Portfolio Optimization Paradox Using Monte Carlo Simulation And Particle Swamp Optimization Algorithms

Author

Malvern F Dumbu

Title

Portfolio Optimization Paradox Using Monte Carlo Simulation And Particle Swamp Optimization Algorithms

Abstract

This dissertation explores the optimization of financial portfolios by integrating Monte carlo simulation with Particle Swarm Optimization (PSO) algorithms. The study addresses the portfolio optimization paradox, which involves balancing the maximization of returns and the minimization of risk. Traditional models like the Capital Asset Pricing Model (CAPM), Modern Portfolio Theory (MPT), and Fama-French models often rely on assumptions that do not fully capture real-world complexities, particularly in emerging markets such as Zimbabwe. By applying Monte carlo simulation and PSO to stocks from both the Zimbabwe Stock Exchange (ZSE) and the New York Stock Exchange (NYSE), this research develops a robust framework that accommodates market uncertainties and adapts to changes. The results indicate that PSO, combined with Monte carlo simulation, outperforms traditional models in terms of risk-adjusted returns, diversification, and stability. This innovative approach provides significant insights for individual and institutional investors, promoting better investment decisions and contributing to financial market stability and inclusivity, especially in diverse economic environments.

Date

2024

Publisher

BUSE

Keywords

Portfolio

Optimization

Paradox

Supervisor

Ms P Hlupo