Chinese convertible bonds are examined in two settings: portfolio construction and relative-value trading. At the portfolio level, a convertible bond index is compared with the HS300 equity index and a broad bond index over 2005–2024 using return, volatility, Sharpe ratio, maximum drawdown, illustrative allocations, and mean–variance frontiers. The convertible bond index earns a return close to equities with lower volatility and a smaller drawdown, while allocations containing convertibles record higher Sharpe ratios than the stock–bond benchmark. At the trading level, mispricing is defined as the gap between GARCH volatility estimated from the underlying stock and the implied volatility embedded in the bond price. A positive gap indicates that the conversion option is priced at a lower volatility than the stock estimate. Following a static delta-hedged Feikai CB example, fixed market-wide thresholds are compared with bond-specific expanding-window percentile rules based only on information available at each date. With a P50 exit, clean-convergence trades increase from 945 to 1,415 and the timeout share falls from 25.0% to 9.4%. The contrast between CITIC and Wencan shows that spread dispersion, rather than spread level alone, is important for determining whether apparent mispricing can be traded.

This paper asks whether the structural consequences of High-Speed Rail (HSR) expansion arise from a city's first connection to the network or from subsequent network densification. This paper matches a hand-coded database of passenger-rail line openings to a panel of Chinese prefecture-level cities from 2008 to 2019 and exploits within-city changes in access in a two-way fixed effects framework. First HSR access increases the tertiary-sector share of Gross Domestic Product (GDP) by 0.925 percentage points and reduces the secondary-sector share by 1.078 percentage points. The reallocation is most pronounced in western cities. By contrast, once initial access is accounted for, additional lines and hub status yield statistically imprecise average effects. A pretreatment placebo and a 2014-cohort difference-in-differences design corroborate the timing and direction of the baseline estimates. These findings identify network entry, rather than subsequent densification, as the principal margin through which HSR expansion reshapes urban industrial structure. They imply that the structural returns to extending network coverage can differ materially from those of adding capacity and connectivity within already-served cities.

Cross-border live streaming e-commerce has emerged as a rapidly growing channel for international trade, yet the role of cultural differences in shaping consumer purchase decisions within this context has received limited empirical attention. This study investigates how cultural differences influence consumer purchase decisions in cross-border live streaming environments, grounded in Hofstede's cultural dimensions theory and the stimulus-organism-response framework. A conceptual model is developed to examine the mechanisms through which cultural dimensions—including individualism-collectivism, uncertainty avoidance, and power distance—affect consumer trust in streamers, perceived product authenticity, and, ultimately, purchase decisions. The analysis reveals that cultural differences moderate the effectiveness of streamer communication styles, the formation of consumer trust, and the weight of social influence in purchase decisions. Specifically, consumers from collectivist cultures respond more strongly to streamer-consumer interaction and community cues, while those from high uncertainty avoidance cultures place greater emphasis on product certification and detailed demonstrations. The findings contribute theoretically to the cross-cultural e-commerce literature and offer practical implications for cross-border live streaming practitioners seeking to tailor their strategies to diverse cultural markets.

Traditional comprehensive budget management suffers from multiple shortcomings such as the absence of strategic orientation, budget slack, and insufficient organizational coordination, making it difficult to adapt to complex and dynamic business environments. Digital technologies such as artificial intelligence and big data provide a technological path for the transformation of budget management. This paper systematically reviews the mechanisms and application scenarios of AI technologies-including time-series forecasting, anomaly detection, causal inference, reinforcement learning, and large language models-throughout the entire process of budget preparation, execution monitoring, analysis and adjustment, and assessment and review. The research shows that AI, relying on data-driven approaches, can achieve accurate forecasting, real-time risk identification, quantitative attribution, and dynamic resource optimization, effectively resolving the inherent pain points of traditional budget management. From the four dimensions of data, technology, organizational talent, and institutional risk control, this paper constructs a guarantee system for the implementation of AI budgeting, while also pointing out the practical obstacles currently faced by intelligent budget transformation, such as insufficient model interpretability, data silos, a shortage of compound financial talent, and imperfect supporting institutions. This paper enriches the theoretical system of intelligent budget management and provides implementation insights for enterprises to advance the digital-intelligent transformation of budgeting.

Agency conflict arising from information asymmetry remains a vital source of governance conflict in corporations, since the insiders usually have access to more relevant and up-to-date information than the external investors, creditors, regulators, and minority stakeholders. In this work, this study presents a new artificial intelligence-based governance intelligence solution that is capable of uncovering information asymmetry gaps, constructing governance relationship networks, and estimating the level of information transparency in each firm-year period. With Chinese A-shares firms from the time interval 2018 to 2024 as the research sample, our model draws its features from annual reports, internal control reports, board statements, regulatory inquiries, ownership structure, management compensation, analyst forecast, and stock trading. As for the methodology, the model utilizes DeBERTa-v3 to extract the governance disclosure information, Sentence-BERT to find out any inconsistency among disclosure variables, Neo4j and graph attention networks to construct the relationship-risk embedding, and LightGBM to classify firms with a higher risk of information asymmetry. From our empirical experiments, it can be concluded that the whole AI-based model yields AUC at 0.874 ± 0.018 and macro-F1 at 0.812 ± 0.021, which is an improvement of 0.116 compared to the conventional governance metrics in terms of AUC.

