Analyzing Insolvency Prediction Models in the Period Before and After the Financial Crisis: A Case Study on the Example of US Firms

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2022/11/21 19:30
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George Giannopoulos Sophia Ali Sardar Rebecca Salti and Nicos Sykianakis
Purpose: The study aims to assess the most accurate bankruptcy prediction model for US firms. Design/methodology/approach: Validating the accuracy of bankruptcy prediction models can provide management with a handy tool as it can decrease potential damage and carry out corrective actions by intervening and preventing insolvency. The impetus of this paper is not to create a new prediction model but to validate the practical application of 3 widely accepted models to determine accuracy in predicting corporate insolvency for; Altman’s Taffler’s and Ohlson’s models. The Logit regression framework is employed to estimate the 3 aforementioned models. Findings: The results revealed that: i) Taffler’s and Ohlson’s models are the most accurate for correctly predicting failed and non-failed firms with an average predictive ability of 75% and 87% respectively ii) Altman’s model had a rather lower predicting ability of 57% iii) Altman’s model predicts high accuracy for only solvent firms iv) Taffler’s and Ohlson’s models can subsequently assist lenders auditors executives investors and corporations to evaluate bankruptcy risk. Practical implications: An early warning system can protect a firm from running into insolvency. Furthermore a country with healthy economic conditions can attract national and international investors. In view of that a robust bankruptcy predictor reduces the probability of large number of insolvencies occurring. Originality value: This study found that failed US f
Insolvency Prediction Models ; Bankruptcy ; US firms. (search for similar items in EconPapers)
Year Published
International Journal of Finance Insurance and Risk Management 2022 vol. 12 issue 3 23-45