Behavioural aspects of China's P2P lending

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.

KEY WORDS:

• P2P lending
• information asymmetry
• lending sentiments
• herding
• speculation
• China

Acknowledgement

Anonymous referees are acknowledged for comments on earlier versions of this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Notes

1 According to the Home of Online Lending, an organisation that compiles P2P data in China, ‘problematic’ or ‘troubled’ P2P platforms are the ones that have difficulty paying off investors, have been investigated by national economic crime investigation department, or whose owners have run away with investors’ money.

2 For example, in 2015, the central bank issued guidance on promoting healthy development of internet finance. In August 2016, the Chinese Banking Regulatory Commission (CBRC) issued ‘Interim Measures for the Administration of the Business Activities of Online Lending Information Intermediary Institutions’. In June 2017, China issued regulations on internet financial information disclosure, and a series of notices on special rectification, capital management rectification, compliance supervision, and self-regulatory inspection.

3 We include a few macro-level variables in the empirical model. The monthly observation of these variables is the same for all cross-section units (P2P platforms). This makes the GMM estimation procedure difficult to implement. Therefore, we use a panel data fixed effect model. We use the lagged-one observations of explanatory variables to consider the endogeneity issue.

4 Although this is a plausible explanation, it appears to be very difficult to formally test it due to data restrictions.