P2P Lending Platform Reputation in China

The core purpose of this research is to analyze how the reputation of Chinese P2P lending platforms influences the investment decisions of lenders. The study specifically aims to determine both the direct and indirect effects (mediated effects) of platform reputation on investor choices. This is a crucial area of investigation given the rapid growth and subsequent instability within the Chinese P2P lending market. The researchers aim to quantify the influence of reputation, moving beyond qualitative assessments to provide empirical evidence of its impact on investor behavior. This is important because understanding this relationship can help mitigate risks and promote the sustainable development of the P2P lending sector in China. The research acknowledges the existence of information asymmetry and moral hazard within the industry and aims to explore how platform reputation can serve as a mechanism to reduce these risks. The study uses a quantitative approach, relying on statistical analysis of data collected from a significant number of platforms to reach its conclusions.

The research employs a quantitative methodology, utilizing data collected from 478 Chinese P2P lending platforms. These platforms were sourced from two influential third-party portals: WDZJ (网贷之家) and P2Peye (网贷天眼). The study utilizes a beta function to calculate platform reputation, a choice informed by a preceding game-theoretic analysis of the reputation mechanism. This model allows for the calculation of a platform reputation score based on investor evaluations. The research then employs statistical techniques to examine the relationship between the calculated platform reputation scores and investor investment choices. This quantitative approach allows for the rigorous testing of hypotheses regarding the direct and indirect effects of platform reputation on investment decisions. The large sample size of 478 platforms provides a robust basis for the analysis and enhances the generalizability of the research findings. The choice of data sources and the use of game theory to inform the reputation model underscore the methodologically rigorous nature of this research.

This section establishes a theoretical foundation for understanding platform reputation and its impact on investor behavior in the context of Chinese P2P lending. It begins by referencing Akerlof's 'Market for Lemons' theory, highlighting the problem of information asymmetry where sellers (platforms) possess more information than buyers (investors) about asset quality. The inherent uncertainty in the used car market, where buyers risk purchasing a 'lemon', is analogous to the risk investors face in the P2P lending market. The theory establishes the challenges of evaluating platform quality in a context where information is incomplete and potentially misleading. To address this, the research then introduces Spence's signaling theory, suggesting that platforms with higher quality can signal this through costly actions (like providing warranties in the used car example). This concept informs the study's approach to defining and measuring platform reputation. Ultimately, the section leverages game theory to build a reputation mechanism model which shows that investor ratings serve as signals distinguishing high-quality platforms from low-quality ones, creating a dynamic where high-quality platforms maintain better reputations over repeated lending cycles. This framework informs the subsequent analysis of the relationship between reputation and investor behavior.

The research explicitly addresses the presence of moral hazard in the Chinese P2P lending market, defining it as a situation where platforms, due to information asymmetry, engage in risky behaviors or conceal information to maximize their own profits at the expense of investors. Krugman's definition of moral hazard, "any situation in which one person makes the decision about how much risk to take, while someone else bears the cost if things go badly," serves as the starting point. The authors explain how this manifests in the P2P lending context, with platforms having better information about borrowers than investors. This asymmetry incentivizes platforms to take on more risk or misrepresent information to increase short-term gains, potentially leading to investor losses. The severe consequences of this moral hazard are illustrated by the 'financial explosion' ('Baolei') of 2018, where the collapse of numerous platforms caused significant financial losses and public distress. The lack of formal regulations in the initial stages of the Chinese P2P lending market (2006-2015) exacerbated this problem. The contrast between the well-regulated P2P environments in the UK and US and the relatively unregulated Chinese market highlights the vulnerability of Chinese P2P investors and emphasizes the importance of platform reputation as a mitigating factor in this high-risk environment.

The paper posits that platform reputation acts as a critical mechanism for mitigating moral hazard in the Chinese P2P lending market. It builds on existing literature on reputation mechanisms, such as the KMRW model, which emphasizes the role of reputation premiums in fostering cooperation and avoiding prisoner's dilemma scenarios in repeated games. The analysis suggests that investor ratings, accumulated over time, act as signals that help differentiate platforms of varying quality and trustworthiness. Platforms with consistently positive ratings are perceived as less likely to engage in moral hazard. This concept of reputation is further described as a form of institution-based trust, contrasting with interpersonal trust. Institution-based trust, rooted in the platform’s mechanisms designed to protect investors, is crucial in the largely anonymous online environment. In a repeated game context, investors who experience negative outcomes (due to a platform's dishonest actions) can spread information to other potential investors, impacting future investment. This self-regulating feature of the reputation mechanism can reduce the incentive for platforms to engage in morally hazardous behavior, emphasizing the essential role of platform reputation in building investor confidence and fostering the stability of the Chinese P2P lending market.

