Predicting the Factors Influencing the Probability of Failure of Central Public Sector Enterprises

Authors

  • Dr. Bhushan Pardeshi Assistant Professor, Pimpri Chinchwad Education Trust’s , S.B.Patil Institute of Management Sector 26, Nigdi Pradhikaran, Pune 411044, Maharashtra, India Author
  • Dr. Padmalochana Bisoyi Assistant Professor, Pimpri Chinchwad Education Trust’s, S.B.Patil Institute of Management Sector 26, Nigdi Pradhikaran, Pune 411044, Maharashtra, India Author
  • Ms. Pranita Burbure Assistant Professor, Pimpri Chinchwad Education Trust’s, S.B.Patil Institute of Management Sector 26, Nigdi Pradhikaran, Pune 411044 Author

DOI:

https://doi.org/10.61841/je5ed782

Keywords:

Central Public Sector Enterprises, bankruptcy, failure& non failure factors, Altman’s Z Score

Abstract

In this study, the researchers tried to discover the extent to which internal factors explain the probability of failure of manufacturing central public sector enterprises. This study is apparent because of the increasing number of failures. The policies, regulations, and new strategies should be developed to assist the management and policymakers by investigating the factors that influence the probability of failure. For the purpose of this study, six medium and light engineering enterprises were selected as a sample, covering a study period of ten years. 15 variables were selected from the extensive review of past research that are identically correlated with the occurrence of failure. These variables were tested by using binary logistic regression. This model uses a binary dependent variable, a dummy variable for failure. The dummy variable is ‘o’ if the enterprise is non-failure and ‘1’ for failure. The result of logistic regression shows that working capital, net profit, return of assets, gross value added to capital employed, labor cost to sales, capital output ratio, and sales to total assets significantly influence the probability of failure. This study reveals the magnitude of firm-specific factors in determining and/or explaining the failure of enterprises. The study also examined financial health by using the Altman’s Z score model. The results show that the failed Central Public Sector Enterprises have registered a negative Z score and fall under the category of distress zone. The failure of Central Public Sector Enterprises may be avoided if indications and influencing factors are timely established and proper measures are taken to improve the financial situation. The study recommends testing the factors of failure every year after preparation of the financial report. 

Downloads

Download data is not yet available.

References

1. Agrawal, AN., Vara, H.O. and Gupta, R.C. (1989).India: Economic Information Year Book. New Delhi: National Publishing House. p. 32.

2. Altman, E. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4), p. 589.

3. Avenhuis, J. (2013). Testing the generalizability of the bankruptcy prediction models of Altman, Ohlson, and Zmijewski for Dutch listed companies. Netherlands: University of Twente.

4. Bernhardsen, E. (2001). A Model of Bankruptcy Prediction. Oslo: Norges bank

5. Blanchard P., Huiban J.-P., and Mathieu. C. (2012). The determinants of firm exit in the French food industries, Review of Agricultural and Environmental Studies, Vol. 93(2), pp. 193-212.

6. Bureau of Public Enterprises. (1992). Public Enterprises Survey 1990-91 Vol. 1. New Delhi: Ministry of Industry, Government of India. p. 27.

7. Daily, C.M. and Dalton, D.R. (1994). 'Bankruptcy and corporate governance: The Impact of Board Composition and Structure', Academy of Management Journal, 37(6), pp. 1603-1617.

8. Department of Public Enterprises (2008). Second Pay Revision Committee Report. Department of Public Enterprises, Government of India.

9. Department of Public Enterprises (2014). Public Enterprise Survey 2012-13, Vol. I. New Delhi: Ministry of Heavy Industries and Public Enterprises, Government of India.

10. Jackson, R. and Wood, A. (2013). The performance of insolvency prediction and credit risk models in the UK: A comparative study. The British Accounting Review, 45(3), pp. 183–202.

11. Jovanovic, B. (1982). Selection and the Evolution of Industry, Econometrica, Vol. 50(3), pp. 649-670.

12. Lukason, O., Hoffman, R. (2014). Firm Bankruptcy Probability and Causes: An Integrated Study. International Journal of Business and Management, vol. 9, issue 11, 80.

13. Mackevicius, J., and Sneidere, R. (2010).Insolvency of an Enterprise and Methods of Financial Analysis for Predicting it. Eknonomika, Vol. 89(1).

14. Ma-Ju Wang and Heng-RueiShiu (2014). Research on the common characteristics of firms in financial distress into bankruptcy or recovery. Investment Management and Financial Innovations, 11(4-1).

15. Marathe, S.S. (1995, March). Re-assessing the Public Sector. Indian Management, p. 22.

16. Mohd. Talha. (1986, September 16-30). Public Undertaking: White Elephants. YojanaVol 30, No. 17, p.1.

17. Ohlson, J. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research, 18(1), p.109

18. Ohlson, J. A. (1980). Financial Ratios as Predictors of Failure.Journal of Accounting Research 4, 71-102

19. Olley G.S. and Pakes, A. (1996).The Dynamics of Productivity in the Telecommunications Equipment Industry, Econometrica, Vol. 64(6), pp. 1263-1297.

20. Skogsvik, K. (1990). Current cost accounting ratios as predictors of business failure: the Swedish case. Journal of Business Finance & Account.

21. Tomasz Korol, Evaluation Of The Factors Influencing Business Bankruptcy Risk In Poland. Financial Internet Quarterly e-Finanse,” 2017, vol. 13/nr 2, s. 22-35.

22. Venkatachalam, C. (1986). Financing of public enterprises in India. Bombay: Himalaya Publishing House. p. 1.

23. Wooldridge, J. (2014). Introduction to econometrics. Europe, Middle East, and Africa edition. United Kingdom: Cengage Learning.

Downloads

Published

30.04.2020

How to Cite

Pardeshi, B., Bisoyi, P., & Burbure, P. (2020). Predicting the Factors Influencing the Probability of Failure of Central Public Sector Enterprises. International Journal of Psychosocial Rehabilitation, 24(2), 1448-1459. https://doi.org/10.61841/je5ed782