Gated News-Event Fusion: Mathematical Properties and Prospective Evaluation for Stock Direction Forecasting

Next-day stock direction forecasting combines market signals with news, but many trading days contain no firm-specific articles. We develop Gated News-Event Fusion (GNEF), an availability-aware model mapping price sequences, engineered market features, and news features to direction probabilities and a nonnegative return-magnitude estimate. GNEF retains price as a residual representation and uses a scalar sigmoid gate to interpolate between technical and news representations. We prove that the gated component is a norm-bounded convex combination whose gate-logit sensitivity is at most one quarter of the distance between these representations. We also establish conditional Lipschitz stability and forward complexity linear in sequence length. GNEF has 67,625 trainable parameters. Evaluation covers ten large-cap equities under prospective expanding walk-forward testing, paired moving-block bootstrap inference, and transaction-cost analysis. Across five random initializations, the residual design has higher mean directional accuracy than a gated mixture of price, technical, and news representations on 7 of 10 tickers. In a single-seed ablation, full GNEF exceeds no-gate and no-news variants in directional accuracy on 3 of 10 tickers. A broader validation-selected policy exceeds momentum in directional accuracy on 5 of 10 tickers. These findings provide selective support for residual fusion but do not establish a consistent advantage across equities.