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Price predictability in limit order book with deep learning model

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  • Kyungsub Lee

Abstract

This study explores the prediction of high-frequency price changes using deep learning models. Although state-of-the-art methods perform well, their complexity impedes the understanding of successful predictions. We found that an inadequately defined target price process may render predictions meaningless by incorporating past information. The commonly used three-class problem in asset price prediction can generally be divided into volatility and directional prediction. When relying solely on the price process, directional prediction performance is not substantial. However, volume imbalance improves directional prediction performance.

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  • Kyungsub Lee, 2024. "Price predictability in limit order book with deep learning model," Papers 2409.14157, arXiv.org.
    • Handle: RePEc:arx:papers:2409.14157
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    References listed on IDEAS

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    1. Yuanyuan Ma & Chenglong Liu & Jie Tian Zhang & Yanze Liu, 2023. "Reliability study of stock index forecasting in volatile and trending cities using public sentiment ——based on word2Vec and LSTM models," Applied Economics, Taylor & Francis Journals, vol. 55(43), pages 5013-5032, September.
    2. Adamantios Ntakaris & Martin Magris & Juho Kanniainen & Moncef Gabbouj & Alexandros Iosifidis, 2018. "Benchmark dataset for mid‐price forecasting of limit order book data with machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 852-866, December.
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