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.

Overwork intensity (OI) is a salient issue that directly affects employees’ motivation and productivity. By using a novel dataset of overwork intensity constructed from daily high-resolution nightlight satellite images, we examine whether overwork intensity is a priced risk in the cross-section of stock returns. We show that a zero-investment portfolio that buys the highest OI quintile stocks and shorts the lowest OI quintile stocks earns 0.495% returns per month. This result is robust when controlling for various well-known risk factors. We argue and empirically verify that profftability, corporate governance, investor sentiment and lottery preference are the potential channels that drive the result.

This study examines the pricing effect of global economic policy uncertainty (GEPU) in the cross-section of individual stocks and portfolios in the Chinese stock market. Employing the GEPU index as a systematic risk factor, our empirical analysis demonstrates that stocks in the lowest decile of βGEPU generate risk-adjusted annualized returns that are 5.16% higher than those in the highest decile. Our analysis reveals that this βGEPU premium is driven by the outperformance of stocks with negative βGEPU and the underperformance of those with positive βGEPU. These findings suggest that uncertainty-averse investors not only demand compensation for holding stocks with negative βGEPU exposure but are also willing to pay a hedging premium for assets that serve as positive βGEPU hedges. The results prove robust across multiple specifications, persisting in both bivariate portfolio sorts and Fama-MacBeth cross-sectional regressions that control an extensive set of classic pricing factors.

This study aims to elucidate the behaviors of the Shanghai and Shenzhen stock exchanges during extreme volatilities—China’s 2015 Stock Market Crash and the 2020 COVID-19 pandemic. Using topological data analysis (TDA), the study identiffes early warning signals within the Shanghai–Hong Kong (SHHK) and Shenzhen–Hong Kong Stock (SZHK) -Stock Connect markets. This timeliness ensures proactive market stabilization and portfolio adjust-ments. The results also reveal that the interconnected market signals are more stable, supporting multidimensional crisis detection and offering valu-able tools for policymakers and investors to effectively mitigate ffnancial risks.

In emerging markets characterized by signiffcant information asymmetry, mitigat-ing firm-level risk is paramount for market stability. While the governance role ofinstitutional investors is known, the impact of their direct, on-the-ground engagementremains underexplored. This study’s objective is to investigate how institutionalinvestor site visits, a crucial hands-on governance mechanism, affect stock returnvolatility. Using a sample of Chinese-listed A-share firms from 2012 to 2022, wefind that frequent site visits significantly reduce firm-level stock return volatility.This risk-reduction effect is more pronounced for firms with greater agency problems,poorer ESG performance, and higher expropriation risk. Our analysis, robust toendogeneity concerns, indicates this effect is driven by improved external oversight.We conclude that direct institutional engagement is a vital channel for reducinginformation asymmetry, enhancing corporate governance, and ultimately promotingmarket stability by lowering investment risk.

This study constructs a three-dimensional space model based on fund investment styles, using a sample of open-end equity and mixed funds from 2005 to 2021 to measure the degree of style convergence. The research explores how style convergence impacts fund managers’ window dressing behavior. The results indicate that, after accounting for the effects of fund performance, style convergence exacerbates window dressing behavior among fund managers. Specifically, this is reflected in fund managers increasing their holdings in winning stocks and selling off losing stocks, which indirectly highlights the intense competition within China’s open-end fund industry. The findings remain robust after a series of endogeneity and robustness tests. Further analysis reveals that style convergence contributes to the risk of client attrition, thereby intensifying the agency problem within the fund industry. The window dressing effect due to style convergence is particularly pronounced in funds managed by individuals with lower educational backgrounds, lower investment skills, smaller family sizes, and lower institutional investor ownership. The paper offers valuable insights into the agency problems arising from investment style convergence and provides guidance for mitigating fund managers' self-interested behavior.

Contrary to the U.S., Chinese stock markets exhibit negative overnight returns, which further decrease with information asymmetry. We demonstrate that 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. Prices are hence initially discounted at opening and recovered by the market close, generating negative overnight returns that are inversely related to information asymmetry risks. Consistent with adverse selection, empirical evidence reveals lower overnight returns during market declines and high-volatility periods, with robust negative associations between overnight returns and information asymmetry proxied by ffrm size, analyst coverage, and earnings announcement proximity. A model is introduced to rationalize our findings. The framework also sheds light on China’s "opening return puzzle", the phenomenon that intraday price rises concentrate predominantly in the initial 30 minutes of trading, by showing how reduced adverse selection enables rapid price recovery during opening session.

This study investigates how the development of digital finance influences the geographical distribution of corporate supply chains using data from Chinese A-share listed companies from 2010 to 2023. We examine whether digital finance enables firms to overcome traditional geographical constraints and adopt different supply chain distribution strategies. The analysis identifies two primary mechanisms through which digital finance influences supply chain geography: governance effects, which operate through enhanced risk management and information transparency, and financing effects, which function through alleviated capital constraints and trade credit provision. We further explore heterogeneous impacts across four dimensions: regional economic development, regional digital infrastructure, industry market competition, and enterprise lifecycle stages. By examining the geographical distribution of supply chains as an outcome of digital finance development, this study provides novel evidence on the micro-governance implications of digital finance. Our findings contribute to understanding how digital finance fundamentally changes the geographical constraints that have historically shaped supplier selection decisions and enables firms to develop more flexible supply chain configurations.

We study the role of superstition in China’s peer-to-peer lending market by ex-amining whether lenders time their bids according to “lucky hours” from the Chinese farmer’s calendar. Loans funded during lucky hours perform better—but only because the platform lists higher-rated loans at those times. This pattern is consistent with a screening mechanism: highly risk-averse lenders place greater value on both true risk reductions and auspicious-day signals, so the platform maximizes surplus by bundling the two—listing low-risk loans on auspicious days. Moreover, listing safer loans at lucky hours can further boost proffts because biased beliefs decay more slowly under asymmetric (bad-news-heavy) learning.

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.