4 papers in the archive (2023–2026), average empirical rigor 6.6/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×4.
Works mostly in Machine Learning for Market Forecasting, Volatility Modeling & Forecasting, NLP & LLMs in Finance, Fixed Income & Interest Rates.
Frequent co-authors: Zhaofeng Zhang (1), Banghao Chen (1), Shengxin Zhu (1), Qinwen Zhu (1), Wen Chen (1), Othmane Zarhali (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%).
A fast simulation framework for stochastic Volterra processes based on Random Fourier Features (RFF) approximation of the kernel is developed. After recalling the main properties of Volterra processes and reviewing existing numerical simulation methods, an accelerated scheme is introduced that relie
In this paper, we introduce two novel methods to solve the American-style option pricing problem and its dual form at the same time using neural networks. Without applying nested Monte Carlo, the first method uses a series of neural networks to simultaneously compute both the lower and upper bounds
This paper develops a deep learning-based framework for pricing convertible bonds with path-dependent contractual features, namely downward conversion price reset and issuer call clauses under rolling-window trigger rules, which are widespread in the convertible bond market. We formulate the valuati
In traditional quantitative trading practice, navigating the complicated and dynamic financial market presents a persistent challenge. Fully capturing various market variables, including long-term information, as well as essential signals that may lead to profit remains a difficult task for learning