This paper examines the impact of green mergers and acquisitions (M&As) on firms’ transition to green exports. We develop a “Technology-Qualification” theoretical framework and conduct the empirical analysis using a matched dataset of Chinese listed manufacturing firms and customs records. The findings show that green M&As significantly promote firms’ green exports, and this effect remains consistent across a series of robustness test. Mechanism analysis reveals that green M&As promote green exports through two key channels: green innovation spillovers and green qualification spillovers. Further heterogeneity analysis indicates that the positive impact of green M&As on green exports is more pronounced among firms with stronger operational performance, weaker green foundations, and those involved in processing trade. In addition, green M&As not only stimulate green exports but also prevent the entry of polluting products and reduce the exit of green product, thereby driving a green-oriented dynamic restructuring of firms’ export structure. This paper offers micro-level insights into how firms can navigate the dual challenges of enhancing green production capabilities and overcoming barriers to green trade during their transition to green exports.

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.

Background Economic stability depends on the ability to foresee financial risk, particularly in markets that are extremely volatile. Unbalanced financial data is difficult for traditional Support Vector Machine (SVM) models to handle, which results in subpar crisis detection capabilities. In order to improve financial risk early warning models, this study combines Gaussian SVM with stochastic gradient descent (SGD) optimisation (SGD-GSVM). Methods The suggested model was developed and assessed using a dataset from China's financial market that included more than 2,000 trading days (January 2022–February 2024). Missing value management, Min-Max scaling for normalising numerical characteristics, and ADASYN oversampling for class imbalance were all part of the data pretreatment process. Key evaluation metrics, such as accuracy, recall, F1-score, G-Mean, AUC-PR, and training time, were used to train and evaluate the SGD-GSVM model to Standard GSVM, SMOTE-SVM, CS-SVM, and Random Forest. Results Standard GSVM (76% accuracy, 1,200s training time) and CS-SVM (81% accuracy, 1,300s training time) were greatly outperformed by the suggested SGD-GSVM model, which obtained the greatest accuracy of 92% with a training time of just 180 seconds. Additionally, it showed excellent recall (90%) and precision (82%), making it the most effective and efficient model for predicting financial risk. Conclusion This work offers a new method for early warning of financial risk by combining SGD optimisation with Gaussian SVM and employing adaptive oversampling for data balancing. The findings show that SGD-GSVM is the best model because it strikes a balance between high accuracy and computational economy. Financial organisations can create real-time risk management plans with the help of the suggested technique. For additional performance improvements, hybrid deep learning approaches might be investigated in future studies.

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.

This research examines the impact of short selling on the financing cost of corporate bonds using panel data from Chinese A-share listed companies spanning the period from 2007 to 2022. The study aims to investigate the potential cross-market information spillover effects within the short selling system. The findings indicate that short selling significantly reduces the financing cost of corporate bonds, with a more pronounced effect observed under greater short selling forces. The robustness of the results is confirmed by controlling for various potential influencing factors and addressing the endogeneity issue through Propensity Score Matched Difference in Differences (PSM-DID) methodology. Moreover, the research reveals that the alleviation of information asymmetry serves as the primary mechanism through which short selling exerts its impact, particularly in regions with well-developed financial markets and favorable legal environments. This study offersa novel perspective of short selling in China and it sheds light on its cross-market spillover effects. By effectively enhancing resource allocation efficiency in capital markets, short selling emerges as a potent tool for mitigating information disparities between bond investors and enterprises.

In this study, we investigate whether and how risk disclosure in peer firms’ management discussion and analysis (MD&A) influences analyst earnings forecast accuracy. We find that peer MD&A risk disclosure significantly improves forecast accuracy, demonstrating a positive spillover effect. Moreover, the impact of peer MD&A risk disclosure on analysts’ forecast accuracy strengthens with the comparability and reliability of peer firms’ information, while weakens with the disclosure quality of the focal firm. Finally, peer MD&A risk disclosure also reduces stock price crash risk, providing further evidence that it improves information environment of the focal firm.

This paper investigates how the geographical distribution of shareholders in Chinese mutual fund management companies influences investment decisions. We show that mutual funds are more inclined to hold and overweight stocks from regions where their shareholders are located, thus capitalizing on a local information advantage. By examining changes in fund holdings in response to shifts in the shareholder base, we rule out the possibility that these effects are driven by fund managers’ local biases. Our findings reveal that stocks from the same region as the fund’s shareholders tend to outperform and significantly contribute to the fund’s overall performance.

In the late 1980s, Mexico opened itself to international trade and foreign investment, followed in the early 1990s by China. China and Mexico are still the two countries characterized as middle-income by the World Bank with the highest levels of merchandise exports. Although their measures of openness have been comparable, these two countries have had sharply different economic performances: China has achieved spectacular growth, whereas Mexico’s growth has been disappointingly modest. In this article, we extend the analysis of Kehoe and Ruhl (2010) to account for the differences in these experiences. We show that China opened its economy while it was still achieving rapid growth from shifting employment out of agriculture and into manufacturing while Mexico opened long after its comparable phase of structural transformation. China is only now catching up with Mexico in terms of GDP per working-age person, and it still lags behind in terms of the fraction of its population engaged in agriculture. Furthermore, we argue that China has been able to move up a ladder of quality and technological sophistication in the composition of its exports and production, while Mexico seems to be stuck exporting a fixed set of products to its North American neighbors.

In the mid-1990s, the concept of smart growth emerged in the United States as a critical response to the phenomenon of suburban sprawl. To promote sustainable urban development, it is necessary to further investigate the principles and applications of smart growth. In this paper, we proposed a Smart Growth Index (SGI) as a standard for measuring the degree of responsible urban development. Based on this index, we constructed a comprehensive 3E evaluation model—covering economic prosperity, social equity, and environmental sustainability—to systematically assess the level of smart growth. For empirical analysis, we selected two medium-sized cities from different continents: Wuhu County, China, and Colima, Mexico. Using an improved entropy method, we evaluated the degree of smart growth in recent years and analyzed the contributions of various policies to sustainable urban development. Then, guided by the ten principles of smart growth, we linked theoretical insights to practical challenges and formulated a development plan for both cities. To forecast long-term trends, we employed trend extrapolation based on historical data, enabling the prediction of SGI values for 2020, 2030, and 2050. The results indicate that Wuhu demonstrates a greater potential for smart growth compared with Colima. We also simulated a scenario in which the population of both cities increased by 50 percent and then re-evaluated the SGI. The analysis suggests that while rapid population growth tends to slow the pace of smart growth, it does not necessarily exert a negative impact on the overall trajectory of sustainable development. Finally, a study on the application of Transit-Oriented Development (TOD) theory in Wuhu County was conducted. Based on this analysis, we proposed several policy recommendations aimed at enhancing the city’s sustainable urban development.

The benchmark discrepancy phenomenon arises when fund managers deviate from their stated benchmarks. We investigate benchmark discrepancy in China's mutual fund market by analyzing holdings data from all actively managed funds and document its widespread prevalence. However, in China – unlike in the U.S. – benchmark discrepancy reduces relative performance and capital inflows. We also examine the characteristics of fund managers exhibiting benchmark discrepancies and find they are more likely to be male, highly educated, and professionally experienced.