Exploring the sentiment in Borsa Istanbul with deep learning

Date

2024-01-01

Author

Atak Atalık, Alev

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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Sentiment analysis holds immense importance in finance and economics, addressing crucial issues such as principal–agent dynamics and information imbalances. The rise of natural language processing signifies a groundbreaking era in sentiment analysis, enabling the effective extraction of insights from textual data. Our research investigates the impact of qualitative financial data on firm valuation, utilizing sentiment extracted from annual financial disclosures, focusing on companies listed on the Borsa Istanbul Stock Exchange from 1998 to 2022. Employing a pre-trained transformer model, we develop sentiment indices and integrate textual data using a system-generalized method of moments. Our study aims to uncover how sentiment expressed in financial disclosures aids in mitigating challenges related to asymmetric information.

Subject Keywords

Asymmetric information, Emerging market, Endogeneity, Financial disclosure, FinBERT, FinRoBERTa, GMM, NLP, Sentiment, Tone

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85182373832&origin=inward
https://hdl.handle.net/11511/108059

Collections

Department of Economics, Article