Prediction of Auditor Selection Using a Combination of PSO Algorithm and CART in Iran

Salehi, Mahdi; Kamalahmadi, Sharifeh; Bahrami, Mostafa

doi:10.13106/jds.2014.vol12.no3.33.

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…The results suggest that firms that have high debt level are more likely to choose high-quality auditors. [26] compared a neural networks auditor choice prediction model with decision trees and support vector machines models. The sample of the study are 338 UK and Irish firms from 2003 to 2005 which consists of 181 Big 4 auditors and 157 non Big 4 auditors.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Further, study by [27] aims to predict auditor selection decision of 95 Iranian firms listed on the Tehran Stock Exchange from 2005 to 2010 using a combination of particle swarm optimization (PSO) and classification and regression trees (CART). In line with [25] and [26], the study use sixteen input factors as predictors of firm auditor selection. The input factors consists of firm specific characteristics, firm financial ratios and auditor's fees.…”

Section: Literature Reviewmentioning

confidence: 99%

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Auditor Choice Prediction Model Using Corporate Governance and Ownership Attributes: Machine Learning Approach

Rahman¹,

Masrom²,

Zakaria³

et al. 2021

IJETAE

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-External auditor is one of the governance mechanisms in mitigating corporate managerial misconduct and thereby enhance the credibility of accounting information. Thus, the main objective of this study is to develop machine learning prediction model on auditor choice of the firm which signal the quality of auditing and financial reporting processes.This paper presents the fundamental knowledge on the design and implementation of machine learning model based on four selected algorithms tested on the real dataset of 2,262 firm-year observations of companies listed on Malaysian stock exchange from 2000 to 2007. The performance of each machine learning algorithm on the auditor choice dataset has been observed based on three groups of features selection namely firm characteristics, governance and ownership. The findings indicated that the machine learning models present better accuracy performance with ownership features selection mainly with the Naïve Bayes algorithm. Keywords-Auditor Choice, Machine Learning, Prediction

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Section: Literature Reviewmentioning

confidence: 99%