The enforcement of peer-to-peer (P2P) registration requirements in mid-2018 triggered a P2P market meltdown, highlighting the inherent challenge faced by Chinese market participants in distinguishing between genuine and fraudulent fintech firms. The difference-in-difference results suggest that the too-big-to-fail (TBTF) perception can effectively halve investor outflows and borrower outflows during periods of uncertainty. Dynamic analysis further validates the parallel-trend assumption and underscores the persistent influence of TBTF perception. Moreover, the empirical findings suggest that, in the face of a market downturn, fintech market participants become unresponsive to all other certification mechanisms, including venture capital participation, custodian banks, and third-party guarantees.

Utilizing a unique P2P dataset, this study employs the PSM-DID method to explore the learning effect brought about by default events on investors. The findings reveal that investors who experience their first default event demonstrate an improved ability to select a higher-quality project the next time. Notably, this positive effect is more pronounced when facing substantial defaults, as opposed to cases where overdue principal and interest are eventually settled. Investors' initial confidence in defaulted projects contributes to a greater enhancement of their investment skills. Furthermore, the beneficial impacts of defaulted events diminish as investors’ investment experience accumulates.

In recent years, numerous Chinese peer-to-peer (P2P) lending platforms have collapsed, prompting us to investigate the regulation and monitoring of the fintech industry. Using a unique dataset of P2P lending platforms in China, we examine the effect of government monitoring on platform collapses. Exploiting platforms’ locational proximity to regulatory offices as a proxy for government monitoring, we show that greater geographical distance results in a higher likelihood of platform collapse. Specifically, for every 10% increase in the driving distance from the platform to the local regulatory office, the likelihood of collapse increases by 10.2%. To establish causality, we conduct a differencein-differencesanalysis that exploits two exogenous shocks: government office relocation and subway station openings. We further explore two underlying channels: the information channel through which greater regulatory distance reduces the likelihood of regulators’ onsite visits and the resource constraint channel, through which greater regulatory distance significantly increases the local regulatory office’s monitoring costs. Overall, this study highlights the importance of onsite regulatory monitoring to ensure the viability of online lending platforms.

This chapter introduces alternative financial institutions (AFIs) in China that do not fall within traditional financial institution (FI) models. We describe their business models and development dynamics in the context of economic and financial reforms and technological advancement. We find that various AFIs are formed based on social, business, and virtual networks to overcome capital allocation barriers, reduce costs, or improve efficiency, providing financial services for the underserved. However, without proper regulations, these AFIs could pose alarming levels of risk on financial stability. They repeat a boom-and-bust pattern, in parallel with the government's initial laissez faire approach but later harsh interferences: being taken over by formal FIs or shut down as illegal practices until the exceptional Ant-Financial case. Improving investors' financial knowledge and regulators’ competency is critical for China to advance its financial system and develop mature FIs and AFIs. We recommend key features required in such a regulatory framework.

Over the last decade, China’s P2P lending industry has been seen as an important credit source but it has recently suffered from a wave of bankruptcies. Using 126,090 P2P loan deals from RenRen Dai, one of the biggest online P2P websites in China, this paper attempts to predict credit default probabilities for P2P lending by implementing machine-learning techniques. More specifically, thisstudy proposes a stacking ensemble machine-learning model to assess credit default risk for P2P lending platforms. A Max-Relevance and Min-Redundancy (MRMR) method is used for feature selection and then irrelevant features are eliminated by using k-means clustering method. Finally, the stacking ensemble model is performed to produce accurate and stable predictions in the feature subset. Experimental results show that stacking ensemble model yields high performance, not only in prediction accuracy but also in precision and recall. In comparison to single classifiers, the stacking ensemble machine-learning model has a minimum error rate and provides more accurate credit default risk prediction. The results also confirm the efficiency of the proposed stacking ensemble model through the area under the ROC curve.

In recent years, numerous Chinese peer-to-peer (P2P) lending platforms have collapsed, prompting us to investigate the regulation and monitoring of the fintech industry. Using a unique dataset of P2P lending platforms in China, we investigate the effect of the information environment on regulatory monitoring and platform collapse. Using the platforms’ proximity to regulatory offices as a proxy for information asymmetry, we show that an increase in distance reduces regulatory monitoring and increases the likelihood of platform collapse. Specifically, for every 1% increase in the driving distance between the local regulatory office and a P2P lending platform’s office, the platform’s likelihood of collapse increases by 1.011%. To establish causality, we conduct a difference-in-differences analysis that exploits two exogenous shocks: government office relocation and subway station openings. We provide evidence that proximity enhances monitoring quality by facilitating soft information collection, reducing platform failures. We further find two channels of this effect: (1) the information channel through which greater regulatory distance reduces the likelihood and frequency of regulators’ on-site visits and (2) the resource-constraint channel, through which greater regulatory distance significantly increases the local regulatory office’s monitoring costs. Overall, this study highlights the importance of the acquisition of soft information for regulatory monitoring to ensure the viability of fintech firms.

我们发现，股市泡沫和信贷市场中的信用风险存在因果关系。我们分析了来自人人贷（国内头部P2P众筹平台）的超过45万笔贷款数据，时间为2015年，当时A股正经历非理性的大起大落。随着上证综指突破3500点，散户们积极进入股市，并通过P2P平台融资，我们发现，此时P2P平台贷款的违约率以及违约程度都有了大幅提高。对于低质量贷款以及过分自信的贷款者，这种效应更加显著。其他一系列P2P市场状态指标，也都出现恶化。总之，我们认为，金融科技的发展会扩大金融风险，促使风险在不同市场之间蔓延。

Peer-to-Peer lending platforms encourage borrowers to obtain various credit certificates for information disclosure. Using unique data from one of China's largest Peer-to-Peer platforms, we show that borrowers of lower credit quality obtain more certificates to boost their credit profiles, while higher-quality ones do not. Uninformed credulous lenders take these nearly costless certificates as a positive signal to guide their nvestments. Consequently, loans applied by borrowers with more credit certificates have higher funding success but worse repayment performance. Overall, we document credit certificates fail to accurately signal borrowers' qualities due to adverse selection, resulting in distorted credit allocation and investment inefficiency.

随着互联网金融在我国的发展，网络借贷产品在家庭资产配置中逐渐受到青睐。本文以 2010 年 1 月 1 日至 2014 年 12 月 31 日中国日均交易量最大的 P2P 平台——红岭创投（www.my089.com）所有有效成功借款为研究样本， 发现股票市场投资者情绪对于网络借贷溢酬的整体线性影响为正向的溢出效应， 但适度情绪下的溢出效应与极端情绪下的传染效 应共同作用的结果在高次项加入回归时发现存在降低-提高-降低的 S 型效应， 说明在极端情绪下，负向的传染效会成为主导。 进一步利用借款项目逾期情况与借款基本信息进行 Probit 回归， 发现高潜在违约率的项目股市的投资者情绪对借款溢酬的影响仍符合 S 型， 但低潜在违约率的项目传染效应会弱化， 这说明投资者能够在极端情绪下识别低风险项目， 并能发 现它的避险功能。

In view of the growing importance of P2P lending in China, we investigate the role of the level of education of a borrower on the demand for funding. We collect and analyze data for more than 10,000 transactions obtained from Renrendai.com, a popular P2P company operating in China. Our analysis indicates that individuals with higher levels of education demand smaller loans for any given interest rate and lower interest rates for any given loan amount, controlling for a variety of factors. This finding applies to individuals demanding both personal and business loans. The same result holds, moreover, when an individual’s credit information is available to a P2P company online. Several robustness tests confirm our basic empirical findings.