3 papers in the archive (2023–2026), average empirical rigor 7.2/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×3.
Works mostly in Machine Learning for Market Forecasting, Risk Management & Tail Risk, Stochastic Control & Optimal Stopping, Insurance & Actuarial Risk.
Frequent co-authors: Yuying Li (2), German Nova Orozco (1), Duy-Minh Dang (1), Chendi Ni (1), Pieter M. van Staden (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%).
Money-back guarantees (MBGs) are features of pooled retirement income products that address bequest concerns by ensuring the initial premium is returned through lifetime payments or, upon early death, as a death benefit to the estate. This paper studies optimal retirement decumulation in an individu
We present a parsimonious neural network approach, which does not rely on dynamic programming techniques, to solve dynamic portfolio optimization problems subject to multiple investment constraints. The number of parameters of the (potentially deep) neural network remains independent of the number o
Motivated by the current global high inflation scenario, we aim to discover a dynamic multi-period allocation strategy to optimally outperform a passive benchmark while adhering to a bounded leverage limit. To this end, we formulate an optimal control problem to outperform a benchmark portfolio thro