Six runs across the spectrum.
Every model tells a story about its assumptions. These six are picked to span the range — a mature compounder where DCF and market sit close, a hyperscaler where the sensitivity grid matters more than the point estimate, a financial that needs a different methodology entirely, and a name the model pushes back on.
A mature, capital-efficient business with a deep earnings base. The kind of name where DCF and comps should both anchor close to where the market trades. They do here, within 4% of spot.
A DCF on a hyperscaler-tier name is mostly an argument about what year growth tapers and to what. The single intrinsic is less interesting than the sensitivity grid — for hot names the model's job is to stress-test the bull case, not produce a point estimate.
DCF does not translate to a leveraged balance sheet. Residual Income asks the right question instead: is the bank earning more than the cost of its equity capital? When ROE clears Ke, RI is positive and book value undershoots fair value.
A high-quality compounder, but at current prices a base-case DCF struggles to clear spot. You would need the bull cloud thesis — margin expansion, AI monetisation — to justify the multiple. The model says no, and that is useful to know.
Retail is low-margin by design, but AWS and ads are pulling the consolidated EBIT margin upward year by year. The DCF projects that trajectory continuing, which produces meaningful upside vs spot.
Smaller, less-followed names trade with more dislocation than mega-caps. A reasonable model can find genuine pricing gaps without resorting to heroic assumptions. Hoka and UGG do the heavy lifting here.