be effective on a wide range of NLP problems, given enough training sample. This is the case in stock market prediction where the prices of stocks are available together with a great collection of relevant text data. This provides a good setting for exploring deep learning-based models for stock price prediction. More specifically, dense representations can represent related sentiment, events, and factual information effectively, which can then be extremely challenging to represent using sparse indicator features.

Advance of deep learning models has inspired increasing efforts on stock market prediction by analyzing stock-related text. For example, Ding et al. [@ding2015deep] showed that deep learning representation of event structures yields better accuracy compared to discrete event features. They further augmented their approach  [@ding2016knowledge] to incorporating an outside knowledge graph into the learning process for event embedding. As another example, Chang et al. [@chang2016measuring] used neural networks to directly learn representations for news abstracts, showing that it is effective for predicting the cumulative abnormal returns of public companies. Other work,e.g., [@kim2012simultaneous; @goccken2016integrating; @adebiyi2014comparison], has proposed different models of neural network that improve the prediction accuracy.

Methodology
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In this section, we first introduce some relative background on our prediction models. Followed by, we 