A comparison of nearest neighbours, discriminant and logit models for auditing decisions

Abstract

This study investigates the efficiency of k‐nearest neighbours (k‐NN) in developing models for estimating auditors' opinions, as opposed to models developed with discriminant and logit analyses. The sample consists of 5276 financial statements, out of which 980 received a qualified audit opinion, obtained from 1455 private and public UK companies operating in the manufacturing and trade sectors. We develop two industry‐specific models and a general one using data from the period 1998–2001, which are then tested over the period 2002–2003. In each case, two versions of the models are developed. The first includes only financial variables. The second includes both financial and non‐financial variables. The results indicate that the inclusion of credit rating in the models results in a considerable increase both in terms of goodness of fit and classification accuracies. The comparison of the methods reveals that the k‐NN models can be more efficient, in terms of average classification accuracy, than the discriminant and logit models. Finally, the results are mixed concerning the development of industry‐specific models, as opposed to general models. Copyright © 2007 John Wiley & Sons, Ltd.

Logistic Regression for Detecting Fraudulent Financial Statement of Listed Companies in China

“…Two studies, Gaganis et al [5] and Kirkos et al [8], used three methods simultaneously. Gaganis et al [5] investigated the efficiency of k-nearest neighbours (k-NN) in developing models for estimating auditors' opinions, as opposed to models developed with discriminant and logit analyses.…”

“…Gaganis et al [5] investigated the efficiency of k-nearest neighbours (k-NN) in developing models for estimating auditors' opinions, as opposed to models developed with discriminant and logit analyses. Kirkos et al [8] investigated the usefulness of these models, which included neural network, decision tree and Bayesian, in the identification of FFS.…”

Pertimbangan Auditor Dalam Memberikan Opini Audit Going Concern

“…Studi lain oleh Gaganis et al (2007) dengan sampel perusahaan di UK menunjukkan hubungan antara peringkat kredit yang buruk dan penerbitan opini GC.…”

Audit‐firm group appointment: an artificial intelligence approach

“…In the fi eld of auditing, relevant studies have only recently employed AI techniques, such as NNs (e.g. Lenard et al, 1995;Gaganis et al, 2007a), support vector machines (Doumpos et al, 2005), nearest neighbours (Gaganis et al, 2007b), DTs and Bayesian belief networks (Kirkos et al, 2007a,b).…”

“…Three methods derived from AI will be used here for the fi rst time, namely decision trees (DTs), neural networks (NNs) and k-nearest neighbours. These methods were selected on the basis of their previous successful application in auditing-related topics (Fanning and Cogger, 1998;Lin and McClean, 2001;Gaganis et al, 2007b;Kirkos et al, 2007a,b). The models developed are compared in terms of their accuracy rate.…”