At a time when sovereign bonds are being downgraded and the very concept of a ‘risk-free’ rate is open to question, the task of analysing and measuring credit risk effectively has never been so critical. Such is the level of uncertainty about the prospects for the global economy, however, that banks, ratings agencies and credit investors face huge challenges as they seek to find new ways of measuring risk in a vastly changed, post-credit crisis world. These challenges are so great that mispricings are inevitable, and where there are mispricings, there are opportunities – for those who know how to exploit them.
With this in mind, in August GAM launched its first market neutral credit fund, GAM Star Diversified Market Neutral Credit . The fund seeks to produce absolute returns with low correlation to fixed income markets by combining quantitative systems and intensive credit analysis to create a portfolio of mispriced investment grade companies. It joins GAM’s growing suite of UCITS III absolute return funds and targetsnet returns of Libor +5-8% pa, with volatility of 5-7% pa.
The fund is managed by DCI, a San Francisco-based asset management firm specialising in corporate credit strategies. The company’s approach is based on its core belief that credit markets contain exploitable information gaps and are poorly diversified, meaning that accurate modelling and ‘active diversification’ can lead to successful credit investing.
Stephen Kealhofer, DCI’s Chief Investment Officer and one of its co-founders, says that the ‘market neutral’ element of GAM Star Diversified Market Neutral Credit is ideal for the kind of macroeconomic environment the world is likely to face over the next few years. “Market neutral strategies are very effective in volatile periods,” he says. “When volatility exists, there are more mispricing opportunities for us to exploit, and it is obviously easier to exploit them when you can go both short and long. And because volatility in credit markets is generally associated with middling or down markets, the market neutral strategy tends to perform well when long-biased portfolios struggle.”
Although the company was only launched six years ago, DCI’s approach to calculating credit default risk derives from a body of research that has been developed and refined over decades, stretching back to the work of Merton, Black and Scholes in the 1960s and 70s. The origins of the firm can be traced back to 1968, when John McQuown, then director of management sciences at Wells Fargo Bank, recruited Oldrich Vasicek, a mathematician who had recently fled Czechoslovakia just before the Russian tanks rolled in. Together, they worked extensively on finding ways to use the mass of information contained in equity prices and apply it to corporate debt markets.
McQuown and Vasicek left Wells Fargo separately in the mid 1970s, but teamed up again in 1983 to resume their work on applying option theory to live credit markets. They asked Kealhofer, then a professor at Columbia University Graduate School of Business, to do some empirical work on their models. “I went to work for them and I became immediately entranced by the application,” Kealhofer recalls. “But when I started to dig into the existing literature I quickly realised that very little empirical research had been done on defaulting firms.”
Kealhofer convinced McQuown and Vasicek that in order to truly get to grips with default risk, it was necessary to develop a deep understanding of actual default experience. This led them to undertake a massive data collection effort, which culminated in 1987 in their first attempt at a default database, covering about 12 years of defaults of all publicly traded firms in the US. “As a consequence of that, we had an unprecedented insight into what was going on from a credit risk standpoint,” Kealhofer says. The default database was continually developed and expanded, and was ultimately pivotal for their credit analysis developments.
The three men worked closely with a number of banks on assessing the creditworthiness of bank loan portfolios. “It became quite clear that the banks were really hamstrung by not having good, objective measures of default risk,” recalls Kealhofer. “Credit was really viewed as more of an art form and very few people were considered to be good at it. Banks really struggled with credit training and getting consistency across their groups – they relied very heavily on a small number of people to review the large exposures across their books, and these guys tended to have a very case-by-case view of the world, which made it difficult to institutionalise the credit process.”
It was this realisation that led Kealhofer, McQuown and Vasicek to form the consulting firm that bore their initials – KMV – in 1991. KMV worked with banks, insurance companies and credit managers to help them improve the way they analysed and quantified default risk. A key focus of the company’s work was diversification. “We realised early on that one of the things that distinguished credit from equity was that individual equity risks were very much in the same order of magnitude – a high-volatility stock has a 50-60% standard deviation, while a low volatility stock has something like a 20% standard deviation, so a ratio of around two- or three-to-one,” Kealhofer says. “This means that if you create an approximately equally-weighted portfolio of stocks, it’s actually going to be fairly diversified.
“With credit, however, we found that there were very wide divergences in the levels of risk associated with individual names. We could go into any bank with our eyes closed and put our fingers on exposures with 2 bps of default risk and others with 400 bps of default risk. That’s a 200-fold difference in the probability of defaulting, which can translate into a 15x differential in the level of volatility. This meant that the higher-risk exposures – particularly if they were large exposures – contributed so much risk that it was impossible to diversify them away, no matter what else was held in the portfolio. This was a direct challenge to the conventional wisdom of the time, which was basically a bean-counting approach to diversification – ie, ‘I’ve got X number of names in my portfolio in Y number of sectors, so I must be ok’.”
