with skip-gram algorithm on the data set shown in table \[tab:Data set\], the size of the trained vocabulary is 153,214. In addition, firm names and an UNK token to represent any words out of the vocabulary are added to the vocabulary set, having an initial embedding initialized randomly with Gaussian samples. The word embedding are fine-tuned during model training. The character embedding has 15 dimensions, and CNN filters length are \[1,3,5\] respectively, each of those are 32 dimensions. The news level Bi-LSTM and day-level Bi-LSTM both have 300 hidden units, the day level LSTM window size $N$ has been set to 7. Hyper-parameters $d_a$ and $r$ in the self attention layer are set to 600 and 10, respectively. Mentioned in section 3, we use Adadelta for our optimization algorithm, the initial learning rate has been set to 0.04. Our model are trained for 200 Epoch.

Base Lines and Proposed Model
-----------------------------

In this subsection, we propose a few baselines to compare with our proposed model. For the sake of simplicity, the following notation identifies each model:

-   ***SVM***: Luss and d’Aspremont et al.  [@luss2015predicting] propose using Bags-of-Words to represent news documents, and constructed the prediction model using Support Vector Machines 