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Predicting stock price and spread movements from news

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Title:Predicting stock price and spread movements from news
Authors:Wistbacka, Pontus
Rönnqvist, Samuel
Vozian, Katia
Sagade, Satchit
Keywords:Machine Learning and Predictive Analytics in Accounting, Finance, and Management
liquidity shocks
machine learning
news
text mining
Date Issued:05 Jan 2021
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.
Pages/Duration:8 pages
URI:http://hdl.handle.net/10125/70804
ISBN:978-0-9981331-4-0
DOI:10.24251/HICSS.2021.192
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Machine Learning and Predictive Analytics in Accounting, Finance, and Management


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