Evaluation of a Supervised Learning Approach for Stock Market Operations
Incremental application of an existing method to stock trading, potentially aiding financial analysts.
The paper evaluated Random Forests for stock market decision support, achieving good success rates and returns per operation.
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.