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Predicting Financial Distress In Indonesia, Malaysia, And Thailand Transportation & Tourism Companies: A Comparative Analysis Of Altman, Springate, Ohlson, Zmijewski, And Grover Models
Abstract
This study delves into the world of predictive models, aiming to anticipate financial distress in the tourism and transportation sectors across Indonesia, Thailand, and Malaysia. The research scrutinizes five distinguished models – Altman, Springate, Ohlson, Zmijewski, and Grover – to illuminate the evolving financial distress patterns. The research starts by precisely evaluating these financial models' forecasting capabilities, leveraging an extensive dataset from 2018 to 2022. This thorough assessment gauges the accuracy inherent in each model, unveiling their performance within the unique economic landscapes of these three nations.
The research identifies models that consistently hit the mark in accuracy through the prism of cross-country and multi-year evaluations. The findings underscore the predictive prowess of each model in foreseeing financial distress across the selected companies. However, these models exhibit distinct accuracy rates in the grand scheme and when zooming into individual countries. The Grover Model is the champion across the chosen countries, boasting the highest accuracy scores. In country-specific analyses, Zmijewski's predictive prowess shines as the most reliable for Malaysia and Thailand. Meanwhile, Grover steals the spotlight in Indonesia by outperforming its counterparts in predictive accuracy.
Keywords
Altman, Grover , Springate, Ohlson, Zmijewski,
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DOI: https://doi.org/10.26905/ap.v9i2.11191
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