In this paper, we investigate whether those enterprises adopting digital finance exhibit higher values. On the basis of the constructed fintech-related lexicon developed by the machine learning-based Word2Vec model, we employ the frequency of fintech-related words (phrases) in the management discussion sections of annual reports as a proxy variable for the degree to which enterprises apply digital finance. We utilize panel data regression and mediation models based on data of Chinese A-share listed companies from 2016 to 2022 and explore the impact of this degree of digital finance application on enterprise value. We find that the degree to which enterprises apply digital finance elevates their values. The in-depth integration of digital technology and finance directly enhances enterprise value by reducing financing costs. Additionally, the effects are more evident among small-scale firms and enterprises located in regions with lower marketization levels. However, in the face of the impact of the COVID-19 pandemic, the positive effects on enterprises are relatively low.

This paper investigates the impact of local fintech development on analysts’ earnings forecast accuracy. We use the method of web text mining to construct the local fintech development index for empirical test and find that local fintech development significantly improves analysts’ earnings forecast accuracy by promoting firm digital transformation, improving firm information transparency, and alleviating the information asymmetry between firms and outsiders. Furthermore, this effect is more significant for analysts without equity pledge associations and those with weaker buy-side pressure. This study shows that local fintech development can optimize the capital market information environment.

A lack of sufficient information about potential borrowers is a major obstacle to access to financing from the traditional financial sector. In response to the need for better information to prevent fraud, to increase access to finance and to support balanced sustainable development, countries around the world have moved over the past several decades to develop credit information reporting requirements and systems to improve the coverage and quality of credit information. Until recently, such requirements mainly covered only banks. However, with the process of digital transformation in China and around the world, a range of new credit providers have emerged, in the context of financial technology (FinTech, TechFin and BigTech). Application of advanced data and analytics technologies provides major opportunities for both market participants – both traditional and otherwise – as well as for credit information agencies: by utilizing advanced technologies, participants and credit reporting agencies can collect massive amounts of information from various online and other activities (‘Big Data’), which contributes to the analysis of borrowing behavior and improves the accuracy of creditworthiness assessments, thereby enhancing availability of finance and supporting growth and development while also moderating prudential, behavioral and conduct related concerns at the heart of financial regulation. Reflecting international experience, China has over the past three decades developed a regulatory regime for credit information reporting and business. However, even in the context of traditional banking and credit, it has not come without problems. With the rapid growth and development of FinTech, TechFin and BigTech lenders, however, have come both real opportunities to leverage credit information and data but also real challenges around its regulation. For example, due to fragmented sources of borrower information and the involvement of many players of different types, there are difficulties in clarifying the business scope of credit reporting and also serious problems in relation to customer protection. Moreover, inadequate incentives for credit information and data sharing pose a challenge for regulators to promote competition and innovation in the credit market. Drawing upon the experiences of other jurisdictions, including the United States, United Kingdom, European Union, Singapore and Hong Kong, this paper argues that China should establish a sophisticated licensing regime and setout differentiated requirements for credit reporting agencies in line with the scope and nature of their business, thus addressing potential for regulatory arbitrage. Further, there is a need to formulate specific rules governing the provision of customer information to credit reporting agencies and the resolution of disputes arising from the accuracy and completeness of credit data. An effective information and data sharing scheme should be in place to help lenders make appropriate credit decisions and facilitate access to finance where necessary. The lessons from China’s experience in turn hold key lessons for other jurisdictions as they move from credit information to credit data regulation in their own financial systems.

Using four datasets of individuals’ digital payment and mutual fund investment records from a dominating fintech platform, we observe a robust U-shaped relation between individuals’ consumption and their financial wealth shocks. Contrary to the prediction of the wealth effect, individuals increase their consumption shortly after experiencing large positive and negative wealth shocks. The unexpected increase in consumption following negative wealth shocks is particularly pronounced among consumption categories with a “hedonic” nature, such as entertainment-related items. We show that this effect, termed “financial retail therapy,” is consistent with a dynamic model of Prospect Theory and evidence from a controlled laboratory experiment.

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.

