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EVENT STUDY METHODS AND EVIDENCE ON THEIR PERFORMANCE
Author(s) -
Armitage Seth
Publication year - 1995
Publication title -
journal of economic surveys
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.657
H-Index - 92
eISSN - 1467-6419
pISSN - 0950-0804
DOI - 10.1111/j.1467-6419.1995.tb00109.x
Subject(s) - econometrics , variance (accounting) , event study , rank (graph theory) , economics , regression , statistical hypothesis testing , abnormal return , efficient market hypothesis , event (particle physics) , test (biology) , statistics , computer science , mathematics , accounting , paleontology , context (archaeology) , physics , horse , combinatorics , quantum mechanics , stock market , biology , finance , stock exchange
. The paper outlines widely used methods of estimating abnormal returns and testing their significance, highlights respects in which they differ conceptually, and reviews research comparing results they produce in various empirical contexts. Direct evidence on the performance of different methods is available from simulation experiments in which known levels of abnormal return are added. The market model is most commonly used to generate expected returns and no better alternative has yet been found despite the weak relationship between beta and actual returns. Choice of procedure for significance testing depends on the characteristics of the data. The evidence indicates that in many cases the best procedure is to standardise market model abnormal returns by their time series standard errors of regression and use the t ‐test. Alternatively a rank test appears to be at least as powerful. If errors are cross‐correlated or increase in variance during the test period, other methods discussed should be used.