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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| Soggetti: | |
| Accesso online: | https://arxiv.org/abs/2511.12563 |
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| _version_ | 1866908656999596032 |
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| author | Linna, Eljas Baltakys, Kestutis Iosifidis, Alexandros Kanniainen, Juho |
| author_facet | Linna, Eljas Baltakys, Kestutis Iosifidis, Alexandros Kanniainen, Juho |
| contents | Modeling the dynamics of financial Limit Order Books (LOB) at the message level is challenging due to irregular event timing, rapid regime shifts, and the reactions of high-frequency traders to visible order flow. Previous LOB models require cumbersome data representations and lack adaptability outside their original tasks, leading us to introduce LOBERT, a general-purpose encoder-only foundation model for LOB data suitable for downstream fine-tuning. LOBERT adapts the original BERT architecture for LOB data by using a novel tokenization scheme that treats complete multi-dimensional messages as single tokens while retaining continuous representations of price, volume, and time. With these methods, LOBERT achieves leading performance in tasks such as predicting mid-price movements and next messages, while reducing the required context length compared to previous methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_12563 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | LOBERT: Generative AI Foundation Model for Limit Order Book Messages Linna, Eljas Baltakys, Kestutis Iosifidis, Alexandros Kanniainen, Juho Artificial Intelligence Modeling the dynamics of financial Limit Order Books (LOB) at the message level is challenging due to irregular event timing, rapid regime shifts, and the reactions of high-frequency traders to visible order flow. Previous LOB models require cumbersome data representations and lack adaptability outside their original tasks, leading us to introduce LOBERT, a general-purpose encoder-only foundation model for LOB data suitable for downstream fine-tuning. LOBERT adapts the original BERT architecture for LOB data by using a novel tokenization scheme that treats complete multi-dimensional messages as single tokens while retaining continuous representations of price, volume, and time. With these methods, LOBERT achieves leading performance in tasks such as predicting mid-price movements and next messages, while reducing the required context length compared to previous methods. |
| title | LOBERT: Generative AI Foundation Model for Limit Order Book Messages |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2511.12563 |