News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis
News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis
This study used textual analysis of 34,209 news articles to quantify news sentiment into three main clusters—positive, negative and neutral—before analysing how they co-move with international equity indices, using the time-varying connectedness of Diebold and Yilmaz (2009, 2012). Better understanding of the spillover of news sentiment to stock markets could aid the decision-making of institutional investors when strong uncertainty is present across major economies. We found that limited spillover from news sentiment to equity markets existed for both the European and international indices examined in the analysis, with spillover being stronger among smaller subsets of news articles more relevant for financial market participants. Additionally, the results indicated that, in the full sample, directional spillover was especially strong in times of larger uncertainty concerning BREXIT developments, whereas the smaller subsets, although also displaying stronger spillover during BREXIT uncertainty, revealed additional spillover peaks at times less related to major BREXIT developments. Differentiation between news about UK-based and EU-based companies also showed less spillover from news sentiment regarding EU-based companies, possibly implying that investors saw BREXIT developments as less relevant for the latter.
BREXIT, connectedness, international equity, news sentiment, textual analysis
Koch, Alexander
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Huynh, Luu Duc Toan
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Wang, Mei
771b4c8f-0903-4062-b903-2aaadfab3dc8
17 May 2022
Koch, Alexander
bf2fed22-ed45-4f52-b2f0-201e30dbf7b1
Huynh, Luu Duc Toan
5ce01bb6-0184-49cc-8f34-aae3a1169e47
Wang, Mei
771b4c8f-0903-4062-b903-2aaadfab3dc8
Koch, Alexander, Huynh, Luu Duc Toan and Wang, Mei
(2022)
News sentiment and international equity markets during BREXIT period: A textual and connectedness analysis.
International Journal of Finance & Economics.
(doi:10.1002/ijfe.2635).
Abstract
This study used textual analysis of 34,209 news articles to quantify news sentiment into three main clusters—positive, negative and neutral—before analysing how they co-move with international equity indices, using the time-varying connectedness of Diebold and Yilmaz (2009, 2012). Better understanding of the spillover of news sentiment to stock markets could aid the decision-making of institutional investors when strong uncertainty is present across major economies. We found that limited spillover from news sentiment to equity markets existed for both the European and international indices examined in the analysis, with spillover being stronger among smaller subsets of news articles more relevant for financial market participants. Additionally, the results indicated that, in the full sample, directional spillover was especially strong in times of larger uncertainty concerning BREXIT developments, whereas the smaller subsets, although also displaying stronger spillover during BREXIT uncertainty, revealed additional spillover peaks at times less related to major BREXIT developments. Differentiation between news about UK-based and EU-based companies also showed less spillover from news sentiment regarding EU-based companies, possibly implying that investors saw BREXIT developments as less relevant for the latter.
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News sentiment and international equity markets during BREXIT period A textual and
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Accepted/In Press date: 2 April 2022
e-pub ahead of print date: 17 May 2022
Published date: 17 May 2022
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© 2022 The Authors. International Journal of Finance & Economics published by John Wiley & Sons Ltd.
Keywords:
BREXIT, connectedness, international equity, news sentiment, textual analysis
Identifiers
Local EPrints ID: 457753
URI: http://eprints.soton.ac.uk/id/eprint/457753
ISSN: 1076-9307
PURE UUID: ff098438-915c-4a13-9a05-ffee29a7b619
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Date deposited: 16 Jun 2022 00:31
Last modified: 16 Mar 2024 17:39
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Author:
Alexander Koch
Author:
Luu Duc Toan Huynh
Author:
Mei Wang
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