ATLASMonte Carlo simulation for stocks: the range, not a single forecast
A Monte Carlo simulation runs thousands of random but realistic price paths and reads the outcomes as a distribution, so you see how wide the possibilities are instead of one guess.
What it does
Nobody can say where a stock will be in a year, but you can say how likely different places are. Monte Carlo simulates many possible futures; sorting their end points gives a bad case, a middle case and a good case, and the chance of ending below today.
It is the same logic behind ATLAS's goals planner and its portfolio and stock ranges. The method matters more than the headline number: if two stocks have the same expected return but one is twice as volatile, Monte Carlo shows the risk difference clearly.
Worked example with live data
Real ATLAS numbers, prices as of Oct 26, 2026.
Live example: 4,000 simulated one-year paths for AAPL, starting at $346.86. The bad case (5th percentile) ends near $261, the middle near $366 and the good case (95th) near $506. About 40% of paths end below today's price.
| Horizon | Bad case | Lower middle | Middle | Upper middle | Good case |
|---|---|---|---|---|---|
| After 1 months | $317 | $337 | $349 | $360 | $382 |
| After 3 months | $298 | $329 | $352 | $374 | $412 |
| After 6 months | $281 | $324 | $357 | $391 | $447 |
| After 1 year(s) | $261 | $316 | $366 | $418 | $506 |
How ATLAS calculates it
Each path advances month by month. The monthly return has a drift (the long-run market return of about 7% a year, applied to every stock) and a random shock scaled by the stock's volatility (75% recent, 25% long-run). The shock follows a fat-tailed Student-t curve, so large moves occur more often than a bell curve would allow. The same seed is used each time, so results are reproducible.
ATLAS's one-month stock ranges use the closed-form version of the same distribution, calibrated on about 148,000 past predictions. With enough paths, simulation and formula agree.
Limits you should know
A simulation is only as good as its inputs. It assumes volatility stays near its estimate, ignores sudden regime changes and treats every stock as having the same long-run return.
Where it sits in the plans
Stock ranges and simulation are part of analysis: the Free plan covers the 200 largest US companies and funds, Starter and above cover all companies.
Run it on your own numbers
Create an account to use this tool with your stocks, your amounts and your horizon.
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Frequently asked questions
What is a Monte Carlo simulation for stocks?
It is a method that generates thousands of random price paths consistent with a stock's volatility and expected return, then reads the spread of results as the range of possible outcomes.
How many simulations are enough?
A few thousand paths are enough for stable percentiles in simple problems. ATLAS uses 4,000 paths in its examples and planner.
Does Monte Carlo predict the stock price?
No. It estimates a range under assumptions. The middle outcome is not a target price.
What is the difference between Monte Carlo and a historical backtest?
A backtest replays what happened once. Monte Carlo explores many outcomes that could have happened, including ones worse than history.
Related tools
- Stock probabilityFor any stock, ATLAS estimates the probability that it closes higher in 21 trading sessions (about one month) and the range where it should land. Most answers sit close to a coin flip, and that is the honest result.
- Portfolio simulatorA portfolio simulator shows the range of values your holdings could reach, not a single forecast. In ATLAS you enter any stocks and quantities and simulate horizons from one day to 20 years.
- Goals plannerThe goals planner runs a Monte Carlo simulation on your savings plan and tells you the chance of reaching your target, instead of assuming a smooth average return.
Stocks used in this example
Open any of them to see the full public page, with fair value, odds and a one-year chart:
Not investment advice. ATLAS estimates come from statistical models and public data; they can be wrong and past results do not predict future ones. Disclaimer