Capturing the timing of crisis evolution : a machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19

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dc.contributor.author Szczygielski, Jan Jakub
dc.contributor.author Charteris, Ailie
dc.contributor.author Obojska, Lidia
dc.contributor.author Brzeszczynski, Janusz
dc.date.accessioned 2024-07-11T13:11:19Z
dc.date.available 2024-07-11T13:11:19Z
dc.date.issued 2024-08
dc.description DATA AVAILABILITY : Data will be made available on request. en_US
dc.description.abstract The phases of a crisis are critical to understanding its evolution. We construct an economic agent-determined machine learning-based Google search index that associates search terms with uncertainty to isolate COVID-19-related uncertainty from overall uncertainty. Subsequently, we apply directional wavelet analysis that discriminates between positive and negative associations to study the evolving impact of the COVID-19 pandemic on financial market uncertainty and financial markets. Our approach permits us to delineate crisis phases with high precision according to information type. The analysis that follows suggests that policy responses impacted uncertainty and that the novelty of the COVID-19 outbreak had a significant impact on global stock markets. Regression analysis, wavelet entropy and partial wavelet coherence confirm the informational content of our uncertainty index. The approach presented in this study is applied to the COVID-19 crisis but is generalisable beyond the pandemic and can assist in decision-making during times of economic and financial market turmoil and should be of interest to policymakers, researchers and econometricians. en_US
dc.description.department Financial Management en_US
dc.description.librarian hj2024 en_US
dc.description.sdg SDG-01:No poverty en_US
dc.description.sdg SDG-08:Decent work and economic growth en_US
dc.description.uri https://www.elsevier.com/locate/techfore en_US
dc.identifier.citation Szczygielski, J.J., Charteris, A., Obojska, L. et al. 2024, 'Capturing the timing of crisis evolution: A machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19', Technological Forecasting and Social Change, vol. 205, art. 123319, pp. 1-20, doi : 10.1016/j.techfore.2024.123319. en_US
dc.identifier.issn 0040-1625 (print)
dc.identifier.issn 1873-5509 (online)
dc.identifier.other 10.1016/j.techfore.2024.123319
dc.identifier.uri http://hdl.handle.net/2263/96945
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.rights © 2024 The Authors. Published by Elsevier Inc. This is an open access article under the CC BY license. en_US
dc.subject COVID-19 pandemic en_US
dc.subject Coronavirus disease 2019 (COVID-19) en_US
dc.subject Google search trends (GST) en_US
dc.subject Machine learning en_US
dc.subject Financial markets en_US
dc.subject Crisis evolution en_US
dc.subject Uncertainty en_US
dc.subject SDG-08: Decent work and economic growth en_US
dc.subject SDG-01: No poverty en_US
dc.title Capturing the timing of crisis evolution : a machine learning and directional wavelet coherence approach to isolating event-specific uncertainty using Google searches with an application to COVID-19 en_US
dc.type Article en_US


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