A Machine Learning Approach to Stochastic Optimal Control

Pablo Ever Avalos Robles, University of Texas at El Paso


Merton’s portfolio optimization problem is a well-renowned problem in financial mathematics which seeks to optimize the investment decision for an investor. In the simplest situation, the market consists of a risk-less asset (i.e. a bond) that pays back a relatively low interest rate, and a risky asset (i.e. a stock) that follows a geometric Brownian motion. The optimal allocation strategy of the investor’s wealth is found by optimizing the expected utility along the stochastic evolution of the market. This thesis focuses on several different applications of this optimization problem. We look at pre-constructed analytical solutions and showcase the results. We formulate simulated allocation strategies and compare results. Lastly, we approach this optimization problem using machine learning, specifically, by training neural networks.

Subject Area

Statistics|Computer science|Finance|Applied Mathematics

Recommended Citation

Avalos Robles, Pablo Ever, "A Machine Learning Approach to Stochastic Optimal Control" (2022). ETD Collection for University of Texas, El Paso. AAI29211084.