Using subsidiary-level data of 3,863 Chinese nonfinancial listed firms, we find their capital expenditures increase with foreign sales, and the difference arises from the investments of the firms’ foreign subsidiaries. We show that the foreign sales-foreign investment association becomes more sensitive when the economic policy uncertainty (EPU) increases in the domestic market. However, foreign EPU does not play such a significant role. We provide one possible explanation that due to global diversification, MNEs can hedge foreign EPU using their international subsidiary network, resulting in the overall investments unchanged. However, given China’s tight regulatory capital controls, the MNEs may be less able to hedge the domestic EPU, so that they reallocate investments from the domestic markets to the foreign markets, consistent with the transaction cost assumption underlying the real options theory. Robust tests show that access to foreign capital, profitability and institutional factors have little explanatory power over the MNEs’ foreign investment.

This study investigates the relationship between investors’ prior gains or losses and their adoption of extrapolative beliefs. Our findings indicate that investors facing prior losses tend to rely on optimistic extrapolative beliefs, whereas those experiencing prior gains adopt pessimistic extrapolative beliefs. These results support the theory of motivated beliefs. The interaction between the capital gain overhang and extrapolative beliefs results in noteworthy mispricing, yielding monthly returns of approximately 1%. Motivated extrapolative beliefs comove with investors’ survey expectations and trading behavior, and help explain momentum anomalies. Additionally, households are susceptible to this belief distortion. Institutional investors can avoid overpriced stocks associated with motivated (over-)optimistic extrapolative beliefs.

This paper studies a novel channel through which climate risks affect households’ choices of risky asset allocation: a stringent climate change regulation elevates labor income risk for households employed by high-emission industries which in turn discourages households' financial risk-taking. Using staggered adoptions of climate change action plans across states, we find that climate change action plans lead to a reduction in the share of risky assets by 15% for households in high-emission industries. We also find a reduction in risky asset holdings after the stringent EPA regulation. These results are stronger with experiences of climate change-related disasters. Our study implies an unintended consequence of climate regulations for wealth inequality by discouraging low-wealth households' financial risk-taking.

Rural banking competition may promote or hinder agricultural total factor productivity (TFP). We analyze a novel dataset on all commercial bank branches in rural China, combined with measures of productivity based on stochastic frontier analysis. To identify causality, we use: 1) an instrumental variable approach based on the administrative division of banks, and 2) a propensity score matching difference-in-difference approach exploiting banking de-regulations in 2009. Both methods reveal that competition has a positive impact on TFP. A heterogeneity analysis finds that the effect is primarily significant along the Beijing-Kowloon railway and its East side. Technology adoption is the typical channel through which lending is hypothesized to impact TFP. We find that the positive effect of competition is larger in areas with greater technology use, but we find an insignificant direct impact of concentration on technology adoption, suggesting the channels of effect may be more complex than previously thought.

There are large regional disparities in relation to the development of digital finance in China. This study expands on the human–land relationship to analyze the impact of terrain relief on digital finance adoption using China Household Finance Survey data. The results show an inverted U-shaped relationship. Further, mechanism analysis indicates that terrain relief influences the wealth creation environment and stimulates the likelihood of entrepreneurship, especially through small and medium-sized enterprises. In addition, the impact is more pronounced in rural and western areas. These findings provide insights enabling the development of a more inclusive financial system.

This study investigates the relationship between investors’ prior gains or losses and their adoption of extrapolative beliefs. Our findings indicate that investors facing prior losses tend to rely on optimistic extrapolative beliefs, whereas those experiencing prior gains adopt pessimistic extrapolative beliefs. These results support the theory of motivated beliefs. The interaction between the capital gain overhang and extrapolative beliefs results in noteworthy mispricing, yielding monthly returns of approximately 1%. Motivated extrapolative beliefs comove with investors’ survey expectations and trading behavior, and help explain momentum anomalies. Additionally, households are susceptible to this belief distortion. Institutional investors can avoid overpriced stocks associated with motivated (over-)optimistic extrapolative beliefs.

This paper will partially solve the puzzle of implied volatility spreads from the perspective of short-selling (option-implied borrowing rate). Specifically, we use Chinese SSE 50 ETF options data to examine the relationship between the option-implied volatility spreads and option-implied borrow rate. Using nonparametric regression models, we find that there is a clear negative correlation between the implied volatility spreads and the implied borrowing rate. Furthermore, our results show that there is a significant nonlinearity between these two variables. Finally, it is interesting to note that the option volatility spreads are zero when the option prices include the short selling cost.

Amongst major economies, China has been taking a lead in the development of central bank digital currency (CBDC), which has generated widespread interest and impact around the globe. China’s CBDC, commonly known as e-CNY, is designed with several distinctive features, enabling it to compare favorably to other payment methods such as credit cards, mobile payment, unbacked cryptocurrency, and stablecoins. A variety of social, economic, political, and regulatory reasons can be identified to help explain China’s active pursuit of CBDC. However, the prospect of success will be affected by many factors and may vary between the domestic and international markets. This paper argues that the adoption of eCNY will likely succeed domestically, but may face more challenges in the international markets. The development of e-CNY seems to have created a catfish effect on other major economies in the race for CBDC. It is not fully clear, however, that the CBDC race will be better explained by the first-mover or the late-mover advantage theory. The CBDC project will have both public and private law implications, and several legal issues warrant particular attention in relation to the legal status of CBCD, the role and responsibility of the central bank, legal remedies for losses suffered by CBDC users from cybersecurity issues and operational problems, and the issue of data privacy and protection.

The increasing awareness and adoption of technology, particularly artificial intelligence, are reshaping industries and daily life. This study explores how adopting artificial intelligence (AoAI) influences stock price crash risk for Chinese A-share listed companies between 2010 and 2020. The primary findings emphasize AoAI's significant role in reducing stock price crash likelihood, enhancing financial market performance, and mitigating manager opportunism. Further, the research identifies varied effects of AoAI on crash risk among different enterprise types, notably benefiting non-state-owned and non-foreign businesses. Additionally, the study finding supports the notion that financial analysts enhance transparency, reducing the risk of stock price crashes. These results underscore the Chinese government's role in shaping the digital economy. Overall, the study's findings remain consistent and robust across statistical methods like 2SLS, PSM, SysGMM, and instrumental variable analysis.

From 2013 onwards, the Chinese central government has subjected the largest state-owned enterprises (SOEs) to ‘disciplinary inspections’ to weed out and punish graft and other corruption. While this policy has been somewhat successful in punishing corruption—over 160 top SOE officials have been indicted—we show that the principal economic impact of these inspections has been to significantly cut investment by targeted firms, leading to a major decline in profitability, innovation and Tobin’s Q. Expenditures on R&D, entertainment, and travel also decline dramatically. The most obvious explanation is that the fear induced in SOE managers, who have limited risk-promoting equity holdings or incentive compensation and few external employment options, deterred them from taking risky but value-enhancing investments post-audit.