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The predictive ability of “conservatism” and “governance” variables in corporate financial disclosures

Author

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  • Malcolm Smith
  • Yun Ren
  • Yinan Dong

Abstract

Purpose - The purpose of this paper is to examine the extent to which “corporate governance” and “conservatism” variables can contribute to the predictive ability of corporate financial disclosures. Design/methodology/approach - Multiple discriminant analysis is used to differentiate between good and poor companies in Australian manufacturing industry on the basis of their 2009 performance. A classification model including size, governance and conservatism variables, together with financial ratio data is constructed based on 2008 data, and used to predict 2009 performance. Findings - A model with conservatism, total debt/total assets, company size, and “percentage of shareholdings held by non‐executive directors” (representing corporate governance) as its independent variables, has a classification accuracy of 80.6 percent, and a predictive accuracy of 62.2 percent. Research limitations/implications - The relatively small sample size, for Australian manufacturing companies, limits both the predictive ability of the model and its generalisability elsewhere. Practical implications - The findings of the paper demonstrate the importance of both “conservatism” and “corporate governance” measures in determining corporate financial performance. Originality/value - The paper uses familiar discriminant methods in an unfamiliar context – focusing on surviving companies exhibiting extremes of financial performance.

Suggested Citation

  • Malcolm Smith & Yun Ren & Yinan Dong, 2011. "The predictive ability of “conservatism” and “governance” variables in corporate financial disclosures," Asian Review of Accounting, Emerald Group Publishing Limited, vol. 19(2), pages 171-185, July.
    • Handle: RePEc:eme:arapps:v:19:y:2011:i:2:p:171-185
    DOI: 10.1108/13217341111181096
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    References listed on IDEAS

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    1. Balcaen, Sofie & Ooghe, Hubert, 2006. "35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems," The British Accounting Review, Elsevier, vol. 38(1), pages 63-93.
    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    3. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    4. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    5. Basu, Sudipta, 1997. "The conservatism principle and the asymmetric timeliness of earnings," Journal of Accounting and Economics, Elsevier, vol. 24(1), pages 3-37, December.
    6. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    7. A. D. Castagna & Z. P. Matolcsy, 1981. "The Prediction of Corporate Failure: Testing the Australian Experience," Australian Journal of Management, Australian School of Business, vol. 6(1), pages 23-50, June.
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