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http://dx.doi.org/10.3837/tiis.2021.05.003

Relation Between News Topics and Variations in Pharmaceutical Indices During COVID-19 Using a Generalized Dirichlet-Multinomial Regression (g-DMR) Model  

Kim, Jang Hyun (Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University)
Park, Min Hyung (Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University)
Kim, Yerin (Department of Applied Artificial Intelligence/Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University)
Nan, Dongyan (Department of Interaction Science/Department of Human-Artificial Intelligence Interaction, Sungkyunkwan University)
Travieso, Fernando (Department of Interaction Science, Sungkyunkwan University)
Publication Information
KSII Transactions on Internet and Information Systems (TIIS) / v.15, no.5, 2021 , pp. 1630-1648 More about this Journal
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.
Keywords
Generalized Dirichlet-multinomial regression; KOSPI; KOSDAQ; Pharmaceutical Index; Topic Modeling;
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