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US Β· Financial Intelligence

United States

SIGMA Risk Score Β· Updated Daily
SIGMA Risk Score
43.3/100
Structural Regime
STABLE
Kairos Window
29.5d
Early Warning
CLEAR

⚠ SIGMA regime reflects structural systemic risk, not short-term price direction. Elevated regime classifications indicate fundamental fragility that can persist alongside rising markets. The regime score measures where United States sits on its financial cycle β€” a leading indicator, not a market timing signal.

United States financial intelligence β€” Federal Reserve quantitative tightening, commercial real estate loan crisis, regional bank stress, dollar reserve currency pressure. SIGMA systemic risk score.

External Validation
SIGMA score corroborated by independent authoritative sources

SIGMA score of 43.3/100 (STABLE regime) is consistent with Fed Funds Rate currently reading 3.63% per Federal Reserve FRED β€” an independent benchmark confirming United States's macro stress trajectory.

Federal Reserve FRED β€” Live Macro Data
FEDFUNDS
3.63%
Fed Funds Rate
T10Y2Y
0.40pp
10Y-2Y Yield Spread
UNRATE
4.30%
Unemployment Rate
Authoritative External Sources
43.3/100
SIGMA Score Β· STABLE Regime
Consistent with live FRED indicators
Advanced Metrics
Hurst Exponent
0.622
trend persistence
Rβ‚€ Financial
0.75
contagion coefficient
Fragility Index
β€”
Taleb fragility score
Days to Transition
267
regime shift estimate

Full United States intelligence brief: 8-layer SIGMA analysis, Phantom Chain scenarios, actionable signals.

Updated daily Β· KAIROS Β· SILENCE Β· PHASE SPACE engines included

Access Full United States Analysis β†’

United States Financial Risk Analysis β€” 2026

United States financial risk analysis for 2026 shows a SIGMA score of 43.3/100, placing the country in the stable regime as of the most recent SIGMA Engine calibration. The SIGMA Engine integrates 8 analytical dimensions β€” sovereign, banking, currency, political, network, metabolic, physical, and NLP β€” to compute a deterministic risk composite that cannot be reverse-engineered from market prices alone. A 43.3 SIGMA score reflects manageable systemic stress with identifiable vectors that require continued tracking.

Primary Risk Drivers β€” United States 2026

The primary risk vectors for United States in 2026 converge on banking sector stress β€” capital adequacy under pressure, interbank contagion risk and commercial and residential real estate overvaluation β€” collateral deflation risk. United States financial intelligence β€” Federal Reserve quantitative tightening, commercial real estate loan crisis, regional bank stress, dollar reserve currency pressure. SIGMA systemic risk score. The North American context amplifies these risks through cross-border contagion channels that the SIGMA Network Layer quantifies using Rβ‚€ financial contagion coefficients β€” measuring how many secondary institutions would be stressed by a failure at the first-order node. The SIGMA Early Warning System shows no active pre-crisis flags for United States at present, though the 267-day estimated transition window should be monitored.

SIGMA Engine Methodology: United States

The SIGMA Engine applies an 8-layer mathematical framework to compute the United States risk score. The Hurst Exponent for this entity measures 0.622 β€” above 0.5, indicating persistent trend-following behavior in risk accumulation, meaning current conditions are more likely to continue than reverse. The KAIROS temporal arbitrage window identifies optimal intelligence entry and exit points based on regime transition probability curves. The PHANTOM Chain multi-agent AI system then generates conditional scenario trees: what happens if the primary risk vector materializes, and which secondary countries enter the contagion path.

United States vs Regional Peers

In the context of North American peers, United States's 43.3 SIGMA score sits near the regional median, with outlier risk concentrated in specific sectors. The Silence-Noise Matrix analysis for United States examines the divergence between SIGMA-measured risk and media attention β€” high-SIGMA, low-media entities (the "silent danger" quadrant) represent the highest-value intelligence, as markets have not yet priced the risk. The Consensus Capture module tracks IMF, World Bank, and ECB institutional stance alignment or divergence with the SIGMA Engine's independent mathematical assessment.

Related Risk Intelligence

Frequently Asked Questions β€” United States Financial Risk

What is United States's financial risk score in 2026?

United States's SIGMA financial risk score is 43.3/100 as of 2026, placing it in the stable regime. This score integrates sovereign debt, banking, currency, and political risk dimensions across 8 analytical layers using the Noosphere Prime SIGMA Engine v5.0.

Is United States at risk of a financial crisis in 2026?

With a SIGMA score of 43.3, United States shows stable-level systemic risk β€” not an immediate crisis probability, but identifiable vulnerabilities in banking sector stress β€” capital adequacy under pressure, interbank contagion risk that require monitoring. The SIGMA Engine projects 267 days to potential regime transition.

What are the main financial risks in United States?

The primary SIGMA-identified risk vectors for United States are: (1) banking sector stress β€” capital adequacy under pressure, interbank contagion risk; (2) commercial and residential real estate overvaluation β€” collateral deflation risk; (3) technology sector credit exposure β€” overextension in digital asset class. These interact through cross-sector amplification channels quantified by the SIGMA network contagion coefficient.

How does Noosphere Prime calculate United States's risk score?

The SIGMA Engine computes United States's risk score through 8 deterministic layers: sovereign/fiscal dimension (debt sustainability, primary balance), banking dimension (capital adequacy, NPL ratio), currency dimension (FX reserves, current account), political dimension (institutional stability, policy continuity), network contagion (Rβ‚€ coefficient), metabolic/cycle analysis, physics-based fragility (Minsky moment probability), and NLP analysis of official communications. Each dimension scores 0–100 and the composite SIGMA_FINAL is computed through calibrated weights.

How does United States compare to other North American countries?

United States ranks within the North American risk landscape with a SIGMA score of 43.3. Peer comparisons are available on the Country Comparison page, which provides side-by-side SIGMA dimension breakdown for any two monitored countries. The North American region's systemic interconnection means that contagion from higher-risk peers can elevate United States's effective risk even when its standalone score is moderate.

Methodology & Attribution
SIGMA Engine v5.0 β€” Analytical Foundation

All SIGMA scores are computed deterministically from 8 mathematical layers using peer-reviewed quantitative finance models. Predictions are SHA256-anchored before events and verified at T+30 / T+60 / T+90 against real market data.

Academic Foundations
Hamilton (1989)
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle β€” Econometrica
Markov regime-switching models β€” SIGMA regime classification
Reinhart & Rogoff (2009)
This Time Is Different: Eight Centuries of Financial Folly β€” NBER Working Paper 14898
Sovereign debt crisis indicators β€” SIGMA sovereign/fiscal layer
Minsky (1986)
Stabilizing an Unstable Economy β€” Yale University Press
Financial instability hypothesis β€” SIGMA fragility / Minsky phase engine
Hurst (1951)
Long-Term Storage Capacity of Reservoirs β€” Transactions of the American Society of Civil Engineers, 116
Long-term memory in time series (H exponent) β€” SIGMA trend persistence layer
Hawkes (1971)
Spectra of Some Self-Exciting and Mutually Exciting Point Processes β€” Biometrika 58(1)
Self-exciting point processes β€” SIGMA volatility clustering / EWS module
Data Sources
All predictions SHA256-anchored before events. Verified at T+30/60/90 against real market data. Not investment advice. CC-BY-4.0
Full Methodology β†’