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

arXiv: https://arxiv.org/abs/2308.01915 · HTML: https://mlsanity.com/paper/2308.01915/

## 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 (most cited first, 30 of 34)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Informer: Beyond Efficient Transformer for Long Sequence Time-Series Forecasting](https://mlsanity.com/paper/2012.07436/) (2020)
- [Effective Approaches to Attention-based Neural Machine Translation](https://mlsanity.com/paper/1508.04025/) (2015)
- [Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecasting](https://mlsanity.com/paper/1912.09363/) (2019)
- [Tabular Data: Deep Learning is Not All You Need](https://mlsanity.com/paper/2106.03253/) (2021)
- [Time Series Forecasting With Deep Learning: A Survey](https://mlsanity.com/paper/2004.13408/) (2020)
- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Improving Reproducibility in Machine Learning Research (A Report from the NeurIPS 2019 Reproducibility Program)](https://mlsanity.com/paper/2003.12206/) (2020)
- [Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019](https://mlsanity.com/paper/1911.13288/) (2019)
- [Deep Learning for Financial Applications : A Survey](https://mlsanity.com/paper/2002.05786/) (2020)
- [AI in Finance: Challenges, Techniques and Opportunities](https://mlsanity.com/paper/2107.09051/) (2021)
- [Applications of deep learning in stock market prediction: recent progress](https://mlsanity.com/paper/2003.01859/) (2020)
- [Benchmark Dataset for Mid-Price Forecasting of Limit Order Book Data with Machine Learning Methods](https://mlsanity.com/paper/1705.03233/) (2017)
- [Temporal Attention augmented Bilinear Network for Financial Time-Series Data Analysis](https://mlsanity.com/paper/1712.00975/) (2017)
- [TACTiS: Transformer-Attentional Copulas for Time Series](https://mlsanity.com/paper/2202.03528/) (2022)
- [Towards Realistic Market Simulations: a Generative Adversarial Networks Approach](https://mlsanity.com/paper/2110.13287/) (2021)
- [Using Deep Learning for price prediction by exploiting stationary limit order book features](https://mlsanity.com/paper/1810.09965/) (2018)
- [Learning to simulate realistic limit order book markets from data as a World Agent](https://mlsanity.com/paper/2210.09897/) (2022)
- [Data Normalization for Bilinear Structures in High-Frequency Financial Time-series](https://mlsanity.com/paper/2003.00598/) (2020)
- [Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data](https://mlsanity.com/paper/1809.07861/) (2018)
- [Neural Stochastic Agent-Based Limit Order Book Simulation: A Hybrid Methodology](https://mlsanity.com/paper/2303.00080/) (2023)
- [Multi-Horizon Forecasting for Limit Order Books: Novel Deep Learning Approaches and Hardware Acceleration using Intelligent Processing Units](https://mlsanity.com/paper/2105.10430/) (2021)
- [Temporal Logistic Neural Bag-of-Features for Financial Time series Forecasting leveraging Limit Order Book Data](https://mlsanity.com/paper/1901.08280/) (2019)
- [Deep Learning for Market by Order Data](https://mlsanity.com/paper/2102.08811/) (2021)
- [Attention-based Neural Bag-of-Features Learning for Sequence Data](https://mlsanity.com/paper/2005.12250/) (2020)

## Built on by (most cited first, 9 of 9)

- [A Survey of Mamba](https://mlsanity.com/paper/2408.01129/) (2024)
- [HLOB -- Information Persistence and Structure in Limit Order Books](https://mlsanity.com/paper/2405.18938/) (2024)
- [Deep Limit Order Book Forecasting](https://mlsanity.com/paper/2403.09267/) (2024)
- [TRADES: Generating Realistic Market Simulations with Diffusion Models](https://mlsanity.com/paper/2502.07071/) (2025)
- [LOB-Bench: Benchmarking Generative AI for Finance -- an Application to Limit Order Book Data](https://mlsanity.com/paper/2502.09172/) (2025)
- [TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data](https://mlsanity.com/paper/2502.15757/) (2025)
- [Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking](https://mlsanity.com/paper/2505.02139/) (2025)
- [An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book](https://mlsanity.com/paper/2505.22678/) (2025)
- [TABL-ABM: A Hybrid Framework for Synthetic LOB Generation](https://mlsanity.com/paper/2510.22685/) (2025)

## 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.