Insider Trading and Expert Networks - Efficiency vs. Fairness

Not a day goes by that we don’t read about the SEC’s deepening investigation into insider trading and its heightened scrutiny of expert networks. To some this is little more than a witch-hunt fueled by a chastened-yet-highly-motivated regulator; to others this is a boon to rooting out information asymmetry that has created an increasingly uneven playing field among investors. As always, the truth lies somewhere in between, but I believe to truly understand the issues one needs to look at the underlying principles: efficiency and fairness.

Many market theorists have argued that “insider trading” (e.g., acting on information that is not publicly available) enhances market efficiency, smooths price volatility and reduces the likelihood of price shocks arising from unexpected events. Where insider trading was once thought to be the province of people skulking around, listening to whispers from company executives, placing trades and delivering bags of money representing a share of the winnings, there is now an entire industry that has emerged to institutionalize the collection of hard-to-obtain information: expert networks. Note that I distinguish between hard-to-obtain information and material non-public information, thought the SEC is currently doing a deep-dive into whether such hard-to-obtain information has, in fact, crossed into the realm of material non-public information. So the “If it looks like a duck…” test has gotten much more complicated. Ivan Boesky and Dennis Levine - now that was insider trading and looked like it without much analysis required. However, are phone calls to professionals with domain expertise insider trading or simply gathering additional research towards validating or invalidating an investment thesis? Well…

From a theoretical perspective, I think it is hard to argue that insider trading doesn’t enhance the smooth functioning of the markets. More high-quality information is out in the marketplace being incorporated into stock prices. News of material events would leak out and be factored into trading activity prior to its release, limiting the potential shock that would arise were the information to be released into the market all at once. Therefore, stock prices would better reflect all the relevant information that is available, more closely representing the true intrinsic value of public companies. What is a boon for market efficiency, however, does not necessarily promote fairness. But that then begs the question - what exactly is fair?

As it relates to the research process, I think fairness should be judged by whether a motivated investor could learn the information through resourcefulness and hard work. If an investor was focused on better understanding the prospects for a particular product, could they read published research, explore primary sources and perform surveys? Sure. Would it be difficult? Absolutely. But it could be done if the individual was sufficiently motivated. Alternatively, they could obtain this information by paying money for it via an expert network. Is it “fair” that someone has the resources to pay for access to experts where another might not? I think so. Those who have built careers around investing and are willing and able to spend money to streamline their investment process are ok with me - provided that the information to which they have access could be obtained by a highly motivated person. What isn’t fair, however, is when experts provide information that couldn’t be obtained through any amount of hard work, e.g., if an official at the FDA was on an expert network panel and had non-public information about the likelihood of a drug making it through clinical trials, sharing this information with a subscriber would unquestionably be unfair. Another example would be a company executive handicapping the prospects of an M&A deal. In these circumstances the recipient of the information would have the ability to trade on it, capturing the benefit between the current stock price and the target stock price reflecting the information they have received. Does this promote efficient markets? Yes. Does it support fair markets? Clearly not.

I believe the current set of cases being brought by the SEC will create a further stratification of the expert network marketplace. The behemoth - Gerson Lehrman Group - will consolidate and enhance their position due to their strong compliance culture and the time they’ve invested with the SEC in figuring out the appropriate business model. Smaller players, however, will be driven out by the costs associated with building and supporting the necessary compliance infrastructure, almost like a Sarbanes-Oxley standard for the expert industry. This will create a brighter line between what constitutes hard-to-obtain information and material non-public information.

Market efficiency is an important goal to which we should always be aspiring, but not at the expense of fairness. But let’s be clear: fairness does not mean equal. It means equal opportunity. And the sharing of material non-public information does not permit equal opportunity.

