In this thesis, several machine learning techniques are explored and applied to the investment decision problem. Some indicators of technical analysis are used as inputs of a neural network which is trained to determine, with each input vector, a buy, sell or hold signal. Likewise, the structure of the network is optimized by applying a genetic algorithm, in order to determine its adequate depth. With this work, some bases are established to perform different empirical studies that improve and deepen the analyzed topics.
