Utilizing microevaluation data on the extent to which firms advance the achievement of the UN’s Sustainable Development Goals (SDGs) provided by Robeco, this paper examines the influence of corporate sustainability on stock price performance and its underlying economic mechanisms. The empirical results suggest that firms’ sustainability has a significant negative effect on excess returns, particularly the contribution of firms to the social dimension of sustainability. Firms’ SDG performance can alleviate financing constraints and reduce financial risk, but it does not significantly enhance financial performance, leading to market capital outflows from high SDG-performing firms, especially from individual investors. Furthermore, our results suggest that high SDG-performing firms are undervalued and do not increase the information content in their stock prices, which may be the main reason for the negative effect of SDG performance. We also conduct a series of heterogeneity tests, which show that firms from regions with high environmental regulatory intensity and less economic development, as well as heavily polluting firms and firms with poorer information environments, experience greater negative effects. These findings have implications for investors to properly understand corporate sustainability and for regulators to promote the development of a low-carbon economy.

While observational studies have long suggested a positive correlation between social relationships and online transactions, surprisingly little research demonstrates a causal link. Effects identified in observational data generally conflate the Information effect, which bears the counterfactual causal interpretation, with the Homophily/environment effect. Against this background, this study conducted a pioneering a randomized field experiment design to isolate the Information effect of friendship disclosure from confounding homophily factors. We exploit a rare opportunity to conduct a field experiment on a large Chinese online second-hand platform, in which we manipulate buyer and seller’s awareness of their preexisting friendship ties. We provide the first empirical evidence that the effect of revealing friendship information between transaction parties turns out to be insignificant. We demonstrate that reliance on observational estimates of the “total effect” of friendship significantly overstates the benefits of providing friendship information in online marketplaces. Our findings contribute to a better understanding of social commerce and highlight the potential fallacy of relying on observational data in business studies.

This study examines how firm-specific characteristics and monetary tightening influence the composition and dynamics of trade credit received by Chinese listed firms. Using panel data, the analysis distinguishes among three sources of trade credit: related parties, non-related parties, and controlling shareholders. The findings reveal a clear asymmetry in firms’ financing responses to monetary tightening: while trade credit from non-related parties declines, credit from related parties—especially controlling shareholders—increases. This underscores the strategic role of intra-group financing in buffering firms against external financial shocks during periods of constrained liquidity. Moreover, firm-specific factors such as size, profitability, market power, and ownership have differing effects depending on the source of trade credit. These effects are most pronounced when the credit is extended from controlling shareholders, reflecting the influence of intra-group trust and reduced information asymmetries. The results also highlight a substitute relationship between bank credit and trade credit, which weakens when trade credit is sourced from related parties and disappears entirely in the case of controlling shareholders. By shedding light on the distinct mechanisms of intra-group trade credit in China’s underdeveloped financial system, this study contributes to a deeper understanding of corporate financing strategies of Chinese firms.

This study applies large language models (LLMs) to measure technological links and examines its predictive power in the Chinese stock market. Using the BAAI General Embedding (BGE) model, we extract semantic information from patent textual data to construct the technological momentum measure. As a comparison, the measure based on traditional International Patent Classification (IPC) is also considered. Empirical analysis shows that both measures significantly predict stock returns and they capture complementary dimensions of technological links. Further investigation through stratified analysis reveals the critical role of investor inattention in explaining their differential performance: in stocks with low investor inattention, IPC-based measure loses its predictive power while BGE-based measure remains significant, indicating that straightforward information is fully priced in while complex semantic relationships require greater cognitive processing; in stocks with high investor inattention, both measures exhibit predictability, with BGE-based measure showing stronger effects. These findings support behavioral finance theories suggesting that complex information diffuses more slowly in markets, especially under significant cognitive constraints, and demonstrate LLMs’ advantage in uncovering subtle technological connections that traditional methods overlook.

