We propose to solve large scale Markowitz mean-variance (MV) portfolio allocation problem using reinforcement learning (RL). By adopting the recently developed continuous-time exploratory control framework, we formulate the exploratory MV problem in high dimensions. We further show the...

Epstein and Schneider (2007) develop a framework of learning under ambiguity, generalizing maxmin preferences of Gilboa and Schmeidler (1989) to intertemporal settings. The specific belief dynamics in Epstein and Schneider (2007) rely on the rejection of initial priors that have become...

This paper proposes a model of how biased individuals update beliefs in the presence of informational ambiguity. Individuals are ambiguous about the actual signal-generating process and interpret signals according to the model that can best support their biases. This paper provides a complete...

We propose a new, highly effective and easy-to-implement algorithm for solving large-scale mean-variance optimization problems --- with weight upper bound constraints and without short sales --- when the size of mean-variance portfolios is much smaller than the number of assets, which is almost...