TIIS (한국인터넷정보학회)
Current Result Document :
| 한글제목(Korean Title) |
Relation Between News Topics and Variations in Pharmaceutical Indices During COVID-19 Using a Generalized Dirichlet-Multinomial Regression (g-DMR) Model |
| 영문제목(English Title) |
Relation Between News Topics and Variations in Pharmaceutical Indices During COVID-19 Using a Generalized Dirichlet-Multinomial Regression (g-DMR) Model |
| 저자(Author) |
Xiaorui Shao
Lijiang Wang
Chang Soo Kim
Ilkyeun Ra
Jang Hyun Kim
Min Hyung Park
Yerin Kim
Dongyan Nan
Fernando Travieso
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| 원문수록처(Citation) |
VOL 15 NO. 05 PP. 1630 ~ 1648 (2021. 05) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
Owing to the unprecedented COVID-19 pandemic, the pharmaceutical industry has attracted considerable attention, spurred by the widespread expectation of vaccine development. In this study, we collect relevant topics from news articles related to COVID-19 and explore their links with two South Korean pharmaceutical indices, the Drug and Medicine index of the Korea Composite Stock Price Index (KOSPI) and the Korean Securities Dealers Automated Quotations (KOSDAQ) Pharmaceutical index. We use generalized Dirichlet-multinomial regression (g-DMR) to reveal the dynamic topic distributions over metadata of index values. The results of our analysis, obtained using g-DMR, reveal that a greater focus on specific news topics has a significant relationship with fluctuations in the indices. We also provide practical and theoretical implications based on this analysis.
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| 키워드(Keyword) |
CNN
DWT
Fault Diagnosis
Multi-domain
Time series Classification
Generalized Dirichlet-multinomial regression
KOSPI
KOSDAQ
Pharmaceutical Index
Topic Modeling
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