Efficacy of industry factors for corporate default prediction
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RAVI aNSHUMAN V. IIMB Management ReviewSummary: The paper aims to assess whether a sensitivity variable, industry beta, has a significant impact on the firm's likelihood of default, as an independent predictor variable. The study uses logistic regression and multiple discriminant analysis for matched pair sample of defaulting and non-defaulting listed Indian firms. The industry beta is estimated by regressing the monthly stock return of each individual firm on the monthly return of the respective industry index. The sensitivity variable for industry factors, industry beta, is found to be statistically significant in predicting defaults. Higher sensitivity to industry factors leads to an increased probability of default.
| Item type | Current library | Call number | Vol info | Status | Notes | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|---|
Journal Article
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Main Library | Vol 31, Issue 1/ 55510416JA6 (Browse shelf(Opens below)) | Available | 55510416JA6 | |||||
Journals and Periodicals
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Main Library On Display | JRNL/GEN/Vol 31, Issue 1/55510416 (Browse shelf(Opens below)) | Vol 31, Issue 1 (30/07/2018) | Not for loan | March, 2019 | 55510416 |
The paper aims to assess whether a sensitivity variable, industry beta, has a significant impact on the firm's likelihood of default, as an independent predictor variable. The study uses logistic regression and multiple discriminant analysis for matched pair sample of defaulting and non-defaulting listed Indian firms. The industry beta is estimated by regressing the monthly stock return of each individual firm on the monthly return of the respective industry index. The sensitivity variable for industry factors, industry beta, is found to be statistically significant in predicting defaults. Higher sensitivity to industry factors leads to an increased probability of default.
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