Aditya Bihani, Arushi Sahay, Aliza Khan, Divyang Sharma, Kanishk Shetty, Vaishaki Chowta,
Vani Viswanathan Third year, Bachelor’s of Science in Economics, NMIMS University, Mumbai
MLA 8 Bihani, Aditya, et al. "PREDICTING THE PROBABILITY OF DEFAULT OF LONG TERM INDIAN CORPORATE BONDS USING A LOGISTIC REGRESSION MODEL." Int. j. of Social Science and Economic Research, vol. 5, no. 11, Nov. 2020, pp. 3312-3337, doi:10.46609/IJSSER.2020.v05i11.002. Accessed Nov. 2020.
APA 6 Bihani, A., Sahay, A., Khan, A., Sharma, D., Shetty, K., Chowta, V., & Viswanathan, V. (2020, November). PREDICTING THE PROBABILITY OF DEFAULT OF LONG TERM INDIAN CORPORATE BONDS USING A LOGISTIC REGRESSION MODEL. Int. j. of Social Science and Economic Research, 5(11), 3312-3337. doi:10.46609/IJSSER.2020.v05i11.002
Chicago Bihani, Aditya, Arushi Sahay, Aliza Khan, Divyang Sharma, Kanishk Shetty, Vaishaki Chowta, and Vani Viswanathan. "PREDICTING THE PROBABILITY OF DEFAULT OF LONG TERM INDIAN CORPORATE BONDS USING A LOGISTIC REGRESSION MODEL." Int. j. of Social Science and Economic Research 5, no. 11 (November 2020), 3312-3337. Accessed November, 2020. doi:10.46609/IJSSER.2020.v05i11.002.
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Abstract: This paper aims to present methods for directly estimating corporate probability of default (PD)
using financial variables. A logistic regression model is employed to directly estimate the
probability of default. The financial variables used in the model have been shortlisted on the basis
of literature reviewed on this topic. We look at companies, both listed and unlisted, whose bonds
defaulted during FY 2018 and 2019. The results are satisfactory with four out of five explanatory
variables having statistically significant coefficients and with the expected signs. Whilst work on
this field has been done for markets like the US, study on Indian bond defaults is relatively scarce
and thus this paper seeks to help fill this void.
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