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Sentiment-based stock prediction systems aim to explore sentiment or eventsignals from online corpora and attempt to relate the signals to stock pricevariations. Both the feature-based and neural-networks-based approaches havedelivered promising results. However, the frequently minor fluctuations of thestock prices restrict learning the sentiment of text from price patterns, andlearning market sentiment from text can be biased if the text is irrelevant tothe underlying market. In addition, when using discrete word features, thepolarity 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 asentiment extractor to extract the opinion on the market trend and a summarizerthat predicts the direction of the index movement of following week given theopinions of the news over the current week. We adopt BERT with multitasklearning which additionally predicts the worthiness of the news and propose ametric called Polarity-Over-Time to extract the word polarity among differentevent periods. A Weekly-Monday prediction framework and a new dataset, the10-year Reuters financial news dataset, are also proposed.

Stock Index Prediction with Multi-task Learning and Word Polarity Over Time