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.

Complexity

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.

An Aversion to Mean Reversion

Last Wednesday’s Financial Times hosted a scathing column by Luke Johnson, which questions the usefulness of economists, as a whole (see “The dismal science is bereft of good ideas.”)

The column’s title is misleading: Johnson focuses his frustrations only on economists – not economics. Importantly, it is the application of the science, not the science itself, which seems to have caused Johnson's concern.

Indeed the purity of all mathematical sciences can be spoiled by its application. Johnson comments that he “[fails] to see the point of professional economists,” that economists “pronounce on capitalism for a living, yet do not participate in private enterprise, which is its underlying engine.” He ends off his piece by prescribing “[the] best move for the world’s economists would be to each start their own business. Then they would experience at first hand the challenges of capitalism on the front line.”

To be fair, the direct application of economic theory was never intended to satisfy the depths of the dynamic puzzle we put before them, a puzzle for which the answer lay not in the data but in the incentives. [1]

We cannot pretend not to have known that economic models work best in reductionist environments, and that the introduction of complications (like off-balance sheet derivatives) tend to reduce the effectiveness of economic models. Conceptually, once models start to consider too many inter-related variables, or degrees of freedom as statisticians call them, they become so rich and sensitive that no empirical observation can either support or refute them. And so any failures of economists to spot the housing bubble or predict the credit crisis, as Johnson mentions, become our failures too. We would have done better to equip our economists (or academics) with the tools necessary to perform the “down and dirty” analyses that take into account the complex and changing nature of our economy. [2]

Seeing no reason why they ought to have succeeded, we’re perhaps a little more forgiving (than Mr. Johnson) of economists’ shortcomings. But we share his concerns that mathematical sciences are being too directly applied, that the practices and the incentives are being largely ignored.

On Endlessly Assuming “Mean Reversion”
Rather, we ought to encourage our researchers to go into the proverbial field – and to learn to think, and study dynamics, differently.

We can no longer allow ourselves to be informed purely by static analyses of historical data and trends, without seeking a keener appreciation for the underlying dynamics at play. The lazy assumption of mean reversion is simply an assumption, not a rule. When the fundamentals are out of whack – and a direct analysis of data alone cannot tell you that – the market can and will act very differently from a mean-reverting economic model.

Thinking Differently

Given that many market participants have emotions (one could argue that computer algorithms are to an extent emotionless), the tendency for panic or at least the capacity for panic ought to make the direct application of mean reversion models less appealing – and their results less informative, predictive or meaningful.

Ask not “is this a buying opportunity” based on a simple historical trend. But what are the underlying fundamentals? If the game changed based on underlying issues, have they been resolved? Or were they underestimated or overestimated. If the latter is determined after sufficient exploration, one could recommend a "buy." If the former, initiate a "sell." To do otherwise - to simply present a graph and suggest an idea, is folly – it's simply a guess.


Mean reverting economic forecast models continue to be constructed to this day without the thought necessary to support their assumptions (despite the realization that we’re in a very different world).

The outputs, unfortunately, are never better than the inputs.


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[1] See our earlier commentary “The Data Reside in the Field”

[2] In light of this fact, it is perhaps troublesome that when questioned by JP Morgan CEO Jamie Dimon as to the extent of the government's investigation of the effect of its banking regulations, Bernanke purportedly responded "has anybody done a comprehensive analysis of the impact on -- on credit? I can't pretend that anybody really has," ... "You know, it's -- it's just too complicated. We don't really have the quantitative tools to do that." Source

The Data Reside in the Field

In the New York Times' “Needed: A Clearer Crystal Ball” (Sunday Business pg. 4) professor Robert Shiller claims that if we sharpen our risk measurement tools we will better understand the risk of another financial shock. He argues that improved data collection can substantially increase the predictive power of our financial models.

As mathematicians we must agree with his sentiment: more complete data is always useful. But as market participants we wonder if professor Shiller has missed out on a valuable lesson to be learnt from this financial crisis.

