Magnitude of Financial Distress in Micro, Small and Medium Enterprises (MSMES) in Bihar, India: A Test of Altman Z’ Score

  • Bishwajeet PRAKASH Rukmini Devi Institute of Advance Studies, Delhi, India
  • Jainendra Kumar VERMA Department of Economic Studies, Central University of Punjab, Bathinda, India

Abstract

Micro, Small and Medium Enterprises (MSMEs) play a crucial role in equitable upliftment and development of economy. Although these sectors are contributing to economy through generation of employment, supply products, services through indigenous and modern technology, promote social entrepreneurship, reduce regional disparities, and promote equitable income opportunities as well strengthen the balance of trade of the economy. MSMEs are facing actuate deficiency of management which leads to organization in to financial deficiency. Financial Distress in the MSMEs is common for all economy of the world, which has been major concern for the academicians, experts, professional, society, creditors and government. This study identifies the prediction of sickness in MSMEs in Bihar with the help of Altman Z’ Score. The study employs the statistical and financial methods to analyze the data collected in the study. The study identifies financial distress in small and medium enterprises through their balance sheet and profit and loss account. The study came out with proper strategy and policy related to revival of the enterprise from the financial sickness and preventing them from industrial sickness.

References

[1] Abdullah, N.A., Hallim, A., Ahmad, H., and Rustom, R.M. 2008. Predicting corporate failure of Malaysia's listed companies: Comparing multiple discriminant analysis, logistic and hazard model. International Journal of Finance and Economics, 15(1): 201-217.
[2] Agarwal, V., and Taffler, R.J. 2007. Twenty-five years of the Taffler z-score model: Does it really have predictive ability? Accounting and Business Research, 37(4): 285-300.
[3] Altman, E. 2002. Financial ratios, discriminant analysis, and prediction of corporate bankruptcy. Journal of Finance, 23(4): 589-610.
[4] Altman, E.I. 1968. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. Journal of Finance, 23(4): 589-603.
[5] Anant, T.C.A., and Goswami, O. 1995. Getting everything wrong: India‘s policies regarding sick firms in Indian industry: Policy and performance. New Delhi, India: Oxford University Press. ISBN 019563666X, 236-288 pp.
[6] Baldwin, C., and Manson, S.P. 1983. The resolution of claims in financial distress the case of Massey Ferguson. The Journal of Finance, 38(2): 505-516.
[7] 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.
[8] Baninoe, R. 2010. Corporate bankruptcy prediction and equity returns in the UK. Cranfield School of Management, Cranfield University.
[9] Bhatt, S.N. 2012. Capital structure and turnaround strategies using Altman‘s Z-score models. Asian Journal of Research in Business, Economics and Management, 2(7): 102-113.
[10] Bhattacharya, C.D. 1982. Discriminant analysis between sick and healthy units. The Chartered Accountant, Working Paper, WP1980-07-01_00397, Indian Institute of Management Ahmedabad, Research and Publication Department.
[11] Bidani, S.N., and Mitra, P.K. 1986. Industrial sickness - identification and rehabilitation, New Delhi, India: Vision Books Pvt. Ltd
[12] Campbell, J.Y., Hilscher, J.D., and Szilagyi, J. 2011. Predicting financial distress and the performance of distressed stocks. Journal of Investment Management, 9(2): 14-34.
[13] Celli, M. 2015. Can Z-score model predict listed companies’ failures in Italy? An empirical test. International Journal of Business and Management, 10(3): 1-15.
[14] Chung, C.K., Tan, S.S., and Holdworth, D.K. 2008. Insolvency prediction model using multivariate discriminant analysis and artificial neural network for the finance industry in New Zealand. International Journal of Business and Management, 1(1): 19-29.
[15] Deakin, E. 1972. A discriminant analysis of predictors of business failure. Journal of Accounting Research, 10(1): 167-179.
[16] Gopinath, C. 1995. External influence of firms: An exploratory model of bank strategies. Journal of Business Research, 34(1): 133-143.
[17] Goswami, O. 1996. Corporate bankruptcy in India: A comparative perspective. Paris, France: OECD Publications and Information Center. ISBN: 978-9264152991, 117 pp.
[18] Gupta, L.C. 1983. Financial ratios for signaling corporate failure. The Chartered Accountant, 2(1): 697-714.
[19] Jayadev, M. 2006. Predictive power of financial risk factors: An empirical analysis of default companies. Vikalpa, 31(3): 45-57.
[20] Jena, R.N., Thatte, R.L, and Ket, V.G. 2018. Performance of the micro, small and medium enterprises (MSMEs) manufacturing sector in select states in India: The concept of MSME manufacturing business facilitator (MSME-MBF) index. Academy of Entrepreneurship Journal, 24(1): 1-22. Available at: https://www.ab academies.org/articles/Performance-of-the-Micro-Small-&-Medium-Enterprises-(Msmes)-1528-2686-24-1-122.pdf.
