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The Influence Of Sample Size And Selection Of Financial Ratios In Bankruptcy Model Accuracy

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  • Yusuf Ali Al-Hroot

    (Faculty of Administrative & Financial Sciences, Philadelphia University)

Abstract

This paper aims to clarify the influence of changing both the sample size and selection of financial ratios in bankruptcy models accuracy of companies listed in the industrial sector of Jordan. The study sample is divided into three sub-samples counting 6, 10 and 14 companies respectively; each sample is composed of bankrupt companies and the solvent ones during the period from 2000 to 2013. Financial ratios were calculated and categorized into two groups. The first group includes: liquidity, profitability, debt, and activity, while the second group includes ten most popular financial ratios found to be useful in earlier studies and expected to predict financial distress. The results show that when 18 models built using discriminant analysis, the model based on most popular financial ratios, found to be useful in earlier studies, has the highest classification accuracy with 100% and consistently for all the samples before bankruptcy. The prediction ac curacy varies among models when increasing the sample size from 6 to 14 companies for the models that developed from the financial ratios of the first group.

Suggested Citation

  • Yusuf Ali Al-Hroot, 2015. "The Influence Of Sample Size And Selection Of Financial Ratios In Bankruptcy Model Accuracy," Economic Review: Journal of Economics and Business, University of Tuzla, Faculty of Economics, vol. 13(1), pages 7-19, May.
  • Handle: RePEc:tuz:journl:v:13:y:2015:i:1:p:7-19
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    References listed on IDEAS

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    1. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    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. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    4. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    5. Hian Koh & Sen Tan, 1999. "A neural network approach to the prediction of going concern status," Accounting and Business Research, Taylor & Francis Journals, vol. 29(3), pages 211-216.
    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. Gupta, Manak C, 1969. "The Effect of Size, Growth, and Industry on the Financial Structure of Manufacturing Companies," Journal of Finance, American Finance Association, vol. 24(3), pages 517-529, June.
    8. du Jardin, Philippe, 2012. "The influence of variable selection methods on the accuracy of bankruptcy prediction models," MPRA Paper 44383, University Library of Munich, Germany.
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    Cited by:

    1. Yusuf Ali Al-Hroot, 2016. "A Comparison of Jordanian Bankruptcy Models: Multilayer Perceptron Neural Network and Discriminant Analysis," International Business Research, Canadian Center of Science and Education, vol. 9(12), pages 121-130, December.

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    More about this item

    Keywords

    Financial ratios; sample size; bankruptcy; discriminant analysis; Jordan;
    All these keywords.

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

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