Stock market uncertainty determination with news headlines : a digital twin approach

GUTIÉRREZ-DIEZ, Pedro José

doi:10.3934/math.2024083

Stock market uncertainty determination with news headlines : a digital twin approach

dc.contributor.author	GUTIÉRREZ-DIEZ, Pedro José
dc.date.accessioned	2023-12-19T09:26:10Z
dc.date.available	2023-12-19T09:26:10Z
dc.date.issued	2023
dc.description	Published online: 13 December 2023	en
dc.description.abstract	We present a novel digital twin model that implements advanced artificial intelligence techniques to robustly link news and stock market uncertainty. On the basis of central results in financial economics, our model efficiently identifies, quantifies, and forecasts the uncertainty encapsulated in the news by mirroring the human mind’s information processing mechanisms. After obtaining full statistical descriptions of the timeline and contextual patterns of the appearances of specific words, the applied data mining techniques lead to the definition of regions of homogeneous knowledge. The absence of a clear assignment of informative elements to specific knowledge regions is regarded as uncertainty, which is then measured and quantified using Shannon Entropy. As compared with standard models, the empirical analyses demonstrate the effectiveness of this approach in anticipating stock market uncertainty, thus showcasing a meaningful integration of natural language processing, artificial intelligence, and information theory to comprehend the perception of uncertainty encapsulated in the news by market agents and its subsequent impact on stock markets.	en
dc.format.mimetype	application/pdf	en
dc.identifier.citation	AIMS mathematics, 2023, Vol. 9, No. 1, pp. 1683-1717	en
dc.identifier.doi	10.3934/math.2024083
dc.identifier.endpage	1717	en
dc.identifier.issn	2473-6988
dc.identifier.issue	1	en
dc.identifier.startpage	1683	en
dc.identifier.uri	https://hdl.handle.net/1814/76193
dc.identifier.volume	9	en
dc.language.iso	en	en
dc.orcid.upload	true	*
dc.publisher	AIMS Press	en
dc.relation.ispartof	AIMS mathematics	en
dc.rights	info:eu-repo/semantics/openAccess	en
dc.rights.license	Attribution 4.0 International	*
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	*
dc.title	Stock market uncertainty determination with news headlines : a digital twin approach	en
dc.type	Article	en
dspace.entity.type	Publication
person.identifier.orcid	0000-0003-4816-615X
person.identifier.other	48364
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