Financial Distress and Forewarning Bankruptcy: An Empirical Analysis of Textile Sector in Pakistan

Authors

  • Nosheen Rasool Chairperson, Department of Commerce &Finance, GC University, Lahore, Pakistan
  • Muhammad Sohail Faculty Member, Department of Commerce & Finance, GC University Lahore, Pakistan
  • Muhammad Usman Assistant Professor, Hailey College of Commerce University of the Punjab, Lahore, Pakistan
  • Muhammad Mubashir Hussain Assistant Professor, Management Studies Department, GC University, Lahore, Pakistan

DOI:

https://doi.org/10.47067/ramss.v3i3.92

Keywords:

Financial Distress, Bankruptcy, Z-Score, Ohlson O-Score, Blums D-Score

Abstract

This study aims to measure the financial distress and forewarn bankruptcy in Textile Sector of Pakistan using popular statistical measures i.e., Z-Score, O-Score, Probit and D-Score. First, applicable financial ratios (profitability, liquidity, leverage, market ratios) and scores (Z-Score, O-Score, Probit and D-Score) of all 77 textile companies were calculated then estimated scores were compared with cut-off point of respective model. Based on findings, models are categorized in two groups: (a) Group-I (Z-Score and O-Score), (b) Group-II (Probit  and D-Score). Results indicate that some of the textile firms are about to face financial distress in near future, which could ultimately lead those firms to bankruptcy. The findings of Group-I indicate that about 43% - 44% companies in the textile sector are in the phase of financial distress; whereas the results of Group-II reveal that about 8% - 16% companies are in financial distress phase. Thus, we could draw two conclusions: (1) the two models (Z-Score and O-Score) in Group-I were found to be robust for assessing financial distress and (2) the two models  (Probit  and D-Score) in Group-II were found to be less rigorous in forecasting financial distress. The previous studies attempted to compare the prediction accuracy of various models by examining the data of both financially distress firms and financially stable firms. But this study is aimed to foretell bankruptcy using comprehensive models (Z-Score, O-Score, Probit and D-Score), to compare the consistency of results across all four models of the study and to categorize financially stable and financially distress companies under each model. The findings of the study are expected to be beneficial at coutry level, firm level and indiviual level such as government and regulatory bodies of Pakistan can intervene to avert bankruptcy rate, management can devise appropriate strategies  to reduce financial distress. Moreover. investors can safeguard their investment by making right decissions based on the findings.

References

Agarwal, V., & 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. https://doi.org/10.1080/00014788.2007.9663313

Ali Hasanain, & Syed Ahsan Ahmad Shah. (n.d.). Investigating the Proposed Changes to Pakistan’s Corporate Bankruptcy Code [Research]. Center for Research in Econmoics and Business. http://www.creb.org.pk/uploads/Ali%20Hasnain%20Working%20Paper%20No.%2001-12%20Complete.pdf

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/10.1111/j.1540-6261.1968.tb00843.x

Altman, E. I., & Saunders, A. (n.d.). Credit risk measurement: Developments over the last 20 years. 22.

An Analysis of Assessment of Financial Distress in Textile Sector of Pakistan (2012-2018). (2019). European Journal of Business and Management. https://doi.org/10.7176/EJBM/11-19-01

Ashraf, S., G. S. Félix, E., & Serrasqueiro, Z. (2019). Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress? Journal of Risk and Financial Management, 12(2), 55. https://doi.org/10.3390/jrfm12020055

Beaver, W. H. (1966). Financial Ratios As Predictors of Failure. Journal of Accounting Research, 4, 71. https://doi.org/10.2307/2490171

Blum, M. (1974). Failing Company Discriminant Analysis. Journal of Accounting Research, 12(1), 1. https://doi.org/10.2307/2490525

Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In Search of Distress Risk. The Journal of Finance, 63(6), 2899–2939. https://doi.org/10.1111/j.1540-6261.2008.01416.x

Chen, C., Chen, C., & Lien, D. (2020). Financial distress prediction model: The effects of corporate governance indicators. Journal of Forecasting, for.2684. https://doi.org/10.1002/for.2684

Cultrera, L., & Brédart, X. (2016). Bankruptcy prediction: The case of Belgian SMEs. Review of Accounting and Finance, 15(1), 101–119. https://doi.org/10.1108/RAF-06-2014-0059

Cybinski, P. (2001). Description, explanation, prediction – the evolution of bankruptcy studies? Managerial Finance, 27(4), 29–44. https://doi.org/10.1108/03074350110767123

Davydenko, S. A. (2010). When Do Firms Default? A Study of the Default Boundary. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.672343

Deakin, E. B. (1972). A Discriminant Analysis of Predictors of Business Failure. Journal of Accounting Research, 10(1), 167. https://doi.org/10.2307/2490225

