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

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  • Sanjeev Bhojraj
  • Charles M. C. Lee
  • Derek K. Oler

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)-super-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. Copyright 2003 Institute of Professional Accounting, University of Chicago.

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Bibliographic Info

Article provided by Wiley Blackwell in its journal Journal of Accounting Research.

Volume (Year): 41 (2003)
Issue (Month): 5 (December)
Pages: 745-774

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Handle: RePEc:bla:joares:v:41:y:2003:i:5:p:745-774

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Cited by:
  1. Dalziel, Margaret, 2007. "A systems-based approach to industry classification," Research Policy, Elsevier, vol. 36(10), pages 1559-1574, December.
  2. Hrazdil, Karel & Zhang, Ray, 2012. "The importance of industry classification in estimating concentration ratios," Economics Letters, Elsevier, vol. 114(2), pages 224-227.
  3. How, Janice & Lam, Jennifer & Yeo, Julian, 2007. "The use of the comparable firm approach in valuing Australian IPOs," International Review of Financial Analysis, Elsevier, vol. 16(2), pages 99-115.
  4. Clarke, Jonathan & Khorana, Ajay & Patel, Ajay & Rau, P. Raghavendra, 2007. "The impact of all-star analyst job changes on their coverage choices and investment banking deal flow," Journal of Financial Economics, Elsevier, vol. 84(3), pages 713-737, June.
  5. Christensen, Jesper Lindgaard, 2013. "The ability of current statistical classifications to separate services and manufacturing," Structural Change and Economic Dynamics, Elsevier, vol. 26(C), pages 47-60.
  6. Elgers, Pieter T. & Porter, Susan L. & Emily Xu, Le, 2008. "The timing of industry and firm earnings information in security prices: A re-evaluation," Journal of Accounting and Economics, Elsevier, vol. 45(1), pages 78-93, March.
  7. Jalal, Abu M. & Prezas, Alexandros P., 2012. "Outsider CEO succession and firm performance," Journal of Economics and Business, Elsevier, vol. 64(6), pages 399-426.
  8. Cohen, Alma & Wang, Charles C.Y., 2013. "How do staggered boards affect shareholder value? Evidence from a natural experiment," Journal of Financial Economics, Elsevier, vol. 110(3), pages 627-641.
  9. Hrazdil, Karel & Trottier, Kim & Zhang, Ray, 2013. "A comparison of industry classification schemes: A large sample study," Economics Letters, Elsevier, vol. 118(1), pages 77-80.
  10. Lucian A. Bebchuk & Alma Cohen & Charles C.Y. Wang, 2011. "Staggered Boards and the Wealth of Shareholders: Evidence from Two Natural Experiments," NBER Working Papers 17127, National Bureau of Economic Research, Inc.
  11. Brown, Stephen J. & Lajbcygier, Paul & Wong, Woon Weng, 2012. "Estimating the cost of capital with basis assets," Journal of Banking & Finance, Elsevier, vol. 36(11), pages 3071-3079.
  12. Chen, Sheng-Syan & Chen, Po-Jung & Lin, Wen-Chun, 2012. "The impact of strategic interaction on earnings expectations associated with corporate product strategies," Journal of Banking & Finance, Elsevier, vol. 36(1), pages 66-77.
  13. Hicks, Diana, 2011. "Structural change and industrial classification," Structural Change and Economic Dynamics, Elsevier, vol. 22(2), pages 93-105, June.
  14. Christian Weiner, 2005. "The Impact of Industry Classification Schemes on Financial Research," SFB 649 Discussion Papers SFB649DP2005-062, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
  15. Kelly, Bryan & Ljungqvist, Alexander P., 2009. "Testing Asymmetric-Information Asset Pricing Models," CEPR Discussion Papers 7180, C.E.P.R. Discussion Papers.
  16. Karel Hrazdil & Thomas Scott, 2013. "The role of industry classification in estimating discretionary accruals," Review of Quantitative Finance and Accounting, Springer, vol. 40(1), pages 15-39, January.
  17. Bradley, Daniel & Choi, Hyung-Suk & Clarke, Jonathan, 2011. "Working for the enemy? The impact of investment banker job changes on deal flow," Journal of Empirical Finance, Elsevier, vol. 18(4), pages 585-596, September.
  18. Kadan, Ohad & Madureira, Leonardo & Wang, Rong & Zach, Tzachi, 2012. "Analysts' industry expertise," Journal of Accounting and Economics, Elsevier, vol. 54(2), pages 95-120.
  19. Lyócsa, Štefan & Výrost, Tomáš & Baumöhl, Eduard, 2012. "Stock market networks: The dynamic conditional correlation approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(16), pages 4147-4158.
  20. Chou, Pin-Huang & Ho, Po-Hsin & Ko, Kuan-Cheng, 2012. "Do industries matter in explaining stock returns and asset-pricing anomalies?," Journal of Banking & Finance, Elsevier, vol. 36(2), pages 355-370.
  21. Garbellini, Nadia & Wirkierman, Ariel Luis, 2014. "Blocks and circularity in labour requirements: An interplay between clusters and subsystems in the EU," Structural Change and Economic Dynamics, Elsevier, vol. 28(C), pages 60-74.
  22. Kousenidis, Dimitrios V. & Ladas, Anestis C. & Negakis, Christos I., 2013. "The effects of the European debt crisis on earnings quality," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 351-362.

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