Financial distress analysis for the prediction of corporate bankruptcy – a case study of a public sector company in India
DOI:
https://doi.org/10.56294/sctconf2024904Keywords:
Financial Distress, Insolvency, Bankruptcy, Liquidity, Financial Ratios, Financial Distress ModelsAbstract
Purpose: the present study examines the liquidity of the firm and its impact on financial distress, which may or may not increase the chances of bankruptcy. The study also analyzes the profitability, cash position, and solvency of the firm.
Design/methodology/approach: we use the data of a listed Government manufacturing company and measure the financial distress and probabilities of bankruptcy to test the chances of financial distress during the period between 2015 and 2019. The financial models used for evaluation in the study are the Altman z-score model, Logit Probability model, and Falmur model.
Findings: The study found that there was a chance of bankruptcy in the initial years, but later, it survived the bankruptcy. The study also established that the liquidity and solvency of the firm were not up to the standard.
Practical implications: the result of the study extends our theoretical understanding and also provides valuable guidelines to reduce the chance of insolvency, bankruptcy, and financial distress of firms and to maintain the proper financial health of the firm.
Originality/value: while many empirical studies investigate the relationship between liquidity position and its impact on financially distressed firms in the industry as a whole, but most do not consider the impact of financial distress in an individual firm or company. Most of the published studies use statistical tools for the evaluation of financial distress. This study uses Multiple Discriminant financial model analysis. Multiple Discriminant financial model Analyses are very useful in deciding remedial actions for financial distress problems
References
1. Altman EI. Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance. 1968 Sep 1; 23(4): 589-609. https://doi.org/10.2307/2978933
2. Altman EI, Hotchkiss E. Corporate financial distress and bankruptcy. New York: John Wiley & Sons; 1993. http://ci.nii.ac.jp/ncid/BA20382005
3. Altman EI, Marco G, Varetto F. Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of banking & finance. 1994 May 1; 18(3): 505-529. https://doi.org/10.1016/0378-4266(94)90007-8
4. Altman EI, Iwanicz‐Drozdowska M, Laitinen EK, Suvas A. Financial distress prediction in an international context: A review and empirical analysis of Altman's Z‐score model. Journal of international financial management & accounting. 2017 Jun; 28(2): 131-171. https://doi.org/10.1111/jifm.12053
5. Amendola A, Restaino M, Sensini L. An analysis of the determinants of financial distress in Italy: A competing risks approach. International Review of Economics & Finance. 2015 May 1; 37: 33-41. https://doi.org/10.1016/j.iref.2014.10.012
6. Ananto RP, Mustika R, Handayani D. The influence of good corporate governance (GCG), leverage, profitability and company size on financial distress in consumer goods companies listed on the Indonesian Stock Exchange. Dharma Andalas Journal of Economics and Business. 2017 Mar 7; 19(1): 92.
7. Arnold G. Corporate Financial Management (2nd Edition ed.). Pearson Education Limited. 2007. http://ci.nii.ac.jp/ncid/BA8072774X
8. Udayakumar R, Chowdary PB, Devi T, Sugumar R. Integrated SVM-FFNN for Fraud Detection in Banking Financial Transactions. Journal of Internet Services and Information Security. 2023; 13(3): 12-25.
9. Balcaen S, Ooghe H. 35 years of studies on business failure: an overview of the classic statistical methodologies and their related problems. The British Accounting Review. 2006 Mar 1; 38(1): 63-93. https://doi.org/10.1016/j.bar.2005.09.001
10. Beaver WH. Financial ratios as predictors of failure. Journal of accounting research. 1966 Jan 1: 71-111. https://doi.org/10.2307/2490171
11. Campbell JY, Hilscher J, Szilagyi J. In search of distress risk. The Journal of finance. 2008 Dec; 63(6): 2899-2939. https://doi.org/10.1111/j.1540-6261.2008.01416.x
12. Cielen A, Peeters L, Vanhoof K. Bankruptcy prediction using a data envelopment analysis. European Journal of Operational Research. 2004 Apr 16; 154(2): 526-532. https://doi.org/10.1016/s0377-2217(03)00186-3
13. Du Jardin P. Bankruptcy prediction using terminal failure processes. European Journal of Operational Research. 2015 Apr 1; 242(1): 286-303. https://doi.org/10.1016/j.ejor.2014.09.059
14. Edmister RO. An empirical test of financial ratio analysis for small business failure prediction. Journal of Financial and Quantitative analysis. 1972 Mar; 7(2): 1477-1493. https://doi.org/10.2307/2329929
15. Fathi MR, Rahimi H, Minouei M. Predicting financial distress using the worst-practice-frontier data envelopment analysis model and artificial neural network. Nankai Business Review International. 2023 Jun 5; 14(2): 295-315. https://doi.org/10.1108/nbri-01-2022-0005
