China's SMIC and Taiwan's TSMC are caught on opposite sides of the "Global Chip War." TSMC, despite having extensive commercial ties and fabs in the Mainland, is a beneficiary of U.S. efforts to stifle competition from Mainland competitors like SMIC. Geopolitical considerations, therefore, are increasingly influencing TSMC’s business decisions, as shown by TSMC’s construction of fabs in Japan and the United States despite founder Morris Chang’s longstanding opposition to overseas fabs due to their high costs. SMIC, meanwhile, is the Mainland’s best hope for creating a “red chip supply chain” and achieving 70% semiconductor self-sufficiency via domestic suppliers, which has taken on even more importance due to U.S. sanctions on advanced chips for AI model development. This article analyzes SMIC founder Richard Chang’s dream of building a red chip giant on the Mainland that can rival or even replace TSMC, which will directly conflict with Chang's former co-worker and fellow Taiwanese Morris Chang’s dream of solidifying TSMC and Taiwan’s position as the irreplaceable center of the semiconductor industry well into the 21st century.

China's tech companies are making waves with their recent achievements, including a "trifold" phone from Huawei and the revolutionary AI reasoning model from DeepSeek. Much discussion has centered on the founders of these companies and their ability to gain an edge on American rivals. But what is less appreciated or understood among foreign analysts of China’s tech giants is the role that innovation and transformation in corporate governance and organizational structure has played in these companies’ successes. Moreover, there are unique aspects of these companies from a corporate governance perspective that are not commonly seen in tech companies in other parts of the world or even within China itself. For instance, Huawei is 99% employee owned, while Alibaba is primarily governed by an "Alibaba Partnership." These unique corporate structures have arisen due to several factors, including the rapid changes to China’s regulatory landscape over the past three decades, distinct characteristics of Chinese business culture, geopolitical tensions and preoccupations with national security, and the “socialism with Chinese characteristics” model. In this article I overview some of the more distinctive corporate governance mechanisms of four Chinese tech companies: Huawei, Alibaba, Bytedance, and DeepSeek, and explain why these structures were adopted.

We combine a new collection of microdata from China with a natural policy experiment to investigate the extent to which reductions in rural-urban migration barriers affect flows of trade and investments between cities and the countryside. We find that increases in worker eligibility for urban residence registration (Hukou) across origin-destination pairs increase rural-urban exports, imports, capital inflows and outflows, both in terms of bilateral transaction values and the number of unique buyer-seller matches. To quantify the implications at the regional level, we interpret these estimates through the lens of a spatial equilibrium model in which migrants can reduce buyer- seller matching frictions. We find that a 10% increase in a rural county’s migration market access on average leads to a 1.5% increase in the county’s trade market access and a 2% increase in investment market access. In the context of China’s recent Hukou reforms, we find that these knock-on effects on market integration were on average larger among the urban destinations compared to the rural origins, reinforcing incentives for rural-urban migration.

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.

The U.S. Treasury yields reached a 20-year high under acute inflation pressure in the post-pandemic era amid aggravated geopolitical conflicts. To quantify the underlying effects of regional geopolitical risks (GPRs) of key U.S. strategic interests, we employ an extended affine term structure model with unspanned GPRs and conventional macroeconomic drivers. We find that GPR shocks, particularly those manifesting U.S.-China rivalry, contribute more to expectations and variations of inflation and yields than shocks to U.S. macroeconomic variables. The results warn on the adequacy of monetary policy in curbing inflation in a fragmented global order with escalating GPRs.

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.

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.

This study introduces an innovative approach for constructing multimodal investor sentiment indices and explores their varying impacts on stock market returns. We employ the RoBERTa model to quantify text-based sentiment, the Google Inception(v3) model for image-based sentiment measurement, and a multimodal semantic correlation fusion model to comprehensively consider the interplay between textual and visual sentiment features. These sentiment indices are further categorised into industry-specific investor sentiment and market-wide investor sentiment, enabling separate analyses of their effects on stock markets. Furthermore, we leverage these indices to build a multifactor stock selection model and timing strategies. Our research findings demonstrate that multimodal sentiment analysis yields superior predictive accuracy. Industry-specific investor sentiment exerts bidirectional positive influences on stock market returns, whereas market-wide investor sentiment indices exhibit unidirectional impacts. Integrating industry-specific investor sentiment into our multifactor stock selection model effectively enhances portfolio returns. Furthermore, combining market-wide investor sentiment with timing strategy optimisation further augments this advantage.

We introduce a hypergraph-based approach to analyze information flow and investor attention transfers through news outlets in financial markets. Extending traditional graph models that focus on pairwise interactions, our hypergraph framework captures higher order relationships between firms that are simultaneously mentioned in the same news article. We develop a random walk based centrality framework that considers both the properties of the hyperedges (news articles) and the nodes (firms). This framework allows us to more accurately simulate investor attention flows and to incorporate different theories of investor behavior, such as category learning and investor attention theory. To demonstrate the effectiveness of our attention centrality, we apply it to the Chinese CSI500 market index from 2016 to 2021, where our centrality measures improve the prediction of future returns, with improvements ranging from 6.3% to 14.0% compared to traditional graph-based models. This improvement implies that our centrality measure can better capture investor attention transfers on the news hypergraph. In particular, we find that investors pay more attention to news that covers both a greater number of firms and firms on which the sentiments are more negative. Although we focus on financial markets in this research, our hypergraph framework holds potential for broader applications in information systems — for example, in understanding social or collaboration networks.

This study investigates the impact of early leadership experiences on innovation performance in small and medium-sized enterprises (SMEs) in China. Using Enterprise Survey for Innovation and Entrepreneurship in China (ESIEC) cross-sectional datasets, it examines the mediating role of psychological traits and the moderating effect of age in this relationship. The analysis employs fixed effects models to control for regional and industry-specific unobserved characteristics. Results indicate a significant positive relationship between early leadership experiences and innovation, with psychological traits mediating this relationship strongly in younger entrepreneurs. For older entrepreneurs, early leadership has a more direct and stronger impact on innovation. These findings underscore the importance of early leadership development in education phase and suggest that the influence and pathways evolve with age, offering particular insights into the formation and application of social and human capital in the entrepreneurial journey