Papers by Keiko Harimoto
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction (D19-51)
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| Challenge: | Existing studies on forex prediction ignore related text completely and focus on forex trade data only, which loses important semantic information. |
| Approach: | They propose a BERT-based Hierarchical Aggregation Model to summarize forex news . they group news from different aspects and extract the most crucial news in each group . |
| Outcome: | The proposed model outperforms baseline methods and grouping methods and summarizes the influence patterns for forex trading. |
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. |