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Multimodal stock trading volume movement prediction with stock-related newsis one of the fundamental problems in the financial area. Existing multimodalworks that train models from scratch face the problem of lacking universalknowledge when modeling financial news. In addition, the models ability may belimited by the lack of domain-related knowledge due to insufficient data in thedatasets. To handle this issue, we propose the Prompt-based MUltimodal StockvolumE prediction model (ProMUSE) to process text and time series modalities.We use pre-trained language models for better comprehension of financial newsand adopt prompt learning methods to leverage their capability in universalknowledge to model textual information. Besides, simply fusing two modalitiescan cause harm to the unimodal representations. Thus, we propose a novelcross-modality contrastive alignment while reserving the unimodal heads besidethe fusion head to mitigate this problem. Extensive experiments demonstratethat our proposed ProMUSE outperforms existing baselines. Comprehensiveanalyses further validate the effectiveness of our architecture compared topotential variants and learning mechanisms.

Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction