FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty

Financial markets exhibit complex, non-linear dynamics characterized by high volatility and uncertainty, making accurate stock movement prediction a challenging task. This study introduces FusionLSTM-CNF, a hybrid deep learning framework that integrates multi-modal data fusion, Long Short-Term Memory (LSTM) networks, and confidence calibration for stock movement prediction under uncertainty. Our model leverages a late fusion architecture, combining the outputs of three parallel LSTM sub-models trained on technical indicators, textual sentiment from financial news, and cross-asset correlation signals. A confidence-aware neural fusion (CNF) layer adaptively reweights modality contributions based on learned uncertainty estimates. We validate our model across multiple financial indices including S&P 500, NASDAQ, and FTSE 100. Experimental results show a 12.3% relative improvement in prediction accuracy over single-modal LSTM baselines and 23.7% reduction in prediction variance. Compared to recent state-of-the-art hybrid architectures, improvements are more modest (1.1–1.8% absolute accuracy gain) but statistically significant (Diebold-Mariano test, $p < 0.05$). The framework provides calibrated confidence estimates (Expected Calibration Error: 0.031) that enable confidence-filtered trading strategies with improved risk-adjusted returns (Sharpe ratio: 0.267 for top-40% confidence trades versus 0.131 unfiltered). This work contributes to financial artificial intelligence by demonstrating the practical benefits of unifying uncertainty quantification with heterogeneous time-series fusion, providing practitioners with both predictions and confidence estimates for risk-aware decision-making.