 30% (see my earlier blog post entitled Correction: The US Has Defaulted Before and it Can Default Again).

With a distribution of ratio realizations and critical point both in hand, rating the issuer is then simply a matter of calculating the proportion of the distribution beyond the default point. If this proportion is very low (perhaps 0.25% for a thirty year Treasury), the issuer is AAA. If the proportion of “default-indicative” realizations is higher, then a lower rating is appropriate.

What would such a model conclude about the US? Since the model only exists in the form of the rough outline above, I can’t be certain – but I have a pretty good idea. In April, I calculated a projected interest expense to revenue ratio based on an adjusted version of the June 2010 CBO Long Term outlook. The adjustments mostly reflected some inputs from CBO’s more recent March 2011 10-year forecast. This calculation yielded a ratio of 37.82% in FY 2041. (Although the debt ceiling deal lowers this figure somewhat, CBO may also have to make an offsetting adjustment due to the disappointing GDP numbers published last week). Assuming that the 30% ratio is indeed the default point, the implied default probability is quite substantial.

This rough calculation is based on a number of assumptions, most important of which is that the CBO forecast provides a reasonable expected value. While CBO’s economic forecasts are well within the mainstream, CBO more controversially assumes no substantive policy change. If major tax increases or entitlement reforms are implemented, the expected ratio could be far lower. But the recent debt ceiling debate has shown just how difficult major policy change is in an environment of divided government and party polarization (the concept of Congressional polarization can be quantified as Political Scientist Keith Poole regularly does at VoteView).

Contrary to what we hear from politicians and the media, I do not see much reason to expect this situation to be resolved by the 2012 election. The likelihood that divided government will continue can be estimated by consulting political markets, such as InTrade, which reflects the expectation that Obama will be President and that the Republicans will control the House and Senate in 2013. Also, the further we get into the cycle of baby boomer retirements, which started in 2008, the more difficult entitlement changes will become given the economic inflexibility and heightened voting participation of seniors.

One assumption in the CBO projections that could be questioned is the reversion of interest rates to modern historical averages over the next few years. If, instead, the US has entered a period of sustained low interest rates a la Japan, the terminal interest expense to revenue ratio would be far lower.

Regardless of which interest rate, policy or growth assumptions are used, the simulation model outlined above provides a formal way of evaluating the implications of a range of political and economic scenarios for sovereign creditors. Further, if rating agencies make the simulation model and their assumptions publicly available, investors could substitute their own exogenous variables and form their own conclusions. The benefit is that evaluations of US and other advanced sovereign credits become more rigorous and more transparent. Quantitative approaches do not guarantee objectivity because the choice of assumptions can itself be biased, but any bias and its effect on the rating becomes more apparent and much easier to address.

[1] Application of models to structured products has proven more controversial, but I would argue that the problems of 2007-2008 are not an indictment of quantitative assessment in general. Instead, the crisis is an indictment of the specific modeling procedures and assumptions employed. Just because models based on Gaussian Copulas failed to adequately weight tail risk does not mean that models relying on more empirically appropriate distributions will not work. And just because RMBS models failed to consider negative Housing Price Appreciation in 2006 is not evidence that models that included a proper set of HPA scenarios would not have been effective.

* Marc Joffe (joffemd@yahoo.com) is a consultant in the credit assessment field. He previously worked as a Senior Director at Moody’s Analytics. This article reflects his personal opinion of sovereign rating practice. Although previously employed by Moody’s Analytics, the author no longer works at Moody’s and, when he did work there, his area of professional responsibility was software development and data collection. He had no professional experience as a ratings analyst, and no knowledge of Moody’s ratings practices beyond what is in the public record.

** It is worthwhile to note that rating agency BMI, though not an SEC-licenced NRSRO, divulges each of the two components separately in its rating analysis.