This section outlines the hypotheses that guide the empirical analysis. The central hypothesis explores the direct effect of P2P platform reputation on investor lending decisions, specifically examining its impact on the volume of transactions. The study acknowledges that other factors might influence both platform reputation and investment volume indirectly; therefore, the research also investigates these indirect or mediating effects. The hypotheses anticipate a positive relationship between platform reputation and investment volume, suggesting that platforms with higher reputations attract greater investor participation. However, the research carefully considers the potential mediating role of other variables that can influence both reputation and investment decisions. The study anticipates that credit-enhancing mechanisms such as registered capital, guarantee methods, and platform ownership structure will play a mediating role, meaning these factors influence both platform reputation and the investor's final lending choice. This nuanced approach accounts for the complexity of the Chinese P2P lending environment and allows for a more comprehensive understanding of the factors driving investor behavior.

This section defines the key variables used in the study and explains how they are measured. The dependent variable is the natural logarithm of transaction volume (lnVolume), representing the cumulative transaction volume of each platform from June 1st to December 31st, 2017. The core independent variable is platform reputation, which is calculated using a beta function, reflecting the aggregated investor evaluations. The study acknowledges and adjusts for limitations in simple beta function calculations, acknowledging that negative reviews carry more weight than positive reviews and that recent reviews have a stronger influence. Other key independent variables include the natural logarithm of registered capital (lnCapital), types of guarantee methods used (Guarantee), platform type (categorized as private, bank-involved, or publicly listed), and the geographic location of the platform (categorized into eastern, central, and western regions of China). These variables are chosen to account for the various factors that potentially influence both platform reputation and investor decisions. By carefully defining and measuring these variables, the study lays a robust foundation for its quantitative analysis and ensures that the results are meaningful and interpretable.

To isolate the impact of platform reputation, the study incorporates control variables that might influence investor decisions independently. These control variables include factors relating to credit enhancement, such as registered capital, guarantee methods used by the platforms, and the type of platform (private, bank-involved, or publicly listed). The inclusion of these control variables helps to account for other factors that contribute to investor trust and risk assessment, thus improving the accuracy of the analysis. The geographic location of the platforms (eastern, central, or western China) is also included as a control variable, recognizing that regional differences in economic development and social trust may influence investor behavior. The data for the analysis comes from 478 P2P platforms listed on two major Chinese online portals: WDZJ (网贷之家) and P2Peye (网贷天眼). This large dataset is vital for the statistical rigor of the study, ensuring enough observations to perform robust statistical tests and to draw meaningful conclusions about the relationship between platform reputation and investor investment choices. The researchers also validate the reliability of their reputation calculation by comparing it with rankings from WDZJ, finding a significant level of agreement.

The initial analysis employs Spearman's rank correlation coefficient to explore the relationships between platform reputation and other variables. A statistically significant positive correlation is found between platform reputation and both transaction volume (lnVolume) and the diversity of guarantee methods (Guarantee). This suggests that platforms with stronger reputations tend to have higher transaction volumes and employ a wider range of risk mitigation strategies. Furthermore, a positive correlation is observed between transaction volume and several other variables, including registered capital (lnCapital), loan duration (Month), and platform location in the eastern region (Address1). Conversely, negative correlations are found between platform reputation and interest rates (Interest), as well as between transaction volume and interest rates and private platform ownership (Type 1). These initial findings indicate that platform reputation is a significant factor influencing transaction volume, but also highlight the importance of other factors, such as capital adequacy, geographical location, and ownership structure, in driving investor behavior. Interest rates, contrary to expectations, show a negative relationship with both reputation and transaction volume, suggesting that interest rates are not the primary driver of investor choices.

