Multi-Task Learning for Features Extraction in Financial Annual Reports
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arXiv
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| Auteurs principaux: | , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866909163219582976 |
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| author | Montariol, Syrielle Martinc, Matej Pelicon, Andraž Pollak, Senja Koloski, Boshko Lončarski, Igor Valentinčič, Aljoša |
| author_facet | Montariol, Syrielle Martinc, Matej Pelicon, Andraž Pollak, Senja Koloski, Boshko Lončarski, Igor Valentinčič, Aljoša |
| contents | For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) information. This textual data can provide valuable weak signals, for example through stylistic features, which can complement the quantitative data on financial performance or on Environmental, Social and Governance (ESG) criteria. In this work, we use various multi-task learning methods for financial text classification with the focus on financial sentiment, objectivity, forward-looking sentence prediction and ESG-content detection. We propose different methods to combine the information extracted from training jointly on different tasks; our best-performing method highlights the positive effect of explicitly adding auxiliary task predictions as features for the final target task during the multi-task training. Next, we use these classifiers to extract textual features from annual reports of FTSE350 companies and investigate the link between ESG quantitative scores and these features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2404_05281 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Task Learning for Features Extraction in Financial Annual Reports Montariol, Syrielle Martinc, Matej Pelicon, Andraž Pollak, Senja Koloski, Boshko Lončarski, Igor Valentinčič, Aljoša Computation and Language For assessing various performance indicators of companies, the focus is shifting from strictly financial (quantitative) publicly disclosed information to qualitative (textual) information. This textual data can provide valuable weak signals, for example through stylistic features, which can complement the quantitative data on financial performance or on Environmental, Social and Governance (ESG) criteria. In this work, we use various multi-task learning methods for financial text classification with the focus on financial sentiment, objectivity, forward-looking sentence prediction and ESG-content detection. We propose different methods to combine the information extracted from training jointly on different tasks; our best-performing method highlights the positive effect of explicitly adding auxiliary task predictions as features for the final target task during the multi-task training. Next, we use these classifiers to extract textual features from annual reports of FTSE350 companies and investigate the link between ESG quantitative scores and these features. |
| title | Multi-Task Learning for Features Extraction in Financial Annual Reports |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2404.05281 |