Accurate assessment of financial distress of SMEs is critical as it has strong implications for various stakeholders to understand the firm's financial health. Recent studies start to leverage network data and suggest the effect of event propagation for predicting financial distress. Yet such methods face methodological challenges in determining and explaining event propagation due to heterogeneous entities and events. In this research, we propose to extend graph contrastive learning and interpretable machine learning in the context of a firm network formed by distinct entities (e.g., firms and persons) and events (i.e., positive and negative), and employ the propagation influence of events in firm networks for financial distress assessment of SMEs. To this end, we design a novel artifact, i.e., adaptive interpretable heterogeneous graph contrastive learning, by drawing on homophily and social learning theories. Our experimental results demonstrate the effectiveness of the proposed artifacts and suggest the differing effects of positive vs. negative events on the financial distress of SMEs. This research contributes to the IS and explainable graph AI literature by improving the assessment and interpretability of network-based financial distress of SMEs.

中文翻译：

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通过事件传播评估中小企业的财务困境：自适应可解释图对比学习模型

准确评估中小企业的财务困境至关重要，因为它对于各利益相关者了解公司的财务健康状况具有重要意义。最近的研究开始利用网络数据并提出事件传播对预测财务困境的影响。然而，由于异构实体和事件，这些方法在确定和解释事件传播方面面临方法论挑战。在这项研究中，我们建议在由不同实体（例如公司和个人）和事件（即正面和负面）形成的公司网络的背景下扩展图对比学习和可解释机器学习，并利用事件的传播影响中小企业财务困境评估的坚实网络。为此，我们利用同质性和社会学习理论设计了一种新颖的工件，即自适应可解释异构图对比学习。我们的实验结果证明了所提出的工件的有效性，并表明积极事件与消极事件对中小企业财务困境的不同影响。这项研究通过改善中小企业网络财务困境的评估和可解释性，为信息系统和可解释的图人工智能文献做出了贡献。