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S&P 500 Sector Reconstruction: A Structural Analysis Using Network Theory
Executive Summary
This analysis challenges traditional sector classifications by applying advanced network theory and machine learning to S&P 500 return data. Our findings reveal that official GICS sectors often fail to capture true market relationships, with significant implications for portfolio diversification and risk management.
- Market structure is statistically significant (p < 0.001)
- Official sectors show weak alignment with return-based clusters
- Energy sector exhibits unique risk-return characteristics
- Nonlinear dimensionality reduction reveals market curvature
- Spinglass algorithm outperforms other community detection methods
- Traditional sector diversification may leave hidden risks
- Data-driven clustering improves portfolio optimization
- Sector "defectors" present arbitrage opportunities
- Advanced analytics enhance risk assessment capabilities
- Return-based clustering reveals true correlations
Algorithm Performance Comparison
ARI vs Official Sectors
Sector Classification Accuracy
Community Size Distribution
Neural Network Sector Classification Performance
| Sector | Stocks Analyzed | Accuracy | False Positive Rate | Key Insight |
|---|---|---|---|---|
| Energy | 10 | 90% | 0.06 | Most distinct return patterns |
| Technology | 10 | 80% | 0.08 | High correlation cluster |
| Healthcare | 10 | 70% | 0.12 | Mixed with defensive stocks |
| Financials | 10 | 65% | 0.15 | Overlaps with industrials |
| Consumer Staples | 10 | 60% | 0.18 | Defensive sector blend |
| Utilities | 10 | 85% | 0.07 | Strong defensive cluster |
| Communication | 10 | 45% | 0.25 | High sector misclassification |
| Industrials | 10 | 55% | 0.20 | Diverse subsectors |
| Materials | 10 | 70% | 0.13 | Commodity correlation |
| Real Estate | 10 | 75% | 0.10 | Interest rate sensitivity |
| Consumer Disc. | 10 | 50% | 0.22 | High within-sector variance |
Methodology & Data Sources
Data Acquisition
Top 10 S&P 500 stocks per sector by market cap, 1-year daily returns from Yahoo Finance
Network Construction
Correlation-based adjacency matrix with 0.35 threshold, community detection algorithms
Machine Learning
Neural networks for sector classification, UMAP dimensionality reduction techniques
Statistical Testing
Spectral tests, triangle tests, modularity optimization, confusion matrix analysis
Download Full Report
Get the complete analysis including detailed methodology, R code, visualizations, and comprehensive results.
Download PDF Report (623 KB)2026 Forecast: The Input Efficiency Ratio (IER) Divergence
December 21, 2025Abstract
While 2025 Net Farm Income (NFI) is artificially buoyant ($4.79B) due to ad-hoc government support, underlying efficiency metrics are flashing red. DakotaAI analysis of NDSU crop budget data reveals a critical divergence in the Input Efficiency Ratio (Revenue generated per $1 of Input Expense).
The Analysis
We normalized inflation-adjusted input costs against projected crop receipts for Wheat/Canola rotations in Cass and Barnes counties:
For every $1 spent on inputs, farmers kept $0.18 profit
Propped up by federal payments
Negative ROI without subsidy
IER Trend Analysis
DakotaAI Conclusion
The data indicates that 2026 is not a "marketing" problem; it is a "variable cost" problem. Producers operating with an IER below 1.00 must automate chemical application (reduce variable load) or face liquidity crises in Q3 2026.
North Dakota Soybean Basis: A 3-Sigma Dislocation Event
December 21, 2025 Global MarketsAbstract
Current spot bids for Soybeans in the Red River Valley are trading at -$1.40 to -$1.75 under the March '26 (ZSH26) futures contract. While local sentiment attributes this to "China trade issues," our statistical analysis confirms this is a historical outlier.
Statistical Analysis
We computed the 10-year rolling mean for Q4 North Dakota basis levels:
Under futures contract
Under futures contract
Basis Distribution (10-Year)
DakotaAI Conclusion
A 3-sigma event (occurring statistically once every ~30 years) implies that standard "store and wait" hedging strategies are mathematically flawed in this environment. The market is signaling a structural break in PNW (Pacific Northwest) rail logistics, not just a temporary price dip.
Recommendation: Co-ops should switch to "Back-to-Back" automated hedging rather than speculating on basis narrowing.
Bakken Gas-to-Compute: The $0.04/kWh Arbitrage Window
December 21, 2025 EnergyAbstract
September 2025 data shows North Dakota natural gas production hitting an all-time high of ~3.79 BCF/day, while crude export capacity remains flat. This has created a surplus of "associated gas" that is aggressively discounted at the wellhead.
Pipeline Injection Value
After compression and transport fees:
Netback to operator
Onsite Compute Value
Converting 1 MCF to ~100 kWh for tier-4 compute:
At $0.04/kWh industrial equivalent
The Arbitrage Spread
Difference between Pipeline and Digital value:
Value premium for compute conversion
Gas Value Comparison
DakotaAI Conclusion
The spread between "Pipeline Value" and "Digital Value" is currently 227%. Operators continuing to inject gas into constrained pipelines are leaving significant margin on the table compared to onsite modular data center deployment.
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