mlsanityData to 14 Oct 2026

LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study

arXiv 2308.01915 ↗First posted 5 Jul 2023q-fin.TR cs.LGcited by 9

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Abstract (arXiv)

The recent advancements in Deep Learning (DL) research have notably influenced the finance sector. We examine the robustness and generalizability of fifteen state-of-the-art DL models focusing on Stock Price Trend Prediction (SPTP) based on Limit Order Book (LOB) data. To carry out this study, we developed LOBCAST, an open-source framework that incorporates data preprocessing, DL model training, evaluation and profit analysis. Our extensive experiments reveal that all models exhibit a significant performance drop when exposed to new data, thereby raising questions about their real-world market applicability. Our work serves as a benchmark, illuminating the potential and the limitations of current approaches and providing insight for innovative solutions.

Key facts. Cited by 9 papers in the map (data through 14 Oct 2026). Builds on 34 of its 102 references that are in the map. First posted on arXiv 5 Jul 2023.

Builds on

34 of 102 references.

References · 34

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22 more
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Built on by

Cited by year

Citing papers per year: 2024: 3; 2025: 6.

9 papers.

  1. 2024A Survey of Mambacited by 39
  2. 2024HLOB -- Information Persistence and Structure in Limit Order Bookscited by 8
  3. 2024Deep Limit Order Book Forecastingcited by 7
  4. 2025TRADES: Generating Realistic Market Simulations with Diffusion Modelscited by 5
  5. 2025LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Datacited by 5
  6. 2025TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Datacited by 4
  7. 2025Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarkingcited by 2
  8. 2025An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Bookcited by 0
  9. 2025TABL-ABM: A Hybrid Framework for Synthetic LOB Generationcited by 0