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Classification techniques in accounting research: Empirical evidence of comparative performance

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  • DUANE B. KENNEDY

Abstract

. Many accounting research problems involve classification of observations into discrete categories. A number of statistical techniques are used in accounting research involving discrete categories. This study examines the performance of seven techniques that can be used when there are more than two discrete categories. The techniques are linear discriminant analysis, quadratic discriminant analysis, McKelvey and Zavoina n†chotomous probit, Walker and Duncan ordinal logit, Nerlove and Press polytomous logit, ordered classification trees, and unordered classification trees. Technique performance is measured using classification accuracy. The study finds that the Walker and Duncan ordinal logit, Nerlove and Press polytomous logit, and linear discriminant analysis techniques have the highest performance. The study also finds that the theoretical assignment rule for McKelvey and Zavoina n†chotomous probit produces lower classification accuracy than an assignment rule based on maximum probability. Résumé. De nombreux problèmes de recherche comptable exigent la classification des observations en catégories discrètes. Maintes techniques statistiques qui font intervenir des catégories discrètes sont utilisées en recherche comptable. L'auteur examine le rendement de sept techniques pouvant être utilisées en présence de plus de deux catégories discrètes. Ces techniques sont l'analyse discriminante linéaire, l'analyse discriminante quadratique, le probit n†chotomique de McKelvey et Zavoina, le logit ordinal de Walker et Duncan, le logit polytomique de Nerlove et Press, les arbres de classification ordonnée et les arbres de classification non ordonnée. Le rendement de ces techniques est évalué en fonction de la précision de la classification. L'auteur conclut que le logit ordinal de Walker et Duncan, le logit polytomique de Nerlove et Press et l'analyse discriminante linéaire sont les techniques qui ont le rendement le plus élevé. Il en vient également à la conclusion que la règle d'affectation théorique pour le probit n†chotomique de McKelvey et Zavoina offre une précision moindre sur le plan de la classification qu'une règle d'affectation basée sur la probabilité maximum.

Suggested Citation

  • Duane B. Kennedy, 1992. "Classification techniques in accounting research: Empirical evidence of comparative performance," Contemporary Accounting Research, John Wiley & Sons, vol. 8(2), pages 419-442, March.
  • Handle: RePEc:wly:coacre:v:8:y:1992:i:2:p:419-442
    DOI: 10.1111/j.1911-3846.1992.tb00853.x
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    1. Stewart Jones & David A. Hensher, 2007. "Evaluating the Behavioural Performance of Alternative Logit Models: An Application to Corporate Takeovers Research," Journal of Business Finance & Accounting, Wiley Blackwell, vol. 34(7‐8), pages 1193-1220, September.
    2. J.E. Boritz & D.B. Kennedy & Augusto de Miranda e Albuquerque, 1995. "Predicting Corporate Failure Using a Neural Network Approach," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 4(2), pages 95-111, June.
    3. Carlos Serrano-Cinca, 1997. "Feedforward neural networks in the classification of financial information," The European Journal of Finance, Taylor & Francis Journals, vol. 3(3), pages 183-202.
    4. David Feldman & Shulamith Gross, 2005. "Mortgage Default: Classification Trees Analysis," The Journal of Real Estate Finance and Economics, Springer, vol. 30(4), pages 369-396, June.
    5. Michel L. Magnan, 1994. "Boritz, J.E. The “Going Concern†Assumption: Accounting and Auditing Implications," Contemporary Accounting Research, John Wiley & Sons, vol. 10(2), pages 787-792, March.
    6. Barniv, Ran & Mehrez, Abraham & Kline, Douglas M., 2000. "Confidence intervals for controlling the probability of bankruptcy," Omega, Elsevier, vol. 28(5), pages 555-565, October.
    7. Takashi Obinata, 2000. "Choice of Pension Discount Rate in Financial Accounting adn Stock Prices," CIRJE F-Series CIRJE-F-82, CIRJE, Faculty of Economics, University of Tokyo.

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