Comparative Analysis of Financial Distress Prediction Models in U.S. Oilfield Services Firms: Evidence from 2010-2023

Rio Budiman; Abdul Mukti Soma

doi:10.33005/jasf.v9i1.739

Authors

Rio Budiman Telkom University
Abdul Mukti Soma Telkom University

DOI:

https://doi.org/10.33005/jasf.v9i1.739

Keywords:

financial distress prediction, oilfield services industry, profitability, model sensitivity, oil price volatility, panel data analysis

Abstract

Purpose: This study examines financial distress in U.S. oilfield services firms by comparing classification outcomes across four prediction models and investigating how industry characteristics influence financial distress detection within a cyclical and capital-intensive environment.

Method: Using panel data from ten publicly listed firms over the period 2010–2023 (140 firm-year observations), this study applies the Altman Z″, Zmijewski, Grover, and Springate models. Differences among models are evaluated using non-parametric tests, including the Friedman test, Kendall’s W, Cochran’s Q, and McNemar test. Binary logistic regression is subsequently employed to examine the effects of oil price, leverage, profitability (ROA), firm size, and oil price volatility on financial distress.

Findings: The results reveal significant differences in financial distress classifications across models, indicating strong model dependency. The Springate model appears more responsive to early-stage financial deterioration than the Altman Z″, Zmijewski, and Grover models. Profitability (ROA) is the only variable that significantly affects financial distress, while oil price, leverage, firm size, and oil price volatility do not exhibit significant direct effects. The findings further suggest that external shocks influence financial distress indirectly through firm-level financial performance.

Implications: The findings highlight the importance of profitability and operational performance in maintaining financial resilience within cyclical industries. Managers, investors, and creditors should therefore place greater emphasis on profitability as an indicator of financial vulnerability than on external market conditions alone.

Novelty/Value: This study contributes by explaining how the structural characteristics of a cyclical and capital-intensive industry shape the sensitivity of financial distress prediction models. The findings suggest that profitability-oriented models identify financial deterioration earlier than leverage-oriented models because industry downturns initially affect asset utilization, revenue generation, and profitability before materially affecting leverage and solvency indicators.

Downloads

Download data is not yet available.

References

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., Hotchkiss, E., & Wang, W. (2019). Corporate financial distress, restructuring, and bankruptcy: Analyze leveraged finance, distressed debt, and bankruptcy (4th ed.). John Wiley & Sons. https://doi.org/10.1002/9781119541929

American Petroleum Institute. (2025). State of American energy 2025. https://www.api.org

Amin, M. R., Ben-Nasr, H., & Masum, A. A. (2025). Oil price uncertainty and corporate debt choice: International evidence. International Review of Economics & Finance. https://doi.org/10.1016/j.iref.2025.104864

Arifin, A. L., & Koerniawan, K. A. (2025). Gender-diverse boards, liquidity, and financial distress: Pathways to fraud deterrence in auditor judgments. Edelweiss Applied Science and Technology, 9(5), 2549–2564. https://doi.org/10.55214/25768484.v9i5.7517

Bellovary, J. L., Giacomino, D. E., & Akers, M. D. (2023). A review of bankruptcy prediction studies: 1930 to present. Journal of Financial Education, 49(1), 1–34.

Cathcart, L., El-Jahel, L., & Jabbour, R. (2020). The differential impact of leverage on the default risk of small and large firms. Journal of Corporate Finance, 60, 101541. https://doi.org/10.1016/j.jcorpfin.2019.101541

Chen, Y., Dong, S., Qian, S., & Chung, K. (2024). Impact of oil price volatility and economic policy uncertainty on business investment: Insights from the energy sector. Heliyon, 10, e26533. https://doi.org/10.1016/j.heliyon.2024.e26533

Dianova, A., & Nahumury, J. (2019). Investigating the Effect of Liquidity, Leverage, Sales Growth, and Good Corporate Governance on Financial Distress. JASF: Journal of Accounting and Strategic Finance, 2(2), 143–156. https://doi.org/10.33005/jasf.v2i2.49

Fan, Z., Zhang, Z., & Zhao, Y. (2021). Does oil price uncertainty affect corporate leverage? Evidence from China. Energy Economics, 98, 105252. https://doi.org/10.1016/j.eneco.2021.105252

Farida, A. L., & Sugesti, P. F. (2023). Determinant of Earnings Management: Financial Distress, Tax Planning, Audit Quality, and Public Accountant Firm Size. JASF: Journal of Accounting and Strategic Finance, 6(1), 1–15. https://doi.org/10.33005/jasf.v6i1.386

Field, A. (2024). Discovering statistics using IBM SPSS Statistics (6th ed.). SAGE Publications.

