Short-term stock price trend prediction with imaging high frequency limit order book data

Predicting price movements over a short period is a challenging problem in highfrequency trading. Deep learning methods have recently been used to forecast shortterm prices via limit order book (LOB) data. In this paper, we propose a framework to convert LOB data into a series of standard images in 2D matrices and predict the mid-price movements via an image-based convolutional neural network (CNN). The empirical study shows that the image-based CNN model outperforms other traditional machine learning and deep learning methods based on raw LOB data. Our findings suggest that the additional information implicit in LOB images contributes to short-term price forecasting. (c) 2023 International Institute of Forecasters. Published by Elsevier B.V. All rights reserved.