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.

This study identifies an incentive-compatible mechanism to foster individual environmental engagement. Utilizing a dataset comprising 100,000 randomly selected users of Ant Forest—a prominent personal carbon accounting platform embedded within Alipay, China's leading BigTech super-app—we provide causal evidence that individuals strategically engage in eco-friendly behaviors to enhance their credit limits, particularly when approaching borrowing constraints. These behaviors not only illustrate the green nudging effect of BigTech but also generate value for the platform by leveraging individual green actions as soft information, thereby improving the efficiency of credit allocation. Using a structural model, we estimate an annual green value of 427.52 million US dollars generated by linking personal carbon accounting with BigTech credit. We also show that the incentive-based mechanism surpasses green mandates and subsidies in improving consumer welfare and overall societal welfare. Our findings highlight the role of an incentive-aligned approach, such as integrating personal carbon accounts into credit reporting frameworks, in addressing environmental challenges.

This paper investigates the impact of curation algorithms through the lens of stock market dynamics. By innovatively incorporating the dynamic interactions between social media platforms, investors, and stock markets, we construct the Social-Media-augmented Artificial Stock marKet (SMASK) model under the agent-based computational framework. Our findings reveal that curation algorithms, by promoting polarized and emotionally charged content, exacerbate behavioral biases among retail investors, leading to worsened stock market quality and investor wealth levels. Moreover, through our experiment on the debated topic of algorithmic regulation, we find limiting the intensity of these algorithms may reduce unnecessary trading behaviors, mitigates investor biases, and enhances overall market quality. This study provides new insights into the dual role of curation algorithms in both business ethics and public interest, offering a quantitative approach to understanding their broader social and economic impact.

While the popularity of non-fungible tokens (NFTs) has brought signiffcant proffts, legal practitioners have been exposed to unanswered legal concerns behind the frenzy of NFT transactions. Generally, such concerns include those related to the applicability of copyright to NFTs, the legal relationship between an NFT and the tokenized work, and the copyrights associated with the NFT in transactions. TTe Hangzhou Internet Court released the ffrst NFT-related copyright case, setting a course for the subsequent judicial and business practice of IP-related NFTs nationally and internationally. With these general considerations in mind, the paper brieffy introduces what non-fungible tokens are and how they relate to copyright law. Speciffcally, by interpreting the ffrst NFT-related copyright decision in detail, the paper addresses the legal status of NFT and NFT transactions from the perspective of Chinese Copyright Law, with particular focus on the liability of online platforms and the applicability of the exhaustion doctrine.

This paper examines the impact of the rating of equity crowdfunding platforms in China on funding campaign success. We gather information from 2014 to 2021 on 583 fund raising campaigns. Our results suggest that campaign success is positively correlated with the reputation of the platforms but especially for the most reputable one. We also show that the level of technological intensity of the industries and services is positively correlated with the amount raised. Overall, our paper suggests that platform ratings provide a valuable signal to investors, especially when projects are risky and when information asymmetry is high.

How does information sharing affect consumers' usage of FinTech credit? Using a unique dataset of ``Buy Now, Pay Later (BNPL)" users from a large digital platform and exploiting a credit reporting policy change, we document that consumers significantly reduce their usage of BNPL credit when the BNPL lender becomes subject to credit reporting regulation. This reduction is more pronounced among borrowers with previous default records, who also become more disciplined in repayment behaviors, than those without such records. The decrease in BNPL usage also leads to a reduction in online consumption, supporting the financial constraint hypothesis. Our results suggest that information sharing can help alleviate overborrowing and overspending, with stronger effects observed among younger borrowers, those who previously consumed more, or those with credit cards. We also highlight the synergies between BNPL lending and Big Tech platforms' ecosystems, which imperfectly substitute for formal enforcement institutions.

We examine the impact of firm-investor communication on ESG issues through investor interactive platforms in Chinese stock exchanges from 2010 to 2022. Our regression analysis finds that increased ESG-based questions from investors and firms’ responses lead to increased stock liquidity, suggesting that investor-firm dialogues beyond financial aspects to include ESG-related themes contribute to greater information transparency. We posit that investors use such communication as a “voice” strategy, advocating firms for enhanced ESG disclosures and performance. This strategy yields a two-fold benefit: it aligns with investors’ ESG objectives and, alternatively, facilitates their exit through improved stock liquidity. Our robustness tests suggest a probable causal relationship between investor engagement on ESG issues and stock liquidity. Moreover, we find that a positive tone in ESG-based communications strengthens this relationship, prompting managers to enhance ESG disclosure transparency in response to investor pressure.

Analyzing data from Chinese online interactive investor platforms, our study reveals that Minority Shareholder Activism (MSA) effectively curtails informed insider trading by voting with their hands or feet, particularly in firms with weaker external monitoring. MSA not only reduces the profitability of insider trading but also encourages firms and regulators to implement stricter ex-post disciplinary measures. Moreover, MSA alleviates the negative impact of insider trading on the stock market by enhancing stock liquidity, increasing stock price informativeness, and reducing crash risk.

This study investigates the impact of investor attention on stock market reactions to ChatGPT using dialogues on the Chinese interactive investor platforms (IIPs). We measure investor attention by the number of investors’ questions toward ChatGPT on the IIPs and categorize the firms’ answers as Investing, Speculative, and Absent. The research reveals positive and statistically significant market reactions surrounding the initial questions that occur before firm responses. Positive abnormal returns are also observed around the initial answer dates, with Investing firms evoking the highest market response, followed by Speculative firms, and Absent firms exhibiting the lowest reactions. Furthermore, positive market reactions persist even as firms modify their ChatGPT involvement statements or face stock exchanges inquiries, suggesting that the stock price upswing may primarily be fueled by ChatGPT-related mania. Our findings imply the potential of ChatGPT fervor: collusion caused by investor attention to ChatGPT and firm’s responses catering to investors.

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.