Design and Verification of a Deep Learning-Driven Risk Warning System for High-Frequency Quantitative Trading

Sifan Lin ()
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Sifan Lin: The London School of Economics and Political Science

A chapter in Proceedings of the 2025 4th International Conference on Public Service, Economic Management and Sustainable Development (PESD 2025), 2025, pp 447-453 from Springer

Abstract: Abstract The rise of high-frequency trading (HFT) has drastically transformed financial market microstructure and brought about unheard-of speed and complexity. While HFT enhances liquidity and facilitates price discovery, it also introduces significant systemic risks, such as flash crashes and liquidity vacuums, which can occur within milliseconds. Risk management systems designed for slower trading environments are insufficient to monitor and address these rapidly emerging risks. This paper proposes the design and validation of a novel HFT risk warning system through the application of deep learning. We use a Long Short-Term Memory (LSTM) network, which is a type of Recurrent Neural Network (RNN), to analyze high-dimensional data from limit order books in a time series format. When the model is trained on historical tick-level data, it learns to predict the upcoming probability of a sharp price drop or liquidity crisis. We conduct a rigorous validation: comparing the LSTM model’s performance against simpler machine learning models such as Logistic Regression and Support Vector Machines.

Keywords: High-Frequency Trading; Quantitative Trading; Risk Warning; Deep Learning; LSTM; Financial Risk Management (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:spr:advbcp:978-94-6463-916-2_48

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DOI: 10.2991/978-94-6463-916-2_48

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