Chinese enterprises are currently facing unprecedented economic transformations accompanied by a diverse array of challenges. This article delves into these challenges and provides management recommendations to assist companies in addressing these pressing issues. First, China's economic growth is gradually slowing, prompting companies to explore new avenues for growth, such as diversifying their products and markets, enhancing research and development, and expanding into emerging markets. Second, the uncertain global trade landscape has impacted exports and supply chains, necessitating diversified supply chains, new trade partnerships, and proactive strategies to navigate potential trade policy changes. Additionally, the pressure of technological innovation cannot be underestimated, urging companies to increase R&D investment, collaborate with other enterprises on research, and recruit and nurture high-quality tech talent. Furthermore, with the Chinese government's growing focus on environmental concerns, companies need to invest in clean production technologies, build sustainable supply chains, and actively fulfill their social responsibilities. Other challenges including rising labor costs, intellectual property protection, financial risks, regulatory compliance, talent recruitment and retention, and digital transformation all require proactive responses. By adopting proactive management strategies, Chinese enterprises can thrive in this era filled with both opportunities and risks, achieving sustainable growth and enhanced competitiveness.

As a vital documentation for assessments, rewards and punishments in terms of political promotions, the intellectual property pilot city policy (IPPC), an strategic incentive measure to enhance innovation capacities at the city and firm level, may play a prominent role in innovation fostering in China. Yet patent bubbles that focus more on quantity over quality have been thrown into doubt, as local cadres and firms shall pay more attention to the easier observable low-quality innovation performance amid the pressure of political task. this paper conducts an investigation that drew upon listed firm data from 270 prefecture-level cities and employs a PSM-DID design to evaluate the IPPC policy effectiveness on innovation quality and innovation quantity. The results are obvious: The policy have boosted the number of innovations, but has a limited effect on improving the quality of innovation. We further apply a hierarchical liner modeling approach to deal with the stratified cityand firm-level data and to verify the mechanism through which policy distortions may affect corporate innovation. There also gives evidence that the IPPC policy comes into effect mainly through financial subsidies, institutional supply and the intensity of IPR protection at the local scale. This report concludes by proposing further policy implementations for the future optimization of China’s innovation strategies.

The study explores the impacts and processes of traditional Confucianism on the green technology innovation behavior of organizations from the perspective of the informal system, using samples of Chinese A-share listed companies from 2003 to 2022. The findings indicate that Confucianism has a significant promotional effect on green technological innovation, which remains robust after using the cross-multiplier term between the number of regional Confucius temples and the mean ESG of firms as an instrumental variable to mitigate the endogeneity problem and a series of tests. According to mechanistic research, Confucianism works largely through two channels: reducing agency conflicts and raising environmental consciousness. Further investigation reveals that there is a substitution impact between Confucianism in the informal institution and environmental legislation in supporting green technology innovation in firms. To encourage green technological innovation in enterprises, it is critical to emphasize the integration of informal and formal systems, as well as to fully use traditional culture’s governance efficacy in supporting the enterprise green transformation.

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.