The COVID-19 pandemic has reshaped regional economic landscapes. However, academic research has not yet sufficiently addressed how external investment in regions has been transformed under the impact of the COVID-19 pandemic and its influence on industrial evolution patterns. In this paper, we integrate insights from economic geography literature and develop a conceptual framework to further theorise the relationship between external shocks, changes in the industrial heterogeneity of investment from regional core cities, and local industrial dynamics. Using the COVID-19 pandemic and the Yangtze River Delta region as a case study, we employ an intensity-based Difference-in-Differences (DID) approach and draw on business registration and enterprise investment databases to estimate the impact of the pandemic shock on the evolution of local industrial dynamics between 2018 and 2024. Our findings indicate that after the core cities underwent the shock of the COVID-19 pandemic, the path dependence of industrial evolution in their surrounding areas significantly increased. The stronger the economic linkage with Shanghai, the more pronounced this effect. However, this impact also exhibits spatial heterogeneity across the Yangtze River Delta, associated with regional industrial division of labour and cooperation. This paper offers an innovative examination of how changes in the industrial heterogeneity of investment inflows from core cities—specifically, dimensions such as relatedness to local industries, industrial upgrading, and diversification—shape the mechanisms of local industrial evolution following the COVID-19 shock. Our findings offer important implications for regional development and adaptive responses in the post-pandemic era.

Purpose – Estimates disclosure of human capital management for Chinese listed companies. Investigate the patterns ofthe disclosure of human capital management acrossindustries and regions. Examine the determinants of human capital management disclosure in China. Examine the association between human capital management disclosure and firm performance. Design/methodology/approach – We employ natural language processing techniques on annual reports’ management discussion and analysissections.We construct exposuremeasuresforten human capitalmanagement dimensions and synthesize them into one comprehensive measure of human capital management disclosure. We conduct empirical analysis on the measure using a sample of Chinese listed companies during 2009–2022. Findings – We construct a measure of human capital management disclosure for 5,153 Chinese companies during 2009–2022. We find that firms with high HCM disclosure are more labor intensive and have more cash holdings and R&D expenditure but have lower sales growth, market-to-book ratio and leverage. HCM disclosure is associated with better future accounting performance but poor future market valuation. There are substantial variations in HCM disclosure across industries, geographic regions and ownership types. HCM disclosure has increased significantly during the COVID-19 pandemic. Social implications – The increased HCM disclosure and its association with firm operating performance and market valuation indicate the relevance of HCM in corporate management and underscore the need for more robust and standardized disclosure of HCM in China. Our findingssupport recent regulatory efforts by CSRC to enhance the transparency and accountability in HCM disclosures and advocate for more explicit and specific HCM disclosure requirements in the future. Originality/value – We propose a quantitative measure of human capital management disclosure, which can be modified to apply to other markets. We construct a comprehensive, ready-to-use dataset for HCM disclosure for Chinese listed companies and conduct descriptive analysis on the dataset. We identify the patterns of human capital management disclosure and its determinants in China.

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.

Against the backdrop of recurrent global crises, economic uncertainty, and mounting environmental and social pressures, corporate resilience—defined as a firm’s capability to withstand external systemic shocks—has emerged as a critical determinant of long-term sustainability. This study empirically exames the effect of ESG (Environmental, Social, and Governance) performance on corporate resilience in China’s A-share market, using the COVID-19 pandemic as a natural experiment to identify causal effects. The sample comprises 651 A-share listed firms, excluding financial institutions, real estate firms, and ST/*ST companies, over the period from January 20, 2020, when the pandemic was officially announced in China, to June 30, 2024. ESG performance is measured as the average of 2018–2019 ratings issued by three major domestic agencies, thereby capturing firms’ pre-shock conditions and mitigating concerns of reverse causality. Corporate resilience is evaluated along two dimensions: resistance, measured by the severity of losses in net income, revenue, and stock price, and recovery, measured by the time required for ROA, EBIT, stock price, and Tobin’s Q to return to pre-shock levels. To ensure the robustness of the findings, this study employs linear regression models with industry-clustered robust standard errors, an instrumental-variable approach using R&D intensity and analyst coverage as instruments, and a Cox accelerated failure time model to estimate recovery duration. The empirical results indicate that stronger pre-shock ESG performance significantly enhances corporate resistance and shortens recovery time. Mechanism analyses further reveal that ESG strengthens corporate resilience by improving total factor productivity, alleviating financing constraints, and enhancing corporate reputation. These findings remain robust to multicollinearity diagnostics and a range of additional robustness tests. Overall, this study provides empirical evidence of the value of ESG in strengthening corporate resilience and offers important implications for firms, policymakers, and investors.

