This study aims to examine the causal effect of carbon regulatory risk on corporate bond yield spreads in emerging markets through empirical analysis. Exploiting China's commitment to peak CO2 emissions before 2030 and achieve carbon neutrality before 2060 as an exogenous shock to an unexpected increase in carbon regulatory risk, we perform a difference-in-difference-in-differences (DDD) strategy. We find that exposure to carbon regulatory risk leads to an increase in bond yield spreads for carbon-intensive firms located in regions with stricter regulatory enforcement. This positive relationship is more pronounced for firms with financing constraints, belonging to more competitive industries, and located in regions with a high marketization process. We further identify that higher earnings uncertainty and increased investor attention serve as two mechanisms by which carbon regulatory risk influences the yield spreads of corporate bonds. Moreover, the spread decomposition reveals that the rise in bond yield spreads after an increase in carbon regulatory risk is primarily driven by the rise in default risk rather than the rise in liquidity risk. Overall, our findings highlight the importance of considering carbon regulatory risk exposure in financial markets, especially in developing economies like China.

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.

This paper investigates the gambling preference of Chinese investors in the convertible bond (CB) market through a natural experiment—the 2018 amendment of Article 142 of the Company Law. Utilizing CB issuance data from 2016 to 2023, we employ a cohort difference-in-difference approach and find a 4% to 7% increase in IPO premiums for high-repurchase-expectation CBs across various measures. This significant increase indicates that the legal revision reshapes investors’ expectation and adjusts their valuation of CBs. Furthermore, the event-study analysis reveals the escalating impact of legal revision, driven by the herding behavior of gambling investors.

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 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 investigate whether a firm’s decision on green bond issuances is influenced by the green bond issuances by other firms in the same industry. We find that a firm is significantly more likely to issue green bonds after observing that other firms in the same industry have previously issued green bonds. This effect cannot be explained by the issuer’s supplement to their previous issuances, incentive policies, and industry competition. Furthermore, we show that issuing green bonds can bring significant positive stock excess returns, which increases the motivation for institutional investors to learn and drive other firms in the same industry they hold to issue green bonds. Our findings indicate that the peer effect can be driven by social learning of the common ownership among firms and explain the reason for the rapid increase in green bond issuance.

Green bonds have emerged as a novel funding mechanism specifically aimed at addressing environmental challenges. Focusing on A-share listed companies in China that went public with bond issues domestically from 2012 to 2021, we reveal that companies with higher energy usage and better environmental disclosure quality are the most inclined to issue green bonds. Such issuance is identified as a pathway towards real green transformation, markedly boosting the green transformation index, green innovation efficiency, and ESG performance. Further analysis indicates that the effect of substantial transformation is particularly pronounced among companies in the eastern regions of China.

We construct a new geospatial measure using high-resolution river vector data from National Geomatics Center of China (NGCC) to study how urban riparian exposure shapes local government debt financing costs. Our base-line results show that cities with higher riparian exposures have significantly lower credit spreads, with a one-standard-deviation increase in riparian exposure reducing credit spreads by approximately 12 basis points. By comparing cities crossed by natural rivers with those intersected by artificial canals, we disentangle the dual role of riparian zones as sources of natural capital benefits (e.g., enhanced transportation capacity) versus climate risks (e.g., flood vulnerability). We find that climate change has amplified the impact of natural disasters, such as floods and droughts, particularly in riparian zones, thus weakening the cost-reducing effect of riparian exposure on bond financing. In contrast, improved water infrastructure and flood-control facilities strengthen the cost-reduction effect. Our findings contribute to the literature on natural capital and government financing, offering valuable implications for public finance and risk management.

Green bonds, pivotal to green finance, draw growing attention from scholars and investors. Social media’s proliferation has amplified the influence of investor sentiment, necessitating robust analysis of its market impact. However, general sentiment lexicons often fail to capture domain-specific slang and nuanced expressions unique to China’s bond market, leading to inaccuracies in sentiment analysis. Thus, this study constructs a specialized sentiment lexicon for the green bond market, namely the COBC (Chinese online bond comments sentiment lexicon), to dissect bond market slang and investor remarks. Compared to three general lexicons (Textbook, SnowNLP, and VADER), it improves the average prediction accuracy by approximately 87.2% in sentiment analysis of Chinese online language within the green bond domain. Sentiment scores derived from COBC-based dictionary analysis are systematically integrated as predictive features into a two-stage hybrid predictive model is proposed integrating Support Vector Machine (SVM), Auto-Regressive Moving Average (ARMA), Bidirectional Long Short-Term Memory Networks (BiLSTM), and Attention Mechanisms to forecast China's green bond market, represented by the China Bond 45 Green Bond Index. First, ARMA-SVR is employed to extract residuals and statistical features from the green bond index. Then, the BiLSTM-Attention model is applied to assess the impact of investor sentiment on the index. Empirical results show that incorporating investor sentiment significantly enhances the predictive accuracy of the green bond index, achieving an average of 67.5% reduction in Mean Squared Error (MSE), and providing valuable insights for market participants and policymakers.

This paper examines bond trading activity and transaction cost differences between the bilateral Over-the-Counter (OTC) and the centralized Central Limit Order Book (CLOB) venues in the China interbank government bonds market, structured as all-to-all. Using a novel trade-level dataset, we estimate that CLOB reduces transaction costs by 0.66 basis points compared to OTC, highlighting the efficiency of its centralized trading mechanism. Furthermore, our analysis of cross-venue selection patterns reveals that the CLOB venue disproportionately facilitates core traders, orders with standardized sizes and settlement speeds, and newly issued bond trades. Despite CLOB’s cost advantages, the continued use of OTC is justified by its unique benefits, including mitigating information leakage, enabling designated counterparties, and facilitating position rebalancing. These findings offer insights into how market microstructure and trading mechanism affect asset liquidity.