The study examines the impact of Government-Backed Financing Guarantee (GFG) programs on employment in small and medium-sized enterprises (SMEs) using data from the Zhejiang Guarantee Group and non-listed SMEs in China. The findings demonstrate that these programs have a significant positive effect on employment in SMEs, particularly in private firms, and non-ZhuanJingTeXin firms. Furthermore, the study demonstrates that GFGs can enhance firm employment rates by mitigating financing constraints. It also contributing to firm revenue growth.

It remains unclear whether state shareholding (SS) truly enhances firms’ fulfillment of their corporate social responsibility (CSR) or merely motivates them to strategically release “enhanced” CSR reports. Utilizing the reform that permits state–owned equity to participate in privately–owned enterprises (POEs) in China, we find that the participation of SS enhances POEs’ access to resources and alleviates their needs for legitimacy, leading to disparities in CSR disclosure and substantive CSR activities for POEs, consistent with the notion of greenwashing. The greenwashing behavior is particularly pronounced in the presence of large state-owned shareholder and when CSR disclosure is compulsory.

This paper utilizes the COVID-19 pandemic as an exogenous shock to investor uncertainty and examines the effect of uncertainty on stock liquidity. Analyzing data from Chinese listed firms, we find that stock liquidity dries up significantly in response to an increase in uncertainty resulting from regional pandemic exposure. The underlying reason for the decline in stock liquidity during the pandemic is a combination of earnings and information uncertainty. Funding constraints, market panic, risk aversion, inattention rationales, and macroeconomics factors are considered in our study. Our findings corroborate the substantial impact of uncertainty on market efficiency, and also add to the discussions on the pandemic effect on financial markets.

The level of corporate risk-taking impacts debt default as a crucial investment decision. Hence, this must be examined considering resource allocation. This study uses A-share listed companies from 2007 to 2021 as samples to empirically explore the impact and mechanism of corporate risk-taking level on debt default risk. The results show that corporate risk-taking can significantly inhibit debt default and the risk of debt default by promoting total factor productivity. Further, the higher the level of enterprise financialization of the firm, the higher the stock liquidity, and the higher the level of managerial confidence, the stronger the inhibitory effect of corporate risktaking on debt default. The heterogeneity analysis reveals that the inhibitory effect of corporate risk-taking on debt default is more significant in large-scale enterprises, enterprises with lower regulatory shareholdings, and enterprises with standard unqualified audit opinions. The study provides guidance for enterprises to improve the level of risk-taking and resource allocation efficiency effectively. Moreover, it provides empirical support for regulators to effectively prevent "waves of defaults" and even "waves of bankruptcies" in the real economy.

This study investigates the relationship between trade policy uncertainty (TPU) and market diversification with risk-averse firms. We build a model to demonstrate how a risk-averse firm diversifies risks stemming from escalating TPU through entering new markets whose trade policies are negatively correlated with ones in its alreadyentered markets. The positive effect of TPU on market diversification is moderated if the firm has lower risk hedging ability and/or is less risk-averse. Conditional on the TPU in the already-entered markets, there is an inverted-U relationship between TPU in the new market and the probability of entering it. Using a unique firm-productlevel dataset on Chinese exporters, we find robust evidence supporting our theoretical predictions.

This research study primarily examines the digital transformation’s internal mechanism promoting enterprises’ total factor productivity (TFP) based on the matching and learning mechanism. Afterward, this research article empirically examines the digital transformation’s influential mechanism on enterprises’ TFP, using the Chinese listed companies’ data on the “A” stock market for the time period ranging from 2007 to 2019. The major study findings are as follows: (1) the improvement of the digital transformation significantly increases enterprises’ TFP. The proposed conclusion remains robust after a series of robustness- and the endogeneity test. (2) Furthermore, mechanism analysis reveals that digital transformation effectively enhances enterprises’ TFP by eliminating resource misallocation in the industry. In addition to this, digital transformation relies on the mechanism of “learning by doing” to promote the technological innovation’s spillover effect; hence, effectively enhancing enterprises’ TFP. (3) Heterogeneity analysis demonstrates that the digital transformation’s impact on enterprises’ TFP is heterogeneous in the context of enterprise size, enterprise type, and enterprise ownership. Lastly, this study puts forward that government bodies should intensify the construction and investment in digital infrastructure, promote a series of institutional reforms, and support digital technological R&D practices.

This paper studies the impact of AI technology on the mutual fund industry. I develop a new measure of AI adoption based on hiring practices and find that this measure can predict fund performance. The funds with high AI ratio outperform non-AI funds, after I controlling for standard factors and fund characteristics. Further empirical evidence shows that funds with a high AI ratio tilt their portfolios toward high information intensity stocks, indicating that mutual funds benefit from AI technology adoption by improving their information capacity. Consistent with this channel, I find that the outperformance of these mutual funds mainly comes from better stock picking skills. Finally, AI technology adoption has a negligible effect on fund manager turnover.

This study investigates whether corruption differentially affects contracting through auctions and negotiations. Using data on Chinese land-market transactions, where corruption is known to be present, we first show that, on average, it exerts similar effects on transactions carried out via auctions and negotiation. However, this finding masks important heterogeneity – auctions featuring healthy competition are less affected by corruption, and significantly less so than negotiation. We then develop a simple model of bidding under the possibility of corruption that rationalizes our findings.

Compared to previous literature on external FinTech, this paper is more interested in the role played by bank FinTech. Based on panel data from Chinese commercial banks spanning 2010 to 2021, this paper investigates the impact of digital transformation on bank soundness and its potential mechanisms. The empirical findings demonstrate a positive association between digital transformation and bank soundness, driven primarily by strategic and management digitization. Mechanistic analysis indicates that digital transformation improves bank soundness by mitigating risk-taking behavior and promoting diversification. The positive effect of digital transformation is more pronounced in state-owned and joint-stock banks, banks with higher liquidity mismatch as well as in sub-samples with greater levels in external FinTech development and economic policies uncertainty. Additional analysis suggests that digital transformation can still enhance bank soundness even in the presence of relatively easy monetary and macroprudential policies, highlighting the harmonization and complementarity between internal innovation from digital transformation and external regulatory policies in maintaining banking stability. Overall, this paper contributes to the literature on bank FinTech, factors influencing bank stability. And it also provides a novel explanation for the relationship between financial innovation and financial stability.

Biodiversity conservation incurs substantial economic costs. We investigate how financial markets price the risks such costs induce, exploiting the “Green Shield Action,” a major regulatory initiative launched in China in 2017 to enforce biodiversity preservation rules in national nature reserves. While improving biodiversity, the initiative led to significant increases in bond yields for municipalities with these reserves. The effects are driven by increases in local governments’ fiscal risk due to expected increases in transition costs resulting from shutting down illegal economic activities within reserves and additional public spending on biodiversity. Investors show little non-financial consideration towards endeavors counteracting biodiversity loss.