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Regulation vs. Retribution

The United States Congress, at the urging of our President, is in the midst of passing a “comprehensive” package of financial reforms in response to the recent financial crisis. Both the Executive Branch and Congressional Majority leaders have specifically stated that these tough regulations should be enacted, bipartisanship be damned. Democratic leaders sense a window of opportunity to play upon public anger and fear in order to roll back the clock on Wall Street and the financial innovations of the past 30 years. The problem is, however, that lost in the discussion is an honest accounting of who and what precipitated the financial crisis, the underlying motivations for the proposed regulations and a reasoned analysis of the structure of Wall Street by people who actually know what they are talking about. And because of this opacity and dishonesty, the desire to leverage populist rhetoric into votes and a fundamental lack of understanding of how Wall Street and capital formation works, we will likely get a package of regulations that will hurt the US and the global economy fall more than they will help. And this would be a shame, because it will reflect the loss of a golden opportunity to do something truly positive.

The financial crisis was merely the tail-end of a daisy-chain of events seeded by two policy disasters: (1) the Greenspan-led credit bubble; and (2) Congressional approval of a multi-trillion dollar expansion of Fannie Mae and Freddie Mac’s balance sheets (GSEs) and the resulting diminution of underwriting standards. This was neither caused by CDOs and other derivative securities nor the existence of Wall Street proprietary trading desks. As all manner of entities lined up to take advantage of the Federal Reserve’s and Congress’s largess - mortgage brokers, borrowers, banks, structured finance operations, derivatives desks and rating agencies - fraud, deceit and poor risk management emerged in its wake. A breakdown of conduct on this scale and associated conflicts-of-interest were enabled by poor rules and regulations as promulgated by the Financial Accounting Standard Board (FASB), the SEC and Congress. I would posit that this was due to a lack of understanding of the forces at work coupled with the influence of lobbyists, greed and self-interest. Nobody looks good coming out of the crisis and hundreds of billions of dollars were lost, so in our media and PR-driven society somebody has to pay - now. But the last thing Congress and the President should be doing is agitating for change without truly understanding its impacts, and focusing on payback instead of fundamentally reforming elements of the system that are truly broken. I am deeply concerned that this is exactly what they are doing.

Given the severe flaws in macroeconomic policy underpinning the crisis, the outcome was not surprising. But as we look at th subsequent chain of events what might have dampened the magnitude of the crisis? I see four core principles that, if they had been in place prior to the crisis, could have materially altered the outcome: (1) financial markets transparency; (2) enhanced accounting disclosures; (3) clear and punitive rules against conflicts-of-interest and (4) elimination of the US Government as a perceived back-stop for creditors.

Transparency should be the cornerstone of any discussion around legislation. Proposals agitating for banks to shut down or spin-off their swap operations are nonsensical and destructive. When used properly and with adequate collateral to handle changes in mark-to-market value, they are powerful tools for risk management and speculation to support efficient, two-sided markets. Moving the lion’s share of over-the-counter derivatives volume to exchanges will substantially enhance the transparency around market pricing, how derivatives desks make money and reduce risk of inadequate capital provision. Accounting disclosures have recently been tightened to better address off-balance sheet exposures, but still fall woefully short in areas such as fair value accounting. Conflicts-of-interest are still embedded in many aspects of our system; it is incomprehensible that rating agencies still retain their position considering their pivotal role in the credit markets crisis. Not smart enough to understand the possible impacts of highly structured instruments? Then they shouldn’t slap on a rating. The excuses provided for their miserable performance are divorced from reality: they were greedy, and they did what they had to in order to maximize short-term profits. Case closed. Open-sourcing credit ratings is likely the right avenue for dealing with this particular conflict, but many other conflicts remain. And until the US Government is no longer perceived as 100% certain to bail out the creditors of complex financial institutions, we will see a repeat of 2008 again and again. Could the answer be a tax based upon the complexity, scale and risk of a bank’s operations, rather than an open checkbook provided by the US taxpayer? Perhaps, provided that such rules were applied globally and in conjunction with regulators of the other major financial centers. This would also help address the “Too Big To Fail” issue, as super-sized institutions would pay out-sized taxes because of the risk they pose to the global financial system.  But one thing is certain: without elimination of the implicit US Government guarantee, private and public/private (GSEs) institutions will revisit the sins of the past decade without adopting fundamental change.

I penned a little-read post back in November 2007 where I touched on certain of these issues; my fundamental views have not changed much over the past two and a half years. It is almost as if the aftermath of the crisis has turned into a soap opera; painfully slow-moving and not particularly entertaining. And the way things are looking, the outcome might be of similar quality to what is being served on daytime TV.

