Corporate Default Prediction Model: Evidence from the Indian Industrial Sector (Record no. 133678)
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000 -LEADER | |
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fixed length control field | 02072nas a2200205Ia 4500 |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
fixed length control field | 240802c99999999xx |||||||||||| ||und|| |
022 ## - INTERNATIONAL STANDARD SERIAL NUMBER | |
International Standard Serial Number | 0972-2629 |
100 ## - MAIN ENTRY--PERSONAL NAME | |
Personal name | H, Shilpa Shetty |
9 (RLIN) | 120278 |
245 #0 - TITLE STATEMENT | |
Title | Corporate Default Prediction Model: Evidence from the Indian Industrial Sector |
260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
Name of publisher, distributor, etc. | Vision |
260 ## - PUBLICATION, DISTRIBUTION, ETC. | |
Date of publication, distribution, etc. | 2024 |
300 ## - PHYSICAL DESCRIPTION | |
Extent | 344-360 |
520 ## - SUMMARY, ETC. | |
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. |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Financial Distress |
9 (RLIN) | 120279 |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Indian Industrial Sector |
9 (RLIN) | 120280 |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | Insolvency and Bankruptcy |
9 (RLIN) | 120281 |
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM | |
Topical term or geographic name entry element | COVID-19 |
9 (RLIN) | 118594 |
700 ## - ADDED ENTRY--PERSONAL NAME | |
Personal name | Vincent, Theresa Nithila |
9 (RLIN) | 120282 |
856 ## - ELECTRONIC LOCATION AND ACCESS | |
Uniform Resource Identifier | <a href="https://doi.org/10.1177/09722629211036207">https://doi.org/10.1177/09722629211036207</a> |
999 ## - SYSTEM CONTROL NUMBERS (KOHA) | |
Koha biblionumber | 133678 |
Withdrawn status | Lost status | Damaged status | Not for loan | Home library | Current library | Date acquired | Serial Enumeration / chronology | Total Checkouts | Barcode | Date last seen | Price effective from | Koha item type |
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Dr VKRV Rao Library | Dr VKRV Rao Library | 02/08/2024 | Vol. 28, No. 3 | AI331 | 02/08/2024 | 02/08/2024 | Article Index |