InvariantStock: Learning Invariant Features for Mastering the Shifting Market
Fuente:
arXiv
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| Autores principales: | , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866913488303030272 |
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| author | Cao, Haiyao Zou, Jinan Liu, Yuhang Zhang, Zhen Abbasnejad, Ehsan Hengel, Anton van den Shi, Javen Qinfeng |
| author_facet | Cao, Haiyao Zou, Jinan Liu, Yuhang Zhang, Zhen Abbasnejad, Ehsan Hengel, Anton van den Shi, Javen Qinfeng |
| contents | Accurately predicting stock returns is crucial for effective portfolio management. However, existing methods often overlook a fundamental issue in the market, namely, distribution shifts, making them less practical for predicting future markets or newly listed stocks. This study introduces a novel approach to address this challenge by focusing on the acquisition of invariant features across various environments, thereby enhancing robustness against distribution shifts. Specifically, we present InvariantStock, a designed learning framework comprising two key modules: an environment-aware prediction module and an environment-agnostic module. Through the designed learning of these two modules, the proposed method can learn invariant features across different environments in a straightforward manner, significantly improving its ability to handle distribution shifts in diverse market settings. Our results demonstrate that the proposed InvariantStock not only delivers robust and accurate predictions but also outperforms existing baseline methods in both prediction tasks and backtesting within the dynamically changing markets of China and the United States. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_00671 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | InvariantStock: Learning Invariant Features for Mastering the Shifting Market Cao, Haiyao Zou, Jinan Liu, Yuhang Zhang, Zhen Abbasnejad, Ehsan Hengel, Anton van den Shi, Javen Qinfeng Computational Engineering, Finance, and Science Accurately predicting stock returns is crucial for effective portfolio management. However, existing methods often overlook a fundamental issue in the market, namely, distribution shifts, making them less practical for predicting future markets or newly listed stocks. This study introduces a novel approach to address this challenge by focusing on the acquisition of invariant features across various environments, thereby enhancing robustness against distribution shifts. Specifically, we present InvariantStock, a designed learning framework comprising two key modules: an environment-aware prediction module and an environment-agnostic module. Through the designed learning of these two modules, the proposed method can learn invariant features across different environments in a straightforward manner, significantly improving its ability to handle distribution shifts in diverse market settings. Our results demonstrate that the proposed InvariantStock not only delivers robust and accurate predictions but also outperforms existing baseline methods in both prediction tasks and backtesting within the dynamically changing markets of China and the United States. |
| title | InvariantStock: Learning Invariant Features for Mastering the Shifting Market |
| topic | Computational Engineering, Finance, and Science |
| url | https://arxiv.org/abs/2409.00671 |