  This paper proposed a method for stock prediction. In terms of feature
extraction, we extract the features of stock-related news besides stock prices.
We first select some seed words based on experience which are the symbols of
good news and bad news. Then we propose an optimization method and calculate
the positive polar of all words. After that, we construct the features of news
based on the positive polar of their words. In consideration of sequential
stock prices and continuous news effects, we propose a recurrent neural network
model to help predict stock prices. Compared to SVM classifier with price
features, we find our proposed method has an over 5% improvement on stock
prediction accuracy in experiments.
