We study Merton's expected utility maximization problem in an incomplete market,
characterized by a factor process in addition to the stock price process, where all the model …

We study optimal stopping for diffusion processes with unknown model primitives within the
continuous-time reinforcement learning (RL) framework developed by Wang et al.(2020) …

We propose a reinforcement learning (RL) approach to address a multiperiod optimization
problem in which a portfolio manager seeks an optimal constant proportion portfolio strategy …

In this paper, we study a continuous-time exploratory mean-variance (EMV) problem under
the framework of reinforcement learning (RL), and the Choquet regularizers are used to …

This paper studies continuous-time risk-sensitive reinforcement learning (RL) under the
entropy-regularized, exploratory diffusion process formulation with the exponential-form …

We study optimal stopping for a diffusion process with unknown model primitives within the
continuous-time reinforcement learning (RL) framework developed by Wang et al.(2020). By …

This thesis studies four mathematical problems in investment management. All four
problems arise from practical challenges and are data-driven. Chapter 2 investigates the …

In this paper, we investigate a competitive market involving two agents who consider not
only their own wealth but also the wealth gap with their opponent. Both agents can invest in …