Corporate Financial Distress: The Impact of Profitability, Liquidity, Asset Productivity, Activity and Solvency

  • Karikari Amoa-Gyarteng Ghana Baptist University College

Abstract

This study aims to determine the importance of liquidity, profitability, asset productivity, activity, and solvency in cases of corporate financial distress. One hundred and five firms in the extractive industry in the United States were analyzed. Firms must be publicly traded and have filed form 10-K reports with the securities and exchange commission of the United States to be considered for the study’s population. The measure of corporate financial distress is the Altman Z-score. By using the Altman discriminant function, this study identifies the precipitants of corporate financial distress. This is especially important because widespread corporate financial distress could cause global financial system volatility. The indicators were measured in the last two years before the distressed firms declared bankruptcy. The results indicate that liquidity, profitability, asset productivity and solvency have an impact on the financial health of firms and therefore, on financial distress. The study further determines that activity ratio does not have a statistically significant relationship with financial distress.

Downloads

Download data is not yet available.

References

Al-Kassar, T. A., & Soileau, J. S. (2014, October). Financial performance evaluation and bankruptcy prediction (failure). Arab Economic & Business Journal, 9(2), 147-155.
Altman, E. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589-609.
Altman, E. I. (1984). The success of business failure prediction models: An international survey. Journal of Banking & Finance, 8(2), 171-198.
Altman, E. (1993). Corporate Financial distress and bankruptcy (3rd ed.). New York, USA: John Wiley & Sons, Inc.
Altman, E. I. (2013). Predicting financial distress of companies: revisiting the Z-score and ZETA® models. In Adrian R. Bell & Marcel Prokopczuk (Eds.), Handbook of research methods and applications in empirical finance (chapter 17, pp. 428-456). Camberley Surrey, UK: Edward Elgar Publishing.
Amoa-Gyarteng, K. (2019a, January). Explanatory and predictive values of the drivers of corporate bankruptcy. Journal of Finance & Marketing, 3(2), 1-8.
Amoa-Gyarteng, K. (2019b, April). Financial characteristics of distressed firms: An application of the Altman algorithm model. Journal of Corporate Accounting & Finance, 30(1), 63-76.

Atkan, S. (2011). Early warning system for bankruptcy prediction. Unpublished Doctoral Dissertation, Economics and Management Department, Karlsruhe Institute of Technology, Germany.
Beaver, W. H. (1966). Financial ratios as predictors of failure. Journal Accounting Research, 4, 71-111.
Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2007). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 33, 1-42.
Biddix, P. J. (2009). Research rundowns: Uncomplicated reviews of educational research methods. Quantitative research (QUANT)–descriptive and inferential statistics. Retrieved August 21, 2017, from https://researchrundowns.com/quantitative-methods/ instrument-validity-reliability/.
Brindescu-Olariu, D. (2016). Profitability ratio as a tool for bankruptcy prediction. SEA–Practical Application of Science, 4(11), 369-372.
Bryman, A. & Bell, E. (2011). Business research methods (3rd ed.). New York, USA: Oxford University Press Inc.
Chung, K.-C., Tan, S. S., & Holdsworth, D. K. (2008, January). Insolvency prediction model using multivariate discriminant analysis and artificial neural network for the finance industry in New Zealand. International Journal of Business & Management, 3(1), 19–29.
Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Earlbaum Associates.
Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods approaches
(3rd ed.). Thousand Oaks, CA: Sage Publications.
Doina, P., & Mircea, M. (2008). Analysis of a company’s liquidity based on its financial statements. Anuals of the University of Oradea, Economic Science Series, 17(3), 1366- 1371.
Dugan, M. T., & Zavgren, C. V. (1988). Bankruptcy prediction research: A valuable instructional tool. Issues in Accounting Education, 3(1), 48-64.
El-Temtamy, O. S. (1995). Bankruptcy prediction: A comparative study of logit and neural networks. Unpublished Doctoral Dissertation, Economics and Finance Department, Middle Tennessee State University, Murfreesboro, USA.
Epstein, D. G., Markell, B. A., Nickles, S. H., & Perris, E. L. (2010, June). Bankruptcy: Materials and cases (3rd ed.). M.N., USA: West Academic Publishing.
Ferrouhi, E. M. (2014). Bank liquidity and financial performance: Evidence from Moroccan banking industry. Verslas: Teorija ir praktika, 15(4), 351-361.
Giddaiah, D. (2016). Types of data collection tools. Retrieved July 15, 2017, from http://www.uni-mysore.ac.in/courseworklisc/ppt/pptsem_Giddaiich.pptx.
Gopalan, R., Kadan, O., & Pevzner, M. (2012, April). Asset liquidity and stock liquidity.
The Journal of Financial & Quantitative Analysis, 47(2), 333-364.
ICAP. (2006). Financial ratios explanation. ICAP Group South Africa. Retrieved August 27, 2016, from https://www.icapb2b.gr/b2b_web/CMSContent/FINANCIAL
_RATIOS.pdf.
Jackendoff, N. (1962). A study of published industry financial and operating ratios (vol. 52).
Bureau of Economic and Business Research. Philadelphia: Temple University.
Khani, A. H., & Guruli, M. R. (2015). Predicting bankruptcy by liquidity ratios analysis.
Journal UMP Social Sciences & Technology Management, 3(2), 372-380.
Kyung-Sung, T., Chong, N., & Gunhee, L.C. (1999). Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems, 10(1), 63-85.