[21] Karels, G.V., and Prakash, A.J. 1987. Multivariate normality and forecasting of business bankruptcy. Journal of Business Finance Accounting, 14(4): 573-593.
[22] Keige, P. 1991. Business failure prediction using discriminate analysis. Doctoral Dissertation, University of Nairobi, Kenya. Available at: http://erepository.uonbi.ac.ke:8080/xmlui/handle/123456789/38070.
[23] Khaliq, A., et al. 2014.Identifying financial distress firms: A case study of Malaysia’s Government Linked Companies (GLC). International Journal of Economics, Finance and Management, 3(3): 141-150.
[24] Kumar, R.G., and Kumar, K. 2015. Predicting financial distress: A comparison of survival analysis and decision tree techniques. International Multi-Conference on Information Processing, 54(4): 396-404.
[25] Lennox, C. 1999. Identifying failing companies: A re-evaluation of the logit, probit and MDA approaches. Journal of Economics and Business, 51(4): 347-364.
[26] Leutkenhost, W. 2004. Corporate social responsibility and the development agenda. Inter Economics-Review of European Economic Policy, 39(3): 157- 168.
[27] Mayer, P.A., and Pifer, H.W. 1970. Prediction of bank failures. The Journal of Finance, 25(4): 853-868.
[28] Michalkovaa, L., Adamkob, P., and Kovacova, M. 2018. The analysis of causes of business financial distress, advances in economics. Business and Management Research, 56(3): 49-52.
[29] Ohlson, J.A. 1980. Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1): 109-131.
[30] Pachouri, A., and Sharma, S. 2016. Barriers to innovation in Indian small and medium-sized enterprises. Asian Development Bank Institute, Working Paper Series, No. 588.
[31] Pervan, M., and Visic, J. 2012. Influence of firm size on its business success. Croatian Operational Research Review, 3: 213-223.
[32] Prakash, B. 2019. Growth and performance of Micro, Small and Medium Enterprises in India: A case study of Bihar. Research Review International Journal of Multidisciplinary, 4(4): 942-947.
[33] Prakash, B., and Rajput, R. 2018. ‖financial distress: An analysis of selected hospitality industry in India, Mishra, P.K., and Verma, J.K. (1st Eds), New Delhi, India: APH Publication.
[34] Ramaratnam, M.S., and Jayaraman, R. 2010. A study on measuring the financial soundness of select firms with special reference to Indian steel companies- An empirical view with Z score. Asian Journal of Management Research, 2(3): 724-735.
[35] Satchkov, D. 2010. When swans are grey: Var as an early warning signal. Journal of Risk Management in Financial Institution, 3(4): 366-379.
[36] Sharma., R. 1985. Industrial sickness in India: An analysis. ASCI Journal of Management, 15(1): 1-27.
[37] Shumway, T. 2001. Forecasting bankruptcy more accurately: A simple hazard model. Journal of Business, 74(1): 101-24
[38] Srivastava, S.S., and Yadav, R.A. 1986. Management and monitoring of industrial sickness. New Delhi: Concept Publishing Company. ISBN: 978-8170220817
[39] Sulphey, M.M., and Nisa, S. 2013. The analytical implication of Altman‘s Z score analysis of BSE listed small companies. Global Journal of Commerce & Management Perspective, 2(4): 145-155.
[40] Weitzel, W., and Jonsson, E. 1989. Decline in organizations: A literature integration and extension. Administrative Science Quarterly, 34(1): 91-109.
[41] Yadav, S., and Tripathi, V. 2018. Challenges and obstacles faced by Micro, Small and Medium Sized enterprises (MSMEs) In India. International Journal of Business and Management Invention, 7(4): 48-54.
[42] *** Ministry of Finance 2019. Economic Survey (2018-2019). Government of Bihar. Patna, India.
[43] *** Ministry of Micro Small and Medium Enterprises. 2007. Quick result of fourth all India census. Government of India, New Delhi, India.
[44] *** Ministry of Micro Small and Medium Enterprises. 2016. Annual Report (2015-2016). Government of India, New Delhi, India.
[45] *** Ministry of Micro Small and Medium Enterprises. 2017. Annual Report (2016-2017). Government of India, New Delhi, India.
[46] *** Ministry of Micro Small and Medium Enterprises. 2018. Annual Report (2017-2018). Government of India, New Delhi, India.
[47] *** OECD. 2016b. Entrepreneurship at a Glance 2016, OECD Publishing, Paris.
[48] *** Reserve Bank of India. 2016. Annual Report (2015-2016). Mumbai, India.
[49] *** IFC. 2010. Scaling-Up SME access to financial services in the developing world. International Finance Corporation, World Bank Group, Washington D.C. Available at: http://www.enterprise development.org/wpcontent /uploads/ScalingUp_SME_Access_to_Financial_Services.pdf.
Published
2019-06-30
How to Cite
PRAKASH, Bishwajeet; VERMA, Jainendra Kumar. Magnitude of Financial Distress in Micro, Small and Medium Enterprises (MSMES) in Bihar, India: A Test of Altman Z’ Score. Journal of Advanced Research in Law and Economics, [S.l.], v. 10, n. 4, p. 1227-1239, june 2019. ISSN 2068-696X. Available at: <https://journals.aserspublishing.eu/jarle/article/view/4889>. Date accessed: 25 apr. 2024. doi: https://doi.org/10.14505//jarle.v10.4(42).22.