DCI’s Market Neutral Credit UCITS and AIF Strategy

Founded in 2004, San Francisco-based DCI was one of the first firms to run a market neutral credit strategy, which launched in 2007. The firm’s unique systematic investment process, including fundamental, quantitative and statistical elements, dates back to groundbreaking academic research in the 1960s. Multiple innovations have been honed and refined over time but the one constant at DCI has been a focus on idiosyncratic alpha, which distinguishes the company from many peers. 

DCI CEO, Tim Kasta, asserts that, “most long/short credit strategies are taking some kind of beta risk; or basis risk such as cash versus CDS; or are looking to pick up illiquidity premia; or they may be taking factor risks, such as small cap, value, growth or momentum. Our return stream is not correlated to any of the beta benchmarks, nor to factors, nor to other managers”.

The source of investment returns can be characterised as a pyramid, with beta at the base; liquidity premia at the next level; alternative beta/risk premia/style premia further up, and true alpha at the pinnacle. DCI’s strategy aims to be a purveyor of pure alpha – and moreover to extract it mainly from idiosyncratic single corporate security selection. 

Performance attribution analysis (using the back-test of the latest models between April 2006 and June 2016) bears this out: of gross average annual returns of 15.8% since 2006, 10.8% came from single name selection and sector exposures made up most of the rest at 4.3%. “While the strategy is run with various constraints including sector constraints, the strategy is not completely sector neutral because analysis has shown that companies in the same sector will often move in clusters,” says Kasta. There have been consistently near zero, and sometimes slightly negative, correlations to conventional asset classes such as equities, credit, and bonds – and no meaningful correlation to factors such as quality, value, size and momentum.

“The main factor risk is a plus, namely that higher volatility environments typically lead to more mispricing in corporate credit CDS,” he continues.

At first sight, DCI’s UCITS strategy seems simple: the long and short books both trade single name corporate CDS, and both are expected to be profit centres; the short book is not a hedge for the long book, though it is constructed so that the systematic risks of the long and short portfolios offset. The core concept is valuation convergence, which decades of research and pratical experience at DCI suggests that credit spreads of cheaply priced names will narrow while those of richly priced names will widen. But the devil is always in the detail. 

Proprietary cleaning of accounting data is needed in a process that has now been almost completely automated (a human eye still checks for the latest adjustments to capital structures or other corporate events). Information gleaned from public equities and derivative markets, and numerous other inputs, help DCI infer default probabilities and thereby fair value credit spreads, using a proprietary, structural default probability and credit valuation model on which the co-founders have been innovating for over three decades.

DCI’s alpha forecasts also incorporate information gleaned from the ongoing dynamics of a firm’s underlying credit quality. This refined approach – which has been employed in its current iteration since mid-2016 – has helped the strategy to perform better in lower volatility climates and is now applied across all strategies at DCI: investment grade credit including long duration/LDI; high yield credit; absolute return credit and long/short credit. 

The current CDS investment universe for the DCI Market Neutral Credit UCITS Fund is filtered down to 300-400 unique entities, but excluding private issuers and allowing only the most liquid CDS contracts to be traded. The contracts are cleared at ICE (thereby mitigating counterparty risk). In this context, the CDS spreads traded are viewed as being as close as possible to pure credit risk premia (rather than illiquidity or other valuation premia).

Opportunistic name weightings 

The CDS reference entities traded in the strategy are all based in developed markets, with typically 70-75% in North America and the balance in Europe. Roughly half of the percolated subset is traded at any time, with the long and short books each typically containing about 80 names. “This is enough to diversify the idiosyncratic risk,” says Kasta. “The long and short books are credit beta-matched, and also happen to have broadly comparable credit rating, term, geographic, and sector profiles.”

Average individual position sizes average around 1% – 1.5% notional, with position sizing in proportion to expected return in what is dubbed “maximum convergence”. Trading is subject to the constraint that forecast alpha must cover transaction costs.

Good and bad climates 

Though wagers on individual names are scaled according to their perceived potential, the overall strategy does not time its opportunity set. “The strategy runs near its maximum leverage of 600% gross exposure at all times, and does not attempt to predict or time regimes,” declares Kasta. A scatterplot shows how weekly returns have risen in more volatile climates, but the strategy has still been able to generate some reasonable returns in calmer markets. 

Hamlin Lovell – Read more on thehedgefundjournal.com

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