Use a cross-sectional regression on fundamentals to predict a P/E, explain the result, and explain where such regressions fall short
The idea
A cross-sectional regression relates the P/E ratios of many shares to fundamentals such as payout ratio, growth and beta, fitting an equation such as P/E = a + b1 × payout + b2 × growth + b3 × beta. Inserting a company's own values into the fitted equation gives its predicted P/E; an actual P/E below the prediction suggests undervaluation. The limitations are serious. The relationship holds only for the sample and period estimated and may shift, so predictive power out of sample is often weak. The explanatory variables tend to be correlated with one another, making individual coefficients unreliable. And the fitted line may reflect mispricing of the whole sample. The trap is applying the equation mechanically without asking whether the coefficient signs make economic sense.
In the app: 1 practice questions on this outcome, a narrated walkthrough with diagrams, spaced review that brings it back before you forget it.
Take the free diagnostic →More in Market-Based Valuation: Price and Enterprise Value Multiples
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