3 papers in the archive (2024), average empirical rigor 4.3/10, average math complexity 3.7/10. Quadrant mix: Philosophers ×2, Street Traders ×1.
Works mostly in Machine Learning for Market Forecasting, Risk Management & Tail Risk, Fixed Income & Interest Rates, Factor Investing & Asset Pricing.
Frequent co-authors: Yuhan Wang (1), Yue Yao (1), Jinsong Liu (1), Jiating Lin (1), Wenying Sun (1), Wenqing Zhang (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%).
In the context of globalization and the rapid expansion of the digital economy, anti-money laundering (AML) has become a crucial aspect of financial oversight, particularly in cross-border transactions. The rising complexity and scale of international financial flows necessitate more intelligent and
With the development of the financial industry, credit default prediction, as an important task in financial risk management, has received increasing attention. Traditional credit default prediction methods mostly rely on machine learning models, such as decision trees and random forests, but these
This paper aims to study the prediction of the bank stability index based on the Time Series Transformer model. The bank stability index is an important indicator to measure the health status and risk resistance of financial institutions. Traditional prediction methods are difficult to adapt to comp