The study of financial markets has been addressed in many works during the last years. Different
methods have been used in order to capture the non-linear behavior which is characteristic of these complex
systems. The development of profitable strategies has been associated with the predictive character
of the market movement, and special attention has been devoted to forecast the trends of financial markets.
This work performs a predictive study of the principal index of the Brazilian stock market through
artificial neural networks and the adaptive exponential smoothing method, respectively. The objective is
to compare the forecasting performance of both methods on this market index, and in particular, to evaluate
the accuracy of both methods to predict the sign of the market returns. Also the influence on the
results of some parameters associated to both methods is studied. Our results show that both methods
produce similar results regarding the prediction of the index returns. On the contrary, the neural networks
outperform the adaptive exponential smoothing method in the forecasting of the market movement,
with relative hit rates similar to the ones found in other developed markets.