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.

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 paper examines how profits from mutual funds’ participation in initial public offerings (IPOs) shape fund performance, investor flows, and market stability in China. Using comprehensive fund–IPO matched data from 2016 to 2023, we decompose fund returns into an IPO-lottery component and residual performance. At the aggregate level, IPO allocations add 2.05% to annualized excess returns; net of IPOs, excess return is −0.35% per year. At the individual level, the contribution of IPO profits varies substantially across funds and is most pronounced among mid-sized funds, inflating perceived managerial skill. Funds with higher IPO-driven gains attract greater inflows despite the absence of performance persistence, leading to capital misallocation. At the market level, IPO-profit-induced trading (PIT) predicts short horizon price run-ups that dissipate and reverse over subsequent months, while raising both total and idiosyncratic volatility. Overall, IPO profits temporarily enhance reported performance but erode market stability by propagating non-fundamental shocks through secondary markets.

This paper analyzes the impact of risk disclosures in bond prospectuses on the credit risk premium in the Chinese corporate bond market through six textual characteristics comprehensively. In the empirical analysis, the collected 5199 bond prospectuses and structured data concerning control variables from 2006 to 2021 are used to perform the fixed effect regression analysis. The results show that fewer Words, less Boilerplate, higher Fog Index, more HardInfoMix, more Redundancy, and higher Specificity of risk disclosures in bond prospectuses will lead to a higher credit risk premium. Further tests demonstrate that ceteris paribus, the negative impact of Words and Boilerplate will be strengthened by implicit government guarantees carried by a state-owned enterprise but be weakened by better corporate business performance. However, ceteris paribus, positive effects of the Fog Index, HardInfoMix, Redundancy, and Specificity will be weakened when the bond issuer is state-owned but be strengthened by better corporate business performance.

We investigate the relationship between the intensity of intraday return reversals and future stock returns in the Chinese stock market. We find that a high frequency of positive overnight returns followed by negative daytime returns predicts one-month ahead returns positively. The analysis shows that daytime retail investors tend to overly sell their own rising stocks at market open, accepting lower stock prices in exchange for liquidity. As the price pressure attenuates, these stocks experience subsequent price increases, implying a positive relationship between return reversals and future returns.

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 paper investigates whether and how managers of highly polluting firms adjust their earnings forecast behaviors in response to the introduction of environmental legal institutions. Using the establishment of environmental courts in China as a quasi-natural experiment, our triple difference-in-differences (DID) estimation shows that environmental courts significantly increase the likelihood of management earnings forecasts for highly polluting firms compared to non-highly polluting firms. This association becomes more pronounced for firms with stronger monitoring power, higher environmental litigation risk, and greater earnings uncertainty. Additionally, we show that highly polluting firms improve the precision and accuracy of earnings forecasts following the establishment of environmental courts. Furthermore, we provide evidence that our results do not support the opportunistic perspective that managers strategically issue more positive earnings forecasts to inflate stakeholders‘ expectations subsequent to the implementation of environmental courts. Overall, our research indicates that environmental legal institutions make firms with greater environmental concerns to provide more forward-looking information, thereby alleviating stakeholders’ apprehensions regarding future profitability prospects.

The influence of informal institutions such as history and culture on corporate behavior has been widely recognized, but few studies have been analyzed from the perspective of the ruling party culture. Based on the data of the old revolutionary base areas (ORBA) in China, this paper makes an empirical test on the role of Red Culture in promoting enterprises green innovation. First, this paper finds that the stronger the Red Culture in the region where the enterprise is located, the higher the level of green innovation.Secondly, in the samples with high political sensitivity and less cultural conflict, the promoting effect of Red Culture is more obvious. This paper not only expands the relevant literature on the influence of informal system on enterprise green innovation, but also enriches the research on the influence of Chinese unique culture on enterprise management decision-making.