3 papers in the archive (2025–2026), average empirical rigor 7.5/10, average math complexity 5.8/10. Quadrant mix: Holy Grail ×2, Street Traders ×1.
Works mostly in Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, NLP & LLMs in Finance, Reinforcement Learning for Trading.
Frequent co-authors: Yanlong Wang (3), Shao-Lun Huang (3), Danny Dongning Sun (3), Xiao-Ping Zhang (3), Hongkang Zhang (2), Fei Ma (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%).
Financial time series forecasting is both highly significant and challenging. Previous approaches typically standardized time series data before feeding it into forecasting models, but this encoding process inherently leads to a loss of important information. Moreover, past time series models genera
This study seeks to advance the understanding and prediction of stock market return uncertainty through the application of advanced deep learning techniques. We introduce a novel deep learning model that utilizes a Gaussian mixture distribution to capture the complex, time-varying nature of asset re
Formulaic alpha factor mining is a critical yet challenging task in quantitative investment, characterized by a vast search space and the need for domain-informed, interpretable signals. However, finding novel signals becomes increasingly difficult as the library grows due to high redundancy. We pro