The opening of high-speed rail (HSR) has significantly boosted business development in China. This study constructs a credit rationing model based on the theory of information asymmetry, and takes the opening of HSR as a quasi-natural experiment to empirically examine its impact on the investment and financing decisions among firms with different risk profiles using data from A-share listed companies from 2005 to 2019. The findings reveal that HSR opening significantly reduces corporate short-term loans while increasing long-term loans, without affecting loan costs. Lowriskfirms, as opposed to high-risk ones, experience notable reductions in short-term loan amounts and extended loan terms post-HSR opening. This is attributed to HSR mitigating information asymmetry between banks and firms. Additionally, HSR opening suppresses "short-term debt for long-term use" behaviors, thereby enhancing investment efficiency and quality. The study empirically supports the idea of leveraging HSR's economic stimulus in terms of firm investment and financing.

The emergence of deep learning has yielded substantial advancements in computer vision, hence offering novel opportunities for the interdisciplinary exploration of finance and computer science. This paper adopts a cognitive dissonance theory viewpoint to investigate the impact of managers face emotion on market performance and risk in Chinese listed companies from 2016 to 2022. We employ deep learning model to analyze managers’ facial emotion. We find that the more positive facial expressions of managers in earnings conference call predict better market performance, lower volatility and share price crash risk. This study deepens the application of cognitive dissonance theory.

We construct a stock learning index in China A-share market, which is based on a theoretical model of information and investment choice. The higher the learning index value, the more thoroughly the individual stock is learned. Our study shows that a stock with a high learning index will have a lower expected future return compared to a stock with a low learning index. Additionally, decomposition of predictive power shows that the predictive power of the learning index mainly comes from the persistence of its own predictive power, while the rest cannot be explained by changes in the volume of news (proxy for information flow). Moreover, the learning index can explain many market anomalies in China A-share market.

This paper examines the effects of superstitious psychology on investors’ decision making in the context of Mercury retrograde, a special astronomical phenomenon meaning “everything going wrong”. Using natural experiments in the Chinese stock market, we find a significant decline in stock prices, approximately -3.14% in the vicinity of Mercury retrogrades, with a subsequent reversal following these periods. The Mercury effect is robust after considering seasonality, the calendar effect, and well-known firm-level characteristics. Our mechanism tests are consistent with model-implied conjectures that stocks covered by higher investor attention are more influenced by superstitious psychology in the extensive and intensive channels. A superstitious hedge strategy motivated by our findings can generate an average annualized market-adjusted return of 8.73%.

This study examines factors that determine prefectural industrial policies and their impact on firm total factor productivity (TFP), utilizing a natural language processing algorithm and data from the Report on the Work of the Government in China. We find that compliance with upper-level governments is crucial in shaping prefectural industrial policies. When an industry is favored by the upper-level government, the probability of the prefectural government’s favoring that industry increases. However, prefectural policies driven by political compliance have a minimal positive impact on TFP, due to inadequate implementation of policy measures like tax deductions, preferential loans, and land price discounts.

This study investigates the impact of CIO on the Environment, Social, and Governance (ESG) performance. Our analysis is based on a panel dataset comprising 2395 Chinese listed companies throughout the period from 2007 to 2020. Evidence from empirical results shows that CIO is positively correlated with ESG performance. In other words, CIO enhance the corporate ESG performance. The issue of endogeneity was duly considered, and appropriate measures were made to address it. Furthermore, robustness tests were conducted, and the findings remained consistent and reliable. The examination of the mechanism indicates that CIO enhance internal control quality that facilitates the advancement of ESG activities within firms. This paper contributes to the existing body of knowledge by examining the impact of external governance systems on the promotion of ESG activities in Chinese enterprises. This study adds to the existing body of scholarship on the implications of Common institutional ownership. Findings recommend several possible policy and economic ramifications that might support Chinese enterprises in their endeavors to incorporate ESG initiatives and contribute to the overall sustainability of society.

Utilizing a unique P2P dataset, this study employs the PSM-DID method to explore the learning effect brought about by default events on investors. The findings reveal that investors who experience their first default event demonstrate an improved ability to select a higher-quality project the next time. Notably, this positive effect is more pronounced when facing substantial defaults, as opposed to cases where overdue principal and interest are eventually settled. Investors' initial confidence in defaulted projects contributes to a greater enhancement of their investment skills. Furthermore, the beneficial impacts of defaulted events diminish as investors’ investment experience accumulates.

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.

Using the formal implementation of the 2012 Green Credit Guidelines (GCG) as an exogenous shock to construct a quasi-natural experiment, we study the impact of green credit policies on the inefficient investment of heavily polluting firms in China's listed companies from 2008 to 2020. We find that green credit policies can significantly alleviate the inefficient investment of heavily polluting enterprises. By reducing agency costs and long-term liabilities, green credit policies mitigate the problem of inefficient investment in heavily polluting firms. Moreover, the mitigating effect of green credit policies on the inefficient investment of heavily polluting enterprises has significant heterogeneity in terms of property rights, internal characteristics.

This study examines a sample of Chinese listed companies from 2009 to 2021 and finds that Environmental, Social, and Governance (ESG) performance significantly reduces bankruptcy risk. Robustness tests support the bankruptcy risk mitigation effect of ESG performance. Mechanism analysis shows that ESG performance reduces bankruptcy risk by decreasing systematic risk and alleviating financing constraints. Further analysis indicates that the performance of the three dimensions of ESG, namely Environment (E), Social (S), and Governance (G), contributes to the reduction of bankruptcy risk.