mlsanityData to 7 Oct 2026

Deep Learning for Market by Order Data

arXiv 2102.08811 ↗First posted 17 Feb 2021q-fin.TR cs.AIcited by 8

Not on the map. Why?

Abstract (arXiv)

Market by order (MBO) data - a detailed feed of individual trade instructions for a given stock on an exchange - is arguably one of the most granular sources of microstructure information. While limit order books (LOBs) are implicitly derived from it, MBO data is largely neglected by current academic literature which focuses primarily on LOB modelling. In this paper, we demonstrate the utility of MBO data for forecasting high-frequency price movements, providing an orthogonal source of information to LOB snapshots and expanding the universe of alpha discovery. We provide the first predictive analysis on MBO data by carefully introducing the data structure and presenting a specific normalisation scheme to consider level information in order books and to allow model training with multiple instruments. Through forecasting experiments using deep neural networks, we show that while MBO-driven and LOB-driven models individually provide similar performance, ensembles of the two can lead to improvements in forecasting accuracy - indicating that MBO data is additive to LOB-based features.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 13 of its 42 references that are in the map. First posted on arXiv 17 Feb 2021.

Builds on

13 of 42 references.

References · 13

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1 more
  1. 2020Deep Learning modeling of Limit Order Book: a comparative perspectivecited by 11

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 1; 2022: 0; 2023: 4; 2024: 2; 2025: 0; 2026: 1.

8 papers.

  1. 2023Generative AI for End-to-End Limit Order Book Modelling: A Token-Level Autoregressive Generative Model of Message Flow Using a Deep State Space Networkcited by 19
  2. 2021Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Unitscited by 10
  3. 2023LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Studycited by 9
  4. 2024HLOB -- Information Persistence and Structure in Limit Order Bookscited by 8
  5. 2023Asynchronous Deep Double Duelling Q-Learning for Trading-Signal Execution in Limit Order Book Marketscited by 3
  6. 2023Liquidity takers behavior representation through a contrastive learning approachcited by 0
  7. 2024Large Investment Modelcited by 0
  8. 2026Repurposing Deep Limit Order Book Forecasting for Scenario-Conditioned Market Impact Modelingcited by 0