# Using Deep Learning for price prediction by exploiting stationary limit order book features

arXiv: https://arxiv.org/abs/1810.09965 · HTML: https://mlsanity.com/paper/1810.09965/

## Key facts

- Cited by 27 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Oct 2018.

## Built on by (most cited first, 27 of 27)

- [Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020-2022](https://mlsanity.com/paper/2305.04811/) (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)
- [LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study](https://mlsanity.com/paper/2308.01915/) (2023)
- [HLOB -- Information Persistence and Structure in Limit Order Books](https://mlsanity.com/paper/2405.18938/) (2024)
- [Optimal Estimation of Generic Dynamics by Path-Dependent Neural Jump ODEs](https://mlsanity.com/paper/2206.14284/) (2022)
- [Deep Limit Order Book Forecasting](https://mlsanity.com/paper/2403.09267/) (2024)
- [Tensor-Train Recurrent Neural Networks for Interpretable Multi-Way Financial Forecasting](https://mlsanity.com/paper/2105.04983/) (2021)
- [How Robust are Limit Order Book Representations under Data Perturbation?](https://mlsanity.com/paper/2110.04752/) (2021)
- [Bayesian Bilinear Neural Network for Predicting the Mid-price Dynamics in Limit-Order Book Markets](https://mlsanity.com/paper/2203.03613/) (2022)
- [Augmented Bilinear Network for Incremental Multi-Stock Time-Series Classification](https://mlsanity.com/paper/2207.11577/) (2022)
- [Axial-LOB: High-Frequency Trading with Axial Attention](https://mlsanity.com/paper/2212.01807/) (2022)
- [Transformers versus LSTMs for electronic trading](https://mlsanity.com/paper/2309.11400/) (2023)
- [TLOB: A Novel Transformer Model with Dual Attention for Price Trend Prediction with Limit Order Book Data](https://mlsanity.com/paper/2502.15757/) (2025)
- [Architecture of Automated Crypto-Finance Agent](https://mlsanity.com/paper/2107.07769/) (2021)
- [Low-Rank Temporal Attention-Augmented Bilinear Network for financial time-series forecasting](https://mlsanity.com/paper/2107.06995/) (2021)
- [Causal Analysis of Generic Time Series Data Applied for Market Prediction](https://mlsanity.com/paper/2204.12928/) (2022)
- [Optimum Output Long Short-Term Memory Cell for High-Frequency Trading Forecasting](https://mlsanity.com/paper/2304.09840/) (2023)
- [Representation Learning of Limit Order Book: A Comprehensive Study and Benchmarking](https://mlsanity.com/paper/2505.02139/) (2025)
- [The Limit Order Book Recreation Model (LOBRM): An Extended Analysis](https://mlsanity.com/paper/2107.00534/) (2021)
- [Market Making with Deep Reinforcement Learning from Limit Order Books](https://mlsanity.com/paper/2305.15821/) (2023)
- [Leveraging Deep Learning and Online Source Sentiment for Financial Portfolio Management](https://mlsanity.com/paper/2309.16679/) (2023)
- [Detecting Multilevel Manipulation from Limit Order Book via Cascaded Contrastive Representation Learning](https://mlsanity.com/paper/2508.17086/) (2025)
- [Deep reinforcement learning for optimal trading with partial information](https://mlsanity.com/paper/2511.00190/) (2025)
- [1D-CapsNet-LSTM: A Deep Learning-Based Model for Multi-Step Stock Index Forecasting](https://mlsanity.com/paper/2310.02090/) (2023)
- [Exploring Microstructural Dynamics in Cryptocurrency Limit Order Books: Better Inputs Matter More Than Stacking Another Hidden Layer](https://mlsanity.com/paper/2506.05764/) (2025)
- [FINN-GL: Generalized Mixed-Precision Extensions for FPGA-Accelerated LSTMs](https://mlsanity.com/paper/2506.20810/) (2025)
- [UQ-LOB: Uncertainty-Aware Limit Order Book Mid-Price Forecasting](https://mlsanity.com/paper/2609.31491/) (2026)

## Abstract (arXiv)

The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges must be overcome before DL is fully utilized. In this work a new method to construct stationary features, that allows DL models to be applied effectively, is proposed. These features are thoroughly tested on the task of predicting mid price movements of the Limit Order Book. Several DL models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Finally a novel model that combines the ability of CNNs to extract useful features and the ability of LSTMs' to analyze time series, is proposed and evaluated. The combined model is able to outperform the individual LSTM and CNN models in the prediction horizons that are tested.