  Sentiment-based stock prediction systems aim to explore sentiment or event
signals from online corpora and attempt to relate the signals to stock price
variations. Both the feature-based and neural-networks-based approaches have
delivered promising results. However, the frequently minor fluctuations of the
stock prices restrict learning the sentiment of text from price patterns, and
learning market sentiment from text can be biased if the text is irrelevant to
the underlying market. In addition, when using discrete word features, the
polarity of a certain term can change over time according to different events.
To address these issues, we propose a two-stage system that consists of a
sentiment extractor to extract the opinion on the market trend and a summarizer
that predicts the direction of the index movement of following week given the
opinions of the news over the current week. We adopt BERT with multitask
learning which additionally predicts the worthiness of the news and propose a
metric called Polarity-Over-Time to extract the word polarity among different
event periods. A Weekly-Monday prediction framework and a new dataset, the
10-year Reuters financial news dataset, are also proposed.
