Papers with stock movement prediction
Incorporating Fine-grained Events in Stock Movement Prediction (D19-51)
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| Challenge: | Existing studies mainly adopt coarse-grained events, which loses the specific semantic information of diverse event types. |
| Approach: | They propose to use a finance event dictionary to extract fine-grained events from finance news to train a neural model that uses the extracted events as the distant supervised label to train stock prediction. |
| Outcome: | The proposed method outperforms baselines and has good generalizability. |
News2vec: News Network Embedding with Subnode Information (D19-1)
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| Challenge: | Existing approaches to embed news as vectors do not integrate features and inter-textual knowledge of news. |
| Approach: | They propose a model that integrates news features and inter-textual knowledge into a dense vector representation. |
| Outcome: | The proposed model can be used to represent news as a dense vector . it is compared with existing models on stock movement prediction and news recommendation tasks . |
Causality-Guided Multi-Memory Interaction Network for Multivariate Stock Price Movement Prediction (2023.acl-long)
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| Challenge: | Existing models for stock price movement prediction use auxiliary data, but we assume other stocks should be utilized as auxiliary information to enhance performance. |
| Approach: | They propose a Causality-guided multi-memory interaction network for stock movement prediction which transforms basic attention into Causal Attention by calculating transfer entropy between multivariate stocks. |
| Outcome: | The proposed model outperforms existing models on three real-world datasets from the U.S. and Chinese markets. |