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.

This study investigates the compliance of local Chinese officials with the zero-Covid policy throughout the COVID-19 pandemic. By examining biographical data from political elites and using a prefecture-day data set on risk levels – an indicator reffecting the status of zero-Covid policy - we discover a significant impact of prefecture leaders’ promotion incentives on their response to COVID-19 outbreaks. Our empirical analysis reveals that leaders with stronger promotion incentives tend to exhibit increased reactions to emerging cases. Evidence shows that such a phenomenon is driven by the different choices of the prefecture leaders facing relatively larger-scale COVID-19 outbreaks. Furthermore, local governors whose jurisdictions are more economically developed tend to enforce more stringent mobility restrictions. However, for prefecture leaders who oversee more developed regions and possess strong promotion incentives, the combined effects of these two factors tend to balance each other out in terms of pandemic response. These results suggest a natural tension between demands for crisis management during the pandemic and routine performance in economic development within the political framework of China.

This research analyses the ramifications of the COVID-19 pandemic on China's economy, examining the divergent epidemic prevention policies used by local governments. Empirical evidence highlights that the emergence of COVID-19 cases correlates with a 1.13% reduction in quarterly GDP growth. However, when a city's secretary maintains an informal ties with the provincial secretary, GDP growth remains resilient. Analyzing micro-level data, we observe that city secretaries with informal ties tend to enact flexible anti-contagion measures. This flexibility stems from a decreased likelihood of reprimand for virus transmission. Such shields exclusively manifests when incumbent provincial secretaries share informal ties with central leadership. This underscores the interplay of political networks in shaping localized economic responses.

This work models the spillover of liquidity and volatility and their joint dynamics in the Chinese stock market. Methodologically, we implement a copula-based vector multiplicative error model for sectors. Utilizing intraday data from 2014 to 2022, our empirical analysis reveals strong interdependence between liquidity and volatility at the sectoral level. Moreover, different sectors dominate the transmission of liquidity and volatility shocks at different times. In normal times, sector volatilities transmit shocks notably (though not always dominantly), while in turbulent times, illiquidity is the key channel through which shocks spread. We also pay special attention to how two catastrophic events impacted the Chinese stock market: the 2015/16 stock market crash and the COVID-19 pandemic. Our ffndings are useful for policymakers monitoring and making policy at the sectoral level, as well as for institutional and private investors making investment decisions.

The COVID-19 pandemic has inflicted substantial impacts on global financial markets and the economy. This study explores the impact of two pandemic outbreaks in China on its stock market industries. It employs the Conditional Autoregressive Value at Risk (CAViaR) model to compute tail risks across 16 selected industry sectors. Additionally, risk correlation networks are constructed to illustrate the risk correlations among industry sectors during different phases of the two outbreaks. Furthermore, risk contagion networks are built based on the Granger causality test to examine the similarities and differences in the contagion mechanisms between the two outbreaks. The findings of this study show that (i) the two outbreaks of COVID-19 have resulted in tail risks for most industries in the Chinese stock market. (ii) The risk correlation network became more compact because of both outbreaks. The impact of the second outbreak on the network was less severe than that of the first outbreak. (iii) During the first outbreak of COVID-19, the financial industry was the primary source of risk output; during the second outbreak, the concentrated outbreak in Shanghai led the industries closely related to the city's economy and trade to become the most significant risk industries. These findings have practical implications for researchers and decision-makers in terms of risk contagion among stock market industries under major public emergencies.

Using administrative universal business registration data as well as primary offline and online surveys of small businesses (including unregistered self-employments) in China, we examine (i) whether digitization helps small and medium enterprises (SMEs) better cope with the COVID-19 pandemic, and (ii) whether the pandemic has spurred digital technology adoption. We document significant economic benefits of digitization in increasing SMEs' resilience against such a large shock, as seen through mitigated demand decline, sustainable cash flow, ability to quickly reopen, and positive outlook for growth. Post the January 2020 lockdown, firm entries exhibited a V-shaped pattern, with entries of e-commerce firms experiencing a less pronounced immediate drop and a quicker rebound. Moreover, the pandemic has accelerated the digital transformation of existing firms and the industry in multiple dimensions (e.g., altering operation scope to include e-commerce, allowing remote work, and adopting electronic information systems). The effect persists more than one year after reopening, and is more pronounced for certain sectors, firms in industrial clusters, and areas with more digital inclusion but less financial efficiency, constituting initial evidence for the long-term impact of the pandemic and the supposedly transitory mitigation policies.

The aim of the study is to investigate the behaviour of the Chinese government bond yield curve before and during the COVID-19 pandemic. Its methodology comprises the techniques of time series analysis, correlation analysis and dimensionality reduction. The main empirical results show that in the pandemic period, the behaviour of the Chinese government bond yield curve differs significantly from that before the outbreak of COVID-19. This is evidenced by the weaker correlations among the analysed yields, the presence of anomalies, heterogeneous behaviour and probable arbitrage opportunities at the long-term end of the studied yield curve, as well as the significant changes in the main factors of its dynamics. The research also reveals that prior to the COVID-19 pandemic, portfolios composed of Chinese government bonds could be well protected against interest rate risk even by using traditional parallel shift immunization techniques. However, after the outbreak of the COVID-19 pandemic the use of such techniques would be relatively effective for portfolios of Chinese government bonds with maturities between 1 and 5 years, while portfolios that include Chinese government bonds with maturities greater than 7 years should be either hedged against all the three factors of the yield curve dynamics or be used only for arbitrage strategies.