A Critical Analysis on Anomaly Detection in High-Frequency Financial Data Using Deep Learning for Options
Bibliographic record
Abstract
High-frequency financial markets churn out massive amounts of data filled with intricate microstructural patterns, which makes them vulnerable to issues like spoofing, layering, and market manipulation. Traditional methods for detecting anomalies often fall short when it comes to capturing these complex patterns, especially given the fast-paced and ever-changing nature of financial transactions. In this study, we introduce a deep learning-based framework designed for spotting anomalies in high-frequency trading (HFT) data. This framework utilizes advanced techniques such as graph neural networks (GNN), recurrent neural networks (RNN), and transformer-based autoencoders. By integrating multi-modal feature extraction, attention to temporal dependencies, and adaptive learning strategies, we aim to boost detection accuracy. We tested our approach on real-world high-frequency limit order book (LOB) data, along with synthetic anomalies added to the dataset. The results from our experiments show notable enhancements in anomaly detection accuracy, precision, and recall, surpassing existing methods while keeping false positive rates low. Our findings underscore the promise of deep learning models in enhancing market surveillance, ensuring regulatory compliance, and mitigating financial risks in HFT settings.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".