We evaluate the impact of digital finance on the ESG performance of manufacturing enterprises and whether digital and traditional finance play a complementary or substitute role in promoting the ESG performance. First, we find that developing digital finance can alleviate financing constraints and promote technological innovation, thereby increasing enterprises' investment in environmental, social, and governance, providing sufficient technical support, and improving their ESG performance. Furthermore, digital finance and traditional finance have a direct impact on the ESG performance and further enhance their influence through complementary effects. Therefore, this paper may provide a valuable reference for finance to support manufacturing enterprises' development effectively.

The study indicates that under conditions of financial repression, a enterprise’s ESG performance significantly impacts the extent of its short-term debt used for long-term purposes. The mechanism test reveals that ESG performance mitigates the degree of short-term debt for long-term use through three pathways: enhancing information transparency, alleviating financing constraints, and curbing excessive investment. Further research suggests that the influence of ESG performance on the use of short-term debt for long-term purposes is more pronounced among private enterprises, high-pollution and high-energy-consuming enterprises, and enterprises in underdeveloped regions. This paper enriches the research on the relationship between ESG performance and corporate financing decisions.

This paper investigates whether Environmental, Social, and Governance (ESG) performance influences the stock idiosyncratic risk and extreme risk. We find that the ESG performance of listed companies significantly reduces the stock idiosyncratic risk and extreme risk. Furthermore, we identify that this mitigating effect is shaped by the nature of enterprise ownership and the firm life cycle. Through additional mechanistic analysis, we confirm that ESG performance affects the stock price volatility risk of listed companies by reducing levels of corporate earnings management and bolstering corporate reputation, thereby alleviating both idiosyncratic risk and extreme risk in stock prices.

This paper investigates whether regional negative public sentiment associated with extreme non-financial social shocks (e.g., violence or crime) will affect the resident firms’ M&A announcement return. Using a sample of 3,200 M&A deals in China, our empirical results consistently show that M&A announcement return is significantly lower after the firm’s headquarter city has experienced negative social shocks. We further find that better CSR performance helps to mitigate the impact of these negative shocks. Overall, we show that firm operations will be largely affected by the resident environment and location, and better CSR performance acts as an effective risk management strategy.

This paper examines how the integration of artificial intelligence (AI) and big data affects banking operations, emphasizing the crucial role of big data in unlocking the full potential of AI. Leveraging a comprehensive dataset of over 4.5 million loans issued by a leading commercial bank in China and exploiting a policy mandate as an exogenous shock, we document significant improvements in credit rating accuracy and loan performance, particularly for SMEs. Specifically, the adoption of AI and big data reduces the rate of unclassified credit ratings by 40.1% and decreases loan default rates by 29.6%. Analyzing the bank's phased implementation, we find that integrating big data analytics substantially enhances the effectiveness of AI models. We further identify significant heterogeneity: improvements are especially pronounced for unsecured and short-term loans, borrowers with incomplete financial records, first-time borrowers, long-distance borrowers, and firms located in economically underdeveloped or linguistically diverse regions. Our findings underscore the powerful synergy between big data and AI, demonstrating their joint capability to alleviate information frictions and enhance credit allocation efficiency.

Despite nearly twenty years of privatization, mixed-ownership reform has been the mainstay of SOE reform in China in recent years. This raises the question of whether the financial performance of mixed-ownership firms (Mixed firms) is better than private-owned enterprises (POEs). Although Mixed firms suffer more from government intervention, unclear property rights, and interest conflicts between state shareholders and private shareholders, they can also benefit from the external resources controlled by the state. Therefore, the performance of Mixed firms is still unclear. Collecting data from the Chinese A-share listed market, we divide the firms into POEs, Mixed firms controlled by the state (MixedSOEs), and Mixed firms controlled by the private sectors (MixedPOEs). Measuring profitability using ROA and ROE, we find that on average, POEs perform better than Mixed firms, and MixedPOEs have a higher profitability than MixedSOEs. Within Mixed firms, more state shares are related to lower profitability, and more private shares are related to higher profitability. Using the NBS survey data, we further find that on average, SOEs exhibit the lowest profitability, with MixedSOEs and MixedPOEs in the middle, and POEs have the highest profitability. We try to address the endogeneity challenge in several ways and get similar results. Overall, our analysis provides new evidence on the financial performance of mixed-ownership firms.

This study explores how Chinese mutual fund managers’ degrees of disagreement (DOD) on stock market returns affect investor capital allocation decisions using a novel text-based measure of expectations in fund disclosures. In the time series, the DOD neg-atively predicts market returns. Cross-sectional results show that investors correctly perceive the DOD as an overpricing signal and discount fund performance accordingly. Flow-performance sensitivity (FPS) is diminished during high dispersion periods. The ef-fect is stronger for outperforming funds and funds with substantial investments in bubble and high-beta stocks, but weaker for skilled funds. We also discuss ffnancial sophisti-cation of investors and provide evidence that our results are not contingent upon such sophistication.

This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

Why do firms appoint expired independent directors? How do expired independent directors affect corporate governance and thus impact investment decisions? By taking advantage of the sharp increase in expired independent directors’ re-employment in China caused by exogenous regulatory shocks, Rule No. 18 and Regulation 11, this paper adopts a PSM-DID design to test the impact of expired independent directors on CSR performance. We find that firms experience a significant decrease in CSR performance after re-hiring expired independent directors and the effect is stronger for CSR components mostly related to internal governance. The results of robustness tests show that the main results are robust to alternative measures of CSR performance, an extended sample period, alternative control groups, year-by-year PSM method, and a staggered DID model regarding Rule No. 18 as a staggered quasi-natural experiment. We address the endogeneity concern that chance drives our DID results by using exogenous regulatory shock, an instrumental variable (the index of regional guanxi culture), and placebo tests. We also find that the negative relation between the re-employment of expired independent directors and CSR performance is more significant for independent directors who have more relations with CEOs and raise less objection to managers’ decisions, and for firms that rely more on expired independent directors’ monitoring roles (e.g., a lower proportion of independent directors, CEO duality, high growth opportunities, and above-median FCF). The mediating-effect test shows that the re-employment of expired independent directors increases CEOs’ myopia and thus reduces CSR performance. In addition, we exclude the alternative explanation that the negative relation is caused by the protective effect brought by expired independent directors’ political backgrounds. Our study shows that managers may build reciprocal relationships with expired independent directors in the Chinese guanxi culture and gain personal interest.

This study reconsiders the timing of the equity risk factors by using the flexible neural networks specified for image recognition to determine the timing weights. The performance of each factor is visualized to be standardized price and volatility charts and `learned' by flexible image recognition methods with timing weights as outputs. The performance of all groups of factors can be significantly improved by using these ``deep learning--based'' timing weights. In addition, visualizing the volatility of factors and using deep learning methods to predict volatility can significantly improve the performance of the volatility-managed portfolio for most categories of factors. Our further investigation reveals that the timing success of our method hinges on its ability in identifying ex ante regime switches such as jumps and crashes of the factors and its predictability on future macroeconomic risk.