Predicting the type of auditor opinion: Statistics, machine learning, or a combination of the two?

Author:

Stanišić NemanjaORCID,Radojević TijanaORCID,Stanić Nenad

Publisher

Centre for Evaluation in Education and Science (CEON/CEES)

Reference71 articles.

1. Abad, D., Sánchez-Ballesta, J. P., & Yagüe, J. (2017). Audit opinions and information asymmetry in the stock market. Accounting & Finance, 57(2), 565-595. https://doi.org/10.1111/acfi.12175;

2. ASB GAAS Section 315. (2013). GAAS section 315, Understanding the Entity and Its Environment and Assessing the Risks of Material Misstatement. Retrieved from https://www.aicpa.org/Research/Standards/AuditAttest/DownloadableDocuments/AU-C-00315.pdf;

3. Ashbeck, E. L., & Bell, M. L. (2016). Single time point comparisons in longitudinal randomized controlled trials: power and bias in the presence of missing data. BMC Medical Research Methodology, 16(1), 43. https://doi.org/10.1186/s12874-016-0144-0;

4. Baayen, R. H., Davidson, D. J., & Bates, D. M. (2008). Mixed-effects modeling with crossed random effects for subjects and items. Journal of Memory and Language, 59(4), 390-412. https://doi.org/10.1016/J.JML.2007.12.005;

5. Bartov, E., Gul, F. A., & Tsui, J. S. L. (2000). Discretionary-accruals models and audit qualifications. Journal of Accounting and Economics, 30(3), 421-452. https://doi.org/10.1016/S0165-4101(01)00015-5;

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