16. Foster G. Financial Statement Analysis (2nd ed.) [English]. Prentice-Hall, Englewood Cliffs. 1986.
17. Geng R, Bose I, Chen X. Prediction of financial distress: An empirical study of listed Chinese companies using data mining. European Journal of Operational Research. 2015 Feb 16; 241(1): 236-247. https://doi.org/10.1016/j.ejor.2014.08.016
18. Bellovary JL, Giacomino DE, Akers MD. A review of bankruptcy prediction studies: 1930 to present. Journal of Financial education. 2007 Dec 1: 1-42. https://epublications.marquette.edu/account_fac/25/
19. Halteh K, Tiwari M. Preempting fraud: a financial distress prediction perspective on combating financial crime. Journal of Money Laundering Control. 2023 Nov 28; 26(6): 1194-1202. https://doi.org/10.1108/jmlc-01-2023-0013
20. Jabeur SB. Bankruptcy prediction using partial least squares logistic regression. Journal of Retailing and Consumer Services. 2017 May 1; 36: 197-202. https://doi.org/10.1016/j.jretconser.2017.02.005
21. John TA. Accounting measures of corporate liquidity, leverage, and costs of financial distress. Financial management. 1993 Oct 1: 91-100.
22. Kashyap S, Bansal R. Modeling Financial Distress Prediction of Indian Companies. International Journal of Recent Technology and Engineering (IJRTE). 2019; 8(1C2).
23. Konstantaras K, Siriopoulos C. Estimating financial distress with a dynamic model: Evidence from family owned enterprises in a small open economy. Journal of Multinational Financial Management. 2011 Oct 1; 21(4): 239-255. https://doi.org/10.1016/j.mulfin.2011.04.001
24. Korol T. Early warning models against bankruptcy risk for Central European and Latin American enterprises. Economic Modelling. 2013 Mar 1; 31: 22-30. https://doi.org/10.1016/j.econmod.2012.11.017
25. Kumar PR, Ravi V. Bankruptcy prediction in banks and firms via statistical and intelligent techniques–A review. European journal of operational research. 2007 Jul 1; 180(1): 1-28. https://doi.org/10.1016/j.ejor.2006.08.043
26. Lu Z, Zhuo Z. Modelling of Chinese corporate bond default–A machine learning approach. Accounting & Finance. 2021 Dec; 61(5): 6147-6191. https://doi.org/10.1111/acfi.12846
27. Lütz S, Kranke M. The European rescue of the Washington Consensus? EU and IMF lending to Central and Eastern European countries. Review of International Political Economy. 2014 Mar 4; 21(2): 310-338. https://doi.org/10.1080/09692290.2012.747104
28. Mohamed SS. Suggested model for explaining financial distress in Egypt: Toward a comprehensive model. In Financial Issues in Emerging Economies: Special Issue Including Selected Papers from II International Conference on Economics and Finance, Bengaluru, India). Emerald Publishing Limited. (https://doi.org/10.1108/S0196-382120200000036005). 2020 Nov 10; 36: 99-122.
29. Muñoz-Izquierdo N, Segovia-Vargas MJ, Pascual-Ezama D. Explaining the causes of business failure using audit report disclosures. Journal of Business Research. 2019 May 1; 98: 403-414. https://doi.org/10.1016/j.jbusres.2018.07.024
30. Friday JE, Godfrey VZ. Perception of and Attitude to Marketing of Library and Information Products and Services by Librarians in Public University Libraries in Bayelsa and Rivers States of Nigeria. Indian Journal of Information Sources and Services. 2023; 13(1): 39–48.
