features. As Ding et al. concluded in [@ding2014using], the news titles might contain more useful information where as the abstract or article might cause some negative effect to the model. The Ab-At-LSTM and Doc-At-LSTM confirmed this viewpoint since their accuracy are lower than the proposed model that only used the information from the title.

Our proposed model has a 65.53% max accuracy and a average accuracy of 63.06% which is lower than the KGEB-CNN proposed by Ding et al., this is likely due to Knowledge graph event embedding (KGEB) is a more powerful method for model the content in news titles than the sequence embedding shown in this work.

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Model & Average Accuracy & Max Accuracy\
SVM & 56.38% & –\
Bag-At-LSTM & 61.93% & 63.06%\
WEB-At-LSTM & 62.51% & 64.42%\
Ab-At-LSTM & 60.6% & 61.93%\
Doc-At-LSTM & 59.96% & 60.6%\
Tech-At-LSTM & 62.51% & 64.42%\
CNN-LSTM & 61.36% & 63.06%\
E-NN & 58.83% & –\
EB-CNN & 64.21% & –\
KGEB-CNN & **66.93**% & –\
At-LSTM & 63.06% & **65.53**%\

We use the At-LSTM model for individual stock price prediction after confirmed that it out performs other approaches. The results are shown in table \[tab:indiResult\], each stock shown 