This paper investigates the sales seasonality premium (Gustavo et al., 2020) in the Chinese stock market. We document a significant sales seasonality premium in the cross section of stocks listed on Chinese market. A long-short strategy of buying low-sales stocks and shorting high-sales season stocks generate a monthly return of 0.84% with a Newey-West t statistic of 2.94. The return spreads between low-sales season stocks and high-sales season stocks are robust to well-known anomalies and are larger in magnitude among large-cap companies. We also find that the return spreads are more pronounced within industries with relatively fixed patterns in product demand. The sales seasonality premium in the Chinese stock market is found to be a combined result of investor rationality and irrationality.

The global financial system is currently navigating a profound transformation driven by digitalization and the rise of decentralized financial innovations. In response, central banks are increasingly exploring or implementing Central Bank Digital Currencies (CBDCs) as a sovereign digital evolution of fiat money. This study investigates the dual nature of CBDCs, evaluating whether they represent a strategic opportunity to modernize the economy or a systemic threat to existing financial stability. Employing a qualitative methodology, the research analyzes official policy frameworks from the IMF and BIS alongside diverse real-world case studies, including China’s e-CNY, the Bahamas’ Sand Dollar, Nigeria’s eNaira, and the European Central Bank’s Digital Euro. The findings suggest that while CBDCs offer significant benefits—such as enhanced payment efficiency, reduced transaction costs, and improved financial inclusion—they also introduce critical risks. These include the potential disintermediation of commercial banks, heightened cybersecurity vulnerabilities, and concerns regarding individual data privacy and government surveillance. The study concludes that the successful integration of CBDCs is not merely a technical challenge but a social and strategic one. Adoption is heavily dependent on infrastructure, digital literacy, and public trust. Ultimately, the research highlights that there is no "one-size-fits-all" model; the future of money will be shaped by how effectively individual nations balance technological innovation with the preservation of financial architecture.

This article examines the link between foreign institutional holdings and firms’ efficiency in labor investment in the setting of Chinese markets. We find that foreign institutional investors enhance firms’ labor investment outcomes primarily through mitigating asymmetric information and by strengthening internal governance. Specifically, the influence of foreign institutional investors on a firm’s labor investment efficiency is stronger when the firm faces greater labor adjustment frictions. This effect is more evident when foreign institutional investors are originated from countries or areas with stronger cultural connections to China, stronger governance quality, common law traditions, or stronger bargaining power in the firms’ governance. Our paper contributes to the literature in that it documents the monitoring role of foreign institutional investors from the perspective of firms’ labor investment decisions, and adds to the literature on the drivers of firms’ labor investment choices.

This study examines the mean-reversion behavior of trading volume in China’s A-share market, with a focus on the speed at which abnormal surges dissipate. We compare two competing hypotheses: the stealth-trading hypothesis, where persistent volume reflects order-splitting by informed traders, and the informational-efficiency hypothesis, which interprets faster reversion as a sign of efficient information absorption. Using the Ornstein–Uhlenbeck (OU) model, we estimate the reversion speed for over 3,000 stocks and link it to firm- and industry-level characteristics. We find that trading volume is strongly mean-reverting, with over 98% of stocks classified as stationary. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger size, higher analyst coverage, lower volatility, and greater liquidity. Notably, reversion speed increased after the 2006 IFRS reform but declined following Stock Connect, suggesting that stock market policies can influence informational efficiency. Our OU-based methodology offers a simple, observable proxy for monitoring how quickly markets process information. These results position trading volume as a core variable in market microstructure research and policy evaluation.

Traditional threshold-based stock networks suffer from subjective parameter selection and inherent limitations: they constrain relationships to binary representations, failing to capture both correlation strength and negative dependencies. To address this, we introduce statistically validated correlation networks that retain only statistically significant correlations via a rigorous t-test of Pearson coefficients. We then propose a novel structure termed the largest strong-correlation balanced module (LSCBM), defined as the maximum-size group of stocks with structural balance (i.e., positive edge-sign products for all triplets) and strong pairwise correlations. This balance condition ensures stable relationships, thus facilitating potential hedging opportunities through negative edges. Theoretically, within a random signed graph model, we establish LSCBM’s asymptotic existence, size scaling, and multiplicity under various parameter regimes. To detect LSCBM efficiently, we develop MaxBalanceCore, a heuristic algorithm that leverages network sparsity. Simulations validate its efficiency, demonstrating scalability to networks of up to 10,000 nodes within tens of seconds. Empirical analysis demonstrates that LSCBM identifies core market subsystems that dynamically reorganize in response to economic shifts and crises. In the Chinese stock market (2013–2024), LSCBM’s size surges during high-stress periods (e.g., the 2015 crash) and contracts during stable or fragmented regimes, while its composition rotates annually across dominant sectors (e.g., Industrials and Financials).

