Financial Sentiment Index with Natural Language Processing

Date

2023-04-12

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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In this paper, we aim to create a financial sentiment index by investigating the company’s voluntary information disclosures using 10-K reports. We extract relevant financial information for sentiment analysis through Natural Language Processing (NLP). We measure strategy-related disclosures, and their cross-sectional variation and classify report content into generic sections using synonym lists divided into four main categories according to their liquidity risk profile, risk positions, intra-annual information, and their exposure to risk. We create an adequate analytical tool and a financial dictionary to depict the importance of granular financial disclosure for investors to identify correctly the risk-taking behaviour and hence make the aggregated effects traceable.

URI

https://ebesweb.org/proceedings/
https://hdl.handle.net/11511/104931

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Department of Economics, Conference / Seminar