Structural credit risk models are widely used in default prediction practice, but standard formulations do not explicitly account for external support that may prevent default. We incorporate external support into the Merton and, more importantly, the Leland--Toft (LT) model with endogenous default. In the Merton model, support reduces realized default mechanically through a joint-default channel. In the LT model, anticipated support additionally raises debt value and affects equity holders' default decision. We derive closed-form expressions for the support-adjusted default boundary and default probability, and show that, under an economically relevant condition, stronger support lowers the endogenous default boundary, generating an additional indirect reduction in default risk. Empirically, we construct a newly assembled dataset of corporate debt defaults among Chinese A-share listed firms that combines bank loan defaults with bond default events. Using a composite measure of external support, we find that incorporating support significantly improves default prediction in both models. For the LT model, both the mechanical and endogenous channels contribute to this improvement, while the specification incorporating both channels delivers the strongest predictive performance. Our results highlight the importance of institutional support for structural credit risk modeling in China.

This paper examines how liquidity is priced in the Chinese corporate bond market. Unlike the pervasive liquidity premium across bonds traded in the interbank market, we document a puzzle in the liquidity-yield relation in the exchange market. Before the first significant credit event in March 2014, there was a "liquidity discount" in this market-the more illiquid a corporate bond, the lower its yield spread. This liquidity discount disappears after the first bond default event. The liquidity premium, i.e., a positive relation between illiquidity and yield, emerged when the credit condition for corporate bonds worsened after April 2018. We find that retail investors' yield-chasing behavior in the exchange market is the driving factor behind the liquidity discount effect. The liquidity discount is stronger for bonds favored by retail investors and during the period with a high intensity of yieldchasing behavior. Our empirical analysis further shows that our findings are not due to two alternative hypotheses based on the market microstructure effects of liquidity concentration and nonsynchronous trading.

Using the Automatic Earthquake Information Release System (AEIRS) that China launched in 2013 as a quasi-natural experiment, which disseminates real-time seismic information to the public, we explore the impact of frequent information release on the pricing in the credit markets and whether such pricing is efficient. The results show a significant increase in loan pricing for firms with higher peak ground acceleration (PGA) scores following the AEIRS. Such effect is more pronounced in firms in regions where seismic risk was previously unrecognized. The baseline results still stand with several robustness checks including parallel trend analysis, spatial regression discontinuity design, placebo tests, and controlling for loan demand effect, etc. Moreover, the release of earthquake information does not alter the actual frequency of seismic events, ex-post loan defaults, or corporate fundamentals. The increase in loan pricing is primarily driven by banks’ overreaction induced by the availability heuristic, which in turn hinders corporate capital investment and asset growth. Collectively our empirical evidence reveals the dark side of frequent information release.

We examine whether and how the cultural background of corporate chairpersons, specifically clan culture, influences firm-to-firm lending decisions. Using hand-collected data on Chinese entrusted loans, a major segment of the country’s shadow banking sector, we find that firms led by chairpersons from prefectures with stronger clan culture are more likely to originate entrusted loans and charge lower spreads. We identify distinct mechanisms governing affiliated versus nonaffiliated transactions. In affiliated lending, our evidence suggests an in-group favoritism channel: culturally embedded chairpersons support affiliated parties despite higher ex-post default risk. Conversely, non-affiliated lending operates via an informational channel, where the positive impact of clan culture is more pronounced when geographic barriers are low, and manifests in larger size dispersion. Overall, our findings highlight the dual role of cultural norms in shadow banking, demonstrating that clan culture facilitates credit allocation while generating sharply different efficiency implications across relationship-based and arm’s-length markets.

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 companies and investors globalize, we are increasingly faced with estimation questions about the risk associated with this globalization. When investors invest in China Mobile, Infosys or Vale, they may be rewarded with higher returns, but they are also exposed to additional risk. When Siemens and Apple push for growth in Asia and Latin America, they clearly are exposed to the political and economic turmoil that often characterize these markets. In practical terms, how, if at all, should we adjust for this additional risk? We will begin the paper with an overview of overall country risk, its sources and measures. We will continue with a discussion of sovereign default risk and examine sovereign ratings and credit default swaps (CDS) as measures of that risk. We will extend that discussion to look at country risk from the perspective of equity investors, by looking at equity risk premiums for different countries and consequences for valuation. In the fourth section, we argue that a company’s exposure to country risk should not be determined by where it is incorporated and traded. By that measure, neither Coca Cola nor Nestle are exposed to country risk. Exposure to country risk should come from a company’s operations, making country risk a critical component of the valuation of almost every large multinational corporation. In the final section, we will also look at how to move across currencies in valuation and capital budgeting, and how to avoid mismatching errors.

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 study examines the role of negative peer events, specifically initial bond defaults, in driving leverage manipulation of non-defaulting firms within the same region. Controlling for firm-specific time-varying characteristics, we find that initial bond defaults within a province are associated with an increase in leverage manipulation among non-defaulting firms. Two potential mechanisms underlying this relationship include increased financial constraints for these firms and elevated investor risk perception of the local bond market. The positive impact of bond defaults on leverage manipulation is more pronounced for financially constrained firms, firms with severe information asymmetry, and those affected by high-rated bond and principal defaults. We further show that companies that manipulate their debt ratios experience higher default risk. Our findings have important implications for transparent disclosure and highlight the negative effect of regional bond defaults on corporate financial reporting practices.

This study examines the economic consequences of China’s Three-Red-Lines policy, introduced in 2021 to cap real estate developers' leverage by imposing strict thresholds on debt ratios and liquidity. Developers breaching these thresholds experienced sharp declines in financing, land acquisitions, and financial performance. Privately owned developers(POE) are hit harder than state-owned firms (SOE), with larger drops in sales and higher default risk. Using granular project-level data, we show that the policy reduces developer sales primarily by curtailing new-project supply: breached developers launch fewer projects. On the demand side, homebuyers reallocate purchases from privately owned developers to SOEs, further widening the POE-SOE gap. The policy also reduced local governments’ land-transfer revenues and increased reliance on local government financing vehicles (LGFVs) for land purchases. These LGFV-acquired parcels exhibit very low subsequent development rates, which may increase local governments’off-balance-sheet debt risks.

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.