Systematic Strategies Turning Towards U.S. Credit To Find Alpha
Systematic trading strategies have historically been the domain of the equity, derivatives and rates markets. It’s hardly surprising; these markets have enjoyed deep liquidity and a healthy depth and breadth of the data that systematic models thrive on.
The U.S. credit market – specifically, U.S. investment grade and high yield corporate credit - is fast emerging as the next great opportunity for systematic trading models. Historically, most credit instruments were traded over the counter but companies like global bond trading fintech MarketAxess provide the infrastructure to execute trades systematically. Max Callaghan, MarketAxess Head of Hedge Fund and ETF Sales says that the emergence of systematic strategies trading U.S. credit can be put down to two reasons.
“There is just an enormous pool of liquidity in U.S. credit instruments, now more than ever, and it’s continuing to grow. Access to liquidity is critical for the success of a systematic trading strategy that a hedge fund might employ,” he said. “Also, there’s been a significant increase in the quality and cleanliness of the data on these instruments and about the overall market so that firms can construct their models better. They’re able to develop much more robust models to exploit the signals that they produce.”
It is not solely the improvements in data or solely the improvements and changes in trading infrastructure that is driving the increased systematic trading of U.S. credit instruments – it is that both have occurred and can now be combined. The symbiotic relationship between systematic strategies and data can be a hindrance, as well as a help; however; but it’s not just a case of bad data in, bad results out.
“The beauty of data is that it’s in the eye of the beholder in the sense that the foundational aspect is rules-based analysis. The hedge funds have to select the right data sets that fit those rules. Data in and of itself isn’t necessarily bad,” said Callaghan.
The enrichment and contextualization of the U.S. TRACE (Trade Reporting and Compliance Engine) data is an area where Callaghan says he is seeing increased interest. TRACE data in of itself is limited given it is capped (investment grade credit transactions are capped at $5mn and high yield transactions at $1mn). The caps are lifted after 6 months but there’s nothing within the TRACE system that tracks it back to the original transaction. Also, TRACE only provides information on what traded, but not necessarily what hasn’t traded.
“Products that help hedge funds to link the newly available uncapped amounts with the original transaction and enrich the dataset is definitely an area where we’re fielding more enquiries,” said Callaghan.
Perhaps more notably, and something that’s a sign of the times, is that demand is increasing specifically for machine learning-supplemented products.
“It’s important to remember that you’re building a strategy to tell you where to trade next, not to tell you what you have already traded. Systematic credit hedge funds are increasingly looking for products where there is a predictive tool overlayed on the underlying data,” he said.
Credit trading volume from systematic funds on the MarketAxess platform reached $116.5bn in the first three quarters of 2020, up over 150% from the same period in 2019; the increase can be attributed to the increased availability of high-quality data and the availability of more seamless market access through trading technology makes it more viable for systematic funds to enter the U.S. credit market. Whilst Callaghan says that there is nothing Covid-specific about the increase, the pandemic is a case in point to support the continued growth of systematic credit trading.
“Systematic is so relevant to now because it helps remove the emotional assessment of an investment. In situations that are emotive, like we’re seeing now, not just because of the pandemic but because of the U.S. election and Brexit, the power of a rules-based strategy, and the execution of that, is where systematic strategies can be so powerful,” he said.
Systematic trading in other credit instruments – for example emerging market debt – is still to be adopted en masse due to the opacity of the available data and a lack of regulatory impetus. Similarly, in Europe, the lack of an EU version of TRACE is holding back some systematic strategies from entering the market. Whilst there is regulatory impetus to figure out a consolidated tape provider for European bond markets, none has emerged yet.
Credit investors are stuck in somewhat of a quandary – the zero lower bound interest rate environment which many developed markets inhabit means that buy and hold sovereign bond allocations are offering a miserly yield. For investors fed up with government bonds but who still want to access less risky fixed income instruments like investment grade U.S. credit, the evolution of the trading infrastructure for these strategies is providing strong benefits.
“The nature of the architecture now means that systematic strategies can be a price-maker, not simply a price-taker. They’re not being charged the bid-offer, they’re making it. That’s an incredibly powerful tool that these strategies can benefit from,” Callaghan said.
Furthermore, for those investors who don’t want to get rid of their buy and hold sovereign bond allocation, they don’t have to – systematic credit can be additive to a diversified portfolio.
“The systematic credit story is not about reallocation,” said Callaghan. “This is a relatively new and very exciting area for a trading style – a systematic one which previously hasn’t been as applicable to U.S. credit markets. It’s not an opportunity cost of investing in one over the other; it’s not saying, ‘I don’t want to be in emerging markets relative value anymore, I want to be in systematic U.S. credit’. It’s about the investors’ risk profile. Hedge funds which trade systematic credit can absolutely exist alongside a traditional bonds allocation in a portfolio and I think that now, more than ever, they can offer something differentiated.”
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