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.

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 most comprehensive bankruptcy database of Chinese firms to date and document significant real effects arising from the establishment of specialized bankruptcy courts. Specifically, the recovery rate for unsecured creditors increases by 38.6 percentage points after the reform. This improvement is not driven by shorter case durations or lower direct bankruptcy costs, as intuition might suggest. Instead, it results primarily from greater efficiency in the discovery and disposal of assets during bankruptcy proceedings. The reform also increases the likelihood of reorganization and promotes capital infusion in such cases. Higher recovery rates generate broader spillovers: reductions in non-performing loans, expansion of unsecured lending by local banks, relaxation of firms’ financial constraints, shifts in capital structure and investment, and greater public willingness to file for bankruptcy when distressed.

Contrary to the US, Chinese stock markets exhibit negative overnight returns that appear to be highly affected by the extent of information asymmetry. 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. An information asymmetry-driven price discount thus emerges at market open, generating negative overnight returns, which further decrease with information asymmetry. Consistent with adverse selection, empirical evidence reveals lower overnight returns during market declines and high-volatility periods, with robust negative relationship between overnight returns and information asymmetry proxied by firm size, analyst coverage, and earnings announcement proximity. A model is introduced to rationalize our findings. This framework also sheds light on China's "opening return puzzle", the phenomenon that prices rise rapidly in the initial 30 minutes of trading, by showing how reduced adverse selection enables rapid price recovery during opening session.

This study shows how price limit rules, intended to stabilize markets, inadvertently distort price dynamics by fostering strategic speculation. Through a dynamic rational expectations model, we demonstrate that price limits induce post limit-up price jumps by impeding full information incorporation, enabling speculators to artificially push prices to upper bounds and exploit uninformed traders. The model predicts two distinct patterns: (1) stocks closing at price limits exhibit positive overnight returns followed by long-term reversals, and (2) stocks retreating from upper bounds suffer sharp reversals with partial recovery. Empirical analysis confirms these predictions. A natural experiment from China’s 2020 GEM reform —- which widened the price limit -— further provides causal evidence that relaxed limits mitigate speculative distortions.

Can bank competition help attenuate the prevalence of zombie firms? Motivated by a stylized model, this paper studies the effect of bank competition on the formation of zombie firms in two stages: the formation of distressed firms and distressed firms obtaining zombie lending. Using China’s 2009 bank entry deregulation as a quasi-natural experiment, the paper finds that bank competition lowers the probability of the formation of distressed firms, while it increases the probability of distressed firms obtaining zombie lending. Overall, bank competition decreases the formation of zombie firms. In addition, the findings show that a higher ex ante proportion of bad loans and higher probability of bad loan recovery will lead to a higher probability of distressed firms receiving zombie lending. Both factors encourage banks to sustain lending to distressed firms to keep them alive and to gamble that those firms may recover in the future.

How to shape a firm’s stock price resilience in the increasingly uncertain environment has become an important topic. This paper investigates the effect of important market participantsfinancial analysts-on stock price resilience. Based on data from 3,444 listed firms from China, we find that firms with higher analyst coverage are more resilient during the Covid-19 induced crisis, which is manifested by a lower pandemic-induced decline in stock price, shorter duration of decline period, higher recovery probability, and shorter duration of the recovery period after the shock. This positive relationship is more prominent for small firms but does not depend on ownership type, and the ratio of star analyst coverage. Further channel tests show that analysts could help in attracting attention from media and institutional investors, improving corporate governance, and reducing financial constraints, which in turn enhance the ability of stock prices to absorb pandemic shocks.

Using the staggered introduction of regional specialized debt recovery courts as a quasi-natural experiment, we estimate the causal effect of law enforcement on financing cost of corporate bonds in China. With primary market issuing data, we show that the introduction of specialized courts reduces issuers’bond financing cost by 15%. The analysis of secondary market trading data confirms the results that the yield spreads of existing bonds reduce significantly. Exploring regional-, firm- and bond-level heterogeneity, we find the effects to be much stronger when ex-ante default risk is high. Our case-level analyses further support that enforcement cost reduction in debt dispute resolution is a channel for the reduction of cost of bond. Our paper has important policy implications in light of the recent bond default wave in China, suggesting that creditors protection through highly efficient law enforcement is important for bond market development and will eventually benefit bond issuers as well.

In this paper, I build a continuous-time macro-finance model in which firms can access both bond credit and bank credit. The model captures the simple idea that the presence of bond financing increases the price elasticity of demand for bank loans. I find that the optimal capital adequacy ratio is quantitatively sensitive to the presence of bond financing and that models would overstate the banking sector's recovery rate if they omit bond financing. Furthermore, the model highlights that an economy's optimal capital requirement highly depends on the efficiency of its bankruptcy procedure and the risk profile of its real sector.

Although big data technologies such as digital contact tracing and health certification apps have been widely used to combat the COVID-19 pandemic, little empirical evidence regarding their effectiveness is available. This paper studies the economic and public health effects of the "Health Code" app in China. By exploiting the staggered implementation of this technology across 322 Chinese cities, I find that this big data technology significantly reduced virus transmission and facilitated economic recovery during the pandemic. A macroeconomic Susceptible-Infectious-Recovered (SIR) model calibrated to the micro-level estimates shows that the technology reduced the economic loss by 0.5% of GDP and saved more than 200,000 lives by alleviating informational frictions during the COVID-19 outbreak.