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

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 firms had low liquidity, low profitability and high gearing. Therefore, these three aspects should be measured as the primary concern when examining a US firm’s financial condition.