Analysis of Factors Affecting Liquidity Risk of Listed Commercial Banks in China-- Based on the Panel Data Model

In this paper, 16 commercial banks listed on Shanghai and Shenzhen A-shares are selected as research samples and data from 2010 to 2019 are adopted. According to their mechanism of operation, scale of asset and etc, the research samples are divided into three groups. They are 16 listed commercial ba...

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Main Authors: Zhang Liulu, Zhao Qiujing
Format: Article
Language:English
Published: EDP Sciences 2021-01-01
Series:E3S Web of Conferences
Online Access:https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/29/e3sconf_eem2021_03006.pdf
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spelling doaj-48ddc4ccdac4497f8e2cb9426a180bea2021-05-28T12:41:13ZengEDP SciencesE3S Web of Conferences2267-12422021-01-012530300610.1051/e3sconf/202125303006e3sconf_eem2021_03006Analysis of Factors Affecting Liquidity Risk of Listed Commercial Banks in China-- Based on the Panel Data ModelZhang Liulu0Zhao Qiujing1Faculty of Humanities Shanghai Institute of TechnologyFaculty of Humanities Shanghai Institute of TechnologyIn this paper, 16 commercial banks listed on Shanghai and Shenzhen A-shares are selected as research samples and data from 2010 to 2019 are adopted. According to their mechanism of operation, scale of asset and etc, the research samples are divided into three groups. They are 16 listed commercial banks as a whole, 5 large state-owned listed commercial banks and 11 other medium and large listed commercial banks. The study makes an empirical analysis on the factors affecting the liquidity risk of listed commercial banks. Firstly, the factors affecting liquidity risk of listed commercial banks are divided into external and internal levels, and then descriptive analysis carried out on the factors at two levels. Then the stability of data is tested. After the data is tested, the panel data models applicable to 16 listed commercial banks as a whole, 5 large state-owned listed commercial banks and 11 other medium and large listed commercial banks are established through Hausman test and F test. The regression analysis of groups are conducted. Finally, according to regression analysis results, some reasonable recommendations are put forward.https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/29/e3sconf_eem2021_03006.pdf
collection DOAJ
language English
format Article
sources DOAJ
author Zhang Liulu
Zhao Qiujing
spellingShingle Zhang Liulu
Zhao Qiujing
Analysis of Factors Affecting Liquidity Risk of Listed Commercial Banks in China-- Based on the Panel Data Model
E3S Web of Conferences
author_facet Zhang Liulu
Zhao Qiujing
author_sort Zhang Liulu
title Analysis of Factors Affecting Liquidity Risk of Listed Commercial Banks in China-- Based on the Panel Data Model
title_short Analysis of Factors Affecting Liquidity Risk of Listed Commercial Banks in China-- Based on the Panel Data Model
title_full Analysis of Factors Affecting Liquidity Risk of Listed Commercial Banks in China-- Based on the Panel Data Model
title_fullStr Analysis of Factors Affecting Liquidity Risk of Listed Commercial Banks in China-- Based on the Panel Data Model
title_full_unstemmed Analysis of Factors Affecting Liquidity Risk of Listed Commercial Banks in China-- Based on the Panel Data Model
title_sort analysis of factors affecting liquidity risk of listed commercial banks in china-- based on the panel data model
publisher EDP Sciences
series E3S Web of Conferences
issn 2267-1242
publishDate 2021-01-01
description In this paper, 16 commercial banks listed on Shanghai and Shenzhen A-shares are selected as research samples and data from 2010 to 2019 are adopted. According to their mechanism of operation, scale of asset and etc, the research samples are divided into three groups. They are 16 listed commercial banks as a whole, 5 large state-owned listed commercial banks and 11 other medium and large listed commercial banks. The study makes an empirical analysis on the factors affecting the liquidity risk of listed commercial banks. Firstly, the factors affecting liquidity risk of listed commercial banks are divided into external and internal levels, and then descriptive analysis carried out on the factors at two levels. Then the stability of data is tested. After the data is tested, the panel data models applicable to 16 listed commercial banks as a whole, 5 large state-owned listed commercial banks and 11 other medium and large listed commercial banks are established through Hausman test and F test. The regression analysis of groups are conducted. Finally, according to regression analysis results, some reasonable recommendations are put forward.
url https://www.e3s-conferences.org/articles/e3sconf/pdf/2021/29/e3sconf_eem2021_03006.pdf
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