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In order to make good investment decisions, it is vitally important for aninvestor to know how to make good analysis of financial time series. Withinthis context, studies on the forecast of the values and trends of stock priceshave become more relevant. Currently, there are different approaches to dealingwith the task. The two main ones are the historical analysis of stock pricesand technical indicators and the analysis of sentiments in news, blogs andtweets about the market. Some of the most used statistical and artificialintelligence techniques are genetic algorithms, Support Vector Machines (SVM)and different architectures of artificial neural networks. This work proposesthe improvement of a model based on the association of three distinct LSTMneural networks, each acting in parallel to predict the opening, minimum andmaximum prices of stock exchange indices on the day following the analysis. Thedataset is composed of historical data from more than 10 indices from theworld's largest stock exchanges. The results demonstrate that the model is ableto predict trends and stock prices with reasonable accuracy.

Previs\~ao dos pre\c{c}os de abertura, m\'inima e m\'axima de \'indices de mercados financeiros usando a associa\c{c}\~ao de redes neurais LSTM