@kim2012simultaneous].

Analyzing stock market using relevant text is complicated but intriguing [@wu2009stock; @xie2013semantic; @ding2014using; @ding2015deep; @ding2016knowledge; @chang2016measuring; @peng2015leverage; @luss2015predicting; @skuza2015sentiment; @sehgal2007sops]. For instance, a model with the name Enalyst was introduced in Lavrenko et al. Their goal is to predict stock intraday price trends by analyzing news articles published in the homepage of YAHOO finance. Mittermayer and Knolmayer implemented several prototypes for predicting the short-term market reaction to news based on text mining techniques. Their model forecast 1-day trend of the five major companies indices. Wu et al. [@wu2009stock] predicted stock trends by selecting a representative set of bursty features (keywords) that have impact on individual stocks. Vivek Sehgal et al. [@sehgal2007sops] introduced a method to predict stock market using sentiment. Similarly, Michał Skuza et al. [@skuza2015sentiment] used sentiment from postings on twitter to predict future stock prices. However, these methods have many limitations including unveiling the rules that may govern the dynamics of the market which makes the prediction model incapable to catch the impact of recent trends.

More recently, neural networks have been leveraged to further improve the accuracy of prediction. In general, neural networks are able to learn dense representations of text, which have been shown to 