PhD Seminar • Scientific Computing • A Deep Learning Approach to Solving Time Consistent Mean-variance Optimal Trade Execution

Please note: This PhD seminar will take place in DC 2102.

Andrew Na, PhD candidate
David R. Cheriton School of Computer Science

Supervisor: Professor Justin Wan

We explore using deep learning to solve the time consistent mean variance optimal trade execution problem. We formulate an HJB equation for the time-consistent mean-variance optimal trade execution problem, which is extended to a multi-asset setting. We present a deep learning method to solve the problem. Given time we will also look at the extension of the problem into the multi-agent setting.