Hybrid bankruptcy forecasting for Indian firms: Integrating financial ratios, macroeconomic indicators, and random forest

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Type of the article: Research Article

Abstract
Bankruptcy forecasting in emerging markets is complicated by macroeconomic and regulatory volatility. This study evaluates whether a hybrid model that integrates firm financial ratios, macro indicators, and a Random Forest classifier outperforms traditional ratio-only approaches for Indian firms. Each bankrupt company is analyzed over a five-year window preceding its actual failure date, resulting in ten bankrupt firms paired with ten matched healthy peers. Using these firm-specific five-year pre-bankruptcy panels, we estimate logistic regression and Random Forest models with stratified 5-fold cross-validation and derive a parsimonious four-factor risk score.
Relative to ratio-only baselines, the hybrid design improves accuracy from 0.76→0.80 (logit) and 0.82→0.86 (Random Forest), and lifts the Area Under the ROC Curve (AUC) from 0.70→0.78, indicating that the model correctly ranks a bankrupt firm as riskier than a healthy firm 78% of the time. Debt-to-Equity, Current Ratio, Net Profit Margin, and GDP Growth dominate feature importance, and rising risk scores typically cross ~0.40 two to three years before failure.
Robustness checks, including alternative class-balance weights, sector dummies, and rolling-window estimation, yield comparable gains and stable feature rankings. The resulting bankruptcy Early-Warning System (EWS) is transparent, portfolio-scalable, and easily embedded into bank risk dashboards. The evidence shows that multidimensional hybrid models provide earlier and more reliable warnings than ratio-based formulas, offering practical value to lenders, investors, and regulators in volatile settings.

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    • Figure 1. Bankruptcy risk scores: Initial vs. signaling score jump across analyzed companies
    • Table 1. Group means and t-tests (five-year pre-bankruptcy window)
    • Table 2. DHFL bankruptcy probability (2015–2019)
    • Table 3. RCom bankruptcy probability (2015–2019)
    • Table 4. Baseline vs. hybrid model performance (validation set)
    • Conceptualization
      Marco Bonelli
    • Data curation
      Marco Bonelli
    • Formal Analysis
      Marco Bonelli
    • Funding acquisition
      Marco Bonelli
    • Investigation
      Marco Bonelli
    • Methodology
      Marco Bonelli
    • Project administration
      Marco Bonelli
    • Resources
      Marco Bonelli
    • Software
      Marco Bonelli
    • Supervision
      Marco Bonelli
    • Validation
      Marco Bonelli
    • Visualization
      Marco Bonelli
    • Writing – original draft
      Marco Bonelli
    • Writing – review & editing
      Marco Bonelli