Tian Wen WangYong Sook Lu0000-0003-0793-3308Chin Soon KuOkmi MohammedBryan RajLip Yee Por2026-10-132026-10-13202610.1109/ACCESS.2026.3654950https://dspace-cris.utar.edu.my/handle/123456789/11710Stock movement prediction remains challenging due to the complexity of integrating heterogeneous data sources and capturing intricate temporal-semantic relationships. Existing approaches suffer from incomplete semantic preservation, shallow fusion mechanisms, and inadequate multi-scale temporal modeling. In this paper, we propose a multimodal deep fusion framework that addresses these limitations through three synergistic components: 1) An SRTE module based on pretrained BERT that preserves 100% of textual content by segmenting long sequences and applying average pooling, recovering 17.82% more tokens than conventional truncation; 2) An MsLSTM architecture that simultaneously captures short-term price dynamics and long-term market trends through parallel processing of original and temporally smoothed sequences, and; 3) A CMCF mechanism that performs dual-level integration via across-temporal interval fusion and along-temporal step aggregation. We compare our proposed method against baseline models on the StockNet dataset, achieving superior predictive performance. Additionally, we conduct comprehensive ablation studies, back-testing analysis, long-text modeling comparisons, and interpretability analysis. The results validate the effectiveness of deep multimodal fusion for financial forecasting and provide actionable insights for data-driven decision-making in volatile market environments. © 2013 IEEE.en-USBERTdeep learning modelFinancial time series forecastingheterogeneous feature embeddingLSTMmultimodal fusion strategystock movement predictionData integrationDecision makingDeep learningElectronic tradingForecastingInformation fusionLearning systemsPrediction modelsSemanticsDeep learning modelFeature embeddingFusion strategiesHeterogeneous feature embeddingHeterogeneous featuresLearning modelsMovement predictionMulti-modal fusionMultimodal fusion strategyStock movementStock movement predictionCommercetext::journal::journal article