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Effect of accruals on financial, non-financial, and audit information in payment default prediction

Author

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  • Erkki K. Laitinen
  • Teija Laitinen

Abstract

This study analyses the contingency effects of accruals on default prediction. Information is classified into three categories: financial statement, audit report, and non-financial information. It is proposed that absolute accruals moderate the relationship between default risk and financial statement information: the more accruals, the less important is the information. It is also proposed that absolute accruals moderate audit report information: the more accruals, the more important is the information. It is also proposed that absolute accruals do not moderate non-financial information. Estimation data consist of 300 default and 300 non-default Finnish firms. Empirical results are validated in large validation data. The logistic regression analysis is used to estimate two kinds of prediction models. First, financial statement variables, audit report information, and non-financial variables are used to predict default. Second, a model where financial statement, audit report, and non-financial information are moderated by absolute accruals is estimated. Empirical evidence largely supports the hypotheses.

Suggested Citation

  • Erkki K. Laitinen & Teija Laitinen, 2009. "Effect of accruals on financial, non-financial, and audit information in payment default prediction," International Journal of Accounting, Auditing and Performance Evaluation, Inderscience Enterprises Ltd, vol. 5(4), pages 353-383.
    • Handle: RePEc:ids:ijaape:v:5:y:2009:i:4:p:353-383
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    Cited by:

    1. Andrea Caputo & Alberto Tron, 2016. "The attestation of corporate turnaround plans in Italy: operating problems and possible solutions," International Journal of Critical Accounting, Inderscience Enterprises Ltd, vol. 8(1), pages 30-44.
    2. Nora Muñoz-Izquierdo & María-del-Mar Camacho-Miñano & María-Jesús Segovia-Vargas & David Pascual-Ezama, 2019. "Is the External Audit Report Useful for Bankruptcy Prediction? Evidence Using Artificial Intelligence," IJFS, MDPI, vol. 7(2), pages 1-23, April.
    3. Muñoz-Izquierdo, Nora & Segovia-Vargas, María Jesús & Camacho-Miñano, María-del-Mar & Pascual-Ezama, David, 2019. "Explaining the causes of business failure using audit report disclosures," Journal of Business Research, Elsevier, vol. 98(C), pages 403-414.

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