One key lesson was that the source of the failure was neither data-driven nor model-driven, but rather a direct result of the expected behavior of poorly incentivized parties.

In fact, one could argue that for the large part the data were comprehensive. The models were highly sophisticated, perhaps too sophisticated. But what caused the crisis was that originating parties were financially rewarded for structuring and selling low quality mortgage loans. The incentive was clear and by mid-2005 the FBI was already commenting on the pervasive and growing nature of mortgage fraud.

Misrepresented financial documentation skews the data and cannot be spotted simply by poring over ever more abundant realms of data: you have to go into the field itself to follow the incentives.

The financial downturn was made worse by financial institutions’ lack of confidence in the creditworthiness of their counterparts. Absent a level of certainty as to the true nature of others’ balance sheets, a lending freeze precipitated an illiquidity crisis. But a more thorough examination of data won’t tell you what resides off-balance sheet: you have to understand the prevailing accounting environment (that led to the mechanical reproduction of the negative basis trade) and the fundamental nature of the opaque shadow banking system.

It is a concern that intellectuals and academics risk being lulled into a false sense of security based on their access to copious amounts of statistical data.

Copious analysis of imperfect data is unlikely, alone, to help our regulators hone in on an inevitable crisis – much less prevent it. Let's not strive to build complex economic models whose success hinges on sensitive data. Let us rather encourage a keener appreciation of the limitations of data, the intentional proliferation of informational asymmetries, and the incentives that can cause a meltdown.

Risk - Discombobulated

Investing brings with it many risks. When things go wrong, they often tend to go wrong in concert: credit risk and market risk and illiquidity risk and complexity and legal and operational risks can all be confused and are often indistinguishable, especially when they need to be realized.

Certain rating agencies have specific ratings for these "non-credit risk" measures. One can get a liquidity rating, a market risk rating, even a trustee quality rating or a hedge fund operational quality rating.

But credit risk doesn’t exist and typically isn’t measured in a vacuum – at least not according to recent ratings criteria. When a product is exposed to operational risk, for example, failures in operational quality can bring down the credit rating itself, making it very difficult for an investor to separate the different risks being measured.

For example, Moody’s today announced that its soon-to-be-released operational risk guidelines could result in rating actions (primarily downgrades from the sounds of it) on the senior ratings of up to 200 structured finance ratings.

"The performance of a securitisation transaction depends not only on the creditworthiness of the underlying pool of obligors, i.e. the quality of the collateral, but is also closely linked to the operational performance of various transaction parties such as the servicer, trustee and cash manager"

With investors looking increasingly to non-traditional investments for heightened returns, and with banks pushing risky activities into the opaque shadow banking system (to minimize regulation and oversight), it’s high time we all started to manage our risks out of one centralized division. That way credit risk doesn’t escape the market risk team, and funding risks don’t escape the credit guys.

Corporate Governance for the Shareholders (Part 1)

2010 and the Dodd-Frank Act ("DFA") brought to the fore Say-on-Pay and certain other delights for those investing in shares of financial institutions.

DFA enhances the SEC's enforcement abilities, while creating an additional watchdog (the Consumer Financial Protection Bureau) which has both examination and enforcement capabilities. It also demands that both companies and regulators reduce their dependence on credit ratings: over-reliance on credit ratings served to exacerbate the depth of the financial crisis, as rating downgrades precipitated further pricing pressures.

Indeed, in our experience several regulatory bodies have approached the new regulatory landscape with a zest and energy that was perhaps absent in the years leading up to the crisis.

Having said that, many critics feel that financial reform measures fell short; some are critical of the regulators' enforcement intent (see here and here), especially as they experience budget constraints; others are skeptical of the newly-created FSOC's ability to even define systemic risk, never mind recognize or measure it.

What other improvements, then, can be introduced to protect against large-scale business risks at financial institutions?