This study develops a comprehensive methodological framework for identifying and quantifying investor echo chambers in online stock discussion forums. Motivated by a dynamic model of endogenous echo chamber formation, which formalizes how investors optimally allocate attention and update beliefs under cognitive and informational constraints, we construct a two-layer multiplex investor network that integrates common-attention similarity and semantic similarity to jointly capture the informational and cognitive linkages among investors. This framework enables the systematic examination of how shared information sources and convergent opinions emerge within investor communities. We compute both community-level and individual-level (node-level) echo-chamber intensity by integrating measures of social homophily, semantic reinforcement, and community insularity. At the firm level, we further aggregate these micro-level indicators using attention-weighted indices, community concentration (HHI), and semantic polarization metrics to characterize how echo-chamber dynamics manifest in firm-related discussions. In addition, we propose a general empirical panel framework to examine the relationship between investor echo-chamber intensity and firm-level outcomes. Overall, this paper provides a methodological foundation for the broader Investors’ Echo Chamber Project, offering scalable tools for network-based behavioral analysis and laying the groundwork for future research linking online social dynamics, financial market efficiency, and corporate decision-making.

This study investigates the mean-reversion behavior of trading volume, using China’s A-share market as a representative setting characterized by dispersed retail investors, frequent public disclosures, and active policy interventions. We compare two competing interpretations:the stealth-trading hypothesis, in which persistent volume reflects order-splitting by informed investors, and the informational efficiency hypothesis, which links faster volume reversion to more effective information processing. Using the Ornstein–Uhlenbeck (OU) model, we estimate reversion speeds for over 3,000 stocks and relate these to firm- and industry-level characteristics. We find that trading volume is broadly mean-reverting, with over 98% of stocks exhibiting stationarity. The OU model forecasts reversion speed with less than 7% error. Faster reversion is associated with larger firm size, greater analyst coverage, lower volatility, and higher liquidity. Notably, reversion speed increased after accounting reforms but declined following capital access liberalization, suggesting that regulatory policy can both enhance and impair informational efficiency. These findings position reversion speed as an observable proxy for market responsiveness and highlight trading volume as a central variable in empirical market microstructure research.

This paper examines how artificial intelligence (AI) adoption influences the total factor productivity (TFP) of Chinese A-share manufacturing firms from 2010 to 2023. Results show that AI significantly raises TFP, robust across multiple specifications and instrumental variable tests. AI also boosts operational efficiency by accelerating accounts receivable and inventory turnover, revealing a “technology–operation–productivity” pathway. The positive effect is stronger in regions with better digital infrastructure and in firms with stronger governance. The findings provide fresh evidence on AI’s productivity effects and offer policy implications for intelligent transformation and high-quality manufacturing development.

In the context of digital transformation, artificial intelligence (AI) has emerged as a pivotal driver for enhancing tourism resource allocation efficiency and promoting the high-quality development of the tourism industry. Grounded in the Technology–Organization–Environment (TOE) framework, this study constructs a multidimensional indicator system by integrating heterogeneous data sources, including Baidu search indices, corporate annual reports, and policy documents. Using a balanced panel dataset covering 31 provincial-level regions in China from 2015 to 2023, we empirically examine the mechanisms through which AI penetration affects the efficiency of tourism resource allocation. The super-efficiency SBM-DEA model is employed to measure allocation efficiency, while the spatial Durbin model (SDM) and geographically weighted regression (GWR) are used to identify spatial spillover effects and regional heterogeneity. Furthermore, tourist satisfaction is quantified using a natural language processing (NLP)-based sentiment index derived from online reviews. The results indicate that AI penetration significantly improves tourism resource allocation efficiency, with stronger effects observed in regions with advanced technological infrastructure. Smart tourism pilot policies demonstrate significant spatial spillover effects, positively influencing scenic areas within a 100-kilometer radius. However, diminishing marginal returns are evident, highlighting capacity absorption thresholds and institutional constraints. Based on the empirical findings, the study proposes targeted policy recommendations, including the establishment of provincial tourism data hubs, promotion of AI toolkit systems, enhancement of scenic area evaluation mechanisms, and reinforcement of collaborative governance between government and enterprises. These insights aim to provide both theoretical and practical guidance for the intelligent transformation and coordinated regional development of China’s tourism industry.

This study examines the evolution and spillover effects of informational efficiency across carbon markets following the launch of China ’s national carbon emissions trading system (NCET). Using a time-varying parameter VAR model, we analyze efficiency transmission among the National Carbon Emission Allowance (CEA), six China’s pilot markets, and the European Union Allowances (EUA). The results reveal substantial heterogeneity in efficiency dynamics. Since early 2023, the CEA and Shenzhen have shown improved efficiency and stability, while the EUA and other pilot markets have experienced declines in efficiency and increased volatility. Despite progress in domestic markets’ efficiency, the EUA remains the primary source of efficiency spillover effects, followed by the CEA, Shenzhen, and Beijing, whereas other pilot markets—particularly Shanghai—act mainly as net recipients. Spillover intensity increases significantly during major regulatory periods, especially around China’s annual “Two Sessions,” highlighting the influence of policy signals on market linkages. These findings offer empirical insights into the time-varying transmission of efficiency under institutional reform and inform the coordinated design of carbon trading policies.