Collected Item: “Financial Distress Prediction Using the Original Altman's Z-Score and Logistic Regression: Evidence from Indian Listed Non-Financial Companies”
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S25008776
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Financial Distress Prediction Using the Original Altman's Z-Score and Logistic Regression: Evidence from Indian Listed Non-Financial Companies
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Tanushree Kotresh Bachenahalli
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2026
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This study examines the effectiveness of financial distress prediction using the original Altman Z-Score model and Logistic Regression, focusing on Indian listed non-financial companies. The research analyses 195 companies across four sectors, using 1,853 company-year observations covering the period 2015–2025. Financial data were obtained from LSEG Workspace.
Financial distress was identified using four financial indicators: negative Return on Assets, a Current Ratio below 1, an Interest Coverage Ratio below 1, and negative Operating Cash Flow. A company-year observation was classified as financially distressed when at least two of these conditions were met.
The study applies the original Altman Z-Score model and Logistic Regression to assess their ability to predict financial distress. Logistic Regression examines the relationship between financial distress and four explanatory variables: Return on Equity, Quick Ratio, Total Asset Turnover and Debt Ratio. Statistical analysis was conducted using EViews, supported by descriptive statistics, correlation analysis, panel-data estimation and diagnostic testing.
The out-of-sample evaluation, covering 2023–2025, found that the original Altman Z-Score achieved an overall prediction accuracy of 84.03%, compared with 79.86% for Logistic Regression. The findings indicate that the original Altman model performed better in the overall prediction of financial distress within the sample. The study highlights the importance of financial ratios, liquidity, profitability, asset efficiency and leverage in assessing corporate financial health.
The research contributes to the understanding of financial distress prediction among Indian listed non-financial companies and demonstrates the value of evaluating established and statistical prediction models using empirical financial data.
Financial distress was identified using four financial indicators: negative Return on Assets, a Current Ratio below 1, an Interest Coverage Ratio below 1, and negative Operating Cash Flow. A company-year observation was classified as financially distressed when at least two of these conditions were met.
The study applies the original Altman Z-Score model and Logistic Regression to assess their ability to predict financial distress. Logistic Regression examines the relationship between financial distress and four explanatory variables: Return on Equity, Quick Ratio, Total Asset Turnover and Debt Ratio. Statistical analysis was conducted using EViews, supported by descriptive statistics, correlation analysis, panel-data estimation and diagnostic testing.
The out-of-sample evaluation, covering 2023–2025, found that the original Altman Z-Score achieved an overall prediction accuracy of 84.03%, compared with 79.86% for Logistic Regression. The findings indicate that the original Altman model performed better in the overall prediction of financial distress within the sample. The study highlights the importance of financial ratios, liquidity, profitability, asset efficiency and leverage in assessing corporate financial health.
The research contributes to the understanding of financial distress prediction among Indian listed non-financial companies and demonstrates the value of evaluating established and statistical prediction models using empirical financial data.
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Finance
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MSc in Finance and Financial Technology
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Tanushree Kotresh Bachenahalli