LaBiondo-Wood, G., & Haber, J. (1998). Nursing research: Methods, critical appraisal, and utilization (6th ed.). St. Louis, MO: Mosby.
Laerd Statistics (2015). How to perform a multiple regression analysis in SPSS statistics.
Retrieved January 9, 2017 from http://www.statistics.laerd.com.
Latinen, E. K., & Latinen, T. (2000, December). Bankruptcy prediction: Application of the Taylors expansion in logistic regression. International Review of Financial Analysis, 9(4), 327-349.
Li, W. G. (2014). Corporate financial distress and bankruptcy prediction in the North American construction industry (pp. 1-43). Duke University, Durham, North Carolina.
Mecham, L. R. (2004, April). Bankruptcy basics: Applicable to cases filed before October 17, 2005 (revised 2nd ed.). The Bankruptcy Judges Division, Administrative Office of the United States Court, Washington, D.C.
Meigs, W. B., & Meigs, R. F. (2003). Financial accounting (13rd ed.). New York, USA: McGraw-Hill, Inc.
Merwin, C. L. (1942). Financing small corporations in five manufacturing industries. New York: National Bureau of Economic Research.
Muller, G. H., Steyn-Bruwer, B. W., & Hamman, W. D. (2009, March). Predicting financial distress of companies listed on the JSE-A comparison of techniques. South African Journal of Business Management, 40(1), 21-32.
New Generation Report. (2016). Corporate bankruptcy recap: Bankruptcies up 41% of filings from oil & gas/energy. Retrieved January 9, 2017, from http://www.bankruptcydata.com.
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. D. (1994, February). Financial distress and corporate performance. The Journal of Finance, 49(3), 1015-1040.
Outecheva, N. (2007). Corporate financial distress: An empirical analysis of distress risk. Unpublished Doctoral Dissertation. Graduate School of Business Administration, University of St. Gallen, Switzerland.
Polit, D. F., & Hungler, B. P. (1999). Nursing research: Principles and methods (6th ed.).
Philadelphia: Lippincott.
Platt, H. D., Platt, M. B., & Pedersen, J. G. (1994, June). Bankruptcy discrimination with real variables. Journal of Business Finance & Accounting, 21(4), 491-509.
Rashid, A., & Abbas, Q. (2011). Predicting bankruptcy in Pakistan. Theoretical & Applied Economics, 18(9), 103-128.
Rindfleish, A., & Heide, J. B. (1997, October). Transaction cost analysis: Past, present and future applications. Journal of Marketing, 61(4), 30-54.
Rose-Green, E., & Lovata, L. (2013). The relationship between firms’ characteristics in the periods prior to bankruptcy filing and bankruptcy outcome. Accounting & Finance Research, 2(1), 97-109.
Rosslyn-Smith, W., De Abreu, N. V. A., & Pretorius, M. (2019). Exploring the indirect costs of a firm in business rescue. South African Journal of Accounting Research, 34(1), 24-44.
Securities and Exchange Commission (SEC) (2009). Interactive data for financial reporting. Retrieved February 20, 2016, from http://www.sec.gov/info/smallbus/ secg/interactivedata-secg.html.
Shiu, H.-R., & Wang, M.-J. (2014, January). Research on the common characteristics of firms in financial distress into bankruptcy or recovery. Investment Management & Financial Innovations, 2(4), 233-243.

Skogvik, K. (1990). Current cost accounting ratios as predictors of business failure: The Swedish case. Journal of Business Finance & Accounting, 17(1), 137-160.
Steyn-Bruwer, B. W., & Hamman, W. D. (2006). Company failure in South Africa: Classification and prediction by means of recursive partitioning. South African Journal of Business Management, 34(4), 1-12.
Sung, T. K., Chang, N., & Lee, G. (1999). Dynamics of modeling in data mining: Interpretive approach to bankruptcy prediction. Journal of Management Information Systems, 16(1), 63-85.
Szívós, L., & Orosz, I. (2014). The role of data authentication and security in the audit of financial statements. Acta Polytechnica Hungarica, 11(8), 161-176.
Triola, M. F. (2009). Elementary statistics (11th ed.). New York, USA: Addison Wesley.
Trivedi, S. M. (2010, March). An analysis of financial performance of state road transport corporation in Gujarat. Unpublished Doctoral Dissertation, Department of Business Management, Saurashtra University, Gujarat, India.
Whitaker, R. (1999). The early stages of financial distress. Journal of Economics & Finance, 23(2), 123-132.
Winer, R. S. (1999, July). Experimentation in the 21st century: The importance of external validity. Journal of the Academy of Marketing Science, 27(3), 349-358.
Yang, Z. R., Platt, M. B., & Platt, H. D. (1999, February). Probabilistic neural networks in bankruptcy prediction. Journal of Business Research, 44(2), 67-74.
Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59-82.
Published
2021-11-07
How to Cite
AMOA-GYARTENG, Karikari. Corporate Financial Distress: The Impact of Profitability, Liquidity, Asset Productivity, Activity and Solvency. Journal of Accounting, Business and Management (JABM), [S.l.], v. 28, n. 2, p. 104-115, nov. 2021. ISSN 2622-2167. Available at: <http://journal.stie-mce.ac.id/index.php/jabminternational/article/view/447>. Date accessed: 08 dec. 2021. doi: https://doi.org/10.31966/jabminternational.v28i2.447.
Section
Articles