This paper examines how economic policy uncertainty (EPU) affects mergers and acquisitions (M&As) between companies with different levels of financing constraints. Existing literature overlooks the interactive effect of EPU and financing constraints on M&As, and empirical evidence regarding EPU's influence on financially constrained firms remains limited. China's unique ownership structure provides a valuable context for this analysis, as state-owned enterprises (SOEs) face fewer financing constraints than private firms. Using a 2007-2021 sample of Chinese listed state-owned enterprises (SOEs) and private companies, we find that high EPU decreases the likelihood of private firms acquiring SOEs, while increases the likelihood of private firms being acquired by SOEs. These results suggest that under high EPU, financially constrained firms experience greater survival pressure, limiting their capacity to alleviate constraints by acquiring less-constrained targets. Conversely, less-constrained firms enhance their bargaining power and are more likely to acquire financially stressed counterparts. EPU facilitates control transfers from high-constraint to low-constraint firms, contributing to long-term market returns and improving financial market allocation efficiency. Our study contributes to the literature by shedding light on how EPU shapes divergent M&A behaviors based on firms’ financing constraints.

Climate change presents a global threat, prompting nations to adopt low-carbon development pathways to mitigate its potential impacts. However, current research lacks a comprehensive framework capable of integrating multiple variables and providing dynamic optimization capabilities. This article focuses on designing pathways for developing a low-carbon economy to tackle climate challenges. Specifically, we construct a low-carbon economy model that incorporates economic, environmental, social, energy, and policy factors to analyze the drivers of economic growth and carbon emissions. We utilize economic model predictive control and tracking model predictive control to optimize development pathways aligned with various low-carbon targets, creating and validating a comprehensive framework for low-carbon policy design using historical data from China. This study highlights significant advantages in analyzing low-carbon pathways through advanced techniques like hierarchical regression and model predictive control, providing a robust framework that enhances our understanding of causal relationships within the LCE system, captures system feedback, dynamically optimizes pathways, and accommodates diverse policies within a comprehensive low-carbon economy system.

Adverse selection remains a significant challenge in the insurance industry, often resulting in substantial financial losses for insurers. The primary hurdle in addressing the issue lies in accurately identifying and quantifying adverse selection. Traditional methods often fail to adequately account for the heterogeneity of insurance purchasers and the endogenous nature of their insurance decisions. This study introduces an innovative approach that integrates the Gaussian Mixture Model and the regression-based model from Dionne et al. (2001) to assess adverse selection, addressing the limitations of previous methods. Through comprehensive simulations, we demonstrate that our method yields unbiased estimates, outperforming existing approaches. Applied to China’s automobile insurance market, leveraging IoT devices to track telematics data, this method captures risk heterogeneity among the insured. The results offer robust evidence of adverse selection, in contrast to conventional methods that fail to detect this phenomenon due to their inability to capture the underlying relationship between customer risk and claim behavior. Our approach offers insurers a robust framework for identifying information asymmetries in the market, thereby enabling the development of more targeted policy interventions and risk management strategies.

China faces significant environmental challenges, including reducing pollutants, improving environmental quality, and peaking carbon emissions. Industrial restructuring is key to achieving both emission reductions and economic transformation. This study uses the Environmentally Extended Multi-Regional Input-Output model and multi-objective optimization to analyze pathways for China’s industrial transformation to synergistically reduce emissions. Our findings indicate that under a compromise scenario, China’s carbon emissions could stabilize at around 10.9 billion tonnes by 2030, with energy consumption controlled at approximately 5 billion tonnes. The Papermaking sector in Guangdong and the Chemicals sector in Shandong are expected to flourish, while the Coal Mining sector in Shanxi and the Communication Equipment sector in Jiangsu will see reductions. The synergy strength between carbon emission reduction and energy conservation is highest at 11%, followed by a 7% synergy between carbon emission and nitrogen oxide reduction. However, significant trade-offs are observed between carbon emission reduction and chemical oxygen demand, and ammonia nitrogen reduction targets at -9%. This comprehensive analysis at regional and sectoral levels provides valuable insights for advancing China’s carbon reduction and pollution control goals.

