Speaker
Description
This research project presents a combination of techniques to develop a
sound mathematical approach to the portfolio optimization problem. The problem
is formulated as a Linear Quadratic Regulator and solved using Dynamic Program-
ming. The key contributions include integrating multivariate regression modeling of
returns with structure estimation for the regressor subset and employing exponential
forgetting with an algorithm for varying forgetting factor. The optimal allocation
is obtained by solving a constrained quadratic programming problem featuring a
custom reward function. We highlight the importance of structure estimation and
the sequential approach, while also exploring the potential of modeling optimal al-
location using the same regression framework as for returns.