Individual institutions marginal contributions to the systemic risk contain predictive power for its potential future exposure and provide early warning signals to regulators and the public. We use higher-order co-skewness and co-kurtosis to construct systemic risk contribution measures, which allow us to identify and characterize the co-movement driving the asymmetry and tail behavior of the joint distribution of asset returns. We illustrate the usefulness of higher-order moment augmented approach by using 4868 stocks living in the Chinese market from June 2002 to March 2022. The empirical results show that these higher-order moment measures convey useful information for systemic risk contribution measurement and portfolio selection, complementary to the information extracted from a standard principal components analysis.

We examined reference point adaptation following gains or losses in security trading using participants from China, Korea, and the US. In both questionnaire studies and trading experiments with real money incentives, reference point adaptation was larger for Asians than for Americans. Subjects in all countries adapted their reference points more after a gain than after an equal-sized loss. When we introduced a forced sale intervention that is designed to close the mental account for a prior outcome, Americans showed greater adaptation toward the new price than their Asian counterparts. We offer possible explanations both for the cross-cultural similarities and the cross-cultural differences.

The use of green finance to slow down global warming in support of sustainable development remains widely discussed. This study examines whether investment structure moderates the impact of green finance on the environment in China, one of the top carbon-emitting nations and the second-largest economy in the world. We primarily used the moments-quantile regression approach with fixed-effect models on panel data from 1992Q1 to 2020Q4. First, the results confirmed that green finance and public and private investments worked synergistically to lower CO2 emissions, especially in Central and Western China. However, there was no proof that green finance and foreign direct investment were complementary in reducing CO2 emissions in China, unlike the Central region. Second, green finance marginally lowered CO2 emissions in all provinces, mainly in Eastern and Western China; this reduction was largely dependent on private investment in the Western region’s most polluting areas and foreign direct investment in Eastern and Western China’s least polluting provinces. Third, the beneficial effect of green finance occurred at varying optimal thresholds and investment-related conditions across Chinese regions at different quantiles. Lastly, we showed that in contrast to the variable impacts of urbanization, oil prices, and economic growth across Chinese regions at different quantiles, renewable energy, and trade openness reduced CO2 emissions. In conclusion, the study makes some policy recommendations for China’s sustainable economic development, an important model from which other countries can tailor their investment strategies and environmentally friendly policies.

This study examines how short-sale constraints affect investors’ information acquisition and thereby shape stock price efficiency. By exploiting two settings that relax short-sale constraints in the US and China, respectively, we find that the removal of short-sale constraints increases investors’ information acquisition in both markets, but the effect is more prompt in China. Investors acquire value-relevant information, especially bad news, and improve their short-selling decisions in both markets. Lastly, information acquisition induced by the removal of short-sale constraints improves price efficiency. Our evidence shows that a reduction in trading frictions promotes information acquisition and improves price efficiency.

This paper employs a difference-in-differences approach to study whether a Chinese audit firm improves its competencies through organizational learning after one of its audit teams has a client cross-listed in the US. Among a group of companies that are listed only in China, we define those audited by firms that have cross-listed clients as the treatment group, and companies audited by other firms as the control group. We find an improvement in audit quality for the treatment group after their audit firms have cross-listed client experience in the US. A large-scale survey of auditors corroborates these findings and sheds light on specific actions undertaken by audit firms to facilitate learning. Both the empirical and survey results highlight the benefits of auditing crosslisted clients in the US and its positive externality on improving the audit quality of non-US-listed companies.

As the world’s two largest economies, China and the US are currently experiencing political and economic friction. This conflict brings high uncertainty to financial markets. Assessing risk spillover effects in a sector level will help us to characterize international risk contagions. We construct a multilayer network to examine tail risk spillovers between China and the US and find that (1) the value of total connectedness rises amidst tensions but declines during reconciliations; (2) interlayer spillovers mainly manifest as extreme pulses instead of steady outflows, which implies a significant increase in the frequency and magnitude of interlayer spillovers requires vigilant monitoring; and (3) compared with the in-strength, the out-strength is more concentrated, which represents that some sectors may play the role of major interlayer transmitter in tail risk spillovers. Monitoring interlayer spillovers helps policymakers and investors respond to emerging systemic threats.

This study explores the intricate relationship between emotional cues present in food delivery app reviews, normative ratings, and reader engagement. Utilizing lexicon-based unsupervised machine learning, our aim is to identify eight distinct emotional states within user reviews sourced from the Google Play Store. Our primary goal is to understand how reviewer star ratings impact reader engagement, particularly through thumbs-up reactions. By analyzing the influence of emotional expressions in user-generated content on review scores and subsequent reader engagement, we seek to provide insights into their complex interplay. Our methodology employs advanced machine learning techniques to uncover subtle emotional nuances within user-generated content, offering novel insights into their relationship. The findings reveal an inverse correlation between review length and positive sentiment, emphasizing the importance of concise feedback. Additionally, the study highlights the differential impact of emotional tones on review scores and reader engagement metrics. Surprisingly, user-assigned ratings negatively affect reader engagement, suggesting potential disparities between perceived quality and reader preferences. In summary, this study pioneers the use of advanced machine learning techniques to unravel the complex relationship between emotional cues in customer evaluations, normative ratings, and subsequent reader engagement within the food delivery app context.

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.

This paper employs a difference-in-differences approach to study whether a Chinese audit firm improves its competencies through organizational learning after one of its audit teams has a client cross-listed in the US. Among a group of companies that are only listed in China, we define those audited by Chinese audit firms that are not international Big 4 affiliates and have cross-listed clients as the treatment group, and companies audited by other audit firms as the control group. We find an improvement in audit quality for the treatment group after their audit firms have cross-listed client experience in the US, and this improvement is not attributable to the effect of joining an international accounting firm network, registration with the PCAOB, or the consolidation in the audit market. A survey of auditors corroborates these findings and provides evidence on audit firms’ specific actions to facilitate learning. Our findings shed light on the benefits of auditing cross-listed clients in the US and its positive externality on improving the audit quality of non-US-listed companies in China.

Interest rate liberalization in China intensifies the exposure of commercial banks' interest rate risks and further increases the difficulty for commercial banks to effectively control interest rate risks, thus putting forward higher requirements for the normal operation and management of commercial banks. With the development of China's financial derivatives market, banking institutions begin to use basic interest rate derivatives to hedge interest rate risks. It is very important to give full play to the maturity transformation Function of commercial banks to enhance the ability of financial services to the real economy. Based on the semi annual unbalanced panel data of 37 listed banks in A-share stock markets from 2006 to 2020, this paper empirically tests the impact of the use of off balance sheet interest rate derivatives on the Maturity Transformation Function of banks in the case of interest rate fluctuations. The empirical results show that: (1) the use of interest rate derivatives helps to weaken the negative impact of interest rate fluctuations on the Maturity Transformation Function of banks. (2) The analysis of the mechanism shows that the use of interest rate derivatives improves the stability of the bank's asset side term structure and liability side term structure, so as to support the effective play of the bank's financial intermediary role. (3) Further analysis shows that the of interest rate derivatives significantly reduces the volatility of bank earnings. This study makes it clear that the use of interest rate derivatives has a positive impact on the commercial banks, which provides evidence for the further development of interest rate derivatives market in China.