Page 15 - FINAL CFA II SLIDES JUNE 2019 DAY 8

LOS 31.i: Calculate and interpret a predicted P/E, READING 31: MARKET-BASED VALUATION: PRICE AND
given a cross-sectional regression on ENTERPRISE VALUE MULTIPLES
fundamentals, and explain limitations to the cross-
sectional regression methodology. MODULE 31.4: EV AND OTHER ASPECTS

A predicted P/E can be estimated from linear regression of historical P/Es on its fundamental variables, including expected
growth and risk.

EXAMPLE: An analyst is valuing a public utility with a dividend payout ratio of 0.50, a beta of 0.95, and an expected earnings
growth rate of 0.06. A regression on other public utilities produces the following regression equation:
• predicted P/E = 6.75 + (4.00 × dividend payout) + (12.35 × growth) − (0.5 × beta)
The firm’s P/E ratio is 12.0. Calculate the predicted P/E on the basis of the values of the explanatory variables for the
company, and determine whether the stock is over- or underpriced.

Answer: predicted P/E = 6.75 + (4.00 × 0.50) + (12.35 × 0.06) − (0.5 × 0.95) = 9.02: Over or under priced?

Actual P/E is greater than predicted P/E, so the firm is overpriced.

Predicted P/E – limitations!
• Uncertainty in the predictive power of the estimated P/E regression for a different time period and/or sample of stocks.
• The relationships between P/E and the fundamental variables examined may change over time.
• Multicollinearity is often a problem in these time series regressions, thus interpreting individual regression coefficients is
difficult!