DeepLOB: Deep Convolutional Neural Networks for Limit Order Books

TL;DR

DeepLOB introduces a deep learning architecture combining convolutional and LSTM layers to predict stock price movements from limit order book data, outperforming existing methods and demonstrating robustness across different instruments.

Contribution

The paper presents a novel deep neural network architecture that captures spatial and temporal features of limit order books, achieving superior prediction accuracy and transferability.

Findings

Outperforms state-of-the-art algorithms on benchmark datasets.

Maintains stable out-of-sample accuracy across different instruments.

Extracts universal features applicable to various stocks.

Abstract

We develop a large-scale deep learning model to predict price movements from limit order book (LOB) data of cash equities. The architecture utilises convolutional filters to capture the spatial structure of the limit order books as well as LSTM modules to capture longer time dependencies. The proposed network outperforms all existing state-of-the-art algorithms on the benchmark LOB dataset [1]. In a more realistic setting, we test our model by using one year market quotes from the London Stock Exchange and the model delivers a remarkably stable out-of-sample prediction accuracy for a variety of instruments. Importantly, our model translates well to instruments which were not part of the training set, indicating the model's ability to extract universal features. In order to better understand these features and to go beyond a "black box" model, we perform a sensitivity analysis to understand the rationale behind the model predictions and reveal the components of LOBs that are most relevant. The ability to extract robust features which translate well to other instruments is an important property of our model which has many other applications.