3 papers in the archive (2025–2026), average empirical rigor 8/10, average math complexity 6.8/10. 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: Ke-Wei Huang (2), Jan Chen (1), Yunshan Ma (1), Huanhuan Zheng (1), Tat-Seng Chua (1), Xiaoyu 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%).
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
While Large Language Models have been used to produce interpretable stock forecasts, they mainly focus on analyzing textual reports but not historical price data, also known as Technical Analysis. This task is challenging as it switches between domains: the stock price inputs and outputs lie in the