This paper investigates the causal impact of pre-trade transparency on the market liquidity of an over-the-counter-style market by leveraging a natural experiment in China’s interbank corporate bond market. We find that turnover, market liquidity, and aggregate bond returns significantly declined when the regulators unexpectedly suspended real-time quote dissemination in March 2023. Consistent with our expectation, these effects were mainly focused on interbank bonds, not exchange bonds, and bonds with lower credit ratings and longer maturities. This study contributes novel evidence to the transparency literature and provides insights for policymakers in emerging markets weighing the trade-offs between data governance and market efficiency.

The high-penetration integration of renewable energy requires huge demand for reliable capacity resources, and the coal-fired units are the main providers of the reliable capacity in China. This study proposes a future-oriented approach to facilitate coal-fired power’ transition through capacity market development. Focusing on China’s power market reform context, we propose a two-stage capacity market mechanism integrating annual capacity auctions and monthly capacity bidding, and design the procedural and transactional framework for coal-fired power participation. We further outline three market strategies including energy market trading, centralized capacity market trading, and renewable energy alliance leasing. Environmental costs are incorporated to construct revenue models and derive boundary conditions for coal-fired units’ decision-making. Research results reveal that current capacity prices fail to cover costs, requiring substantial market-driven price increases to achieve profitability. While stable capacity revenue can reduce medium-to-long-term and spot market prices, fostering competition between coal-fired power and renewable energy resources. However, coal-fired power remains highly sensitive to price volatility, demanding robust resilience to fluctuations. Carbon prices significantly influence capacity prices, yet excessive free carbon quota allocations weaken carbon price transmission effects, necessitating optimized quota ratios to enhance market responsiveness. Finally, policy implications are proposed according to the research results.

This study identifies a significant settlement effect in China’s equity options market, where price decline and pre-settlement return momentum exists on the settlement Friday (T+2) due to a temporal misalignment between option expiration (T) and the T+1 trading rule for the underlying asset. We attribute this phenomenon to three distinct behavioral channels: closing pressure from put option unwinding, momentum-generating predatory trading by futures-spot arbitrageurs exploiting liquidity fragility, and an announcement effect that attenuates the anomaly by adjusting spot speculators' expectations. Robust empirical analysis identifies predatory trading as the primary driver of the settlement effect.These findings offer critical insights for market microstructure theory and the design of physically-delivered derivatives.

Climate change presents a global threat, prompting nations to adopt low-carbon development pathways to mitigate its potential impacts. However, current research lacks a comprehensive framework capable of integrating multiple variables and providing dynamic optimization capabilities. This article focuses on designing pathways for developing a low-carbon economy to tackle climate challenges. Specifically, we construct a low-carbon economy model that incorporates economic, environmental, social, energy, and policy factors to analyze the drivers of economic growth and carbon emissions. We utilize economic model predictive control and tracking model predictive control to optimize development pathways aligned with various low-carbon targets, creating and validating a comprehensive framework for low-carbon policy design using historical data from China. This study highlights significant advantages in analyzing low-carbon pathways through advanced techniques like hierarchical regression and model predictive control, providing a robust framework that enhances our understanding of causal relationships within the LCE system, captures system feedback, dynamically optimizes pathways, and accommodates diverse policies within a comprehensive low-carbon economy system.

Central bank digital currency (CBDC) is intended to boost financial inclusion and limit threats to bank stability posed by private cryptocurrencies. Our study examines the impact of implementing CBDC on the bank stability of two countries in Asia and the Pacific, the People’s Republic of China (PRC) and India, that initiated research on CBDC within the last ten years (2013 to 2022). We construct a bank stability index by utilizing five dimensions, namely capital adequacy, profitability, asset quality, liquidity, and efficiency, using a novel “benefit-of-the-doubt” approach. Employing panel estimation techniques, we find a significant positive impact of adopting CBDC on bank stability and a moderating role of monetary policy. We also find that the effect is greater in India, a lower-middle-income country, than in the PRC, an upper-middle-income nation. We conclude that by taking an accommodative monetary policy stance, adopting CBDC favors bank stability. We confirm our results with various robustness tests by introducing proxies for bank stability and other model specifications. Our findings underscore the potential of adopting CBDC, when carefully managed alongside appropriate monetary policy, for enhancing bank or overall financial stability.

This paper proposed an innovative WTC-LSTM-ASSA-NSTformer framework for gold price forecasting. The model integrates Wavelet Transform Convolution, Long Short-Term Memory networks (LSTM), and an improved Nyström Spatial-Temporal Transformer (NSTformer) based on Adaptive Sparse Self-Attention (ASSA), effectively capturing the multi-scale features and long- and short-term dependencies of gold prices. Additionally, for the first time, various financial and economic uncertainty indices (including VIX, GPR, EPU, and T10Y3M) are innovatively incorporated into the forecasting model, enhancing its adaptability to complex market environments. An empirical analysis based on a large-scale daily dataset from 1990 to 2024 shows that the model significantly outperforms traditional methods and standalone deep learning models in terms of MSE and MAE metrics. The model’s superiority and stability are further validated through multiple robustness tests, including varying sliding window sizes, adjusting dataset proportions, and experiments with different forecasting horizons. This study not only provides a highly accurate tool for gold price forecasting but also offers a novel methodological pattern to financial time series analysis, with important practical implications for investment decision-making, risk management, and policy formulation.

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.

This study investigates the influence of smart supply chain management on the Environmental, Social, and Governance (ESG) performance of Chinese manufacturing firms spanning from 2009 to 2022. Our findings reveal a positive association between smart supply chain management and enhanced ESG performance, a relationship consistently upheld across various analytical methodologies. Additionally, we uncover that smart supply chain practices stimulate corporate social responsibility (CSR) disclosure, contributing to heightened transparency and subsequently bolstering ESG metrics within firms. Furthermore, our analysis demonstrates that the positive effect of smart supply chain management on ESG outcomes is particularly pronounced among firms that are operating in less competitive and more environmentally impactful industries, receiving heightened media scrutiny, and influenced by Confucian principles. This research provides actionable insights for firms seeking to advance their ESG initiatives.

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 significance of sustainable development has underscored the importance of integrating corporate sustainability indicators into corporate strategies. As external stakeholders increasingly emphasize corporate environmential performance, social responsibility and governance (ESG), understanding its impact on corporate value becomes essential, especially in emerging markets like China. This research aims to bridge these knowledge gaps by empirically investigating the influence of ESG ratings on firms’ value among Chinese listed firms, with a special emphasis on the mediating roles played by financial distress and financing constraints. By analyzing data from listed companies of China over the period 2018 to 2022, this research explores the correlation between firms’ value and ESG ratings. The findings indicate a positive association between firms’ value and ESG ratings. Enhanced ESG ratings directly boost market valuation and indirectly elevate firm value by mitigating financing constraints and financial distress. Further analysis reveals the positive effects of ESG ratings are more noticeable in industries that are not heavily polluting and in state-owned enterprises. This research provides valuable insights for enterprise management by systematically examining how ESG ratings contribute to corporate value through the mitigation of financial distress and constraints, while also highlighting the variations in ESG strategy implementation across different types of enterprises.