Forecasting Stock Returns Through an Efficient Aggregation of Mutual Fund Holdings

• 成果类型: Article
• 署名作者: Wermers, Russ; Yao, Tong; Zhao, Jane
• 署名单位: University System of Maryland; University of Maryland College Park; University of Iowa
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454
• DOI: 10.1093/rfs/hhs111
• 发表日期: 2012
• 页码: 3490
• 关键词: cross-section; PICK STOCKS; performance; liquidity; persistence; size; momentum; Managers; MARKET; IMPACT

摘要

We develop a stock return-predictive measure based on an efficient aggregation of the portfolio holdings of all actively managed U.S. domestic equity mutual funds, and use this model to study the source of fund managers' stock selection abilities. This generalized inverse alpha (GIA) approach reveals differences in the ability of managers to predict firms' future earnings from fundamental research. Notably, the GIA's return-forecasting power is not subsumed by publicly available quantitative predictors, such as momentum, value, and earnings quality, nor is it subsumed by methods shown in past research to forecast stock returns using fund holdings or trades.