Pretrained Financial Language Model-Guided Multimodal Sensing with Hardware Provenance and Cross-Frequency Temporal...

Financial media risk is jointly driven by multisource content, including news reports, corporate announcements, social media posts, short videos, and livestreams, while content authenticity, propagation velocity, asset relevance, and trading infrastructure conditions can simultaneously influence short-term market fluctuations. To address the limitations of existing methods, including their reliance on either textual information or market sequences, insufficient source verification, and inadequate alignment of asynchronous multimodal signals, FMRP-Net is proposed for artificial intelligence-driven sensing. Event semantics, risk categories, and asset association information are first extracted through a pretrained financial language model and cross-modal consistency analysis. A dual-layer hardware reliability perception module is then employed to integrate sensing evidence from cameras, microphones, terminal inertial signals, server temperature, power consumption, network traffic, and transmission latency. Heterogeneous temporal propagation graphs, cross-frequency alignment, and bidirectional propagation-market coupling are further incorporated to jointly predict market direction, volatility, risk level, and propagation trends. Experimental results demonstrate that FMRP-Net achieved an Accuracy of 0.832, a Macro-F1 of 0.824, a ROC-AUC of 0.891, an MCC of 0.665, and a PR-AUC of 0.883 for market direction prediction over future horizons of 5, 15, 30, and 60 min, indicating a balanced performance in terms of Precision and Recall. For volatility prediction, MAE, RMSE, and MAPE values of 0.0178, 0.0271, and 12.46% were obtained, respectively, together with an R2 of 0.812. In the ablation study, the media risk Macro-F1 and source reliability AUC reached 0.842 and 0.929, respectively, while the propagation-scale prediction error was reduced to 0.109 and the average early-warning lead time reached 10.6 min. These results demonstrate that the integration of multimedia semantics, hardware sensing evidence, and propagation structures can effectively improve the accuracy, stability, and interpretability of financial market prediction and risk early warning.