4 papers in the archive (2025–2026), average empirical rigor 5.9/10, average math complexity 4.5/10. Quadrant mix: Holy Grail ×1, Lab Rats ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Reinforcement Learning for Trading, Volatility Modeling & Forecasting, Portfolio Optimization.
Frequent co-authors: Zining Wang (3), John Cartlidge (2), Wei Wei (2), Rongwei Liu (1), Shiyu Zhang (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)
The optimal asset allocation between risky and risk-free assets is a persistent challenge due to the inherent volatility in financial markets. Conventional methods rely on strict distributional assumptions or non-additive reward ratios, which limit their robustness and applicability to investment go
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