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Black Box Trading: Panacea or Promotion?

Without question, quantitative trading approaches - carrying names such as “black box trading,” “algorithmic trading” and “statistical arbitrage” - are all the rage. Lumped in with these mysterious-sounding approaches are high-IQ terms like “pattern matching,” “genetic algorithms” and “neural networks.” At the essence of these strategies are two distinct features: (1) humans aren’t involved in the decision-making process; and (2) models are designed to either “learn” like humans or to detect non-intuitive relationships among a sea of data that can’t be readily seen by humans. Basically, creating models and approaches that are, ultimately, better than humans because they can act faster, trade more cheaply, make decisions dispassionately, process more information and see things humans simply can’t due to the limits of our ceberal cortex.

An interesting article in Saturday’s New York Times raises an array of interesting issues and uses a new hedge fund started by one of the brainiacs of all-time, Ray Kurzweil, as the vehicle for exploring this fascinating topic. For context, it should be noted that rocket-scientist types running hedge funds is not new: figures such as David Shaw (DE Shaw) and Jim Simons (Renaissance Technologies) have been using higher-order math and computer science to extract value from market data for the last three decades. These skills are now being more broadly applied to news feeds, government filings and other data pools where entities can be extracted, sentiment gleaned and metadata created and analyzed. Other hedge funds as well as both buy- and sell-side firms are using similar technologies and approches in their businesses.

The computerization of trading and investment is a logical and noble pursuit. However, attempts such as these are not without their pitfalls for a variety of reasons. Statistical arbitrage strategies have become progressively less and less attractive as more capital has flowed into the area. Where a super-smart quantitative manager could once design a high frequency, quasi-market making strategy that was both very profitable, required little capital and entailed a small degree of market risk, they now need to extend signal horizons and seek to generate returns by doing what everybody else does when they reach for return - take on more risk and accept greater variation in returns. Further, these high frequency strategies are often not very scalable, a real hinderance for a manager that wants to grow and leverage their brand into a multi-billion dollar operation. Returns in the various statistical arbitrage strategies display asymptotic profiles, where early alpha generation is eventually squeezed to zero as more brains and assets focus on the strategy in question. Managers innovate, enjoy attractive returns for a period after which they need to move on and develop the next set of algorithms. It may appear to a layperson that a black box trading strategy would be great - few PMs with huge egos, relatively modest investment in programmers and hardware to build a scalable platform and a nimble, easy-to-adjust model to adapt to changing market conditions. This, my friends, is simply not the case.

Consider the two black box managers with the most successful long-term records and asset growth - the aforementioned DE Shaw and Renaissance. They both have armies of PhD.s of all stripes - computer scientists, mathematicians, physicists, biologists, chemists, linguists, etc. This is not exactly the cheap and scalable infrastructure many have in mind. It takes a lot of money, relentless and effective recruiting and a culture to support the degree of innovation required to succeed. I think about it as the “cycle of the 4 M’s:”

1. Man, who develops the



2. Model, which is operated by the



3. Machine, which executes the Model in the



4. Market, which generates returns, results in feedback interpreted by Man, who modifies the Model, etc.

It is usually not the cute, campy story of a smart technician with his trusty computer building a successful and scalable hedge fund. Few have done it well, and it remains to be seen whether Ray Kurzweil and his lot will be able to make it into the pantheon of black box gurus like David and Jim. Do these new entrants have brains? Yes, and often in spades. But success ultimately requires A LOT more than brains, like:

1. Managerial skill



2. Risk management skill



3. Recruiting skill, and



4. Business-building skill, to name a few.

So let’s turn to the NYT story for their take on things. Some interesting excerpts from the NYT story are as follows.

But in recent years, as algorithms and traditional quantitative techniques have multiplied, their successes have slowed.

“Now it’s an arms race,” said Andrew Lo, director of the Massachusetts Institute of Technology’s Laboratory for Financial Engineering. “Everyone is building more sophisticated algorithms, and the more competition exists, the smaller the profits.”

