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Abstract
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.
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