Abstract
[Purpose]This study examines the impact of Korean news articles on stock valuations, exploring how news content and themes affect market participants’ predictions of corporate value and stock prices, thereby highlighting news’ role in shaping market perceptions. [Methodology]Our research method involved creating a deep learning－based news classification model to sort approximately 57 million Naver news articles from 2013 to 2021 into about 20 themes, including capital financing and technological advancements. We also analyzed market responses (stock returns) to these news themes and assessed how positive and negative news differently influenced market reactions. [Findings]Informative news significantly affects returns and trading volume within a minute, showing quick market responses. Positive news triggers faster and stronger market reactions than negative news. Moreover, market reactions vary by news theme, with earnings and macroeconomic news prompting rapid and significant responses. [Implications]This pioneering study on analyzing a large scale of Korean news data offers valuable insights for text analysis in accounting and finance. It helps investors better predict stock price reactions to news and could lessen the information gap between institutional and individual investors, ultimately improving stock market transparency and efficiency.
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