We conducted a nationwide field experiment with 4,800+ Chinese-listed companies, randomly raising ESG concerns to their management teams via high-visibility and high-stakes online platforms. Tracking the full impact-generating process, we find that companies respond to our concerns by providing high-quality answers, publishing ESG reports, and making commitments to investors. Over time, Environmental (E) inquiries boost stock valuations, while Governance (G) concerns prompt skepticism. Productive and opaque firms are more likely to respond, consistent with a signaling model where costly ESG actions signal firm quality under information asymmetry. Overall, ESG actions are likely driven by profit-oriented signaling rather than values-based motives.

The growing emphasis on Environmental, Social, and Governance (ESG) principles, particularly in corporate sectors, shapes investment trends and operational strategies, whose shift is supported by the increasing role of media in monitoring and influencing corporate ESG performance, thereby driving the energy innovation. Therefore, based on reported events from Baidu News and patent text information of Chinese A-share listed companies from 2012 to 2022, this study innovatively applied machine learning and text analysis to measure ESG news sentiment and corporate energy innovation indicators. Combing with reputation, stakeholder, and agency theories, we find that a good reputation conveyed by positive ESG textual sentiments in the media significantly promotes corporate energy innovation, and the effect is mainly realized through alleviating financing constraints and agency problems and promoting green investment. Further analysis shows that ESG news sentiment promotes corporate energy innovation mainly among private firms, non-growth-stage firms, high-energy-consuming firms, and regions with better green finance development and higher ESG governance intensity. From the perspective of ESG news content and information content, greater ESG news attention can also exert an energy innovation incentive effect, in which the incentive effect exerted by positive media sentiment in the environmental (E) and social (S) dimensions, as well as excellent attention, is more robust. This study provides new insights for promoting green and low-carbon development and understanding the external governance role of media in corporate ESG development.

It has been widely recognized that firms’ environmental, social, and governance (ESG) performances are crucial for shaping their information environments. Nonetheless, the impact of ESG performances on important analyst report attributes still remains clear. Our study reveals that superior firm. ESG performances significantly enhance the analyst report readability. The mechanism analysis demonstrates that this effect is primarily driven by increased information accessibility (the information acquisition channel) and greater analysts’ research efforts (the analyst effort channel). As expected, this effect is more pronounced in firms operating in highly polluted industries, firms with opaque financial infomration and state-owned enterprises (SOEs). Finally, our findings reveal that the release of analyst reports triggers higher market reactions for firms with superior ESG performances. In overall, our study highlights the criticial role of firm ESG performances in boosting financial analysts’ information production process.

Previous studies on the impact of corporate information disclosure on stock price crash risk have largely focused on financial statements. In contrast, China’s unique monthly operating report disclosure system—featuring high frequency and realtime operational data—offers a distinct information channel. Using data from A-share listed firms from 2010 to 2021, we find that monthly operating report disclosures significantly reduce stock price crash risk by alleviating information asymmetry between firms and external stakeholders. The underlying mechanisms involve restraining managerial opportunism and correcting investor expectation biases. Further analysis shows that firms’ official responses to investor inquiries has no significant effect on crash risk once monthly operational disclosures are accounted for, underscoring that the quality of information disclosed is as important as its frequency. The risk-reducing effect is more pronounced among firms with greater business complexity, weaker internal controls, and lower institutional ownership.

This paper examines how profits from mutual funds’ participation in initial public offerings (IPOs) shape fund performance, investor flows, and market stability in China. Using comprehensive fund–IPO matched data from 2016 to 2023, we decompose fund returns into an IPO-lottery component and residual performance. At the aggregate level, IPO allocations add 2.05% to annualized excess returns; net of IPOs, excess return is −0.35% per year. At the individual level, the contribution of IPO profits varies substantially across funds and is most pronounced among mid-sized funds, inflating perceived managerial skill. Funds with higher IPO-driven gains attract greater inflows despite the absence of performance persistence, leading to capital misallocation. At the market level, IPO-profit-induced trading (PIT) predicts short horizon price run-ups that dissipate and reverse over subsequent months, while raising both total and idiosyncratic volatility. Overall, IPO profits temporarily enhance reported performance but erode market stability by propagating non-fundamental shocks through secondary markets.