31. Alifiah MN. Prediction of financial distress companies in the trading and services sector in Malaysia using macroeconomic variables. Procedia-Social and Behavioral Sciences. 2014 May 15; 129: 90-98.
32. Platt HD, Platt MB. Predicting corporate financial distress: Reflections on choice-based sample bias. Journal of economics and finance. 2002 Jun; 26(2): 184-199. https://doi.org/10.1007/bf02755985
33. Plumley D, Serbera JP, Wilson R. Too big to fail? Accounting for predictions of financial distress in English professional football clubs. Journal of Applied Accounting Research. 2021 Jan 25; 22(1): 93-113. https://doi.org/10.1108/jaar-05-2020-0095
34. Rana S, Mahmud A, Mawa Z. A Study of the Applicability of Bankruptcy Prediction Models: An Empirical Analysis of Steel Manufacturing Companies of Bangladesh. Emperor International Journal of Finance and Management Research. 2020; 6: 1-22.
35. Karas M, Režňáková M. The stability of bankruptcy predictors in the construction and manufacturing industries at various times before bankruptcy. E+M. Ekonomie a Management. 2017; 20(2): 116–133. https://doi.org/10.15240/tul/001/2017-2-009
36. Rohmadini A, Saifi M, Darmawan A. The influence of profitability, liquidity and leverage on financial distress (Study of food & beverage companies listed on the Indonesia Stock Exchange for the 2013-2016 period). Journal of Business Administration. 2018; 61(2): 11-9.
37. Rujoub MA, Cook DM, Hay LE. Using cash flow ratios to predict business failures. Journal of Managerial Issues. 1995 Apr 1: 75-90. https://www.jstor.org/stable/40604051
38. Camgözlü Y, Kutlu Y. Leaf Image Classification Based on Pre-trained Convolutional Neural Network Models. Natural and Engineering Sciences. 2023 Dec 1; 8(3): 214-232.
39. Rose PS, Giroux GA. Predicting corporate bankruptcy: an analytical and empirical evaluation. Review of Financial Economics. 1984 Apr 1; 19(2): 1–12.
40. Tanja M, Milica V. The Impact of Public Events on the Use of Space: Analysis of the Manifestations in Liberty Square in Novi Sad. Arhiv za tehničke nauke, 2023; 2(29): 75-82.
41. Simanjuntak CE, Krist FT, Aminah W. The Influence of Financial Ratios on Financial Distress. eProceedings of Management. 2017 Aug 1; 4(2). https://libraryeproceeding.telkomuniversity.ac.id/index.php/management/article/view/1411
42. Tandon D, Tandon N. Credit Default Analytics in Banks Using Altman Z score. BULMIM Journal of Management and Research. 2016; 1(1): 1-1. https://doi.org/10.5958/2455-3298.2016.00001.5
43. Tykvova T, Borell M. Do private equity owners increase risk of financial distress and bankruptcy? Journal of Corporate Finance, 2012; 18(1): 138–150. https://doi.org/10.1016/j.jcorpfin.2011.11.004
44. Muparuri L, Gumbo V. On logit and artificial neural networks in corporate distress modelling for Zimbabwe listed corporates. Sustainability Analytics and Modeling. 2022 Jan 1; 2: 100006. https://doi.org/10.1016/j.samod.2022.100006
45. Srinadi NL, Hermawan D, Jaya AA. Advancement of banking and financial services employing artificial intelligence and the internet of things. Journal of Wireless Mobile Networks, Ubiquitous Computing, and Dependable Applications. 2023; 14(1): 106-117.
46. Sari A, Saputra H, Siahaan AP. Financial distress analysis on Indonesia stock exchange companies. International journal for innovative research in multidisciplinary field. 2018; 4(3): 73-74.
47. S E, K. (2012). Analysis of financial statements. Raja Grafindo.
48. Asutay M, Othman J. Alternative measures for predicting financial distress in the case of Malaysian Islamic banks: assessing the impact of global financial crisis. Journal of Islamic Accounting and Business Research. 2020 Dec 6; 11(9): 1827-1845. https://doi.org/10.1108/jiabr-12-2019-0223
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