  In light of the power problems of statistical tests and undisciplined use of
alpha-based statistics to compare models, this paper proposes a unified set of
distance-based performance metrics, derived as the square root of the sum of
squared alphas and squared standard errors. The Bayesian investor views model
performance as the shortest distance between his dogmatic belief (model-implied
distribution) and complete skepticism (data-based distribution) in the model,
and favors models that produce low dispersion of alphas with high explanatory
power. In this view, the momentum factor is a crucial addition to the
five-factor model of Fama and French (2015), alleviating his prior concern of
model mispricing by -8% to 8% per annum. The distance metrics complement the
frequentist p-values with a diagnostic tool to guard against bad models.
