Performance determinants of China's internet lending business: a study on the regulated and unregulated companies

In recent years, commercial internet lending has become a major industry in China, and internet lending platforms have also begun using innovative ways to mobilize and disseminate small business capitals. However, these platforms are not well managed as there has been an increase in the number of de...

Full description

Saved in:
Bibliographic Details
Main Authors: Tian, Zhongkai, Sheikh Hassan, Ahmad Fahmi
Format: Article
Published: Human Resource Management Academic Research Society 2021
Online Access:http://psasir.upm.edu.my/id/eprint/94539/
https://hrmars.com/index.php/IJARBSS/article/view/10976/Performance-Determinants-of-Chinas-Internet-Lending-Business-A-Study-on-the-Regulated-and-Unregulated-Companies
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.94539
record_format eprints
spelling my.upm.eprints.945392022-12-02T07:50:15Z http://psasir.upm.edu.my/id/eprint/94539/ Performance determinants of China's internet lending business: a study on the regulated and unregulated companies Tian, Zhongkai Sheikh Hassan, Ahmad Fahmi In recent years, commercial internet lending has become a major industry in China, and internet lending platforms have also begun using innovative ways to mobilize and disseminate small business capitals. However, these platforms are not well managed as there has been an increase in the number of defaults in recent years. In order to strengthen the governance of the internet lending business, China’s newly established National Internet Finance Association (NIFA) has introduced some regulations aiming at reducing the risk of defaults and enhancing performance of its regulated members as a way to improve competitiveness. The objective of this study is to identify the performance determinants of China’s regulated and unregulated internet lending companies. Using the binary logistic regression method, this study will investigate the differences of the two types of lending platforms through comparison. We used 343 platforms based on one-month’s data so as to explore the differences. Our study provides evidence showing the existence of four performance determinants for both regulated and unregulated companies; namely, total transactions, number of investors, loan periods, and interest rates. These determinants could be used by individual investors to avoid potential risk and investment decision making while being instrumental for the policymakers when regulating this market. Human Resource Management Academic Research Society 2021-11 Article PeerReviewed Tian, Zhongkai and Sheikh Hassan, Ahmad Fahmi (2021) Performance determinants of China's internet lending business: a study on the regulated and unregulated companies. International Journal of Academic Research in Business and Social Sciences, 11 (11). 65 - 78. ISSN 2222-6990 https://hrmars.com/index.php/IJARBSS/article/view/10976/Performance-Determinants-of-Chinas-Internet-Lending-Business-A-Study-on-the-Regulated-and-Unregulated-Companies 10.6007/IJARBSS/v11-i11/10976
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
description In recent years, commercial internet lending has become a major industry in China, and internet lending platforms have also begun using innovative ways to mobilize and disseminate small business capitals. However, these platforms are not well managed as there has been an increase in the number of defaults in recent years. In order to strengthen the governance of the internet lending business, China’s newly established National Internet Finance Association (NIFA) has introduced some regulations aiming at reducing the risk of defaults and enhancing performance of its regulated members as a way to improve competitiveness. The objective of this study is to identify the performance determinants of China’s regulated and unregulated internet lending companies. Using the binary logistic regression method, this study will investigate the differences of the two types of lending platforms through comparison. We used 343 platforms based on one-month’s data so as to explore the differences. Our study provides evidence showing the existence of four performance determinants for both regulated and unregulated companies; namely, total transactions, number of investors, loan periods, and interest rates. These determinants could be used by individual investors to avoid potential risk and investment decision making while being instrumental for the policymakers when regulating this market.
format Article
author Tian, Zhongkai
Sheikh Hassan, Ahmad Fahmi
spellingShingle Tian, Zhongkai
Sheikh Hassan, Ahmad Fahmi
Performance determinants of China's internet lending business: a study on the regulated and unregulated companies
author_facet Tian, Zhongkai
Sheikh Hassan, Ahmad Fahmi
author_sort Tian, Zhongkai
title Performance determinants of China's internet lending business: a study on the regulated and unregulated companies
title_short Performance determinants of China's internet lending business: a study on the regulated and unregulated companies
title_full Performance determinants of China's internet lending business: a study on the regulated and unregulated companies
title_fullStr Performance determinants of China's internet lending business: a study on the regulated and unregulated companies
title_full_unstemmed Performance determinants of China's internet lending business: a study on the regulated and unregulated companies
title_sort performance determinants of china's internet lending business: a study on the regulated and unregulated companies
publisher Human Resource Management Academic Research Society
publishDate 2021
url http://psasir.upm.edu.my/id/eprint/94539/
https://hrmars.com/index.php/IJARBSS/article/view/10976/Performance-Determinants-of-Chinas-Internet-Lending-Business-A-Study-on-the-Regulated-and-Unregulated-Companies
_version_ 1753789922960998400
score 13.211869