# Machine Learning for Forecasting Mid Price Movement using Limit Order Book Data

arXiv: https://arxiv.org/abs/1809.07861 · HTML: https://mlsanity.com/paper/1809.07861/

## Key facts

- Cited by 14 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2018.

## Built on by (most cited first, 14 of 14)

- [LOB-Based Deep Learning Models for Stock Price Trend Prediction: A Benchmark Study](https://mlsanity.com/paper/2308.01915/) (2023)
- [Deep Limit Order Book Forecasting](https://mlsanity.com/paper/2403.09267/) (2024)
- [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)
- [FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents](https://mlsanity.com/paper/2206.06952/) (2022)
- [Novel Modelling Strategies for High-frequency Stock Trading Data](https://mlsanity.com/paper/2212.00148/) (2022)
- [Feature Selection with Annealing for Forecasting Financial Time Series](https://mlsanity.com/paper/2303.02223/) (2023)
- [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)
- [Visualising Deep Network's Time-Series Representations](https://mlsanity.com/paper/2103.07176/) (2021)
- [Non-negative isomorphic neural networks for photonic neuromorphic accelerators](https://mlsanity.com/paper/2310.01084/) (2023)
- [SenDaL: An Effective and Efficient Calibration Framework of Low-Cost Sensors for Daily Life](https://mlsanity.com/paper/2502.10460/) (2025)
- [An Efficient deep learning model to Predict Stock Price Movement Based on Limit Order Book](https://mlsanity.com/paper/2505.22678/) (2025)
- [An `Inverse' Experimental Framework to Estimate Market Efficiency](https://mlsanity.com/paper/2604.18130/) (2026)

## Abstract (arXiv)

Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that Machine Learning highly befits this task carving the path towards future research in this field.