As a growing number of banks, hedge funds and asset managers begin deploying more systematic and quantitative strategies across fixed income trading, Max Callaghan, lead for hedge fund and ETF sales at MarketAxess in London, explains some of the fundamental requirements for their success.
Finding the right conditions for systematic growth
Fixed income credit investment and trading has changed substantially over the past decade. Electronic trading in corporate bonds reached all-time highs in 2020. The use of automated trading protocols and algorithms has become more mainstream across all market participants. And the development of all-to-all trading has driven the growth of alternative liquidity providers.
The way in which investors seek to allocate capital has also evolved, as have the strategies they favour and the returns they expect. This has been most evident in the ‘rise of passive’ credit investment, as well as the growing penetration and assets-under-management of ETF asset managers.
However, a strategy previously reserved for other asset classes has recently really started to make greater inroads in fixed income credit – Systematic.
Systematic investing has become more applicable to fixed income credit precisely as a result of the market structure changes taking place. Through the growth of alternative liquidity providers – and, vitally, because electronic platforms can now connect end-investors to that alternative and/or natural liquidity – fixed income systematic credit strategies now have a higher likelihood of execution when signals are received. In essence, we’ve reached a point where systematic trading strategies can realistically be employed and the signals won’t be wasted.
Alongside this developing execution landscape, markedly better-quality data is helping to improve signal generation and accuracy. The improved quality and cleanliness of the data helps to validate the applicability and profitability of prospective systematic strategies. And, once launched, the same data allows those strategies to identify live and relevant signals and act accordingly.
Choosing the ‘right’ data is important, though – not all data is equal, especially in markets where regulatory reporting requirements differ. The key is finding data solutions and predictive pricing tools that provide the right cross-market coverage and liquidity depth to allow signals to be captured and deciphered.
Finally, contrary to some observers, systematic fixed income investment does not always seek to eradicate the role of humans from the investment process. In fact, it seeks to apply human input where its value is most keenly felt. And although, as I note, the market dynamics have evolved, fixed income credit remains relatively illiquid when set against standard commodity trading advisor (CTA) trading products, underlining the continuing need for some (important) level of human involvement.
Developments in trading desk construction and in trader profiling are helping to ensure that the mix of human and machine is optimised. In part, this is made possible because these machines are not actually black-box, mysterious operations. Without devaluing the complexity involved, particularly with regards the construction of the machines, the machine processes must be simple to the extent that they can be explained.
In short, as the global credit markets become more open and electronic, the foundations are now laid for systematic fixed income credit strategies to better prosper and exploit the available opportunities.
The best data wins
Data is a key prerequisite to creating and running systematic strategies. Without data, the strategy cannot build or subsequently respect a rules-based structure. Reflecting on traditional systematic trading products in other asset classes, each one typically has robust and available data which, in several cases, is drawn from trading exchanges and then applied to the strategy.
The availability (or lack) of data, alongside the market’s structure, has meant that systematic strategies have remained relatively nascent in fixed income credit. However, recently this has changed as fixed income electronic platforms have been able to enrich existing data sets and build sophisticated data tools.
The fixed income credit market does not have trading exchanges like those in equity markets. Instead it relies heavily on voice execution and, increasingly, electronic trading systems. Estimates from Greenwich Associates show that electronic trading in US credit is now approximately 30% of market volume, up from 20% a year ago. This shift in execution structure means that bond trading venues like us possess an increasingly valuable data set from which we can build more sophisticated and insightful tools. Regulatory rules have also given us access to data, such as TRACE, that we can then analyse and enrich to create data sets not previously available in fixed income credit.
A mix of enriched TRACE and historical data alongside AI-powered pricing analytics can create compelling pictures of prior market activity and predictive pricing indicators. That gives systematic funds the ability to assess opportunities across sectors, industries and even individual bonds. What we’ve seen, though, is that with hundreds of thousands of individual bonds available in the credit market, strategy optimisation relies on close collaboration between the data provider and fund. We have worked with several clients to develop a consultative approach that leverages the expertise of our own data scientists and their analytics tools to help appraise and validate the potential opportunity for a systematic fixed income trading strategy. And to then help to build the right pipes to allow the strategy to consume the right data once live.
So, to finish: Data allows systematic funds to see through the fog, establishing the validity and viability of the strategy. The same data then allows assessment of live market opportunities, framed within the rules of the strategy. It is the enrichment of existing data, alongside the construction and development of bespoke data solutions, which has helped fill data gaps left as a result of different regulatory regimes or comparably opaque market trading dynamics and data capture. These continued improvements in data leave clients with systematic fixed income credit strategies with cleaner, clearer signals upon which to execute.
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