Deciphering news sentiment and stock price relationships in Indonesian companies: An AI-based exploration of industry affiliation and news co-occurrence
The rapid increase of textual data has transformed the way we understand and forecast financial market behavior. Investor sentiments, often swayed by news, are pivotal in determining stock prices. Analyzing a dataset of 192.582 Indonesian financial news articles published between 2018 and 2023. This...
| Main Authors: | , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Springer Nature
2025
|
| Subjects: | |
| Online Access: | https://umpir.ump.edu.my/id/eprint/45284/ |
| _version_ | 1848827374557200384 |
|---|---|
| author | Alamsyah, Andry Ramadhani, Dian Puteri Kristanti, Farida Titik Nasution, Arbi Haza Mohd Sham, Mohamad Sokkalingam, Rajalingam Widiyanesti, Sri Saputra, Muhammad Apriandito Arya |
| author_facet | Alamsyah, Andry Ramadhani, Dian Puteri Kristanti, Farida Titik Nasution, Arbi Haza Mohd Sham, Mohamad Sokkalingam, Rajalingam Widiyanesti, Sri Saputra, Muhammad Apriandito Arya |
| author_sort | Alamsyah, Andry |
| building | UMP Institutional Repository |
| collection | Online Access |
| description | The rapid increase of textual data has transformed the way we understand and forecast financial market behavior. Investor sentiments, often swayed by news, are pivotal in determining stock prices. Analyzing a dataset of 192.582 Indonesian financial 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 affects stock prices at the levels of individuals, industries, and news co-occurrence clusters. While earlier research has addressed the effect of sentiment on stock prices at both the company and industry levels, there is a significant lack of studies focused on media co-occurrence clusters, which is vital for comprehending the collective media portrayal of interconnected firms. 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 reflect business relationships beyond traditional industry lines. It demonstrates that companies frequently mentioned together in the news exhibit stronger and more stable sentiment-price correlations, offering a new analytical perspective for AI-driven investment strategies and underscoring the potential of big data analytics in Indonesia's capital market. |
| first_indexed | 2025-11-15T03:59:42Z |
| format | Article |
| id | ump-45284 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T03:59:42Z |
| publishDate | 2025 |
| publisher | Springer Nature |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | ump-452842025-08-07T01:41:53Z https://umpir.ump.edu.my/id/eprint/45284/ Deciphering news sentiment and stock price relationships in Indonesian companies: An AI-based exploration of industry affiliation and news co-occurrence Alamsyah, Andry Ramadhani, Dian Puteri Kristanti, Farida Titik Nasution, Arbi Haza Mohd Sham, Mohamad Sokkalingam, Rajalingam Widiyanesti, Sri Saputra, Muhammad Apriandito Arya HG Finance QA Mathematics The rapid increase of textual data has transformed the way we understand and forecast financial market behavior. Investor sentiments, often swayed by news, are pivotal in determining stock prices. Analyzing a dataset of 192.582 Indonesian financial 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 affects stock prices at the levels of individuals, industries, and news co-occurrence clusters. While earlier research has addressed the effect of sentiment on stock prices at both the company and industry levels, there is a significant lack of studies focused on media co-occurrence clusters, which is vital for comprehending the collective media portrayal of interconnected firms. 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 reflect business relationships beyond traditional industry lines. It demonstrates that companies frequently mentioned together in the news exhibit stronger and more stable sentiment-price correlations, offering a new analytical perspective for AI-driven investment strategies and underscoring the potential of big data analytics in Indonesia's capital market. Springer Nature 2025 Article PeerReviewed pdf en cc_by_nc_nd_4 https://umpir.ump.edu.my/id/eprint/45284/1/Deciphering%20news%20sentiment%20and%20stock%20price%20relationships%20in%20Indonesian%20companies.pdf Alamsyah, Andry and Ramadhani, Dian Puteri and Kristanti, Farida Titik and Nasution, Arbi Haza and Mohd Sham, Mohamad and Sokkalingam, Rajalingam and Widiyanesti, Sri and Saputra, Muhammad Apriandito Arya (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 (1). pp. 1-29. ISSN 2731-0809. (Published) https://doi.org/10.1007/s44163-025-00350-5 https://doi.org/10.1007/s44163-025-00350-5 https://doi.org/10.1007/s44163-025-00350-5 |
| spellingShingle | HG Finance QA Mathematics Alamsyah, Andry Ramadhani, Dian Puteri Kristanti, Farida Titik Nasution, Arbi Haza Mohd Sham, Mohamad Sokkalingam, Rajalingam Widiyanesti, Sri Saputra, Muhammad Apriandito Arya Deciphering news sentiment and stock price relationships in Indonesian companies: An AI-based exploration of industry affiliation and news co-occurrence |
| title | Deciphering news sentiment and stock price relationships in Indonesian companies: An AI-based exploration of industry affiliation and news co-occurrence |
| title_full | Deciphering news sentiment and stock price relationships in Indonesian companies: An AI-based exploration of industry affiliation and news co-occurrence |
| title_fullStr | Deciphering news sentiment and stock price relationships in Indonesian companies: An AI-based exploration of industry affiliation and news co-occurrence |
| title_full_unstemmed | Deciphering news sentiment and stock price relationships in Indonesian companies: An AI-based exploration of industry affiliation and news co-occurrence |
| title_short | Deciphering news sentiment and stock price relationships in Indonesian companies: An AI-based exploration of industry affiliation and news co-occurrence |
| title_sort | deciphering news sentiment and stock price relationships in indonesian companies: an ai-based exploration of industry affiliation and news co-occurrence |
| topic | HG Finance QA Mathematics |
| url | https://umpir.ump.edu.my/id/eprint/45284/ https://umpir.ump.edu.my/id/eprint/45284/ https://umpir.ump.edu.my/id/eprint/45284/ |