  Portfolio sorting is ubiquitous in the empirical finance literature, where it
has been widely used to identify pricing anomalies. Despite its popularity,
little attention has been paid to the statistical properties of the procedure.
We develop a general framework for portfolio sorting by casting it as a
nonparametric estimator. We present valid asymptotic inference methods and a
valid mean square error expansion of the estimator leading to an optimal choice
for the number of portfolios. In practical settings, the optimal choice may be
much larger than the standard choices of 5 or 10. To illustrate the relevance
of our results, we revisit the size and momentum anomalies.
