3 papers in the archive (2024–2025), average empirical rigor 5.7/10, average math complexity 5.5/10, 2 with linked code. Quadrant mix: Lab Rats ×1, Street Traders ×2.
Works mostly in Machine Learning for Market Forecasting, Risk Management & Tail Risk, Insurance & Actuarial Risk, NLP & LLMs in Finance.
Frequent co-authors: Xu Zhang (1), Zhengang Huang (1), Yunzhi Wu (1), Xun Lu (1), Erpeng Qi (1), Yunkai Chen (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%).
Time series forecasting is important in finance domain. Financial time series (TS) patterns are influenced by both short-term public opinions and medium-/long-term policy and market trends. Hence, processing multi-period inputs becomes crucial for accurate financial time series forecasting (TSF). Ho
Developing professional, structured reasoning on par with human financial analysts and traders remains a central challenge in AI for finance, where markets demand interpretability and trust. Traditional time-series models lack explainability, while LLMs face challenges in turning natural-language an
In economic analysis, rational decision-makers often take actions to reduce their risk exposure. These actions include purchasing market insurance and implementing prevention measures to modify the shape of the loss distribution. Under the assumption that the insureds’ actions are fully observed by