A COMPARISON OF NEAREST NEIGHBOURS, DISCRIMINANT AND LOGIT MODELS FOR AUDITING DECISIONS

被引:19
作者
Gaganis, Chrysovalantis [1 ]
Pasiouras, Fotios [2 ]
Spathis, Charalambos [3 ]
Zopounidis, Constantin [1 ]
机构
[1] Tech Univ Crete, Dept Prod Engn & Management, Financial Engn Lab, Univ Campus, Khania 73100, Greece
[2] Univ Bath, Sch Management, Bath BA2 7AY, Avon, England
[3] Aristotles Univ Thessaloniki, Dept Econ, Div Business Adm, Thessaloniki 54124, Greece
关键词
D O I
10.1002/isaf.283
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
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 (C) 2007 John Wiley & Sons, Ltd.
引用
收藏
页码:23 / 40
页数:18
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