In the early 1970s, Professor Robert Merton had developed an influential framework for credit analysis which relied upon market value measures of leverage as well as accurate assessments of business risk, but no one had been able to implement it successfully. Armed with its historical data, KMV found that the key difficulty was the measure of business risk. “We discovered that we could summarise the volatility of the asset value of a firm by looking at the claims on its future cash flows,” Kealhofer says. “Put simply, if a firm has volatile future cash flows, its value today is more volatile. We could make inferences about that volatility using the option pricing tools that we had developed, and that turned out to be a key ingredient in credit analysis.”
KMV developed its own measures of default probabilities and quickly found that its ratings were often at odds with those of the major ratings agencies, particularly when credit quality had been changing. “Ratings did not evolve to monitor default risk; they were a gate-keeping tool to separate sheep from goats,” says Kealhofer. “As a consequence, ratings were not useful for managing credit portfolios because the risks were realised by the time the ratings had changed. By using equity market based measures of leverage, our default probabilities provided more timely indication of changes in default risk.”
KMV was sold to Moody’s for US$210 million in 2002, and in 2004 Kealhofer and McQuown joined forces with David M Solo to form DCI. Since launch, the firm has grown rapidly and now has more than 20 employees, managing over US$3.5 billion of assets in long-only, enhanced and long/short market neutral strategies.
Starting in 2004, DCI developed a new generation of default probability estimates. Kealhofer breaks down DCI’s approach to measuring the default risk of a firm into two key elements, beginning with gaining an accurate understanding of the firm’s liabilities. “There’s a lot of variation in how people undertake liability analysis, but with our default database we are able to do it very scientifically. For example, if a firm books prepaid revenues as a liability, should that really be a liability or not? We’re able to sort through all kinds of non-debt liabilities and figure out how they should be treated from a credit analysis standpoint.”
The second element of DCI’s approach is its focus on understanding the overall value of the firm, rather than just the value of its debt. “We do that by viewing the equity of a business as a call option on the underlying value of the firm – in other words, we do option pricing backwards in order to infer a value for the whole business. And finally, we use that same approach to translate information from the volatility of the equity to the volatility of those future cash flows. Once we’ve got all this information, we put it all back into the option framework to assess the likelihood of default and, ultimately, the value of the risk.”
Using these more accurate measures of corporate default probabilities, DCI next focuses on portfolio construction. The hallmark of KMV portfolio analysis was something they called ‘active diversification’. In order to diversify credit portfolios, it was necessary to reduce holding sizes as risk increased; passive diversification was inadequate to offset the amount of risk in the riskier exposures. DCI began applying this approach in backtesting corporate bond portfolios, and unexpectedly discovered that the better-diversified, lower-risk portfolios actually produced greater returns. This shifted their focus to understanding the pricing of credit, explains Kealhofer. “It was only as we got some way down the road that we realised that there was a surprising amount of mispricing up for grabs, and that the diversification process was revealing the mispricing,” he says. “Shifting exposure away from higher default risk credits towards lower risk credits simultaneously shifts exposure away from overpriced assets to underpriced assets, boosting returns.”
The result is a three step portfolio process. First, DCI defines the investable universe. It does this by starting with the North American and European CDS and bond populations, limited to corporations with publicly traded equity, and then filtering them on strict liquidity criteria. After further removing issuers with exogenous risks, DCI then compiles an approved list of approximately 400-600 issuers. Next it separates them into two groups of about 100 issuers each, one consisting of the most underpriced credits and one of the most overpriced, using the DCI default risk measures. It then constructs a portfolio from each of the groups, using its active diversification principles to minimise idiosyncratic risk.
Finally, DCI creates the overall portfolio by shorting the overpriced portfolio against the underpriced portfolio in a fashion designed to minimize the systematic risk exposures of the total portfolio. “If we look at a name and say, ‘this is positively mispriced’, the odds of that name producing a superior return are not 100%,” he says. “Each one of those risks may have a 60-65% chance of paying off, so what you want in a portfolio is a large number of them, with no individual position too large. We keep very small positions, concentrate them into exposures that are most attractively priced and least attractively priced, and run them as long and short portfolios against each other.”
Kealhofer believes that DCI’s combination of intensive credit analysis, leading-edge quantitative systems, active diversification and market neutral approach makes it stand out from its competitors. Most credit investors, he says, spend all their time either trying to time the market correctly or trying to pick the winners – both of which are difficult to do successfully over a long period and therefore carry high risks. “People still ask us, ‘why are you so diversified? Why can’t you just pick the winners?’” Kealhofer says. “Well, we think we can pick the winners, but by taking a lot of small bets over time we ensure that no one name can take down the portfolio. And because we’re market neutral, we ensure that our portfolios are never driven by the direction of the market – that is something that we are very rigorous about.”
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Stephen Kealhofer is the Founder, Chief Investment Officer and Head of Research at DCI, LLC. Prior to co-founding DCI in 2004, Stephen co-founded and served as managing partner of KMV, a credit analytic firm, which was acquired by Moody’s in 2002. Before founding KMV in 1989, he was director of research for Diversified Corporate Loans. Previously, Stephen was a visiting assistant professor at the Haas School of Business and assistant professor at the Graduate School of Business, Columbia University.