Using a large language model (LLM), we construct a narrative sentiment index to investigate the predictability of risk premia in the Chinese Treasury bond market. Both in-sample and out-of-sample analyses demonstrate that narrative sentiment significantly and negatively forecasts future bond excess returns, yielding significant economic value for mean-variance investors. By incorporating this measure into an affine term structure model (ATSM), we identify a dual-channel effect: narrative sentiment relates positively to expected future short rates but negatively to excess returns, with the impact intensifying at longer maturities. Risk premia estimated via this sentiment-augmented ATSM exhibit more pronounced countercyclical dynamics. Finally, we establish that narrative sentiment carries forward-looking signals regarding both subjective expectations and future fundamentals, providing a coherent economic foundation for its predictive power in the bond market.

This study investigates the pricing of convertible bonds by focusing on the underlying stock price limits, a market structural feature overlooked in conventional models, and provides a comprehensive framework integrating price limits with embedded options (including conversion, call, put and reset clauses) based on Monte Carlo simulation. Numerical analysis reveals that convertible bond prices across varying moneyness states are insensitive to price limits at low underlying stock volatility, while higher volatility amplifies the pronounced impact of price limits on in-the-money bonds. Empirical tests based on data on the Chinese convertible bond market spanning the period from 2018 to 2024 indicate an average overvaluation in the market. The result identifies that there are negative pricing errors for deep-out-of-the-money bonds and positive errors for in-the-money bonds. A trading strategy based on pricing errors yields positive returns, demonstrating the model's practical applicability.

Structural credit risk models are widely used in default prediction practice, but standard formulations do not explicitly account for external support that may prevent default. We incorporate external support into the Merton and, more importantly, the Leland--Toft (LT) model with endogenous default. In the Merton model, support reduces realized default mechanically through a joint-default channel. In the LT model, anticipated support additionally raises debt value and affects equity holders' default decision. We derive closed-form expressions for the support-adjusted default boundary and default probability, and show that, under an economically relevant condition, stronger support lowers the endogenous default boundary, generating an additional indirect reduction in default risk. Empirically, we construct a newly assembled dataset of corporate debt defaults among Chinese A-share listed firms that combines bank loan defaults with bond default events. Using a composite measure of external support, we find that incorporating support significantly improves default prediction in both models. For the LT model, both the mechanical and endogenous channels contribute to this improvement, while the specification incorporating both channels delivers the strongest predictive performance. Our results highlight the importance of institutional support for structural credit risk modeling in China.

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.

This paper utilizes data from Chinese A-share listed companies and, based on the limited attention theory, reexamines the impact of data asset disclosure on capital market pricing efficiency. The study finds that the economic consequences of data asset disclosure are not static but exhibit a significant marginal decreasing feature as the overall market disclosure level rises. Specifically, when the overall market disclosure level is low, corporate disclosure of data asset information can significantly reduce stock price synchronization and enhance capital market pricing efficiency by increasing the supply of idiosyncratic information, enhancing information diffusion, and accelerating information integration. However, with the recent widespread disclosure behavior, there is no evidence that the above mechanisms remain effective. Further analysis indicates that disclosure behavior can maintain its positive effect only when the overall market disclosure level of data assets is low, and the firm's disclosed idiosyncratic information is relatively limited, the information dissemination environment is favorable, or institutional investor attention is not excessively diffused. This paper expands the boundaries of the limited attention theory, extending the research perspective to the dimension of the dispersion of investor attention by homogenized information, and reveals the value dilution problem faced by data asset information disclosure in the context of universalization.

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.

We develop a theoretical model in which investors evaluate dual-listed stocks through the prospect theory value of their historical return distributions. When the prospect theory value (PTV) of A shares exceeds that of H-shares, the A-share price will be higher than the H-share price, generating a persistent equilibrium price premium despite identical fundamentals. Using Chinese A–H dual-listed firms, we find that the PTV difference positively explains the AH premium. A gain–loss decomposition shows that A-shares have higher PTV because their less negative loss contribution more than offsets their smaller gain contribution. Consistent with this result, loss aversion and value-function curvature exhibit stronger pricing relationships than probability weighting. Investor composition shapes the pricing influence of PTV. Our results also explain why the AH premium increased following deeper market integration, like the Shanghai–Hong Kong Stock Connect.

Using Chinese A-share listed firms from 2007 to 2024, this study examines whether supply chain shareholding (SCS) affects stock price synchronicity. We find that SCS significantly reduces stock price synchronicity. This finding remains robust to a series of robustness and endogeneity tests. Mechanism tests provide evidence consistent with information asymmetry and agency costs as two channels linking SCS to lower stock price synchronicity. Heterogeneity analyses show that the effect is more pronounced among firms with low supply chain concentration and those operating in unfavorable business environments. Overall, these findings identify SCS as a relationship-based governance arrangement that improves capital market information efficiency, particularly where relational governance and informal institutional substitution are more valuable.

This study evaluates whether electric vehicles (EVs) in China have delivered on their “green” promise by improving air quality and reducing private ownership costs. Combining city–month data on EV adoption and air pollution with detailed vehicle-level transaction records, we find little evidence that rising EV adoption reduces concentrations of major pollutants or greenhouse gases. Both the traditional instrumental variable (IV) and the Bartik IV estimations show that the null effects persist after addressing potential endogeneity. The limited environmental gains are largely attributable to the continued dominance of coal in electricity generation and to a sizable rebound effect whereby lower operating costs increase vehicle usage. On the private side, although EVs reduce energy expenditures by roughly CNY 16,747, their faster depreciation leads to an average net loss of about CNY 18,175 over the ownership period relative to fuel vehicles (FVs). Counterfactual simulations show that EVs match the emissions of fuel vehicles when the coal share falls to about 55%, deliver measurable benefits below 49%, and require a reduction to around 31% once higher utilization is considered. The results show that the environmental benefits from EV adoption depend on a transition toward cleaner electricity generation.