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.

We study the influence of investor attention on mutual fund investors' fund selection and fund managers' portfolio choice. Using the Google Search Volume Index to measure investor attention on individual stocks, we find fund investors tend to direct more capital to mutual funds holding more high-attention stocks; fund managers tend to perform window-dressing trading to increase the portfolio holdings of high-attention stocks displayed to investors. Our results suggest that funds, particularly those with strong incentives, strategically trade on stock attention to attract investor flows. This strategic trading behaviour is also associated with fund underperformance and leads to larger non-fundamental volatility of holding stocks.

We investigate the economic impact of artificial intelligence (AI) adoption in the mutual fund industry by introducing a novel measure of AI adoption based on the presence of AI skilled personnel at fund management firms. We provide robust evidence that AI adoption enhances fund performance, primarily by improving risk management, increasing attentive capacity, and enabling faster information processing. Furthermore, we find that mutual funds with higher levels of AI adoption experience greater investor net flows and exhibit lower flow-performance sensitivity. While AI adoption benefits individual funds, we find no evidence of aggregate performance improvements at the industry level.

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.

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.

This paper studies the impact of AI technology on the mutual fund industry. I develop a new measure of AI adoption based on hiring practices and find that this measure can predict fund performance. The funds with high AI ratio outperform non-AI funds, after I controlling for standard factors and fund characteristics. Further empirical evidence shows that funds with a high AI ratio tilt their portfolios toward high information intensity stocks, indicating that mutual funds benefit from AI technology adoption by improving their information capacity. Consistent with this channel, I find that the outperformance of these mutual funds mainly comes from better stock picking skills. Finally, AI technology adoption has a negligible effect on fund manager turnover.

We examine the performance of actively managed Chinese mutual Funds over the period 2002-2020. Using the bootstrap-based false discovery technique, we find that 19.25% of Chinese actively managed mutual funds produce positive-alpha, which contrasts with existing studies documented by others in developed markets. Our findings survive a battery of robustness tests. Unlike in developed markets, equilibrium accounting may not hold in China as the Chinese stock market is dominated by retail investors instead of mutual funds, and thus the mutual funds in China can be more skilled at the expense of the retail investors. We find supportive evidence of the applicability of the bootstrap-based false discovery rate method by conducting simulations.

By using a unique proprietary dataset and implementing the Wuhan (China) lockdown from January to April 2020 as a natural experiment, we find that individual mutual fund investors in Wuhan significantly reduced their trading frequency, total investment of their portfolios, and risk level of their invested funds during the lockdown period as compared to investors in other cities. These changes are stronger for older investors and are reversed soon after the lifting of the lockdown. Our results suggest that the elimination of face-to-face interaction among individual investors reduced their information sharing, which led to more conservatism in their financial trading. These results are not supported by the alternative explanations of limited investor attention and temporary changes in personal circumstances, including depression and/or income reduction, during the lockdown period. Finally, consistent with the theory of naïve investor trading, we also find that investors received higher trading returns during the lockdown.

This study examines the impact of quantitative investment on stock price crash risk from the perspective of quantitative mutual funds holdings. The results show that quantitative mutual funds holdings can significantly reduce stock price crash risk, and this effect is more pronounced in subsamples characterized by executives with overseas backgrounds, higher internal governance efficiency, greater analyst attention, and higher profit volatility. Further research finds that quantitative mutual funds holdings can suppress the risk of stock price crash by smoothing the volatility of stock returns and optimizing the valuation of firms. This study sheds light on the effects of quantitative investment on stock price crash risk.

This study explores how Chinese mutual fund managers’ degrees of disagreement (DOD) on stock market returns affect investor capital allocation decisions using a novel textbased measure of expectations in fund disclosures. In the time series, the DOD negatively 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 effect 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 sophistication of investors and provide evidence that our results are not contingent upon such sophistication.