Nonparametric Rank Tests for Event Studies

Abstract

In event study analyses of abnormal returns on a single day, Corrado's (1989) nonparametric rank test and its modification in Corrado and Zivney (1992) have good empirical power properties, but problems arise in their application to cumulative abnormal returns (CARs). This paper proposes a generalized rank (GRANK) testing procedure that can be used for testing both single day and cumulative abnormal returns. Asymptotic distributions of the associated test statistics are derived and empirical properties of the test statistics are studied with simulations of CRSP returns. The results show that the proposed GRANK procedure outperforms previous rank tests of CARs and is robust to abnormal return serial correlation and event-induced volatility. Moreover, the GRANK procedure exhibits superior empirical power relative to parametric tests by Patell (1976) and Boehmer, Musumeci, and Poulsen (1991).