The China-specific three-factor model has dominated the explanation of cross-sectional stock returns in the Chinese A-share market since the work of Liu et al. However, their evidence is limited to the sample period of 1995–2014, while the Chinese equity market has undergone substantial changes. Whether the original size and value premiums persist and whether the three-factor model retains its explanatory power in the post-2014 remains an open empirical question. This study replicates and extends the Liu–Stambaugh–Yuan three-factor framework using updated A-share data spanning 25 years, from January 2000 to December 2024. Strictly following the original data-cleaning rules, portfolio sorting procedures, and factor construction methodologies, this paper constructs the market factor, size factor, and value factor. Based on full-sample time-series regressions, the replicated model achieves an average R² of 0.6365, which is notably higher than the 0.55 reported in the original study, indicating strengthened explanatory power. The sub-sample analysis further confirms that the size premium is largely driven by micro-cap stocks in the bottom 30% market capitalization segment, whereas the value premium remains robust across all firm sizes. This paper verifies the long-term validity and reliability of the Chinese three-factor model. The results reveal that size and value anomalies continue to persist in China's maturing stock market, providing new evidence for asset pricing research and quantitative investment applications in the Chinese market.

This article explores how subjective confidence and objective knowledge relate to investment decision behavior. The data are the investor sub-sample of the 2024 U.S. National Financial Capability Study (N=2861). Subjective knowledge uses a seven-point self-assessment, objective knowledge a twelve-item test; they correlate at only 0.376, and 29.4% of investors are in a high-self-assessment, low-ability state. Controlling objective knowledge, each standard deviation of self-assessment is associated with high-frequency trading 7.87 percentage points higher, complex product holding 11.04 higher and social-media-driven decisions 9.96 higher. Objective knowledge varies by outcome: positive for high-frequency trading (5.84 points), not significant for complex products, negative for social-media-driven decisions and fraud susceptibility. Those who participated in financial education score 0.320 standard deviations higher on self-assessment but only 0.116 higher on objective knowledge, so their calibration gap is larger. The data are cross-sectional; conclusions are conditional correlations, not causal effects.

Against the dual backdrop of unprecedented global changes in a century and the domestic effort to build a new development paradigm, the international landscape is undergoing profound adjustments. China is gradually transforming from a passive recipient of globalization to an active shaper and leader of the globalization landscape. The Hainan Free Trade Port (hereinafter referred to as "Hainan FTP"), as a landmark project in China's expansion of high-standard opening-up in the new era, holds significant policy value and academic importance for exploring its development path. Grounded in the comprehensive development model of Hainan FTP, this study constructs a multi-dimensional collaborative analytical framework of "opening-up—endogenous development". By comprehensively employing policy text analysis, statistical data description, and case study methods, it systematically analyzes the current situation and bottlenecks of Hainan FTP across four dimensions of opening-up: overseas project cooperation, tariff policy innovation, healthcare industry cultivation, and private equity industry development. While the policy dividends of Hainan FTP have been initially realized, it faces multiple challenges, including insufficient international recognition, underutilized policy effectiveness, imbalanced industrial structure, a shortage of high-end talent, and a marginally optimized business environment.

China's central government policies have not evolved at a uniform pace in the transition from e-government to digital government. Instead, gradual policy accumulation has been accompanied by periods of concentrated adjustment. Drawing on 61 central-level policy documents, this study combines content analysis, annual change measurement, structural distance analysis, Bayesian Information Criterion (BIC) model comparison, and policy sequence analysis to examine how accumulated governance pressures are translated into policy change at particular moments. The findings identify two periods of pronounced policy change: 2015–2018 and 2022–2025. During the first period, policy priorities shifted from departmental informatization toward interdepartmental coordination, government service integration, and business process reengineering. During the second period, digital government policy expanded to encompass data-related responsibilities, data development and utilization, security safeguards, and organizational coordination, marking a further transition from service delivery to holistic governance. These changes did not constitute abrupt replacements of existing policies. Rather, they reflected the accelerated release of long-standing governance pressures under high-level political attention and agenda authorization. Policy image reconstruction, institutional venue adjustment, and the institutionalization of policy tool mixes jointly promoted policy reconfiguration, while departmental fragmentation and cross-level implementation frictions persisted. This study conceptualizes this pattern as "stage-based concentrated reconfiguration," revealing the coexistence of gradual accumulation and concentrated adjustment within a centralized policy system. The concept extends punctuated equilibrium theory by improving its capacity to explain policy changes that are substantial but not fully discrete. The findings also suggest that sustainable digital government reform requires not only high-level political support but also stable coordination mechanisms, clearly defined responsibilities, and continuous policy instruments capable of converting temporary policy mobilization into lasting governance capacity.

China passed a demographic milestone in 2025: 323.38 million people, or 23 percent of the population, were aged 60 or over. Almost all of them will grow old at home rather than in institutions, which makes the physical condition of ordinary apartment buildings an economic question and not only a welfare question. Millions of older residents live in walk-up blocks built before 2000 that have no elevator, and a flight of stairs is enough to keep a person with weak knees indoors for weeks. This paper argues that retrofitting elevators should be understood as consumption infrastructure for the silver economy. It sets out a threshold model in which a trip is taken only when its value exceeds ordinary costs plus stair-climbing costs, and shows that the model predicts the pattern found in the data: benefits concentrate on upper floors and on frailer residents. Using official statistics and published studies of Guangzhou and Hangzhou, the paper replicates the lifetime cost and benefit calculation for one retrofitted unit block and finds a break-even price premium of roughly 1.8 percent against an estimated premium of 5.53 percent. A further calculation shows that the cost-sharing ladder used in practice is only loosely aligned with those gains, leaving two of the four paying floors close to break-even.

