Monte Carlo Stock Simulator
Simulate thousands of price paths using geometric Brownian motion.
Frequently Asked Questions
What is Monte Carlo simulation?
Monte Carlo simulation uses random sampling to generate thousands of possible price paths, helping you understand the probability distribution of potential outcomes.
How reliable are the results?
Results are probabilistic estimates based on assumed volatility and return. They are not predictions but help quantify the range of possible outcomes.
What does the median price mean?
The median price is the middle value — 50% of simulations result in a price above it and 50% below it.
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What is this tool?
Monte Carlo simulation for stocks uses Geometric Brownian Motion to model random price paths. Running thousands of simulations generates a probability distribution of possible future prices, helping assess upside and downside risk. Formula: S(t) = S0 x exp((mu - sigma2/2)t + sigma x sqrt(t) x Z).
How to use
- 1
Enter current price
Input the current stock price.
- 2
Set return and volatility
Enter annual return (drift) and annual volatility.
- 3
Choose time horizon
Set the simulation period in trading days.
- 4
Run simulations
Select number of simulations (100-500).
- 5
View distribution
See price percentiles, profit probability, and histogram.
Frequently Asked Questions
What is Monte Carlo simulation?
Monte Carlo simulation uses random sampling to model uncertain outcomes, generating thousands of possible price paths.
How accurate are the results?
Results provide probability ranges, not predictions. Accuracy depends on input parameters. Use as risk assessment, not crystal ball.
What is Geometric Brownian Motion?
GBM is a stochastic model where logarithmic returns follow a normal distribution. It is the foundation of Black-Scholes pricing.