use the entire news title content as our models input and use LSTM-based encoder to encode it to a distributional representation to tackle with sparsity problem.\
***Word and Character Embedding***: For each input news, we remove the punctuation and then use a word embedding layer to calculate the embedded vectors for each word. The embedding layer takes a sequence of sentences as input, this sequence corresponds to a set of titles of news articles. These embedding are unique vectors of continuous values with length $w = (w_1,...,w_l)$ and $w_i\in R^{m}$ for each word in the training corpus, $m$ is the word level embedding dimension.

Existing pre-trained word embedding such as Glove [@pennington2014glove] and Word2Vec [@mikolov2013efficient] typically comes from general domains( Google News or Wikipedia, etc). However, these word embeddings often fail to capture rich domain specific vocabularies. We therefore train our own word embedding with financial domain news text consisting of news articles from Reuters an Bloomberg. The data are further described in Section 4.

In addition, we leverage character composition from Chen et al. [@chen2017recurrent] and concatenate the character level composition with the original word embedding to gain rich represent for each word. The character composition feeds all characters 