  It has been shown that financial news leads to the fluctuation of stock
prices. However, previous work on news-driven financial market prediction
focused only on predicting stock price movement without providing an
explanation. In this paper, we propose a dual-layer attention-based neural
network to address this issue. In the initial stage, we introduce a
knowledge-based method to adaptively extract relevant financial news. Then, we
use input attention to pay more attention to the more influential news and
concatenate the day embeddings with the output of the news representation.
Finally, we use an output attention mechanism to allocate different weights to
different days in terms of their contribution to stock price movement. Thorough
empirical studies based upon historical prices of several individual stocks
demonstrate the superiority of our proposed method in stock price prediction
compared to state-of-the-art methods.
