4 papers in the archive (2023–2025), average empirical rigor 5.9/10, average math complexity 7.5/10. Quadrant mix: Holy Grail ×3, Lab Rats ×1.
Works mostly in Stochastic Control & Optimal Stopping, Machine Learning for Market Forecasting, Options & Derivatives Pricing, Portfolio Optimization.
Frequent co-authors: Peter A. Forsyth (2), Peter Forsyth (1), Pieter van Staden (1), Robert Scriba (1), Jingbo B Wang (1), Chendi Ni (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 examine strategically incorporating broad stock market leveraged exchange-traded funds (LETFs) into investment portfolios. We demonstrate that easily understandable and implementable strategies can enhance the risk-return profile of a portfolio containing LETFs. Our analysis shows that seemingly
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
Financial derivative pricing is a significant challenge in finance, involving the valuation of instruments like options based on underlying assets. While some cases have simple solutions, many require complex classical computational methods like Monte Carlo simulations and numerical techniques. Howe