SYMMETRIC OR ASYMMETRIC INFORMATION? A MACHINE LEARNING APPROACH FOR FINANCIAL SENTIMENT

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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Natural language processing has been widely used for financial applications in recent years. In this paper, we Word Error Rate and Cosin Similarity for comparing and measuring text similarity and derivation in sets of financial disclosures from BIST100 companies. In addition to performing text extraction, we will provide a range of text analysis options, such as the readability metrics, word counts using pre-determined lists (e.g., forward-looking, uncertainty, tone, etc.), and comparison with reference corpus (word, parts of speech and semantic level). We aim to extract relevant financial information for financial sentiment analysis through Natural Language Processing and understand whether the information is symmetric or asymmetric. Therefore, 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.

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https://hdl.handle.net/11511/104959

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Department of Economics, Conference / Seminar