000			02072nas a2200205Ia 4500
008			240802c99999999xx |||||||||||| ||und||
022			_a0972-2629
100			_aH, Shilpa Shetty _9120278
245		0	_aCorporate Default Prediction Model: Evidence from the Indian Industrial Sector
260			_bVision
260			_c2024
300			_a344-360
520			_aThe 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.
650			_a Financial Distress _9120279
650			_a Indian Industrial Sector _9120280
650			_a Insolvency and Bankruptcy _9120281
650			_aCOVID-19 _9118594
700			_a Vincent, Theresa Nithila _9120282
856			_uhttps://doi.org/10.1177/09722629211036207
999			_c133678 _d133678