We study how financial markets integrate news about pollution abatement costs into firm values. Using China’s automation of pollution monitoring, we find that firms with factories in bad-news cities---cities that used to report much lower pollution than the automated reading---see significant declines in stock prices. This is consistent with the view that investors expect firms in high-pollution cities to pay significant adjustment and abatement costs to become “greener.” However, the efficiency with which such information is incorporated into prices varies widely---while the market reaction is quick in the Hong Kong stock market, it is considerably delayed in the mainland ones, resulting in a drift. The equity markets expect most of these abatement costs to be paid by private firms and not by state-owned enterprises, and by brown firms and not by green firms.

We examine how mutual funds form stock market expectations and the implications of these beliefs for asset returns, using a novel text-based measure extracted from Chinese fund reports. Funds extrapolate from recent stock market and fund returns when forming expectations, with more recent returns receiving greater weight. This recency tendency is weaker among more experienced managers. At the aggregate level, consensus expectations positively predict short-term future market returns, both in and out of sample. At the fund level, expectations are positively related to subsequent fund performance in the time series. In the cross-section, however, superior performance arises only when funds accurately forecast market direction and adjust their portfolios accordingly. This effect is stronger for optimistic forecasts and among funds with greater exposure to liquid stocks. Our findings highlight the conditional nature of belief-driven performance, shaped jointly by forecasting skill and the ability to implement views in the presence of execution frictions such as short-selling and liquidity constraints.

Using unique account-level data from a leading Chinese fintech platform, we investigate how financial influencers, the key information intermediaries in social finance, attract followers through a process of social transmission bias. We document a robust performance-following pattern wherein retail investors overextrapolate influencers’ past returns rather than rational learning in the social network from their past performance. The transmission bias is amplified by two mechanisms: (1) influencers’ active social engagement and (2) their index fund-heavy portfolios. Evidence further reveals influencers’self-enhancing reporting through selective performance disclosure. Crucially, the dynamics ultimately increase risk exposure and impair returns for follower investors.

We construct a novel risk-based similarity peer network by applying machine learning techniques to extract a comprehensive set of disclosed risk factors from firms' annual reports. We find that a firm's future returns can be significantly predicted by the past returns of its risk-similar peers, even after excluding firms within the same industry. A long-short portfolio, formed based on the returns of these risk-similar peers, generates an alpha of 84 basis points per month. This return predictability is particularly pronounced for negative-return stocks and those with limited investor attention, suggesting that the effect is driven by slow information diffusion across firms with similar risk exposures. Our findings highlight that the risk factors disclosed in 10-K filings contain valuable information that is often overlooked by investors.

How do Held-to-Maturity (HTM) securities that limit the impacts of banks’ unrealized capital loss on the regulatory capital measures affect banks’ exposure to deposit run risks when policy rates increase? And how should regulators design policies on classifying securities as HTM jointly with bank capital regulation? To answer these questions, we develop a model of bank runs in which banks classify long-term assets as HTM or Asset-for-Sale (AFS). Banks trade off the current cost of issuing equity to meet the capital requirement when the interest rate increases against increasing future run risks when the interest rate increases further in the future. When banks underestimate interest rate risks or have limited liability to depositors in the event of default, capping held-to-maturity long-term assets and mandating more equity capital issuance may reduce the run risks of moderately capitalized banks. Using bank-quarter-level data from Call Reports, we provide empirical support for the model’s testable implications.

Exploiting a unique market-oriented media reform initiated in 1996 in China, we investigate the role of media environment in affecting firm behaviour. We find robust evidence that market-oriented media environment is conductive to firm innovation, with the reform promoting patent quantity and quality substantially. The effect is more pronounced for firms with higher information asymmetry. Matching firm data with 1.3 million news reports, we find the market-oriented media reform significantly improves the criticalness and unbiasedness of news coverage and shapes an innovation-friendly environment. Our findings highlight economic outcomes of relaxing media control and underline substantial gains from deepening the reform.

We examine how the sudden drop in public information search capability caused by Google’s withdrawal from China affects Chinese analysts’ earnings forecasts. We observe, after Google’s withdrawal, a decline in analysts’ forecast accuracy for firms with foreign trade relative to those without it. This decline suggests that the withdrawal hinders analysts’ acquisition of foreign information about firms, which decreases the quality of their earnings forecasts. We also find that the effect of the withdrawal on forecast accuracy is stronger for firms with higher business complexity and more opaque financial reporting and for analysts with weaker information processing capacity and more attention constraints. Additionally, we find that corporate site visits serve as an alternative information source that can compensate for the information loss caused by the Google withdrawal. Last, we document that the withdrawal reduces analysts’ forecast timeliness and increases their forecast dispersion.

This study investigates the effect of government digital attention on firm digital innovation. Using data from Chinese listed firms over 2012–2020, we find government digital attention can significantly propel the improvement of firms' digital innovation levels, primarily driving an increase in the quantity of digital innovations rather than a qualitative enhancement. Further analysis indicates that government attention achieves this impact by elevating the regional digital infrastructure, increasing firms' digital subsidies, alleviating firms' financing constraints, encouraging firms to intensify R&D investment, fostering a positive attitude towards digital transformation, and consequently, boosting the overall level of firms' digital innovation.

This study investigates the impact of analysts' information source diversity on forecast bias and investment returns. We combine the GPT-4o model and text similarity, to extract the names of information sources from the text of analyst in-depth reports. Using 349,200 sources, we calculate information diversity scores based on the variety of data sources to measure analysts’ ability of selecting relevant information. The findings reveal that higher information diversity significantly reduces forecast bias and enhances portfolio returns. The effect is particularly pronounced for large companies, state-owned enterprises, those with low analyst coverage, low firm-specific experience, and reports with positive forecast revisions. Institutional investors recognize the value of this skill, while retail investors remain largely unaware, which contributes to financial inequality. This study highlights the critical role of information diversity in analyst performance.

This study identifies an incentive-compatible mechanism to foster individual environmental engagement. Utilizing a dataset comprising 100,000 randomly selected users of Ant Forest—a prominent personal carbon accounting platform embedded within Alipay, China's leading BigTech super-app—we provide causal evidence that individuals strategically engage in eco-friendly behaviors to enhance their credit limits, particularly when approaching borrowing constraints. These behaviors not only illustrate the green nudging effect of BigTech but also generate value for the platform by leveraging individual green actions as soft information, thereby improving the efficiency of credit allocation. Using a structural model, we estimate an annual green value of 427.52 million US dollars generated by linking personal carbon accounting with BigTech credit. We also show that the incentive-based mechanism surpasses green mandates and subsidies in improving consumer welfare and overall societal welfare. Our findings highlight the role of an incentive-aligned approach, such as integrating personal carbon accounts into credit reporting frameworks, in addressing environmental challenges.