(SVMs).

-   ***Bag-At-LSTM***: At-LSTM without sentence encoder. We take the average of the word embedding inputs instead of using Bi-LSTM to encode the news title.

-   ***WEB-At-LSTM***: Same as our proposed model but without the character level composition.

-   ***Ab-At-LSTM***: Instead of the news title, we use the news abstract as input for our model, the model structure remains the same.

-   ***Doc-At-LSTM***: We further leverage a Hierarchical Attention Networks proposed by Yang et al. [@yang2016hierarchical] to represent the entire news Document, this model adds a sentence level attention layer into our proposed model to differentiate more and less important content when constructing the document representation.

-   ***Tech-At-LSTM***: We concatenate seven Technical indicator leveraged from Zhai et al. [@zhai2007combining] shown in figure  \[fig:tech\] with the vector $V$ after the day level LSTM layer and fed together into the prediction layer.

    ![\[fig:tech\]Self attention mechanism](tech.PNG){width="100.00000%" height="3.0in"}

-   ***CNN-LSTM***: We use CNN instead of the news level self-attention layer. Note that the word and day level self-attention layer still remains the same. We want to see how well the self-attention layer works compared to CNN which is good at capturing local and semantic information from texts.

-   ***E-NN***: Ding et al. [@ding2014using] reported 