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Model Predictive Ability Evaluated through Receiver Operating Characteristic Curve

A recent study published in [Journal Name] has evaluated the predictive ability of several models for identifying financial distress in Vietnamese listed firms using the receiver operating characteristic (ROC) curve. The ROC curve is a powerful tool for model evaluation, as it provides a graphical representation of the trade-off between true positives and false positives.

Model Evaluation Results

The study analyzed 10 different models, each using either the interest coverage ratio (ICR) or times-interest-earned (TIE) as a proxy for financial distress. According to the results presented in Table 4, all models demonstrated statistical significance with ROC values greater than 0.5, indicating that they can predict the financial distress likelihood for Vietnamese listed firms.

  • Models using TIE as a proxy for financial distress performed slightly better than those using ICR.
  • Comprehensive models 9 and 10 showed ROC values of 0.973 and 0.977, respectively, among the top-performing models.

Predictor Selection

In addition to evaluating model performance through ROC curves, the study also used stepwise logistic regression to identify well-fitted predictors for financial distress. The results presented in Table 5 showed that:

  • Eight out of 11 widely used financial indicators were considered appropriate for predicting financial distress when ICR was used as a proxy.
  • However, only four indicators were found suitable when TIE was used as a proxy.

Robustness Analysis

The study also performed a robustness analysis by dividing the sample into four sub-samples and re-estimating the models. The results presented in Table 6 showed that:

  • The findings remained largely similar across different sub-samples.

Industry-Level Analysis

Finally, the industry-level analysis using the emerging-market score (EMS) model revealed that:

  • “Administrative & Support Services” was the riskiest industry over the entire period from 2012 to 2021, with a financial distress likelihood of [Value].
  • This highlights the importance of considering industry-specific risks when evaluating corporate financial distress.

Conclusion

Overall, this study demonstrates the effectiveness of using ROC curves and stepwise logistic regression in evaluating model predictive ability for financial distress prediction. The results provide valuable insights into the performance of different models and the importance of considering industry-specific risks in predicting corporate financial distress.