From ancient bronze coinage to modern blockchain-based tokens, the form of money has undergone a clearly identifiable process of evolution. Yet its fundamental dilemma has remained unchanged: how can a symbol devoid of intrinsic value—the "name"—be made to assume the function of measuring real social wealth—the "substance"? At first glance, the "treasure currency system" introduced by Wang Mang in the late Western Han dynasty appears entirely unrelated to contemporary stablecoins. In essence, however, both confront the same predicament—the separation of name and substance. This study therefore presents a perceptive and logically rigorous comparison of their respective credit mechanisms. Wang Mang's monetary reform ultimately collapsed under the issuance of "nominal values" driven by state power, whereas the central challenge for stablecoins lies in how to anchor their nominal value to credible "substance" within a decentralized framework. From this perspective, the paper naturally arrives at the conclusion that only when name and substance are unified through a credible credit anchor can monetary stability be achieved. The historical experience thus provides a valuable point of reference not only for the study of ancient monetary reforms, but also for contemporary discussions on the regulation of digital currencies, the internationalization of the Renminbi, and the ongoing process of monetary digitalization.

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Digital platforms and business analytics are fundamentally reshaping how organizations acquire, distribute, interpret, and retain knowledge; however, few studies have systematically examined how digital platform capabilities map onto specific dimensions of organizational learning. This paper addresses this gap through a conceptual literature synthesis that integrates scholarship on digital platforms, business analytics, and organizational learning theory. Drawing on Huber's four-process model of organizational learning—knowledge acquisition, information distribution, information interpretation, and organizational memory—as well as March's exploration–exploitation framework, this study develops an integrative conceptual framework comprising four propositions linking digital platform and analytics capabilities to each learning dimension. The framework identifies reinforcing feedback loops through which enhanced learning drives deeper analytics adoption and digital platform utilization. Theoretical implications for updating organizational learning theory in the digital age are discussed, alongside practical implications for financial institutions pursuing digital transformation. Future research directions include empirical validation through firm-level surveys, industry-specific case studies, and longitudinal investigations of digital learning evolution.

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This paper employs a simple linear regression model, based on provincial panel data from the National Bureau of Statistics of China spanning 2008 to 2024, to explore the impact of the real estate economy on regional employment development, and conducts a robustness check using the added value of the real estate sector, followed by a heterogeneity analysis from the perspectives of time, region, and industry. The study arrives at the following findings: 1. The real estate economy is positively correlated with regional employment. 2. Prior to 2018, when real estate regulatory policies were unprecedentedly stringent, the real estate sector exerted a stronger driving force on employment development. 3. The real estate economy drives employment development in the eastern, central, and western regions, with the strongest effect observed in the east and the weakest in the central and western regions. Conversely, it acts as a hindrance to employment in the northeastern region. 4. Regarding industrial sectors, the promotion effect is most significant in the services sector, followed by the industrial sector, whereas it exerts a negative and obstructive impact on the agricultural sector.

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This paper examines the viability of high-frequency pairs trading in China's A-share market using minute-level data across three GICS sectors. Applying a Bollinger-band framework to 4,500 cointegrated stock pairs, we document consistent—though moderate—gross profitability. We then conduct trade-level regressions to isolate drivers of profitability and identify three key determinants: spread volatility, market-cap disparity within pairs, and multi-frequency return correlations. Further diagnostics show that execution latency significantly erodes returns, convergence reliability closely aligns with estimated half-lives, and exit effectiveness varies asymmetrically across long and short trades. Sector-level results reveal that Consumer Discretionary delivers the strongest risk-adjusted performance, while Retailing features the fastest mean-reversion and highest trade frequency. Robustness checks—including random-pair bootstraps, threshold sensitivity tests, and stress-period performance during the COVID-19 shock—confirm that profitability is not driven by data-snooping or microstructure artifacts. Overall, the findings provide new evidence that high-frequency statistical arbitrage remains feasible in an emerging market setting, while highlighting the critical roles of execution speed, volatility conditions, and behavioral inattention in shaping trade-level outcomes.

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