Differences between COMPUSTAT and CRSP SIC codes and related effects on research

Guenther, David A.; Rosman, Andrew J.

doi:10.1016/0165-4101(94)90021-3

Cited by 136 publications

(65 citation statements)

References 13 publications

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“…Naveh and Marcus (2005) uses a three-digit SIC criterion where possible while Corbett et al (2005) used a looser two-digit SIC criterion as they wished to avoid discrepancies at the three-and four-digit level that have been found in their database by previous researchers (Guenther and Rosman, 1994). Although it is clear that each of the longitudinal studies uses different detailed selection methods for their control groups all apart from Häversjö have used a systematic approach that attempts to align their control group as closely as possible to the industries of their QCert sample along with a control for the firms' size.…”

Section: Iso 9001 Quality Management and Performance Literaturementioning

confidence: 99%

Shedding light on causation between ISO 9001 and improved business performance

Dick

Heras²,

2008

International Journal of Operations &Amp; Production Management

129

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Purpose -The adoption of the ISO 9001 Quality Management Systems Standard has proven to be a persistent and growing phenomenon in services and manufacturing, yet to date little research has been done that can indicate how far improved business performance can be attributed to it rather than counter-intuitive causes. The paper aims to examine the evidence for the causal links between quality management system certification and improved performance in the empirical literature. Design/methodology/approach -A method is proposed for testing how far performance improvement can be attributed to quality management system certification and how far attribution to other causes applies. This method is illustrated on a longitudinal study and then utilised to interpret the findings of other longitudinal studies. Findings -It is concluded that although there is some evidence to indicate that quality management system certification has some causal influence on business performance, there is also evidence for the existence of a substantial mechanism whereby better performing firms self-select to adopt certification. Possible causes for this mechanism are discussed. Research limitations/implications -The existence of a self-select mechanism has profound implications for interpreting business performance achievements associated with quality management system certification because the benefits found may well be inflated by its presence. The authors suggest that richer theory is needed that can incorporate bi-directional influences and new research is needed to explore the underlying causes of adoption selection effects. Originality/value -The paper provides researchers with a method for testing and discussing causation influences on results. It provides evidence that a substantial part of the association found in the research on quality management system certification and business benefits may be due to counterintuitive causes.Keywords Performance management, ISO 9000 series, Cause and effect analysis, QualityPaper type Research paper Introduction Although most "new" ideas in management have short life spans and are discarded when eclipsed by the next fad (Carson et al., 2000), adoption of third party accredited ISO quality management system standards has proven to be a persistent and growing phenomenon. Its persistence suggests that it is not simply another management fad but will remain an influential global management meta-standard (Uzumeri, 1997).

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Section: Iso 9001 Quality Management and Performance Literaturementioning

confidence: 99%

Shedding light on causation between ISO 9001 and improved business performance

Dick

Heras²,

2008

International Journal of Operations &Amp; Production Management

129

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“…Guenther and Rosman (1994) document substantial SIC difference between Compustat and CRSP, with 38% classification disagreement at the two-digit level. Kahle and Walkling (1996) further point out that the two major data vendors disagree on 80% of classifications at the four-digit level.…”

Section: Different Industry Classification Systemsmentioning

confidence: 99%

“…Because the firms within the same industry employ similar production technologies and/or compete in similar markets, news concerning technology shocks or market demand should affect these firms' cash flows and stock returns in a similar manner. Moreover, the external shocks would also be reflected in key accounting variables (Guenther and Rosman (1994)). I thus choose the six variables as the basis for judging the effectiveness of SIC codes in this section, and also the basis for constructing a measure of misclassification in the next section.…”

Section: Testing the Effectiveness Of Sic Codesmentioning

confidence: 99%

The Potential Misinterpretation Problem of Residual Based Models Due to Industry Misclassification

Wang¹,

Shao²,

Zhang³

2017

AJBR

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Accounting researchers frequently employ industry-specific residual based models to draw inferences. Examples include discretionary accruals in Jones (1991) and modified Jones model (1995), and accruals quality in Francis et al. (2005). This paper illustrates that the interpretation of the residual terms is potentially subject to the problem that arises from industry misclassification. In an industry-specific crosssectional regression, the assumed homogeneity within the same industry is problematic because the industry classification system is noisy, and thus large magnitude residuals are potentially caused by misclassified observations. Moreover, the misclassification may not happen randomly. If firms with certain characteristics are more likely to be misclassified, directional biases rather than pure noise may emerge. In firm-specific time-series settings, the implied stationarity over time for the same firm is also questionable. Firms face external shocks and/or internal changes. The big magnitude residuals could capture those "shock" or "change" years. Given that researchers often investigate whether there exists earnings management around special events, such as M&A or equity issuance, it would be necessary to distinguish between whether abnormal accruals around the event is due to earnings management or simply non-stationarity.

