applied in the result section.

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Data set & Training & Development & Testing\
Time interval & 20/10/2006-27/06/2012 & 28/06/2012-13/03/2013& 14/03/2013-20/11/2013\
News& 445,262 & 55,658 & 55,658\

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Company Symbol & Training news& Development news& Testing news\
GOOG & 5744 & 718 & 718\
AMZN & 3245 & 406 & 405\
CSCO & 2471 & 309 & 308\
MSFT & 4056 & 406 & 405\
AAPL & 9720 & 1215 & 1214\
INTC & 4355 & 545 & 544\
IBM & 2016 & 252 & 252\
AMD & 1224 & 153 & 153\
NVDA & 1440 & 168 & 167\
QCOM & 924 & 116 & 115\
WMT& 2793 & 350 & 349\
T& 1228 & 154 & 153\

Following Ding et al. [@ding2014using] we focus on the news headlines instead of the full content of the news articles for prediction since they found it produced better results. We use news articles on each day and predict if the S&P 500 index closing price movement (increase or decrease) in the day $t+1$ compared with the closing price on day $t$.

### Implementation Details

As mentioned in the previous section, We pre-trained 100 dimentional word embedding [@mikolov2013efficient] 