ABSTRACT Traditional quantitative finance models, fundamentally rooted in the Efficient Market Hypothesis (EMH) and continuous random walks, frequently struggle to predict extreme liquidity events, such as Flash Crashes. During high-entropy transitions, financial markets are modeled to exhibit characteristics of coupled physical systems undergoing structural failure rather than independent stochastic agents. To address this gap, this paper proposes the theoretical application of the HEPOE Theory (High Entropy Predictive Organization Efficiency), originally developed for bioenergetic systems and solid-state materials, to the financial microstructure. We introduce a theoretical leading indicator, the Financial Vectance Operator (νfin(t)), a dimensionless metric derived from Spatio-Temporal Graph Neural Networks (ST-GNN), aimed at estimating the thermodynamic pressure and order flow toxicity on the limit order book. Furthermore, we propose Entropic Damping, the targeted injection of counter-phase liquidity, as a theoretical mitigation protocol designed to potentially restore market homeostasis without trading halts. Preliminary in silico stochastic simulations suggest that the HEPOE Vectance can theoretically identify destructive phase synchrony and estimate potential liquidity rupture Δt before macroscopic price collapse. By mapping the homological relationship between physical material failure and financial market crashes, this paper proposes a theoretical mathematical framework for predicting and potentially mitigating insolvency in high-entropy economic systems. Keywords: Econophysics. Flash Crashes. Financial Vectance. High-Frequency Trading (HFT). Spatio-Temporal Graph Neural Networks (ST-GNN). Entropic Damping. Liquidity Rupture. Structural Fatigue. HEPOE Theory.