Date of Award
2022-05-01
Degree Name
Master of Science
Department
Statistics
Advisor(s)
Michael Pokojovy
Abstract
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.
Language
en
Provenance
Received from ProQuest
Copyright Date
2022-05
File Size
79 p.
File Format
application/pdf
Rights Holder
Pablo Ever Avalos
Recommended Citation
Avalos, Pablo Ever, "A Machine Learning Approach to Stochastic Optimal Control" (2022). Open Access Theses & Dissertations. 3470.
https://scholarworks.utep.edu/open_etd/3470
Included in
Computer Sciences Commons, Finance and Financial Management Commons, Statistics and Probability Commons