Business and Economic Horizons

  Previous Article | Back to Volume | Next Article
  Abstract | References | Citation | Download | Preview | Statistics
Volume 14
Issue 2
Online publication date 2018-02-22
Title Predicting financial distress: Applicability of O-score model for Pakistani firms
Author Hamid Waqas, Rohani Md-Rus
Abstract
Predicting financial distress have significant importance in corporate finance as it serves as an effective early warning system for the related stakeholders. The study applies the most admired financial distress prediction O-score model and compares its predictive accuracy with estimated logit model. The study estimates logit model by including the profitability ratios, liquidity ratios, leverage ratios, and cash flow ratios. This study filled the gap by using the cash flow ratios to predict financial distress for Pakistani listed firms. The sample for the estimation model consists of 290 firms with 45 distressed and 245 healthy firms for the period 2006-2016 and covers all sectors of Pakistan Stock Exchange. The study provides important insights on the role of different financial ratio in predicting financial distress and shows that estimated logit model produces higher accuracy rate in predicting financial distress.
Citation
References
Abdullah, N. A. H., Halim, A., Ahmad, H., & Rus, R. M. (2008). Predicting corporate failure of Malaysia’s listed companies: comparing multiple discriminant analysis, logistic regression and the hazard model. International Research Journal of Finance and Economics, 15, 201-217.

Agarwal, V., & Taffler, R. (2008). Comparing the performance of market-based and accounting-based bankruptcy prediction models. Journal of Banking & Finance, 32(8), 1541-1551.

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.

Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The journal of finance, 23(4), 589-609.

Altman, E. I., Iwanicz-Drozdowska, M., Laitinen, E. K., & Suvas, A. (2014). Distressed Firm and Bankruptcy Prediction in an International Context: A review and empirical analysis of Altman's Z-score model.
http://dx.doi.org/10.2139/ssrn.2536340

Altman, E. I., & Hotchkiss, E. (2010). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt (Vol. 289). John Wiley & Sons.

Altman, E. I., & Loris, B. (1976). A financial early warning system for over‐the‐counter broker‐dealers. The Journal of Finance, 31(4), 1201-1217.

Altman, E. I., Marco, G., & Varetto, F. (1994). Corporate distress diagnosis: Comparisons using linear discriminant analysis and neural networks (the Italian experience). Journal of banking & finance, 18(3), 505-529.

Arlov, O., RANKOV, S., & KOTLICA, S. (2010). Cash Flow in Predicting Financial Distress and Bankruptcy.

Adnan Aziz, M., & Dar, H. A. (2006). Predicting corporate bankruptcy: where we stand?. Corporate Governance: The international journal of business in society, 6(1), 18-33.

Bandyopadhyay, A. (2006). Predicting probability of default of Indian corporate bonds: logistic and Z-score model approaches. The Journal of Risk Finance, 7(3), 255-272.

Agarwal, V., & Bauer, J. (2014). Distress risk and stock returns: The neglected profitability effect. In FMA Annual Meeting, Nashville, TN, October (pp. 15-18).

Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal of Accounting Research, 4, 71-111.

Bhunia, A., Khan, I., & MuKhuti, S. (2011). A study of managing liquidity. Journal of Management Research, 3(2), 1-22.

Biddle, G. C., Ma, M. L., & Song, F. M. (2010). Accounting Conservatism and Bankruptcy Risk.  http://dx.doi.org/10.2139/ssrn.1621272

Brealey, R. A., Myers, S. C., & Allen, F. (2011). Principles of corporate finance (10th ed.). McGraw-Hill.

Bunyaminu, A., & Issah, M. (2012). Predicting corporate failure of UK’s listed companies: Comparing multiple discriminant analysis and logistic regression. International Research Journal of Finance and Economics, 94, 6-22.

Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2008). In search of distress risk. The Journal of Finance, 63(6), 2899-2939.

Charitou, A., Neophytou, E., & Charalambous, C. (2004). Predicting corporate failure: empirical evidence for the UK. European Accounting Review, 13(3), 465-497.

Chiaramonte, L., & Casu, B. (2016). Capital and liquidity ratios and financial distress. Evidence from the European banking industry. The British Accounting Review, 49(2), 138-161.

Yap, B. C. F., Yong, D. G. F., & Poon, W. C. (2010). How well do financial ratios and multiple discriminant analysis predict company failures in Malaysia. International Research Journal of Finance and Economics, 54(13), 166-175.

