Every publicly traded company in the US is required to file an annual 10-K
financial report, which contains a wealth of information about the company. In
this paper, we propose an explainable deep-learning model, called FinBERT-XRC,
that takes a 10-K report as input, and automatically assesses the post-event
return volatility risk of its associated company. In contrast to previous
systems, our proposed model simultaneously offers explanations of its
classification decision at three different levels: the word, sentence, and
corpus levels. By doing so, our model provides a comprehensive interpretation
of its prediction to end users. This is particularly important in financial
domains, where the transparency and accountability of algorithmic predictions
play a vital role in their application to decision-making processes. Aside from
its novel interpretability, our model surpasses the state of the art in
predictive accuracy in experiments on a large real-world dataset of 10-K
reports spanning six years.

本文介绍了一种名为 FinBERT-XRC 的可解释深度学习模型，它以 10-K 报告作为输入，自动评估其关联公司的事后收益波动风险，并在词语、句子和文集三个层面上同时提供分类决策的解释，此模型的预测透明性和准确性在金融领域的应用中起着重要作用。