Digital mergers and acquisitions (M&As) are increasingly becoming a critical strategic approach for enterprises to advance digital transformation. This study conceptualizes digital M&As as positive shock events for corporate digital transformation. Using a dataset of digital M&As by Chinese listed companies from 2005 to 2024, this study applies the propensity score matching combined with difference-in-differences (PSM-DID) method to empirically examine the impact of digital M&As on the market value of acquiring firms. The results show that digital M&As significantly enhance acquirers’ market value. Mechanism tests reveal that this effect is driven by digital resource empowerment, operating through increased digital factor inputs and strengthened digital innovation capabilities. Heterogeneity analysis further indicates that the market value enhancement effect of digital M&As is predominantly significant in non-digital firms, non-state-owned enterprises, and firms located in eastern China. This study expands the research scope of the micro-level effects of the digital economy and offers useful references for the Chinese government in refining its digital economy strategies, as well as practical guidance for firms in formulating their own digital investment decisions.

Robo-advisors have been hailed as financial innovations that combine Artificial Intelligence (AI) and low-cost advisory services, with the potential to democratize stock market participation and improve financial inclusion, especially in less developed countries. However, to date their adoption has been slower than expected and existing research that has attempted to understand this puzzle focuses exclusively on existing users of robo-advisors. In this paper, we study the intention to adopt robo-advisors as an antecedent of actual adoption. Using data from a survey of 1,277 Chinese adults, a country with one of the highest saving rates in the world but also very low stock market participation rate, we find that financial literacy and technology acceptance strongly influence the intention to adopt robo-advisors. A one-unit increase in financial literacy (technology acceptance) is associated with a 5.69% (4.74%) increase in the probability of adopting robo-advisors. Importantly, financial confidence partially mediates the literacy-adoption link, highlighting a key psychological mechanism in improving stock market participation rates. Our results shed light on the underlying drivers that facilitate financial inclusion.

This study examines the effect of artificial intelligence (AI) adoption on stock price crash risk using panel data from Chinese A-share listed firms from 2001 to 2022. We find that higher levels of AI application significantly reduce crash risk, primarily by enhancing information transparency, easing financial constraints, and promoting innovation. Notably, AI improves transparency within supply chains by reducing information asymmetry between upstream and downstream firms, thereby enhancing information flow and reducing market frictions. Among AI types, machine learning proves most effective in lowering crash risk due to its data-processing and forecasting capabilities, while natural language processing and computer vision show weaker effects. The impact of AI is particularly pronounced in non-government-regulated industries and high-tech firms. Moreover, its risk-mitigating effect becomes increasingly significant over time. These results are robust to instrumental variable estimation and staggered difference-in-differences (DID) designs. These findings highlight the strategic role of AI in risk management and offer practical implications for firms and policymakers aiming to enhance transparency, financial resilience, and long-term value creation.

This paper examines the impact of Exchange-Traded Fund (ETF) ownership on corporate earnings management. We find that ETF ownership is associated with a significant reduction in earnings management, and this result remains robust across a wide range of endogeneity tests and robustness checks. Further analyses reveal that ETFs exert a pronounced mitigating effect on sales manipulation, production manipulation, and expense manipulation. Mechanism tests indicate that ETFs curb earnings management by improving stock liquidity and strengthening external monitoring. We also find that the influence of ETFs is stronger in private firms, in firms with lower information transparency, and in firms with CEO duality, suggesting that ETFs serve as a more prominent external governance force when internal governance mechanisms are relatively weak. Overall, this study enriches the literature on the economic consequences of ETFs and provides new empirical evidence that financial innovation in emerging markets can help alleviate the information risk faced by investors.

This study develops a novel deep learning framework for bank distress prediction, designed to overcome the limitations of static network analysis and to enhance model interpretability. We propose a Spatio-Temporal Attention Network that uniquely captures the time-varying nature of systemic risk. Methodologically, it introduces two key innovations: (1) a dynamic interbank network whose connection weights are adjusted by the volatility of the Shanghai Interbank Offered Rate (SHIBOR), reflecting real-time market liquidity changes; and (2) a dual spatio-temporal attention mechanism that identifies critical time steps and pivotal contagion pathways leading to a distress event. Empirical results demonstrate that the model significantly outperforms traditional benchmarks across key metrics including accuracy and F1-score. Most critically, the architecture proves exceptionally effective at reducing Type II errors, substantially minimizing the failure to identify at-risk banks. The model also offers high interpretability, with attention weights visualizing intuitive risk evolution patterns. We conclude that incorporating dynamic, liquidity-adjusted networks is crucial for superior predictive performance in systemic risk modeling.