Background: Traditional foam-based aquaculture in Dongshan Bay, Fujian, caused severe white pollution and ocean eutrophication. This study examines the multidimensional value of the transition from traditional to ecological aquaculture in the Xiaxikeng area. Methods: Field interviews with fishermen, on-site inspections, statistical analysis, and literature review were conducted. Results: Replacement of ~400,000 EPS foam buoys with HDPE plastic buoys, removal of 41,407 mu of illegal aquaculture, and installation of 100,000 plastic cages achieved significant ecological restoration. The 10,600-mu shellfish-algae co-culture area yields an annual carbon sink of 16,772.41 t, CO2-equivalent (~¥946,100). Village collective income rose from ¥360,000 (2018) to ¥2.22 million (2024). Conclusions: The transition creates a positive feedback loop among ecological restoration, economic growth, and social governance, providing a replicable model for coastal China.

This paper investigates the impact of AI on the access of disabled job seekers to the labor market, using a mixed-method research design. Based on a survey of 91 disabled job seekers in China, this study carried out a systematic meta-review of correspondence experiments regarding disability employment and examined three Ordinary Least Squares (OLS) models. The results show that the ease of use of the platform is crucial for the successful job search. While the findings seem to indicate a negative association between the use of AI and successful job search, they suggest a selection effect: AI is more likely adopted by the most disadvantaged job seekers, who face more barriers. Nevertheless, the study argues that AI tools offer limited benefits because they are unable to tackle structural issues, including the inaccessibility of platforms, discrimination by employers, and stigma around welfare enterprises. Across the three models, platform ease of use is the only predictor significant in all three, and satisfaction with platform accessibility is significant in two of the three, while education and age are never significant (all p > 0.10). Although 78.02% of respondents report having used AI tools, 45.05% received no response to their applications and 40.66% identify inaccurate automated resume screening as a major obstacle. The paper concludes that stable accommodation subsidies, enforced platform accessibility standards, and fairness audits of screening algorithms are preconditions for AI to deliver genuine inclusion gains.

Cross-border supply chain finance supports the financing of Small and Medium-sized Enterprises (SMEs) participating in the global division of labor by integrating logistics, information flow, and capital flow in cross-border trade. However, its development faces prominent challenges, such as cross-border information asymmetry, exchange rate and policy risks, difficulties in credit transmission, and complex regulatory coordination. In recent years, the application of fintech and blockchain technologies has driven the evolution of cross-border supply chain finance from a traditional bank-led single financing model toward a platform-based, digital, and ecosystem-oriented direction. This paper adopts a systematic literature review method to systematically examine the research progress of cross-border supply chain finance from three dimensions: challenges—responses—model innovation. At the challenge level, it focuses on barriers to cross-border credit transmission, exchange rate and compliance risks, and the difficulty of coordinating logistics, capital and information flows. At the response level, it summarizes strategies such as fintech empowerment, blockchain-based credit penetration, and multi-agent collaborative governance. At the model innovation level, it synthesizes emerging models, including cross-border e-commerce platform-led financing, blockchain-driven digital credit, and cross-border asset securitization. The study finds that existing research still has deficiencies in risk pricing, technology integration, and cross-regional regulatory coordination for cross-border supply chain finance. This paper proposes a three-dimensional analytical framework of "cross-border credit—technology empowerment—ecosystem synergy" and points out that future research can be deepened in directions such as digital credit infrastructure, smart contract risk control, cross-border regtech, and scenario expansion in emerging markets.

This paper examines how financial econometric models can be used to identify financial statement fraud in listed companies, using Luckin Coffee Inc. as a case study. Luckin Coffee was selected because it is a well-known publicly listed company whose accounting scandal has been documented by regulatory authorities, while its rapid-growth business model provides a suitable setting for quantitative fraud-risk analysis. The study combines a qualitative analysis of the mechanisms underlying the fraud with a concise empirical assessment based on the Beneish M-score and accrual-based indicators. The results reveal several measurable warning signs in the company's reporting environment in 2019, including abnormal sales growth, a widening gap between reported revenue and operating cash flow, inflated costs and expenses used to support fictitious sales, and increased leverage pressure following aggressive external financing. Even when the unstable accounts receivable ratio is treated conservatively, the resulting M-score remains above commonly used thresholds for manipulation risk. The study argues that financial econometric models cannot replace audit evidence, but they can serve as an effective early-warning tool for investors, auditors, and regulators. The primary contribution of this study is to demonstrate how model-based screening can be integrated with governance analysis into a practical framework for detecting fraud in listed companies.

Live music events have become a significant part of the urban experience economy, yet empirical evidence on how concert attendance translates into local economic activity remains concentrated in Western settings and at the macro level. This study examines the spending behavior of concert, music festival, tour and Livehouse audiences in Chinese host cities, and asks whether such events generate economic activity beyond ticket revenue. A cross-sectional design was adopted, and a structured questionnaire of forty-one items was distributed through purposive sampling combined with snowball sampling, yielding forty-one valid responses. Local off-ticket spending, defined as the sum of intra-city transport, accommodation, catering, merchandise, sightseeing and other shopping expenditure, serves as the principal dependent variable, and is supplemented by total expenditure and estimated new local spending. Descriptive statistics, Pearson and Spearman correlations, and ordinary least squares regression with HC3 robust standard errors were applied. Respondents reported mean local off-ticket spending of CNY 2,710.70 against a median of CNY 1,100, confirming a pronounced right-skewed distribution. Length of stay showed the most consistent positive association with local spending in both the correlation and the regression analyses, whereas fan intensity, monthly disposable amount, non-local status and price sensitivity did not reach conventional levels of significance. The findings suggest that the duration of an audience member's stay, rather than travel distance or fan identity alone, is the more proximate mechanism through which performances stimulate local consumption. Given the small sample and the self-reported amounts, the results are interpreted as associational rather than causal.