Fitriani, F., & Soma, A. M. (2026). The influence of financial literacy, perceived ease of use, perceived risk, and lifestyle on QRIS adoption. Multidisciplinary Indonesian Center Journal. https://doi.org/10.62567/micjo.v3i1.1981

Fridson, M. S., & Alvarez, F. (2022). Financial statement analysis: A practitioner’s guide (5th ed.). Wiley. https://doi.org/10.1002/9781119457176

Georgievski, B., Velickovska, K., Dzenopoljac, V., Pahwa, N., & Dzenopoljac, A. (2024). Bankruptcy trends among European football clubs. Journal of Physical Education and Sport. https://doi.org/10.7752/jpes.2024.04104

Grover, J., & Lavin, A. (2001). Financial ratios, discriminant analysis, and the prediction of corporate bankruptcy [Working paper]. University of Toledo.

Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2019). Multivariate data analysis (8th ed.). Cengage Learning.

Hasan, M. M., Asad, S., & Wong, J. B. (2022). Oil price uncertainty and corporate debt maturity structure. Finance Research Letters, 46, 102278. https://doi.org/10.1016/j.frl.2021.102278

Haynes and Boone LLP. (2021). Oil patch bankruptcy monitor. https://www.haynesboone.com

Huang, K., Chi, J., Liao, J., & Yuen, M. K. (2025). In the heat of the moment, secrets will out: Oil price uncertainty and firm green innovation disclosure. International Review of Economics & Finance, 98, 103934. https://doi.org/10.1016/j.iref.2025.103934

Ibrahim, N., Rahman, N. A., & Hamzah, A. H. (2024). A comparative model for financial distress in Malaysian companies using Altman, Springate, Grover, and Zmijewski models. AIP Conference Proceedings, 3189(1), 090005. https://doi.org/10.1063/5.0212345

Ilyas, M., Khan, A., Nadeem, M., & Suleman, M. T. (2021). Economic policy uncertainty, oil price shocks and corporate investment: Evidence from the oil industry. Energy Economics, 99, 105193. https://doi.org/10.1016/j.eneco.2021.105193

Insani, F., Pontoh, G., & Said, D. (2024). Corporate bankruptcy prediction model: A systematic literature review. Journal of Risk and Financial Management, 17(2), 85. https://doi.org/10.3390/jrfm17020085

International Energy Agency. (2023). World energy outlook 2023. https://www.iea.org/reports/world-energy-outlook-2023

Isynuwardhana, D., & Putri, M. L. (2021). Event study analysis before and after COVID-19 in Indonesia. Academy of Accounting and Financial Studies Journal, 25(6), 1–11. https://www.abacademies.org/articles/event-study-analysis-before-and-after-covid19-in-indonesia-11775.html

Kebede, T. N., Tesfaye, G. D., & Erana, O. T. (2024). Determinants of financial distress: Evidence from insurance companies in Ethiopia. Journal of Innovation and Entrepreneurship, 13(1), 17. https://doi.org/10.1186/s13731-024-00369-5

Kamal, J. B., Costa, M., & Habib, A. (2022). Oil price volatility, organization capital, and firm performance. American Business Review, 25(2), 488–514. https://doi.org/10.37625/abr.25.2.488-514

Kilian, L., & Zhou, X. (2022). Oil prices, gasoline prices, and inflation expectations. Journal of Applied Econometrics, 37(5), 867–881. https://doi.org/10.1002/jae.2911

Kostrzewa, M., Furman, O., Furman, R., Tomczak, S., & Zięba, M. (2025). Are foundation models useful for bankruptcy prediction? arXiv Preprint. https://arxiv.org/abs/2511.16375

Kristanti, F. T. (2019). Financial distress: Teori dan perkembangannya dalam konteks Indonesia. Intelegensia Media.

