[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125152-en":3,"doc-seo-125152-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125152,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","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","The phases of a crisis are critical to understanding its evolution. An economic-agent-determined machine learning framework builds a Google search index that links search terms to uncertainty, isolating COVID-19-related uncertainty from broader uncertainty. Directional wavelet analysis then distinguishes positive versus negative associations to trace how the pandemic shapes financial-market uncertainty and market behavior. Results indicate that policy responses influenced uncertainty and COVID-19 novelty affected global stock markets.","Technological Forecasting & Social Change 205 (2024) 123319  \nContents lists available at ScienceDirect  \nTechnological Forecasting & Social Change  \njournal [homepage:](homepage: www.elsevier.com/locate/techfore)[ www.elsevier.com/locate/techfore](homepage: www.elsevier.com/locate/techfore)  \n| 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\u003Cbr>Jan Jakub Szczygielski a, b, Ailie Charteris c, *, Lidia Obojskad, Janusz Brzeszczy´nski e, f\u003Cbr>a Department of Finance, Kozminski University, ul. Jagiello´nska 57/59, 03-301, Warsaw, Poland\u003Cbr>b Department of Financial Management, University of Pretoria, Private Bag x20, Hatfield, Pretoria, 0028, South Africa c Department of Finance and Tax, University of Cape Town, Rondebosch, 7700, Cape Town, South Africa\u003Cbr>d Department of Quantitative Methods & Information Technology, Kozminski University, ul. Jagiello´nska 57/59, 03-301, Warsaw, Poland\u003Cbr>e Department of Accounting and Finance, Business School, Edinburgh Napier University (ENU), 219 Colinton Road, Edinburgh, EH14 1DJ, Scotland, United Kingdom f Department of Capital Market and Investments, Faculty of Economics and Sociology, University of Ł´od´z, ul. POW 3/5, 90-225, Ł´od´z, Poland |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords: COVID-19\u003Cbr>Google search trends Machine learning Financial markets Crisis evolution Uncertainty |  | 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. |\n\n1. Introduction  \nAs crises evolve, intensifying and waning, their effect on financial markets and the broader economy varies. Distinct phases of a crisis can be identified by a strengthening and weakening impact arising from related events and the implementation of certain policies. For example, the Lehman Brothers bankruptcy, the freezing and unfreezing of credit markets, and quantitative easing changed the course of the impact of the Global Financial Crisis (GFC) on financial markets (Dooley and Hutchison, 2009; Corbet et al., 2019). Policymakers need to understand phases, their timing and drivers in order to ascertain appropriate fiscal, monetary and other policy responses with the aim of limiting the deleterious impact of a crisis (Jana et al., 2022; Lai, 2022). Market participants also seek knowledge and insight into the distinct phases of a crisis to be able to hedge downside risk throughout its evolution.  \nTraditionally, the identification of crisis phases has relied on the timing of pre-selected major events, the introduction of policies or by analysing stock price movements (see Dimitriou et al., 2013; Ramelli and Wagner, 2020; Akhtaruzzaman et al., 2021). More recently, Lai (2022) proposed the use of stock options, which reflect investors’ risk preferences ","cbCaibzAqlSX9uNu","https://ap.wps.com/l/cbCaibzAqlSX9uNu","pdf",4418902,1,20,"English","en",105,"# Introduction\n## Crisis phase identification and timing\n## COVID-19 policy timing and response variation","[{\"question\":\"How does the study isolate COVID-19-related uncertainty from overall uncertainty?\",\"answer\":\"It constructs an economic-agent-determined machine learning-based Google search index that associates search terms with uncertainty, separating COVID-19-related uncertainty from broader uncertainty.\"},{\"question\":\"What role does directional wavelet analysis play in the research?\",\"answer\":\"Directional wavelet analysis discriminates between positive and negative associations, enabling an examination of how the COVID-19 pandemic affects financial market uncertainty over time.\"},{\"question\":\"What do the follow-up validation methods confirm about the uncertainty index?\",\"answer\":\"Regression analysis, wavelet entropy, and partial wavelet coherence confirm the informational content of the proposed uncertainty index.\"}]","Capturing the timing of crisis evolution - 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