3 papers in the archive (2024), average empirical rigor 3.2/10, average math complexity 2.8/10. Quadrant mix: Philosophers ×3.
Works mostly in Machine Learning for Market Forecasting, Risk Management & Tail Risk, NLP & LLMs in Finance.
Frequent co-authors: Yu Cheng (3), Ao Xiang (2), Jingyu Zhang (2), Xingxin Gu (1), Zhizhong Wu (1), Qin Yang (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%).
With the increasing complexity of financial markets and rapid growth in data volume, traditional risk monitoring methods no longer suffice for modern financial institutions. This paper designs and optimizes a risk monitoring system based on big data and machine learning. By constructing a four-layer
In the realm of globalized financial markets, commercial banks are confronted with an escalating magnitude of credit risk, thereby imposing heightened requisites upon the security of bank assets and financial stability. This study harnesses advanced neural network techniques, notably the Backpropaga
This paper explores the application of Natural Language Processing (NLP) in financial risk detection. By constructing an NLP-based financial risk detection model, this study aims to identify and predict potential risks in financial documents and communications. First, the fundamental concepts of NLP