Predicting stock price and spread movements from news

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dc.contributor.authorWistbacka, Pontus
dc.contributor.authorRönnqvist, Samuel
dc.contributor.authorVozian, Katia
dc.contributor.authorSagade, Satchit
dc.date.accessioned2020-12-24T19:18:23Z
dc.date.available2020-12-24T19:18:23Z
dc.date.issued2021-01-05
dc.description.abstractWe 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.extent8 pages
dc.identifier.doi10.24251/HICSS.2021.192
dc.identifier.isbn978-0-9981331-4-0
dc.identifier.urihttp://hdl.handle.net/10125/70804
dc.language.isoEnglish
dc.relation.ispartofProceedings of the 54th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectMachine Learning and Predictive Analytics in Accounting, Finance, and Management
dc.subjectliquidity shocks
dc.subjectmachine learning
dc.subjectnews
dc.subjecttext mining
dc.titlePredicting stock price and spread movements from news
prism.startingpage1593

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