Edwards, D. J., Owusu-Manu, D.-G., Baiden, B., Badu, E., & Love, P. E. (2017). Financial distress and highway infrastructure delays. Journal of Engineering, Design and Technology, 15(1), 118–132. https://doi.org/10.1108/JEDT-02-2016-0006

Manalu, S., Octavianus, R. J. N., Faculty of Economics and Business Universitas Ma Chung Malang, Kalmadara, G. S. S., & Faculty of Economics and Business Universitas Ma Chung Malang. (2017). Financial Distress Analysis with Altman Z-Score Approach and Zmijewski X-Score on Shipping Service Company. Jurnal Aplikasi Manajemen, 15(4), 677–682. https://doi.org/10.21776/ub.jam.2017.015.04.15

Farooq, U., & Qamar, M. A. J. (2019). Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria. Journal of Forecasting, 38(7), 632–648. https://doi.org/10.1002/for.2588

Garlappi, L., Shu, T., & Yan, H. (2006). Default Risk, Shareholder Advantage and Stock Returns. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.890348

Grice, J. S., & Ingram, R. W. (2001). Tests of the generalizability of Altman’s bankruptcy prediction model. Journal of Business Research, 54(1), 53–61. https://doi.org/10.1016/S0148-2963(00)00126-0

Ijaz, M. S., Hunjra, A. I., & Azam, R. I. (2017). Forewarning Bankruptcy: An Indigenous Model for Pakistan. Business & Economic Review, 9(4), 261–288. https://doi.org/10.22547/BER/9.4.12

Jaffari, A. A., & Ghafoor, Z. (2017). Predicting Corporate Bankruptcy in Pakistan A Comparative Study of Multiple Discriminant Analysis (MDA) and Logistic Regression. Research Journal of Finance and Accounting, 20.

Jahur, M. S., & Quadir, S. M. N. (2012). Financial Distress in Small and Medium Enterprises (SMES) of Bangladesh: Determinants and Remedial Measures. 15(1), 16.

Jiang, Y., & Jones, S. (2018). Corporate distress prediction in China: A machine learning approach. Accounting & Finance, 58(4), 1063–1109. https://doi.org/10.1111/acfi.12432

Jones, S., Johnstone, D., & Wilson, R. (2017). Predicting Corporate Bankruptcy: An Evaluation of Alternative Statistical Frameworks: An Evaluation of Alternative Statistical Frameworks. Journal of Business Finance & Accounting, 44(1–2), 3–34. https://doi.org/10.1111/jbfa.12218

Laitinen, T., & Kankaanpaa, M. (1999). Comparative analysis of failure prediction methods: The Finnish case. European Accounting Review, 8(1), 67–92. https://doi.org/10.1080/096381899336159

Lizares, R. M., & Bautista, C. C. (2020). Corporate financial distress: The case of publicly listed firms in an emerging market economy. Journal of International Financial Management & Accounting, jifm.12122. https://doi.org/10.1111/jifm.12122

Muhammad, D., Malik, S., Muzammal, B., & Amin, A. (2017). Evaluating Financial Distress in Developing Economies: A Case Study of Pakistani and Indian Public Sector Banks using Altman’s Z score. 03, 19.

Muñoz?Izquierdo, N., Laitinen, E. K., Camacho?Miñano, M., & Pascual?Ezama, D. (2020a). Does audit report information improve financial distress prediction over Altman’s traditional Z ?Score model? Journal of International Financial Management & Accounting, 31(1), 65–97. https://doi.org/10.1111/jifm.12110

Muñoz?Izquierdo, N., Laitinen, E. K., Camacho?Miñano, M., & Pascual?Ezama, D. (2020b). Does audit report information improve financial distress prediction over Altman’s traditional Z ?Score model? Journal of International Financial Management & Accounting, 31(1), 65–97. https://doi.org/10.1111/jifm.12110

Rim, E. K., & Roy, A. B. (2014). Classifying Manufacturing Firms in Lebanon: An Application of Altman’s Model. Procedia - Social and Behavioral Sciences, 109, 11–18. https://doi.org/10.1016/j.sbspro.2013.12.413

U?urlu, M., & Aksoy, H. (2006). Prediction of corporate financial distress in an emerging market: The case of Turkey. Cross Cultural Management: An International Journal, 13(4), 277–295. https://doi.org/10.1108/13527600610713396

VenkataRamana, N., Azash, S., & Ramakrishnaiah, K. (2012). Financial Performance and Predicting the Risk of Bankruptcy: A Case of Selected Cement Companies in India. 1, 17.

Downloads

Published

2020-12-31

How to Cite

Rasool, N. ., Sohail, M. ., Usman, M. ., & Hussain, M. M. . (2020). Financial Distress and Forewarning Bankruptcy: An Empirical Analysis of Textile Sector in Pakistan. Review of Applied Management and Social Sciences, 3(3), 493-506. https://doi.org/10.47067/ramss.v3i3.92