Mutual fund style drift measured using higher moments and its cash flow incentive

This paper evaluates the contribution of higher moment information to the identification of mutual fund investment styles. We develop a multi-objective optimization model that identifies fund investment styles by simultaneously considering returns and higher moments risks. Our results indicate that this model, by incorporating variance and skewness, can more accurately identify fund investment styles. We then quantify the degree of style drift exhibited by funds utilizing the proposed model. Employing a dataset of 1327 open-ended equity funds in China between 2008 and 2023, we find that style drift is both pervasive and persistent. Funds susceptible to style drift tend to be smaller, have higher portfolio turnover and expense ratios, and are supervised by less seasoned managers. In addition, we explore how cash flows affect funds' style drift behavior. Our analysis reveals a positive relationship between low cash inflows and the magnitude of style drift. This finding remains consistent after addressing potential endogeneity concerns. Finally, we find no support for the hypothesis that style drift enhances future fund performance.