Price predictability in limit order book with deep learning model
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866929509666652160 |
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| author | Lee, Kyungsub |
| author_facet | Lee, Kyungsub |
| contents | This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14157 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Price predictability in limit order book with deep learning model Lee, Kyungsub Statistical Finance Trading and Market Microstructure Machine Learning This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance. |
| title | Price predictability in limit order book with deep learning model |
| topic | Statistical Finance Trading and Market Microstructure Machine Learning |
| url | https://arxiv.org/abs/2409.14157 |