Cross-firm momentum effects via shared analyst coverage are well-documented in de-veloped markets, but their robustness remains unclear in emerging markets, where information diffusion is asymmetric and analyst coverage is highly concentrated. Our work revisits this effect in an environment of extreme informational frictions — the Chinese market. We reconstruct the information transmission channel within the an-alyst coverage network by introducing a novel weighting scheme based on strength centrality (SC). This measure identiffes inffuential leader firms that command dis-proportionate attention from both analysts and the market. Our results demonstrate that SC-weighted connected-firm returns robustly predict cross-sectional stock returns, yielding significant and persistent profits even under a rigorous stock filter. This per-formance cannot be subsumed by strategies based on alternative weighting schemes or by explanations such as intra-industry cross-firm momentum and information discreteness. Further analysis reveals that the superiority of the SC-based approach stems from its ability to effectively identify firms with stronger cross-period fundamental linkages. In addition, high-SC stocks are characterized by higher investor attention, more efficient information processing, lower arbitrage costs, and greater internationa exposures. With this evidence, we further confirm a directional spillover: cross-firm momentum effects flow exclusively from these high-SC leaders to low-SC laggards, and there is no reverse spillover. Our findings suggest that cross-firm momentum may be systematically underestimated in many international markets due to methodological limitations rather than economic irrelevance. The SC-based framework therefore of-fers a portable tool for global investors and researchers operating in environments with asymmetric information.

We document “foreign bias” in AI financial predictions, reversing the classic home bias. U.S.-based ChatGPT is systematically more optimistic than China-based DeepSeek about Chinese firms—in price predictions and directional forecasts—yet significantly less accurate. Evidence supports an information-availability mechanism: bias is strongest when U.S. media coverage of Chinese firms is limited and attenuates for cross-listed firms. Crucially, injecting Chinese news eliminates the prediction gap. Both models produce similar forecasts for U.S. firms, consistent with broader worldwide coverage. LLMs trained in different information environments can create divergent signals, with implications for investors and policymakers as AI increasingly intermediates global markets.

Strengthening disclosure regulation and improving disclosure quality are central to China's transition to a full registration system and crucial for preventing capital market risks. Using prospectuses disclosed by IPOs on the STAR Market, ChiNext, and the Beijing Stock Exchange from 2019 to 2023, this study constructs four textual indicators from prospectuses—length, sentence complexity, technical term density, and uncertainty—and examines how they affect resource allocation efficiency under the registration system. We find that text length and sentence complexity improve resource allocation efficiency, consistent with an information effectiveness effect. In contrast, technical term density and uncertainty reduce efficiency, reflecting information redundancy. Further analysis shows that the registration system reform enhances the comprehensiveness and complexity of disclosures, but its net effect on efficiency depends on the balance between information effectiveness and redundancy. This study contributes to the international literature on “institutional environment—disclosure—resource allocation” with evidence from an emerging market, while also extending theories of information asymmetry and impression management. Our findings support Chinese regulators in optimizing prospectus standards and strengthening review oversight, and provide policy insights for other emerging markets seeking to improve capital allocation through more effective disclosure design.

Against the backdrop of recurrent global crises, economic uncertainty, and mounting environmental and social pressures, corporate resilience—defined as a firm’s capability to withstand external systemic shocks—has emerged as a critical determinant of long-term sustainability. This study empirically exames the effect of ESG (Environmental, Social, and Governance) performance on corporate resilience in China’s A-share market, using the COVID-19 pandemic as a natural experiment to identify causal effects. The sample comprises 651 A-share listed firms, excluding financial institutions, real estate firms, and ST/*ST companies, over the period from January 20, 2020, when the pandemic was officially announced in China, to June 30, 2024. ESG performance is measured as the average of 2018–2019 ratings issued by three major domestic agencies, thereby capturing firms’ pre-shock conditions and mitigating concerns of reverse causality. Corporate resilience is evaluated along two dimensions: resistance, measured by the severity of losses in net income, revenue, and stock price, and recovery, measured by the time required for ROA, EBIT, stock price, and Tobin’s Q to return to pre-shock levels. To ensure the robustness of the findings, this study employs linear regression models with industry-clustered robust standard errors, an instrumental-variable approach using R&D intensity and analyst coverage as instruments, and a Cox accelerated failure time model to estimate recovery duration. The empirical results indicate that stronger pre-shock ESG performance significantly enhances corporate resistance and shortens recovery time. Mechanism analyses further reveal that ESG strengthens corporate resilience by improving total factor productivity, alleviating financing constraints, and enhancing corporate reputation. These findings remain robust to multicollinearity diagnostics and a range of additional robustness tests. Overall, this study provides empirical evidence of the value of ESG in strengthening corporate resilience and offers important implications for firms, policymakers, and investors.

In view of the divergence of existing research results on the relationship between ESG and investment returns, this paper constructs an S-score metric, which comprehensively measures corporate sustainability performance. It further tests the applicability of a sustainability-based investment strategy using this metric in China's A-share market. Using Shanghai and Shenzhen A-shares from May 2016 to April 2024 as the research sample, the S-score is constructed across five dimensions: Profitability, Growth Opportunities, Investment Efficiency, Risk Mitigation, and ESG Performance. The S-score is calculated using Z-score standardization and entropy weighted. Strategy effectiveness was tested through univariate grouping, bivariate grouping, and Fama-Macbeth regression, further examining strategy performance under varying market conditions, holding periods, and information environments. The study finds that the S-score demonstrates significant discriminative power for cross-sectional stock returns. The hedge portfolio based on this metric achieved an annualized excess return of 7.943% after adjusting for the China three-factor (CH-3) model. Its predictive power remains robust after controlling for variables such as market capitalization and book-to-market ratio, delivering significant positive returns across bull and bear markets, extreme pandemic conditions, and holding periods of up to eight years. From a behavioral finance perspective, this paper reveals that explanations such as the gradual diffusion of information and investors' limited attention span help elucidate the profitability of the S-score strategy. The findings demonstrate the effectiveness of Sustainable Investing strategies in China's A-share market, indicating that ESG-integrated factor investing can optimize resource allocation. This research contributes empirical evidence on Sustainable Investing in emerging markets, providing insights for policy formulation and practical implementation while supporting the virtuous cycle between Sustainable Investing and long-termism.

