  Stock market prediction is one of the most attractive research topic since
the successful prediction on the market's future movement leads to significant
profit. Traditional short term stock market predictions are usually based on
the analysis of historical market data, such as stock prices, moving averages
or daily returns. However, financial news also contains useful information on
public companies and the market. Existing methods in finance literature exploit
sentiment signal features, which are limited by not considering factors such as
events and the news context. We address this issue by leveraging deep neural
models to extract rich semantic features from news text. In particular, a
Bidirectional-LSTM are used to encode the news text and capture the context
information, self attention mechanism are applied to distribute attention on
most relative words, news and days. In terms of predicting directional changes
in both Standard & Poor's 500 index and individual companies stock price, we
show that this technique is competitive with other state of the art approaches,
demonstrating the effectiveness of recent NLP technology advances for
computational finance.
