This paper examines whether a socially embedded soft trait, systematically omitted from AI-automated credit decisions, can predict financial outcomes in fully automated lending environments, and through which economic channel. We develop theoretically and examine empirically a labor-market competitiveness mechanism in which facial trustworthiness can serve as a soft signal under information asymmetry, shaping income growth and repayment capacity. Leveraging a unique proprietary dataset from a nationwide FinTech platform in China with AI-based underwriting that explicitly excludes facial information, we link loan performance to employment histories and construct trustworthiness scores using computer-vision algorithms. We find that borrowers with more trustworthy facial appearances experience greater upward job mobility and significantly lower default risk, conditional on rich hard information. A one-standard-deviation increase in facial trustworthiness is associated with a 4.6 percent reduction in default relative to the sample mean. The effect is strongest among borrowers with limited formal credentials, consistent with facial trustworthiness substituting for hard-to-observe labor-market signals. Our findings identify an economically grounded channel through which biometric appearance predicts financial resilience and highlight limits of algorithmic models that omit socially embedded information.

We study how mutual fund managers update beliefs and whether such updating reflects managerial skill. Using regulator-mandated, forward-looking investment outlook disclosures from Chinese mutual funds, we construct a novel measure of belief updating intensity based on year-over-year changes in stated beliefs, and further decompose these updates into macro and micro themes. We show that belief updating is strongly state contingent. During recessions, funds with more intensive updating shift their outlooks toward macroeconomic conditions, reduce procyclical stock exposures, and scale back on-site company visits, while the opposite pattern emerges in expansions. Belief-updating intensity positively predicts future performance, and this payoff is strongest when managers reweight beliefs in ways that align with prevailing economic conditions, indicating that belief updating captures an ex ante dimension of managerial skill. Consistent with a signaling interpretation, investors allocate more capital to funds with stronger belief updating, particularly when past returns are less informative.

Using 4724 A-share listed firms in the Chinese stock market from 2009 to 2024, we investigate how CPU, an index constructed from textual analysis of major Chinese newspapers, affects investors' behavior, measured by turnover ratios and abnormal returns. The baseline regression shows that higher CPU significantly reduces stock turnover ratio and abnormal returns, indicating heightened investor caution and negative sentiment. Extended regression with provincial-level CPU further supports these findings. Heterogeneity analysis reveal that low-carbon firms, non-state-owned enterprises (non-SOEs), and firms in Central and Western China are more negatively affected by CPU, while high-carbon firms and SOEs are relatively resilient when facing higher CPU, mainly due to expected policy support and institutional investor dominance. Further mechanism analysis uncovers that ESG performance moderates CPU's effects. Firms with higher ESG ratings experience smaller valuation declines but lower trading volumes. In addition, consumer sentiment mediates CPU's effects. CPU suppresses public confidence, which in turn reduces investor participation and firm valuations. These findings highlight the importance of improving policy transparency, promoting standardized ESG disclosure, and mitigating retail investor overreaction. The study contributes to behavioral and climate finance literature by linking policy-driven uncertainty with micro-level investor responses in a key emerging market.

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.

This paper investigates how firm-wide organizational competence shapes corporate tax outcomes. Exploiting China's Tax Compliance Rating (TCR) system, which evaluates the administrative execution of daily transaction flows across procurement, human resources, and supply chain operations, we examine whether operational rigor translates into superior tax performance. We document that top-tier (Grade A) firms achieve significantly lower effective tax rates. This association persists after controlling for general internal control quality, managerial ability, statutory tax incentives, auditor expertise, and state ownership. Importantly, this tax efficiency does not stem from aggressive or risky tax positions, as Grade A firms exhibit significantly lower tax volatility and no elevation in accounting or tax restatements. Dynamic tests further show that these tax advantages compound steadily with cumulative compliance experience. Overall, our findings demonstrate that corporate tax efficiency represents a tangible economic return on day-to-day administrative execution and cross-departmental coordination, establishing compliance ratings as a credible market signal of organizational competence.

