Content analysis of XBRL filings as an efficient supplement of bankruptcy prediction? Empirical evidence based on US GAAP annual reports
AbstractMost of the bankruptcy prediction models developed so far have in common that they are based on quantitative data or more precisely financial ratios. However, useful information can be lost when disregarding soft information. In this work, we develop an automated content analysis technique to assess the bankruptcy risk of companies using XBRL tags. We develop a list of potential red flags based on the U.S. GAAP taxonomy and assign the elements to 2 categories and 7 subcategories. Then we test our red flag item list based on U.S. GAAP annual reports of 26 companies with Chapter 11 bankruptcy filings and a control group. The empirical results show that in total, the red flag item list has predictive power of bankruptcy risk. Logistic regression results also show that the predictive power increases the nearer the bankruptcy filing date approaches. We furthermore observe that the category 2 red flags (bankruptcy characteristics and influencing factors) have higher discriminatory power than category 1 red flags (earnings management indicators) for one year before the bankruptcy filing date. This difference narrows for two years before the bankruptcy filing date and may turn in favor of category 1 red flags for three years before the bankruptcy filing date. --
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Friedrich-Alexander-Universität Erlangen-Nürnberg, Lehrstuhl für Rechnungswesen und Prüfungswesen in its series Working Papers in Accounting Valuation Auditing with number 2012-2.
Date of creation: 2012
Date of revision:
content analysis; red flags; XBRL; bankruptcy prediction; risk assessment; earnings management; Inhaltsanalyse; Red Flags; XBRL; Insolvenzprognose; Risikobewertung; Bilanzpolitik;
Find related papers by JEL classification:
- M41 - Business Administration and Business Economics; Marketing; Accounting - - Accounting - - - Accounting
- C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
- C81 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Methodology for Collecting, Estimating, and Organizing Microeconomic Data
This paper has been announced in the following NEP Reports:
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Godbillon-Camus, Brigitte & Godlewski, Christophe, 2005. "Credit risk management in banks: Hard information, soft Information and manipulation," MPRA Paper 1873, University Library of Munich, Germany.
- Laurel A. Franzen & Kimberly J. Rodgers & Timothy T. Simin, 2007. "Measuring Distress Risk: The Effect of R&D Intensity," Journal of Finance, American Finance Association, vol. 62(6), pages 2931-2967, December.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (ZBW - German National Library of Economics).
If references are entirely missing, you can add them using this form.