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.

This paper finds that reported fund returns do not necessarily represent the returns of mutual fund investors, especially over long investment periods. We show that mutual fund’s reported returns are calculated using NAV and represent the mutual fund manager’s skill in extracting value from the capital market. However, the real returns earned by mutual fund investors depend not only on the mutual fund manager’s skill but also on the subscription and redemption activities. Using the inflow and outflow information reported in the mutual funds’ semi-annual reports in China, we are able to calculate mutual fund investors’ real returns. We further derive the adjusted gain coefficient (AGC) to capture the difference between the reported mutual fund returns and the mutual fund investors’ real returns. We find that the AGC is significantly lower than 1, which suggests that the real returns of mutual fund investors are significantly lower than reported mutual fund returns in China. The underperformance of mutual fund investors relative to the mutual fund managers they invest in is very persistent and is stronger in more recent years. A further investigation reveals that this underperformance is largely attributed to investors’ poor timing skills and additional fees incurred as a result of excessive subscription and redemption activities. We also identify skilled mutual fund investors using AGC and find that fund managers can benefit from investors’ timing skills. Skilled mutual fund investors flow in when the mutual fund managers have good investment opportunities and flow out when the mutual fund managers have extra cash. The synchronization of the mutual fund investors’ flow and mutual fund managers’ investment strategies can reduce the need for liquidity management and improve mutual fund performance. Using Chinese mutual funds data, we show that a 1% increase in AGC can increase fund riskadjusted return by 0.2% in the next six months.

The adequate evaluation of mutual fund performance and of the fund managers’ ability to add value is an issue to which it has been given special attention in the recent financial literature. One of the traditional evaluation measures most commonly used is Carhart's alpha. However, one of the main problems of the evaluation methods that use the beta of the portfolios as a measure of risk and, therefore, Carhart's alpha is its sensitivity to the definition of the market portfolio. In this work we study the importance of defining the market portfolio using Carhart's alpha for a sample of UK mutual funds, and the influence of this market portfolio in the funds´ excess returns and in the performance ranking classification of the fund sample.