3 papers in the archive (2025–2026), average empirical rigor 7.3/10, average math complexity 4.3/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in Crypto Markets & DeFi, Machine Learning for Market Forecasting, Factor Investing & Asset Pricing, Volatility Modeling & Forecasting.
Frequent co-authors: Sangmi Chai (3), Gyuyeon Na (2), Soyoun Kim (2), Jungbin Shin (1), Jieun Yu (1), Sunyoung Moon (1).
Scores are our LLM-assisted heuristics (methodology), not peer review or citation counts. Author pages are generated from paper metadata; name variants may split one person across pages. arXiv author page →
Ordered by a blend of empirical rigor (60%) and math complexity (40%).
Abnormal cryptocurrency transactions - such as mixing services, fraudulent transfers, and pump-and-dump operations – pose escalating risks to financial integrity but remain notoriously difficult to detect due to class imbalance, temporal volatility, and complex network dependencies. Existing approa
Detecting anomalous trajectories in decentralized crypto networks is fundamentally challenged by extreme label scarcity and the adaptive evasion strategies of illicit actors. While Graph Neural Networks (GNNs) effectively capture local structural patterns, they struggle to internalize multi hop, log
This study aims to detect pump and dump (P&D) manipulation in cryptocurrency markets, where the scarcity of such events causes severe class imbalance and hinders accurate detection. To address this issue, the Synthetic Minority Oversampling Technique (SMOTE) was applied, and advanced ensemble learni