A Comparative Analysis of Unsupervised AI Anomaly Detection Algorithms for Identifying Suspicious Trading Patterns Around U.S. Corporate Earnings Announcements

Xujia Chen

Journal of Sustainability, Policy, and Practice, 2026, vol. 2, issue 4, 114-128

Abstract: Suspicious trading activity around U.S. corporate earnings announcements remains a central concern for the Securities and Exchange Commission. This study conducts a comparative evaluation of four unsupervised AI anomaly detection algorithms---Isolation Forest, One-Class Support Vector Machine (OC-SVM), Long Short-Term Memory (LSTM) Autoencoder, and UnSupervised Anomaly Detection (USAD)---applied to identifying suspicious timing patterns around quarterly earnings disclosures. The analysis is built on a twelve-feature multi-dimensional representation that combines price, volume, and order-flow indicators, computed over a [-20, +2] trading-day event window. Model fitting is fully unsupervised throughout; label information from a held-out validation split of the training period is used only at the threshold-calibration stage, and percentile-only operating points that do not consume any labels are reported alongside the F1-maximizing operating point for transparency. Ground-truth labels are constructed from 127 prosecuted insider trading cases, drawn from a four-stage pipeline applied to SEC Litigation Releases issued during the 2015--2023 sample period, with the case-selection conventions of a published academic sample (1996--2013) adopted purely as a methodological template. The main benchmark is reported on the subset of NASDAQ-listed S&P 500 constituents for which complete LOBSTER Level-1 order-book coverage was available in the study data environment, comprising 217 tickers, 6,884 event windows, 61 positives across the full period, and 22 positives in the 2021--2023 test split, with the full 14,416-window sample (46 test positives) used as a sensitivity analysis. Empirical results indicate that, within this sample, deep temporal detectors directionally favor classical baselines, with USAD attaining a balanced-threshold F1 of 0.58 (5-seed std 0.05) and ROC-AUC of 0.82, against 0.46 (std 0.04) and 0.72 for the Isolation Forest baseline. Bootstrap 95% confidence intervals overlap meaningfully across detectors, and the small number of test positives limits resolution, so the reported numbers should be interpreted as an exploratory benchmark rather than a definitive ranking. The study further quantifies the sensitivity-to-false-positive trade-off under three threshold configurations, reports flagged-window counts and false-positive rates so that downstream review burden can be assessed directly, and provides computational efficiency benchmarks at one-hour, one-minute, and one-second aggregation, yielding practical reference values for regulatory surveillance applications in which review capacity is the binding operational constraint.

Keywords: Anomaly detection; Market surveillance; Insider trading; Earnings announcement (search for similar items in EconPapers)
Date: 2026
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