So investment firms have increasingly begun exploring mathematics’ furthest edges and turning to people like Mr. Kurzweil, who became an expert in pattern recognition building a reading machine for the blind.

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So Mr. Kurzweil and others took a different tack: instead of creating sequential rules to instruct a computer to read, they thought, why not create thousands of random rules and let the computer figure out what works?

The result was nonlinear decision making processes more akin to how a brain operates. So-called “neural networks” and “genetic algorithms” have become common in higher-level computer science. Neural networks permit computers to create new rules and automatically change underlying assumptions by experimenting with thousands of random sequences and processes. Genetic algorithms encourage software to “evolve” by letting different rules compete, and combining the most successful outcomes.

Wall Street has rushed to mimic the techniques. Because arbitrage opportunities disappear so quickly now, neural networks have emerged that can consider thousands of scenarios at once. It is unlikely, for instance, that Microsoft will begin selling ice-cream or I.B.M. will declare bankruptcy, but a nonlinear system can consider such possibilities, and thousands of others, without overtaxing computers that must be ready to react in milliseconds.

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“The downside with these systems is their black box-ness,” Mr. Williams said. “Traders have intuitive senses of how the world works. But with these systems you pour in a bunch of numbers, and something comes out the other end, and it’s not always intuitive or clear why the black box latched onto certain data or relationships.”

********************

“Right now, everyone basically has access to the same data,” said John Bates, a Progress Software executive. “To get an edge, we want to give investors the ability to immediately turn news into numbers. We want to automate what before required human analysis.”

But as these new techniques proliferate, some worry that promotion is outpacing reality. These techniques may be better for marketing than stock picking.

“Investment firms fall over themselves advertising their latest, most esoteric systems,” said Mr. Lo of M.I.T., who was asked by a $20 billion pension fund to design a neural network. He declined after discovering the investors had no real idea how such networks work.

“There are some pretty substantial misconceptions about what these things can and cannot do,” he said. “As with any black box, if you don’t know why it works, you won’t realize when it’s stopped working. Even a broken watch is right twice a day.”

So what are some of the key themes? There is an

1. Arms race, being led by the development of



2. Better models, though



3. Machines lack human intuitition, but can benefit from



4. Digitization of data, which reduces the need for humans, but



5. Is the hype around black box strategies outstripping the reality?

The arms race has been going on for decades. This is nothing new. It is simply the nature of the arms race that has changed. The last leg of the race was largely played with hardware and platforms, with FTP, execution costs being driven towards zero, competitive, low-latency platforms, real-time architectures fueled by incomprehensible processing power and “smart” trade execution systems. But the arms race is changing, with the next leg being driven by highly intelligent software and models. Programs that can take in feeds across different formats, analyze (and possibly create) the metadata at lightning speed, look for statistical and linguistic relationships among elements in the data set, and “learn” from history through enhanced algorithms leading to better performance. Ok, I get it. But it still doesn’t answer the question of whether or not these new entrants will have the stuff to generate consistent, sustainable performance across a progressively larger asset base without killing returns and/or blowing up.

I wonder if this new-found emphasis on black box trading will, over time, drive alpha back towards the fundamental bottoms-up strategies. And I’m really not sure if neural networks, genetic algorithms and other ultra high-IQ approaches really change the calculus of how the markets and investor behavior works. Capital tends to flow from the “cold” strategies to the “hot” strategies, which naturally causes hot strategies to become cold and vice versa. Anyone remember convertible arbitrage? What was a darling in 2000-03 was a dog in 2004-05 and a darling once again in 2006. This is an inexorable game of “asset allocation tennis” that has taken place (and likely will continue) for time immemorial. So does it really matter if ever-more sophisticated tools and techniques are used? Or is Ray Kurzweil’s knowledge base and its application to the markets so differentiated that he will enjoy a competitive advantage for a material amount of time that would enable him to build a true hedge fund firm with a lasting legacy? Maybe, but I’m cynical. No knock on Ray (he is clearly one of the most brilliant thinkers of our generation), but I think there are enough brilliant minds working in enough related areas with enough access to capital to make any demonstrable advantage fleeting at best. Call me a cynic, but after 20 years kicking around the markets and with a sense of history it takes a lot more than a few good years to convince me that a new paradigm is upon us. Only time will tell.

