TREFIN: Interpretable Financial Forecasting through Typed State Dynamics

An interpretable financial forecast should expose the measurable state on which it depends. We introduce TREFIN (Typed Relational Evolution for Financial Forecasting), a probabilistic framework that links asset concepts, residual dependence, and market conditions through an observed-factor measurement procedure. Concepts and a signed residual graph evolve jointly, with distinct propagation rules for directional and risk-like quantities. Future measurements supervise the predicted states, which provide the sole information interface to an asymmetric, heavy-tailed return decoder. A system-level theorem establishes invariant state domains, distributional validity, finite-horizon error propagation, and a conditional downside-scale response. On the 32-asset FinMultiTime task, the validation-calibrated model achieves the lowest mean squared and root mean squared errors and ranks among the top two methods on all six metrics in the main five-seed comparison. State diagnostics reveal how named quantities affect the forecast, while chronological evaluations and retraining expose complementary strengths and remaining calibration limitations. TREFIN makes intermediate financial states testable without giving up competitive nonlinear prediction.