  Data mining methods have been widely applied in financial markets, with the
purpose of providing suitable tools for prices forecasting and automatic
trading. Particularly, learning methods aim to identify patterns in time series
and, based on such patterns, to recommend buy/sell operations. The objective of
this work is to evaluate the performance of Random Forests, a supervised
learning method based on ensembles of decision trees, for decision support in
stock markets. Preliminary results indicate good rates of successful operations
and good rates of return per operation, providing a strong motivation for
further research in this topic.
