Portfolio Optimization Methods: The Mean-Variance Approach and the Bayesian Approach

Nguyen, Hoang (2019) Portfolio Optimization Methods: The Mean-Variance Approach and the Bayesian Approach. Undergraduate thesis, under the direction of Andrew Lynch from Finance, The University of Mississippi.

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Abstract

This thesis is a discussion on the mean-variance approach to portfolio optimization and an introduction of the Bayesian approach, which is designed to solve certain limitations of the classical mean-variance analysis. The primary goal of portfolio optimization is to achieve the maximum return from investment given a certain level of risk. The mean-variance approach, introduced by Harry Markowitz, sought to solve this optimization problem by analyzing the means and variances of a certain collection of stocks. However, due to its simplicity, the mean-variance approach is subject to various limitations. In this paper, we seek to solve some of these limitations by applying the Bayesian method, which is mainly based on probability theory and the Bayes’ theorem. These approaches will be applied to form optimal portfolios using the data of 27 Dow Jones companies in the period of 2008-2017 for a better comparison. The topic of portfolio optimization is extremely broad, and there are many approaches that have been and are being currently researched. Yet, there is no approach that is proven to perform most efficiently. The purpose of this paper is to discuss two potential and popular approaches in forming optimal portfolios.

Item Type: Thesis (Undergraduate)
Creators: Nguyen, Hoang
Student's Degree Program(s): B.A. in Mathematics, B.B.A. in Finance
Thesis Advisor: Andrew Lynch
Thesis Advisor's Department: Finance
Institution: The University of Mississippi
Subjects: Q Science > QA Mathematics
Depositing User: Hoang Nguyen
Date Deposited: 10 May 2019 04:30
Last Modified: 10 May 2019 04:30
URI: http://thesis.honors.olemiss.edu/id/eprint/1398

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