3 papers in the archive (2026), average empirical rigor 6.8/10, average math complexity 3.5/10. Quadrant mix: Holy Grail ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Crypto Markets & DeFi, Risk Management & Tail Risk.
Frequent co-authors: Jin Zheng (3), Wei Wei (2), Shiyu Zhang (1), John Cartlidge (1), Weibin Feng (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%).
Systemic risk refers to the overall vulnerability arising from the high degree of interconnectedness and interdependence within the financial system. In the rapidly developing decentralized finance (DeFi) ecosystem, numerous studies have analyzed systemic risk through specific channels such as liqui
The exponential growth of financial research has rendered traditional systematic literature reviews (SLRs) increasingly impractical, as manual screening and narrative synthesis struggle to keep pace with the scale and complexity of modern scholarship. While the existing artificial intelligence (AI)
Recent advances in artificial intelligence (AI) and natural language processing (NLP) have enabled tools to support systematic literature reviews (SLRs), yet existing frameworks often produce outputs that are efficient but contextually limited, requiring substantial expert oversight.The framework em