individual companies stock price. Our model consists a Recurrent Neural network(RNN) to encode the news text and capture the context information, self attention mechanism is applied to distribute attention on most relative words, news and days . The model input are financial news titles extracted from Reuters and Bloomberg. Our model take advantages from the rapid development of deep neural networks and we show that our model is competitive with other state-of-the-art approaches, demonstrating the effectiveness of recent NLP technology advances for computational finance.

The rest of the paper are organized as follows. We introduce related work in Section 2. Followed by, we present some background and the methodologies for our proposed prediction model in Section 3. Experimental setup and results are demonstrated in Section 4. The paper finally concludes and point out future directions in Section 5.

Related work
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Stock market prediction is an intriguing time-series learning problem in finance and economics, which has attracted a considerable amount of research. Efforts on predicting stock market have been carried out based on different resources and approaches. For example, one of the most widely studied approach relies on analyzing recent prices and volumes on the market [@schoneburg1990stock; @akgiray1989conditional; @goccken2016integrating; @adebiyi2014comparison; 