2024/05/03 by Xue Wen Tan, Tan, Xue Wen, Stanley Kok +1 · 1 citation
Business, Management and Accounting · Decision Sciences · Economics, Econometrics and Finance · #Credit Risk and Financial Regulations #FOS: Computer and information sciences #FOS: Economics and business #Financial Distress and Bankruptcy Prediction #Machine Learning (cs.LG) #Risk Management (q-fin.RM) #Stock Market Forecasting Methods
paper · pdf · doi:10.48550/arxiv.2405.01881
openalex publication_date 2024/05/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.