Audit Opinion Prediction: A Comparison of Data Mining Techniques

IF 1.6 Q3 BUSINESS, FINANCE Journal of Emerging Technologies in Accounting Pub Date : 2020-11-18 DOI:10.2308/jeta-19-10-02-40
A. Saeedi
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引用次数: 3

Abstract

This study compares the ability of four data mining techniques in the prediction of audit opinions on companies' financial statements. The research data consists of 37,325 firm-year observations for companies listed on the New York Stock Exchange (NYSE), the American Stock Exchange (AMEX), and the NASDAQ from 2001 to 2017. The dataset consists of U.S. companies' variousfinancial and non-financial variables. This study uses Decision Trees (DT), Support Vector Machines (SVM), K-Nearest Neighbors (k-NN), and Rough Sets (RS) to develop the prediction models. While all models developed by these four techniques predict the audit opinions with relatively high accuracy, the SVM models developed by RBF kernel demonstrate the highest performance in terms of overall prediction accuracy rates and Type I and Type II errors. The results indicate that all models developed using different algorithms demonstrate their highest performance in predicting going-concern modifications, ranging from 84.2 to 100 percent.
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审计意见预测:数据挖掘技术的比较
本研究比较了四种数据挖掘技术预测公司财务报表审计意见的能力。研究数据包括2001年至2017年在纽约证券交易所(NYSE)、美国证券交易所(AMEX)和纳斯达克上市的37325家公司的年度观察数据。该数据集由美国公司的各种财务和非财务变量组成。本研究使用决策树(DT)、支持向量机(SVM)、k-近邻(k-NN)和粗糙集(RS)来建立预测模型。虽然这四种技术开发的模型预测审计意见的准确率都比较高,但RBF核开发的SVM模型在整体预测准确率和I型和II型误差方面表现出最高的性能。结果表明,使用不同算法建立的模型在预测持续经营变更方面表现出最高的性能,其预测率从84.2%到100%不等。
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来源期刊
CiteScore
4.30
自引率
27.80%
发文量
14
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