Feature-Group Diagnostics for Weak-Signal AUD/USD Direction Forecasting

Abstract

Short-horizon AUD/USD direction forecasting is a noisy classification problem relevant to risk monitoring and foreign-exchange exposure management. Many exchange-rate forecasting studies evaluate model classes after combining technical, financial, and macro-related predictors, which can obscure whether performance differences arise from the learner itself or from economic information channels. This paper uses a paired feature-group diagnostic design that combines a full-feature benchmark, OwnMarket-based inclusion, AllGroups-based ablation, window-length sensitivity, and expanding-window robustness analysis. The 45 daily predictors are grouped as OwnMarket, CrossFX, Commodities, EquityRisk, and DollarRatesSpreads. After conservative lag-1 alignment, the best full-feature MCC is 0.076, achieved by the 1D-CNN. Validation-based inclusion-ablation diagnostics rank EquityRisk first among the external groups, driven mainly by its positive ablation contribution. Commodity effects are mixed across models, lookback windows, and expanding-window periods, while overall directional skill remains limited. The results show that paired feature-group diagnostics provide a disciplined way to interpret information-source contributions when model-only rankings are weakly informative.