Probability of Hitting a Price Target (GBM)
Estimate the probability that a stock or crypto reaches a target price within a given time horizon using Geometric Brownian Motion (GBM).
Optional. Set to 0% for pure volatility-based probability.
Max 5 years.
Max 500.
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How the Price Target Probability Simulator Works
This Price Target Probability Simulator estimates the likelihood that a stock or cryptocurrency will touch or exceed a specific price level within a chosen time horizon. Instead of relying on subjective opinions or simple trend lines, this tool uses a quantitative framework called Geometric Brownian Motion (GBM), one of the most widely used mathematical models for asset prices in quantitative finance, derivatives pricing, and risk management. GBM assumes that prices evolve through continual compounding and random market shocks. Under these assumptions, the simulator runs multiple Monte Carlo paths to determine how often the price crosses the target level.
The process works by breaking the total horizon into daily steps using 252 trading days per year. At each step, the model applies the GBM equation S(t + Δt) = S(t) × exp[(μ − 0.5σ²)Δt + σ√Δt·Z], where μ is the expected drift, σ is annual volatility, and Z is a standard normal random variable. Because volatility plays a much stronger role than drift in short-term movements, this model is ideal for traders exploring breakouts, price targets, stop-loss probabilities, or upside risk scenarios.
After running the Monte Carlo engine across all simulated paths, the tool records the percentage of runs in which the price hits or exceeds the target. This number provides an intuitive, probability-based interpretation of market uncertainty. Traders can use this to understand how aggressive or conservative a given target is under realistic volatility conditions. The simulator also reports the approximate time it takes to reach the target in successful paths, along with the final price distribution at the end of the horizon. As with all Monte Carlo tools, the results are intended for educational exploration and should not be treated as financial, tax, legal, or trading advice.
Price Target Probability Simulator – FAQ
What does this simulator estimate?
It estimates the probability that a stock or crypto asset will reach or exceed a specific target price within a chosen time frame by simulating many price paths using Geometric Brownian Motion.
Does this tool predict actual prices?
No. The simulator shows hypothetical outcomes under the GBM model using your chosen volatility and drift assumptions. Real-world results may vary, especially during news events or market regime changes.
Why is volatility so important?
Volatility determines how widely price paths can spread. Higher volatility increases both the chance of reaching very high targets and very low values. In short-term forecasting, volatility usually matters more than drift.
How accurate is this model?
GBM is a standard model in quantitative finance and is used in many pricing and risk applications. However, it does not include jumps, volatility clustering, news shocks, or macroeconomic changes. It is most accurate for broad statistical insight, not precise predictions.