The Systematic Credit Gap: Why This Frontier Remains Underexplored

Systematic investing has profoundly reshaped equity and futures markets. From high-frequency trading to long-horizon factor investing, quant models have established themselves as foundational. Yet in corporate credit – a multi-trillion dollar market ripe with inefficiencies – systematic strategies remain an outlier.

Why?

After months of research and conversations with allocators, portfolio managers, and systematic pioneers, the picture is clear: credit’s path to quantification has been slower not due to lack of opportunity, but due to structural headwinds. Encouragingly, that is beginning to change.

Let’s unpack why systematic credit strategies have lagged, and why their time may finally be arriving.

Market Microstructure: The Friction That Slowed Innovation

Unlike equities or futures, corporate bonds don’t trade on centralized exchanges. Liquidity is fragmented across OTC markets, where price discovery still leans on request-for-quote (RFQ) systems and bilateral dealer relationships. Until recently, this meant patchy data, indicative pricing, and wide bid-ask spreads – all dealbreakers for model-driven execution.

Moreover, many bonds simply don’t trade daily. Some don’t trade for weeks. In such an environment, building a live, clean, and actionable dataset is a massive lift. Equities offer one share class per company, traded continuously. Credit gives you hundreds of thousands of securities, each with idiosyncratic features – callability, covenants, seniority, and maturity profiles.

We’re finally seeing progress. TRACE has improved post-trade transparency. Electronic platforms like MarketAxess and Tradeweb are pushing execution into the digital realm. Credit ETFs and portfolio trading have helped “equify” parts of the market. But compared to the millisecond precision of stock exchanges, credit remains semi-analog.

Innovations Including Portfolio Trading Starting to See Growth

Modeling Credit Is Hard – But Getting Easier

Even with better data, modeling credit requires tools beyond typical quant playbooks. Default risk is discontinuous – one event can wipe out years of carry. A company might issue 15 different bonds, all reacting differently to news, capital structure changes, or even tax treatment.

Systematic credit strategies must marry statistical rigor with credit-specific nuance. That means modeling probability of default and loss given default. It means accounting for issuer fundamentals, macro exposures, and bond-specific quirks. There’s no elegant “value factor” without estimating fair spread versus risk.

Credit markets are more complex than equity markets

Source: BlueCove

Machine learning doesn’t save you here either. Defaults are rare events, and many bonds have short histories, limiting the depth of training data. Quants often consolidate to issuer level or use proxies – but this increases abstraction and model complexity. Still, we’re seeing funds build serious infrastructure to ingest balance sheet data, parse ratings dynamics, and combine it with price signals.

Credit-specific alpha may be more elusive than in equities – but it’s there for those with the tooling and patience.

Execution: The Last Barrier to True Systematic Scaling

A trading signal means nothing if you can’t execute. In credit, you can’t just hit a bid on a central book. You send an RFQ, negotiate, maybe get an indicative quote, maybe not. Bid-ask spreads, especially in high yield, are wide enough to make some trades uneconomical – unless your alpha is unusually strong.

But the gap is narrowing. Man Numeric reports that ~90% of its IG and HY trades are now electronic – up from ~30% a year prior. Open trading platforms, all-to-all networks, and algorithmic smart order routers are transforming how bonds trade.

Systematic credit investing has only recently become feasible at scale

Source: BlueCove

Still, execution risk must be embedded into models: from slippage and capacity constraints to the reality that some signals may be “theoretical” until liquidity matches. Many funds also use substitution techniques – approximating exposures via ETFs or similar bonds when the target security is illiquid.

In equities, signal to trade is often one step. In credit, it’s more like four.

Top challenges to investing in systematic credit

Source: BNP Paribas Prime Services Capital Introduction Flash Survey, September 2021 

Investors Are Interested – But Still Watching Closely

Systematic credit strategies face a chicken-and-egg challenge. Allocators want track records and scale before allocating serious capital. But managers need capital to build scalable strategies and infrastructure. Historically, most credit mandates went to discretionary managers – especially in high yield, distressed, or event-driven strategies.

But this is changing. Higher rates and wider spreads have revived interest in credit. Investors are rethinking their sources of alpha. A 2021 BNP Paribas survey showed over half of institutional respondents exploring systematic credit. Consultants and funds-of-funds have been early movers – now pensions and insurers are following.

Most important factors when making an allocation to systematic credit

Source: BNP Paribas Prime Services Capital Introduction Flash Survey, September 2021

Credible managers with multi-year live performance – especially those who navigated 2020 or 2022 – are gaining attention. Systematic credit can offer diversifying return streams, low beta to traditional credit, and transparency. But liquidity remains a gating concern for many allocators – and rightly so.

The New Frontier: Systematic Credit Takes Shape

For years, systematic credit strategies faced steep skepticism – not only due to structural and technical challenges, but also cultural inertia. Allocators were accustomed to discretionary credit managers navigating high-touch, idiosyncratic markets. The quant playbook felt ill-suited.

But this narrative is shifting. Systematic credit investing is no longer just a curiosity. It is becoming a credible, scalable, and diversifying complement to both traditional credit and systematic equity portfolios.

Today, pioneers are proving that with the right architecture – both technological and investment – systematic credit can scale. Firms like AQR, Man Numeric, Acadian, and BlueCove are leading the charge, while multi-strategy platforms like ExodusPoint are embedding systematic credit pods alongside discretionary teams, slowly bridging the cultural divide. Their approaches vary: from style premia and relative value to high-throughput signal processing and execution automation, to the application of equity-style factor investing to over 20,000 bonds across developed markets.

On the allocator side, interest is building. Institutions are seeking uncorrelated alpha. Consultants and early adopters have paved the way, and now pensions, insurers, and OCIO platforms are evaluating how systematic credit fits into broader fixed-income diversification strategies.

Conclusion

There is still caution, particularly around liquidity, model explainability, and drawdown resilience. But the direction of travel is clear.

Yes, credit’s structural quirks persist. But the technological, data, and execution advancements of the last five years have reshaped what’s possible. Those who invest early – in infrastructure, talent, and trust – may find themselves ahead of a structural trend.

The next five years could see systematic credit follow the same adoption curve that quant equity saw in the 2000s. And as history shows, when the infrastructure is in place, capital tends to follow.