Mapeando fundamentos do Constructo Neuromarketing com profissionais de empresa de publicidade

This study aims to acknowledge the domain level and influence of the neuromarketing construct. This is done considering professionals at advertising agencies in Brazil. The presence of concepts related to this new approach is very little divulged, and there are little analysis performed on this area. Thus, the research is of qualitative and exploratory nature and used as primary fonts books, articles related to marketing, neuroscience, and psychology as well as secondary fonts. A profound interview was realized aiming the main advertising agencies in Brazil. The public was composed by managers responsible for planning. A content analysis was performed afterwards. The advances related to the brain science have permitted the development of technological innovation. These go primarily towards knowledge and unconscious experiences of consumers, which are responsible for the impulse of decision making and consumer behavior. These issues are related to Neuromarketing, that in turn, uses techniques such as FMRI, PET and FDOT. These scan the consumer s brain and produces imagines on the neuron s structures and functioning. This is seen while activities such as mental tasks for the visualization of brands, images or products, watching videos and commercials are performed. It is observed that the agencies are constantly in search of new technologies and are aware of the limitations of the current research instruments. On the other hand, they are not totally familiar with concepts related to neuromarketing. In relation to the neuroimage techniques it is pointed out by the research that there is full unawareness, but some agencies seem to visualize positive impacts with the use of these techniques for the evaluation of films and in ways that permit to know the consumer better. It is also seen that neuroimage is perceived as a technique amongst others, but its application is not real, there are some barriers in the market and in the agencies itself. These barriers as well as some questioning allied to the scarce knowledge of neuromarketing, make it not possible to be put into practice in the advertising market. It is also observed that even though there is greater use of neuromarketing; there would not be any meaningful changes in functioning and structuring of these agencies. The use of the neuro-image machines should be done in research institutes and centers of big companies. Results show that the level of domain of the neuromarketing construct in the Brazilian advertising agencies is only a theoretical one. Little is known of this subject and the neurological studies and absolutely nothing of neuroimage techniques

Previsão do índice bovespa por meio de redes neurais artificiais: uma análise comparada aos métodos tradicionais de séries de tempo

Forecast is the basis for making strategic, tactical and operational business decisions. In financial economics, several techniques have been used to predict the behavior of assets over the past decades.Thus, there are several methods to assist in the task of time series forecasting, however, conventional modeling techniques such as statistical models and those based on theoretical mathematical models have produced unsatisfactory predictions, increasing the number of studies in more advanced methods of prediction. Among these, the Artificial Neural Networks (ANN) are a relatively new and promising method for predicting business that shows a technique that has caused much interest in the financial environment and has been used successfully in a wide variety of financial modeling systems applications, in many cases proving its superiority over the statistical models ARIMA-GARCH. In this context, this study aimed to examine whether the ANNs are a more appropriate method for predicting the behavior of Indices in Capital Markets than the traditional methods of time series analysis. For this purpose we developed an quantitative study, from financial economic indices, and developed two models of RNA-type feedfoward supervised learning, whose structures consisted of 20 data in the input layer, 90 neurons in one hidden layer and one given as the output layer (Ibovespa). These models used backpropagation, an input activation function based on the tangent sigmoid and a linear output function. Since the aim of analyzing the adherence of the Method of Artificial Neural Networks to carry out predictions of the Ibovespa, we chose to perform this analysis by comparing results between this and Time Series Predictive Model GARCH, developing a GARCH model (1.1).Once applied both methods (ANN and GARCH) we conducted the results' analysis by comparing the results of the forecast with the historical data and by studying the forecast errors by the MSE, RMSE, MAE, Standard Deviation, the Theil's U and forecasting encompassing tests. It was found that the models developed by means of ANNs had lower MSE, RMSE and MAE than the GARCH (1,1) model and Theil U test indicated that the three models have smaller errors than those of a naïve forecast. Although the ANN based on returns have lower precision indicator values than those of ANN based on prices, the forecast encompassing test rejected the hypothesis that this model is better than that, indicating that the ANN models have a similar level of accuracy . It was concluded that for the data series studied the ANN models show a more appropriate Ibovespa forecasting than the traditional models of time series, represented by the GARCH model