Bryan Krug - High Yield Credit Investing

Principles & Lessons:

• Credit investing is fundamentally about understanding how a company will delever over time, not about optimizing for upside. Bryan Krug emphasizes that credit investors are primarily underwriting to the company’s ability to reduce its debt burden through cash flow generation: “The primary metric we look at is the company’s ability to basically delever the business.” This focus on downside protection means credit analysis tends to privilege stable, recurring revenues, low capital intensity, and high free cash flow relative to debt. Unlike equity investors, who often tolerate or even celebrate high leverage in pursuit of growth, credit investors must prioritize the reliability of a return of capital, not just return on capital.
• Valuing asset-backed lending over cash flow is a dangerous illusion because asset values are not invariant—they collapse under stress. Krug highlights a key epistemic mistake among many credit investors: believing asset value provides downside protection, when in fact asset value is a function of prevailing assumptions and prices, not an anchor point. He cites energy bonds in early 2016: “The average E&P... dropped roughly 55, 60 points from par to 35 to 40 cents on the dollar,” despite ostensibly strong asset coverage, because oil prices collapsed. This reveals that asset-based valuations fail under systemic re-pricing—meaning the only robust anchor is recurring cash generation.
• Credit spreads function as an imperfect but revealing market-wide barometer of expected default risk, liquidity conditions, and macro sentiment. The spread over treasuries—say, 400 basis points—embeds expected losses from defaults and other risk premia. Krug walks through the math: “If you assume a 2% default rate... and a 60% loss... you should be paying 120 basis points for just pure default risk.” The rest of the spread compensates for liquidity and uncertainty. Spreads near their historical tights, as they were at the time of this conversation, suggest low perceived risk—implying credit markets are not pricing in economic deterioration, even when equities are volatile.
• Credit markets are structurally inefficient because the largest weightings go to the most indebted issuers, creating a distorted benchmark. Krug underscores a critical flaw: fixed income indices are weighted by debt outstanding, not quality. “Having a lot of debt... is obviously not a very good outcome potentially.” This leads to a paradox: passive investing in high yield allocates more to the riskiest borrowers, while active managers who avoid these names can outperform. This is one reason credit markets remain one of the few domains where active management routinely beats passive—though the edge depends on resisting index-conformity.
• Ratings agencies are not epistemically reliable—rating transitions are slow, backward-looking, and often introduce technical distortions that can be exploited. Krug states flatly: “We think [rating agencies] are terrible.” He provides numerous examples: AIG was rated AAA just months before collapse, and in energy, rating models overweighted operating history and underweighted cost structure, leading to misratings. The most actionable implication is around “fallen angels”—companies downgraded from investment grade to high yield. These transitions often force institutional selling, independent of fundamentals, presenting opportunities for patient buyers who understand the capital structure better than the rating label.
• Data analysis in credit investing should focus on idiosyncratic, company-specific information flows rather than cross-sectional abstractions. Unlike in equity markets, where quant signals often involve broad rankings or style factors, Krug’s team uses real-world proxies to verify cash flow or revenue trends—storm data for roofing companies, weekend box office trends for theaters, or census data for vinyl siding penetration. These narrow, contextual data sets don’t scale easily, which paradoxically makes them more defensible: “There’s a lot of data out there I don’t think people realize exists.” The epistemic edge here comes not from generalized metrics but from precisely tailored informational triangulation.
• The critical difference between equity and credit is not just upside versus downside orientation, but also the asymmetry of outcomes and the fixed nature of payoffs. Credit returns are capped—bonds trade at par—so mistakes are disproportionately costly. Equity investors might tolerate volatility or deterioration in the hopes of upside; credit investors cannot. This asymmetry changes how investors think: “We want to avoid permanent impairment.” It also changes the methodology. Equity valuation is often about modeling future potential; credit is about modeling failure modes and cash sufficiency under adverse paths. This different framing leads to different inference strategies and different tolerances for uncertainty.
• The high yield cycle is driven not just by credit quality but by issuance trends and capital availability—most notably from private equity—and its spillover effects are nonlinear. Krug notes that “risky issuance is kind of a precursor to future defaults,” but that “despite a trillion dollars of PE dry powder,” issuance has been light, which he views as both surprising and benign. However, when PE deployment accelerates, it often comes with looser covenants and structurally riskier capital structures. Since most PE deals involve 60–70% leverage, an issuance surge can shift the entire risk profile of the high yield market. The timing and intensity of that shift—often driven by incentives, not fundamentals—cannot be forecasted by historical default rates alone.

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