Date: 16 Sep 2021 (Thu)
Webinar
Monthly Webinar Series 2021:
Capital Market Development: China and Asia
10:00 am – 11:10 am, Thursday (Beijing-Singapore Time)
10:00 pm, Wednesday (US Time / Eastern Time)
The use of massive amounts of data by large technology firms (big techs) to assess firms’ creditworthiness could reduce the need for collateral in solving asymmetric information problems in credit markets. Using a unique dataset of more than two million Chinese firms that received credit from both an important big tech firm (Ant Group) and traditional commercial banks, this paper investigates how different forms of credit correlate with local economic activity, house prices and firm characteristics. The authors find that big tech credit does not correlate with local business conditions and house prices when controlling for demand factors, but reacts strongly to changes in firm characteristics, such as transaction volumes and network scores used to calculate firm credit ratings. By contrast, both secured and unsecured bank credit react significantly to local house prices, which incorporate useful information on the environment in which clients operate and on their creditworthiness. This evidence implies that a greater use of big tech credit – granted on the basis of machine learning and big data – could reduce the importance of collateral in credit markets and potentially weaken the financial accelerator mechanism.
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Speaker
Yiping HUANG, Sinar Mas Chair Professor of Finance of Economics, Deputy Dean of the National School of Development, and Director of the Institute of Digital Finance, Peking University
Co-authors:
Leonardo GAMBACORTA, Head of Innovation and the Digital Economy, Monetary and Economic Department, Bank for International Settlements
Zhenhua LI, Executive Director, Ant Group Research Institute
Han QIU, Economist, Bank for International Settlements
Shu CHEN, Algorithm Engineer, Ant Group
Discussant
Wei JIANG, Arthur F. Burns Professor of Free and Competitive Enterprise, Columbia Business School, Finance & Economics Division, Columbia University
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Session Format
Each session lasts for 1 hour 10 minutes (25 minutes for the author, 25 minutes for the discussant and 20 minutes for participants' Q&A). Sessions will be recorded and posted on ABFER's web, except in cases where speakers or discussants request us not to.
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Registration
Please register in http://abfer.org/events/abfer-events/212:webinarseries2021reg. A unique Zoom webinar link will be sent to you two days before the event. (Notice: Videos and screenshots will be taken during each session for the purpose of marketing, publicity purposes in print, electronic and social media)
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Organizing Committee
Zhiguo HE, Jun PAN, Michael SONG, Bernard YEUNG, Bohui ZHANG, Xiaoyan ZHANG (co-chaired by Zhiguo HE and Bernard YEUNG)
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Hosting Organizers
ABFER and University of Chicago's Becker Friedman Institute China (BFI-China)
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Collaborating Organizers
National University of Singapore (NUS) Business School, Shanghai Advanced Institute of Finance (SAIF), The Chinese University of Hong Kong (CUHK) Department of Economics, CUHK-Shenzhen and Tsinghua University PBC School of Finance (Tsinghua PBCSF)
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Event Website: http://abfer.org/events/abfer-events/monthly-webinar-series/211:webinarseries2021