<![CDATA[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.]]>
