Enregistré dans:
| Auteur principal: | |
|---|---|
| Format: | Artículo científico |
| Langue: | en |
| Publié: |
Fundação Escola de Comércio Álvares Penteado
2014
|
| Sujets: | |
| Accès en ligne: | https://www.redalyc.org/articulo.oa?id=94732743007 |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
Table des matières:
- Accounting Fraud: an estimation of detection probability Artur Filipe Ewald Wuerges José Alonso Borba Administración y Contabilidad AAER Logit Factor analysis Accounting fraud Misclassification Financial statement fraud (FSF) is costly for investors and can damage the credibility of the audit profession. To prevent and detect fraud, it is helpful to know its causes. The binary choice models (e.g. logit and probit) commonly used in the extant literature, however, fail to account for undetected cases of fraud and thus present unreliable hypotheses tests. Using a sample of 118 companies accused of fraud by the Securities and Exchange Commission (SEC), we estimated a logit model that corrects the problems arising from undetected frauds in U.S. companies. To avoid multicollinearity problems, we extracted seven factors from 28 variables using the principal factors method. Our results indicate that only 1.43 percent of the instances of FSF were publicized by the SEC. Of the six significant variables included in the traditional, uncorrected logit model, three were found to be actually non-significant in the corrected model. The likelihood of FSF is 5.12 times higher when the firm’s auditor issues an adverse or qualified report. 2014 artículo científico 1806-4892 https://www.redalyc.org/articulo.oa?id=94732743007 en http://www.redalyc.org/revista.oa?id=947 Revista Brasileira de Gestão de Negócios - RBGN application/pdf Fundação Escola de Comércio Álvares Penteado Revista Brasileira de Gestão de Negócios - RBGN (Brasil) Num.52 Vol.16