We examine communication of political connections in corporate China, and show that politically inclined positive words—words in connotation of political sentiment—serve as a distinct and effective signaling device for corporate political connections. Using a large sample of corporate news, we find that news’ political sentiment, instead of orthodox political measures such as occurrences of political nouns and political entities, reflects executives’ political connections for private enterprises, and is related to rent-seeking benefits in government subsidy, tax refund, financing constraints and political risk. Our results demonstrate that political sentiment is an effective way to decode subtle corporate political connections in modern China’s “Mind Politics” environment that infiltrates into private corporations.

Using sample of Chinese listed firms from Q1 2017 to Q4 2022, this article examines the impact of economic policy uncertainty on share repurchases. We find that economic policy uncertainty significantly increases the probability and scale of open market share repurchases. Private enterprises, government-supported enterprises, innovative enterprises, and investment hotspot enterprises repurchased more shares during periods of high economic policy uncertainty. Additionally, the market value of repurchase programs issued during periods of high economic policy uncertainty is larger. We also discover that economic policy uncertainty substantially influences the characteristics, timing, and outcomes of the repurchase programs. Lastly, this article confirms that share repurchase behavior has a similar effect to voluntary disclosures and can alleviate the information asymmetry triggered by economic policy uncertainty. In summary, Chinese listed firms have resorted to more share repurchases during periods of high economic policy uncertainty to convey their actual value and boost investor confidence, aligning with the signaling motive. Open market share repurchases surface as an efficacious instrument to cope with the risk from economic policy uncertainty.

The caliber of export products is a microcosmic reffection of economic development quality. This study seeks to elucidate the inffuence of environmental regulation on product quality, integrating the role of the skill premium as shaped by environmental regulation within a Dixit-Stiglitz CES production function model. Additionally, we empirically scrutinize the interplay between environmental regulation, skill premium, and product quality, utilizing Chinese customs export data in conjunction with data from listed companies spanning 2003-2015. The conclusions drawn from our theoretical analysis and empirical veriffcation reveal an inverted U-shaped relationship between environmental regulation and product quality, which is tempered by the skill premium. Moreover, a signiffcant positive correlation exists between environmental regulation and the skill premium. Similarly, the relationship between the skill premium and product quality manifests an inverted U-shape. Notably, an elevated skill premium markedly bolsters the enhancement of product quality through green innovation. These insights underscore the need for balanced environmental regulations and strategic investment in skilled labor to augment product quality. This serves as a valuable compass for policymakers and businesses endeavoring for green innovation and high-quality, sustainable economic growth.

Based on the matching data of China’s industrial enterprise database and enterprise patent database, this study investigates the effect and mechanism of government subsidy policy on the innovation performance of Chinese enterprises using the panel data matching and multi-period difference-in-difference model. Results show that the incentive effect of government subsidy policy on enterprises’ innovation performance is dominant, thus improving the innovation performance of Chinese manufacturing enterprises. However, the promotion effect on lowquality innovation is greater than that on high-quality innovation. Moreover, the government subsidy policy affects the innovation performance of enterprises by stimulating enterprises to increase their R&D investment, increasing the spending on rent-seeking and taking on more social responsibility. The government subsidy policy significantly affects the innovation performance of non-state-owned enterprises, small and medium-sized enterprises, and technology-intensive industries. The impact of government subsidizing policies varies regionally. The policy effect on enterprise innovation performance is significant in the eastern and central regions but not in the western and northeast regions.

In the process of green transformation of China's economy, it is of great practical significance to study the impact of green finance in supporting the development of the real economy, especially the impact of green credit on enterprise innovation, in order to promote the green transformation of enterprises, industrial structure upgrading and sustainable economic development. This paper takes green credit as a perspective and introduces it into the analytical framework of the impact of environmental regulation on corporate green innovation, through theoretical mechanism analysis and empirical testing, in order to reveal the impact and mechanism of green credit on corporate green innovation. It is found that green credit can effectively promote green innovation in heavy polluting enterprises, and it is mainly reflected in the increase of green utility model patent applications with a low degree of inventiveness. The promotion effect of green credit on green innovation is more obvious in regions with lower levels of economic development. Further mechanism analysis shows that green credit policy promotes green innovation of heavy polluting enterprises mainly through the incentive effect brought by changing financing environment and the pressure effect brought by increasing market competition. The findings of this paper can provide references for policy-making departments, banks and enterprises.

In recent years, the attempt to curb state-owned enterprises (SOEs) has resulted in dedicated rules in trade agreements. This paper reveals significant paradigm shifts in cross-border SOE regulation by exploring the emerging SOE rules and contrasting them with SOE disciplines in WTO agreements. First, the emerging SOE rules shift the emphasis from regulating trade measures to the competitive behaviour of SOEs. More importantly, the emerging SOE rules are characterized by excessive focus on behaviour analysis and a per se approach. Under a per se approach, a violation of the emerging SOE rules could be established regardless of whether the behaviour of an SOE caused a harmful trade or competition effect. Finally, in light of SOE reform in China, the article contends that the emerging SOE rules’ behaviour analysis deviate cross-border SOE regulation from its primary goal of levelling the playing field.

The effects of skyscraper development on surrounding firms’ innovation in China is assessed in this paper, and empirical results show that skyscraper development significantly promotes the innovation of firms within 1 km of skyscrapers. However, such effect only exists around very tall skyscrapers in large cities. This means that skyscraper development in small cities often is unfit for the economic fundamentals of these cities, which may weaken the positive externalities generated by skyscrapers. The mechanism analysis in this paper shows that increased surrounding population density brought by skyscraper development and knowledge sharing between firms surrounding the skyscrapers and other firms in the same city are the main paths through which skyscrapers affect innovation.

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.

This study investigates whether and how the central discipline inspection of the securities regulators affects the information environment and investor valuation in the Chinese capital market. Based on the private equity placement (PEP) events, we find that the self-interested media outlets provide more negative coverage of the passed PEP firms during the inspection period than those passed outside the inspection period, resulting in poorer stock returns. Additionally, we find that the negative effect of the inspection on the PEPs’ market reactions is attenuated in media-connected firms and firms with higher advertising expenditure. However, we do not find significant long-term market performance differences between the passed PEP firms during the inspection period and those passed outside the inspection period. Additional results show that during the inspection period, the securities regulators tend to approve the PEP applicants with better initial announcement returns. Moreover, sophisticated investors pay a higher price for the shares of these passed PEP firms during the inspection period. Collectively, our findings suggest that anti-corruption campaigns have unintended effects that hinder retail investors’ access to objective information.

Widely used volatility forecasting methods are usually based on low frequency time series models. Although some of them employ high frequency observations, these intraday data are often summarized into a point low frequency statistic, e.g., a daily realized measure, before being incorporated into a forecasting model. This paper contributes to the volatility forecasting literature by instead predicting the next-period intraday volatility curve via a functional time series forecasting approach. In contrast with non-functional methods, the proposed functional approach fully exploits the rich intraday information and hence leads to more accurate volatility forecasts. This is further confrmed by extensive comparisons between the proposed functional method and those widely used non-functional methods in out-of-sample volatility forecasting for a number of stocks and equity indices from the Chinese market.