AI 中文总结
针对AI相关半导体企业，提出异构图补丁Transformer，融合多源信号并沿供应链关系传播风险，预测季度财务变化，在116家企业实验中误差最低。
AI 中文摘要
现代半导体生产依赖于全球分布的多层级供应链，其中一家企业的财务压力会延迟传播，并最终影响设计AI芯片公司的收入、库存和盈利能力。大多数企业只能看到其直接合作伙伴，而先前的预测研究主要针对基于市场的风险度量，因此很少有工具能够预测供应链压力将如何体现在报告的财务数据中。在本研究中，我们提出了一种异构图补丁Transformer，用于预测未来一个季度和两个季度的这些季度变化。该模型从涵盖60个季度的15,186家公司网络中学习，通过学习的门控机制将季度基本面与宏观贸易、事件和灾害信号融合，通过类型化、方向特定的传播在供应商、客户、所有权和总部关系中传递风险，并使用基于补丁的标记化对传播的历史进行编码。在对116家重点半导体企业的初步实验中，所提出的模型在两个预测时间范围的所有目标上均实现了最低误差，并且其盈利能力优势在两个季度的时间范围内进一步扩大。这些预测可以帮助供应链管理者和投资者在中断出现在报告的财务数据之前采取行动。
英文摘要
Modern semiconductor production relies on a globally distributed, multi-tier supply chain in which financial stress at one firm spreads with a delay and eventually affects the revenue, inventory, and profitability of the companies that design AI chips. Most firms see only their direct partners, and prior predictive research has mainly targeted market-based risk measures, so few tools forecast how supply chain stress will appear in reported financials. In this study, we propose a heterogeneous graph patch transformer that forecasts these quarterly changes one and two quarters ahead. Learning from a 15,186-company network over 60 quarters, the proposed model fuses quarterly fundamentals with macro-trade, event, and disaster signals through learned gates, carries risk across supplier, customer, ownership, and headquarters relations through typed, direction-specific propagation, and encodes the propagated histories with patch-based tokenization. In preliminary experiments on 116 focal semiconductor firms, the proposed model achieves the lowest error on every target at both horizons, and its profitability advantage widens at the two-quarter horizon. These forecasts can help supply chain managers and investors act before disruptions appear in reported financials.
Comments15 pages, 3 figures, 3 tables