This paper illustrates how firms with publicly disclosed political connections use subsidiaries to obtain preferential treatment in land markets. While the headquarters of politically connected listed firms pay land prices comparable to those paid by other firms, their subsidiaries receive discounts of 12.1%–13.2%. These discounts are more pronounced when land is acquired through less transparent methods, in regions with weaker institutional environments, and among private firms. The anti-corruption campaign launched in 2012 effectively mitigates corruption-related discounts, with the magnitude of the discounts negatively associated with campaign intensity. Additionally, larger discounts for subsidiaries are observed following greater charitable donations, suggesting a reciprocal relationship between firms and officials. Overall, the findings contribute to a broader understanding of how firms with publicly disclosed political ties use complex corporate structures to engage in rent-seeking behavior.

This study investigates the role of contextual cues in memory-based decision-making within high-stakes trading environments. Using trade records from a large Chinese brokerage firm, we provide evidence that both extreme events (COVID-19 quarantines) and everyday contexts (geographic locations) trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to substantial losses for the representative investor's portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which contextual cues affect financial markets.

Can fully agentic AI nowcast stock returns? We deploy a state-of-the-art Large Language Model to evaluate the attractiveness of each Russell 1000 stock each trading day, starting in April 2025 when AI web interfaces enabled real-time search. Our data contribution is unique along three dimensions. First, the nowcasting framework is completely out-of-sample and free of look-ahead bias by construction: predictions are collected at the current edge of time, ensuring the AI has no knowledge of future outcomes. Second, this temporal design is irreproducible once the information environment passes. Third, our framework is fully agentic: we do not feed the model curated news or disclosures; it autonomously searches the web, filters sources, and synthesises information into quantitative predictions. We find that AI possesses genuine stock-selection ability, but that its predictive power is concentrated in identifying future winners. A daily value-weighted portfolio of the 20 highestranked stocks earns a Fama-French five-factor plus momentum alpha of 19.4 basis points and an annualised Sharpe ratio of 2.68 over April 2025–March 2026. The same portfolio accumulates roughly 49.0% cumulative return, versus 21.2% for the Russell 1000 benchmark. The strategy is economically implementable: the average bid-ask spread of the daily Top-20 portfolio is 1.79 basis points, less than 10% of gross daily alpha. However, the signal remains asymmetric. Bottom-ranked portfolios generally exhibit alphas close to zero, while the strongest predictive content sits in the extreme top ranks. Delayed-entry tests further show that predictability does not vanish after a single day; rather, the signal remains positive over a broad window of subsequent entry dates, consistent with slow information diffusion rather than a fleeting overnight anomaly.

This study investigates the role of contextual cues in memory-based decision-making within high-stakestrading environments. Using trade records from a large Chinese brokerage firm and a novel dataset on COVID-19 quarantines, we find that quarantine periods trigger the recall of previously traded stocks, increasing the likelihood of subsequent orders for those stocks. The observed patterns align more closely with similarity-based recall than with alternative channels. Welfare analysis reveals that these memory-induced trades lead to an annualized loss of approximately 70 percentage points for the representative investor’s portfolio. We also find evidence at the market level: when the geographical distribution of quarantine risks is recalled, the probability of recalling the cross-sectional stock return-volume distribution from the same day increases by 1.6 percentage points. This study provides causal evidence from a real-world setting for memory-based theories, particularly similarity-based recall, and highlights a novel channel through which COVID-19 policies affect financial markets.

Purpose: High investment efficiency is a key dimension of high-quality enterprise development. As critical nodes embedded in supply chain networks, corporate investment behaviors are profoundly shaped by the structural characteristics of their supply chains. Concentrated supply chain configuration, as one of the core structural features, has not yet been systematically examined in terms of its impact on corporate investment efficiency and the underlying mechanisms, leaving an important research gap. Design/methodology/approach: Based on a sample of China’s A-share listed enterprises from 2007 to 2023, this study empirically examines the effect of concentrated supply chain configuration on corporate investment efficiency. Findings: First, concentrated supply chain configuration exerts a significant inhibitory effect on corporate investment efficiency, a conclusion that remains robust after a series of tests. Second, mechanism tests indicate that this influence operates primarily through three channels: exacerbating financing constraints, crowding out working capital, and deteriorating the information environment. Third, heterogeneity analysis shows that both supplier concentration and customer concentration inhibit investment efficiency, with the latter having a slightly stronger negative effect. The adverse impact is more pronounced in over-investing enterprises, non-state-owned enterprises, smaller firms, and those in growth or decline stages. Furthermore, regional factor market development, external market power, and internal control quality are found to effectively mitigate the negative effect of concentrated supply chain configuration on corporate investment efficiency. Originality: This study extends the research on determinants of corporate investment efficiency from a supply chain structure perspective, providing new theoretical insights and empirical evidence for understanding corporate investment behavior in China.