Predicting the stock market is a widely studied field, either due to the curiosity in finding an explanation for the behavior of financial assets or for financial purposes. Among these studies the best techniques use neural networks as a prediction technique. More specifically, the best networks for this purpose are called recurrent neural networks (RNN) and provide an extra option when dealing with a sequence of values. However, a great part of the studies is intended to predict the result of few stocks, therefore, this work aims to predict the behavior of a large number of stocks. For this, similar stocks were grouped based on their correlation and later the algorithm K-means was applied so that similar groups were clustered. After this process, the Long Short-Term Memory (LSTM) - a type of RNN - was used in order to predict the price of a certain group of assets. Later, predicted prices are compared to the correct prices in order to analyze prices tendency. Results showed that clustering stocks did not influence the effectiveness of the network, once tendency was predicted correct for an average of 48% of time. Investors and portfolio managers can use proposed techniques to simply their daily tasks.

出于对金融资产行为的解释或出于财务目的的好奇心，预测股票市场是一个广泛研究的领域。在这些研究中，最好的技术使用神经网络作为预测技术。更具体地说，用于此目的的最佳网络称为递归神经网络（RNN），在处理值序列时提供了额外的选择。但是，大部分研究旨在预测少数股票的结果，因此，这项工作旨在预测大量股票的行为。为此，将相似的股票基于它们的相关性进行分组，然后使用算法K均值对相似的股票进行聚类。经过这个过程 长期短期记忆（LSTM）-一种RNN-用于预测某组资产的价格。随后，将预测价格与正确价格进行比较，以分析价格趋势。结果表明，一旦预测趋势可以平均纠正48％，群集库存就不会影响网络的有效性。投资者和投资组合经理可以使用建议的技术来简化其日常任务。