This study develops a binary logistic regression-based early warning model to predict financial distress among small and medium-sized enterprises (SMEs) from China. Drawing on a sample of 90 firms listed on the Chinese SME board, including 45 special treatment firms from 2017 to 2022 and 45 matched non-distressed counterparts as a control group, this study analyses 24 financial indicators covering solvency, operational efficiency, profitability and growth capacity. Principal component analysis identified six core variables, which were subsequently incorporated into the logistic regression framework. The model achieved high predictive accuracy, exceeding 80% in the year of distress (T) and maintaining robust forecasting capability up to three years prior (T-1, T-2, T-3). These findings confirm the effectiveness of the model in offering early identification of financial vulnerabilities. This study advances financial risk management literature by developing a targeted, multi-dimensional early warning system for SMEs, providing actionable insights for managers, investors and policymakers to enhance financial resilience. Empirically validated financial early warning models should emphasize targeted monitoring, proactive intervention and informed decision-making to enhance financial stability among Chinese SMEs.
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