What's My Line? A Comparison of Industry Classification Schemes for Capital Market Research

Authors

  • Sanjeev Bhojraj,

    1. Cornell University. We thank Richard Leftwich (editor) and an anonymous referee for helpful comments. We also thank Thomson Financial Services Inc. for providing earnings per share forecast data, available through the Institutional Brokers Estimate System (IBES). The IBES data have been provided as part of a broad academic program to encourage earnings expectation research. David Blitzer and Maureen Maitland of Standard & Poor's provided patient tutelage on the Global Industry Classification Standard (GICS)SM system; however, this research is independent from and was not financed by Standard & Poor's.
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  • Charles M. C. Lee,

    1. Cornell University. We thank Richard Leftwich (editor) and an anonymous referee for helpful comments. We also thank Thomson Financial Services Inc. for providing earnings per share forecast data, available through the Institutional Brokers Estimate System (IBES). The IBES data have been provided as part of a broad academic program to encourage earnings expectation research. David Blitzer and Maureen Maitland of Standard & Poor's provided patient tutelage on the Global Industry Classification Standard (GICS)SM system; however, this research is independent from and was not financed by Standard & Poor's.
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  • Derek K. Oler

    1. Cornell University. We thank Richard Leftwich (editor) and an anonymous referee for helpful comments. We also thank Thomson Financial Services Inc. for providing earnings per share forecast data, available through the Institutional Brokers Estimate System (IBES). The IBES data have been provided as part of a broad academic program to encourage earnings expectation research. David Blitzer and Maureen Maitland of Standard & Poor's provided patient tutelage on the Global Industry Classification Standard (GICS)SM system; however, this research is independent from and was not financed by Standard & Poor's.
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ABSTRACT

This study compares four broadly available industry classification schemes in a variety of applications common to capital market research. Standard Industrial Classification (SIC) codes have been available since 1939 but are being replaced by North American Industry Classification System (NAICS) codes. The Global Industry Classifications Standard (GICS)SM system, jointly developed by Standard & Poor's and Morgan Stanley Capital International (MSCI), is popular among financial practitioners, whereas the Fama and French [1997] algorithm is used primarily by academics. Our results show that GICS classifications are significantly better at explaining stock return comovements, as well as cross-sectional variations in valuation multiples, forecasted and realized growth rates, research and development expenditures, and various key financial ratios. The GICS advantage is consistent from year to year and is most pronounced among large firms. The other three methods differ little from each other in most applications.

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