the change of price for each individual stock are difficult. Besides, the influences of events to stock prices can occur in indirect ways and act in chain reactions, which sets obstacles for precise market prediction. There are three approaches related to the information required to make a prediction. The first approach, technical analysis, is based on the premise that the future behavior of a financial time series is conditioned to its own past. Secondly, fundamental analysis, is based on external information as political and economic factors. A major source of information are text from the internet, these information are taken from unstructured data as news articles, financial reports or even microblogs. Nofsinger et al [@nofsinger2001impact] shows that in some cases, investors tend to buy after positive news resulting in a stress of buying and higher stock prices, they sell after negative news resulting in a decrease of prices. Finally the third approach considers as all relevant information coming from both, financial time series and textual data.

In this work, our goal is to leverage public released financial news and train a model named Attention-based LSTM (At-LSTM) to make prediction on directional changes for both Standard & Poor’s 500 index and 