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.

翻译：为评估公司各项绩效指标，研究重心正从严格基于财务（量化）的公开披露信息转向定性（文本）信息。此类文本数据可提供有价值的弱信号（例如通过文体特征），从而补充财务绩效或环境、社会与治理（ESG）标准方面的量化数据。本研究采用多种多任务学习方法对金融文本进行分类，重点聚焦金融情感倾向、客观性、前瞻性句子预测以及ESG内容检测。我们提出了不同方法以整合跨任务联合训练所提取的信息；其中表现最佳的方法突出表明，在多任务训练过程中，将辅助任务预测结果显式地作为最终目标任务的特征具有积极效果。随后，我们运用这些分类器从FTSE350公司的年报中提取文本特征，并探究ESG量化评分与这些特征之间的关联。