@inproceedings{wibisono-etal-2024-assessing,
    title = "Assessing the Impact of {ESG}-Related News on Stock Trading in the {I}ndonesian Market: A Text Similarity Framework Approach",
    author = "Wibisono, Okiriza  and
      Septiandri, Ali Akbar  and
      Najogie, Reinhard Denis",
    editor = "Chen, Chung-Chi  and
      Liu, Xiaomo  and
      Hahn, Udo  and
      Nourbakhsh, Armineh  and
      Ma, Zhiqiang  and
      Smiley, Charese  and
      Hoste, Veronique  and
      Das, Sanjiv Ranjan  and
      Li, Manling  and
      Ghassemi, Mohammad  and
      Huang, Hen-Hsen  and
      Takamura, Hiroya  and
      Chen, Hsin-Hsi",
    booktitle = "Proceedings of the Joint Workshop of the 7th Financial Technology and Natural Language Processing, the 5th Knowledge Discovery from Unstructured Data in Financial Services, and the 4th Workshop on Economics and Natural Language Processing",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.finnlp-1.4",
    pages = "34--39",
    abstract = "Environmental, Social, and Governance (ESG) perspectives have become integral to corporate decision-making and investment, with global regulatory mandates for ESG disclosure. The reliability of ESG ratings, crucial for assessing corporate sustainability practices, is compromised by inconsistencies and discrepancies across and within rating agencies, casting doubt on their effectiveness in reflecting true ESG performance and impact on firm valuations. While there have been studies using ESG-related news articles to measure their effect on stock trading, none have studied the Indonesian stock market. To address this gap, we developed a text similarity framework to identify ESG-related news articles based on Sustainability Accounting Standards Board (SASB) Standards without the need for manual annotations. Using news articles from one of the prominent business media outlets in Indonesia and an event study method, we found that 17.9{\%} out of 18,431 environment-related news are followed by increased stock trading on the firms mentioned in the news, compared to 16.0{\%} on random-dates datasets of the same size and firm composition. This approach is intended as a simpler alternative to building an ESG-specific news labeling model or using third-party data providers, although further analyses may be required to evaluate its robustness.",
}

Environmental, Social, and Governance (ESG) perspectives have become integral to corporate decision-making and investment, with global regulatory mandates for ESG disclosure. The reliability of ESG ratings, crucial for assessing corporate sustainability practices, is compromised by inconsistencies and discrepancies across and within rating agencies, casting doubt on their effectiveness in reflecting true ESG performance and impact on firm valuations. While there have been studies using ESG-related news articles to measure their effect on stock trading, none have studied the Indonesian stock market. To address this gap, we developed a text similarity framework to identify ESG-related news articles based on Sustainability Accounting Standards Board (SASB) Standards without the need for manual annotations. Using news articles from one of the prominent business media outlets in Indonesia and an event study method, we found that 17.9{\%} out of 18,431 environment-related news are followed by increased stock trading on the firms mentioned in the news, compared to 16.0{\%} on random-dates datasets of the same size and firm composition. This approach is intended as a simpler alternative to building an ESG-specific news labeling model or using third-party data providers, although further analyses may be required to evaluate its robustness.
Wibisono, Okiriza Septi ri, Ali Akbar Najogie, Reinhard Denis
Assessing the Impact of ESG-Related News on Stock Trading in the Indonesian Market: A Text Similarity Framework Approach