4 papers in the archive (2024–2025), average empirical rigor 4.1/10, average math complexity 7.6/10. Quadrant mix: Holy Grail ×1, Lab Rats ×3.
Works mostly in Insurance & Actuarial Risk, Stochastic Control & Optimal Stopping, Machine Learning for Market Forecasting, Portfolio Optimization.
Frequent co-authors: John Armstrong (4), Cristin Buescu (2), Rohan Hobbs (2), Catherine Donnelly (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%).
We evaluate the performance and level of intergenerational cross-subsidy in flat-accrual and dynamic-accrual collective defined contribution (CDC) schemes which have been designed to be compatible with UK legislation. In the flat-accrual scheme, all members accrue the benefits at the same rate irres
We study the optimal investment problem for a homogeneous collective of $n$ individuals investing in a Black-Scholes model subject to longevity risk with Epstein–Zin preferences. %and with preferences given by power utility. We compute analytic formulae for the optimal investment strategy, consumpt
We use a neural network to identify the optimal solution to a family of optimal investment problems, where the parameters determining an investor’s risk and consumption preferences are given as inputs to the neural network in addition to economic variables. This is used to develop a practical tool t
We present an architecture for managing Collective Defined Contribution (CDC) schemes. The current approach to UK CDC can be described as shared-indexation, where the nominal benefit of every member in a scheme receives the same level of indexation each year. The design of such schemes rely on the u