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.

This study highlights asymmetries in liquidity risk pricing from the perspective of heterogeneously informed traders facing changing levels of preference uncertainty. We hypothesize that higher illiquidity premium and liquidity risk betas may arise simultaneously in circumstances where investors are asymmetrically informed about their trading counterparts’ preferences and their financial firms’ timely valuations of assets . We first test the time-varying state transition patterns of IML, a traded liquidity factor of the return premium on illiquid-minus-liquid stocks, using a Markov regime-switching framework. We then investigate how the conditional price of the systematic risk of the IML fluctuate over time subject to changing levels of preference uncertainty. Empirical results from the Chinese stock market support our hypotheses that investors’ sensitivity to the IML systematic risk conditionally increase in times of higher preference uncertainty as proxied by the stock turnover and order imbalance. Further policy impact analyses suggest that China’s market liberalization efforts, contingent upon its recent stock connect and margin trading programs, reduce the conditional price of liquidity risk for affected stocks by helping the incorporation of information into stock prices more efficiently. Tighter macroeconomic funding conditions, on the contrary, conditionally increase the price of liquidity that investors require.

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.

How much value has been lost in the Chinese bankruptcy system due to excessive liquidation of companies whose going concern value is greater than the liquidation value? I compile new judiciary bankruptcy auction data covering all bankruptcy asset sales from 2017 to 2022 in China. I estimate the valuation of the asset for both the final buyer and creditor through the revealed preference method using an auction model. On average, excessive liquidation results in a 13.5% welfare loss. However, solely considering the liquidation process, an 8% welfare gain is derived from selling the asset without transferring it to the creditors. Firms that are (1) larger in total asset size, (2) have less information disclosure, (3) have less access to the financial market, and (4) possess a higher fraction of intangible assets are more vulnerable to such welfare loss. Overall, this paper suggests that policies promoting bankruptcy reorganization by introducing distressed investors who target larger bankruptcy firms suffering more from information asymmetry will significantly enhance welfare in the Chinese bankruptcy market.

This study explores how Chinese mutual fund managers’ degrees of disagreement (DOD) on stock market returns affect investor capital allocation decisions using a novel text-based measure of expectations in fund disclosures. In the time series, the DOD neg-atively predicts market returns. Cross-sectional results show that investors correctly perceive the DOD as an overpricing signal and discount fund performance accordingly. Flow-performance sensitivity (FPS) is diminished during high dispersion periods. The ef-fect is stronger for outperforming funds and funds with substantial investments in bubble and high-beta stocks, but weaker for skilled funds. We also discuss ffnancial sophisti-cation of investors and provide evidence that our results are not contingent upon such sophistication.

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 paper investigates the impact of curation algorithms through the lens of stock market dynamics. By innovatively incorporating the dynamic interactions between social media platforms, investors, and stock markets, we construct the Social-Media-augmented Artificial Stock marKet (SMASK) model under the agent-based computational framework. Our findings reveal that curation algorithms, by promoting polarized and emotionally charged content, exacerbate behavioral biases among retail investors, leading to worsened stock market quality and investor wealth levels. Moreover, through our experiment on the debated topic of algorithmic regulation, we find limiting the intensity of these algorithms may reduce unnecessary trading behaviors, mitigates investor biases, and enhances overall market quality. This study provides new insights into the dual role of curation algorithms in both business ethics and public interest, offering a quantitative approach to understanding their broader social and economic impact.

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.

Using a unique dataset from China spanning 2005 to 2023, we investigate how superstitious beliefs influence mutual fund managers’ risk-taking behavior and how this influence evolves over their careers. We find a significant 6.82% reduction in risk-taking during managers’ zodiac years, traditionally considered unlucky in Chinese culture. This effect is particularly pronounced among less experienced managers, those without financial education backgrounds, and those with lower management skills. The impact also intensifies during periods of high market volatility. Our findings challenge the traditional dichotomy between retail and professional investors, showing that even professional fund managers can be influenced by irrational beliefs early in their careers. However, the diminishing effect of superstition with experience and expertise suggests a gradual transition towards more rational decision-making. Our results provide insights into the process by which financial professionals evolve from exhibiting behavior akin to retail investors to becoming the rational actors often assumed in financial theory.

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.