Redes interorganizacionais e inovação: o caso do pólo joalheiro de Belém

This work is a case study based on Belém Jewelry Pole, whose main issue is to understand how the social network (which the Pole is inserted) influences on innovation process on this area. The main objective is to analyze how interorganizational networks impacted/impact on the potential for innovation, creating both limits and opportunities for the companies development. The adopted method analyzed the historical jewelry industry trajectory since the beginning of mineral extraction in the city of Itaituba (in the Pará State) until nowadays. Primary and secondary data were used allowing the view of the dynamics of the network during transformation periods of the main involved actors in the process. The prospect of embeddedness structural as analysis technique allowed verifying the quality of interactors ties, as well as the visualization of their structures. During the jewelry industry trajectory was verified a change in the quality of social relations, modifying the information flow, trust and associations of various links in the production chain. Both direct and indirect ties facilitated the access to remote networks entering new information related to new products, processes and market aspects. This interaction has led to raising the innovation potential causing a qualitative and quantitative improvement in competitiveness of organizations. Some embedded ties allowed the formation of partnerships bringing various economic earnings for those involved in the relationship. Thus, it is understood how aspects related to the position, architecture and quality of ties in a wide social network influenced on the innovation process and eventual jewelry industry trajectory

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