This study was conducted to understand better how rules influence China's energy performance; this research on these policies' efficacy that facilitating the transition to sustainable energy sources is of tremendous significance, particularly in light of the severe problems climate change poses. To determine whether or not strict regulations are beneficial to China's energy transition efforts, this research makes use of a substantial amount of data about China's environmental laws and environmental transition policies. This paper thoroughly analyses the impact of strict environmental regulations on various energy transition measures. These metrics include the availability of green energy, carbon emissions, and energy efficiency. The results provide insights into how environmental restrictions have affected China's transition to a different energy source. Policymakers and stakeholders may use this information to build efficient plans to expedite the transition to a low-carbon, renewable energy system in China and abroad.

Adverse selection remains a significant challenge in the insurance industry, often resulting in substantial financial losses for insurers. The primary hurdle in addressing the issue lies in accurately identifying and quantifying adverse selection. Traditional methods often fail to adequately account for the heterogeneity of insurance purchasers and the endogenous nature of their insurance decisions. This study introduces an innovative approach that integrates the Gaussian Mixture Model and the regression-based model from Dionne et al. (2001) to assess adverse selection, addressing the limitations of previous methods. Through comprehensive simulations, we demonstrate that our method yields unbiased estimates, outperforming existing approaches. Applied to China’s automobile insurance market, leveraging IoT devices to track telematics data, this method captures risk heterogeneity among the insured. The results offer robust evidence of adverse selection, in contrast to conventional methods that fail to detect this phenomenon due to their inability to capture the underlying relationship between customer risk and claim behavior. Our approach offers insurers a robust framework for identifying information asymmetries in the market, thereby enabling the development of more targeted policy interventions and risk management strategies.

We study tail-risk contagion in Belt and Road (BRI) stock markets by conditioning on shocks from China and global commodities. We construct time-varying contagion indices from conditional higher co-moments (CoHCM) estimated within a DCC-GARCH model with generalized hyperbolic innovations, and apply them to daily data for 32 BRI markets. The higher-moment index isolates two channels: a China-driven financial-institutional channel and a WTI-driven commodity-real-economy channel, whereas a covariance benchmark fails to recover this separation. Furthermore, the system-GMM estimates link the China-conditional channel to institutional quality and financial depth, and the WTI-conditional channel to real activity. In out-of-sample portfolio tests, the WTI-conditional signal improves risk-adjusted performance relative to equally weighted and mean-variance benchmarks, while the China-conditional signal does not. Tail-based measurement thus sharpens identification of contagion paths and yields information that is economically relevant for risk management in interconnected emerging markets.

Contrary to the US, Chinese stock markets exhibit negative overnight returns that appear to be highly affected by the extent of information asymmetry. China's "T+1" trading rule, which prohibits same-day selling, exacerbates adverse selection for uninformed buyers by limiting them to react to post-trade information. An information asymmetry-driven price discount thus emerges at market open, generating negative overnight returns, which further decrease with information asymmetry. Consistent with adverse selection, empirical evidence reveals lower overnight returns during market declines and high-volatility periods, with robust negative relationship between overnight returns and information asymmetry proxied by firm size, analyst coverage, and earnings announcement proximity. A model is introduced to rationalize our findings. This framework also sheds light on China's "opening return puzzle", the phenomenon that prices rise rapidly in the initial 30 minutes of trading, by showing how reduced adverse selection enables rapid price recovery during opening session.

We examine the effects of issuing green bond on green premium and green signal transmission by matching green bonds with ordinary bonds. We find that the credit spread of green bonds is significantly lower than that of ordinary bonds, especially for those green bonds with lower information disclosure complexity. Besides, issuing green bonds cannot receive a positive response from the stock market, but can significantly reduce issuer’s loan costs and provide more financial subsidies for high polluting issuers. Furthermore, by obtaining discounted loans and financial subsidies, issuing green bonds can increase issuer’s R&D intensity and reduce their carbon emissions. These findings indicate that issuing green bonds can reduce financing costs and convey green signals to market stakeholders with less investment experience.

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.