Mining semantic features in patent text for financial distress prediction

• Published in: Technological forecasting & social change 2023-05, Vol.190, p.122450, Article 122450
• Main Authors: Jiang, Cuiqing, Zhou, Yiru, Chen, Bo
• Format: Article
• Language: English
• Subjects: Financial distress prediction; Semantic features; Technological innovation; Text mining
• ISSN: 0040-1625; 1873-5509
• DOI: 10.1016/j.techfore.2023.122450

Financial distress prediction has been a popular topic over the decades. Most studies have used accounting features from financial statements to predict financial distress. Compared to listed companies, unlisted public companies have longer financial disclosure cycles, less required disclosure of market trading information, and higher financial risk. However, they can also have a strong ability to innovate and great growth potential, attributes that cannot be fully reflected in financial statements. In this study, as a supplement to accounting features, we propose a framework for mining the statistical features and semantic features in patent text by comprehensively analyzing the patent's structured information, abstract, claims, citations, and specifications. The results of empirical evaluation confirm that patent features contain incremental information related to financial distress. This research broadens the feature space of financial distress research and expands the research on patent text. It also provides decision support for banks approving loans, investment decision-making, and patent pledges. •We propose a framework to mine semantic features in patent text for financial distress prediction.•Senmantic features integrates patent text, citation, main products and strategic emerging industry information.•Our results demonstrate that patents contain incremental information in financial distress prediction scenario.