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.

We revisit cross-asset time-series momentum (XTSM) and single-asset time-series momentum (TSM) in China's stock and bond markets. With a fixed-effects model, we find a positive momentum from bonds to stocks and a negative momentum from stocks to bonds, with both momentum persisting for no more than six months. By employing a cross-grouping method, we find that the choice of lookback periods and asset signals impacts the performance of XTSM and TSM. A comparison between XTSM, TSM, and time-series historical (TSH) portfolios reveals that XTSM outperforms in small/midcap stocks and government bonds, while its performance is weak in large-cap stocks and corporate bonds. A spanning test confirms that XTSM generates excess returns that other pricing factors can not explain. XTSM is more prone to momentum crashes. Increased market stress has similarly adverse effects on XTSM and TSM. Furthermore, Market illiquidity, IPO counts, new investor accounts, and consumer confidence index positively correlate with the returns of XTSM and TSM portfolios, while IPO first-day return and turnover rate correlate negatively. The effects of these sentiment indicators exhibit heterogeneity.

We empirically examine how institutional investors react to adjustments in environmental policies in China. We observe a seemingly counterintuitive phenomenon: when environmental policies intensify, fund managers do not increase their holdings in high ESG-rated firms as might typically be expected; instead, they significantly divest from these firms. This behavior stems from the fact that, under stringent environmental policies, maintaining a high level of ESG investing leads to financial losses and fund outflows, especially in the short term, which impair fund managers’ compensation and raise career concerns. Further, within the context of environmental policy adjustments, our heterogeneity analysis tries to disentangle the true motivations behind institutional investors' ESG adoptions. We demonstrate that both pro-social preferences and financial incentives play pivotal roles, and that fund managers do not tolerate unlimited financial losses when ESG investing underperform. Our findings reveal the economic impact of environmental policies on institutional investors and shed light on the contentious and complex nature of the ESG concepts.

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.

The relationship between ESG factors and corporate performance is contentious, partly due to the literature's neglect of organizational demographic differences. Using data from 5,127 Chinese companies (2009-2022), we empirically analyze ESG's impact on corporate performance, factoring in the demographic heterogeneity of executive teams. Our findings indicate that although ESG indeed enhances corporate performance, its conversion effect is significantly influenced by the vertical dyads of gender and education within the top management teams (TMT). Additionally, our extended analysis reveals that these two types of vertical dyads exhibit distinct structural characteristics.

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How enterprises can leverage frontier technologies to achieve synergy between environmental governance and high-quality development has become a critical issue amid the deepening global push for sustainable development and the green economic transition. Based on micro-level data of Chinese enterprises from 2009 to 2023, this study systematically examines the impact of artificial intelligence (AI) on corporate green governance performance and explores the underlying mechanisms. The findings reveal that AI significantly enhances green governance performance at the enterprise level, and this effect remains robust after accounting for potential endogeneity. Mechanism analysis shows that AI empowers green transformation through a dual-path mechanism of “cognition–behavior,” by strengthening environmental tendency and increasing environmental investment. Further heterogeneity analysis indicates that the positive effects are more pronounced in nonheavy polluting industries and state-owned enterprises, suggesting that industry characteristics and ownership structure moderate the green governance impact of AI. This study contributes to the theoretical foundation of research at the intersection of digital technology and green governance, and provides empirical evidence and policy insights to support AI-driven green transformation in practice.

The disparities in green transition have led to the call for a ‘just transition’. However, the large differences in energy efficiency across different regions have been identified as a primary hazard to the just transition. This study examines whether transferring low-carbon technology can improve the efficiency of energy, enhancing the overall energy efficiency, and marketing a sustainable and equitable energy future. In this paper, we utilize the Undesirable-SE-SBM model to estimate the energy efficiency of China's 30 provinces during 2012 to 2022, and empirically tested the impact of low-carbon technology transfer on the convergence of total-factor energy efficiency by convergence analysis. The results showed that: (1) There is evidence of σ convergence and absolute β convergence in the eastern and western regions, but not in the central region. (2) Low-carbon technology transfer can accelerate the convergence of total factor energy efficiency. Lagging regions that adopt low-carbon technologies can catch up with the advanced regions' level of total-factor energy efficiency. (3) There is regional heterogeneity in the effect of low-carbon technology transfer on the accelerating convergence of total factor energy efficiency. The western region experiences the most significant acceleration, followed by the eastern and central regions.

This study examines how full-time external supervisors affect corporate irregularities using listed Chinese state-owned enterprises (SOEs) as a research sample. We find that full-time external supervisors restrain corporate irregularities. This outcome continues to hold after accounting for potential endogeneity concerns. Further mediating effect analysis shows that full-time external supervisors mitigate corporate irregularities by curbing managers' opportunistic behavior. Additionally, the heterogeneity analysis demonstrates that the impact of full-time external supervisors on corporate irregularities varies significantly across different types of SOEs and internal control environments. Overall, this paper enriches and expands the literature on the effectiveness of full-time external supervisors in emerging economies and provides new insights for dealing with corporate irregularities.

This paper investigates how institutional investors’ site visits affect firm productivity by using a dataset of China’s A-share listed firms. The findings reveal that site visits have a constructive effect on firm productivity. Moreover, mechanism analysis indicates that reducing information asymmetry and improving stock price informativeness are two channels through which site visits influence firm productivity. Heterogeneity analysis demonstrates that the nexus between site visits and firm productivity is more pronounced for non-state-owned firms and firms with intenser product market competition. Overall, this study brings new insights into the benefits of site visits and highlights the importance of investor activism.