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Diagnosing the Financial Distress in Oil Drilling and Exploration Sector of India through Discriminant Analysis

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  • Anita Nandi
  • Partha Pratim Sengupta
  • Abhijit Dutta

Abstract

The present study is mainly devoted to the bankruptcy prediction models and their ability to assess a bankruptcy probability for oil drilling and exploration sector of Indian. The study puts an effort to determine the financial health of 12 selected companies from this sector of India for a period of 5 years. These companies serve the backbone of many other industries such as transport industry, manufacturing industry, automobile industry and so on of the Indian economy. The study has taken the reference of Altman’s Z -score model, where ratios such as working capital to total asset, retained earnings to total asset, earnings before interest and tax to total assets, market value of equity to book value of debt and sales to total assets have been taken. The discriminant analysis is conducted to validate the outcomes of Altman’s model to predict group membership and to forecast the overall industry condition. The study reveals that 75 per cent of the companies are in financially healthy zone. The results indicate that working capital/total assets can very well explain the Z -score. The research on financial health using Altman’s score is very limited in Indian context. Therefore, this study will add value to the existing body of literature for financial risk.

Suggested Citation

  • Anita Nandi & Partha Pratim Sengupta & Abhijit Dutta, 2019. "Diagnosing the Financial Distress in Oil Drilling and Exploration Sector of India through Discriminant Analysis," Vision, , vol. 23(4), pages 364-373, December.
    • Handle: RePEc:sae:vision:v:23:y:2019:i:4:p:364-373
    DOI: 10.1177/0972262919862920
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    References listed on IDEAS

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    1. Teija Laitinen & Maria Kankaanpaa, 1999. "Comparative analysis of failure prediction methods: the Finnish case," European Accounting Review, Taylor & Francis Journals, vol. 8(1), pages 67-92.
    2. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    3. Vasile MAZILESCU, 2017. "An Expert System for a Business Problem," Proceedings RCE 2017, Editura Lumen, vol. 0, pages 563-578, November.
    4. Edward I. Altman & Gabriele Sabato, 2013. "MODELING CREDIT RISK FOR SMEs: EVIDENCE FROM THE US MARKET," World Scientific Book Chapters, in: Oliviero Roggi & Edward I Altman (ed.), Managing and Measuring Risk Emerging Global Standards and Regulations After the Financial Crisis, chapter 9, pages 251-279, World Scientific Publishing Co. Pte. Ltd..
    5. Petr Gurný & Martin Gurný, 2013. "Comparison of Credit Scoring Models on Probability of Default Estimation for Us Banks," Prague Economic Papers, Prague University of Economics and Business, vol. 2013(2), pages 163-181.
    6. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    7. Demyanyk, Yuliya & Hasan, Iftekhar, 2010. "Financial crises and bank failures: A review of prediction methods," Omega, Elsevier, vol. 38(5), pages 315-324, October.
    8. Gheorghe H. Popescu, 2014. "FDI and Economic Growth in Central and Eastern Europe," Sustainability, MDPI, vol. 6(11), pages 1-15, November.
    9. Brunso, Karen & Scholderer, Joachim & Grunert, Klaus G., 2004. "Closing the gap between values and behavior--a means-end theory of lifestyle," Journal of Business Research, Elsevier, vol. 57(6), pages 665-670, June.
    10. Vasile MAZILESCU, 2017. "An Expert System for a Business Problem," Risk in Contemporary Economy, "Dunarea de Jos" University of Galati, Faculty of Economics and Business Administration, pages 563-578.
    11. Platt, Harlan D. & Platt, Marjorie B., 2006. "Understanding Differences Between Financial Distress and Bankruptcy," Review of Applied Economics, Lincoln University, Department of Financial and Business Systems, vol. 2(2), pages 1-17.
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