Integrating corporate governance and financial variables for the identification of qualified audit opinions with neural networks

Abstract

Prior studies have contributed to the development of models that help predict audit opinions, and have applied several methodologies in the search of better predictions. Nevertheless, and even though the existing literature on the prediction of audit opinions is profuse, the results achieved by the existing modelling are still considerably far from having obtained high levels of prediction, and the results are not in excess of 80 % in terms of classification. In previous research, prediction models of audit opinions have used financial variables. The main contribution of this paper is to show that combining these financial variables with variables relating to corporate governance of companies, the predictive ability of the models is significantly higher. For this, a sample was selected from Spanish Listed companies during the financial years between 2008 and 2010. From this sample, financial and corporate governance information was obtained, enabling us to rely on a new set of variables, more complete than those used in prior research. The validity of the variables was studied by means of univariant tests, upon completion of the database. Then, the results of the models built upon said variables were compared through neural network tests that have proven to yield a higher level of prediction, according to prior literature, specifically, multilayer perceptron and probabilistic neural network.