Improving audit opinion prediction accuracy using metaheuristics-tuned XGBoost algorithm with interpretable results through SHAP value analysis

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dc.authoridTirkolaee, Erfan Babaee/0000-0003-1664-9210
dc.authoridZivkovic, Miodrag/0000-0002-4351-068X
dc.authoridSimic, Vladimir/0000-0001-5709-3744
dc.authoridBacanin, Nebojsa/0000-0002-2062-924X
dc.authoridStanisic, Nemanja/0000-0002-6543-1472
dc.authorwosidTirkolaee, Erfan Babaee/U-3676-2017
dc.authorwosidZivkovic, Miodrag/AFC-8832-2022
dc.authorwosidSimic, Vladimir/B-8837-2011
dc.authorwosidBacanin, Nebojsa/L-5328-2019
dc.contributor.authorTodorovic, Mihailo
dc.contributor.authorStanisic, Nemanja
dc.contributor.authorZivkovic, Miodrag
dc.contributor.authorBacanin, Nebojsa
dc.contributor.authorSimic, Vladimir
dc.contributor.authorTirkolaee, Erfan Babaee
dc.date.accessioned2024-05-19T14:39:11Z
dc.date.available2024-05-19T14:39:11Z
dc.date.issued2023
dc.departmentİstinye Üniversitesien_US
dc.description.abstractThis study aims to create a machine learning model that can predict opinions in external audits and surpass the benchmark set in a prior study from the literature. This tool could reduce audit risk, which is a crucial task in external audits. Previous studies have shown that it is possible to create models that can predict the audit opinion a company will receive. In these studies, authors used statistics and machine learning models, and both non-financial (e.g. audit lag) and financial data (e.g. financial ratios, or absolute value items available from financial statements) to make predictions. In this study, the performance of the XGBoost model optimized by metaheuristics algorithms is examined and evaluated. This study compares the performance of six different metaheuristic algorithms used to tune the XGBoost model in two separate scenarios. The first scenario represents a realistic client portfolio, where a majority of the clients are known, while the second scenario simulates a new clients-only portfolio, a more difficult scenario where prior information such as audit lag is not available. The study uses a dataset of 12,690 observations of Serbian companies and their audit opinions from 2016 to 2019. The findings indicate an improvement over the benchmark due to a more optimized hyperparameter tuning process and the use of the iterative sine-cosine algorithm for the XGBoost model.en_US
dc.identifier.doi10.1016/j.asoc.2023.110955
dc.identifier.issn1568-4946
dc.identifier.issn1872-9681
dc.identifier.scopus2-s2.0-85175043979en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1016/j.asoc.2023.110955
dc.identifier.urihttps://hdl.handle.net/20.500.12713/4723
dc.identifier.volume149en_US
dc.identifier.wosWOS:001108066100001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofApplied Soft Computingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz20240519_kaen_US
dc.subjectAudit Opinion Predictionen_US
dc.subjectMachine Learningen_US
dc.subjectXgboosten_US
dc.subjectSine Cosine Algorithmen_US
dc.titleImproving audit opinion prediction accuracy using metaheuristics-tuned XGBoost algorithm with interpretable results through SHAP value analysisen_US
dc.typeArticleen_US

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