Effective financial distress prediction (FDP) can discover a company's potential financial risks and support relevant decisions in a timely manner. Previous studies on FDP have mostly focused on using financial indicators and periodic reports. Compared with periodic reports, current reports disclose major events in a timelier manner. But leveraging the information in current reports involves the critical challenges of capturing the complex semantics and measuring the importance of heterogeneous events. To this end, we propose a novel deep learning method, a user-response-guided deep attention network (URGDAN), to predict financial distress using current reports. In the proposed method, we construct a deep learning architecture to integrate financial indicators, current report texts, and user responses. URGDAN leverages the user responses to current reports to guide the semantic feature representation of the reports, it also identifies event information that has a significant correlation with company financial distress. Empirical evaluation shows that URGDAN significantly improves predictive performance and can accurately determine the importance of different current reports. Our work provides practical implications for creditors and investors.

中文翻译：

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使用当前报告预测财务困境：一种基于用户响应引导注意力的新型深度学习方法

有效的财务困境预测（FDP）可以发现企业潜在的财务风险并及时支持相关决策。以往对FDP的研究主要集中于使用财务指标和定期报告。与定期报告相比，定期报告更及时地披露重大事件。但利用当前报告中的信息涉及捕获复杂语义和衡量异构事件重要性的关键挑战。为此，我们提出了一种新颖的深度学习方法，即用户响应引导的深度注意力网络（URGDAN），以使用当前报告预测财务困境。在所提出的方法中，我们构建了一个深度学习架构来集成财务指标、当前报告文本和用户响应。URGDAN 利用用户对当前报告的响应来指导报告的语义特征表示，它还识别与公司财务困境具有显着相关性的事件信息。实证评估表明，URGDAN 显着提高了预测性能，并且可以准确确定当前不同报告的重要性。我们的工作为债权人和投资者提供了实际意义。