IDEAS home Printed from
   My bibliography  Save this paper

Verbesserung der Vergleichbarkeit von Schätzgüteergebnissen von Insolvenzprognosestudien (German version of 'Improving the comparability of insolvency predictions')


  • Martin Bemmann

    (Dresden University of Technology Faculty of Business Management & Economics)


This working paper aims at improving the comparability of forecast quality measures of insolvency prediction studies. For this purpose, in a first step commonly used accuracy measures for categorial, ordinal and cardinal insolvency predictions are presented. It will be argued, that ordinal measures are the most suitable measures for sample spanning comparisons concerning predictive power of rating models, as they are not affected by sample default rates. A method for transforming cardinal into ordinal accuracy measures is presented, by which comparisons of insolvency prediction results of older and present-day studies are enabled. In the second part of the working paper an overview of influencing variables – aside from the quality of the insolvency prediction methods – is given, which affect the accuracy measures presented in the first part of the paper and thus impair sample spanning comparison of empirically obtained forecast quality results. In this context, methods for evaluating information losses that are attributable to the discretization of continuous rating scales or preselection of portfolios are developed. Measure results of various insolvency prognosis studies are envisaged and compared with three benchmarks. First benchmark is the accuracy that can be achieved solely by taking into account legal status and industry classification of corporations. The second benchmark is the univariate prognosis accuracy of single financial ratios. As third benchmark, ALTMAN’s Zscore model is examined, a multivariate insolvency prediction model, that is currently used as reference rating model in many empirical studies. It turns out, however, that the Z-score’s forecast quality is so discontenting, that its application is not recommendable. Instead it is suggested to use those rating models that are cited in this discussion paper, which are fully documented and which therefore can be rebuilt and directly applied to any desired data sample. If applied to the respective target groups, their performance matches with the performance of commercial rating systems, like bureau and business scores for rather small companies, middle market rating models for SMB, or agency ratings for large public companies.

Suggested Citation

  • Martin Bemmann, 2005. "Verbesserung der Vergleichbarkeit von Schätzgüteergebnissen von Insolvenzprognosestudien (German version of 'Improving the comparability of insolvency predictions')," Finance 0507007, EconWPA.
  • Handle: RePEc:wpa:wuwpfi:0507007
    Note: Type of Document - pdf; pages: 150. This document is in German. There is an English version available ('Improving the comparability of insolvency predictions').

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Scholes, Myron & Williams, Joseph, 1977. "Estimating betas from nonsynchronous data," Journal of Financial Economics, Elsevier, vol. 5(3), pages 309-327, December.
    2. J. Cable & K. Holland, 1999. "Modelling normal returns in event studies: a model-selection approach and pilot study," The European Journal of Finance, Taylor & Francis Journals, vol. 5(4), pages 331-341.
    3. Tim Bollerslev & Robert J. Hodrick, 1992. "Financial Market Efficiency Tests," NBER Working Papers 4108, National Bureau of Economic Research, Inc.
    4. Amihud, Yakov & Mendelson, Haim & Lauterbach, Beni, 1997. "Market microstructure and securities values: Evidence from the Tel Aviv Stock Exchange," Journal of Financial Economics, Elsevier, vol. 45(3), pages 365-390, September.
    5. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    6. Kadlec, Gregory B & Patterson, Douglas M, 1999. "A Transactions Data Analysis of Nonsynchronous Trading," Review of Financial Studies, Society for Financial Studies, vol. 12(3), pages 609-630.
    7. Harris, Lawrence, 1991. "Stock Price Clustering and Discreteness," Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 389-415.
    8. Silvio John Camilleri & Christopher J. Green, 2005. "An Analysis of the Impacts of Non-Synchronous Trading On," Finance 0504020, EconWPA.
    9. Brown, Stephen J. & Warner, Jerold B., 1980. "Measuring security price performance," Journal of Financial Economics, Elsevier, vol. 8(3), pages 205-258, September.
    10. Elroy Dimson & Massoud Mussavian, 1998. "A brief history of market efficiency," European Financial Management, European Financial Management Association, vol. 4(1), pages 91-103.
    11. Granger, C W J, 1969. "Investigating Causal Relations by Econometric Models and Cross-Spectral Methods," Econometrica, Econometric Society, vol. 37(3), pages 424-438, July.
    12. A. Craig MacKinlay, 1997. "Event Studies in Economics and Finance," Journal of Economic Literature, American Economic Association, vol. 35(1), pages 13-39, March.
    13. Nikitas Niarchos & Christos Alexakis, 1998. "Stock market prices, 'causality' and efficiency: evidence from the Athens stock exchange," Applied Financial Economics, Taylor & Francis Journals, vol. 8(2), pages 167-174.
    14. Paul V. Azzopardi & Silvio John Camilleri, 2004. "The Relevance of Short Sales to the Maltese Stock Market," Finance 0409009, EconWPA.
    15. Azzopardi, Paul & Silvio John, Camilleri, 2003. "The Relevance of Short Sales to the Maltese Stock Market," MPRA Paper 84566, University Library of Munich, Germany.
    16. Atchison, Michael D & Butler, Kirt C & Simonds, Richard R, 1987. " Nonsynchronous Security Trading and Market Index Autocorrelation," Journal of Finance, American Finance Association, vol. 42(1), pages 111-118, March.
    Full references (including those not matched with items on IDEAS)

    More about this item


    financial ratio analysis; corporate bankruptcy prediction; forecast validation; accuracy ratio; information entropy; sample selection; rating granularity;

    JEL classification:

    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General

    NEP fields

    This paper has been announced in the following NEP Reports:


    This item is featured on the following reading lists or Wikipedia pages:
    1. Schätzgütemaße für kardinale Insolvenzprognosen in Wikipedia German ne '')
    2. Wikipedia:Löschkandidaten/22. September 2008 in Wikipedia German ne '')
    3. Schätzgütemaße für ordinale Insolvenzprognosen in Wikipedia German ne '')


    Access and download statistics


    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:wpa:wuwpfi:0507007. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA). General contact details of provider: .

    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.

    We have no references for this item. You can help adding them by using 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.

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

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.