in the table has more than 66% accuracy and the company WALMART has an average accuracy of 70.36% and max accuracy of 72.06%. Apparently, predicting individual stock price leads to higher accuracy than predicting S&P 500 index, and this is mainly due to the news article we used for input. In terms of the individual stock prediction, the news article we used are more relative to it’s corresponding company. In contrast, we used the full corpus as input for S&P 500 index prediction and certainly this adds noise to our model and hence effects the accuracy.

[|p[3cm]{}||p[3cm]{}|p[3cm]{}|]{}\
Company & Average Accuracy & Max Accuracy\
GOOG & 68.75% & 71.25%\
AMZN & 67.32% & 69.46%\
CSCO & 66.82% & 67.62%\
MSFT & 67.92% & 69.89%\
AAPL & 67.52% & 69.42%\
INTC & 67.12% & 67.63%\
IBM & 69.49% & 71.41%\
AMD & 66.12% & 69.10%\
NVDA & 69.35% & 70.51%\
QCOM & 68.53% & 69.70%\
WMT& **70.36**% & **72.06**%\
T& 68.53% & 69.70%\

Conclusion
==========

This paper has been motivated by the successes of Deep learning methods in Natural Language Processing task. we proposed a Attention-based LSTM model(At-LSTM) to predict the directional movements of Standard & Poor’s 500 index and individual 