A successful systematic credit strategy is not just about predicting returns. It equally relies on accurately forecasting the associated risks and, crucially, the transaction costs, described as avoiding giving a 'liver and a kidney to Goldman Sachs.'
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Identifying flawed investments, especially in opaque markets like private credit, is rarely about one decisive discovery. It involves assembling a 'mosaic' from many small pieces of information and red flags. This gradual build-up of evidence is what allows for an early, profitable exit before negatives become obvious to all.
In credit markets, where transaction costs can reach 70-80 basis points for high-yield bonds, a systematic strategy's success hinges equally on its trading efficiency as on its return forecasts. A good model is useless if its alpha is consumed by trading costs.
Systematic models don't attempt to forecast unpredictable shocks like policy changes. Instead, they build portfolios with 'guardrails'—diversifying away concentrated macro risks like sector or country bets—to ensure resilience and avoid being badly damaged by any single event.
Systematic credit comprises only 2-3% of active funds, versus 20% in equities. This lag is not due to performance but to institutional inertia, incumbent resistance, and the perception of strategies as 'black boxes.' Acadian's Scott Richardson argues transparency is the key to overcoming this.
Goodwin argues against the passive "index-hugging" approach to credit focused on coupon payments and agency ratings. Diameter's edge comes from approaching credit like an equity long-short fund, constantly analyzing what macro and sector trends will change security prices over the next 3 to 24 months to generate total return.
Before seeking exotic alternative data, systematic credit investors must solve a more fundamental problem: correctly mapping standard financial and market data to the specific bond-issuing legal entity within a complex corporate hierarchy. Getting this wrong invalidates any model.
The common practice of bifurcating credit portfolios into 'investment grade' and 'high yield' is an artifact of historical benchmarks and institutional mandates, not an economically optimal approach. A purely systematic view would blend them based on risk characteristics.
A core discipline from risk arbitrage is to precisely understand and quantify the potential downside before investing. By knowing exactly 'why we're going to lose money' and what that loss looks like, investors can better set probabilities and make more disciplined, unemotional decisions.
Barclays' research shows that the best investment performance comes from combining fundamental analysts with systematic signals. The key is to filter out trades where the two perspectives diverge, as this method is exceptionally effective at eliminating potential losing investments and generating alpha.
Instead of focusing on vague metrics like management or margins, the primary measure of a "good business" should be its fundamental return on invested capital (ROIC). This first-principles, quantitative approach is the foundation for sound credit underwriting, especially in illiquid deals.
