Alamsyah, Andry and Ramadhani, Dian Puteri and Kristanti, Farida Titik and Nasution, Arbi Haza (2025) Deciphering news sentiment and stock price relationships in Indonesian companies: an AI-based exploration of industry affiliation and news co-occurrence. Discover Artificial Intelligence, 5 (87). ISSN 2731-0809

Preview

Text J7_Deciphering news .pdf Download (2MB) | Preview

Abstract

The rapid increase of textual data has transformed the way we understand and forecast fnancial market behavior. Investor sentiments, often swayed by news, are pivotal in determining stock prices. Analyzing a dataset of 192.582 Indonesian fnancial news articles published between 2018 and 2023. This study investigates the complex connections between news sentiment and stock market behavior of Indonesian companies. We leverage AI-based sentiment analysis and natural language processing techniques, including identity recognition, network analysis, and correlation assessment, to explore how news sentiment afects stock prices at the levels of individuals, industries, and news co-occurrence clusters. While earlier research has addressed the efect of sentiment on stock prices at both the company and industry levels, there is a signifcant lack of studies focused on media co-occurrence clusters, which is vital for comprehending the collective media portrayal of interconnected frms. Our results show that sentiment-price correlations strengthen hierarchically, with individual companies at 0.26, industry groupings at 0.30, and news co-occurrence clusters at 0.43. This research introduces a unique analytical framework that explores sentiment across various levels, highlighting co-occurrence clusters that refect business relationships beyond traditional industry lines. It demonstrates that companies frequently mentioned together in the news exhibit stronger and more stable sentiment-price correlations, ofering a new analytical perspective for AI-driven investment strategies and underscoring the potential of big data analytics in Indonesia’s capital market.

Actions (login required)

View Item