We study the role of superstition in China’s peer-to-peer lending market by ex-amining whether lenders time their bids according to “lucky hours” from the Chinese farmer’s calendar. Loans funded during lucky hours perform better—but only because the platform lists higher-rated loans at those times. This pattern is consistent with a screening mechanism: highly risk-averse lenders place greater value on both true risk reductions and auspicious-day signals, so the platform maximizes surplus by bundling the two—listing low-risk loans on auspicious days. Moreover, listing safer loans at lucky hours can further boost proffts because biased beliefs decay more slowly under asymmetric (bad-news-heavy) learning.

We conceptually identify and empirically verify the features distinguishing FinTech platforms from non-financial platforms using marketplace lending data. Specifically, we highlight three key features: (i) Long-term contracts introducing default risk at both the individual and platform levels; (ii) Lenders’ investment diversification to mitigate individual default risk; (iii) Platform-level default risk leading to greater asymmetric user stickiness and rendering platform-level cross-side network effects (p-CNEs), a novel metric we introduce, crucial for adoption and market dynamics. We incorporate these features into a model of two-sided FinTech platform with potential failures and endogenous participation and fee structures. Our model predicts lenders’ single-homing, occasional lower fees for borrowers, asymmetric p-CNEs, and the predictive power of lenders’ p-CNEs in forecasting platform failures. Empirical evidence from China’s marketplace lending industry, characterized by frequent market entries, exits, and strong network externalities, corroborates our theoretical predictions. We find that lenders’ p-CNEs are systematically lower on declining or well-established platforms compared to those on emerging or rapidly growing platforms. Furthermore, lenders’ p-CNEs serve as an early indicator of platform survival likelihood, even at the initial stages of market development. Our findings provide novel economic insights into the functioning of multi-sided FinTech platforms, offering valuable implications for both industry practitioners and financial regulators.