Ahead of the Curve: The Future of Quant Credit

This article was originally published in IPE on 1 October 2021.

Introduction

The past several decades have seen quantitative strategies established as an important feature of global equity markets. In 2019, less than a quarter of the more than USD30 trillion of US equities was held by human-managed funds.

The balance was in quantitative investing styles (through a combination of exchangetraded funds, which are essentially passive quant investing, and more than one-third by funds pursuing more active quantitative investing mandates). This is a trend that is playing out across different markets. For example, quant equity funds are burgeoning in China and in new asset classes, particularly credit.

Quant credit has been slower to take off than many expected, largely owing to the over-the-counter nature of trading and the consequent lack of deep pools of data (which quant strategies require to function efficiently). With the rise of electronic trading and settlement in credit, this impediment has fallen away and the past few years have seen volumes in quant credit grow dramatically. This growth looks set to continue and it is likely that quant strategies will increasingly come to dominate the credit markets.

The Evolution of Quant Credit

It is a fascinating time to consider how quant credit will evolve in the decade to come. With the market still in its nascency, there are numerous potential paths for the strategy class to follow. This article will explore several possible iterations. Not all will come to pass, but a few things appear highly likely from our vantage point as early participants in this fast-developing market.

First, it will be a future of increasing data sophistication and the use of machine learning. It is likely more and more traditional quant equity players will move into the quant credit space and use the sophisticated models and data management techniques they have deployed in equities to generate alpha in credit.

The strategy class looks set to move swiftly beyond bonds and credit default swaps to target other fixed-income products through quant strategies. Investors will most likely seek to combine quant approaches to equity, credit and hybrid instruments, leading to greater cross-capital structure relative value. Above all, it is likely to be a market whose essence is radically transformed from one dominated by traditional, buy-and-hold investors to faster-moving, more data-driven and dynamic participants.

The Development of Quant Credit

As far as the next stages of the development of quant credit go, the following areas ought to be of particular interest.

• Loans and convertible bonds: Quant funds look set to target opportunities in leveraged loans and, particularly, convertible bonds. While loans and other private credit instruments are obviously problematic from a data availability perspective at present, the momentum of quant activity in other areas of the fixed-income markets will likely drive innovation and transparency in these historically opaque areas. Already both Refinitiv and IHS Markit are providing daily pricing on 6,500 loan facilities;
• Sub-asset classes: Quant credit will evolve beyond corporate bonds and loans to other sub-asset classes of fixed income – eventually spanning the rest of the fixed-income indices, including sovereigns, mortgage-backed securities and assetbacked securities;
• Emerging-market debt: Emerging markets is a clear area of potential growth for quant credit strategies, as is the Chinese corporate debt market. Issues of data availability and market liquidity are endemic here today, but both of these areas are likely to be addressed by the increasing electronification of markets;
• Distressed: Quant credit is yet to move into distressed debt – this is a high-touch asset class that requires a significant degree of human intervention but further data capture and analysis around restructurings and recovery may make this possible;
• Liability-driven investment (‘LDI’): Beyond the range of different securities covered, quant credit can also broaden investor capabilities to address more idiosyncratic portfolio construction problems. There is further room to expand into short-term cash management strategies. LDI, with its specific key rate profiles, can easily be solved for and managed in a risk and transaction cost-aware manner;
• Alternative data: The increasing availability of alternative data sources in the credit markets will provide particularly fertile ground for quant credit strategies. There are numerous areas where alt data is able to provide information about a company’s fundamentals that is yet to be evidenced in corporate filings or credit ratings and the most sophisticated quant credit managers will take advantage of this to steal a march on the broader market. This might include everything from crowd-sourced social media sentiment to the web scraping of data relating to a company’s pricing, inventory and sales from public retail sites;
• Capital structure arbitrage: The increasing integration of quant credit and equity strategies looks likely. Firms look set to be viewed on an ecosystem basis with quant funds seeking not only to identify relative value opportunities between different companies but also within a firm’s own capital structure. This will require strong data-processing skills as well as sophisticated models able to judge the relative attractiveness of a variety of different securities, and particularly the ability to manage risk across asset classes (risk models are often are single-asset class only).

Precisely how and when these aspects of quant credit play out is unclear, but one thing seems irrefutable: the momentum behind quantitative approaches to the fixed-income markets has increased rapidly in the past few years and that the product now stands on the cusp of enormous growth. Those aspects of bond trading that previously stood in the way of quant credit attaining the kind of dominant position that quant equity has achieved have largely disappeared.

All investors in credit need to recognise the extraordinary transformation that lies ahead and prepare for credit markets in which humans design the algorithms that direct trading activity, rather than carrying out the trades themselves.

Conclusion

Allocators recognise that diversification is crucial when attempting to build an asset mix that is robust in the face of a variety of different market conditions. Quant credit brings multiple different forms of potential diversification to a portfolio – not only the diversification of investment process, but also likely uncorrelated returns.

Looking to quant equity, where there is a much longer and deeper pool of performance data, it is apparent that the historical correlation between quant returns and traditional discretionary returns is less than 0.1. This suggests that, as quant credit scales up and branches out into more areas of the fixed-income landscape, there is the potential for a new and uncorrelated source of diversification. As such, allocators are likely to embrace quant credit enthusiastically in the coming years.