6 papers
PreFER: Interactive Robo-Advisor with Scoring Mechanism
Yuwei Wang, Hoi Ying Wong
We propose an interactive robo-advising framework that learns personalized risk preferences from scores provided by clients. The resulting preference-learning problem is closely re…
Duality and DeepMartingale for High-Dimensional Optimal Switching: Computable Upper Bounds and Approximation-Expressivity Guarantees
Junyan Ye, Hoi Ying Wong
We study finite-horizon optimal switching with discrete intervention dates on a general filtration, allowing continuous-time observations between decision dates, and develop a deep…
DeepMartingale: Duality of the Optimal Stopping Problem with Expressivity and High-Dimensional Hedging
Junyan Ye, Hoi Ying Wong
We propose \textit{DeepMartingale}, a deep-learning framework for the dual formulation of discrete-monitoring optimal stopping problems under continuous-time models. Leveraging a m…
Robust Exploratory Stopping under Ambiguity in Reinforcement Learning
Junyan Ye, Hoi Ying Wong, Kyunghyun Park
We propose and analyze a continuous-time robust reinforcement learning framework for optimal stopping under ambiguity. In this framework, an agent chooses a robust exploratory stop…
Robust dividend policy: Equivalence of Epstein-Zin and Maenhout preferences
Kexin Chen, Kyunghyun Park, Hoi Ying Wong
In a continuous-time economy, this paper formulates the Epstein-Zin preference for discounted dividends received by an investor as an Epstein-Zin singular control utility. We intro…
Duality in optimal consumption--investment problems with alternative data
Kexin Chen, Hoi Ying Wong
This study investigates an optimal consumption--investment problem in which the unobserved stock trend is modulated by a hidden Markov chain that represents different economic regi…