Resumen

BAKHSHI, Ali; YAZDANI, Shohre  y  MALEKI, Ali. Independient educating optimum method of independent auditor report type prediction. Conrado [online]. 2020, vol.16, n.74, pp. 79-92.  Epub 02-Jun-2020. ISSN 1990-8644.

Accountability requires the existence of reliable and valid information and auditing is one of fundamental bases for the accountability process. Thus educating the optimum method is of great importance. With the presence of a great volume of information, using the prediction methods can contribute the auditors in this respect. This research aims to compare a variety of methods of teaching of that. In current research, J48, random forest, vector machine and neural network have been used. Research population involves the corporates accepted by Tehran Stock Exchange in 2008-2017. Here, 19 financial and non-financial independent variables have been applied in two groups of test and training. Also, the independent auditor report has been classified into two groups of acceptable and conditional. Comparing the above-mentioned methods has indicated that random forest algorithm with the prediction accuracy average as 78.83% was the most optimum model to predict the report type in the both groups and other models involving J48, support vector machine, CART decision tree and finally, artificial neural network were of the most accuracy.

Palabras clave : Auditor report type; J48 algorithm; random forest; support vector machine; CART decision tree; artificial neural network..

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