Evaluation of Annual Reports by Text Analysis: An Application in Bist100 Index

Sedat Çerez, Abdullah Kürşat Merter, Yavuz Selim Balcıoğlu and Gökhan Özer
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Sedat Çerez: Gebze Technical University
Abdullah Kürşat Merter: Gebze Technical University
Yavuz Selim Balcıoğlu: Gebze Technical University
Gökhan Özer: Gebze Technical University

Journal of Finance Letters (Maliye ve Finans Yazıları), 2024, vol. 39, issue 121, 175-188

Abstract: In this study, the annual reports of 60 non-financial firms in the Borsa Istanbul (BIST) 100 index are examined, and the topics and sentiment tone of the reports are analyzed. Using the Latent Dirichlet Allocation method, it is found that four main topics are differentiated in the annual reports, and approximately 87% of the disclosures in the reports exhibit a positive sentiment and 13% of the disclosures exhibit a negative sentiment, indicating that there is a generally optimistic tone in the reports. This research highlights the potential of text analysis as a tool for extracting meaningful insights from corporate reports and offers a new approach to understanding corporate performance and strategy.

Keywords: Annual Reports; Text Analysis; Sentiment Analysis; Bist100 (search for similar items in EconPapers)
JEL-codes: C53 C81 C82 G32 (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:acc:malfin:v:39:y:2024:i:121:p:175-188

DOI: 10.33203/mfy.1338486

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