Premium
Know your industry: the implications of using static GICS classifications in financial research
Author(s) -
Katselas Dean,
Sidhu Baljit K.,
Yu Chuan
Publication year - 2019
Publication title -
accounting and finance
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.645
H-Index - 49
eISSN - 1467-629X
pISSN - 0810-5391
DOI - 10.1111/acfi.12285
Subject(s) - matching (statistics) , benchmark (surveying) , series (stratigraphy) , computer science , data mining , econometrics , economics , statistics , mathematics , paleontology , geodesy , biology , geography
Researchers commonly use industry classifications as a means of identifying peer companies to use as a performance benchmark. We describe the structure of commonly used sources of industry classification data available for Australian listed companies, both static and in time series. Next, we run a series of experiments matching firms according to GICS classification data presented in time series versus static data sources. Our results indicate that performance measures are better specified when matching on GICS data from a dynamic relative to a static source. The results of our power tests also underscore the importance of using dynamic industry data.