Critically assess a DCF result, understanding its sensitivity to assumptions and the “garbage in — garbage out” principle.
DCF gives a range, not an exact number
DCF gives a range, not an exact number
DCF looks precise — formulas, numbers, a neat final figure. But behind that precision lies a deep dependence on assumptions. A small change in the discount rate or the terminal growth rate can move the valuation by tens of percent. That's why two professional analysts working on the same company can easily arrive at different valuations — and that's normal.
The main principle: “garbage in — garbage out” (GIGO). If the FCF forecast is unrealistic, the rate is picked arbitrarily, and the terminal growth rate is pulled out of thin air, the final figure is meaningless, however carefully it was calculated. The quality of a DCF model is determined by the quality of its assumptions, not the complexity of its formulas.
To see how robust a valuation is, analysts run a sensitivity analysis: they change the rate and the growth rate by ±1–2 percentage points and watch how the result moves. If the valuation swings sharply on small changes, that's a risk signal. This analysis is often laid out as data tables (Data Table in Excel), the topic of Unit 6.
An important disclaimer: the DCF model here is for learning purposes and illustrates valuation principles. It is not investment advice and does not replace professional financial analysis. Treat the model's results as one point of view, not the final word.
Lesson notes
DCF gives a range, not an exact number
DCF looks precise — formulas, numbers, a neat final figure. But behind that precision lies a deep dependence on assumptions. A small change in the discount rate or the terminal growth rate can move the valuation by tens of percent. That's why two professional analysts working on the same company can easily arrive at different valuations — and that's normal.
The main principle: “garbage in — garbage out” (GIGO). If the FCF forecast is unrealistic, the rate is picked arbitrarily, and the terminal growth rate is pulled out of thin air, the final figure is meaningless, however carefully it was calculated. The quality of a DCF model is determined by the quality of its assumptions, not the complexity of its formulas.
To see how robust a valuation is, analysts run a sensitivity analysis: they change the rate and the growth rate by ±1–2 percentage points and watch how the result moves. If the valuation swings sharply on small changes, that's a risk signal. This analysis is often laid out as data tables (Data Table in Excel), the topic of Unit 6.
An important disclaimer: the DCF model here is for learning purposes and illustrates valuation principles. It is not investment advice and does not replace professional financial analysis. Treat the model's results as one point of view, not the final word.