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 Chinas 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 Sscore 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 bookto-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 Chinas A-share market, indicating that ESGintegrated 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.

Purpose-This study empirically examines the relationship between the blockholder exit threats (ETs) and management tone manipulation, based on two competing hypotheses: the "governance hypothesis" and the "market pressure hypothesis." The study aims to explore the impact and mechanisms of ETs on the quality of textual information disclosure. Design/methodology/approach-This study uses ordinary least squares multivariate regressions on panel data from Chinese A-share listed companies spanning 2007 to 2022. Building on Huang et al. (2014), we construct a benchmark model of managerial tone and use its residuals to measure abnormal positive tone. Blockholder exit threat (ET), following Dou et al. (2018), is proxied by the interaction between stock liquidity and blockholder competition. To address endogeneity, we employ propensity score matching, the Heckman two-stage method and lagged models. Robustness checks include alternative variable definitions and different textual dictionaries. Findings-The results show that external blockholder ETs significantly increase the degree of abnormal positive managerial tone, thereby supporting the market pressure hypothesis. Further analyses reveal that the positive association between ETs and abnormal positive tone is stronger in subsamples where managerial market pressure is higher, the level of blockholder expropriation is greater and insider trading is more prevalentindicating a stronger collusion motive between external blockholders and management. Mechanism tests show that ETs manipulate managerial tone by increasing the sensitivity of managerial wealth to share price fluctuations. Research limitations/implications-This study is limited to Chinese A-share listed companies, whose unique institutional and regulatory environment may restrict the generalizability of the findings to other markets. Differences in disclosure rules, legal enforcement and investor protection between China and developed economies suggest that the identified mechanisms may not apply universally. Additionally, our reliance on annual report narratives for measuring managerial tone may overlook other communication channels. Future research could extend the analysis to other market segments or countries and incorporate additional data sources to test the robustness and broader applicability of our conclusions. Practical implications-The results highlight the need for enhanced regulatory oversight of narrative disclosures to prevent managerial “window dressing” and mitigate information asymmetry. Regulators should strengthen cross-validation between financial and non-financial information and promote mechanisms that increase the cost and risk of blockholder–management collusion. Investors are advised to critically assess managerial tone and remain alert to overly optimistic language. Additionally, increased supervision of tunneling and insider trading, along with stronger legal protections for minority shareholders, is essential for fostering transparency and integrity in China’s capital markets and improving overall market quality. Originality/value-This paper contributes to understanding the impact of external blockholder ETs on managerial tone manipulation in narrative disclosures from the perspective of ownership structure in China’s unique institutional environment. The findings of the study provide new insights into the governance role of blockholders and offer policy implications for improving information transparency and the healthy development of emerging capital markets.

This study investigates a novel calendar anomaly in the Shanghai Composite Index (SSE Composite) based on the Nayin (纳音) Five Elements system, a cyclical classification embedded in the traditional Chinese sexagenary calendar (Heavenly Stems and Earthly Branches). Each two-year period within the sixty-year cycle is assigned one of five elemental attributes: Earth(土), Metal (金), Fire (火), Wood (木), or Water (水). Using daily trading data from December 1990 to February 2025 spanning 18 complete Nayin cycles, the study finds that Earth-element periods exhibit a 100% positive return rate (mean: +123.4%), while Metal-element periods universally display late-cycle declines (4/4 periods, mean late-segment return: −30.4%). Wood element periods tend toward negative returns (mean: −11.4%). Fire-element periods contain structural bottoms, and Water-element periods mirror the directional momentum of their predecessors (3/3 accuracy). A binomial test for Earth-period returns yields p = 0.095 (one tailed), with Cohen’s d = 1.50 versus non-Earth periods. These patterns are not replicated in the S&P 500 Index under the same calendar framework, suggesting a China-specific cultural behavioral channel consistent with theories of culturally embedded market sentiment. This paper contributes to the calendar anomalies literature by documenting a previously unexplored two year cyclical pattern linked to traditional Chinese metaphysical calendrics.

This study investigates the pricing of convertible bonds by focusing on the underlying stock price limits, a market structural feature overlooked in conventional models, and provides a comprehensive framework integrating price limits with embedded options (including conversion, call, put and reset clauses) based on Monte Carlo simulation. Numerical analysis reveals that convertible bond prices across varying moneyness states are insensitive to price limits at low underlying stock volatility, while higher volatility amplifies the pronounced impact of price limits on in-the-money bonds. Empirical tests based on data on the Chinese convertible bond market spanning the period from 2018 to 2024 indicate an average overvaluation in the market. The result identifies that there are negative pricing errors for deep-out-of-the-money bonds and positive errors for in-the-money bonds. A trading strategy based on pricing errors yields positive returns, demonstrating the model's practical applicability.

