  In order to make good investment decisions, it is vitally important for an
investor to know how to make good analysis of financial time series. Within
this context, studies on the forecast of the values and trends of stock prices
have become more relevant. Currently, there are different approaches to dealing
with the task. The two main ones are the historical analysis of stock prices
and technical indicators and the analysis of sentiments in news, blogs and
tweets about the market. Some of the most used statistical and artificial
intelligence techniques are genetic algorithms, Support Vector Machines (SVM)
and different architectures of artificial neural networks. This work proposes
the improvement of a model based on the association of three distinct LSTM
neural networks, each acting in parallel to predict the opening, minimum and
maximum prices of stock exchange indices on the day following the analysis. The
dataset is composed of historical data from more than 10 indices from the
world's largest stock exchanges. The results demonstrate that the model is able
to predict trends and stock prices with reasonable accuracy.
