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Corporate Default Prediction Model: Evidence from the Indian Industrial Sector

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  • Shilpa Shetty H.
  • Theresa Nithila Vincent

Abstract

The unprecedented pandemic COVID-19 has impacted businesses across the globe. A significant jump in the credit default risk is expected. Credit default is an indicator of financial distress experienced by the business. Credit default often leads to bankruptcy filing against the defaulting company. In India, the Insolvency and Bankruptcy Code (IBC) is the law that governs insolvency and bankruptcy. As reported by the Insolvency and Bankruptcy Board of India (IBBI), the number of companies filing for bankruptcy under IBC is on a rise, and the industrial sector has witnessed the maximum number of bankruptcy filings. The present article attempts to develop a credit default prediction model for the Indian industrial sector based on a sample of 164 companies comprising an equal number of defaulting and nondefaulting companies. A total of 120 companies are used as training samples and 44 companies as the testing samples. Binary logistic regression analysis is employed to develop the model. The diagnostic ability of the model is tested using receiver operating characteristic curve, area under the curve and annual accuracy. According to the study, return on assets, current ratio, debt to total assets ratio, sales to working capital ratio and cash flow to total assets ratio is statistically significant in predicting default. The findings of the study have significant implications in lending and investment decisions.

Suggested Citation

  • Shilpa Shetty H. & Theresa Nithila Vincent, 2024. "Corporate Default Prediction Model: Evidence from the Indian Industrial Sector," Vision, , vol. 28(3), pages 344-360, June.
    • Handle: RePEc:sae:vision:v:28:y:2024:i:3:p:344-360
    DOI: 10.1177/09722629211036207
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    References listed on IDEAS

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