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The Saga of Ruchi Soya Industries Limited : Could Credit Risk Models Predict Bankruptcy?


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1 FORE School of Management, Adhitam Kendra, B-18, Qutub Institutional Area, New Delhi - 110 016, India

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Purpose : The primary objective of this study was to examine the efficacy of credit risk models in predicting bankruptcy and evaluating the firm's post-acquisition performance.

Design/Methodology : Ruchi Soya Industries Limited, an Indian listed firm that went into bankruptcy in 2018, was identified for back-testing and evaluating the predictive ability of four models: three accounting-based (Altman Z-score, Altman’s emerging market score, and Zmijewski) and the market-based KMV model. In the second stage of analysis, the operating and financial performance of the company was evaluated for pre-bankruptcy and post-acquisition by Patanjali Foods using t-tests on financial parameters of solvency, profitability, and efficiency.

Findings : The findings of this study revealed that while all the models were accurate in predicting default accurately for up to 1 year before bankruptcy, they failed to do so accurately beyond that. The predictive ability of the models was highest for the KMV model, followed by Zmijewski and Altman's Z-score. The performance of the firm improved significantly post-acquisition on profitability and solvency parameters.

Practical Implications : According to the findings of this research, credit risk models are accurate at predicting financial trouble and bankruptcy up to 1 year in advance. These findings can be used for credit appraisal by lenders to assess any financial trouble and enable effective risk management. These models can also be used to avoid business failure to develop proactive and preventive financial and managerial decisions. The impact on performance parameters post-acquisition can help consultants and advisors evaluate the restructuring process to see whether there has been value creation post-restructuring.


Keywords

Accounting-Based Models, Bankruptcy, Market-Based Models, Predictive.

JELClassification Codes : C52, G33, G13

Paper Submission Date : January 24, 2023 ; Paper sent back for Revision : February 18, 2023 ; Paper Acceptance Date : March 5, 2023 ; Paper Published Online : March 15, 2023

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  • The Saga of Ruchi Soya Industries Limited : Could Credit Risk Models Predict Bankruptcy?

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Authors

Nidhi Gupta
FORE School of Management, Adhitam Kendra, B-18, Qutub Institutional Area, New Delhi - 110 016, India
Vandana Gupta
FORE School of Management, Adhitam Kendra, B-18, Qutub Institutional Area, New Delhi - 110 016, India

Abstract


Purpose : The primary objective of this study was to examine the efficacy of credit risk models in predicting bankruptcy and evaluating the firm's post-acquisition performance.

Design/Methodology : Ruchi Soya Industries Limited, an Indian listed firm that went into bankruptcy in 2018, was identified for back-testing and evaluating the predictive ability of four models: three accounting-based (Altman Z-score, Altman’s emerging market score, and Zmijewski) and the market-based KMV model. In the second stage of analysis, the operating and financial performance of the company was evaluated for pre-bankruptcy and post-acquisition by Patanjali Foods using t-tests on financial parameters of solvency, profitability, and efficiency.

Findings : The findings of this study revealed that while all the models were accurate in predicting default accurately for up to 1 year before bankruptcy, they failed to do so accurately beyond that. The predictive ability of the models was highest for the KMV model, followed by Zmijewski and Altman's Z-score. The performance of the firm improved significantly post-acquisition on profitability and solvency parameters.

Practical Implications : According to the findings of this research, credit risk models are accurate at predicting financial trouble and bankruptcy up to 1 year in advance. These findings can be used for credit appraisal by lenders to assess any financial trouble and enable effective risk management. These models can also be used to avoid business failure to develop proactive and preventive financial and managerial decisions. The impact on performance parameters post-acquisition can help consultants and advisors evaluate the restructuring process to see whether there has been value creation post-restructuring.


Keywords


Accounting-Based Models, Bankruptcy, Market-Based Models, Predictive.

JELClassification Codes : C52, G33, G13

Paper Submission Date : January 24, 2023 ; Paper sent back for Revision : February 18, 2023 ; Paper Acceptance Date : March 5, 2023 ; Paper Published Online : March 15, 2023


References





DOI: https://doi.org/10.17010/ijf%2F2023%2Fv17i3%2F172673