Network Contagion in Financial Labor Markets: Predicting Turnover in Hong Kong
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866918138346471424 |
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| author | AlKetbi, Abdulla Yam, Patrick Marti, Gautier Jaradat, Raed |
| author_facet | AlKetbi, Abdulla Yam, Patrick Marti, Gautier Jaradat, Raed |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_08001 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Network Contagion in Financial Labor Markets: Predicting Turnover in Hong Kong AlKetbi, Abdulla Yam, Patrick Marti, Gautier Jaradat, Raed Social and Information Networks Machine Learning Applications 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. |
| title | Network Contagion in Financial Labor Markets: Predicting Turnover in Hong Kong |
| topic | Social and Information Networks Machine Learning Applications |
| url | https://arxiv.org/abs/2509.08001 |