The market is a distribution,
not a number.
Simulates thousands of EPS paths under mean-reverting growth with occasional shocks, discounts each one back to today, and shows the range of prices that assumption set could justify.
Set your assumptions on the left, then press Run.
Defaults reflect the example stock from the model: price $284, EPS $8.96, 13% expected annual EPS growth, 12% discount rate.
“When investing, one should have very clear the outcome distribution of its positions: if you don't know it, it is better leaving the table.”Read the full entry →
Why a DCF should be a distribution, not a number.
The Monte Carlo DCF values a company as the present value of its future free cash flows plus a terminal value — the same skeleton any analyst uses. What changes here is that instead of typing in one growth rate, one margin, one discount rate and getting back one 'answer,' each assumption is drawn thousands of times from a distribution you control. The output is a full histogram of intrinsic values, not a single point pretending to be truth.
Focus on the P10, P50 and P90 bands. The median is your central estimate; the gap between the tails is your honest uncertainty. If the current price is below P10, you are buying with a wide margin of safety. If it is above P90, the market disagrees with your model and you should ask why. A tight distribution means the model is confident (or the volatility inputs are too low); a very wide one means the business is too hard to value at all.
Revenue growth and EBIT margin drive early cash flows; the discount rate and terminal growth drive the terminal value, which is usually more than half the total. Terminal growth cannot exceed the long-run growth of the economy — around two to three percent — or the model becomes a perpetual-motion machine.