Structured Event Representation and Stock Return Predictability
Fuente:
arXiv
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866909974385393664 |
|---|---|
| author | Li, Gang Qiao, Dandan Zheng, Mingxuan |
| author_facet | Li, Gang Qiao, Dandan Zheng, Mingxuan |
| contents | We find that event features extracted by large language models (LLMs) are effective for text-based stock return prediction. Using a pre-trained LLM to extract event features from news articles, we propose a novel deep learning model based on structured event representation (SER) and attention mechanisms to predict stock returns in the cross-section. Our SER-based model provides superior performance compared with other existing text-driven models to forecast stock returns out of sample and offers highly interpretable feature structures to examine the mechanisms underlying the stock return predictability. We further provide various implications based on SER and highlight the crucial benefit of structured model inputs in stock return predictability. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_19484 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Structured Event Representation and Stock Return Predictability Li, Gang Qiao, Dandan Zheng, Mingxuan General Economics Economics Computational Engineering, Finance, and Science Machine Learning We find that event features extracted by large language models (LLMs) are effective for text-based stock return prediction. Using a pre-trained LLM to extract event features from news articles, we propose a novel deep learning model based on structured event representation (SER) and attention mechanisms to predict stock returns in the cross-section. Our SER-based model provides superior performance compared with other existing text-driven models to forecast stock returns out of sample and offers highly interpretable feature structures to examine the mechanisms underlying the stock return predictability. We further provide various implications based on SER and highlight the crucial benefit of structured model inputs in stock return predictability. |
| title | Structured Event Representation and Stock Return Predictability |
| topic | General Economics Economics Computational Engineering, Finance, and Science Machine Learning |
| url | https://arxiv.org/abs/2512.19484 |