We present the first study of the Public Register of Licensed Persons and Registered Institutions maintained by the Hong Kong Securities and Futures Commission (SFC) through the lens of complex network analysis. This dataset, spanning 21 years with daily granularity, provides a unique view of the evolving social network between licensed professionals and their affiliated firms in Hong Kong's financial sector. Leveraging large language models, we classify firms (e.g., asset managers, banks) and infer the likely nationality and gender of employees based on their names. This application enhances the dataset by adding rich demographic and organizational context, enabling more precise network analysis. Our preliminary findings reveal key structural features, offering new insights into the dynamics of Hong Kong's financial landscape. We release the structured dataset to enable further research, establishing a foundation for future studies that may inform recruitment strategies, policy-making, and risk management in the financial industry.

翻译：本研究首次通过复杂网络分析的视角，对香港证券及期货事务监察委员会（SFC）维护的《持牌人及注册机构公众纪录册》进行了系统性探究。该数据集时间跨度达21年且具有日粒度，为观察香港金融行业中持牌专业人士与其所属机构之间不断演化的社会网络提供了独特视角。我们利用大语言模型对机构（如资产管理公司、银行等）进行分类，并基于员工姓名推断其可能的国籍与性别。这一应用通过添加丰富的人口统计学与组织背景信息增强了数据集的价值，从而支持更精确的网络分析。初步研究结果揭示了关键的结构特征，为理解香港金融生态的动态演变提供了新见解。我们公开了结构化数据集以促进后续研究，为未来可能影响金融行业招聘策略、政策制定及风险管理的研究奠定基础。