Using proprietary data from China’s interbank bond-pledged repo market, we show that the interest-rate risk and credit risk of the pledged bond are key determinants of repo pricing. From a bond-option perspective, we develop arbitrage-free models that anchor the repo yield curve to the pledged-bond yield curve. The fair repo haircut is interpreted as the per-unit price of a call option on the pledged bond. We extend this framework to incorporate bail-in or bail-out potential, which enhances the model’s empirical performance and provides a novel explanation for systematic repo cheapness and existence of negative haircuts.

As China's bond market expands rapidly, the complexity of financial guarantee networks and their implications for credit risk have become critical issues in both academic research and financial practice. Utilizing micro-level data from China's credit bond market spanning 2014 to 2024, this study constructs a multi-layer network incorporating bonds, guarantors, and issuing firms to empirically examine the impact of guarantor network centrality on bond credit spreads. The results reveal a significant U-shaped relationship: moderate centrality reduces spreads by bolstering market confidence, whereas excessive centrality increases them due to heightened systemic risk. Mechanism analyses identify systemic risk and information asymmetry as key mediating channels through which centrality affects credit risk premiums. Heterogeneity tests indicate that this U-shaped pattern is more pronounced among state-owned guarantors, real estate firms, and high-risk clusters within the network. Furthermore, both cross-layer connectivity within the multi-layer structure and regional financial development levels significantly moderate the centrality-spread relationship. These findings offer a structural perspective on credit risk pricing in emerging markets and provide valuable policy insights for credit rating system design, guarantee regulation, and systemic risk prevention. International investors could also leverage these findings to better assess systemic risk in interconnected financial markets across emerging economies.

This paper examines how AI investment and data assets affect corporatecredit risk. Using Chinese listed firms, we construct four complementary measures ofAI investment, asset-based, labor-based, LLM-based, and text-based, and link them tofirms’ distance-to-default. We find that benchmark-level AI investment reduces defaultrisk, while excessive ffrm-speciffc investment increases it by eroding profitability andreffecting risk-taking and competitive pressure. The dominance of this adverse effectyields a negative overall relation between AI investment and credit risk. Cash flow riskis the transmission channel: benchmark-level AI improves cash ffow quality, whereasexcessive investment worsens it. High-quality data assets complement benchmark-levelAI by stabilizing cash ffow, but this benefit fades once investment becomes excessive.Overall, the impact of AI on credit risk depends on both investment intensity and dataquality, operating primarily through cash flow dynamics.

This paper investigates whether firms’ overtime intensity affects the cost of debt financing. Using satellite-based night-time light data for Chinese listed firms between 2013 and 2022, we construct an objective measure of weekday overtime that captures firms’ operational effort and capacity utilization. We find that higher overtime intensity is associated with significantly lower bond offering yield spreads. The effect is stronger among smaller, less-followed, less-profitable, and non-AAA-rated issuers, consistent with an information-asymmetry channel where investors rely more on observable operational behavior when hard information is weaker. The findings suggest that overtime functions as a priced form of soft information in debt markets, offering new evidence that real-time operational signals influence credit risk assessment.

This paper provides a comprehensive analysis of excess returns specific to corporate bonds. We construct a measure of excess returns that uses synthetic Treasury securities with identical cash flows as benchmarks, thereby fully removing interest rate effects and isolating the component of returns specific to corporate bonds. Using a monthly sample from 2002 to 2024, we find that, in addition to being lower on average, the corporate-bond-specific excess return differs significantly in the cross section from both the standard excess return based on T-bills and the duration-adjusted return. We further examine the effects of a broad set of bond-level characteristics and systematic risk factors on bond excess returns. Together, these findings provide a foundational benchmark for future research on corporate bond returns.

I study whether workplace smoking bans change how bond investors assess firm risk. Using staggered state adoption across U.S.\ states from 2002 to 2012 and a heterogeneity-robust difference-in-differences design, I find that smoking bans increase six-month cumulative abnormal bond returns by about 90 basis points. The average effect is only the starting point: the response is much larger for speculative-grade issuers and firms with low interest coverage, indicating that investors reprice the policy where downside operating risk matters most for debt values. Mechanism tests point most clearly to improved operating performance and lower worker turnover, while broader financial-constraint, liquidity, and duration channels remain close to zero. Alternative estimators, placebo diagnostics, and geographic spillover checks all support the interpretation that workplace smoking bans trigger targeted credit-risk reassessment rather than a generic regional shock. My findings connect public-health regulation to capital-market outcomes and show how non-financial policy shocks can reveal economically meaningful information about corporate credit risk.

In the realm of financial stability, accurate credit default discrimination models are crucial for policy-making and risk management. This paper introduces a robust model that enhances credit default discrimination through a sophisticated integration of a filter-wrapper feature selection strategy, instance selection, and an updated version of majority voting. We present a novel approach that combines individual and ensemble classifiers, rigorously tested on datasets from Chinese listed companies and the German credit market. The results highlight significant improvements over traditional models, offering policymakers and financial institutions a more reliable tool for assessing credit risks. The paper not only demonstrates the effectiveness of our model through extensive comparisons but also discusses its implications for regulatory practices and the potential for adoption in broader financial applications.

This paper analyzes the impact of risk disclosures in bond prospectuses on the credit risk premium in the Chinese corporate bond market through six textual characteristics comprehensively. In the empirical analysis, the collected 5199 bond prospectuses and structured data concerning control variables from 2006 to 2021 are used to perform the fixed effect regression analysis. The results show that fewer Words, less Boilerplate, higher Fog Index, more HardInfoMix, more Redundancy, and higher Specificity of risk disclosures in bond prospectuses will lead to a higher credit risk premium. Further tests demonstrate that ceteris paribus, the negative impact of Words and Boilerplate will be strengthened by implicit government guarantees carried by a state-owned enterprise but be weakened by better corporate business performance. However, ceteris paribus, positive effects of the Fog Index, HardInfoMix, Redundancy, and Specificity will be weakened when the bond issuer is state-owned but be strengthened by better corporate business performance.

This study finds that reducing political influence over local courts weakens local government debt capacity. We establish this result by exploiting the staggered roll-out of a judicial centralization reform aimed at alleviating local court capture in China and find reduced judicial favoritism towards local governments post-reform. The majority of local government lawsuits are with contractors over government payment delays. The reform not only increases government lawsuit losses but also exposes their credit risk, as payment delays without court support signal government liquidity constraint. Investors respond by tightening lending and increasing interest rates, which curbs government spending.

This paper creates default-probability-(PD)-implied credit ratings for Chinese firms following the S&P global rating standard. The domestic credit rating agency (DCRA) ratings are higher than the PD-implied ratings by ten notches on average for the identical level of default risk, implying that the domestic ratings are significantly inflated. The PD-implied ratings outperform the DCRA ratings in detecting defaults and complement the latter in predicting yield spreads. The PD-implied ratings draw information from operating efficiency-related variables; in contrast, the DCRA ratings pay attention to scale-based firm characteristics in credit risk assessment.