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More on Derivatives - Necessity vs. Novelty

First of all, I’d like to thank the folks over at the FT for taking my somewhat tough post concerning MSM’s depiction of Wall Street trading risk so seriously. They put up a post in their blog, Alphaville, about my emotional (passionate?) missive in Information Arbitrage yesterday. I had to crack up when their lead-in was as follows:

In the blogosphere, the mainstream media, the FT included, has got Information Arbitrage’s Roger Ehrenberg, the former head of Deutsche Bank’s fund of hedge funds and president of Monitor110, all hot under the collar.

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“A $120 million loss on a trading desk, unless the loss was the result of poor controls or a rogue trader, is neither a show stopper nor something that warrants intense eyebrow-raising and upset stomachs.”

Message received loud and clear, Roger.

Bottom line: they heard me. That’s pretty cool - thanks, Alphaville. I don’t know whether or not it was coincidence, but the FT had two very interesting stories yesterday on the issue of derivatives that I believe warrant discussion. But before that, let me tell you my view of the evolution of the derivatives marketplace since the early 1990’s.

My 2 Cent Perspective on Derivatives History

I think the early-mid 1990’s could be characterized as the wild, wild west period of the derivatives markets. A bit of a gold rush mentality prevailed across most asset classes. Interest rates were low, companies frequently viewed their Treasury operations as profit centers and people sold crazy, crazy instruments to banks. Sales of naked options - principally variations on interest rate puts. Leveraged derivatives (does anyone remember the LIBOR-cubed swaps?). Pure punts via “index amortizing swaps” calibrated to a particular rate view. Risky and massive mortgage books that served to underlie MBS pools. At the same time, banks built huge portfolios of complex correlation risks while the risk management systems themselves weren’t yet sufficiently evolved to deal with these mounting exposures. You can think of the risks inherent in this market environment as something akin to a Ferrari attempting to traverse the cobblestone streets of Rome at 140 mph. Someone is going to hit the wall. It was inevitable.

Then came the Fed’s 300 bps increase in rates starting in early 1994. The party was over, and the derivatives speculators (note that I use this word and not the word “hedgers”), in general, were caught off-guard. This includes both corporate sellers of volatility who were simply trying to collect premium they thought (and hoped) they wouldn’t give back (which they did, in spades) and trading desks that were also poorly positioned for the flattening yield curve environment. MBS buyers also got clobbered as duration was extended in a rising rate environment. And all of this happened in the face of one of the steepest yield curves in history, with short rates pegged at 3% and the long bond trading at 8%. Remember why a yield curve slopes upward? The interest rate market’s expection of higher future rates. Did this scare people? No way - sell forward optionality! Harness that steepness to collect premium today which hopefully won’t have to be paid back later. And this is why things got really ugly when the Fed took the punch bowl away.

First came the P&G/Bankers Trust debacle. A corporation taking a $200 million trading loss on a “hedge?” Sure, a “hedge” that had a duration of 125 years (which offsets precisely what exposure in the business?). Then Air Products. Then Gibson Greetings. And countless other corporations of scale who lost hundreds of millions of dollars when they had to mark these derivatives to market but who suffered in silence when the losses hit. And, of course, the corporations blamed the banks for fleecing them (which is a bunch of crap, to be sure, but hey, you gotta blame somebody other than yourself). Bottom line - a little bit of the luster came off of the derivatives market, but even if corporate use of these tools slowed during the 1994-1996 period, institutions and governments continued to use these tools in quantity.

