4 research outputs found
Incorporating Fine-grained Events in Stock Movement Prediction
Considering event structure information has proven helpful in text-based
stock movement prediction. However, existing works mainly adopt the
coarse-grained events, which loses the specific semantic information of diverse
event types. In this work, we propose to incorporate the fine-grained events in
stock movement prediction. Firstly, we propose a professional finance event
dictionary built by domain experts and use it to extract fine-grained events
automatically from finance news. Then we design a neural model to combine
finance news with fine-grained event structure and stock trade data to predict
the stock movement. Besides, in order to improve the generalizability of the
proposed method, we design an advanced model that uses the extracted
fine-grained events as the distant supervised label to train a multi-task
framework of event extraction and stock prediction. The experimental results
show that our method outperforms all the baselines and has good
generalizability.Comment: Accepted by 2th ECONLP workshop in EMNLP201
Incorporating Pre-trained Model Prompting in Multimodal Stock Volume Movement Prediction
Multimodal stock trading volume movement prediction with stock-related news
is one of the fundamental problems in the financial area. Existing multimodal
works that train models from scratch face the problem of lacking universal
knowledge when modeling financial news. In addition, the models ability may be
limited by the lack of domain-related knowledge due to insufficient data in the
datasets. To handle this issue, we propose the Prompt-based MUltimodal Stock
volumE prediction model (ProMUSE) to process text and time series modalities.
We use pre-trained language models for better comprehension of financial news
and adopt prompt learning methods to leverage their capability in universal
knowledge to model textual information. Besides, simply fusing two modalities
can cause harm to the unimodal representations. Thus, we propose a novel
cross-modality contrastive alignment while reserving the unimodal heads beside
the fusion head to mitigate this problem. Extensive experiments demonstrate
that our proposed ProMUSE outperforms existing baselines. Comprehensive
analyses further validate the effectiveness of our architecture compared to
potential variants and learning mechanisms.Comment: 9 pages, 3 figures, 7 tables. Accepted by 2023 KDD Workshop on
Machine Learning in Financ