An Early-Warning Predictive Framework for Financial Distress in U.S. Small Businesses

Abstract

This study developed and empirically evaluated an early-warning predictive framework for financial distress in U.S. small businesses using a quantitative, time-dynamic modeling approach. The analysis was conducted on a sample of 482 small businesses spanning manufacturing, construction, trade, professional services, and hospitality sectors, with firms distributed across all major U.S. regions. Financial distress was defined using multiple economically meaningful impairment indicators rather than bankruptcy alone. Descriptive results showed notable variability across constructs, with mean liquidity of 0.62 (SD = 0.21), mean leverage of 0.54 (SD = 0.19), and mean credit behavior score of 0.47 (SD = 0.22), indicating heterogeneous financial conditions across firms. Reliability analysis confirmed strong internal consistency, with Cronbach’s alpha values ranging from 0.77 for efficiency indicators to 0.88 for credit behavior measures. Regression analysis revealed statistically significant associations between financial distress and liquidity (β = −0.21, p = 0.002), profitability (β = −0.14, p = 0.008), leverage (β = 0.25, p < 0.001), cash flow dynamics (β = −0.23, p = 0.001), credit behavior (β = 0.34, p < 0.001), and relationship-based indicators (β = −0.18, p = 0.005). Efficiency indicators were not statistically significant at the 5% level. Model explanatory power increased incrementally from R² = 0.31 in the baseline financial model to R² = 0.46 in the full integrated model, while variance inflation factors remained below 1.9 across all specifications. Hypothesis testing resulted in the rejection of six out of seven null hypotheses. Overall, the findings provide quantitative evidence that an integrated early-warning predictive framework can effectively identify emerging financial distress among U.S. small businesses by combining financial, cash flow, behavioral, and relational indicators.