IDEAS home Printed from https://ideas.repec.org/a/sae/ausman/v14y1989i2p211-221.html
   My bibliography  Save this article

Testing Corporate Model Prediction Accuracy

Author

Listed:
  • Murray Nash

    (New Zealand Treasury.)

  • Michael Anstis

    (Department of Economics, University of Auckland.)

  • Michael Bradbury

    (Department of Economics, University of Auckland.)

Abstract

This paper investigates the predictive accuracies of corporate failure models. We find, through the elimination of biases found in these models, that the success rates for the placing of corporations into their correct categories are overstated. The paper confirms the view that the use of predictive corporate distress models should not be the sole criterion for making a lending decision.

Suggested Citation

  • Murray Nash & Michael Anstis & Michael Bradbury, 1989. "Testing Corporate Model Prediction Accuracy," Australian Journal of Management, Australian School of Business, vol. 14(2), pages 211-221, December.
    • Handle: RePEc:sae:ausman:v:14:y:1989:i:2:p:211-221
    DOI: 10.1177/031289628901400205
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/031289628901400205
    Download Restriction: no
    File URL: https://libkey.io/10.1177/031289628901400205?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    2. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    3. Edmister, Robert O., 1972. "An Empirical Test of Financial Ratio Analysis for Small Business Failure Prediction," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 7(2), pages 1477-1493, March.
    4. 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.
    5. 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.
    6. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    7. Hamer, Michelle M., 1983. "Failure prediction: Sensitivity of classification accuracy to alternative statistical methods and variable sets," Journal of Accounting and Public Policy, Elsevier, vol. 2(4), pages 289-307.
    8. Marais, Ml & Patell, Jm & Wolfson, Ma, 1984. "The Experimental-Design Of Classification Models - An Application Of Recursive Partitioning And Bootstrapping To Commercial Bank Loan Classifications," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 87-114.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Hu, Yu-Chiang & Ansell, Jake, 2007. "Measuring retail company performance using credit scoring techniques," European Journal of Operational Research, Elsevier, vol. 183(3), pages 1595-1606, December.
    2. Andrea Bedin & Monica Billio & Michele Costola & Loriana Pelizzon, 2019. "Credit Scoring in SME Asset-Backed Securities: An Italian Case Study," JRFM, MDPI, vol. 12(2), pages 1-28, May.
    3. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    4. Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
    5. Dimitras, A. I. & Zanakis, S. H. & Zopounidis, C., 1996. "A survey of business failures with an emphasis on prediction methods and industrial applications," European Journal of Operational Research, Elsevier, vol. 90(3), pages 487-513, May.
    6. Antonio David Somoza Lopez & Josep Vallverdu Calafell, 2003. "Una comparacion de la seleccion de los ratios contables en los modelos contable-financieros de prediccion de la insolvencia empresarial," Working Papers in Economics 94, Universitat de Barcelona. Espai de Recerca en Economia.
    7. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    8. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    9. 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.
    10. García-Gallego, Ana & Mures-Quintana, María-Jesús, 2013. "La muestra de empresas en los modelos de predicción del fracaso: influencia en los resultados de clasificación || The Sample of Firms in Business Failure Prediction Models: Influence on Classification," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 15(1), pages 133-150, June.
    11. Ha-Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," EconomiX Working Papers 2014-26, University of Paris Nanterre, EconomiX.
    12. Ali DERAN & Omer ISKENDEROGLU & Incilay ERDURU, 2014. "Regional Differences and Financial Ratios: A Comparative Approach on Companies of ISE City Indexes," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 946-955.
    13. Layla Khoja & Maxwell Chipulu & Ranadeva Jayasekera, 2016. "Analysing corporate insolvency in the Gulf Cooperation Council using logistic regression and multidimensional scaling," Review of Quantitative Finance and Accounting, Springer, vol. 46(3), pages 483-518, April.
    14. Onofrei, Mihaela & Lupu, Dan, 2014. "The modelling of forecasting the bankruptcy in Romania," MPRA Paper 95511, University Library of Munich, Germany.
    15. Şaban Çelik, 2013. "Micro Credit Risk Metrics: A Comprehensive Review," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 20(4), pages 233-272, October.
    16. Francesco Ciampi & Valentina Cillo & Fabio Fiano, 2020. "Combining Kohonen maps and prior payment behavior for small enterprise default prediction," Small Business Economics, Springer, vol. 54(4), pages 1007-1039, April.
    17. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    18. Ha Thu Nguyen, 2014. "Default Predictors in Credit Scoring - Evidence from France’s Retail Banking Institution," Working Papers hal-04141336, HAL.
    19. Jayasekera, Ranadeva, 2018. "Prediction of company failure: Past, present and promising directions for the future," International Review of Financial Analysis, Elsevier, vol. 55(C), pages 196-208.
    20. Milagros Vivel-Búa & Rubén Lado-Sestayo & Luis Otero-González, 2016. "Impact of location on the probability of default in the Spanish lodging industry," Tourism Economics, , vol. 22(3), pages 593-607, June.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:ausman:v:14:y:1989:i:2:p:211-221. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: http://www.agsm.edu.au .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.