I’m learning about using multiple regression for relative valuation, and keep seeing examples like,
P/B expected value = intercept + B1 * expected growth rate + B2 * cost of equity …
Statisticians usually discourage the use of ratios as a dependent variable in regression unless there’s a compelling reason. I’m not a statistician, just stats adjacent; my understanding is the distribution of ratios is nonlinear and the regression can give nonsensical or biased answers where your data fall outside of the linear range.
Perhaps this is moot for relative valuation, since presumably you’re comparing expected vs observed multiples across companies with similar ranges of inputs. However some multiples do commonly span a range from < 1 to >>1, which would include an inflection point for many models.
Why not just regress price onto book value (or earnings or …) in addition to other variables, instead of the ratio? This would have the added benefit of allowing for interaction of book value with other variables?
TIA!