Risk Must Have a Voice

We would like to see Risk have a voice. Certainly, many risk managers were very good at measuring risk. But their institutions failed anyway. Why? Often, the objectives of risk management (preservation of capital reserves) run counter to the growth objectives of the CEO, who is incentivized to put capital to work. One could argue that the too-big-to-fail banks are or were long risk, knowing that they had large potential short-term upside and low downside given the (implicit) government guarantee. The government or the taxpayer, in this scenario, is short risk. One option is to ensure that the chief risk officer reports directly to the board, rather than to the CEO. Again, if the CEO is the chairman of the board, risk's voice may be dampened and this may provide a warning sign.

Risk and Compliance Must be Independent

Similarly, it is crucial that risk managers and compliance officers are incentivized, and safe, to voice their concerns. As a cost center with relatively limited bonus potential, shareholders ought to recognize that "at-will" risk and compliance managers -- especially if they are (intentionally) over-paid -- often have little advantage for being right but significant downside for being wrong. (Click here for an example of objections ending poorly for "at will" employees.)

-End Part 1

We will be exploring further avenues for improvement in subsequent pieces of this series, including a discussion of management's communication of its risk appetite. If you have any corporate governance suggestions you would like us to consider or include, feel free to email them to us or leave them in the comments section below this post.

In Due Diligence We Trust

“Guildenstern: We only know what we’re told, and that’s little
enough. And for all we know it isn’t even true.

Player: For all anyone knows, nothing is. Everything has to be taken on trust; truth is only that which is taken to be true. It’s the currency of living. There may be nothing behind it, but it doesn’t make any difference so long as it is honoured. One acts on assumptions. What do you assume?”

- Tom Stoppard’s Rozencrantz and Guildenstern are Dead

As we battle a crisis among crises, the informational asymmetry between sophisticated and unsophisticated investors becomes all the more striking.

The so-called sophistication level requirements have conveniently lingered despite the increasing complexities brought on by financial innovation: the dollar amount of income or net worth for natural persons to be considered “Accredited Investors,” for example, has not been adjusted for inflation in the almost 30 years since it was originally adopted; similarly investments in auction rate securities (ARS) were initially limited to institutional investors with minimums of $250,000. In recent years the minimum level has been brought down to $25,000 in an effort to lure as many market participants as possible, and more worryingly to make ARS available to the general public.

(“Accredited Investor” is defined to include natural persons having an annual income of $200,000 -- or $300k with spouse -- over specified periods or a net worth of $1mm or more. ARS typically refers to either municipal or corporate debt securities or preferred stocks which pay interest at rates set at periodic auctions. The market for ARS, prior to the collapse of the auction market, purportedly stood at around $350 billion.)

As such, Accredit Investors have over time become far less “accredited.” More importantly, the less “accredited” investors were being allowed to purchase increasingly complex securities. Investors, not wanting to look bad, chose to trust others rather than give voice to their lack of understanding. They place their trust in their lawyers, accountants, financial advisers and brokers. And the rating agencies.

They trust these parties differently: they trust their lawyers, financial advisers and brokers to act in their best interests and to be mindful of their risks. They trust them based on their credentials, the lengthy relationship they’ve had with them in the past and the fact that they have supervisory boards (e.g., FINRA, FASB and the SEC) tasked with ensuring they behave properly. They trust the rating agencies to provide an independent, objective opinion that has no party’s ulterior motives at heart and accuracy as its only goal.

Unable to perform their internal analysis, investors increasingly had to blindly trust the rating agencies’ ratings, from both a modeling perspective, and from an unbiased credit risk opinion perspective. Less sophisticated investors tended to rely more heavily on analyses performed by the rating agencies. More sophisticated investors, like Paulson, worked day and night to take advantage of their analytical advantage, by finding and betting against those bonds whose ratings least reflected their true credit quality.

