3 papers in the archive (2026), average empirical rigor 8/10, average math complexity 6.7/10. Quadrant mix: Holy Grail ×3.
Works mostly in NLP & LLMs in Finance, Machine Learning for Market Forecasting, High-Frequency Trading & Optimal Execution, Portfolio Optimization.
Frequent co-authors: Kelvin J. L. Koa (2), Xiangyu Li (1), Fengbin Zhu (1), Xuan Yao (1), Siyu Liu (1), Xiaoluan Liu (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 this work, we study portfolio optimization under the stochastic discount factor (SDF) framework by learning market state representations that capture the underlying risk structures of financial data. This is challenging due to several factors: financial markets exhibit non-stationary dynamics wit
Corporate financial disclosures often unfold over time, with announcements, prepared remarks, and discussion spanning multiple high-frequency return intervals. Financial NLP benchmarks can therefore suffer from a within-event timing mismatch when the prediction target begins before the textual or au
Deep Research (DR) agents have demonstrated strong capabilities in complex, research-oriented tasks through autonomous planning, iterative retrieval, multi-step reasoning, and structured reporting. However, adapting DR agents to finance introduces unique challenges: financial analysis demands the jo