Anomaly pattern detection in high-frequency trading using graph neural networks

HIGHLIGHTS

• Who: SOONG CHINGTSING from the Fordham University, USA have published the paper: Anomaly Pattern Detection in High-Frequency Trading Using Graph Neural Networks, in the Journal: (JOURNAL)
• How: This paper presents a new method for detecting abnormal patterns in high-frequency trading (HFT) using graph neural networks (GNNs). The model is evaluated on highfrequency trading data from five major stocks listed on NASDAQ spanning six months of trading activity with over 10 million events.

SUMMARY

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Licence: cc-by

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