This paper studies whether financial technology (FinTech) changes loan contract design by reducing banks’ reliance on collateral in corporate lending. Using loan-level data on Chinese listed firms from 2007 to 2023 and exploiting the People’s Bank of China’s 2019 FinTech Development Plan as a quasi-natural experiment, we find that banks with stronger pre-policy FinTech capability significantly reduce secured lending after the policy shock. In the benchmark specification, the probability that a loan is secured falls by 1.64 percentage points, or about 2.7% relative to the baseline secured-loan share. The result is robust to alternative loan classifications, matching procedures, alternative measures of FinTech adoption, aggregated lending outcomes, and alternative inference procedures. The pattern is more pronounced among small and medium-sized enterprises, lower-tier branches, and branches located outside bank headquarters’ cities, where borrower information is likely to be more limited. Supplementary analyses are consistent with FinTech reducing banks’ information-production costs and suggest that technological proximity to FinTech-active peers may amplify the collateral-reducing effect. Overall, the evidence indicates that FinTech can enhance banks’ screening capacity and shift lending decisions away from reliance on asset-based guarantees toward information-based credit assessment.

Technology firms are often financed on narratives about products, contracts, customers, and technological progress well before these developments appear in accounting statements. We ask when such narratives become economically informative. Our central idea is that narratives should matter more once they can be linked to later verifiable outcomes rather than treated as stand-alone text.Using listed Chinese technology firms, we develop a validated corporate narrative framework for bank-affiliated investment, a setting in which investors must screen with soft information ex ante and then monitor hard realization and downside risk ex post. We use GPT-5.1 to extract business claims from management discussion, investor-relations records, exchange Q&A, and earnings-roadshow materials, and to label later claim–evidence pairs as support, partial support, conflict, duplicate, or irrelevant. We then connect these labels to official announcements, procurement awards, permits, project updates, and negative-event disclosures to construct a validated firm-month signal. The broad merged panel contains 592 firms and 30,169 firm-month observations; the main return tests use 576 firms and 18,230 firm-month observations over 2022–2024. A simple production rule that combines a low-narrative-premium component with hard-narrative and hard-event anchors, together with a separate downside-risk gate, delivers an implementable annualized long-short return of 8.93% in bank-invested firms after trading costs. The signal is much weaker in non-bank firms, predicts future gross-margin improvement more strongly than future ROE, and improves downside screening.

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 propose a novel dual-screening classification framework for fund selection designed to align statistical objectives with investor goals. Testing on the Chinese mutual funds market, a Gradient Boosting model implementing our framework generates a statistically and economically significant 14.65% annual risk-adjusted alpha, substantially outperforming identical models trained under a standard regression framework. Feature importance analysis confirms that fund-level momentum and flows are the most significant predictors of performance in this market. Our findings provide a robust and practical framework for active management, demonstrating that modelling both upside potential and downside risk is critical for superior performance.

With the accelerating global aging population, the prevalence of cognitive impairments, particularly Alzheimer’s disease and related dementias, is rising steadily, giving rise to a substantial and rapidly expanding market for memory services. This review aims to systematically examine the core development trends, driving factors, innovative service models, and challenges within the memory services market in the context of the aging economy. It provides a comprehensive analysis of the entire industry chain, spanning early screening, diagnosis, non-pharmacological interventions, long-term care, and technological support. By integrating the latest business models, policy directions, and evolving consumer demands, this article explores future development trajectories and investment potential in the memory services sector. The insights offered herein are intended to support practitioners, policymakers, and researchers engaged in this critical field by delivering an in-depth understanding of current market dynamics and emerging opportunities.

The impact of corporate environment, society, and governance (ESG) disclosure on the online market remains uncertain. To address this ambiguity, this study utilizes text analysis to thematically classify research samples to examine the positive influence of corporate ESG disclosure on stock liquidity from a reputational perspective. Interestingly, the reputational effect of ESG disclosure shows asymmetry within the online market, particularly in its highly information-sensitive environment. Notably, negative media reputation insignificantly diminishes the positive impact of ESG disclosure on stock liquidity. A series of robustness tests confirm the reliability of the sample screening method and findings.

In China's labor market, there is a phenomenon that enterprises choose whether to provide "social insurance and housing fund" to laborers autonomously. This paper use micro-data from two leading Internet recruitment platforms and empirically finds that in a labor market with double-side information asymmetry, "social insurance and housing fund" is not only a cost but also a signal. Providing workers with "social insurance and housing fund" can both send a signal of stable operation to the labor market and identify high-quality workers for enterprises. With an instrument variable of local average social security payment rate, this paper excludes the endogenous effect of labor supply on wages while the signaling effect above is still significant. In addition, "housing fund" has a stronger signaling effect than "social insurance", and the strength of the two signaling effects is affected by the scale of the enterprises and the level of local payment rates. This paper also introduces a theoretical framework of two micro-mechanisms — signaling and screening — into the analysis. In terms of policies, this paper proposes to strengthen the information disclosure and the propagation of social security payment, and further reduce the financial burden of enterprises.

Using proprietary individual level loan data, this paper explores the economic consequences of the 2009 bank entry deregulation in China. Such deregulation leads to higher screening standards, lower interest rates, and lower delinquency rates for corporate loans from entrant banks. Consequently, in deregulated cities, private firms with bank credit access increase asset investments, employment, net income, and ROA. In contrast, the performance of state-owned enterprises (SOEs) does not improve following deregulation. Deregulation also amplifies bank credit from productive private firms to inefficient SOEs due mainly to SOEs’ soft budget constraints. This adverse effect accounts for 0.31% annual GDP losses.

Open banking facilitates data sharing consented by customers who generate the data, with a regulatory goal of promoting competition between traditional banks and challenger fintech entrants. We study lending market competition when sharing banks’ customer data enables better borrower screening or targeting by fintech lenders. Open banking could make the entire financial industry better off yet leave all borrowers worse off, even if borrowers could choose whether to share their data. We highlight the importance of equilibrium credit quality inference from borrowers’ endogenous sign-up decisions. When data sharing triggers privacy concerns by facilitating exploitative targeted loans, the equilibrium sign-up population can grow with the degree of privacy concerns.

This paper uses a dataset of more than 100,000 firms over the period of 2000-07 to assess whether and why Chinese firms overinvest. We find that corporate investment in China has become increasingly efficient over time, suggesting that overinvestment has been declining. However, within all ownership categories, we find evidence indicating a degree of overinvestment by firms that invest more than the industry median. The free cash flow hypothesis provides a good explanation for China’s overinvestment in the collective and private sectors, whereas in the state sector, overinvestment is attributable to the poor screening and monitoring of enterprises by banks.