Identifying driving factors of urban digital financial network—based on machine learning methods

Xiaojie Huang; Gaoke Liao; Xiaojie Huang; Gaoke Liao

doi:10.3934/era.2022239

Electronic Research Archive

2022, Volume 30, Issue 12: 4716-4739. doi: 10.3934/era.2022239

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Identifying driving factors of urban digital financial network—based on machine learning methods

Xiaojie Huang ¹,
Gaoke Liao ^{2
,
,}

1.
School of Economics and Statistics, Guangzhou University, Guangzhou 510006, China
2.
Guangzhou Institute of International Finance, Guangzhou University, Guangzhou 510006, China

Received: 19 October 2022 Revised: 30 November 2022 Accepted: 05 December 2022 Published: 19 December 2022

With the continuous development of digital finance, the correlation among urban digital finance has been increasing. In this paper, we further apply machine learning methods to study the driving factors of urban digital finance networks based on the construction of urban digital finance spatial network associated with a sample of 278 cities in China. The results of network characteristics analysis show that the core-edge structure of an urban digital finance network shows the characteristics of gradual deepening and orderly distribution; the core cities show reciprocal relationships with each other, and the edge cities lack connection with each other; the core cities match the structural hole distribution and the edge cities are limited by the network capital in their development. The results of driver analysis show that year-end loan balances, science and technology expenditures and per capita gross regional product are the main drivers of urban digital financial networks.
- digital financial,
- network,
- driving factor,
- machine learning
Citation: Xiaojie Huang, Gaoke Liao. Identifying driving factors of urban digital financial network—based on machine learning methods[J]. Electronic Research Archive, 2022, 30(12): 4716-4739. doi: 10.3934/era.2022239

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Abstract

With the continuous development of digital finance, the correlation among urban digital finance has been increasing. In this paper, we further apply machine learning methods to study the driving factors of urban digital finance networks based on the construction of urban digital finance spatial network associated with a sample of 278 cities in China. The results of network characteristics analysis show that the core-edge structure of an urban digital finance network shows the characteristics of gradual deepening and orderly distribution; the core cities show reciprocal relationships with each other, and the edge cities lack connection with each other; the core cities match the structural hole distribution and the edge cities are limited by the network capital in their development. The results of driver analysis show that year-end loan balances, science and technology expenditures and per capita gross regional product are the main drivers of urban digital financial networks.

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