The opaque, private securitization market provided a handy tool for poorly incentivized parties to take advantage of less sophisticated parties: the immature securitization market is noted for its lack of transparency and disclosure. More recently it has been marked by its misrepresentations. Battles continue to be fought over who owns the rights to the mortgages underlying the mortgage-backed securities vehicles; the bankruptcy process for off-shore vehicles remains underdeveloped relative to the United States; off-shore legal counsel seems nowhere to be found; and the complexity of some of the special purpose entities (and the number of different interests being represented) creates havoc for our litigious society.

In sum, the securitization market was particularly susceptible to being abused. And it was abused. The SEC vs. ICP case, seems only the tip of the iceberg as we begin to examine the repercussions that come from the several incentive misalignments that are apparent throughout the securitization vehicle. We are abounding with conflicts of interest while informational asymmetries, resulting in various forms of moral hazard, proliferate.

Economy-wide, we have realized that we need to encourage, if not force, the investor to perform necessary levels of due diligence both pre and post investing in complex securities. We need to impose adequate safe-guards on these vehicles to ensure that they are not mismanaged, and that managers are incentivized to manage across the capital structures, in the spirit of the deals. We also ought to encourage regulator responsibility, and permit them the authority necessary to step in sooner – to act before the problems become insurmountable. The recent, indefatigable, examiner activity seems too little too late. But it is crucial that the standard be set.

The Carry Trade from Off-Balance-Sheet Heaven

The Citigroup-structured CDO, Adams Square Funding II, Ltd., closed in March 2007 with the $600mm Class A1 Floating Rate Notes (Due 2047) not being offered by Citi; rather the A1 notes were being insured by a swap counterparty by way of the then-convenient-and-now-infamous negative basis trade.

By insuring this A1 tranche trade (Ambac Assurance was reportedly the ultimate swap counterparty), Citi was able to lock in substantial up-front “profits” on the trade in addition to their significant underwriting fee. FASB’s accounting regime (1) enabled the so-called profits on the trade to be recognized immediately, by way of “sale accounting,” and (2) allowed the trade to disappear into off-balance-sheet oblivion, away from Citigroup shareholder verification.

The negative basis trade was perpetuated by several banks for many reasons, as described more comprehensively here. The forthcoming chart provides what we believe to be a thorough breakdown of the minimum estimated up-front profit -- of approximately $9.8mm -- Citigroup would have been able to achieve in having the A1 wrapped by a monoline guarantor.



Indeed UBS’s shareholder report explains that

[UBS’s] CDO desk viewed retaining the Super Senior tranche of CDOs as an attractive source of profit, with the funded positions yielding a positive carry (i.e. return) above the internal UBS funding rate …

and…

Day1 P&L treatment of many of the transactions meant that employee remuneration (including bonuses) was not directly impacted by the longer term development of positions created…

UBS may have made larger sums on the deals they had wrapped: UBS’s cost of credit default swap (CDS) protection was on average as low as 11 bps, or 0.11%.

The ability to lock in such enormous, fictitious, gains (and potentially distribute some of these gains immediately in the form of bonuses to investment bankers) proved to be a major contributor to the financial crisis. With the under-capitalized monolines – such as ACA, AIG, Ambac, CIFG, FGIC, FSA, MBIA, Radian and XL -- struggling or failing to support the credit protection contracts they had over-sold, several of the TBTF banks were forced to rely on the government’s (and taxpayers’) aid to fund the ultimate return to their balance sheets of what we estimate to be $300 billion of off-balance-sheet negative basis trade securities.

Other resources: a diagram describing the trade more generally, in its context relative to the CDO, can be found here.

Credit Ratings vs. Credit Default Swaps

As an alternative to relying overly on ratings produced by credit rating agencies, several ratings reform proposals offer the usage of bond or credit default swap (CDS) prices or spreads as a more plausible option. Some of these proposals are positively suggestive of the fact that market prices are both more accurate and more predictive than credit ratings.

I’m not convinced.

