用职业网络预测香港金融从业者离职，发现离职有传染效应。
Network Contagion in Financial Labor Markets: Predicting Turnover in Hong Kong
- 构建12万+从业者、近5000家公司的时序职业网络。
- 当六个月内超三成同事离职，个人离职概率提升23%。
- 结合网络特征可使预测准确率提高30%，适合监管与人才管理。
员工流动是金融市场的关键挑战，但职业网络在职业变动中的作用仍不明确。利用香港证券及期货事务监察委员会（SFC）公开注册数据（2007–2024），我们构建了包含121,883名专业人士和4,979家公司的时序网络，用于分析与预测员工离职。提出一种基于图的特征传播框架，捕捉同侪影响与组织稳定性。分析显示存在传染效应：当六个月内超过30%的同事离职时，个人离职概率提升23%。将网络信号嵌入机器学习模型后，预测性能相比基线提升30%。结果表明，时序网络效应在劳动力动态中具有强预测力，展示了网络分析在监管监控、人才管理与系统性风险评估中的应用潜力。
原文摘要 · Abstract (English)
Employee turnover is a critical challenge in financial markets, yet little is known about the role of professional networks in shaping career moves. Using the Hong Kong Securities and Futures Commission (SFC) public register (2007-2024), we construct temporal networks of 121,883 professionals and 4,979 firms to analyze and predict employee departures. We introduce a graph-based feature propagation framework that captures peer influence and organizational stability. Our analysis shows a contagion effect: professionals are 23% more likely to leave when over 30% of their peers depart within six months. Embedding these network signals into machine learning models improves turnover prediction by 30% over baselines. These results highlight the predictive power of temporal network effects in workforce dynamics, and demonstrate how network-based analytics can inform regulatory monitoring, talent management, and systemic risk assessment.
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