We construct six monthly abnormal weather indicators by employing physical weather data of temperature, humidity, wind speed, precipitation, sunshine hours and sky coverage in China. We compute the abnormal weather betas to proxy for abnormal weather risks and investigate the pricing effects of these risks in the Chinese stock market. We find a low abnormal weather beta premium in the cross-section, i.e., stocks with high betas to changes in the abnormal weather index significantly underperform those with low betas by 0.10% to 0.37% per month on a risk-adjusted basis. In particular, the low abnormal weather beta premiums of temperature, sunshine hours, and sky coverage are significant over the subsequent six months. The further analyses suggest that the abnormal weather betas exhibit persistent long-term predictive power and have significant explanatory power for size, value, and long-term reversal anomalies. We further explore several potential explanations for the low abnormal weather beta premiums from various aspects, including firm characteristics, industry heterogeneity, firm performance, investor behavior and institutional holdings. The results suggest that neither firm-level characteristics nor industry heterogeneity can adequately explain the low abnormal weather beta premiums. The anomalies related to abnormal weather sensitivity are likely driven by the interaction of investor behavioral bias and arbitrage barriers, rather than compensation for fundamental risk.

This study examines whether stock price fragility—the risk of price volatility arising from non-fundamental demand shifts, such as investor liquidity shocks—affects investment efficiency in real-economy firms. Our research indicates that firms facing higher price fragility exhibit significantly weaker investment performance. Price fragility amplifies noise trading and worsens information asymmetry, undermining the capacity of prices to convey reliable signals for managerial decisions. Further analysis reveals that financing constraints exacerbate the impact of stock price fragility on under-investment, whereas managerial overconfidence plays an asymmetric moderating role, mitigating under-investment while amplifying over-investment. Evidence from a natural experiment–based identification strategy supports a causal interpretation of our results and alleviates endogeneity concerns.

In response to China’s national carbon neutrality goals, this study examines how corporate carbon emissions disclosure affects the financial performance of Chinese A-share listed companies. Leveraging artificial intelligence tools, including natural language processing, we analyzed emissions disclosures for 4,336 companies from 2017 to 2022. The research demonstrates that high-quality carbon disclosure positively impacts financial performance with higher stock returns, improved return on equity, increased Tobin’s Q ratio, and reduced stock price volatility. Our findings underscore the emerging importance of carbon transparency in financial markets, highlighting how environmental reporting can serve as a strategic mechanism to create corporate value and adapt to climate change.

Chinese mutual fund managers are required to file quarterly commentaries that mix fund-specific performance/strategy discussions with broader macroeconomic commentary, alongside financial statements. Using FinBERT Model, we decompose these narratives into substantive discussions (fund-specific) and general discussions (macro) and measure strategic messaging by the relative tone of substantive discussions to general discussions. We then analyze how such strategic narratives affect investor behavior. We posit two channels through which relative tone could affect subsequent fund flows: information revelation or strategic tone management. Using 64,810 fund-quarter observations (2008–2022), we find that a one-standard-deviation increase in optimistic relative tone predicts a 0.98% rise in subsequent fund flows—about 25.36% of the effect of prior-quarter performance. Results hold in difference-in-difference analyses, using fund manager-turnover as identification. The flow effect is stronger for funds with superior past performance and for senior managers, but is weaker when investors are distracted. Relative tone does not predict future fund returns on average, although star managers’ relative tone carries some predictive content. Overall, our evidence is consistent with coexistence of information revelation and strategic tone management.

This paper investigates the utilization of live streaming in online communication with retail investors. Using the earnings communication conference in China, we find that firms with superior performance and higher costs associated with processing earnings information are more inclined to employ live streaming during these conferences. Moreover, in line with the social presence theory, robust evidence suggests that live streaming results in more positive retail investor sentiment following the conference. Further analysis shows that live streaming is positively related to investor attention and substitutes post-conference information acquisition with textual information presented during the conference. On the information effect, it enhances market reactions to textual information from managers. This paper sheds light on how multimedia can transform the process of communication with retail investors.