Against a backdrop of economic transformation and industrial upgrading, financing constraints have become a significant barrier to the growth of microeconomic entities. Investor sentiment, a recognized source of irrationality in capital markets, is understood to influence the financing environment through various channels. Concurrently, advances in generative artificial intelligence (AI) are generating novel methods for the precise measurement of this sentiment. Leveraging data from Chinese A-share listed corporations from 2011 to 2023, this study constructs a double machine learning framework to examine the impact of investor sentiment on financing constraints and its underlying transmission mechanisms. Our findings indicate that elevated investor sentiment significantly reduces these constraints. This effect operates primarily through an enhanced capacity for debt financing, a reduction in market scrutiny, and a stimulation of consumer demand that boosts corporate revenue. Conversely, periods of subdued sentiment tend to trigger risk-averse behavior, intensify liquidity pressures, and further restrict access to capital. A heterogeneity analysis reveals that the effects of sentiment vary across industries, ownership structures, and regional levels of information infrastructure. The impact is particularly pronounced within the real estate sector and in more digitally advanced regions. Interestingly, the effect does not fundamentally differ across ownership types, suggesting a degree of market neutrality. In sum, this study offers fresh empirical evidence on the mechanisms through which sentiment drives financing behavior. It also expands the application of generative AI, demonstrating its value as a tool for financial research.

Using data from Chinese non-financial listed firms over the 2009-2021 period, this study examines the link between cybersecurity and total factor productivity. The results suggest that cybersecurity can improve total factor productivity, a finding robust to multiples tests. Further investigation confirms that this relationship arises from information multiplication and capital deepening. It is particularly pronounced after the implementation of the Cybersecurity Law or among firms with strong digital innovation activities. In contrast, for firms whose senior executives have an information technology background, the moderating effect is not significant. However, the relationship is more pronounced for firms with larger scale, that are more technology-intensive, or operating in industries with above-average competition. Policy wise, these findings provide new evidence on the economic value of cybersecurity and offer policy implications for improving cybersecurity governance and aligning security investment with information and communications technology development to enhance resource allocation efficiency and sustainable digital growth.

Purpose-This study explores how inconsistency in environmental, social and governance (ESG) ratings affects the information environment of publicly traded companies in emerging markets. Design/methodology/approach-Drawing on data from six ESG rating agencies covering listed Chinese firms from 2010 to 2022, we construct measures of pairwise ESG rating divergence and average them across 15 unique rater pairs. We then employ a battery of econometric methods to examine how inconsistencies in ESG ratings affect stock return synchronicity in the Chinese capital market. Findings-Our findings indicate that greater ESG rating divergence correlates with increased stock return synchronicity, suggesting a decline in the dissemination of firm-specific information. Supporting evidence for this relationship includes reduced private information flow, heightened opportunistic executive sales and decreased stock turnover for firms associated with higher ESG rating divergence. Our analysis suggests that such divergence hinders informed trading by institutional investors and the effect is more pronounced for firms with lower transparency, such as those with fewer analysts, limited investor oversight and sparse ESG disclosure, as well as for firms in non-polluting industries. These findings emphasize that ESG rating divergence exacerbates uncertainty about firms' future performance, discourages informed trading and ultimately restricts the integration of firm-specific information into stock prices. Originality/value-This study adds to the literature on ESG ratings and market efficiency by highlighting the importance of standardizing rating methodologies and improving corporate disclosure to counteract the adverse impacts of rating divergence.

In this paper, we investigate the availability of external finance for state-owned enterprises (SOEs) and private enterprises (PEs) across all manufacturing industries in China. Employing a disequilibrium model, we find that SOEs are generally less financially constrained than PEs, but the difference is of small magnitude and has been steadily declining over time. We also reveal a positive relation between the advantage of SOEs in accessing external funds and the level of SOE monopoly at the industry level. In general, while SOEs and PEs are equally constrained in competitive downstream industries, we find a significant comparative advantage in accessing external finance for SOEs in monopolized mid- and upstream industries. The policy implication is straightforward: to finance PEs better, the Chinese government should consider breaking the existing monopolies or near monopolies of SOEs in certain industries further, rather than encouraging banks to favor PEs in their lending.

