Quantitative Geometric Market Structuralism: A Framework for Detecting Structural Endpoints in Financial Markets

Paper: arXiv 2511.16319

Authors: Amir Kavoosi

Abstract

This study introduces the Quantitative Geometric Market Structuralist (QGMS) framework a hybrid analytical methodology integrating geometric pattern recognition with quantitative mathematical modeling to identify terminal zones of large-scale market movements. Unlike conventional econometric or signal-based models, the QGMS framework conceptualizes market dynamics as evolving geometric structures governed by self-organizing principles of price formation. To preserve the proprietary nature of its internal mathematical architecture, the methodology employs a blind-testing validation process, wherein price, symbol, and temporal identifiers are concealed during analysis. This design ensures objective verification without revealing the underlying algorithmic core. The frameworks predictive robustness has been empirically examined across multiple financial crises, including the 2008 Global Financial Collapse, the 2015 EUR CHF SNB event, the 2016 Brexit referendum, and the 2020 COVID-19 market crash. In each case, the system consistently identified structural endpoints preceding major market reversals. The findings suggest that geometric quantitative market interpretation may offer a new class of predictive tools bridging the gap between mathematical formalism and empirical price behavior. By combining academic testability with intellectual property protection, the QGMS framework establishes a viable foundation for institutional evaluation and further research into nonlinear structural forecasting models.

Complexity vs Empirical Score

• Math Complexity: 2.5/10
• Empirical Rigor: 2.0/10
• Quadrant: Philosophers — conceptual discussion, limited math and data

Why this score: The paper relies heavily on conceptual jargon and subjective technical analysis themes while withholding its core mathematical formulas under the guise of intellectual property protection. Without reproducible algorithms, statistical backtests, or concrete mathematical proofs, it fails to meet standard academic rigor for quantitative finance.

Research Flowchart

  flowchart TD
    A[Goal: Identify Terminal Zones of Financial Market Movements] --> B[Data: Multi-Asset Historical Crisis Data]
    B --> C[Method: QGMS Framework]
    C --> D[Blind-Testing Validation]
    D --> E[Computational Analysis: Geometric Pattern Recognition & Math Modeling]
    E --> F[Outcomes: Successfully Identified Structural Endpoints Preceding Reversals]