Predicting Audit Opinion in Consolidated Financial Statements with Artificial Neural Networks

José Ramón Sánchez-Serrano, David Alaminos, Francisco García-Lagos and Angela M. Callejón-Gil
Additional contact information
José Ramón Sánchez-Serrano: Department of Finance and Accounting, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain
David Alaminos: Department of Economic Theory and Economic History, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain
Francisco García-Lagos: Department of Electronic Technology, Bulevar Louis Pasteur, 35. Campus de Teatinos. University of Malaga, 29071 Malaga, Spain
Angela M. Callejón-Gil: Department of Finance and Accounting, Campus El Ejido s/n, University of Malaga, 29071 Malaga, Spain

Mathematics, 2020, vol. 8, issue 8, 1-14

Abstract: The models for predicting audit opinion analyze the variables that affect the probability of obtaining a qualified opinion. This helps auditors to plan revision procedures and control their performances. Despite their apparent relevance, existing models have only focused on the context of individual financial statements and none have referred to consolidated financial statements. The consolidated information is essential for decision-making processes and understanding the true financial situation of a company. Our objective is to provide a new audit opinion prediction model for consolidated financial statements. To this end, a sample of group of Spanish companies was chosen and an artificial neural network technique, the multilayer perceptron, was used. The results show that the developed method managed to predict the audit opinion with accuracy above 86%. Moreover, there exist important differences concerning the most significant variables in the audit opinion prediction for individual accounts, since when using consolidated financial statements, the variables referring to industry, group size, auditor, and board members were converted into the main explanatory parameters of the prediction.

Keywords: audit opinion prediction; multilayer perceptron; consolidated financial statements; auditing (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://www.mdpi.com/2227-7390/8/8/1288/pdf (application/pdf)
https://www.mdpi.com/2227-7390/8/8/1288/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:8:y:2020:i:8:p:1288-:d:394586

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().