The 2008 Labor Contract Law of China stipulates that all employment relationships must be covered by a written labor contract. This regulation considerably strengthened employment protection for workers. Using a unique longitudinal dataset, the China Labor-force Dynamics Survey (2012, 2014, and 2016 waves), this article estimates the impact of the formal contractualization of labor relations on workers’ labor market outcomes, social insurance participation, and job satisfaction. We find that gaining a labor contract was strongly associated with an increase in salary, a decrease in working overtime hours, and greater participation in unemployment and pension insurances. In terms of job satisfaction, workers who gained a labor contract reported being less satisfied with their workplace environment and income than they had anticipated. This finding suggests that workers had higher expectations from the benefits gained through contractualization, than what they actually derived.

This paper is the first to examine the evolution of price discovery in agricultural commodity markets across the four distinct phases determined by trade friction and trade policy uncertainty. Using cointegrated vector autoregressive model and common factor weights, we report that corn, cotton, soybean meal, and sugar (palm oil, soybean, soybean oil, and wheat) futures (spot) play a dominant role in price discovery during the full sample period. Moreover, the leadership in price discovery evolves over time in conjunction with changes in trade friction phases. However, such results vary across commodities. We also report that most of the agricultural commodity markets are predominantly led by futures markets in price discovery during phase Ⅲ, except for the wheat market. Our results indicate that taking trade friction into consideration would benefit portfolio managements and diversifying agricultural trade partners holds significance.

The healthy operation of the stock market relies on sound liquidity. We utilize the semantic information from disclosure texts of listed companies on the China Science and Technology Innovation Board (STAR Market) to construct a daily corporate network. Through empirical tests and performance analyses of machine learning models, we elucidate the relationship between the similarity of company disclosure text contents and the temporal and spatial correlations of stock liquidity. Our liquidity indicators encompass trading costs, market depth, trading speed, and price impact, recognized across four dimensions. Furthermore, we reveal that the information loss caused by employing Minimum Spanning Tree (MST) topology significantly affects the explanatory power of network topology indicators for stock liquidity, with a more pronounced impact observed at the document level. Subsequently, by establishing a neural network model to predict next-day liquidity indicators, we demonstrate the temporal relationship of stock liquidity. We model a liquidity predicting task and train a daily liquidity prediction model incorporating Graph Convolutional Network (GCN) modules to solve it. Compared to models with the same parameter structure containing only fully connected layers, the GCN prediction model, which leverages company network structure information, exhibits stronger performance and faster convergence. We provide new insights for research on company disclosure and capital market liquidity.

This paper explores the impact of government intervention on local analysts’ earnings forecasts, based on a scenario of financial misconduct in Chinese state-owned enterprises (SOEs). The results show that, under the influence of the government, local analysts’ earnings forecasts for SOEs with financial misconduct are less accurate and more optimistically biased. Further heterogeneity analysis reveals that forecast bias by local analysts is greater when officials have stronger promotion incentives, when regions are less market-oriented and have a larger share of the state-owned economy, and when SOEs contribute more to taxation and employment. In further analysis, we find that local analysts have a more optimistic tone in reports targeting non-compliant SOEs. Local analysts who depend heavily on political information will also issue more biased and optimistic forecasts on SOEs with violations. Finally, as a reward for achieving government goals, the local brokerages affiliated with these analysts and providing these optimistic forecasts are more likely to become underwriters in seasoned equity offerings of SOEs. This paper reveals that government intervention significantly influences analyst forecasts, providing implications for understanding the sources of analyst forecast bias.

We study how financial markets integrate news about pollution abatement costs into firm values. Using China’s automation of pollution monitoring, we find that firms with factories in bad-news cities---cities that used to report much lower pollution than the automated reading---see significant declines in stock prices. This is consistent with the view that investors expect firms in high-pollution cities to pay significant adjustment and abatement costs to become “greener.” However, the efficiency with which such information is incorporated into prices varies widely---while the market reaction is quick in the Hong Kong stock market, it is considerably delayed in the mainland ones, resulting in a drift. The equity markets expect most of these abatement costs to be paid by private firms and not by state-owned enterprises, and by brown firms and not by green firms.