Optimal Portfolio Selection Under Catastrophic Events Using Monte Carlo Simulation

Abstract

The global stock market is a critical mechanism for the allocation of scarce financial resources to productive economic activities. However, investors continuously face the dual challenge of minimising risk while simultaneously maximising returns. This tension becomes particularly acute during catastrophic events such as pandemics, which can severely disrupt market stability and undermine conventional investment strategies. The COVID-19 pandemic, for instance, caused significant downturns across major stock markets worldwide, highlighting the vulnerability of concentrated investment portfolios and reinforcing the importance of sound portfolio diversification strategies. This study applies Markowitz’s Modern Portfolio Theory (MPT) to nine selected stocks listed on the United States (US) stock market, spanning sectors including Technology, E-commerce, Energy, Health, Automobile, Transport, and Entertainment. Stock performance is evaluated over two distinct periods: before the pandemic (January 2018 to December 2019) and during the pandemic (January 2020 to December 2021), using data obtained from Yahoo Finance. The expected returns of the selected stocks are estimated using the Capital Asset Pricing Model (CAPM). A diversified portfolio is then formulated, the Sharpe ratio is computed for risk-adjusted performance evaluation, and the efficient frontier is constructed using Monte Carlo simulation implemented in Python. The simulation generates 2,000 portfolio scenarios to identify the optimal risky portfolio. The results demonstrate that a well-diversified portfolio can yield superior risk-adjusted returns, with the optimal portfolio achieving a Sharpe ratio of 1.21 at a return of 27.02% and a standard deviation of 22.36%. These findings underscore the effectiveness of MPT and Monte Carlo simulation as practical tools for optimal portfolio selection, particularly in the context of catastrophic market events.