"In the current economic environment, the value of information is increasingly important in the habits and customs of each citizen. Prices per barrel of oil, Euribor rates, equity indexes, inflation rate, among others, arrive everyday at our homes through the media. Their study is increasingly important, especially by the need to identify their impact on our daily quotidian.
The application of Data Mining (DM) techniques, in an attempt to predict the movement of the stock market, has never been a very well accepted, mainly by economists and the academic world. It is an area of high complexity, and the fact that it involves money, mean that their research, especially successful, is not made public. However, these techniques exist, and are increasingly used not only in financial markets, but also in practically all other activity sectors.
This dissertation begins by making an approach to the complex world of the stock market, in an attempt to demystify it a little. Subsequently, based on investment rules normally applied by practitioners, it’s presented a model of an artificial neural network, which provides in a daily basis, the decision to buy, hold or sell a particular asset, supported by a decision support system to monitor and analyze the evolution of the made decisions."