Mapping Hong Kong's Financial Ecosystem: A Network Analysis of the SFC's Licensed Professionals and Institutions

TL;DR

This study applies complex network analysis to Hong Kong's financial sector using 21 years of SFC licensed professionals and firms data, enhanced by language models classifying demographics and firm types, revealing structural insights.

Contribution

First comprehensive network analysis of Hong Kong's financial ecosystem using SFC data, integrating demographic inference with large language models for enriched insights.

Findings

Identified key structural features of the financial network

Revealed demographic and organizational patterns

Provided a structured dataset for future research

Abstract

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.