Mergers and acquisitions (M&A) are widely regarded as a critical instrument for industrial consolidation and capacity rationalization. Utilizing a dataset of China’s major national and regional steel enterprises from 1998 to 2013, this study empirically evaluates the efficacy of M&A in resolving over1capacity. We document a striking heterogeneity in outcomes: while M&A generally alleviates overcapacity, this effect is exclusively driven by inter-provincial transactions; intra-provincial mergers show no significant impact. Mechanism analysis reveals that inter-provincial M&A facilitates the transfer of control rights, effectively insulating firms from local government intervention. In contrast, intra-provincial mergers are undermined by local intervention through mechanisms of "internal control" and "external defense." Furthermore, despite the reduction in capacity, we find that inter-provincial mergers suffer from incomplete post-merger integration. There is a persistent lack of governance coordination, and no convergence in capacity management performance is observed between the merging entities over time. These findings offer critical policy implications for mitigating local protectionism and enhancing integration efficiency to resolve industrial overcapacity.

Enhancing the performance of corporate green supply chain management (GSCM) represents a critical enabler for delivering on the central imperatives of the global low-carbon transition. Using panel data of Chinese listed companies from 2003 to 2022, this study employs a staggered difference-in-differences (DID) approach to examine the impact of the Low-Carbon City Pilot (LCCP) policy on firms' GSCM performance. The results show that the LCCP policy significantly improves firms' GSCM performance, and this effect remains robust across a range of sensitivity tests. Mechanism analyses reveal that the policy operates primarily by stimulating green innovation and increasing investment in environmental protection equipment. Furthermore, financial constraints play a significant moderating role: the positive effect of the policy on GSCM performance is stronger when firms face fewer financing constraints. Heterogeneity analysis indicates that the policy effects are more pronounced among non-heavy-polluting enterprises, those located in resource-dependent cities, and ISO 14001-certified enterprises. This study provides novel micro-level evidence on the environmental governance effects of place-based climate policies, and offers key policy implications for scaling up low-carbon city pilots and promoting corporate supply chain decarbonization.

This paper exploits China’s culture-oriented institutional reform as a quasinatural experiment to examine the impact of cultural policies on water pollution. Using a difference-in-differences approach with city-level panel data from 2003 to 2020, we find that the reform leads to a significant reduction in water pollution in pilot cities. Heterogeneity analysis indicates that the effect is more pronounced in cities with better economic performance, higher administrative status, richer tourism resources, and more abundant human capital. The reform prompts a shift in local industrial structure toward less-polluting sectors, with a marked contraction in pollution-intensive activities. It also strengthens local environmental regulation and enhances green innovation, thereby contributing to pollution reduction. Our findings highlight the spillover environmental effects of policies not explicitly designed for environmental purposes.

In recent years, despite China’s sustained expansion of public investment in education, pronounced regional disparities in educational development persist, revealing the practical constraints of the existing fiscal sharing structure in advancing equity objectives. In particular, the mechanism through which the allocation of fiscal responsibilities between central and local governments influences educational equity performance remains insufficiently examined in a systematic and empirical manner. From the perspective of fiscal structure, this study seeks to identify and explicate the evolutionary trajectory of educational fiscal equity in China, as well as its underlying institutional determinants. To this end, the study constructs a fiscal performance evaluation framework comprising seven core indicators. Objective weights are assigned using the entropy method, and the TOPSIS model is subsequently employed to conduct a comparative ranking analysis of national education fiscal performance over the period from 2013 to 2023. The results indicate that educational fiscal equity has exhibited an overall upward trend during the study period; however, its evolution displays clear stage-specific characteristics. In the initial phase, improvements in equity were primarily driven by fiscal expansion, leading to rapid gains. In the subsequent period, progress became increasingly constrained by structural optimization bottlenecks, with fiscal performance gradually stabilizing at a plateau in more recent years. Further analysis reveals that the persistently heavy fiscal burden borne by local governments, coupled with the relatively limited regulatory and equalization capacity of the central government, constitutes a key structural impediment to the sustained improvement of educational equity. By elucidating the internal mechanism through which the allocation of fiscal responsibilities shapes educational equity performance, this study provides both theoretical grounding and quantitative support for deepening fiscal system reform and optimizing policies related to the allocation of educational resources.

This paper asks whether a personally experienced bubble crash leaves a lasting imprint on investment style. We study this in China’s stock market, where two technology-growth booms —the 2014–2015 Internet boom and the 2025–2026 AI hardware boom—share a similar narrative but are separated by a decade of generational turnover. Following managers across the mutual funds they run from 2013Q1 to 2026Q1, we compare those who lived through the 2015 crash with otherwise similar managers who entered afterwards. We find that the caution of veteran managers reflects a scar rather than a symptom of ageing. Conditional on age and career length, managers who lived through the 2015 crash hold more value exposure and chase technology stocks less when prices have been rising, and this reluctance deepens with how much the crash hurt them—crash-window tenure, performance shortfalls, redemption pressure—rather than with age. These demand differences reach asset prices as well: stocks held disproportionately by managers without bubble memories carry significantly higher subsequent left-tail risk, whereas crash-experienced ownership stabilises the stock, particularly when ownership is broad. Heterogeneous institutional beliefs rooted in career experience therefore shape not only how managers invest but also the crash risk of the assets they hold.

