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

Efeitos da tilápia do Nilo (Oreochromis niloticus) e do enriquecimento por nutrientes sobre a comunidade planctônica em um lago artificial no semi-árido brasileiro

The major aim of this study was to test the hypothesis that the introduction of the Nile tilapia (Oreochromis niloticus) and the enrichment with nutrients (N and P) interact synergistically to change the structure of plankton communities, increase phytoplankton biomass and decrease water transparency of a semi-arid tropical reservoir. One field experiment was performed during five weeks in twenty enclosures (8m3) to where four treatments were randomly allocated: with tilapia addition (T), with nutrients addition (NP), with tilapia and nutrients addition (T+NP) and a control treatment with no tilapia or nutrients addition (C). A two-way repeated measures ANOVA was done to test for time (t), tilapia (T) and nutrient (NP) effects and their interaction on water transparency, total phosphorus, total nitrogen, phytoplankton and zooplankton. The results show that there was no effect of nutrient addition on these variables but significant fish effects on the biomass of total zooplankton, nauplii, rotifers, cladocerans and calanoid copepods, on the biovolume of Bacillariophyta, Zygnemaphyceae and large algae (GALD ≥ 50 μm) and on Secchi depth. In addition, we found significant interaction effects between tilapia and nutrients on Secchi depth and rotifers. Overall, tilapia decreased the biomass of most zooplankton taxa and large algae (diatoms) and decreased the water transparency while nutrient enrichment increased the biomass of zooplankton (rotifers) but only in the absence of tilapia. In conclusion, the influence of fish on the reservoir plankton community and water transparency was greater than that of nutrient loading. This finding suggests that biomanipulation should be a greater priority in the restoration of eutrophic reservoirs in tropical semi-arid regions