Using comprehensive account-level data and 192 asset pricing anomaly signals, we investigate whether retail investors and institutions trade on anomalies in China. We find that retail investors tend to trade contrary to anomaly prescriptions, suggesting that they have a strong tendency to buy (sell) overvalued (undervalued) stocks. In contrast, institutions trade consistent with anomalies, indicating that they buy (sell) undervalued (overvalued) stocks. Regarding the information content of anomalies, we find that small retail investors trade contrary to trading-based anomalies, whereas institutions trade consistent with both trading- and accounting-based anomalies. Additionally, lottery stock preference and return extrapolation help explain investors’ trading behavior on anomalies.

The scale expansion of low-carbon industries and the green transformation of carbon-intensive industries are two sides of the same coin in achieving the “dual carbon” goals. However, research on transition finance supporting the upgrading of traditional existing carbon-intensive industries remains insufficient. The key to examining the effectiveness of transition finance lies in distinguishing whether the supported enterprises are engaging in greenwashing or green evolution. Based on data of Chinese A-share listed companies in the carbon-intensive industries, an empirical study is conducted and offers the following findings: (1) Transition finance not only does not increase greenwashing but also promotes comprehensive green innovation in carbon-intensive enterprises. (2) In terms of the influencing mechanism, transition finance exerts “resource effects” and “signaling effects,” promoting green innovation by improving debt maturity mismatch and attracting green institutional investors. (3) Heterogeneity analysis shows that the positive impact of transition finance on green innovation is particularly pronounced among enterprises in the eastern region, state-owned enterprises, and those with lower levels of managerial myopia. (4) Further industry spillover effects analysis reveals that transition finance empowers green innovation within industries though peer effects and competitive effects. The findings are essential for understanding the effectiveness of transition finance and offer valuable insights for policymakers.

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.

Why is price momentum absent in China? Since momentum is commonly considered arising from investors’ under-reaction to fundamental news, we decompose monthly stock returns into news- and non-news-driven components and document a news day return continuation along with an offsetting non-news day reversal in China. The non-news day reversal is particularly strong for stocks with high retail ownership, relatively less recent positive news articles, and limits to arbitrage. Evidence on order imbalance suggests that stock returns overshoot on news days due to retail investors' excessive attention-driven buying demands, and mispricing gets corrected by institutional investors on subsequent non-news days. To avoid this tug-of-war in stock price, we use a signal that directly captures the recent news performance and re-document a momentum-like underreaction to fundamental news in China.

We introduce a novel perspective on private information, specifically microstructure-based private information, to unravel how institutional investors predict stock returns. Using tick-by-tick transaction data from the Chinese stock market, we find that in retail-dominated markets, institutional investors positively predict stock returns, consistent with findings from institution-dominated markets. However, in contrast to the traditional view that institutional investors primarily rely on value-based private information, our results indicate that microstructure-based private information contributes almost as much to their predictive power as value-based private information does, with both components jointly accounting for approximately two-thirds of the total predictive power of institutional order flow. This finding reveals that retail investors’ trading activities significantly impact institutional investors, naturally forcing them to balance firm value information with microstructure information, thus profoundly influencing the price discovery process in the stock market.

Site visits allow visitors to physically inspect productive resources and interact with onsite employees and executives face-to-face. We posit that, by allowing visitors to acquire investmentrelated information and monitor the management team, site visits offer disciplinary benefits for corporate investments. Using mandatory disclosures of site visits in China, we find that corporate investments become more responsive to growth opportunities as the intensity of site visits increases, consistent with the notion that site visits yield disciplinary benefits. We also find that the positive association between site visits and investment efficiency is more pronounced when visitors can glean more investment-related information and when they have stronger incentives and greater power to monitor managers. This positive association is also stronger among firms with more severe agency problems and higher asset tangibility. The overall evidence supports the notion that site visits serve as a unique venue for institutional investors and financial analysts to acquire valuable information and serve a monitoring function, which generates disciplinary benefits for corporate investments.

Using Chinese publicly listed firms from 2007 to 2020, this study empirically explores the impact of non-controlling shareholders’ network on the corporate excess goodwill. We find that the centrality of non-controlling shareholders’ network significantly decreases the excess goodwill from mergers and acquisitions, indicating that non-controlling shareholders’ network can restrain the goodwill bubbles. Moreover, the inhibitory effect of non-controlling shareholders’ network on excess goodwill stems from pressure-resistant institutional investors and individual investors. This effect is achieved through the information effect, resource effect, and governance effect. Furthermore, this inhibitory effect is more pronounced in firms located in less developed regions and legal environments, and firms with lower audit quality. In conclusion, non-controlling shareholders’ network plays a positive role in the restriction of excess goodwill in listed companies.

This study explores the proclivity of institutional investors in China towards highESG stocks amidst the growth of ESG investment funds. Using A-share data from 2015-2022 and a Tobit model analysis, it is found that these investors indeed favor such stocks, particularly under extensive analyst coverage and in non-state-owned firms. However, rating discrepancies can impact this preference. The attraction lies in reduced operational risks and improved net profits. Notably, independent investors show a stronger ESG preference, especially within high-pollution industries. Thus, fostering ESG investment among institutional investors can improve resource allocation in China's capital market, favoring eco-friendly companies.

We show that the personal traits of analysts, as revealed by their political donations, influence their forecasting behavior and stock prices. Analysts who contribute primarily to the Republican Party adopt a more conservative fore- casting style. Their earnings forecast revisions are less likely to deviate from the forecasts of other analysts and are less likely to be bold. Their stock recommen- dations also contain more modest upgrades and downgrades. Overall, these analysts produce better quality research, which is recognized and rewarded by their employers, institutional investors, and the media. Stock market participants, how- ever, do not fully recognize their superior ability as the market reaction following revisions by these analysts is weaker.

We examine how pre-announcement weather conditions near a firm’s major institutional in- vestors affect stock market reactions to firms’ earnings announcements. We find that unpleasant weather experienced by institutional investors leads to more delayed market responses to sub- sequent earnings news. Moreover, unpleasant weather of institutional investors is associated with higher earnings announcement premia. The influence of institutional investors’ weather is robust after controlling for New York City weather, extreme weather conditions, and firm local weather. Additional cross-sectional evidence suggests that the strength of this weather effect is related to institutional investors’ trading behavior.