Can fully agentic artificial intelligence predict equity returns in real time? We build a novel dataset of daily AI-generated attractiveness scores spanning the entire Russell 1000 universe, continuously updated since April 2025. Each trading day, the model freely searches the live internet and produces quantitative scores for every constituent stock before the next market open, eliminating both look-ahead bias and attribution bias. A daily value-weighted portfolio of the 20 highest-ranked stocks earns a Fama-French six-factor alpha of 16.5 basis points per day (41.5% per annum), with an average bid-ask spread of 1.92 basis points. Predictability is sharply asymmetric: the model reliably identifies future outperformers but not future underperformers. The evidence points to stock picking rather than market timing: performance is strongest on down-market days, and the alpha does not come from betting on aggregate market direction. The AI’s reasoning traces help explain this predictability. Alpha is concentrated in traces with richer live-web evidence and stronger analytical structure. In the theme attribution, most active alpha reflects firm selection under an investment lens, not broad theme choice.

This study investigates the relationship between the number of stock concepts and future returns, as well as the economic mechanisms underlying this association. Using novel collected data, we find that stocks with a greater number of concepts earn significantly higher returns in the subsequent month, generating a six-factor adjusted annualized alpha of approximately 9.6% for a long-short portfolio. Although these stocks exhibit higher turnover, return volatility, and investor attention - features commonly associated with speculative concept-driven trading - an analysis of cross-listed AH twin stocks reveals that concept counts do not widen the AH premium. Moreover, the return premium persists for up to 15 months without significant reversal, whereas firms that engage in opportunistic concept-chasing exhibit pronounced long-run reversals, suggesting that the positive concept-return relation is not driven by speculative motives. The higher returns are primarily attributable to strong industrial policy support and sustained above-expectation operating performance. Firms with more concepts are more likely to receive government subsidies and deliver positive earnings surprises, effects that are amplified when their core concepts receive stronger national policy backing. In contrast, firms that pursue concepts lacking substantive business relevance are significantly less likely to obtain government subsidies and fail to achieve above-expectation growth. Regarding investor composition, institutional ownership of high-concept stocks increases modestly, while ownership by government-guided funds rises substantially - particularly for stocks whose core concepts are strongly supported by national industrial policies. Conversely, concept-chasing behavior by listed firms significantly reduces the ownership ratio of government-guided funds. Overall, our findings indicate that concept stocks in China’s capital market are not purely speculative but signal underlying national policies.

With the development of China's economy, more and more Chinese enterprises are active on the world stage, and cross-border M&A is the most effective and fastest way for enterprises to go abroad and make overseas investments, and it is also an important path for globalization after the enterprises have reached a certain stage of growth. Compared to domestic M&A, cross-border M&A is a more complex economic activity, requiring more factors to be considered and greater risks to be taken, with the slightest misstep often leading to operational difficulties for the acquiring company. It is important to consider whether cross-border M&A can improve business performance, the factors that influence the performance of cross-border M&A, and how to improve the performance of enterprises in cross-border M&A. This study takes 100 cross-border M&A events of Chinese listed companies in Shanghai and Shenzhen during the period of 2017-2020 as a sample, and on the basis of reviewing the research results of cross-border M&A at home and abroad, combined with the characteristics of cross-border M&A of Chinese enterprises, from different perspectives, a number of financial indicators are selected to construct comprehensive performance evaluation indicators using factor analysis, and the preliminary analysis shows that after cross-border M&A, the companies with increased performance The preliminary analysis showed that the number of companies whose performance increased after cross-border M&A increased year by year. The impact of industry relevance and transaction equity on M&A performance is not significant; the ratio ofM&A amount to current assets negatively affects firm performance in the year of M&A. Finally, based on the empirical results, relevant policy recommendations are made to encourage better development of private enterprises and improving cross-border M&A performance.

We examine the unintended consequences of bankruptcy court establishment on corporate behavior. Using data on Chinese listed firms from 2009 to 2019 and a staggered difference-in-differences model, we find that the establishment of bankruptcy courts increases accrual earnings management by about 17% among high bankruptcy risk firms relative to low-risk firms. While bankruptcy courts improve bankruptcy efficiency and justice, reduce local government intervention, and accelerate the exit of zombie firms, they also induce greater earnings management. This effect is driven mainly by survival pressure and managerial reputation concerns, rather than by efforts to correct external evaluations. Consistent with this interpretation, we do not observe improvements in long-term operations, governance, performance, or real earnings management. Overall, this paper enriches the literature on earnings management from the perspective of judicial governance and on the economic consequences of creditor-friendly bankruptcy institutions.

We investigate the capabilities of Large Language Models (LLMs) in generating novel alpha factors for option returns. Utilizing a structured prompt-engineering approach, LLMs like GPT-5 can directly create factors for two distinct options markets: the mature U.S. market and the emerging Chinese market. Empirical analysis further reveals that the LLM-generated factors exhibit remarkable and robust performance, delivering statistically signifcant returns in both all-sample and extensive out-of-sample tests. Beyond their statistical signifcance, such factors are economically meaningful. They display low self-correlation, indicating genuine innovation, and are grounded in sound economic rationale derived from market microstructure and behavioral fnance principles, showcasing a key advantage over traditional machine learning models.