We study the income inequality among streamers using the administrative data of a leading Chinese live-streaming platform. The live-streaming technology enables a superstar to produce new entertainment products matched with demand and occupies a larger market share. Imagine an extreme case; the best streamer hosts live for 24 hours, earns all possible income, and leaves zero time for other streamers. Our data show that the income distribution of the highest-paid streamers follows Zipf’s Law and appears to be even more concentrated than any offline business: NBA top players, Forbes celebrities, and billionaires. Income inequality increased rapidly as the platform expanded from 2018 to 2020 — for example, the income share of the platform’s top 10 streamers increased from 14.82% to 45.15% as its revenue grew by 142%. To estimate inequality elasticity to the market size, we study four quasi-experimental shocks: potential market size proxied by economic development and Fintech coverage, quarter-end revenue spikes induced by the seasonal incentive regime, user surge induced by capital raising, and the Covid-19 lockdown in Wuhan. Gini coefficient elasticity ranges fromm1.3% to 10.6% estimated from the cross-city variations (local economic development and Covid-19 Wuhan lockdown); the time-series variations (quarter-end and user surge before capital raising) imply an elasticity ranging from 3.6% to 25.5%.

Using unique data on Alipay users' investment accounts, we find that, in addition to generating better performance than investors’ self-directed portfolios, robo-advising has a positive spillover effect on its adopters in terms that it improves their investment behaviors. Investors have more diversified portfolios and exhibit fewer behavioral biases in portfolio management and fund choices in their self-directed accounts after adopting robo-advising. The spillover effect is more prominent for adopters who interact with the service more actively and who were less sophisticated before adopting the app. We also find that adopters learn from the robo-advisor by simply imitating its portfolios or strategies. Collectively, this study provides large-sample, non-laboratory evidence that robo-advising effectively plays a role in educating investors through repeated interactions with its adopters and setting investment models that are easy to follow.

This study investigates the influence of mobile payment application use on crime rates. Using a unique database of verdicts from criminal courts in China and an index measuring the extent of mobile payment usage, we find that a one standard deviation increase in mobile payment adoption and usage leads to an 11% decrease in the theft rate. Furthermore, the effect of widespread mobile payment adoption on theft rates is more pronounced in areas characterized by a higher prevalence of cash transactions. These findings suggest that the decrease in cash circulation in society due to mobile payment use can reduce incentives for theft. However, we do not find evidence linking mobile payment usage to other types of criminal activity, including robbery, arson, brawling, homicide, and serious injury by vehicle.

The past decade has witnessed a phenomenal rise of digital wallets, and the COVID-19 pandemic further accelerated their adoption globally. Such e-wallets provide not only a conduit to external bank accounts but also internal payment options, including the ever-popular Buy-Now-Pay-Later (BNPL). We examine, for the first time, e-wallet transactions matched with merchant and consumer information from a world-leading provider based in China, with around one billion users globally and a business model that other e-wallet providers quickly converge to. We document that internal payment options, especially BNPL, dominate both online and on-site transactions. BNPL has greatly expanded credit access at the extensive margin through its adoption in two-sided payment markets. While BNPL crowds out other e-wallet payment options, it expands FinTech credit to underserved consumers. Exploiting a randomized experiment, we also find that e-wallet credit through BNPL substantially boosts consumer spending. Nevertheless, users, especially those relying on e-wallets as their sole credit source, carefully moderate borrowing when incurring interest charges. The insights likely prove informative for economies transitioning from cash-heavy to cashless societies where digital payments and FinTech credit see the largest growth and market potential.

We explore the relationship between fintech, macroprudential policies, and commercial bank risk-taking. Based on system generalized method of moment modeling on a panel data of 114 commercial banks in China from 2013 to 2020, results show that there are functional differences in the impact of fintech on bank risk-taking. Payment and settlement technology (PST), capital raising technology (CRT) and investment management technology (IMT) are positively correlated with bank risk-taking. In contrast, market facility technology (MFT) negatively correlates with bank risk-taking. We also find that macroprudential policies weaken the promotion effect of CRT on bank risk-taking and strengthen the inhibition effect of MFT on bank risk-taking while having no significant moderating effect on PST and bank risk, IMT and bank risk. Further, the micro characteristics of banks (capital adequacy ratio, asset scale, liquidity level) affects the moderating strength of macroprudential policies. Various robustness tests confirm our conclusions.