TY - JOUR
T1 - A High-Dimensional Approach to Predicting Audit Opinions
AU - Saeedi, Ali
N1 - Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.
PY - 2022
Y1 - 2022
N2 - This study develops a model for the prediction of audit reports. The research data comprises 57881 firm-year observations for public companies listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), and the NASDAQ from 2000 to 2019. The dataset consists of a high dimension of predictor variables (105 variables), including accounting-based, ownership concentration, executive compensation, market price, analysts rating, macroeconomic, and audit-related variables. A commercial version of Gradient Boosting, called TreeNet®, is used to build the prediction model. The results indicate that the developed model demonstrates high performance in predicting going-concern reports with an accuracy rate of 97.5%.
AB - This study develops a model for the prediction of audit reports. The research data comprises 57881 firm-year observations for public companies listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), and the NASDAQ from 2000 to 2019. The dataset consists of a high dimension of predictor variables (105 variables), including accounting-based, ownership concentration, executive compensation, market price, analysts rating, macroeconomic, and audit-related variables. A commercial version of Gradient Boosting, called TreeNet®, is used to build the prediction model. The results indicate that the developed model demonstrates high performance in predicting going-concern reports with an accuracy rate of 97.5%.
KW - Audit opinion prediction
KW - auditing
KW - going-concern opinion
KW - gradient boosting
KW - machine learning
UR - http://www.scopus.com/inward/record.url?scp=85138417367&partnerID=8YFLogxK
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U2 - 10.1080/00036846.2022.2118224
DO - 10.1080/00036846.2022.2118224
M3 - Article
AN - SCOPUS:85138417367
JO - Applied Economics
JF - Applied Economics
SN - 0003-6846
ER -