Question 1

What is a Monte Carlo stock simulation?

Accepted Answer

A Monte Carlo stock simulation uses random sampling to model thousands of possible future price paths for a stock. Each path is generated using geometric Brownian motion calibrated to the stock's real historical volatility and drift. By running 5,000 simulations, you get a probability distribution showing the range of likely outcomes from worst-case to best-case scenarios.

Question 2

How accurate is a Monte Carlo simulation for stock prices?

Accepted Answer

Monte Carlo simulations show the range of statistically plausible outcomes based on historical behaviour, not a single prediction. They are widely used in quantitative finance for risk assessment, portfolio analysis, and option pricing. The accuracy depends on the model assumptions: geometric Brownian motion assumes log-normal returns and constant volatility, which is a reasonable approximation but does not capture tail risks or regime changes.

Question 3

How does this Monte Carlo stock simulator work?

Accepted Answer

Search for any publicly traded stock ticker. The tool fetches real historical price data, calculates the annualised volatility and drift, then generates 5,000 simulated price paths using geometric Brownian motion. You can view confidence bands (P10 to P90), the median expected outcome, a histogram of final prices, and the probability of the stock finishing higher than its current price.

Monte Carlo Stock Price Simulator