We construct the China Crude Oil Volatility Index (CNOVX)—the first model-free, optionimplied measure of forward-looking oil price risk for China—using INE crude oil options from 2021 to 2024 and an adapted CBOE methodology that accounts for sparse strike availability via smooth interpolation and extrapolation. Our results show that CNOVX increases with trading activity in the futures market, declines with option volume, and is strongly predicted by the 30-day realized variance of the SC crude oil futures contract. External shocks, including the Russia–Ukraine conflict and the Geopolitical Risk Index, significantly elevate CNOVX levels. During the COVID-19 pandemic, mortality risk intensifies the volatility-amplifying role of futures trading and strengthens the volatility-dampening effect of options, while confirmed case counts have weaker influence. We further document a pronounced asymmetric leverage effect: negative futures returns raise CNOVX more than positive returns of equal size. However, volatility feedback effects are negligible, as changes in implied volatility respond primarily to contemporaneous market conditions. Overall, CNOVX serves as a timely and informative benchmark for monitoring risk in China’s evolving crude oil derivatives market, with valuable implications for investors, hedgers, and policymakers.

This paper utilizes the COVID-19 pandemic as an exogenous shock to investor uncertainty and examines the effect of uncertainty on stock liquidity. Analyzing data from Chinese listed firms, we find that stock liquidity dries up significantly in response to an increase in uncertainty resulting from regional pandemic exposure. The underlying reason for the decline in stock liquidity during the pandemic is a combination of earnings and information uncertainty. Funding constraints, market panic, risk aversion, inattention rationales, and macroeconomics factors are considered in our study. Our findings corroborate the substantial impact of uncertainty on market efficiency, and also add to the discussions on the pandemic effect on financial markets.

This study examines how firm-level exposure to the COVID-19 pandemic affects the speed of leverage adjustment among 3260 US-listed firms from 2019q1 to 2022q1. Using a novel measure of COVID-19 exposure, we find that higher exposure significantly reduces the speed at which firms adjust their leverage towards target levels. This effect is more pronounced for financially constrained firms and those operating in competitive markets. We further show that COVID-19 exposure adversely impacts corporate liquidity, default risk, and financial flexibility. Our findings highlight the role of exogenous shocks in shaping corporate financing decisions.

This paper utilizes the COVID-19 pandemic as an exogenous shock to investor uncertainty and examines the effect of uncertainty on stock liquidity. Analyzing data from Chinese listed firms, we find that stock liquidity dries up significantly in response to an increase in uncertainty resulting from regional pandemic exposure. The underlying reason for the decline in stock liquidity during the pandemic is a combination of earnings and information uncertainty. Funding constraints, market panic, risk aversion, inattention rationales, and macroeconomics factors are considered in our study. Our findings corroborate the substantial impact of uncertainty on market efficiency, and also add to the discussions on the pandemic effect on financial markets.

Using iterative experiments to uncover causal links between critical policy details and outcomes helps to optimize policy design. This paper studies a large-scale staged fiscal stimulus program conducted during the COVID-19 pandemic, in which a provincial government in China disbursed digital coupons to 8.4 million individual accounts in consecutive waves and updated the program design each time. We find that ruling out unproductive program features leads to a pattern of increasing treatment effects over the waves and that program design matters more than the size of the fiscal stimulus in boosting spending. Our results show that (i) general coupons with no constraints on where the vouchers can be redeemed are more effective than specialized coupons in stimulating consumption in the targeted sectors; (ii) coupon packets with fewer denominations and shorter redemption windows tend to be more effective; and (iii) low-income residents and non-local residents are equally or even more responsive to the coupon program than other groups. Our results illustrate that generating variations in iterative policy experiments, combined with a timely assessment of individuals’ responses to marginal incentives, optimizes program design.

In this paper, we investigate whether those enterprises adopting digital finance exhibit higher values. On the basis of the constructed fintech-related lexicon developed by the machine learning-based Word2Vec model, we employ the frequency of fintech-related words (phrases) in the management discussion sections of annual reports as a proxy variable for the degree to which enterprises apply digital finance. We utilize panel data regression and mediation models based on data of Chinese A-share listed companies from 2016 to 2022 and explore the impact of this degree of digital finance application on enterprise value. We find that the degree to which enterprises apply digital finance elevates their values. The in-depth integration of digital technology and finance directly enhances enterprise value by reducing financing costs. Additionally, the effects are more evident among small-scale firms and enterprises located in regions with lower marketization levels. However, in the face of the impact of the COVID-19 pandemic, the positive effects on enterprises are relatively low.