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.

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This research examines a naive rolling Mean-Variance Optimization (MVO) strategy for multi-stock allocation, in which expected returns and covariances are estimated solely from trailing historical data and re-estimated on a monthly basis. The central aim is to test empirically whether this deliberately simple approach can remain competitive despite the classical concern that MVO's sensitivity to estimation error makes it impractical relative to simpler heuristics such as trend following and momentum ranking. Expected returns and covariance are re-estimated monthly from a common trailing window, and a long-only, max-Sharpe portfolio is held until the next rebalance - deliberately simple, with no trend filter, momentum ranking, or volatility-targeting overlay. It is benchmarked against SPY buy-and-hold, a 60/40 portfolio, QQQ buy-and-hold, and the original design (a trend-filtered, risk-adjusted momentum strategy with volatility targeting and drawdown control). The strategy is evaluated over two independent, non-overlapping periods rather than a single blended sample: an in-sample window (2010-2019) used to fix the design, and a strict out-of-sample holdout (2020-2025) touched only once. On real market data for a nine-stock universe, the naive MVO strategy outperformed all four benchmarks on annual return and Sharpe ratio in both the in-sample (29.11% return, 1.19 Sharpe) and out-of-sample (36.53%, 1.16) periods - though not on maximum drawdown, where the momentum benchmark risk overlay produced a shallower worst-case decline in both periods, highlighting a trade-off between simplicity and downside protection that practitioners should weigh explicitly.

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There is synergy space for dual subsidy policies, but precise threshold management is required to improve the comprehensive benefits of the entire supply chain. Both government promotion subsidies and platform tariff subsidies are regarded as key means to promote the development of cross-border e-commerce supply chains, yet the synergy mechanism of the two subsidies remains unclear. Against the background of intensifying tariff trade frictions, this paper constructs a game model with the participation of the government, cross-border e-commerce platforms, settled merchants and overseas consumers, and compares and analyzes supply chain pricing decisions under four scenarios: no subsidy, platform tariff subsidy only, government subsidy only, and coexistence of dual subsidies. The results show that a single subsidy policy cannot fully offset the negative impact of rising tariffs. Meanwhile, excessive platform subsidies will squeeze their promotion budgets and may induce merchants to raise prices. Although the linkage of dual subsidies can slow down the decline of demand under high tariff conditions, its effect is restricted by multiple factors such as subsidy intensity and tariff level.

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Nowadays, with the development of the Internet economy, virtual communities have become important spaces for consumers to obtain information, communicating with others and making purchase decisions. Key opinion leaders (KOLs), as central actors in these communities, influence consumers not only through direct recommendations but also by shaping the community atmosphere. This study uses thematic synthesis and content analysis to review literature on virtual communities, KOL types, community atmosphere and consumer purchase intention. Based on the different roles of KOLs, this study divides them into three types: cognitive-expert, emotional-resonance and structural-hub KOLs. Cognitive-expert KOLs provide professional information and help form a supportive atmosphere. Emotional-resonance KOLs build emotional connections and strengthen members' sense of belonging. Structural-hub KOLs connect different groups and promote an open community environment. These findings indicate that community atmospheres affect perceived value, trust, emotional identification and purchase intention of consumer. Therefore, the influence of KOLs in virtual communities follows both a direct path and an indirect path through community atmosphere. This study develops a framework of KOL typology, community atmosphere, consumer behavior and suggests that brands should select KOLs according to marketing objectives while platform managers should maintain community trust and prevent excessive commercialization during their daily operations.

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