Evaluating and Comparing Predictive Accuracy of Financial Distress Models in the Banking Sector: A Case Study of Indonesian Banking Entities (2019-2021)
Keywords:
Accuracy Rate, Banking Industry, Financial DistressAbstract
This study aims to comprehensively evaluate and predict financial distress occurrences in the banking sector using four prominent models: Altman's Z-Score, Zmijewski's X-Score, Springate's S-Score, and Grover's G-Score. It endeavors to pinpoint the most accurate predictor among these models, focusing on their applicability in forecasting financial distress for banking entities listed on the Indonesia Stock Exchange from 2019 to 2021. Employing a descriptive research design with a quantitative descriptive approach, the study employs variables and measurements from the Altman, Zmijewski, Springate, and Grover models. Findings highlight the preeminence of the Zmijewski (X-Score) and Springate (S-Score) models, boasting a 100% accuracy rate in predicting and analyzing financial distress in the Indonesian banking industry. Conversely, the Altman (Z-Score) and Grover (G-Score) models exhibit a contrasting 0% accuracy rate across the study's sample. These results underscore the Zmijewski and Springate models' efficacy in predicting financial distress, emphasizing proactive anticipation by companies, regardless of their perceived financial stability. In summary, the originality of this study stems from its specific focus on evaluating and comparing these models within the Indonesian banking sector, its methodology, and the unique insights it offers regarding the efficacy of these models in predicting financial distress occurrences.
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