This repository contains R/Python code and documentation for performing Monte Carlo simulations on stock prices. Monte Carlo simulations are a powerful tool for estimating the range of possible future stock prices based on historical data and assumptions about future returns and volatility.
Monte Carlo simulations use random sampling and statistical techniques to model the uncertainty associated with stock price movements. By running simulations over large iterations, we utilize the Law of Large Numbers to gain insights into the potential outcomes of investment strategies. We must make sure we:
- define assumptions about the expected returns and volatility of a stock
- simulate the future price paths of the stock over a specific time horizon
- visualize the results to understand the range of possible outcomes