Multi-Task Learning for Features Extraction in Financial Annual Reports

arXiv:2404.05281v1 Announce Type: new
Abstract: 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 …

abstract arxiv companies cs.cl data example extraction features financial focus information multi-task learning performance quantitative reports textual through type