4 papers in the archive (2023–2026), average empirical rigor 6/10, average math complexity 5.4/10, 1 with linked code. Quadrant mix: Holy Grail ×1, Lab Rats ×1, Street Traders ×2.
Works mostly in NLP & LLMs in Finance, Portfolio Optimization, Machine Learning for Market Forecasting.
Frequent co-authors: Jun Han (2), Zhi Yang (2), Fangqi Lou (2), Zhaowei Liu (2), Liwen Zhang (2), Shuo 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%).
Transfer learning is an emerging and popular paradigm for utilizing existing knowledge from previous learning tasks to improve the performance of new ones. In this paper, we propose a novel concept of transfer risk and and analyze its properties to evaluate transferability of transfer learning. We a
Financial markets are noisy and non-stationary, making alpha mining highly sensitive to noise in backtesting results and sudden market regime shifts. While recent agentic frameworks improve alpha mining automation, they often lack controllable multi-round search and reliable reuse of validated exper
Multimodal large language models are playing an increasingly significant role in empowering the financial domain, however, the challenges they face, such as multimodal and high-density information and cross-modal multi-hop reasoning, go beyond the evaluation scope of existing multimodal benchmarks.
We derive a functional Itô-formula for non-anticipative maps of rough paths, based on the approximation properties of the signature of càdlàg rough paths. This result is a functional extension of the Itô-formula for càdlàg rough paths (by Friz and Zhang (2018)), which coincides with the change of va