Accepted Unknow ; Published 2024 December 1

Journal of Industrial Engineering and Applied Science, 2024, 2(6), 3005-6071.

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This paper presents a new method for detecting abnormal patterns in high-frequency trading (HFT) using graph neural networks (GNNs). The increasing sophistication of trading algorithms and the large volume of data have often created unprecedented challenges for traditional market analysis. Our framework addresses these challenges by introducing a GNN-based architecture that takes advantage of the physical and structural properties of business data. The proposed method transforms HFT data into graphical models where the nodes represent market conditions and the edges capture their physical and price relationships. A specialized GNN architecture, incorporating attention mechanisms and temporal convolution modules, is developed to learn complex trading patterns and identify potential anomalies. The model is evaluated on high-frequency trading data from five major stocks listed on NASDAQ, spanning six months of trading activity with over 10 million events. Experimental results demonstrate superior performance compared to existing approaches, achieving a 15% improvement in detection accuracy and maintaining robust performance across different market conditions. The framework exhibits particular strength in identifying complex manipulation patterns while maintaining low false positive rates. Our approach processes large volumes of trading data in real time with significantly reduced computational requirements compared to traditional methods. This research contributes to the development of more effective market surveillance systems and provides valuable insights for regulatory authorities in maintaining market integrity.

Graph Neural Networks , High-Frequency Trading , Market Manipulation Detection , Financial Market Surveillance .

APA Style

Li, M., Shu, M. & Lu, T. (2024). Anomaly pattern detection in high-frequency trading using graph neural networks. Journal of Industrial Engineering and Applied Science, 2(6), 77-85. https://doi.org/10.70393/6a69656173.323430

I would like to extend my sincere gratitude to Lin Li, Yitian Zhang, Jiayi Wang, and Ke Xiong for their groundbreaking research on network traffic anomaly detection using deep learning techniques as published in their article titled[19]"Deep Learning-Based Network Traffic Anomaly Detection: A Study in IoT Environments" in the Journal of Computer Technology and Applied Mathematics (2024). Their insights and methodologies have significantly influenced my understanding of network security and anomaly detection, and have provided valuable inspiration for my research in this critical area. I would like to express my heartfelt appreciation to Haoran Li, Gaike Wang, Lin Li, and Jiayi Wang for their innovative study on resource allocation and energy optimization in cloud computing environments, as published in their article titled[20]"Dynamic Resource Allocation and Energy Optimization in Cloud Data Centers Using Deep Reinforcement Learning" in the Journal of Computer Technology and Applied Mathematics (2024). Their comprehensive analysis and optimization approaches have significantly enhanced my knowledge of cloud computing systems and inspired my research in this field.

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