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Know your industry: the implications of using static GICS classifications in financial research
被引:7
|作者:
Katselas, Dean
[1
]
Sidhu, Baljit K.
[2
]
Yu, Chuan
[2
]
机构:
[1] Australian Natl Univ, Res Sch Finance Actuarial Studies & Appl Stat, Canberra, ACT, Australia
[2] Univ New South Wales, Sch Accounting, Sydney, NSW, Australia
关键词:
Industry;
GICS;
Matching;
Accounting;
Finance;
IMPACT;
D O I:
10.1111/acfi.12285
中图分类号:
F8 [财政、金融];
学科分类号:
0202 ;
摘要:
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.
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页码:1131 / 1162
页数:32
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