Metrics

  • citations in SCIndeks: [1]
  • citations in CrossRef:[2]
  • citations in Google Scholar:[]
  • visits in previous 30 days:16
  • full-text downloads in 30 days:9

Contents

article: 2 from 28  
Back back to result list
2019, vol. 16, iss. 2, pp. 1-58
Predicting the type of auditor opinion: Statistics, machine learning, or a combination of the two?
University Singidunum, Belgrade

emailtradojevic@singidunum.ac.rs
Keywords: auditor opinion; financial reports; generalized linear mixed models; random forest; guided regularized random forest; ensembles
Abstract
The goal of this study is to overcome the identified methodological limitations of prior studies aimed at predicting the type of auditor opinion and draw definite conclusions on the relative predictive performance of different predictive methods for this particular task. Predictive performance of twelve candidate models from the realms of statistics and machine learning is assessed separately for the two common real-life scenarios: a) when prior information on the client (i.e. types of audit opinion received in the past) is available and can be used in prediction, and b) when such information is not available (e.g. new companies). The results show that, in the first scenario, several methods from both realms achieve comparable predictive performance of around 0.89, as measured by the Area under the curve (AUC). In the second scenario, however, machine learning algorithms, particularly tree-based ones, such as random forest, perform significantly better, achieving the AUC of up to 0.79. Finally, we develop and assess the predictive performance of two hybrid models aimed at combining the strong points of both statistical (i.e. interpretability of results) and machine learning (i.e. handling a large number of predictors and improved accuracy) approaches. The complete procedure is demonstrated in a reproducible manner, using the largest empirical data set ever used in this stream of research, comprising 13,561 pairs of annual financial statements and the corresponding audit reports. The procedures described in this study allow audit and finance professionals around the globe to develop and test predictive models that will aid their procedures of audit planning and risk assessment.
References
*** (2013) AU-C 315 Understanding the Entity and Its Environment and Assessing the Risks of Material Misstatement. in: Practitioner's Guide to GAAS 2014, John Wiley & Sons, Inc, ASB GAAS Section 315. Retrieved from https://www.aicpa.org/Research/Standards/AuditAttest/DownloadableDocuments/AU-C-00315.pdf
Abad, D., Sánchez-Ballesta, J.P., Yagüe, J. (2017) Audit opinions and information asymmetry in the stock market. Accounting & Finance, 57(2): 565-595
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): 43-43
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
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
Bell, T.B., Tabor, R.H. (1991) Empirical Analysis of Audit Uncertainty Qualifications. Journal of Accounting Research, 29(2): 350-350
Beneish, M.D. (1999) The Detection of Earnings Manipulation. Financial Analysts Journal, 55(5): 24-36
Bergmeir, C., Benítez, J.M. (2012) Neural Networks in R Using the Stuttgart Neural Network Simulator: RSNNS. Journal of Statistical Software, 46(7): 1-26, http://www.jstatsoft.org/v46/i07
Blandón, J.G., Bosch, J.M.A. (2013) Audit firm tenure and qualified opinions: New evidence from Spain. Revista de Contabilidad, 16(2): 118-125
Bürkner, P.C. (2017) brms: An R Package for Bayesian Multilevel Models Using Stan. Journal of Statistical Software, 80(1): 1-28
Caramanis, C., Spathis, C. (2006) Auditee and audit firm characteristics as determinants of audit qualifications. Managerial Auditing Journal, 21(9): 905-920
Chen, T., He, T., Benesty, M., Khotilovich, V., Tang, Y. (2017) xgboost: Extreme Gradient Boosting. Retrieved from https://cran.r-project.org/package=xgboost
Cohen, J. (1960) A Coefficient of Agreement for Nominal Scales. Educational and Psychological Measurement, 20(1): 37-46
Deangelo, L.E. (1986) Accounting numbers as market valuation substitutes: A study of management buyouts of public stockholders. Accounting Review, 61(3), 400-420. Retrieved from /han/GoogleScholar/www.jstor.org/stable/10.2307/247149
Dechow, P.M., Ge, W., Larson, C.R., Sloan, R.G. (2011) Predicting Material Accounting Misstatements. Contemporary Accounting Research, 28(1): 1-58
Dechow, P.M., Sloan, R.G., Sweeney, A.P. (1995) Detecting Earnings Management. Accounting Review, 70(2): 193-225
Dechow, P., Ge, W., Schrand, C. (2010) Understanding earnings quality: A review of the proxies, their determinants and their consequences. Journal of Accounting and Economics, 50(2-3): 344-401
