Post-Covid-19 Financial Distress Analysis: Insights from Indonesian Transportation Sub-Sector Companies
DOI:
https://doi.org/10.34209/equ.v28i1.10594Abstract
The objective of this study is to analyze the prediction of financial distress in transportation sub-sector companies listed on the Indonesia Stock Exchange for the period 2021-2023 using four prediction models: Altman (Z-Score), Springate (S-Score), Zmijewski (X-Score), and Grover (G-Score). The study will calculate the level of accuracy. The analysis utilizes secondary data, specifically financial reports from 12 companies, constituting a total sample of 36. The findings indicate that the Zmijewski and Grover model exhibits the highest accuracy rate of 76%, followed by zmijewski with 71%, springate with 46%, and Altman with 26%. These results suggest that the Zmijewski and Grover model is appropriate model for use in the transportation sub-sector in Indonesia during the observed period. The implications of this research suggest that Zmijewski and Grover's model can be utilized by companies to evaluate financial conditions proactively, by investors to assess investment risks, and by regulators to ensure the stability of the transportation sub-sector. However, this study also underscores that Zmijewski and Grover's model cannot be generalized to all sectors, emphasizing the necessity for further research to test the model in other sectors by considering both financial and non-financial variables.
Keywords: Financial Distress; Accuracy; Post-covid; DAR; Net Profit.
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This work is licensed under a Creative Commons Attribution 4.0 International License.
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