Firstly, with ratings being so deeply embedded throughout our financial structure, the ratings of the assets themselves become an integral component of the market-implied risk assessment. For example, even when analyzing securitized products Vink and Fabozzi (2009) show credit ratings to be a major factor accounting for the movement of primary market spreads. Thus, for any proposal to be convincing it would have to test the accuracy and reliability of CDS spreads on unrated bonds or companies. Alternatively, a study would need to compare the performance of traded securities whose ratings are not publicly known (also known as shadow ratings) to the performance of those shadow ratings.

Secondly, bond yields (or spreads-to-swaps) and credit default swap premiums are largely incomparable to credit ratings for many reasons. These differences will have to be tackled in a separate piece, but at the very least there’s that non-insignificant concept of liquidity. Both CDS premiums and bond yields include the various risks – not just credit risks – that come with investing in, or buying protection on, a security. Credit ratings speak solely to long-term credit risks.

One may argue that the ratings were far less accurate than CDS spreads during the crisis, and that this (i.e., during a market dislocation) is the only time we depend on accurate default projections and we should therefore abolish rating agencies in general. While I don’t wish to complain of these proposals, I fear that they complain unfairly of the rating agencies.

Yes the CDS spreads may better reflect default probability during a crisis. By definition they’re more adaptive to changing market conditions, versus the ratings which are long-term predictors. But would you want ratings to change in as volatile a fashion as CDS spreads? Would you want ratings to depend on headline news, or on audited (or lightly audited) financial data? Also, one shouldn’t forget that CDS spreads on CDOs and RMBS tranches were just as poor reflections of market-perceived asset quality before the crisis. The crisis could only occur, in part, because the banks were able to buy protection so cheaply from the monolines, by way of being long the CDS -- the infamous negative basis trades.

But even if these proposals made sense and even if their hypotheses were correct, they would be missing at least one crucial point: we need ratings. Meaningful ratings are essential – certainly now. Let me explain why, albeit by way of a long-winded explanation.

For financial reform to be successful it needs ultimately to deal with the flaws in our banks’ risk management procedures – and to deal with them in an environment in which the very serious practice of risk mitigation is left by senior management to risk managers, just as the serious business of growing revenues while attending to shareholder pressure is left by risk managers to upper management.

That these two functions are more adversarial than independent in nature is a concept not to be lost on us. Overly cautious risk management might hinder the implementation of growth opportunities, or the extent thereof. At times, indeed, they may be thought by the skeptic to be mutually exclusive.

Indeed the overpowering pressures that come with business initiatives can influence even the most judicious risk manager’s ability to perform her function in an objective manner, even though her function ought to be both separate from and independent of the business strategies. (See for example “Lehman’s Worst Offense: Risk Management.”)

With both traders and management being compensated for revenue generation, and with prudent risk managers acting only as a hindrance to the initiation and exploitation of growth opportunities, there remains little incentive for senior managers to maintain a healthy risk management environment. Instead of cultivating an environment in which risk managers are educated in monitoring the real risks (which requires expensive resources including personnel, data and systems) they are seen rather as a burden and a cost center, and are therefore starved of the resources necessary to question traders, trades, and trading strategies.

In sum, we remain in the infancy of creating a functioning risk control practice in place at our major banks. We are yet to promote adequate business-peer challenge processes and our price verification processes remain immature. Credit ratings, if created and applied properly, can provide a healthy starting point for internal skepticism; they can provide the independent credit risk assessment that supplements an analysis performed by the front-office or by the back-office.

Conclusion

CDS spreads are untested as a predictor of long-term default probability on unrated securities. Perhaps the reliability of CDS spreads depends on the underlying referenced entity being rated. There’s no doubt that CDS spreads are useful indicators – but I seriously doubt that they’re anywhere near as useful as ratings in predicting long-term default probabilities or losses.

I remain convinced there's an important place in our market for one or more independent agencies to provide their objective opinions in the form of a rating. For ratings reform to be successful, however, requires that the necessary measures be put in place to ensure that rating analysts are unfettered by market share concerns, and are incentivized only by ratings quality and accuracy. If we can achieve these objectives, ratings will return to providing a meaningful utility.