'Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network'
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Recurrent Neural Network,Stock Prediction,Attention Mechanism,S&P 500

Introduction
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Stock market prediction is the act of trying to determine the future value of a company stock [@malkiel1985random]. Apparently, the successful prediction of a stock’s future price can yield significant profit, making the prediction problem an area of strong appeal for both academic researchers and industry practitioners. However, Stock market prediction is usually considered as one of the most challenging issues among time series predictions due to its noise and volatile features [@wang2012novel]. During the past decades, machine learning models, such as Support Vector Regression (SVR) [@basak2007support] and Support Vector Machines(SVM) [@hearst1998support], have been widely used to predict financial time series and gain high predictive accuracy  [@refenes1994stock; @das2012support; @lu2009financial].

How to accurately predict stock movement is still an open question with respect to the economic and social organization of modern society. The well-known efficient-market hypothesis (EMH) [@malkiel2003efficient] suggests that stock prices reflect all currently available information and any price changes based on the newly revealed relevant information. However, due to the implicit correlations between daily events and their effect on the stock prices, finding relevant information that contribute to 