GBM Stock Price Path Simulator
Simulate many possible future stock price paths using Geometric Brownian Motion (GBM) and see how the same stock can end up in very different places.
View multiple simulated paths, the distribution of prices at the horizon, and key percentiles (5%, 50%, 95%).
The current price of the stock or index you want to simulate.
Long-run expected annual return. This can be positive or negative.
Standard deviation of annual returns. Higher values create more extreme paths and wider distributions.
The time horizon for the simulation in years. The model uses 252 trading days per year.
Each simulation represents one possible price path. More paths yield smoother statistics.
For clarity, the chart only shows a subset of all simulated paths.
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How the GBM Stock Price Path Simulator Works
The GBM Stock Price Path Simulator lets you explore how a single stock or index can follow many different possible futures, even when you hold the same assumptions for expected return and volatility. Instead of producing a single forecast line, the tool generates multiple random paths using Geometric Brownian Motion (GBM), a widely used model in quantitative finance. This creates a visual and numerical illustration of path dependency and uncertainty: prices can compound in many ways, leading to dramatically different outcomes over the same time horizon.
To use the simulator, you enter a starting price, an expected annual drift (mean return), an annual volatility, the number of years to simulate, and the number of paths to generate. Under the hood, the time horizon is broken into 252 trading steps per year, and the GBM formula is applied at each step. The price at the next time point is calculated by multiplying the current price by an exponential term that combines the drift, volatility, and a random shock drawn from a standard normal distribution. Repeating this process step by step creates one simulated price path; repeating it many times creates a family of possible trajectories that all start at the same initial value.
Once the simulations are complete, the tool focuses on the distribution of prices at the final horizon. It sorts the ending prices and computes the mean, minimum, maximum, and key percentiles such as the 5th, 50th (median), and 95th. It also calculates the probability that the final price finishes above the initial price, given your chosen drift and volatility. This provides a quick way to understand both the upside and downside potential of a position under the assumptions of the GBM model.
While GBM is a standard and convenient model, it does not capture all the complexities of real markets. It assumes constant volatility, log-normal returns, and no jumps, and it ignores factors such as liquidity, trading costs, and market microstructure. As a result, this simulator should be used as an educational and exploratory tool rather than a precise prediction engine. Traders, students, and risk analysts can use it to build intuition about how volatility and time interact, and to see firsthand how the same stock can end up in very different places when driven by Brownian motion.
GBM Stock Price Path Simulator – FAQ
What does this GBM simulator actually show?
It shows many possible future price paths for a stock or index, based on your assumptions for expected annual return (drift) and annual volatility. It also summarizes the distribution of prices at the final time horizon and reports key percentiles and probabilities.
Why use Geometric Brownian Motion for stock prices?
Geometric Brownian Motion is a standard mathematical model that keeps prices non-negative and captures the compounding nature of returns. Although real markets are more complex, GBM is widely used in option pricing, risk management, and academic finance as a baseline for modeling asset prices.
Does this tool predict future stock prices?
No. The tool does not predict what will actually happen in the market. It illustrates hypothetical scenarios under the GBM model using your chosen parameters. Real-world outcomes can differ significantly, especially around major news events, regime changes, or structural shifts.
How should I choose drift and volatility values?
Many users base drift and volatility on historical data for the stock or index they are studying. Others experiment with a range of values to explore different scenarios, such as stress tests or optimistic and pessimistic cases. There is no single correct input; the goal is to understand how assumptions translate into a spread of possible outcomes.