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“…(GICS = Global Industry Classification Standard (by MSCI and Standard & Poor's); BICS = Bloomberg Industry Classification System; ICB = Industry Classification Benchmark (by London Stock Exchange FTSE); NAICS = North American Industry Classification System (by Mexico's Instituto Nacional de Estadística y Geografía, Statistics Canada also known as Statistique Canada, and the United States Office of Management and Budget); SIC = Standard Industrial Classification (by the United States government agencies); TRBC = Thomson Reuters Business Classification.) are widely used in a variety of fields, including economic applications (for economics, financial economics and accounting related literature, see, e.g., ; for a recent review, see, e.g., [24]; for other applications and more generally related literature, see, e.g., [25][26][27][28][29][30][31][32][33][34][35]), general population and healthcare related studies (see, e.g., [36] and references therein), and (quantitative) finance/trading (including risk modeling) (for related literature, see, e.g., ; for applications to risk modeling within quantitative finance, see, e.g., [60][61][62]; for statistical/data mining related methods, see, e.g., [63][64][65][66][67][68][69]). Industry classification (i.e., taxonomy) groups companies into baskets (e.g., industries) based on some kind of a similarity criterion or criteria, which differ from one classification to another.…”

Section: Introductionmentioning

confidence: 99%

Open Source Fundamental Industry Classification

Kakushadze

2017

Data

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Abstract:We provide complete source code for building a fundamental industry classification based on publicly available and freely downloadable data. We compare various fundamental industry classifications by running a horserace of short-horizon trading signals (alphas) utilizing open source heterotic risk models (https://ssrn.com/abstract=2600798) built using such industry classifications. Our source code includes various stand-alone and portable modules, e.g., for downloading/parsing web data, etc.Keywords: industry classification; fundamental; open source; source code; stocks; hierarchy; GICS; BICS; ICB; NAICS; SIC; TRBC; quantitative trading; trading signal; alpha; risk model; mean-reversion; optimization; short-horizon; backtest; simulation; download IntroductionFundamental industry classifications such as GICS, BICS, ICB, NAICS, SIC, TRBC, etc. (GICS = Global Industry Classification Standard (by MSCI and Standard & Poor's); BICS = Bloomberg Industry Classification System; ICB = Industry Classification Benchmark (by London Stock Exchange FTSE); NAICS = North American Industry Classification System (by Mexico's Instituto Nacional de Estadística y Geografía, Statistics Canada also known as Statistique Canada, and the United States Office of Management and Budget); SIC = Standard Industrial Classification (by the United States government agencies); TRBC = Thomson Reuters Business Classification.) are widely used in a variety of fields, including economic applications (for economics, financial economics and accounting related literature, see, e.g., ; for a recent review, see, e.g., [24]; for other applications and more generally related literature, see, e.g., [25][26][27][28][29][30][31][32][33][34][35]), general population and healthcare related studies (see, e.g., [36] and references therein), and (quantitative) finance/trading (including risk modeling) (for related literature, see, e.g., ; for applications to risk modeling within quantitative finance, see, e.g., [60][61][62]; for statistical/data mining related methods, see, e.g., [63][64][65][66][67][68][69]). Industry classification (i.e., taxonomy) groups companies into baskets (e.g., industries) based on some kind of a similarity criterion or criteria, which differ from one classification to another. Such fundamental industry classifications generally are expected to be based on pertinent fundamental/economic data, such as companies' products and services, revenue sources, suppliers, competitors, partners, etc. They are essentially independent of the pricing data and, if well-built, tend to be rather stable out-of-sample as companies seldom jump industries. Many industry classifications are developed commercially and acquiring such data is associated with nontrivial costs. Even government-developed classifications such as NAICS or even SIC (see below) are not exactly free. This is for two main reasons. First, simply specifying a hierarchical structure (e.g., a complete list of, say, sectors, industries and sub-industries as in BICS) is only the tip of th...

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Differences between COMPUSTAT and CRSP SIC codes and related effects on research

Cited by 136 publications

References 13 publications

Shedding light on causation between ISO 9001 and improved business performance

Shedding light on causation between ISO 9001 and improved business performance

The Potential Misinterpretation Problem of Residual Based Models Due to Industry Misclassification

Open Source Fundamental Industry Classification

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