SOAS Research Online

A Free Database of the Latest Research by SOAS Academics and PhD Students

[skip to content]

Shao, Shuai and Bo, Hong (2022) 'Behavioural Aspects of China's P2P Lending.' European Journal of Finance, 28 (1). pp. 30-45.

[img]
Preview
Text - Published Version
Available under License Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 (CC BY-NC-ND 4.0).

Download (1MB) | Preview

Abstract

In this paper, we argue that China's P2P lending is influenced by the behavioural factors of P2P platforms. This is because severe information asymmetry results in high uncertainty surrounding China's P2P lending industry. Specifically, we examine three behavioural aspects of P2P lending: lending sentiments, herding, and speculation. Using a sample of 918 P2P platforms from October 2015-September 2019, we document that positive P2P news release in the media (sentiments) encourages P2P lending; P2P platforms herd on their peers in making lending decisions; and P2P lending contains speculative elements and is driven by real estate bubbles. Moreover, we find that these behavioural effects are less profound if P2P platforms adopt a fund custody mechanism in which commercial banks provide custodian services for investor funds used for P2P lending. This result suggests that behavioural factors are more important in explaining P2P lending when information asymmetry is more severe. We obtain these results by controlling for other usual factors that can explain P2P lending, including characteristics of P2P platforms, macroeconomic variables, and other variables reflecting features of P2P operating environment. Our results suggest that regulators should monitor risk management of P2P platforms and reduce asymmetric information faced by participants in China's P2P lending market.

Item Type: Journal Article
Keywords: P2P lending, information asymmetry, lending sentiments, herding, speculation, China
SOAS Departments & Centres: Departments and Subunits > School of Finance & Management
ISSN: 1351847X
DOI (Digital Object Identifier): https://doi.org/10.1080/1351847X.2021.1880459
Date Deposited: 12 Jan 2021 09:29
URI: https://eprints.soas.ac.uk/id/eprint/34678

Altmetric Data

Statistics

Download activity - last 12 monthsShow export options
Downloads since deposit
6 month trend
67Downloads
6 month trend
167Hits
Accesses by country - last 12 monthsShow export options
Accesses by referrer - last 12 monthsShow export options

Repository staff only

Edit Item Edit Item