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.

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.

Existing literature questions whether mutual fund holdings convey information for predicting stock returns in emerging markets. We argue that this lack of evidence stems from a methodological limitation: conventional value-weighted holding measures are dominated by a few large funds, obscuring the collective signal from numerous smaller, but potentially more informed managers. This scale-induced distortion masks the true breadth of institutional attention. To address this, we propose the Abnormal Fund Holding Count (AFHC), defined as the residual from regressing the log number of funds holding a stock on controls for size, value, and liquidity. Using Chinese mutual fund data from 2005 to 2023, we show that AFHC strongly predicts future returns: a long-short portfolio based on AFHC earns an equal-weighted monthly alpha of 1.13\% (t = 4.21), robust to risk adjustments and controlling related return predictors. Moreover, high-AFHC stocks exhibit subsequent improvements in fundamentals and greater price informativeness, consistent with a price discovery channel. Our findings demonstrate that institutional breadth, rather than aggregate capital weight, better reveals informed trading in developing markets.

This study utilizes the broadcast of the popular television series Game of Hunting in mainland China as a quasi-natural experiment to examine how investor attention impacts stock price volatility. The empirical findings reveal that the broadcast of Game of Hunting increased stock price volatility for headhunting companies. Notably, headhunting companies that attracted lower levels of investor attention before the broadcast exhibited greater stock price volatility. Conversely, headhunting companies with more complex business operations, which posed greater challenges for investors in processing information, experienced smaller fluctuations in stock prices. The broadcast of Game of Hunting drew retail investors’ attention to headhunting stocks, prompting them to purchase these stocks. This attention-driven buying then triggered arbitrage behavior by other retail investors, who sold the stocks, ultimately causing fluctuations in the share prices of headhunting companies. In contrast, institutional investors’ trading of these stocks was neither affected by the popularity of Game of Hunting nor influenced by the trading behavior of retail investors.

This study examines the relationship between price discovery and commodity-related media sentiment in the Chinese futures market through a tone-based natural language processing framework. We integrate a customized Chinese BERT model into a BiLSTM network with an Attention mechanism, enhancing semantic representation through contextual embeddings and salient feature emphasis. Analyzing over 850,000 news articles, we find sentiment provides significant incremental predictive power for commodity portfolio returns. We finally design a long-short trading strategy that consistently outperforms several benchmark approaches. The results emphasize that deep semantic sentiment analysis offers substantive predictive value for asset returns.

As an emerging market, China's capital market is still developing, and stock mispricing due to information asymmetry is common. As the primary spokesperson for information disclosure, the Board Secretary plays a pivotal role in mitigating this issue. This study examines the impact of Board Secretary shareholding on stock mispricing using a sample of Chinese A-share listed companies from 2010 to 2020. The findings show that shareholding significantly reduces mispricing. This effect is more pronounced in firms with lower media attention, suggesting it compensates for weak external monitoring. Furthermore, the reduction is primarily achieved by improving the quality of analyst forecasts. This research provides insights into mitigating mispricing and optimizing the governance of Board Secretaries.

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.

Salience theory posits that investors’ attention is disproportionately drawn to returns that stand out relative to a reference point. Building on this insight, we empirically examine how a stock’s salience theory value (ST) affects its future price crash risk. Using a comprehensive sample of Chinese A-share listed firms from 2005 to 2024, we find that stocks with higher ST are significantly more prone to subsequent price crashes. Mechanism analysis suggests that this relation operates through transient retail speculation and impaired pricing efficiency. High-ST stocks attract retail speculative demand and experience temporary overvaluation, which delays the incorporation of negative information into prices and amplifies the likelihood of abrupt price corrections. We further show that the effect is more pronounced during high-sentiment market conditions and is largely driven by stocks exhibiting salient upside return realizations. Overall, our findings highlight stock salience as a novel behavioral driver of crash risk and offer cautionary insights for investors and regulators.

This paper utilizes data from Chinese A-share listed companies and, based on the limited attention theory, reexamines the impact of data asset disclosure on capital market pricing efficiency. The study finds that the economic consequences of data asset disclosure are not static but exhibit a significant marginal decreasing feature as the overall market disclosure level rises. Specifically, when the overall market disclosure level is low, corporate disclosure of data asset information can significantly reduce stock price synchronization and enhance capital market pricing efficiency by increasing the supply of idiosyncratic information, enhancing information diffusion, and accelerating information integration. However, with the recent widespread disclosure behavior, there is no evidence that the above mechanisms remain effective. Further analysis indicates that disclosure behavior can maintain its positive effect only when the overall market disclosure level of data assets is low, and the firm's disclosed idiosyncratic information is relatively limited, the information dissemination environment is favorable, or institutional investor attention is not excessively diffused. This paper expands the boundaries of the limited attention theory, extending the research perspective to the dimension of the dispersion of investor attention by homogenized information, and reveals the value dilution problem faced by data asset information disclosure in the context of universalization.

We investigate whether China's 2021 mining ban transformed Bitcoin from a speculative vehicle into a macro-sensitive asset. Using daily data from 2017-2025, we document a decisive structural break. Pre-2021, volatility was endogenous, driven by raw trading volume rather than fundamentals. Post-ban, however, internal microstructure noise loses predictive power. Instead, volatility is now driven by macroeconomic anxiety: Wikipedia searches for "Inflation" improve forecast accuracy by over 7%. We further uncover a "dual narrative" where inflation attention predicts crash risk, while "Recession" queries (pivot speculation) drive rallies. These findings suggest the regulatory shock successfully curtailed noise trading, allowing macro-fundamentals to dominate price discovery.