Abstract
Stock market performance is determined by supply and demand of individual, institutional, and foreign investors, who increasingly use media such as news articles for decision-making. We present a bidirectional long short term memory model to forecast trading trends based on statistically significant investor-specific topics from financial news datasets. The application of this study shows three valuable results: (i) topics significantly meaningful to each investor type differ, (ii) investors show different decision-making trends for the same news topics and different sensitivity levels, and (iii) news topics significantly associated with investors’ responses differ according to the stock market and sensitivity.
Data availability
The data that support the findings of this study are openly available in Naver Financial at https://finance.naver.com/news/mainnews.nhn (financial news) and https://finance.naver.com/sise/sise_trans_style.nhn (investors’ trading trends).
Disclosure statement
No potential conflict of interest was reported by the authors.