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.

This paper aims to provide a better basis for understanding the transmission connection between tourism development and sustainable economic growth in the empirical scenario of International countries. In this way, we have applied the dynamic stochastic general equilibrium (DSGE) model in different countries in order to check the power of generalization of this framework to study the tourism development. Also, we extend this model to obtain the long-term effects of tourism development with confidence intervals. The influence of tourism development on sustainable economic growth is proved by our results and show the indirect consequences between tourist activity and other industries produced through the external effects of investment and human capital and public sector. Our study confirms that the DSGE technique can be a generalized model for the analysis of tourism development and, especially, can improve previous precision results with the DSGE-VAR model, where vector autoregression (VAR) is introduced in the DSGE model. The simulation results reveal even more than when the productivity of the economy in general enhances, as the current tourist demand increases in greater proportion than more than the national tourism demand. For its part, the consumption of domestic tourism rises more than the consumption of inbound tourism if the productivity of the tourism production enhances, but non-tourism prices decrease at a slower rate and tourism investment needs a longer time to recover to what is established.

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.

Currency crises have been analyzed and modeled over the last few decades. These currency crises develop mainly due to a balance of payments crisis, and in many cases, these crises lead to speculative attacks against the price of the currency. Despite the popularity of these models, they are currently shown as models with low estimation precision. In the present study, estimates are made with first- and second-generation speculative attack models using neural network methods. The results conclude that the Quantum-Inspired Neural Network and Deep Neural Decision Trees methodologies are shown to be the most accurate, with results around 90% accuracy. These results exceed the estimates made with Ordinary Least Squares, the usual estimation method for speculative attack models. In addition, the time required for the estimation is less for neural network methods than for Ordinary Least Squares. These results can be of great importance for public and financial institutions when anticipating speculative pressures on currencies that are in price crisis in the markets.