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

Recieved from ProQuest

File Size

79 p.

File Format

application/pdf

Rights Holder

Pablo Ever Avalos

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