Attention-based reading, highlighting, and forecasting of the limit order book

Jiwon Jung and Kiseop Lee

Quantitative Finance, 2025, vol. 25, issue 7, 1015-1027

Abstract: Managing high-frequency data in a limit order book (LOB) is complex due to its high dimensionality, irregular timing, and complex spatiotemporal dependencies across price levels. These challenges often exceed the capabilities of conventional time-series models. Accurate prediction of the multi-level LOB, not just the mid-price, is crucial for understanding market dynamics but is difficult due to the interdependencies among attributes like order types, features, and levels. This study introduces advanced sequence-to-sequence models to forecast the entire multi-level LOB, including prices and volumes. Our key contribution is a compound multivariate embedding method that captures spatiotemporal relationships. Empirical results show that our method outperforms others, achieving the lowest forecasting error while maintaining LOB structure.

Date: 2025
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DOI: 10.1080/14697688.2025.2522914

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