In the U.S. restaurant industry, tipped workers receive a large share of their earnings from customers rather than from their employers, and tip credit provisions allow restaurants to pay cash wages below the standard minimum wage. This paper examines why this compensation structure persists and how raising the tipped minimum wage affects wages, employment, and firm behavior. Based on the literature on modern monopsony power and existing evidence on minimum wage policies, labor market concentration, and tipping, this paper develops a search-friction framework in which job-switching costs give individual restaurants wage-setting power even in markets with many employers. The results indicate that restaurants' wage-setting power enables them to maintain wages below competitive levels, implying that a moderate increase in the tipped minimum wage can improve worker earnings and reduce turnover without generating a proportionate loss in employment. However, restaurants respond through multiple channels, including raising menu prices, adjusting service charges and compensation structures, slowing hiring, reducing working hours, and, for low-margin firms, exiting the market. The effects of tipped minimum wage policies therefore rely on the magnitude of the wage increase, restaurant characteristics, and local labor market conditions.

With the rapid expansion of China's micro-drama market, brand integration has become a key marketing channel. This development has produced two distinct approaches to value creation: Hanshu's high-cost production and Proya's distributed placement. At the same time, generative AI tools such as ByteDance's Seedance 2.0 are reshaping marketing agencies' production capabilities. Thus, this study examines why lightweight soft placement creates value and how AI tools reshape marketing intermediaries' capabilities. Specifically, it uses a qualitative comparative case study based on industry reports from iResearch Center, Miaozhen Systems, and ThePaper, public campaign indicators, and Chinese industry media reports. On this basis, Hanshu and Proya are selected as comparative cases with similar market positioning but different brand integration strategies. The comparison indicates that lightweight placement is not a lower-value version of custom production. Its strength lies in risk distribution, iterative learning, and market reach. Moreover, the VRIO analysis shows that AI tool access alone provides limited advantage, while integrated workflows and client networks create more durable value.

The intangible, instantly delivered, and continuously consumed nature of AI compute services exposes three inherent frictions in fiat-based settlement: temporal mismatches, coarse pricing granularity, and cross-border delays. This study investigates whether AI-Token—the atomic unit of model inference and training—can serve as a more efficient medium of exchange than fiat currency in B2B AI transactions, and how such a shift affects firm performance. Integrating transaction cost economics, agency theory, and corporate finance, it constructs a three-layer framework that traces how Token's roles as a means of payment, cost item, and asset class generate distinct causal pathways to revenue-side, cost-side, and asset-side performance. Employing theoretical synthesis and deductive analysis, this paper provides a unified conceptual architecture for future empirical research on programmable settlement media in the digital economy.

In the field of investment decision-making and risk management, maximum drawdown is a key measure of downside risk. Time-domain variables such as returns, volatility, historical drawdowns, trading volume, and turnover are relied on by existing studies, but frequency-domain structures receive less attention. This study aims to examine, for the CSI 1000 Index, whether frequency-domain features can improve machine learning forecasts of future maximum drawdown, and the data is from Eastmoney. Based on the conventional risk characteristics, three Fourier indicators are constructed, namely Frequency Energy Shift (FES), Spectral Entropy (SE) and Stock Market Spectral Synchronization (SSC). The samples are divided into training sets, validation sets, and test sets according to time. The model used for prediction is LightGBM. In drawdown observations, MAE, RMSE and Tail MAE are used to evaluate the overall prediction accuracy and performance. The baseline model only uses conventional features, and the full model adds these three frequency-domain indicators. Finally, the results of the two models are compared. Compared with the baseline model, the MAE of the full model has decreased by 12.67%, RMSE by 11.56%, and Tail MAE by 14.71%. The results show that the frequency-domain features can improve the overall prediction accuracy and provide some information not captured by traditional risk variables. This research framework provides a perspective for the prediction of future maximum drawdown and is also helpful for monitoring the downside risk of the stock market.

The relationship between ESG performance and investment portfolio performance has become a focal point of academic debate. This review systematically examines studies from 2009 to 2024 indexed in Web of Science, Scopus, and CNKI databases, organizing them around three interrelated questions: does ESG integration help or hurt returns, by what mechanisms does it operate, and why do empirical findings differ so sharply across studies? Three broad positions emerge. The positive school argues that ESG leadership builds intangible capital—stakeholder trust, and reputational resilience—that conventional financial metrics tend to miss. The negative school contends that ESG constraints narrow the investment universe and suppress expected returns. A third, more skeptical position holds that the divergence in findings is largely an artifact of inconsistent rating methodologies and sample choices rather than evidence for or against ESG per se. Empirical data from MSCI indices over 2016–2025 show ESG-screened portfolios performing on par with conventional benchmarks, with compound annual growth rates of 12.17% for both. The review finds that ESG integration is most reliably associated with reduced tail-risk exposure and lower financing costs, rather than with systematic outperformance. Whether it improves risk-adjusted returns depends heavily on which rating agency's scores are used, the market in question, and how the portfolio is constructed.