Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
In this article, we develop a finite-sample distribution-free procedure to test the beta-pricing representation of linear factor pricing models. In sharp contrast to extant finite-sample tests, our framework allows for unknown forms of nonnormalities, heteroscedasticity, and time-varying covariances. The power of the proposed test procedure increases as the time series lengthens and/or the cross section becomes larger. So the criticism sometimes heard that nonparametric tests lack power does not apply here, since the number of test assets is chosen by the user. This also stands in contrast to the usual tests that lose power or may not even be computable if the number of test assets is too large. Supplementary materials for this article are available online.