Coats, P.K. & Franklin, L., 1993. Recognizing Financial Distress Patterns using Aneural Network Too. Financial Management, 22(3), pp.142-155

Deakin, E.B., 1972. A discriminant analysis of predictors. Journal of Accounting Research, 10(1), 167-179.

Dichev, I. D. (1998). Is the risk of bankruptcy a systematic risk?. The Journal of Finance, 53(3), 1131-1147.

Duda, M., & Schmidt, H. (2010). Bankruptcy prediction: Static logit model versus discrete hazard models incorporating macroeconomic dependencies. Lund University, 1-60.

Gentry, J. A., Newbold, P., & Whitford, D. T. (1985). Classifying bankrupt firms with funds flow components. Journal of Accounting Research, 23(1), 146-160.

Gilbert, L. R., Menon, K., & Schwartz, K. B. (1990). Predicting bankruptcy for firms in financial distress. Journal of Business Finance & Accounting, 17(1), 161-171.

Gilson, S. C. (2010). Creating value through corporate restructuring: Case studies in bankruptcies, buyouts, and breakups. John Wiley & Sons.

Grice, J. S., & Dugan, M. T. (2001). The limitations of bankruptcy prediction models: Some cautions for the researcher. Review of Quantitative Finance and Accounting, 17(2), 151-166.

Hill, N. T., Perry, S. E., & Andes, S. (1996). Evaluating firms in financial distress: An event history analysis. Journal of Applied Business Research, 12(3), 60-71.

Jantadej, P., 2006. Using the combinations of cash flow components to predict financial distress. University of Nebraska - Lincoln.

Jones, S., & Peat, M. (2014). Predicting Corporate Bankruptcy Risk in Australia: A Latent Class Analysis. Journal of Applied Management Accounting Research, 12(1), 13-25.

Kim, N. & Mangi, F., 2016. What is next for Asia’s best-performing stock market? Bloomberg Markets, (October 2016), pp.1-5.

Kordestani, G., Biglari, V., & Bakhtiari, M. (2011). Ability of combinations of cash flow components to predict financial distress. Business: Theory and Practice, 12(3), 277-285.

Rizwan Khurshid, M. (2013). Determinants of financial distress evidence from KSE 100 Index. IBA Business Review, 8(1), 7-19.

Manab, N. A., Theng, N. Y., & Md-Rus, R. (2015). The determinants of credit risk in Malaysia. Procedia-Social and Behavioral Sciences, 172(27), 301-308.

Monti, N. E., & Garcia, R. M. (2010). A statistical analysis to predict financial distress. Journal of Service Science and Management, 3(3), 309-335.

Ohlson, J. A. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1),109-131.

Opler, T.C. & Titman, S., 1994. Financial distress and corporate performance. The Journal of Finance, 49(3), pp.1015-1040.

Šarlija, N., & Jeger, M. (2011). Comparing financial distress prediction models before and during recession. Croatian Operational Research Review, 2(1), 133-142.

Ijaz, M. S., Hunjra, A. I., Hameed, Z., & Maqbool, A. (2013). Assessing the financial failure using Z-Score and current ratio: A case of sugar sector listed companies of Karachi Stock Exchange.

Sharma, D. S. (2001). The role of cash flow information in predicting corporate failure: the state of the literature. Managerial Finance, 27(4), 3-28.

Shumway, T. (2001). Forecasting bankruptcy more accurately: A simple hazard model. The Journal of Business, 74(1), 101-124.

Uğurlu, 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.

Wruck, K.H., 1990. Financial distress, reorganization, and organizational efficiency. Journal of Financial Economics, 27(2), pp.419-444.

Xiao, Z., Yang, X., Pang, Y., & Dang, X. (2012). The prediction for listed companies’ financial distress by using multiple prediction methods with rough set and Dempster-Shafer evidence theory. Knowledge-Based Systems, 26, 196-206.

Xu, W., Xiao, Z., Dang, X., Yang, D., & Yang, X. (2014). Financial ratio selection for business failure prediction using soft set theory. Knowledge-Based Systems, 63, 59-67.

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82
Keywords Financial distress, bankruptcy, logit regression, O-score model, financial distress, emerging market, Pakistan
DOI http://dx.doi.org/10.15208/beh.2018.28
Pages 389-401
Download Full PDF Download
  Previous Article | Back to Volume | Next Article
Share
Search in articles
Statistics
Journal Published articles
BEH 558
Journal Hits
BEH 1132661
Journal Downloads
BEH 41256
Total users online -