This paper examines the effects of China's 2016 coal capacity reduction policy on firms, local labor markets, and crime. We find that the policy substantially reduced the asset size and employment of coal firms in treated cities while simultaneously increasing their profitability and lowering debt. Policy-induced layoffs generated adverse spillovers in local labor markets, reducing residents' incomes, worsening public perceptions of job opportunities, and increasing divorce rates. These labor market effects were most pronounced among older and less-educated individuals. Employment also shifted away from the mining sector toward wholesale and retail trade and real estate-related industries. In addition, the policy led to an increase in crime, driven primarily by property offenses committed by individual offenders, while violent crime remained largely unchanged. A back-of-the-envelope calculation suggests that the increase in coal firms' profits exceeded the fiscal resources required for worker resettlement.

This paper examines how potential disruptions from generative artificial intelligence (AI) to firms' core businesses affect their cross-industry acquisition decisions. Using data on Chinese A-share listed firms, we employ a large language model to assess the potential positive and negative effects of generative AI on core businesses and construct a measure of net AI risk exposure. We measure cross-industry M&A using the semantic distance between acquirers' and targets' business descriptions. A difference-in-differences analysis exploiting the commercial diffusion of generative AI shows that more exposed firms acquire more distant targets, with an effect equivalent to 8.2% of the sample mean and robust to alternative specifications. The effect is stronger among firms with narrower innovation boundaries and more concentrated business portfolios, consistent with innovation capability reconfiguration and strategic risk diversification. It is also stronger among firms with smaller market shares, in more competitive industries, and in more marketized regions. Further analyses show that high-exposure firms acquire targets with higher AI patent shares and receive more favorable announcement-day market reactions to distant acquisitions. Our findings suggest that potential AI-related disruptions shape acquisition target selection and firms' business boundaries.

This paper studies the unintended spillover effects of China's 2020 "Three Red Lines" policy – a macroprudential intervention aimed at deleveraging real estate developers – on employment in the upstream manufacturing firms. Exploiting variation in industries' exposure to the real estate sector via input-output linkages and employing a generalized difference-in-differences (DID) design on panel data of listed manufacturing firms (2017-2022), we find a significant decline in employment among upstream suppliers, while there is no substantial effect on downstream firms. We identify two transmission channels: a demand propagation channel, evidenced by falling sales and inventory overhang, and a balance sheet contagion channel, where liquidity strain is passed upstream via extended payables and a shift to riskier short-term debt. Furthermore, we show that downward nominal wage rigidity shaped the adjustment, as firms resorted to layoffs rather than wage cuts for ordinary workers. From a regional labor market perspective, the employment losses represent a net reduction at the aggregate level, as displaced workers are not absorbed by other local firms in the same city or province. Our study reveals a significant real economic cost of sectoral macroprudential regulation and clarifies how such shocks propagate through production networks and interact with labor market frictions.

This paper explores the impact of the Integrated Government Service Platform (IGSP) reform, a large-scale digital regulatory initiative in China, on firms’ investment efficiency. Using a firm-level panel dataset of Chinese listed firms, we find that the IGSP reform significantly enhances firms’ investment efficiency. Mechanism analyses indicate that the IGSP enhances firms’ investment efficiency by improving governance service quality and convergence of governance services across cities within a province, thereby reducing economic policy uncertainty and reinforcing market expectations. The heterogeneity analyses reveal that the effect is more pronounced for firms without political connections, those operating in high business volatility and high-tech industries, and regions with stronger informal institutions. Finally, the IGSP encourages firms to expand their investment activities to other cities within the same province. This finding is fully consistent with the underlying mechanisms identified in this study. Our findings provide novel evidence on the economic consequences of digital regulatory integration and offer policy implications for advancing service-oriented and efficiency-driven governance in developing economies.

We identify choke points in global production networks as industries bridging flows across global value chains. These industries exhibit low substitutability: US firms exposed to Chinese choke points during the 2022 Covid-19 lockdowns experienced large and persistent sales declines. The Red Sea crisis demonstrates how negative shocks to water transport, a single choke point, propagate throughout the global network. This structural fragility is priced in global stock markets: firms in choke point industries earn annualized benchmark-adjusted returns exceeding 6%. The premium compensates for aggregate consumption risk, as downturns in choke point industries predict lower future US and global consumption growth.

This paper investigates the sales seasonality premium (Gustavo et al., 2020) in the Chinese stock market. We document a significant sales seasonality premium in the cross section of stocks listed on Chinese market. A long-short strategy of buying low-sales stocks and shorting high-sales season stocks generate a monthly return of 0.84% with a Newey-West t statistic of 2.94. The return spreads between low-sales season stocks and high-sales season stocks are robust to well-known anomalies and are larger in magnitude among large-cap companies. We also find that the return spreads are more pronounced within industries with relatively fixed patterns in product demand. The sales seasonality premium in the Chinese stock market is found to be a combined result of investor rationality and irrationality.