Purpose The study investigates the impact of government intervention policy of capital markets (“National Team”) on firms’ sustainable management, i.e., green mergers and acquisitions (GMAs) in China, aiming to understand how such interventions influence corporate investment activities amidst a growing focus on green transition. Design/methodology/approach The research employs a dynamic analysis of quarterly data from Chinese companies (2014 Q1 to 2022 Q4), utilizing identified strategies, such as double machine learning-DID and multiple panel data regressions to assess the effects of government intervention on GMAs, and examines potential economic channels like liquidity, market stabilization, and informativeness. Findings The study finds that increased government intervention via direct stock purchases significantly boosts both the number and amount of GMAs, with economic significance of 23% and 45%, respectively. It identifies liquidity, market stability, and informativeness efficiency as underlying economic channels for this effect. Practical implications The findings suggest that government interventions can enhance corporate investment in green sectors, guiding firms to align strategies with sustainability goals. This can inform policymakers regarding the effectiveness of direct stock purchases in fostering a green economy, especially for large emerging countries. Social implications By promoting GMAs, government interventions contribute to green innovation and energy transition, ultimately benefiting society through enhanced environmental sustainability and compliance with eco-friendly regulations. Originality/value This research uniquely documents the direct effects of government stock purchases on corporate green financial activities, particularly GMAs, in a Chinese context characterized by tight credit, thereby expanding the understanding of government intervention in emerging markets.

This study employs a Triple-Difference (Triple-DID) model, utilizing balanced panel data at the district level from Beijing, Shanghai, Shenzhen, and Hangzhou between 2018 and 2024, to critically evaluate the effectiveness of the Multi-School Zoning Policy (MSZP) in suppressing the capitalization of educational resources into housing prices and promoting educational equity. The research explicitly accounts for spatial and institutional heterogeneity as well as household strategic behavior.The results indicate that: (1) MSZP significantly reduced the average housing price premium associated with elite school districts by 15.2%, with the strongest effect observed in Beijing and the weakest in Hangzhou; (2) The policy's effectiveness diminishes as the spatial concentration of high-quality educational resources increases, highlighting persistent structural inequalities; (3) In areas characterized by resource monopolization and strong institutional inertia, the policy's suppressive effect on educational capitalization and its gains in educational equity are both constrained.The findings suggest that MSZP alone cannot fully overcome the "spatial lock-in" effect of high-quality educational resources. Achieving lasting equity requires complementary deeper institutional innovations, such as robust cross-district teacher rotation, transparent resource allocation mechanisms, and adaptive zoning algorithms. This research offers quantitative evidence for optimizing policy and institutional tools in the pursuit of comprehensive urban education reform.

This study investigates the impact of Total Factor Productivity(TFP) growth in China's high-tech industry on household consumption, examining the distinct roles of labor and capital factor productivity from the perspective of biased technological progress. We innovatively construct a sequential proportional NDDF-Luenberger index. This index not only provides a theoretically consistent measure of TFP but also enables its precise decomposition into labor factor productivity and capital factor productivity, allowing for the quantitative identification of the degree and direction of technological bias. Our analysis yields three key findings. First, China's high-tech industry TFP evolved through a three-phase pattern of "surge–retreat–recovery," characterized by persistent capital-biased technological progress. Second, at the national level, improvements in overall TFP, labor factor productivity, and capital factor productivity all significantly promote household consumption, validating the theoretical pathway where supply-side efficiency gains stimulate demand. Third, significant regional heterogeneity exists: the Eastern region exhibits a "capital-led" growth pattern with weaker consumption effects from labor productivity; the Central and Western regions show "factor synergy," where both productivities contribute to consumption; whereas the Northeastern region suffers from a blocked transmission mechanism, where technological progress fails to significantly boost local consumption due to insufficient integration with the regional economy. By integrating supply-side TFP with demand-side consumption through the lens of biased technological progress, this research provides critical insights for fostering a virtuous cycle between innovation and domestic demand, offering valuable implications for industrial and regional policy design aimed at sustainable and inclusive growth.

This article examines the rapid growth of Real-World Asset (RWA) tokenisation in Asia, focusing on Hong Kong as an emerging regional hub. It analyses three sectoral case studies in renewable energy, real estate, and financial instruments to illustrate the practical applications, market implications, and regulatory challenges of RWA projects. As of September 2025, the global RWA market reached an estimated value of $30.91 billion and is projected to grow into a trillion-dollar market within the next decade. The article highlights Asia’s proactive regulatory initiatives aimed at developing clear tokenisation standards and promoting the sustainable and responsible growth of the virtual asset sector. Supported by regulatory sandboxes and institutional participation in leading financial centres such as Hong Kong and Singapore, the region has become a focal point of innovation in asset tokenisation. Following the introduction, Section 2 reviews the latest developments in RWA as a fast-emerging area of financial and legal practice. Section 3 presents three case studies, while Section 4 provides practical guidance for asset owners and investors. Section 5 discusses key regulatory models and the overseas expansion of Chinese enterprises through digital assets tokenisation, and Section 6 concludes with implications for regulators, investors, and policymakers.

Innovation is crucial for long-term corporate value and competitive advantage; however, it can misalign the interests of managers and investors. Balancing managers’ short- and long-term goals is a pivotal challenge in promoting innovation incentives. Therefore, this study examines innovative incentives for managers of publicly traded firms to address the issue of agency problems. The study focuses on economic value-added (EVA) reform implemented by China’s State-Owned Assets Supervision and Administration Commission (SASAC), which encourages EVA-driven R&D investments as the primary management metric. The policy effectively motivates key corporate managers by reducing capital costs and stimulating increased innovation. Following this policy’s implementation, notable innovation disparities exist between state-owned enterprises and firms not subject to the reform. Furthermore, innovation incentives significantly affect overconfident company managers, yielding positive effects on innovation.