Trend-Based Encoding of Exogenous Time-Series for Interpretable Financial Prediction

Integrating heterogeneous exogenous data into financial prediction models is challenging due to scale mismatches and semantic ambiguity. We propose a trend-encoding framework that transforms raw exogenous time-series into directional binary representations, improving predictive robustness while preserving interpretability. Using Saudi stock market data with COVID- 19 indicators, we evaluate predictive models under baseline and trend-enhanced configurations. Results show that trend encoding consistently enhances predictive stability over raw inputs. Interpretable models benefit disproportionately, achieving performance comparable to black-box methods. Sectoral analysis reveals heterogeneous sensitivities: Banking responds strongly to case and mortality trends, Energy to recovery indicators, while Food & Beverages shows weaker alignment. These findings show that trend-based encoding of exogenous signals can improve cross-domain financial prediction, particularly for interpretable models.