To delve deeper into the relationships between platform reputation and transaction volume, the study uses more sophisticated statistical techniques. Median regression, OLS regression, and random-effects OLS regression are employed to analyze the direct and indirect (mediating) effects of platform reputation. The direct effects of reputation on transaction volume are tested while controlling for potentially confounding factors including registered capital, guarantee methods, platform type, and location. These analyses confirm a statistically significant positive direct effect of platform reputation on transaction volumes. The results clearly support the hypothesis that higher platform reputation leads to higher transaction volume. However, the investigation extends beyond direct effects. Further regression analysis explores the mediating effects of various variables on the reputation-transaction volume relationship. These analyses reveal that platform reputation has partial mediating effects on registered capital and eastern region location, but a full mediating effect on private platform type. These results suggest that the influence of platform reputation on investment choices operates through multiple channels, reflecting the intricate interplay of various factors within the Chinese P2P lending ecosystem. This complex relationship between reputation and other influencing factors is a key finding of the study.

To address potential endogeneity issues among the independent variables, the study performs further robustness checks. This involves adding instrumental variables – CEO's educational background (Edu) and membership in the Internet Banking Association (IBA, Join) – to the regression models. The inclusion of these instrumental variables helps to address potential biases and confirms the robustness of the findings related to platform reputation's impact on transaction volume. These instrumental variables are selected based on their likely influence on platform reputation without directly affecting the transaction volume. The positive relationship between these instrumental variables and platform reputation supports their suitability. The results of the instrumental variable regression analysis are consistent with the previous findings, strengthening the conclusion that platform reputation is positively associated with transaction volume, even after addressing potential endogeneity problems. The inclusion of these robustness checks enhances the credibility and reliability of the research findings. The Sobel test is also employed to further scrutinize the mediating effect of bank-involved platforms. This test confirms that platform reputation does not have a significant mediating effect on bank-involved platforms.

The study's empirical analysis reveals a strong positive relationship between platform reputation and investor lending decisions, measured by transaction volume. This finding confirms the central hypothesis, demonstrating that platforms with higher reputations attract significantly more investment. The analysis shows that this relationship holds even after controlling for various credit-enhancing factors, emphasizing the independent importance of platform reputation. Furthermore, the study uncovers mediating effects, showing that platform reputation's influence extends beyond a direct impact. The study finds that reputation partially mediates the effect of registered capital and platform location (eastern region) on investment volume, while fully mediating the effect of private platform ownership. This suggests that higher reputation amplifies the positive impact of factors like capital and location on investment decisions. The analysis also highlights that the quality of Chinese P2P platforms varies significantly, with many falling into the low-quality category. The study also reveals that interest rates are not the main determinant of investor choices, indicating that other factors, particularly platform reputation and characteristics, play a more crucial role in investment decisions.

The research's findings have significant implications for policymakers seeking to regulate and stabilize the Chinese P2P lending market. The prevalence of low-quality platforms underscores the urgent need for stronger regulatory frameworks. The study suggests that Chinese central and local governments, along with industry associations like the Internet Banking Association (IBA), should swiftly establish regulations, policies, and industry standards to eliminate low-quality platforms, formalize operations, and improve market stability. This formalization is necessary to prevent future 'financial explosions' as seen in 2018. The findings also indicate a need to support the development of platforms in less-developed regions (particularly the western region), recognizing regional disparities in credit and social trust. The research reveals that privately owned platforms constitute a large portion of the market (80%), yet their reputation is significantly lower than bank-involved platforms. Therefore, the study calls for policies to support and improve the reputation of private platforms. Finally, given the limited use of guarantee methods by Chinese P2P platforms, the study recommends new industrial standards and regulations that strengthen investor protections and minimize market instability. These policy recommendations highlight the need for a comprehensive approach to regulating the P2P lending industry, addressing not only the issues of reputation and risk but also regional disparities and the characteristics of platform ownership.

The authors acknowledge certain limitations in the study. Firstly, the sample size, comprising 478 platforms, represents only about a quarter of the total platform population in China. This limitation stems from data availability for the specific variables analyzed. However, the researchers note that their sample includes a balance of large and small, healthy and unhealthy platforms, suggesting good representativeness. Secondly, the study recognizes the potential limitations of using proxy measurements, acknowledging the inherent challenges in precisely quantifying platform reputation. These limitations provide areas for future research to explore. Future studies could benefit from larger sample sizes, potentially encompassing the entire Chinese P2P lending market, if comprehensive data become available. Improvements to the measurement of platform reputation, possibly incorporating alternative data sources or more sophisticated modeling techniques, could further enhance the robustness of the analysis. Further research could also delve deeper into the mediating effects of specific aspects of credit-enhancing information, providing a more detailed understanding of the mechanisms through which platform reputation influences investor decisions.