This study explores the anchoring effect of previous bid premiums on acquirers’ bidding behavior in serial acquisitions. We demonstrate that, after controlling for deal characteristics, learning, and unobserved factors, the current bid premium is positively correlated with the acquirer’s previous bid premium. The strength of this anchoring effect diminishes with longer time intervals between acquisitions and increases with the industry similarity of targets. Notably, it remains unaffected by the acquirer’s state ownership or acquisition frequency. Additionally, the anchoring effect is less pronounced during periods of high economic uncertainty and can reverse following a change in the acquirer’s CEO. Our findings suggest that serial acquisitions are interrelated events, challenging the notion that each bid is an isolated occurrence. This research provides insights into the underperformance of serial acquirers compared to single acquirers and the declining trend in announcement returns across successive deals.

We study how politicians' activities affect the stock market and firm performance. Using hand-collected data on China's national leaders' corporate visits, we investigate the industry-wide implications of these visits. We find that over the six days surrounding a visit, an average industry peer's value increases by 2\% of its total assets. This result reflects investors' favourable interpretation of leaders' visits as a signal of more government support for the entire industry. However, the industry peer's profitability plummets by more than 15\% in the next three years. Further analysis reveals that after the visits, industry peers increase their investments, presumably in anticipation of additional government subsidies and credits. However, these resources are insufficient, and the profitability of these firms suffers. Our findings suggest that national leaders' visits do not help boost the targeted industries, and firms should carefully interpret the politicians' activities.

Facing external shocks, maintaining and stabilizing imports is a major practical issue for many developing countries. We first document that sourcing market switching (SMS) is widespread for Chinese firms (For 2000-2016, SMS firms account for 76.29% of all import firms and 96.30% of total import value). Then we use Chinese firm-level data to show that SMS can significantly mitigate the negative impacts of international uncertainty on imports, which further stabilizes firm employment and innovation, leading to increases in national and even world welfare. Possible motivations for SMS include stabilizing import supply, lowering import tariffs, raising the real exchange rate, and increasing product switching. We also find that the effects of SMS vary by the type of uncertainty, firm ownership, productivity, credit constraints, trade mode, and product features.

This paper explores the impact of government intervention on local analysts’ earnings forecasts, based on a scenario of financial misconduct in Chinese state-owned enterprises (SOEs). The results show that, under the influence of the government, local analysts’ earnings forecasts for SOEs with financial misconduct are less accurate and more optimistically biased. Further heterogeneity analysis reveals that forecast bias by local analysts is greater when officials have stronger promotion incentives, when regions are less market-oriented and have a larger share of the state-owned economy, and when SOEs contribute more to taxation and employment. In further analysis, we find that local analysts have a more optimistic tone in reports targeting non-compliant SOEs. Local analysts who depend heavily on political information will also issue more biased and optimistic forecasts on SOEs with violations. Finally, as a reward for achieving government goals, the local brokerages affiliated with these analysts and providing these optimistic forecasts are more likely to become underwriters in seasoned equity offerings of SOEs. This paper reveals that government intervention significantly influences analyst forecasts, providing implications for understanding the sources of analyst forecast bias.

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.

We (re)evaluate the general-equilibrium effects of (environmental) policies from the perspectives of input-output networks and firm dynamics and heterogeneity. Using China’s carbon emission trading system (ETS) as an example, we find that ETS leads to more patent applications, especially the ones associated with low-carbon technologies in the targeted sectors. The effects are muted at the firm level due to selection effects, whereby only larger firms are significantly and positively affected. Meanwhile, larger firms occupy a small share in number but a large share of aggregate outcomes, contributing to the discrepancy between the effects of ETS at the individual firm and aggregate sector levels. The effects also diffuse in input-output networks, leading to more patents in upstream/downstream sectors. We build and estimate the first firm dynamics model with input-output linkages and regulatory policies in the literature and conduct policy experiments. ETS’s effects are amplified given input-output networks.