IIFT International Business and Management
Review Journal
issue front

Ankita Nagpal1, Gaurav Nagpal1 and Naga Vamsi Krishna Jasti1

First Published 19 Feb 2024. https://doi.org/10.1177/jiift.231225913
Article Information Volume 1, Issue 2 December 2023
Corresponding Author:

Gaurav Nagpal, Birla Institute of Technology and Science, Pilani, Rajasthan 333031, India.
Email: gaurav19821@gmail.com

Birla Institute of Technology and Science, Pilani, Rajasthan, India

Creative Commons Non Commercial CC BY-NC: This article is distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 License (http://www.creativecommons.org/licenses/by-nc/4.0/) which permits non-Commercial use, reproduction and distribution of the work without further permission provided the original work is attributed. 


The credit ratings play an important role in the allocation of capital among the enterprises, and thereby, in the growth of any economy. The different credit rating methodologies and criteria are used by different credit rating agencies, and the criteria may also differ from industry to industry. This research study tries to explore if it is possible to model credit ratings as the outcome of financial metrics. The study deploys the conjoint analysis approach to forecast the Ind-Ra ratings given to 50 firms, as a dependent variable and the key financial metrics as independent variables. First, the independent variables are computed based on financial statements. Then, the model is executed. It is found that the debt-equity ratio is negatively rated to credit rating, while the other three variables (profitability, asset turnover ratio and current ratio) are positively related to credit ratings. The study shows that financial metrics are not the only influencer of credit ratings but many subjective criteria such as future expected consumer trends, leadership overview, management aptitude and so on. Therefore, the model proposed in this study using financial statements has a 60% accuracy only because the subjective factors as mentioned above are difficult to be quantified and captured in the model as predictor variables.


Credit rating, credit risk, Ind-Ra Ratings, prediction model, conjoint analysis, credit worthiness, non-performing loans


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