Abstract
Stock market provides limitless opportunities for average income earners to grow their wealth. Nevertheless, it is well-known that most people lose their cash by investing in it. This study investigates the statistics of market volatility by obtaining and analysing data sources from financial websites using Python programming language. The implemented method uses a mathematical model called Geometric Brownian Motion (GBM) in order to simulate stock prices in the United States and Malaysia The stocks were used as a data set for the simulations, which in turn were conducted in time periods 1000 cycle. The two main parameters which determine the outcome of the simulations are the mean return of a stock and the standard deviation of historical returns. This study provides useful insights for retail traders through the Monte Carlo Analysis in finance.