Indian Journal of Finance

Open Access Open Access  Restricted Access Subscription Access

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


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

   Subscribe/Renew Journal


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

User
Subscription Login to verify subscription
Notifications
Font Size

  • Abdelsalam, H. M. (2008). A credit assessment model for small businesses in Egypt. Indian Journal of Finance, 2(4), 3–16. https://www.indianjournaloffinance.co.in/index.php/IJF/article/view/71660

  • Agarwal, A., & Patni, I. (2019). Applicability of Altman Z-score in bankruptcy prediction of BSE PSUs. Journal of Commerce & Accounting Research, 8(2), 93–103. http://publishingindia.com/jcar/

  • Agarwal, V., & Taffler, R. (2008). Does financial distress risk drive the momentum anomaly? Financial Management, 37(3), 461– 484. http://dx.doi.org/10.1111/j.1755-053X.2008.00021.x

  • Ahmed, M. A., & Govind, D. (2018). An evaluation of the Altman Z-score model in predicting corporate bankruptcy for Canadian publicly listed firms. Summit Research Repository. http://summit.sfu.ca/item/18342

  • Ahuja, B. R., & Singhal, N. (2014). Assessing the financial soundness of companies with special reference to the Indian textile sector: An application of the Altman Z score model. Indian Journal of Finance, 8(4), 38–48. http://dx.doi.org/10.17010/ijf/2014/v8i4/71922

  • Al-Manaseer, S. R., & Al-Oshaibat, S. D. (2018). Validity of Altman Z-score model to predict financial failure: Evidence from Jordan. International Journal of Economics and Finance, 10(8), 181–189. https://doi.org/10.5539/ijef.v10n8p181

  • Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609. https://doi.org/10.2307/2978933

  • Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2017). Financial distress prediction in an international context: A review and empirical analysis of Altman’s Z-score model. Journal of International Financial Management & Accounting, 28(2), 131–171. https://doi.org/10.1111/jifm.12053

  • Altman, E. I., Haldeman, R. G., & Narayanan, P. (1977). ZETATM analysis a new model to identify bankruptcy risk of corporations. Journal of Banking & Finance, 1(1), 29–54. http://doi.org/10.1016/0378-4266(77)90017-6

  • Arora, P., & Saurabh, S. (2022). Predicting distress : A post insolvency and bankruptcy code 2016 analysis. Journal of Economics and Finance, 46, 604–622. https://doi.org/10.1007/s12197-022-09582-y

  • Bandyopadhyay, A. (2006). Predicting probability of default of Indian corporate bonds: Logistic and Z-score model approaches. Journal of Risk Finance, 7(3), 255–272. https://doi.org/10.1108/15265940610664942

  • Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71–111. https://doi.org/10.2307/2490171

  • Black, F., & Scholes, M. (1973). The pricing of options and corporate liabilities. Journal of Political Economy, 81(3), 637–654. https://doi.org/10.1086/260062

  • Chandra, S., & Awasthi, R. (2019). Insolvency risk: Issues and challenges for public sector commercial banks of India. Indian Journal of Finance, 13(12), 19–33. https://doi.org/10.17010/ijf/2019/v13i12/149266

  • Chitta, S., Jain, R. K., & Sriharsha, R. (2019). Financial soundness of Maharatna companies: Application of Altman Z score model. Indian Journal of Finance, 13(10), 22–33. https://doi.org/10.17010/ijf/2019/v13i10/147745

  • Hussain, F., Ali, I., Ullah, S., & Ali, M. (2014). Can Altman Z-score model predict business failures in Pakistan? Evidence from textile companies of Pakistan. Journal of Economics and Sustainable Development, 5(13), 110–115.

  • Jayadev, M. (2006). Predictive power of financial risk factors: An empirical analysis of default companies. Vikalpa, 31(3), 45–56. https://journals.sagepub.com/doi/pdf/10.1177/0256090920060304

  • Joshi, N. A., & Desai, J. (2019). Financial restructuring and its impact on operating performance in the energy sector in India. Indian Journal of Finance, 13(1), 37–54. http://doi.org/10.17010/ijf/2019/v13i1/141047

  • Kapil, S., & Agarwal, S. (2019). Assessing bankruptcy of Indian listed firms using bankruptcy models, decision tree and neural network. International Journal of Business and Economics, 4(1), 112–136. https://doi.org/10.5281/zenodo.2596723

  • Kattadiyil, B., & Umarov, I. (2021). Alok industries, pre & post corporate insolvency resolution process. Economics and Innovative Technologies, 6, 38–48. https://inlibrary.uz/index.php/economics_and_innovative/article/view/12177

  • Kaur, J. (2019). Financial distress and bank performance: A study of select Indian banks. International Journal of Financial Management, 9(3), 26–35. http://www.publishingindia.com/ijfm/30/financial-distress-and-bank-performance-a-study-of-select-indian-banks/828/5761/

  • Kittur, A. H. (2019). Effectiveness of the Altman Z-Score model: Does the Altman Z-Score model accurately capture the effects of non-performing assets (NPA) in the Indian banking sector? (Dissertation). http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-86144

  • KMV Corporate. (1993). Credit monitor overview. San Francisco California, 60–66.

  • Kumar, R. G., & Kumar, K. (2012). A comparison of bankruptcy models. International Journal of Marketing, Financial Services and Management Research, 1(4), 76–86.

  • Merton, R. C. (1974). On the pricing of corporate debt: The risk structure of interest rates. The Journal of Finance, 29(2), 449–470. https://doi.org/10.2307/2978814

  • Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 81(1), 109–131. https://doi.org/10.2307/2490395

  • Shetty, S., & Vincent, T. N. (2021). Corporate default prediction model: Evidence from the Indian industrial sector. Vision. https://doi.org/10.1177/09722629211036207

  • Taffler, R. J. (1983). The assessment of company solvency and performance using a statistical model. Accounting and Business Research, 13(52), 295–308. https://doi.org/10.1080/00014788.1983.9729767

  • Taffler, R. J. (1984). Empirical models for the monitoring of UK corporations. Journal of Banking & Finance, 8(2), 199–227. https://doi.org/10.1016/0378-4266(84)90004-9 30) Tung, D. T., & Phung, V. T. (2019). An application of Altman Z-score model to analyze the bankruptcy risk: Cases of multidisciplinary enterprises in Vietnam. In

  • vestment Management & Financial Innovations, 16(4), 181–191. http://dx.doi.org/10.21511/imfi.16(4).2019.16

  • Viswanatha Reddy, C. (2012). Analysis of liquidity, profitability, risk and financial distress: A case study of Dr. Reddy’s Laboratories Ltd. Indian Journal of Finance, 6(12), 5–17. https://www.indianjournaloffinance.co.in/index.php/IJF/article/view/72358

  • Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490859


Abstract Views: 100

PDF Views: 0




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

Abstract Views: 100  |  PDF Views: 0

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