Why S&P Global's 'Lost Years' Could Set Up The Next Decade

SPGI reported FY25 revenue growth of 8% and EPS growth of 19% driven by broad-based segment performance. The stock is now trading below its 10-year average P/E and P/FCF, and S&P Global's strong economic moat, proprietary data and brand reduce GenAI-related disruption risk—particularly in ratings and indices.

Analysis

Incumbent data-and-ratings providers sit at the intersection of two offsetting forces: rising demand for high‑quality, auditable inputs (which favors firms with proprietary pipelines and regulatory entrenchment) and accelerating commoditization pressure from cheaper models and open datasets. The practical result is likely a divergence in returns — top-tier franchises will win share and capture pricing power on premium products while lower‑quality suppliers see accelerated churn; expect revenue mix shifts toward higher‑margin SaaS/analytics over the next 12–36 months.

GenAI adoption will drive two cost dynamics that matter for valuation: (1) a step‑up in variable operating expense from model training/inference (GPU/cloud) and labeled‑data procurement that can temporarily compress FCF margins; (2) an opportunity to upsell bespoke, explainable outputs to regulated clients who will pay a premium for provenance and audit trails. The balance of these forces means short‑term margin volatility but persistent long‑term pricing optionality for providers that invest selectively in explainability and workflow integration.

Secondary competitive effects are underappreciated: benchmark and index revenue is sensitive to market beta and AUM flows, so macro drawdowns can depress the most predictable revenue streams and expose operating leverage. Conversely, multi‑year contract wins (index licensing, enterprise analytics) and regulatory reliance (ratings for capital rules) are high‑conviction catalysts that can re‑rate multiples over 6–24 months if communicated with clear revenue visibility.

Contrarian frame: the market either underprices the optionality from softwareized, recurring analytics tied to ESG/benchmarks or overestimates the moat erosion from AI; both cannot be true. My read is modest underpricing of positive optionality — the cleanest path to upside is execution on cross‑sell and margin stabilization after one more year of targeted AI capex rather than a permanent margin decline.

Key Decisions for Investors

• Overweight SPGI (ticker: SPGI) — accumulate into any pullbacks over the next 3 months targeting 12–18 month total return +20–30%. Size as a 3–5% portfolio position and use a 10–12% hard stop or hedge with 6–9 month 5% OTM put protection to cap downside while keeping upside exposure.
• Call‑spread trade to capture re‑rating: buy 12–15 month SPGI ATM calls and sell 6–9 month nearer‑dated calls (calendar/vertical) to finance premium. Aim for a 2–3x upside vs max premium paid; this limits theta decay and monetizes near‑term IV spikes while keeping multi‑quarter exposure to execution/catalyst risk.
• Relative value pair: long SPGI / short MSCI or LSEG (tickers: MSCI, LSEG) to isolate ratings/index execution vs pure index data risk. Make it market‑neutral sized; horizon 6–12 months — target 15–20% relative outperformance if SPGI wins share in analytics or stabilizes margins after AI investments.
• Event/hedge: buy SPGI exposure ahead of two catalysts — next quarterly results and any large index licensing announcements — but purchase protective puts that expire just after each event (30–60 days) to limit event risk. This converts earnings exposure into positive optionality while capping tail losses.