Holdings Data, Security Returns, and the Selection of Superior Mutual Funds
研究发现，基于基金持仓和证券贝塔计算的阿尔法来筛选基金，比基于基金收益时间序列回归的阿尔法更能预测未来表现，且持仓数据越频繁效果越好。
Abstract In this paper we show that selecting mutual funds using alpha computed from a fund’s holdings and security betas produces better future alphas than selecting funds using alpha computed from a time-series regression on fund returns. This is true whether future alphas are computed using holdings and security betas or a time-series regression on fund returns. Furthermore, we show that the more frequently the holdings data are available, the greater the benefit. This has major implications for the Securities and Exchange Commission’s recent ruling on the frequency of holdings disclosure and the information plan sponsors should collect from portfolio managers. We also explore the effect of conditioning betas on macroeconomic variables as suggested by Ferson and Schadt (1996) to identify superior-performing mutual funds as well as the alternative way of employing holdings data proposed by Grinblatt and Titman (1993).