A High-Dimensional Approach to Predicting Audit Opinions

Research output: Contribution to journalArticlepeer-review


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%.

Original languageEnglish (US)
Pages (from-to)3807-3832
Number of pages26
JournalApplied Economics
Issue number33
StatePublished - 2023

Bibliographical note

Publisher Copyright:
© 2022 Informa UK Limited, trading as Taylor & Francis Group.


  • Audit opinion prediction
  • auditing
  • going-concern opinion
  • gradient boosting
  • machine learning


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