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DC Field | Value | Language |
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dc.contributor.author | Asama Liammukda | en_US |
dc.contributor.author | Manad Khamkong | en_US |
dc.contributor.author | Lampang Saenchan | en_US |
dc.contributor.author | Napon Hongsakulvasu | en_US |
dc.date.accessioned | 2021-01-27T03:44:44Z | - |
dc.date.available | 2021-01-27T03:44:44Z | - |
dc.date.issued | 2020-09-25 | en_US |
dc.identifier.other | 2-s2.0-85097331646 | en_US |
dc.identifier.other | 10.1109/IBDAP50342.2020.9245613 | en_US |
dc.identifier.uri | https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85097331646&origin=inward | en_US |
dc.identifier.uri | http://cmuir.cmu.ac.th/jspui/handle/6653943832/71419 | - |
dc.description.abstract | © 2020 IEEE. In this study, we used the trend of COVID-19 from Google trend to represent a panic of investors in COVID-19 and measure the effect of that panic on time-varying volatility of U.S. portfolios by using Fama - French five factor models with GARCH model. The result of analysis, we can capture a time-varying volatility of all portfolios since 11/1/2019 to 4/30/2020 and trend of COVID-19 has affecting on time-varying volatility of the small neutral portfolio, big neutral portfolio, and small growth portfolio. The results of this study coincide with the event that investors panicked that caused a circuit breaker in the stock market. So, we can use Google trend for 'warning sign' of a COVID-19 panic. | en_US |
dc.subject | Computer Science | en_US |
dc.subject | Decision Sciences | en_US |
dc.title | Panic of COVID-19 on the volatility of U.S. portfolios: Applied big data from Google trend | en_US |
dc.type | Conference Proceeding | en_US |
article.title.sourcetitle | 2020 1st International Conference on Big Data Analytics and Practices, IBDAP 2020 | en_US |
article.stream.affiliations | Chiang Mai University | en_US |
Appears in Collections: | CMUL: Journal Articles |
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