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• W4385277637 abstract "Abstract Financial integration in the Association of Southeast Asian Nations (ASEAN) region is a key focus of the ASEAN Economic Community. Whereas many studies focus on modelling corporate default, this paper identifies early warning indicators of financial distress before a default, using multiple discriminant analysis (MDA) models with a sample of listed and delisted companies in the ASEAN region. The analysis examines 720 companies in 10 different industries across six ASEAN countries from 1997 to 2016. The study constructs individual models for each country as well as an overall model for the entire region, using both in‐sample and out‐of‐sample approaches. This overall model could be useful for an integrated banking system. To ensure robustness, the study also separately examines the predictive performance of the MDA models across different economic crises: the Asian financial crisis (AFC) from 1997 to 2000, the global financial crisis (GFC) from 2007 to 2009 and their pre‐ and post‐crisis periods. We find that profitability ratios are the best indicators of financial distress in the ASEAN region, followed by liquidity and leverage ratios. In addition, our findings reveal common indicators that can be used to predict financial distress across ASEAN countries. The single model performs reasonably well in predicting financial distress 1 year ahead. In addition, the model is extended to incorporate a market‐based indicator into the MDA models, the distance to default. However, the inclusion of this indicator does not significantly improve the accuracy of the models in predicting financial distress at listed firms in the ASEAN region." @default.
• W4385277637 created "2023-07-27" @default.
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• W4385277637 date "2023-07-26" @default.
• W4385277637 modified "2023-09-25" @default.
• W4385277637 title "Accounting‐based variables as an early warning indicator of financial distress in crisis and non‐crisis periods" @default.
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• W4385277637 doi "https://doi.org/10.1002/ijfe.2864" @default.
• W4385277637 hasPublicationYear "2023" @default.
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