Paper: arXiv 2409.02277

Abstract

Managing high-frequency data in a limit order book (LOB) is a complex task that often exceeds the capabilities of conventional time-series forecasting models. Accurately predicting the entire multi-level LOB, beyond just the mid-price, is essential for understanding high-frequency market dynamics. However, this task is challenging due to the complex interdependencies among compound attributes within each dimension, such as order types, features, and levels. In this study, we explore advanced multidimensional sequence-to-sequence models to forecast the entire multi-level LOB, including order prices and volumes. Our main contribution is the development of a compound multivariate embedding method designed to capture the complex relationships between spatiotemporal features. Empirical results show that our method outperforms other multivariate forecasting methods, achieving the lowest forecasting error while preserving the ordinal structure of the LOB.

Complexity vs Empirical Score

  • Math Complexity: 8.0/10
  • Empirical Rigor: 4.0/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper introduces advanced attention-based architectures and spatiotemporal embedding techniques, indicating high mathematical complexity. However, while it claims empirical validation with forecasting error comparisons, it lacks specific backtest performance metrics, real-world implementation details, or code/dataset links, placing it on the lower end of empirical rigor.

Research Flowchart

  flowchart TD
  A["Research Goal<br>Predict entire multi-level LOB<br>(prices & volumes)"] --> B["Data Input<br>High-Frequency<br>Limit Order Book Data"]
  B --> C["Methodology<br>Compound Multivariate Embedding"]
  C --> D["Computational Process<br>Spatiotemporal<br>Sequence-to-Sequence Model"]
  D --> E["Outcomes<br>Lowest Forecasting Error<br>Preserved Ordinal Structure"]

Cited References (40)

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and 32 more
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  2. Deeplob: Deep convolutional neural networks for limit order books (2019)
  3. Using deep learning for price prediction by exploiting stationary limit order book features (2020)
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0 of 40 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.