As the late 1990s/early 2000s rolled around, there was a key theme that precipitated the exponential growth of the derivatives marketplace: the blurring of the lines between debt and equity. First the equity derivatives marketplace really exploded, with an amazing amount of innovation benefiting investors and issuers alike. Structured convertible bonds that lowered issuance costs for corporations, equity hedging strategies that created extremely flexible, cash-efficient share buyback programs, private convertible instruments to monetize appreciated stock positions, portable alpha strategies for pension funds, and on and on and on. At the same time, the concept of “capital structure arbitrage” was born, emerging from either the convertible trading desks or the credit trading operations of the large Wall Street firms. This strategy of trading the different strips of the capital structure was facilitated by an innovative but very straight-forward tool - the credit default swap (CDS). CDS allowed credit buyers and sellers to use derivatives in lieu of the actual instruments to create a position. It was this emergence that, to me, accelerated the blurring of distinction between debt and equity. Artificial barriers would no longer be tolerated. How can you optimize the trading of equity without taking advantage of the information and liquidity inherent in the debt? And this, in turn, set in motion the innovation we have continued to witness over the past five years.

CDS, LCDS, CBOs, CDOs, CLOs - these tools have emerged to help buyers and sellers get what they want, either in terms of risk transfer or portfolio return objectives. As these markets have grown they have become more standardized, and issues of weak documentation are being dealt with aggressively by Wall Street trading desks and their counterparties alike. I am sure, by now, you are completely nauseated by my little missive on derivatives history, but I have done this to prove a point - when instruments are created that generate real value, the markets explode. When they are mere gimmicks that fail to materially enhance one’s ability to either make money or manage risk, they falter. And it is here that I’d like to turn to the two stories in the FT.

When Derivatives are Necessary - A Market Emerges

Richard Beales’ article drives home the point that innovation has been rapid and has created real benefits, but not without some concerning arising from fears over how the markets will handle stress and unacceptable levels of undocumented trades:

The pace of financial innovation is quickening. Products that barely existed a few years ago are already multi-billion-dollar markets.

Derivatives and structured instruments have evolved particularly quickly across the capital markets, especially in the worlds of credit, equities and commodities.

Many of these innovations benefit the financial system because they help disperse risk more widely, analysts and regulators say. But there are concerns as they have come at a time of economic growth, low interest rates and low volatility.

********************

Other novel instruments are also designed to serve a practical purpose. Loan-only credit default swaps (LCDSs) allow lenders to hedge exposure by buying a type of insurance or protection against the borrower’s default – though they also allow hedge funds and others to take positions without owning the underlying debt.

The LCDS market is an offshoot of the still rapidly growing market for basic credit default swaps, the most common credit derivatives. While CDSs enable market participants to buy and sell protection against default on unsecured bonds, LCDSs are designed to track the credit of secured loans.

********************

Innovation brings challenges for regulators. In the credit derivatives world, US and European regulators have also had a hand in encouraging the finance industry to put its house in order. Last month was the anniversary of an initiative to clean up paperwork problems and increase automation in the industry.

The Federal Reserve Bank of New York, which hosted a meeting of 16 dealer banks and their regulators, welcomed progress in cleaning up the backlog, which a year earlier had been seen as a potential threat to the stability of the financial system.

But in a sign of the pace of innovation, the regulators’ attention is shifting to other parts of the derivatives world, such as equity derivatives.

“We look forward to seeing the industry improve the automation and standardisation of over-the-counter equity derivatives trading and reduce the current levels of unconfirmed trades,” said the New York Fed.

********************

Sure, when you are building a trillion dollar market there will be bumps in the road. But when you look at the ability for issuers and investors alike to:

1. Benefit from enhanced liquidity;



2. Transfer risk to those best able to absorb it; and



3. Access a vehicle for taking a holistic view of the capital structure and to express a view in a variety of different manners and market

It is hard to underestimate the value of this innovation.

When Derivatives are a Novelty - The Market Flounders

Saskia Scholtes also had an interesting piece on the equity default swap (EDS) market, which has has languished since its development in 2003:

Of the many ideas thrown at the wall by investment banks eager to develop the latest must-do derivative, not all stick that well.

One product that appears to have been consigned to the derivatives larder for now is the equity default swap.

********************

Launched in 2003, when memories of the bursting stock market bubble were still painfully fresh, the concept was logical, say investors.

But equity market volatility has subsided and the attraction has waned. Protection against a fall in share prices is also easily achieved with a simple put option. The promised growth in EDS trading volumes may be on hold, at least until the next bear market.

********************

The idea was to allow banks and investors to fine-tune their positions further. But one credit hedge fund manager suggests that this added level of complexity may have been “one step too far” for broad-based market adoption.

