Man Numeric sees quant credit funds as opportunity “to go against the grain”

Robert Lam, co-head of credit at Man Numeric, the Boston-based investment unit of London-listed hedge fund giant Man Group, said “99 per cent” of high yield assets are managed using traditional discretionary processes. But he suggested such one-side market composition tends to naturally throw up inefficiencies and distortions, in turn offering a “large opportunity to go against the grain” for quantitative funds.

Systematic hedge funds are increasingly well placed to tap into inefficiencies within credit assets – a market often seen as unworkable for most computer-based strategies, credit heads at Man Numeric say.

Robert Lam, co-head of credit at Man Numeric, the Boston-based investment unit of London-listed hedge fund giant Man Group, said “99 per cent” of high yield assets are managed using traditional discretionary processes. But he suggested such one-side market composition tends to naturally throw up inefficiencies and distortions, in turn offering a “large opportunity to go against the grain” for quantitative funds.

Lam and Paul Kamenski, co-head of credit at Man Numeric, explored the growth and evolution of computer-based funds in credit – a sector traditionally dominated by human-led strategies – in Man Group’s latest ‘CIO Agenda’ podcast.

The wide-ranging discussion explored portfolio construction, data availability from private companies, factor-based quants, ESG, and the ways in which trading and execution costs can be effectively engineered into a systematic strategy.

“Not only do we believe that differentiated investment processes can lead to differentiated outcomes, but also there is an opportunity to improve at each step of the investment process that people are using to invest into these corporate bonds,” Lam said, pointing to underlying data, returns forecast, risk modelling and portfolio construction.

Both Lam and Kamenski acknowledged the assortment of hurdles in developing a systematic credit approach, which cut across liquidity, price discovery, data, credit spreads, and modelling.

But Kamenski also highlighted the rapid advances in this area.

“You have an entire capital structure, oftentimes with multiple line items, different subordination, different durations, and less clear issuer curves to try to identify mispricing even within that same capital structure. So at this point early pioneers have really tackled those challenges. And it’s an exciting time when there really is some track record now for some of those earliest adopters.”

He continued: “Once you have that sort of momentum in terms of having more and more liquidity, I think it feeds positively on itself and supports even more growth in this space.”

Lam added: “We think that there is even more interest and more differentiated ways to come up with some orthogonal alphas around alternative data, which is a relatively untouched area within the corporate credit universe.”

Looking ahead, Kamenski said he was “very excited” about the next five years in this space, and said systematic approaches can start to challenge discretionary strategies’ stranglehold on the “99 per cent” of high yield assets.

“Five or 10 years from now that number in terms of the percentage of market share that systematic approaches have will be decidedly higher,” he said.

Asked by Man Group CIO Sandy Rattray whether alpha will gradually decay as systematic credit strategies evolve, Lam conceded that it will be “natural to see some moderation in alpha over time.”

“It’s going to take some time for the market to catch up,” he said, noting the importance of innovation on the research side.

“My base case is that the underlying insights in our strategies today will actually look drastically different in five years, and it’ll actually look drastically different again in the subsequent five years,” he added. “I do expect that moderation over time, but from our perspective, it’s just about continuing to push to that forefront of where you see the inefficiencies at that point in time and how do you exploit them and express them in your model.”