Accuracy of the Financial Distress Model in Transportation Firms: Altman, Springate and Grover Model

This study analyzes the comparison of financial distress prediction models in transportation firms listed on the Indonesia Stock Exchange during 2018–2020. Using a quantitative approach and secondary data, the study focuses on a sample of 7 firms selected through purposive sampling from a population of 27 firms. The analysis evaluates the Altman, Springate, and Grover models for their accuracy and error rates. Results show that the Altman and Springate models exhibit identical accuracy and error rates, with an accuracy level of 42.56% and an error rate of 57.14%. In contrast, the Grover model demonstrates superior performance with an accuracy level of 85.71% and an error rate of 14.29%. These findings establish the Grover model as the most accurate tool for predicting financial distress among the models tested. Managerial implications highlight the importance of utilizing accurate financial distress prediction models to identify potential financial issues early. This can aid transportation firms in improving financial management and ensuring operational sustainability. By adopting effective prediction models like the Grover model, firms can strengthen their capacity to mitigate risks associated with financial instability