Activity, as well as expectations, often exhibit asymmetric dynamics by increasing gradually with occasional large downturns. Such dynamics emerge in a social learning model, which entails strong feedback between activity and information. In the model, activity reveals private information about...

We propose an actor-critic reinforcement learning (RL) algorithm for the optimal execution problem. We consider the celebrated Almgren-Chriss model in continuous time and formulate a relaxed stochastic control problem for execution under an entropy regularized mean-quadratic variation objective....

We consider two data-driven approaches to hedging, Reinforcement Learning and Deep Trajectory-based Stochastic Optimal Control, under a stepwise mean-variance objective. We compare their performance for a European call option in the presence of transaction costs under discrete trading schedules....

A pre-condition for employer learning is that signals at labor market entry do not fully reveal graduates' productivity. I model various distinct sources of signal imperfection-such as noise and multi-dimensional types-and characterize their implications for the private return to skill...

School closures induced by the COVID-19 pandemic led to concerns about student learning. This paper evaluates the effect of school closures on student learning in Uzbekistan, using a unique dataset that allows assessing change in learning over time. The findings show that test scores in math for...

We analyze a repeated principal-agent setting in which the principal cares about the agent's verifiable effort as well as an extra profit that can be generated only if the agent is talented. The agent is overconfident about his talent and updates beliefs using Bayes' rule. An exploitation...