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Predicting stock price and spread movements from news

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dc.contributor.author Wistbacka, Pontus
dc.contributor.author Rönnqvist, Samuel
dc.contributor.author Vozian, Katia
dc.contributor.author Sagade, Satchit
dc.date.accessioned 2020-12-24T19:18:23Z
dc.date.available 2020-12-24T19:18:23Z
dc.date.issued 2021-01-05
dc.identifier.isbn 978-0-9981331-4-0
dc.identifier.uri http://hdl.handle.net/10125/70804
dc.description.abstract We explore several ways of using news articles and financial data to train neural network machine learning models to predict shock events in high-frequency market data, and aggregated shock episodes. We investigate the use of price movements in this context, and separately at a daily interval as well. We describe in detail how training sets are created from our data sources and how our machine learning models are trained. We find that pairing company-related news text with events or movements in financial time series proves less straight-forward than the literature would indicate. We discuss possible reasons for negative results, especially relating to the combination of minute-level news and millisecond-level market data.
dc.format.extent 8 pages
dc.language.iso English
dc.relation.ispartof Proceedings of the 54th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Machine Learning and Predictive Analytics in Accounting, Finance, and Management
dc.subject liquidity shocks
dc.subject machine learning
dc.subject news
dc.subject text mining
dc.title Predicting stock price and spread movements from news
dc.identifier.doi 10.24251/HICSS.2021.192
prism.startingpage 1593
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance, and Management


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