Defond, M.L., Jiambalvo, J. (1994) Debt covenant violation and manipulation of accruals. Journal of Accounting and Economics, 17(1-2), 145-176
Delong, E.R., Delong, D.M., Clarke-Pearson, D.L. (1988) Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach. Biometrics, 44(3): 837-837
Demler, O.V., Pencina, M.J., D'agostino, R.B. (2012) Misuse of DeLong test to compare AUCs for nested models. Statistics in Medicine, 31(23): 2577-2587
Deng, H. (2013) Guided Random Forest in the RRF Package. ArXiv, 1-2. Retrieved from http://arxiv.org/abs/1306.0237
Deng, H. (2014) Package ' inTrees'. Retrieved from https://cran.r-project.org/package=inTrees
Deng, H., Runger, G. (2012) Feature selection via regularized trees. in: 2012 Annual International Joint Conference on Neural Networks, IJCNN 2012, Brisbane, Australia
Deng, H., Runger, G. (2013) Gene selection with guided regularized random forest. Pattern Recognition, 46(12): 3483-3489
Dhaliwal, D.S., Liu, Q., Xie, H., Zhang, J. (2014) Negative Press Coverage, Litigation Risk, and Audit Opinions in China. SSRN Electronic Journal
Dopuch, N., Holthausen, R., Leftwich, R. (1987) Predicting audit qualifications with financial and market variables. Accounting Review, 62(3): 431-454
Doumpos, M., Gaganis, C., Pasiouras, F. (2005) Explaining qualifications in audit reports using a support vector machine methodology. Intelligent Systems in Accounting, Finance and Management, 13(4): 197-215
Fernández-Gámez, M.A., García-Lagos, F., Sánchez-Serrano, J.R. (2016) Integrating corporate governance and financial variables for the identification of qualified audit opinions with neural networks. Neural Computing and Applications, 27(5): 1427-1444
Francis, J.R., Krishnan, J.J. (1999) Accounting Accruals and Auditor Reporting Conservatism. Contemporary Accounting Research, 16(1): 135-165
Gaganis, C., Pasiouras, F. (2006) Auditing models for the detection of qualified audit opinions in the UK public services sector. International Journal of Accounting, Auditing and Performance Evaluation, 3(4): 471-471
Gaganis, C., Pasiouras, F., Doumpos, M. (2007) Probabilistic neural networks for the identification of qualified audit opinions. Expert Systems with Applications, 32(1): 114-124
Gaganis, C., Pasiouras, F., Spathis, C., Zopounidis, C. (2007) A comparison of nearest neighbours, discriminant and logit models for auditing decisions. Intelligent Systems in Accounting, Finance and Management, 15(1-2): 23-40
Gassen, J., Skaife, H.A. (2009) Can Audit Reforms Affect the Information Role of Audits? Evidence from the German Market. Contemporary Accounting Research, 26(3): 867-898
Gibbons, R.D., Hedeker, D., Dutoit, S. (2010) Advances in Analysis of Longitudinal Data. Annual Review of Clinical Psychology, 6(1): 79-107
Glancy, F.H., Yadav, S.B. (2011) A computational model for financial reporting fraud detection. Decision Support Systems, 50(3): 595-601
Healy, P.M. (1985) The effect of bonus schemes on accounting decisions. Journal of Accounting and Economics, 7(1-3): 85-107
Humpherys, S.L., Moffitt, K.C., Burns, M.B., Burgoon, J.K., Felix, W.F. (2011) Identification of fraudulent financial statements using linguistic credibility analysis. Decision Support Systems, 50(3): 585-594
IAASB (2013) ISA 315: Identifying and Assessing the Risks of Material Misstatement through Understanding the Entity and Its Environment. Retrieved from https://www.iaasb.org/system/fles/meetings/ fles/20130415-IAASB-Agenda_Item_5-D_Disclosures - ISA 315 %28Revised%29 for reference ONLY.pdf
IAASB (2013) ISA 570: Going Concern. Retrieved from http://www.ifac.org/system/fles/downloads/a031-2010-iaasb-handbook-isa-570.pdf
Jones, J.J. (1991) Earnings management during import relief investigations. Journal of Accounting Research, 29(2): 193
Jones, K.L., Krishnan, G.V., Melendrez, K.D. (2008) Do Models of Discretionary Accruals Detect Actual Cases of Fraudulent and Restated Earnings? an Empirical Analysis. Contemporary Accounting Research, 25(2), 499-531
Karatzoglou, A., Smola, A., Hornik, K., Zeileis, A. (2004) kernlab: An S4 Package for Kernel Methods in R. Journal of Statistical Sofware, 11(9), 1-20. Retrieved from http://www.jstatsof.org/v11/i09
Kinney, W.R., Mcdaniel, L.S. (1989) Characteristics of firms correcting previously reported quarterly earnings. Journal of Accounting and Economics, 11(1), 71-93
Kirkos, E., Spathis, C., Nanopoulos, A., Manolopoulos, Y. (2007) Identifying Qualified Auditors' Opinions: A Data Mining Approach. Journal of Emerging Technologies in Accounting, 4, 183-197
Krishnan, J., Krishnan, J. (1996) The Role of Economic Trade-Offs in the Audit Opinion Decision: An Empirical Analysis. Journal of Accounting, Auditing & Finance, 11(4), 565-586