The key take-away here is that innovation for innovation’s sake simply doesn’t work, even in the “arcane and esoteric” world (needless to say, I am being extremely sarcastic) of derivatives. The market knows what it needs, and when smart people come up with smart ideas there is rapid adoption followed by exponential growth. It is good to know that there is a self-policing mechanism in place called Mr. Market which makes sure necessary ideas are rewarded while others fall by the wayside. This is the way it should work, right?

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Pricing Event Risk and the CDS Market

Much has been written lately about those getting smashed in the credit derivatives market, particularly in the wake of several recent corporate reorganizations that have introduced new and unexpected volatility into the marketplace - spin-offs, split-offs, new financing entities, subsidiary IPOs, etc. The ball kind of got rolling with VNU’s subsidiary financing, and has picked up steam in light of Verizon’s announced spin-off of its directories unit. Bloomberg issued a story today that highlighted some of the heated emotions over this issue:

Hedge Fund Nightmare

The predicament is akin to battling a rare disease because of the more than 1,000 companies with credit-default swaps bought or sold this year, fewer than 3 percent triggered price swings related to a change of corporate control, or so-called succession event, according to data from Frankfurt-based Deutsche Bank AG.

For hedge funds, unregistered pools of capital where managers participate substantially in the profits of the money invested, the volatility of credit-default swaps is a “nightmare,” said Simon Ballard, head of research in London at ARC Securities Ltd., a fixed-income broker. “Credit derivatives have underpinned the evolution of the hedge fund community for the last few years.”

New York Fed

Even the International Swaps and Derivatives Association, the trade group that has championed credit-default swaps as tools to reduce risks in the debt market, is concerned that increased volatility shows the hazard that the contracts no longer reflect the value of assets they’re mimicking.

These “new problems” are causing widespread confusion, said Kimberly Summe, ISDA’s legal counsel in New York. Summe, who helps set the standards for credit-default swap contracts, coordinates a twice-monthly conference call with 150 bankers, investors and lawyers to tie the contracts more closely to a company’s credit risk.

So far, regulators aren’t voicing concerns. Last August, the Federal Reserve Bank of New York chastised some of the biggest financial firms, including New York-based JPMorgan Chase & Co., the third-biggest U.S. bank, and Goldman Sachs Group Inc., the most profitable securities firm, for allowing 150,000 credit-default swap contracts to remain uncompleted, leaving traders unsure of their obligations.

First of all, a 3% probability is neither akin to the risks of contracting a rare disease nor is it a “tail event.” While there is surely improvement to made to the ISDA credit derivatives template, the bottom line is that this (the risks to CDS prices in the wake of corporate reorganizations) is part of the game. Credit risk is not a static measure, and the derivatives marketplace needs to incorporate the probabilities of restructurings and reorganizations into their prices for these instruments. Whining has no place in the markets, folks. People made boatloads of cash on the ride up, and as markets mature and time passes the ride occasionally gets bumpy. Sorry, that’s life.

This reminds me of my days in equity derivatives in the go-go late 1990’s, when M&A events and spin-offs of dot-com subsidiaries were all the rage. Say we were long some vega (optionality) on a stock (either by owning puts through a synthetic share buyback program or through a short put/long call sold as hedge of an investment position), and the company underlying our position announces a spin-off of its internet subsidiary. What happens? All of a sudden we have positions on two stocks instead of one. So what? The volatility of the basket of two stocks is less than that of the single stock that spawned the two, rendering our long vega position less valuable. Totally sucks, right? Right. But we survived and that was part of the game. The fact is that this potential outcome needed to be factored into our pricing when we initially provided the hedge.

I am glad to hear that regulators aren’t freaked about the current situation. They shouldn’t be. They were right to be concerned about the massive number of unconfirmed trades, a matter on which the Street has made tremendous progress over the past year. But regulators shouldn’t be stepping in where market forces are designed to take care of the problem. Traders can price event risk. They do it every day. If swap spreads need to widen to take these risks into account, ok. But if they don’t, all I can say is caveat emptor - and no whining, please.

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