The Role of Financial Ratios in Bankruptcy Prediction: An Empirical Study Using Contemporary Financial Data
DOI:
https://doi.org/10.33003/fujafr-2025.v3i2.164.43-55Keywords:
Corporate Bankruptcy, Financial Ratios, Bankruptcy Prediction, Financial RiskAbstract
Corporate bankruptcy poses considerable risks to various stakeholders. This study investigated contemporary issues in predicting corporate bankruptcy in the non-financial public companies in the United States, using five key financial ratios: Cash Flow to Total Liabilities (OANCFLT), Net Income to Total Sales (NIREVT), Total Liabilities to Total Assets (LTAT), Total Current Assets to Total Current Liabilities (ACTLCT), and Total Assets to Total Sales (ATREVT). Utilizing a multivariate logistic regression model and monthly data from 2017 to 2021, this research examined the predictive power of these ratios and their effectiveness in identifying early signs of corporate failure. The findings underscore the importance of certain financial ratios, particularly LTAT with 80% predictive power in the year before bankruptcy and OANCFLT at 65% and statistically significant with t-values of -2.85, offering valuable insights for stakeholders aiming to mitigate financial risks.
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. DOI: https://doi.org/10.1080/00014788.2007.9663313
Ajayi, O. L., Kazeem, H. B., & Babalola, E. O. (2021). Financial distress prediction in Nigerian oil and gas industry: A multivariate approach. Al-Hikmah International Journal of Finance, 1(1), 1-12.
Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. Journal of Finance, 23(4), 589-609. DOI: https://doi.org/10.1111/j.1540-6261.1968.tb00843.x
Beaver, W. (1966). Financial Ratios as Predictors of Failures. Journal of Accounting Research, 4(3) (Supplement), 71-102. DOI: https://doi.org/10.2307/2490171
Braga, A. S. A., & Cunha, J. (2022). Impact of macroeconomic indicators on bankruptcy prediction models: Case of the Portuguese construction sector. Quantitative Finance and Economics, 6(3), 405- 432. DOI: https://doi.org/10.3934/QFE.2022018
Chen, Y., Huang, W., & Li, J. (2021). The impact of extreme values on financial distress prediction: A comparative study. Journal of Empirical Finance, 60, 1–18. DOI: https://doi.org/10.1016/j.jempfin.2020.12.003
Edtiyarsih, D. D. (2023). Analysis of bankruptcy prediction with financial ratios Altman Z-Score model: Case study of oil and gas companies listed on IDX in 2017–2021. West Science Interdisciplinary Studies, 1(02), 18-30 DOI: https://doi.org/10.58812/wsis.v1i02.44
Giannopoulos, G., & Sigbjørnsen, S. (2019). Prediction of bankruptcy using financial ratios in the Greek market. Scientific Research Publishing, 92181. DOI: https://doi.org/10.4236/tel.2019.94072
Jackson, R., & Wood, A. (2013). The Performance of Insolvency Prediction and Credit Risk Models in the UK: A Comparative Study. The British Accounting Review, 45 (3), 183-202 DOI: https://doi.org/10.1016/j.bar.2013.06.009
Joos, P., De Bourdeaudhuij, C. and Ooghe, H. (1995), “Financial distress models in Belgium: the results of a decade of empirical research”, The International Journal of Accounting, 30 ( 3), 245-274.
Ncube, T. (2014). Predicting corporate failure: Insights from the financial sector in Zimbabwe. International Journal of Economics, Commerce, and Management, 2(11), 1-15.
Ohlson, J. (1980). Financial ratios and the probabilistic prediction of bankruptcy. Journal of Accounting Research, 18(1), 109-131. DOI: https://doi.org/10.2307/2490395
Rabiu, N. A. B., Aminu, D. A., Olanisebe, M., & Salihu, M. A. (2023). The Mediating Effect of Financial Distress on the Relationship between Profitability and Value of Listed Non-Financial Firms in Nigeria. FUDMA Journal of Accounting and Finance Research [FUJAFR], 1(2), 1-15. DOI: https://doi.org/10.33003/fujafr-2023.v1i2.18.1-15
Taffler, R. J. (1983). The assessment of company solvency and performance using a statistical model. Accounting and Business Research, 13(52), 295-308. DOI: https://doi.org/10.1080/00014788.1983.9729767
Tian, S., & Yu, Y. (2017). Financial ratios and bankruptcy predictions: International evidence. International Review of Economics & Finance, 51, 510-526. DOI: https://doi.org/10.1016/j.iref.2017.07.025
Umar, A., & Dandago, K. I. (2023). The Knowledge Economy: How Intellectual Capital Drives Financial Performance of Non-financial Service Firms in Nigeria. FUDMA Journal of Accounting and Finance Research [FUJAFR], 1(2), 113-122. DOI: https://doi.org/10.33003/fujafr-2023.v1i2.42.113-122
UL Hassan, E., Zainuddin, Z., & Nordin, S. (2017). A review of financial distress prediction models: logistic regression and multivariate discriminant analysis. Indian-Pacific Journal of Accounting and Finance, 1(3), 13-23. DOI: https://doi.org/10.52962/ipjaf.2017.1.3.15
Utami, D. W., Atmaja, H. E., & Hirawati, H. (2021). The Role of Financial Ratios on the Financial Distress Prediction. Kinerja, 25(2), 287-307. DOI: https://doi.org/10.24002/kinerja.v25i2.4661
Veganzones, D. & Severin, E. (2021). Corporate failure prediction models in the twenty-first century: a review. European Business Review, 33 (2), 204-226 DOI: https://doi.org/10.1108/EBR-12-2018-0209
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