Digital technologies are often portrayed as disruptive levers for decarbonisation, yet many digital green initiatives improve transparency and efficiency without redesigning how environmental value is authorised, coordinated, and appropriated. We address this puzzle by theorising green digital business model innovation as the evolution of venture-level configurations under multi-domain selection in policy-steered sustainability transitions. Building on the activity-system view of business models and on innovation-studies, we define business model radicality as coupled reconfiguration across content, structure, and governance rather than technology novelty alone. We further theorise a cross-domain convergence requirement: configurations are retained only when they align simultaneously with institutional role allocations, market interfaces, technological infrastructures, and ecological verification demands. A comparative longitudinal study of three blockchain-enabled initiatives in China’s energy-finance nexus shows that radical disintermediation is screened out early when any single domain withholds authorisation or credible settlement, whereas incumbent-compatible configurations are retained through sequencing, interpretive pruning, and boundary-spanning alignment work. The study contributes to innovation studies by treating business models as meso-level selection objects, sharpening configuration-level radicality, and explaining why digital green opportunities often remain incremental in regulated transition systems.

Can fully agentic artificial intelligence predict equity returns in real time? We build a novel dataset of daily AI-generated attractiveness scores spanning the entire Russell 1000 universe, continuously updated since April 2025. Each trading day, the model freely searches the live internet and produces quantitative scores for every constituent stock before the next market open, eliminating both look-ahead bias and attribution bias. A daily value-weighted portfolio of the 20 highest-ranked stocks earns a Fama-French six-factor alpha of 16.5 basis points per day (41.5% per annum), with an average bid-ask spread of 1.92 basis points. Predictability is sharply asymmetric: the model reliably identifies future outperformers but not future underperformers. The evidence points to stock picking rather than market timing: performance is strongest on down-market days, and the alpha does not come from betting on aggregate market direction. The AI’s reasoning traces help explain this predictability. Alpha is concentrated in traces with richer live-web evidence and stronger analytical structure. In the theme attribution, most active alpha reflects firm selection under an investment lens, not broad theme choice.

We examine whether the characteristics of economically linked firms predict stock returns in the Chinese A-share market. We construct two peer-based signals: a peeraverage (PA) signal that aggregates the characteristics of fundamentally similar firms, and a peer-deviation (PD) signal that summarizes a firm’s characteristic deviations from industry-peer averages. PA captures shared fundamental information, whereas PD benchmarks a firm’s relative position against economically comparable peers. Portfolio sorts, factor-spanning tests, and Fama–MacBeth regressions reveal economically meaningful and statistically significant return predictability for both signals, with generally larger portfolio returns among non-state-owned enterprises. Both signals retain significant incremental predictive power after controlling for the full set of own-firm characteristics and flexible machine-learning forecasts based solely on these characteristics. Changes in both signals also predict future earnings, while return predictability is generally stronger among firms receiving less investor attention, consistent with limited-attention-induced underreaction. These findings highlight the distinct informational and benchmarking roles of economic networks and their predictive value beyond firms’ own characteristics.

The essay delves into the impact of environmental uncertainty on asymmetric mispricing utilizing the data from listed firms in China spanning from 2007 to 2023. Our analysis reveals that environmental uncertainty amplifies stock mispricing within capital markets, whether upward or downward. Diverging from prior research, we distinguish between upward and downward mispricing and reveal the black box of environmental uncertainty affecting stock mispricing from dual channels. Specifically, environmental uncertainty intensifies upward mispricing through heightened earnings management and exacerbates downward mispricing by boosting investor irrationality. Furthermore, we explore the heterogeneous impact of different media coverage. In the downward mispricing sample, negative media exacerbated the relationship between the two, while positive coverage played a mitigating role. In the upward mispricing sample, only negative reports have a significant impact, and mitigate the impact of uncertainty on mispricing. Our research on media heterogeneity once again proves that it is a double-edged sword. Our research indicates that improving the capacity to recognize different mispricing mechanisms in various market directions can greatly boost decision-making efficiency. Meanwhile, it is vital to strengthen professional ethics in media organizations and encourage more objective reporting. These efforts can jointly contribute to improve he efficiency of emerging capital markets.

As China's bond market expands rapidly, the complexity of financial guarantee networks and their implications for credit risk have become critical issues in both academic research and financial practice. Utilizing micro-level data from China's credit bond market spanning 2014 to 2024, this study constructs a multi-layer network incorporating bonds, guarantors, and issuing firms to empirically examine the impact of guarantor network centrality on bond credit spreads. The results reveal a significant U-shaped relationship: moderate centrality reduces spreads by bolstering market confidence, whereas excessive centrality increases them due to heightened systemic risk. Mechanism analyses identify systemic risk and information asymmetry as key mediating channels through which centrality affects credit risk premiums. Heterogeneity tests indicate that this U-shaped pattern is more pronounced among state-owned guarantors, real estate firms, and high-risk clusters within the network. Furthermore, both cross-layer connectivity within the multi-layer structure and regional financial development levels significantly moderate the centrality-spread relationship. These findings offer a structural perspective on credit risk pricing in emerging markets and provide valuable policy insights for credit rating system design, guarantee regulation, and systemic risk prevention. International investors could also leverage these findings to better assess systemic risk in interconnected financial markets across emerging economies.

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.