We propose SGA-DCAT (Sentiment-Gated Dual-stream Cross-Attention Temporal Network), an architecture for stock price prediction that treats news sentiment not as a passive input feature but as a control signal that governs the model’s memory dynamics. Most sentiment-augmented methods simply concatenate sentiment scores with price inputs, which implicitly assumes that the relationship between sentiment and price is the same regardless of market conditions. SGA-DCAT departs from this practice in three ways: (1) a sentiment-gated adaptive LSTM cell modulates the forget and input gates directly through sentiment signals so that memory retention varies with market mood; (2) a dual-stream cross-attention mechanism encodes price and sentiment through separate recurrent networks and fuses them via learned attention, keeping modality-specific representations intact; and (3) a multi-scale adaptive aggregation module combines predictions from 5-day, 10-day, and 20-day windows using a gating network conditioned on the current market state. On the NASDAQ-100 index with FinBERT-derived news sentiment, SGA-DCAT achieves a MAPE of 1.40%, RMSE of 217.83, and MAE of 171.58, outperforming Persistence, ARIMA, GARCH, MLP, LSTM, GRU, Transformer, DA-RNN, TFT, and LSTM + FinBERT baselines (all p<0.001, Diebold–Mariano test). In ablation experiments, each component contributes measurably, and SGA-DCAT reaches 66.93% directional accuracy on the held-out test set. Rolling-window evaluation across four non-overlapping temporal folds and regime-specific analysis confirm that these results are robust across bull, bear, and volatile market conditions.
Liu et al. (Wed,) studied this question.