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PhD research investigating whether Large Language Models can detect structural constraints in financial markets through genuine reasoning rather than training data memorization.
Core Innovation: Temporal obfuscation testing — stripping all dates, ticker symbols, and contextual markers from financial data, forcing LLMs to reason from numerical structure alone.
Application Domain: Options dealer gamma exposure (GEX) — the aggregate hedging constraints that market makers face due to their options inventory positions.
| Metric | Result |
|---|---|
| Detection Rate | 71.5% (obfuscated, unbiased prompts) |
| Predictive Accuracy | 90.9% (forward returns) |
| Raw Chain Superiority | 92.3% vs 61.5% GEX-assisted (+30.8pp) |
| Test Coverage | 242 trading days (SPY, 2024) |
Finding: LLMs reconstruct dealer positioning from raw strike-level data, outperforming pre-calculated metrics — empirical evidence that scalar GEX aggregation discards structural signal.
| Metric | Result |
|---|---|
| 2024 Detection | 81.2% (persistent regimes) |
| 2020 Detection | 12.1% (pre-0DTE baseline) |
| Discrimination | 69.1pp separation (φ = 0.672, p < 0.0001) |
| False Positives | 0% on transitional/low-magnitude controls |
| Coverage | 1,412 windows + 809 controls (2020–2025) |
Finding: Detection tracks 0DTE options adoption — 3.7% (2021) → 100% (2024) — with GEX magnitude growing 360%, revealing a structural market reorganization.
Stable detection (68–74% quarterly) persists while economic profitability collapses (Sharpe 1.8 → 0.1), confirming detected patterns are structural mechanics, not exploitable anomalies.
Title: Inferring Latent Market Forces: Evaluating LLM Detection of Gamma Exposure Patterns via Obfuscation Testing
- arXiv: 2512.17923
- Venue: 2nd IEEE Workshop on LLMs for Finance @ IEEE BigData 2025 (Dec 2025, Macau)
- LaTeX:
docs/papers/paper1/ - Headline: 71.5% obfuscated detection, 90.9% predictive accuracy on forward returns
Title: Validating LLM Structural Reasoning: Detecting Persistent Market Regimes Through Temporal Obfuscation
- Venue: IFIP International Conference on AI Applications and Innovations (Springer LNCS; camera-ready May 2026)
- LaTeX:
docs/papers/paper2/aiai/ - Headline: 81.2% detection (2024) vs 12.1% (2020) — 69.1pp separation, φ = 0.672, p < 0.0001 over 2,221 evaluations
Title: Validating LLM Structural Reasoning: Detecting Persistent Market Regimes Through Temporal Obfuscation
A journal-length submission that combines the obfuscation methodology validated at IEEE BigData 2025 with the multi-day regime-detection results from the AIAI 2026 paper, plus reviewer-driven additions: full prompt reproducibility, bootstrap CIs, χ² + Fisher contingency tests, threshold sensitivity, and a Markov-switching benchmark.
- Venue: Journal of Risk and Financial Management (MDPI)
- LaTeX:
docs/papers/jrfm/ - Status: Major revision (R3 round) submitted April 2026
Research extensions not pursued within this repository (cross-asset generalization, intraday/per-strike analysis, GNN-based cross-asset hedging networks): docs/papers/extensions/.
Raw: SPY, 2024-03-15: Net GEX: -$32.9B, Flip: $485.00
Obfuscated: Day T+0, INDEX_1: Net GEX: -$32.9B, Flip: $485.00
Remove dates, tickers, events → preserve only quantitative structure → force structural reasoning.
Every detection must specify:
- WHO: The constrained actor (e.g., dealers with negative gamma)
- WHOM: The affected parties (e.g., directional traders)
- WHAT: The forced mechanism (e.g., pro-cyclical hedging amplifying volatility)
| Criterion | Threshold | Purpose |
|---|---|---|
| Persistence | ≥ 70% days same sign | Exceeds random binomial (~2.2σ) |
| Magnitude | ≥ $5B average |GEX| | Economically significant positioning |
| Stability | ≤ 5 sign flips | Sustained directional bias |
- PostgreSQL 18.1: 81.8M contracts, 50 symbols, 2020–2025 (20.58 GB)
- Intraday snapshots: Yearly-partitioned table, 21 snapshots/day
- Alpha Vantage Premium: Historical options chains (1000 calls/min)
- Polygon.io: Stock price data (free tier)
- OpenAI o4-mini: Reasoning model via Batch API
- Cost: $11.07 for all 2,221 evaluations
gex-llm-patterns/
├── src/
│ ├── agents/ # LLM market mechanics agent
│ ├── analysis/ # Pattern library (15 patterns)
│ ├── gex/ # GEX calculator (Black-Scholes)
│ ├── llm/ # LLM integration
│ ├── validation/ # Obfuscation & regime classification
│ └── data_sources/ # Alpha Vantage, Polygon clients
├── scripts/
│ ├── validation/ # IEEE BigData + AIAI/JRFM validation pipelines
│ ├── analysis/ # Sensitivity analysis, figures
│ └── data_collection/ # Intraday OI monitor
├── docs/
│ ├── papers/
│ │ ├── paper1/ # Single-day obfuscation source — IEEE BigData 2025 (published)
│ │ ├── paper2/ # Regime detection source — AIAI 2026 (accepted)
│ │ ├── jrfm/ # Combined journal submission — JRFM/MDPI (under review)
│ │ └── extensions/ # Forward-looking research directions (snapshot)
│ └── presentations/ # PhD symposium, fundamentals
├── reports/ # Validation results (YAML)
└── config_defaults/ # Configuration templates
git clone https://github.com/iAmGiG/gex-llm-patterns.git
cd gex-llm-patterns
pip install -r requirements.txt
# Configure API keys
export OPENAI_API_KEY="your_key"
export POLYGON_API_KEY="your_key"
# Verify
python -c "from src.analysis.pattern_library import PatternLibrary; print('OK')"- Academic research only — not trading advice
- Public data only — Alpha Vantage options chains
- Open source — methodology and code fully available
- Not financial advice — detection rates do not imply profitable strategies
@inproceedings{regan2025obfuscation,
author = {Regan, Christopher and Xie, Ying},
title = {Inferring Latent Market Forces: Evaluating {LLM} Detection of
Gamma Exposure Patterns via Obfuscation Testing},
booktitle = {2nd IEEE International Workshop on Large Language Models
for Finance, IEEE International Conference on Big Data},
year = {2025},
publisher = {IEEE},
url = {https://arxiv.org/abs/2512.17923}
}GNU Affero General Public License v3.0 — see LICENSE.
Last Updated: April 2026
Contact: Christopher Regan (cregan1@kennesaw.edu) · Ying Xie (yxie2@kennesaw.edu)
Institution: Kennesaw State University, College of Computing and Software Engineering