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A High-Dimensional Approach to Predicting Audit Opinions

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  • Ali Saeedi

Abstract

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%.

Suggested Citation

  • Ali Saeedi, 2023. "A High-Dimensional Approach to Predicting Audit Opinions," Applied Economics, Taylor & Francis Journals, vol. 55(33), pages 3807-3832, July.
    • Handle: RePEc:taf:applec:v:55:y:2023:i:33:p:3807-3832
    DOI: 10.1080/00036846.2022.2118224
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