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

Increased hippocampal excitability and impaired spatial memory function in mice lacking VGLUT2 selectively in neurons defined by tyrosine hydroxylase promoter activity

Three populations of neurons expressing the vesicular glutamate transporter 2 (Vglut2) were recently described in the A10 area of the mouse midbrain, of which two populations were shown to express the gene encoding, the rate-limiting enzyme for catecholamine synthesis, tyrosine hydroxylase (TH).One of these populations (‘‘TH– Vglut2 Class1’’) also expressed the dopamine transporter (DAT) gene while one did not ("TH–Vglut2 Class2"), and the remaining population did not express TH at all ("TH-Vglut2-only"). TH is known to be expressed by a promoter which shows two phases of activation, a transient one early during embryonal development, and a later one which gives rise to stable endogenous expression of the TH gene. The transient phase is, however, not specific to catecholaminergic neurons, a feature taken to advantage here as it enabled Vglut2 gene targeting within all three A10 populations expressing this gene, thus creating a new conditional knockout. These knockout mice showed impairment in spatial memory function. Electrophysiological analyses revealed a profound alteration of oscillatory activity in the CA3 region of the hippocampus. In addition to identifying a novel role for Vglut2 in hippocampus function, this study points to the need for improved genetic tools for targeting of the diversity of subpopulations of the A10 area