Extracting information and sentiment analysis on dialogue in financial results briefing

Hiromasa Nakatsuka () and Yoshiyuki Suimon ()
Additional contact information
Hiromasa Nakatsuka: Nomura Securities Co., Ltd.
Yoshiyuki Suimon: Keio University

Digital Finance, 2025, vol. 7, issue 4, No 1, 605-621

Abstract: Abstract Financial results briefings are an important corporate disclosure for Japanese companies, and dialogue-style Q&A sessions provide valuable information. However, their conversational and colloquial style is different from the typical targets of financial sentiment analysis, which presents challenges for sentiment classification. In this paper, we propose an approach that leverages large language models (LLMs) to enhance sentiment analysis of Q&A sessions. We segment the Q&A transcripts into conversational chunks and use ChatGPT to summarize and standardize their content, which enables more accurate sentiment classification with a BERT model fine-tuned for financial contexts. Using an event study on stock returns, we find that summary-based sentiment is significantly related to abnormal returns (ARs). We further compare BERT-based sentiment with sentiment scores directly assigned by ChatGPT, and find that both explain ARs. Our findings indicate that LLMs can effectively enhance sentiment analysis across various financial documents with different writing styles, including dialogue-style transcripts.

Keywords: Financial results briefing; Large language model; Machine learning; Sentiment analysis (search for similar items in EconPapers)
JEL-codes: G11 G14 G17 (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s42521-025-00159-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:digfin:v:7:y:2025:i:4:d:10.1007_s42521-025-00159-y

Ordering information: This journal article can be ordered from
https://www.springer.com/finance/journal/42521

DOI: 10.1007/s42521-025-00159-y

Access Statistics for this article

Digital Finance is currently edited by Wolfgang Karl Härdle, Steven Kou and Min Dai

More articles in Digital Finance from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().