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

Wuyi Ye, Jinting Yang and Pengzhan Chen

International Journal of Forecasting, 2024, vol. 40, issue 3, 1189-1205

Abstract: Predicting price movements over a short period is a challenging problem in high-frequency trading. Deep learning methods have recently been used to forecast short-term 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.

Keywords: Limit order book; Mid-price prediction; Convolutional neural network; Image classification; Feature engineering (search for similar items in EconPapers)
Date: 2024
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:40:y:2024:i:3:p:1189-1205

DOI: 10.1016/j.ijforecast.2023.10.008

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