BT Sovereign Signal

Sovereign investors usually look at countries through market data, credit views, and country data. Those lenses overlap, but they are not the same.

A country can trade at a high spread because fundamentals are weak. It can also trade wide because of liquidity, investor positioning, recent restructuring history, election risk, or a global selloff.

That is all BT-SFS tries to answer. Market comparison is a later use case, not part of score construction.

These rules keep BT-SFS open, auditable, and separate from the market prices it is later compared against.

BT-SFS uses public-source data where possible and a published default/restructuring event registry where public datasets lag recent events.

The score blends source dates. Current IMF fields can be estimates or projections, while WDI and WGI observations often lag. Public releases should show the source year for every metric.

The headline score uses nine required metrics plus a separate default/restructuring adjustment.

External debt remains diagnostic for now. It is important, but current open-source coverage is not clean enough across the whole tradable country set to make it a required input.

Institutional quality is a single composite: the simple average of the World Bank WGI Rule of Law, Government Effectiveness, and Control of Corruption estimates, scored as one metric. WGI is perception-based and typically lags by about two years, so the institutions pillar is the slowest-moving quarter of the score and can carry a residue of past market sentiment.

Every metric is converted into an oriented score where 100 is better and 0 is worse. Ordinary metrics use release-relative percentile ranks.

The default/restructuring adjustment is separate because recent default history is not just another macro variable. It captures willingness to pay, market-access track record, and unresolved restructuring complexity.

The adjustment should be source-linked, time-decayed, capped, visible to users, and included in the public release package. The v0.4 parameters are:

The overlay interacts with the metrics by design: a restructuring can improve measured debt ratios while the penalty offsets it, and the offset fades on the published decay schedule rather than on new behavior.

Required metrics should not be neutral-filled in the public score. A neutral fill can be useful internally for diagnostics, but it makes public comparisons look more precise than they are.

Agency ratings and market spreads are useful. They are also exactly the kinds of things we may want to compare against the score.

One practical use case is a chart comparing BT-SFS with selected USD sovereign bond spreads. That chart is a product and research surface. It is not the methodology itself.

BT-SFS reflects several inputs at once: academic work on ratings, spreads, and debt distress; public rating-agency methodology; public scorecard examples; open-data constraints; and our own product requirement that fundamentals stay separate from market pricing. No single external model is the template.

Academic work on sovereign ratings and spreads supports a compact fundamentals vector. Cantor and Packer show that macro, income, inflation, external, and default-history variables explain much of sovereign rating variation. Eichengreen and Mody, Hilscher and Nosbusch, Longstaff et al., and others show that spreads reflect both country fundamentals and global risk conditions. Mauro, Sussman, and Yafeh add a longer historical comparison across the 1870-1913 and modern globalization periods, which is useful because fiscal variables, political events, country-specific developments, and global co-movement can all matter.

Public rating-agency methodologies from S&P, Fitch, and Moody's use broad categories such as institutions, economy, external position, fiscal strength, monetary flexibility, and event risk. BT-SFS uses similar categories, but with published weights and source rules.

That boundary is important. BT-SFS is an inspectable fundamentals score that can later be compared with ratings, spreads, and default outcomes. It is not a rating model, a default model, or a pricing model.

BT-SFS measures observable macro, fiscal, external, institutional, and default-history fundamentals. It does not fully capture policy credibility, fiscal accounting quality, FX-regime fragility, currency mismatch, political transition risk, or market technicals. The external pillar also measures flow adequacy: reserves in months of imports covers trade needs, not rollover risk against short-term external debt, which stays diagnostic until open-source coverage improves. Those forces may appear later as residuals between the fundamentals score and market spreads.

BT-SFS is not a sovereign rating, default-probability model, recovery model, investment recommendation, or fair-value model for spreads.

BT-SFS has a separate validation appendix because the methodology and the evidence should stay distinguishable. The paper explains how the score is built. The validation appendix asks whether the frozen score behaves sensibly against observable outcomes.

The first validation run is encouraging, but limited. It supports BT-SFS as a transparent fundamentals score with construct and concurrent validity. It does not prove forecast skill, and it does not justify statistically re-estimating the weights yet.

A tier-exclusion cut of the spread fit (validation appendix) shows where that concurrent validity lives: most of the explanatory power is between rating tiers; the distressed CCC cohort sits off the single global curve — excluding it lifts the log-fit R² from 0.58 to 0.69 — and the strong-fundamentals tail anchors the slope, an argument for adding high-grade anchor countries in a future release.

The full appendix is published alongside this paper: BT-SFS validation appendix v0.1.

A public release should include the short paper, the technical appendix, score specification lock, country universe, source-traceable inputs, metric scores, pillar scores, base score, default adjustment, final score, source years, fallback flags, event registry, known source failures, provisional countries, and changelog.

Before launch, the public product also needs a rights and framing review for agency-rating references, EMB-holdings-derived universe construction, and language that could be mistaken for investment advice.

This should be an open project, not a closed claim of authority. Good feedback should be concrete enough to test.

The standard for change should be simple: does the proposal make the model more transparent, more reproducible, more stable, or more useful without pretending to be more precise than the data allow?

The exhibits below show how the current v0.4 dry run looks when the methodology is applied. They are deliberately placed after the methodology: the table, spread view, and history view are evidence and reader aids, not inputs into the score.

Snapshot source: BT-SFS v0.4 internal dry run, market date 2026-06-25. Spreads are selected near-5-duration USD sovereign bond observations. History is a current-vintage backcast, not a real-time historical model.

Metric Profile Matrix

The profile is table-first rather than radar-first. Each cell shows the oriented metric score, then the raw value and source year. Higher score is better for every metric after transformation.

1. IMF DataMapper API help
2. IMF DataMapper general government debt/GDP example
3. World Bank API developer documentation
4. World Bank WDI reserves months example
5. World Bank Worldwide Governance Indicators
6. World Bank WGI Rule of Law API example
7. Bank of Canada sovereign default database update
8. Bank of Canada sovereign default database methodology report
9. VanEck Investment Case for Emerging Markets Debt
10. Cantor and Packer, sovereign credit ratings
11. Eichengreen and Mody, emerging-market spreads
12. Mauro, Sussman, and Yafeh, sovereign bond spreads history
13. Afonso, Gomes, and Rother, sovereign rating determinants
14. Hilscher and Nosbusch, determinants of sovereign risk
15. Longstaff et al., sovereign credit risk
16. Manasse, Roubini, and Schimmelpfennig, sovereign debt crises
17. Kraay and Nehru, external debt sustainability
18. Hellwig, machine-learning fiscal-crisis prediction
19. IMF Vulnerability Exercise approach using machine learning
20. Reinhart, Rogoff, and Savastano, debt intolerance
21. FTSE Russell / LSEG Sovereign Risk Monitor methodology
22. S&P sovereign rating methodology
23. Fitch sovereign rating criteria
24. Moody's sovereign methodology overview