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

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An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

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type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Stock Index Prediction with Multi-task Learning and Word Polarity Over Time
Creator	Voigt, Kerstin Zhou, Yue
source	ArXiv
abstract	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.
has issue date	2020-08-17(xsd:dateTime)
has license	arxiv
sha1sum (hex)	68fbb64848798cef0bc2a38f0245c365132fb9f1
resource representing a document's title	Stock Index Prediction with Multi-task Learning and Word Polarity Over Time
resource representing a document's body	covid:68fbb64848798cef0bc2a38f0245c365132fb9f1#body_text
is schema:about of	named entity 'events' named entity 'VARIATIONS' named entity 'METRIC' named entity 'USING' named entity 'RESULTS' »more»

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