Research measures the reliability of audit firms in predicting bankruptcy for US-listed financial institutions. Object of the analysis is the Going Concern Opinion (GCO), widely considered a bankruptcy warning signal to stakeholders. The sample is composed of 42 US-listed financial companies that filed Chapter 11 between 1998 and 2011. To highlight differences between bankrupting and healthy firms, a matching sample composed by 42 randomly picked healthy US-listed financial companies is collected. We concentrate on financial institutions, whereas the existing literature pays considerably heavier attention to the industrial sector. This research imbalance is remarkable and particularly unexpected in the wake of recent financial scandals. Literature points out two main approaches on bankruptcy prediction: 1) purely mathematical; 2) approaches based on a combination of auditor knowledge, expertise and experience. The use of data mining techniques, allow us to benefit from the best features of both approaches. Statistical tools used in the analysis are: Logit regression, Support Vector Machines and an Adaboost Meta-algorithm. Findings show a quite low reliability of GCOs in predicting bankruptcy. It is likely that auditors consider further information in supporting their audit opinions, aside from financial-economic ratios. The scant predictive ability of auditors might be due to critical relationships with distressed clients, as suggested by recent literature.
A statistical analysis of the reliability of audit opinions as bankruptcy predictors
CASERIO, CARLO;
2014-01-01
Abstract
Research measures the reliability of audit firms in predicting bankruptcy for US-listed financial institutions. Object of the analysis is the Going Concern Opinion (GCO), widely considered a bankruptcy warning signal to stakeholders. The sample is composed of 42 US-listed financial companies that filed Chapter 11 between 1998 and 2011. To highlight differences between bankrupting and healthy firms, a matching sample composed by 42 randomly picked healthy US-listed financial companies is collected. We concentrate on financial institutions, whereas the existing literature pays considerably heavier attention to the industrial sector. This research imbalance is remarkable and particularly unexpected in the wake of recent financial scandals. Literature points out two main approaches on bankruptcy prediction: 1) purely mathematical; 2) approaches based on a combination of auditor knowledge, expertise and experience. The use of data mining techniques, allow us to benefit from the best features of both approaches. Statistical tools used in the analysis are: Logit regression, Support Vector Machines and an Adaboost Meta-algorithm. Findings show a quite low reliability of GCOs in predicting bankruptcy. It is likely that auditors consider further information in supporting their audit opinions, aside from financial-economic ratios. The scant predictive ability of auditors might be due to critical relationships with distressed clients, as suggested by recent literature.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