Kristanti, F. T., Isynuwardhana, D., & Rahayu, S. (2019). Market concentration, diversification, and financial distress in the Indonesian banking system. Jurnal Keuangan dan Perbankan, 23(4), 514-524. https://doi.org/10.26905/jkdp.v23i4.2693

Kristanti, F. T., Salim, D. F., & Ihsan, A. F. (2025). Financial distress prediction in emerging markets by using a machine learning approach for financial sustainability. Discover Sustainability, 6, 1296. https://doi.org/10.1007/s43621-025-02199-1

Maghyereh, A., & Al-Zoubi, H. (2025). The effect of oil price uncertainty on the joint default risk of oil and natural gas companies. Energy Research Letters, 6. https://doi.org/10.46557/001c.137236

Maria, E., Halim, A., & Suwardi, E. (2021). Financial Distress, Regional Independence and Corruption: An Empirical Study in Indonesian Local Governments. JASF: Journal of Accounting and Strategic Finance, 4(1), 54–70. https://doi.org/10.33005/jasf.v4i1.159

Michalkova, L., & Ponisciakova, O. (2025). Bankruptcy prediction, financial distress and corporate life cycle. Administrative Sciences, 15(2), 63. https://doi.org/10.3390/admsci15020063

Mousavi, M. M., Gozgor, G., & Acheampong, A. (2024). Do oil market shocks affect financial distress? Evidence from firm-level global data. Journal of Commodity Markets, 36, 100436. https://doi.org/10.1016/j.jcomm.2024.100436

Ramadhani, Z., Soma, A. M., Rikumahu, B., & Salim, D. F. (2026). The influence of digital literacy, financial literacy, gender, and fear of missing out (FOMO) on stock investment decisions. Multidisciplinary Indonesian Center Journal, 3(1), 1110–1122. https://doi.org/10.62567/micjo.v3i1.1968

Rech, F., Meng, F., Musa, H., Šebeňa, M., & Tuo, S. J. (2025). Does firm-level AI adoption improve early warning of corporate financial distress? Evidence from Chinese non-financial firms. arXiv Preprint. https://arxiv.org/abs/2512.02510

Springate, G. L. (1978). Predicting the possibility of failure in a Canadian firm: A discriminant analysis (Master’s thesis, Simon Fraser University).

Song, Y., Liu, C., & Zhang, Y. (2024). ESG performance and financial distress prediction in energy firms. Energy Economics, 131, 107305. https://doi.org/10.1016/j.eneco.2023.107305

The Wall Street Journal. (2020, March 2). Pioneer Energy files for bankruptcy with ‘prepack’ plan. https://www.wsj.com/articles/pioneer-energy-files-for-bankruptcy-with-prepack-plan-11583177194

Toudas, K., Archontakis, S., & Boufounou, P. (2024). Corporate bankruptcy prediction models. Computation, 12(1), 9. https://doi.org/10.3390/computation12010009

U.S. Energy Information Administration. (2026). Annual Energy Outlook 2026. https://www.eia.gov

Vukčević, M., Lakićević, M., Melović, B., Backović, T., & Dudić, B. (2024). Modern models for predicting bankruptcy to detect early signals of business failure: Evidence from Montenegro. PLOS ONE, 19(5), e0303793. https://doi.org/10.1371/journal.pone.0303793

Wooldridge, J. M. (2023). Introductory econometrics: A modern approach (8th ed.). Cengage Learning.

Zhang, Z., Zheng, M., Zhang, T., Lin, L., Wang, Y., & Lin, L. (2026). Bankruptcy prediction from 10-K narratives: Evidence from interpretable text scores and accounting baselines. arXiv Preprint. https://arxiv.org/abs/2606.05623

Zhao, J., Ouenniche, J., & De Smedt, J. (2024). Survey and classification of the literature on corporate bankruptcy prediction. Machine Learning with Applications, 17, 100527. https://doi.org/10.1016/j.mlwa.2024.100527

Zmijewski, M. E. (1984). Methodological issues related to the estimation of financial distress prediction models. Journal of Accounting Research, 22, 59–82. https://doi.org/10.2307/2490856