Physics-Informed Reinforcement Learning for Financial Markets, Deep Hedging and Systemic-Risk-Constrained Portfolio Optimization

Murali Krishna Pasupuleti

Abstract

Abstract: Financial reinforcement learning promises adaptive decision-making under nonlinearity, transaction costs and changing market regimes, yet purely data-driven policies can exploit statistical artefacts, violate self-financing logic and understate externalities transmitted through interconnected institutions. This paper develops a model-based analytical framework that combines physics-informed reinforcement learning, deep hedging and systemic-risk-constrained portfolio optimization. In this context, “physics-informed” denotes the embedding of financial structural laws—stochastic price dynamics, no-arbitrage restrictions, self-financing identities, market-impact equations and network-clearing relations—within policy learning. The proposed Physics-Informed Reinforcement Learning for Hedging and Systemic Resilience (PIRL-HSR) framework integrates a shared state encoder, constrained actor–critic control, coherent tail-risk measures, transaction-cost-aware hedging and network-based systemic-risk penalties. Its mathematical architecture includes stochastic differential dynamics, matrix exposure models, physics-residual regularization, conditional value-at-risk, Eisenberg–Noe-style clearing, Lagrangian constrained optimization and a master objective balancing return, hedge error, liquidity and contagion. An illustrative normalized 0–10 numerical model yields a risk-adjusted resilience score of 7.28 for the proposed configuration, compared with lower scores for mean–variance, unconstrained deep reinforcement learning and conventional deep-hedging benchmarks; these figures are pedagogical rather than empirical. Analytical sensitivity indicates that systemic-risk aversion improves resilience up to an interior range, after which excessive penalties may erode diversification and return capacity. The paper contributes a unified research design for asset managers, banks, insurers and regulators seeking adaptive financial control that remains economically coherent, tail-aware and systemically responsible. Empirical validation requires market, derivatives, balance-sheet, liquidity and network-exposure data across multiple regimes. Keywords: physics-informed reinforcement learning; deep hedging; financial markets; systemic risk; portfolio optimization; constrained Markov decision processes; conditional value-at-risk; network contagion; no-arbitrage learning; self-financing constraint; transaction costs; financial resilience; actor–critic methods; stress testing; model risk.

Source: semanticscholar · PDF

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