The purpose of this study is to apply the methodology proposed by PINHEIRO & SENNA (2015) to a set of agricultural products traded in Brazil. The multivariate and nonlinear character of this methodology has shown to be suitable, as compared to the neural network model, since it allows for a better predictive performance. Results obtained in an out-of-sample period, by using the calculated error and statistical test, confirmed this statement. This study will be useful to farmers as price forecasting based on their tendency is relevant.

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This study aimed to forecast the prices of a group of commodities through the multivariate spectral analysis model and compare them with those obtained by classical forecasting and neural network models. The choice of commodities such as ethanol, cattle, corn, coffee and soy was due to the emphasis in the exports in 2013. The multivariate spectral model has proved to be suitable, when compared with others, by enabling a better predictive performance. The results obtained in the out-of-sample period, through the use of measurement error and statistical test, confirm this. This research may help market professionals in formulating and implementing policies targeted to the agricultural sector due to the relevance of price forecast as a planning instrument and analysis of the finance market behavior for those who need protection against price fluctuations.

This research analyzed stocks listed on Ibovespa -S o Paulo Stock Exchange Index -, in two distinct periods of evaluation and based on 2,003 daily registers for each of the stocks, in order to characterize the historical series as non-correlated or correlated in long range. This paper defined the percentage ratio of portfolio stocks from these series by applying, with contribution from statistic physics, the DFA method. Between stock groups evaluated in annual periods subsequent to the analyses of historical series, the group composed by stocks with correlated in long range series presented superior financial returns than the ones from Ibovespa. Regarding the group composed by stocks with non-correlated historical series, it was not possible to obtain abnormal gains.