This paper examines bond trading activity and transaction cost differences between the bilateral Over-the-Counter (OTC) and the centralized Central Limit Order Book (CLOB) venues in the China interbank government bonds market, structured as all-to-all. Using a novel trade-level dataset, we estimate that CLOB reduces transaction costs by 0.66 basis points compared to OTC, highlighting the efficiency of its centralized trading mechanism. Furthermore, our analysis of cross-venue selection patterns reveals that the CLOB venue disproportionately facilitates core traders, orders with standardized sizes and settlement speeds, and newly issued bond trades. Despite CLOB’s cost advantages, the continued use of OTC is justified by its unique benefits, including mitigating information leakage, enabling designated counterparties, and facilitating position rebalancing. These findings offer insights into how market microstructure and trading mechanism affect asset liquidity.

Shill bidding, the use of non-genuine bids to inflate prices, undermines auction market integrity. Exploiting China’s online judicial housing auctions as a laboratory, we identify 2% of participants as suspected shill bidders, affecting 8% of auctions. They raise price premium by 14.3%, causing an annual deadweight loss of ¥570 million for homebuyers. Mechanism analysis reveals they create bidding momentum and intensify competition. We establish causality using a difference-in-differences analysis leveraging a 2017 regulatory intervention and an instrumental variable approach using dishonest judgment debtors. These findings offer actionable insights for policymakers and auction platforms to combat fraud in online high-stake auctions.

This study examines how housing purchase restrictions (HPRs) affect firms' robotics adoption through labor cost increases. Exploiting policy-driven housing price shocks across Chinese cities, we find firms significantly accelerate robot adoption in response to higher labor costs. Effects are pronounced among financially unconstrained firms, state-owned enterprises, and firms with skilled or educated workforces. Automation investments subsequently improve firm productivity, profitability, and market positions. Our findings highlight unintended spillovers from housing regulations to firm-level technological decisions and suggest policymakers consider these indirect effects when designing local market interventions.

We examine the risk-return trade-off in market anomalies within the A-share market, showing that even decaying anomalies may proxy for latent risk factors. To balance forecast bias and variance, we integrate the 1/N and mean-variance frameworks, minimizing out-of-sample forecast error. Treating anomalies as tradable assets, we construct optimized long-short portfolios with strong performance: an average annualized Sharpe ratio of 1.56 and a certainty-equivalent return of 29.4% for a meanvariance investor. These premiums persist post-publication and are largely driven by liquidity risk exposures. Our results remain robust to market frictions, including shortsale constraints and transaction costs. We conclude that even decaying market anomalies may reflect priced risk premia rather than mere mispricing. This research provides practical guidance for academics and investors in return predictability and asset allocation, especially in the unique context of the Chinese A-share market.

Suzhou and Shenzhen are among the top cities in China by GDP, and both have performed exceedingly well in terms of cultivating technological industries and attracting foreign investment. This is in spite of the fact that neither city is a provincial capital nor a centrally administered city like Shanghai and Beijing. Yet, the two cities embody very different administrative models with respect to their relationship with the provincial and central governments. Shenzhen, in particular, has a closer relationship with the central government than almost any non-centrally administered city in China, whereas Suzhou is a city that remains closely in coordination with the provincial government even as its economy has grown by leaps and bounds. This begs the question of which city's model will prevail moving forward: the Shenzhen model, typified by "re-centralization" of power, or the Suzhou model, which represents more of the conventional regional decentralization model that has been prevalent in China since the 1980s. The article attempts to argue that even though Shenzhen is of pivotal importance to the central government's policies, it will remain an outlier for the time being so as to avoid disturbing the delicate balance between the central and provincial governments, barring an unforeseen economic or political crisis.

We investigate the relationship between the intensity of intraday return reversals and future stock returns in the Chinese stock market. We find that a high frequency of positive overnight returns followed by negative daytime returns predicts one-month ahead returns positively. The analysis shows that daytime retail investors tend to overly sell their own rising stocks at market open, accepting lower stock prices in exchange for liquidity. As the price pressure attenuates, these stocks experience subsequent price increases, implying a positive relationship between return reversals and future returns.

The study examines the impact of Government-Backed Financing Guarantee (GFG) programs on employment in small and medium-sized enterprises (SMEs) using data from the Zhejiang Guarantee Group and non-listed SMEs in China. The findings demonstrate that these programs have a significant positive effect on employment in SMEs, particularly in private firms, and non-ZhuanJingTeXin firms. Furthermore, the study demonstrates that GFGs can enhance firm employment rates by mitigating financing constraints. It also contributing to firm revenue growth.

This paper examines whether employees avoid firms that commit environmental, social and governance (ESG) misconduct in China where ESG norms are weak. We find that the number of employees grows slower when firms have more ESG incidents after accounting for performance, risk, corporate governance, and time-invariant firm characteristics. The result is mostly attributable to social incidents and incidents that affect China, better educated knowledge workers, and high tech and non-labor-intensive industries, and is unlikely to be caused by layoffs. Overall, workers with better job fluidity respond to incidents that affect them personally.

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.

We use a sample of China’s listed firms and employ a naïve Bayesian machine learning algorithm to reveal that environmental information intangibility superficially promotes green innovation. We demonstrate that this effect is channelled through the acquisition of institutional resources, including bank loans and government subsidies. The impact of environmental information intangibility on green innovation is most pronounced within state-owned enterprises, large firms, and politically connected firms. Furthermore, we confirm that environmental information intangibility does not lead to improvements in innovation efficiency or quality. This implies that green innovation may serve as a symbolic environmental activity. Our findings contribute to the understanding of the consequences of environmental information intangibility, greenwashing behaviour, and their relationship to green innovation.