ARTEMIS: A Neuro Symbolic Framework for Economically Constrained Market Dynamics
This work addresses the need for interpretable and economically grounded predictions in quantitative finance, offering a novel framework that bridges deep learning with transparency, though it is domain-specific and incremental in combining existing techniques.
The paper tackles the problem of deep learning models in quantitative finance lacking interpretability and economic plausibility by introducing ARTEMIS, a neuro-symbolic framework that enforces no-arbitrage constraints, resulting in state-of-the-art directional accuracy, such as 64.96% on DSLOB and 96.0% on Time-IMM datasets.
Deep learning models in quantitative finance often operate as black boxes, lacking interpretability and failing to incorporate fundamental economic principles such as no-arbitrage constraints. This paper introduces ARTEMIS (Arbitrage-free Representation Through Economic Models and Interpretable Symbolics), a novel neuro-symbolic framework combining a continuous-time Laplace Neural Operator encoder, a neural stochastic differential equation regularised by physics-informed losses, and a differentiable symbolic bottleneck that distils interpretable trading rules. The model enforces economic plausibility via two novel regularisation terms: a Feynman-Kac PDE residual penalising local no-arbitrage violations, and a market price of risk penalty bounding the instantaneous Sharpe ratio. We evaluate ARTEMIS against six strong baselines on four datasets: Jane Street, Optiver, Time-IMM, and DSLOB (a synthetic crash regime). Results demonstrate ARTEMIS achieves state-of-the-art directional accuracy, outperforming all baselines on DSLOB (64.96%) and Time-IMM (96.0%). A comprehensive ablation study confirms each component's contribution: removing the PDE loss reduces directional accuracy from 64.89% to 50.32%. Underperformance on Optiver is attributed to its long sequence length and volatility-focused target. By providing interpretable, economically grounded predictions, ARTEMIS bridges the gap between deep learning's power and the transparency demanded in quantitative finance.