Krishnan, J., Krishnan, J., Stephens, R.G. (1996) The Simultaneous Relation Between Auditor Switching and Audit Opinion: An Empirical Analysis. Accounting & Business Research, 26(3), 224-236
Kuhn, M. (2017) caret: Classification and Regression Training. Retrieved from https://cran.r-project.org/package=caret
Kuhn, M., Ross, Q. (2017) C5.0 Decision Trees and Rule-Based Models. Retrieved from https://cran.rproject.org/package=C50
Laitinen, E.K., Laitinen, T.K. (1998) Qualified audit reports in Finland: Evidence from large companies. European Accounting Review, 7(4): 639-653
Liaw, A., Wiener, M. (2002) Classification and Regression by randomForest. R News, 2(3): 18-22, http://cran.r-project.org/doc/Rnews
Maggina, A., Tsaklanganos, A.A. (2011) Predicting Audit Opinions Evidence from the Athens Stock Exchange. Journal of Applied Business Research (JABR), 27(4): 53-53
Monroe, G.S., Teh, S.T. (2009) Predicting uncertainty audit qualifications in Australia using publicly available information. Accounting & Finance, 33(2): 79-106
Mutchler, J.F., Hopwood, W., McKeown, J.M. (1997) The Influence of Contrary Information and Mitigating Factors on Audit Opinion Decisions on Bankrupt Companies. Journal of Accounting Research, 35(2): 295-295
Ngai, E.W.T., Hu, Y., Wong, Y.H., Chen, Y., Sun, X. (2011) The application of data mining techniques in financial fraud detection: A classification framework and an academic review of literature. Decision Support Systems, 50(3): 559-569
Pedersen, A.B., Mikkelsen, E.M., Cronin-Fenton, D., Kristensen, N.R., Pham, T.M., Pedersen, L.B., Petersen, I. (2017) Missing data and multiple imputation in clinical epidemiological research. Clinical Epidemiology, Volume 9: 157-166
Perols, J.L., Bowen, R.M., Zimmermann, C., Samba, B. (2017) Finding needles in a haystack: Using data analytics to improve fraud prediction. In Accounting Review, 92(2): 1-58
Perols, J. (2011) Financial Statement Fraud Detection: An Analysis of Statistical and Machine Learning Algorithms. AUDITING: A Journal of Practice & Theory, 30(2): 19-50
Pourheydari, O., Nezamabadi-Pour, H., Aazami, Z. (2012) Identifying qualified audit opinions by artificial neural networks. African Journal of Business Management, 6(44): 11077-11087
R Foundation for Statistical Computing-R Core Team (2017) R: A language and environment for statistical computing. Vienna, Austria, http://www.R-project.org/
Ridgeway, G. (2017) gbm: Generalized Boosted Regression Models. Retrieved from https://cran.r-project.org/package=gbm
Ruiz-Barbadillo, E., Gómez-Aguilar, N., de Fuentes-Barberá, C., García-Benau, M.A. (2004) Audit quality and the going-concern decision-making process: Spanish evidence. European Accounting Review, 13(4): 597-620
Saif, S.M., Sarikhani, M., Ebrahimi, F. (2012) Finding Rules for Audit Opinions Prediction Through Data Mining Methods. European Online Journal of Natural and Social Sciences, 1(2), 28-36
Saif, S.M., Sarikhani, M., Ebrahimi, F. (2013) An Expert System with Neural Network and Decision Tree for Predicting Audit Opinions. IAES International Journal of Artificial Intelligence (IJ-AI), 2(4): 151-158, http://iaesjournal.com/online/index.php/IJAI/article/view/3950
Schafer, J.L., Graham, J.W. (2002) Missing data: Our view of the state of the art. Psychological Methods, 7(2): 147-177
Spathis, C., Doumpos, M., Zopounidis, C. (2003) Using client performance measures to identify pre-engagement factors associated with qualified audit reports in Greece. International Journal of Accounting, 38(3): 267-284
Stice, J. (1991) sing Financial and Market Information to Identify Pre-Engagement Factors Associated with Lawsuits against Auditors. Accounting Review, 66, 3, 516-533
Venables, W.N., Ripley, B.D. (2002) Modern Applied Statistics with S. New York: Springer, 4th ed. Retrieved from http://www.stats.ox.ac.uk/pub/MASS4
Yasar, A., Yakut, E., Gutnu, M.M. (2015) Predicting Qualified Audit Opinions Using Financial Ratios: Evidence from the Istanbul Stock Exchange. International Journal of Business and Social Science, 6(8): 57-67
Yeh, C., Chi, D., Lin, Y. (2014) Going-concern prediction using hybrid random forests and rough set approach. Information Sciences, 254: 98-110
Zdolšek, D., Jagrič, T., Odar, M. (2015) Identification of auditor's report qualifications: An empirical analysis for Slovenia. Economic Research / Ekonomska Istraživanja, 28(1): 994-1005
Zhou, W., Kapoor, G. (2011) Detecting evolutionary financial statement fraud. Decision Support Systems, 50(3), 570-575
 

About

article language: English
document type: Original Scientific Paper
DOI: 10.5937/EJAE16-21832
published in SCIndeks: 27/10/2019
peer review method: double-blind
Creative Commons License 4.0