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投资者情绪与国家治理对新冠疫情期间意外条件波动性的影响:来自全球股票市场的证据

Effects of investor sentiment and country governance on unexpected conditional volatility during the COVID-19 pandemic: Evidence from global stock markets.

作者信息

Hsu Yu-Lin, Tang Leilei

机构信息

Department of Accounting and Finance, University of Strathclyde. Level 3, Stenhouse Wing, 199 Cathedral Street, Glasgow G4 0QU, UK.

出版信息

Int Rev Financ Anal. 2022 Jul;82:102186. doi: 10.1016/j.irfa.2022.102186. Epub 2022 Apr 22.

DOI:10.1016/j.irfa.2022.102186
PMID:36532086
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9033296/
Abstract

This paper first investigates the relationship between investor sentiment, captured by internet search behaviour, and the unexpected component of stock market volatility during the COVID-19 pandemic. According to data on 12 major stock markets, our research indicates a positive correlation between the Google search volume index on COVID-19 and the unexpected volatility of stock markets. The result suggests that greater COVID-19-related investor sentiment during this pandemic is associated with higher stock market uncertainty. Our study further examines whether country-level governance plays a role in protecting stock markets during this pandemic and reveals that the unexpected conditional volatility is lower when a country's governance is more effective. The impact of investor sentiment and country governance on unexpected volatility after the initial shock of COVID-19 is also investigated. The findings demonstrate the importance of establishing good country-level governance that can effectively reduce stock market uncertainty in the context of this pandemic, and support continual policy development related to investor protection.

摘要

本文首先研究了通过互联网搜索行为捕捉到的投资者情绪与新冠疫情期间股市波动的意外成分之间的关系。根据12个主要股票市场的数据,我们的研究表明,关于新冠疫情的谷歌搜索量指数与股市的意外波动之间存在正相关关系。这一结果表明,在此次疫情期间,与新冠疫情相关的投资者情绪越高,股市不确定性就越高。我们的研究进一步考察了国家层面的治理在此次疫情期间对股市的保护作用,结果显示,当一个国家的治理更有效时,意外条件波动性更低。我们还研究了在新冠疫情的初始冲击之后,投资者情绪和国家治理对意外波动的影响。研究结果表明,建立有效的国家层面治理对于在此次疫情背景下有效降低股市不确定性非常重要,并支持了与投资者保护相关的持续政策发展。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40c4/9033296/3bc8b2a00948/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40c4/9033296/c2889488f23b/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40c4/9033296/9684dd6d333c/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40c4/9033296/3bc8b2a00948/gr3_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40c4/9033296/c2889488f23b/gr1_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40c4/9033296/9684dd6d333c/gr2_lrg.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/40c4/9033296/3bc8b2a00948/gr3_lrg.jpg

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COVID19-MLSF: A multi-task learning-based stock market forecasting framework during the COVID-19 pandemic.COVID19-MLSF:一种基于多任务学习的新冠疫情期间股票市场预测框架。
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