mlsanityData to 7 Oct 2026

How Robust are Limit Order Book Representations under Data Perturbation?

arXiv 2110.04752 ↗First posted 10 Oct 2021q-fin.TR cs.LGcited by 6

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Abstract (arXiv)

The success of machine learning models in the financial domain is highly reliant on the quality of the data representation. In this paper, we focus on the representation of limit order book data and discuss the opportunities and challenges for learning representations of such data. We also experimentally analyse the issues associated with existing representations and present a guideline for future research in this area.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Builds on 6 of its 19 references that are in the map. First posted on arXiv 10 Oct 2021.

Builds on

6 of 19 references.

References · 6

  1. 2019Quant GANs: Deep Generation of Financial Time Seriescited by 107
  2. 2018Deep Hedgingcited by 106
  3. 2018Universal features of price formation in financial markets: perspectives from Deep Learningcited by 59
  4. 2017Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methodscited by 49
  5. 2017Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysiscited by 46
  6. 2018Using Deep Learning for price prediction by exploiting stationary limit order book featurescited by 27

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Cited by year (2026 to 7 Oct)

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

6 papers.

  1. 2023Alpha-GPT: Human-AI Interactive Alpha Mining for Quantitative Investmentcited by 54
  2. 2023LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Studycited by 9
  3. 2025From Deep Learning to LLMs: A survey of AI in Quantitative Investmentcited by 9
  4. 2022Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligencecited by 5
  5. 2025Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layercited by 0
  6. 2026An `Inverse' Experimental Framework to Estimate Market Efficiencycited by 0