OPTIMIZATION OF FINANCIAL PORTFOLIOS USING MONTE CARLO SIMULATIONS AND PYTHON

Abstract

This study simulates portfolio construction using 10 stocks from the Dow Jones index, as well as from the stock exchanges of Mexico, Japan, and Colombia, applying Monte Carlo simulations to generate various weight combinations. The analysis calculates three key metrics: expected return, volatility, and Sharpe ratio. Historical closing prices of the selected stocks from 2014 to 2024 are used, which are standardized and scaled for easier comparison. After performing 1000 simulations, the portfolio with the highest Sharpe ratio from the market is identified as the most efficient. The results are visualized in an efficient frontier graph, highlighting the optimal portfolio, enabling investors to select the best risk-return combination.