全球智库信息集成服务系统(G2TT): Rethinking Performance Evaluation

G2TT

来源类型	Working Paper
规范类型	报告
DOI	10.3386/w22134
来源ID	Working Paper 22134
	Rethinking Performance Evaluation
	Campbell R. Harvey; Yan Liu
发表日期	2016-04-04
出版年	2016
语种	英语
摘要	We show that the standard equation-by-equation OLS used in performance evaluation ignores information in the alpha population and leads to severely biased estimates for the alpha population. We propose a new framework that treats fund alphas as random effects. Our framework allows us to make inference on the alpha population while controlling for various sources of estimation risk. At the individual fund level, our method pools information from the entire alpha distribution to make density forecast for the fund's alpha, offering a new way to think about performance evaluation. In simulations, we show that our method generates parameter estimates that universally dominate the OLS estimates, both at the population and at the individual fund level. While it is generally accepted that few if any mutual funds outperform, we find that the fraction of funds that generate positive alphas is accurately estimated at over 10%. An out-of-sample forecasting exercise also shows that our method generates superior alpha forecasts.
主题	Financial Economics ; Financial Markets ; Portfolio Selection and Asset Pricing ; Financial Institutions
URL	https://www.nber.org/papers/w22134
来源智库	National Bureau of Economic Research (United States)
引用统计
资源类型	智库出版物
条目标识符	http://119.78.100.153/handle/2XGU8XDN/579807
推荐引用方式 GB/T 7714	Campbell R. Harvey,Yan Liu. Rethinking Performance Evaluation. 2016.

条目包含的文件
文件名称/大小	资源类型	版本类型	开放类型	使用许可
w22134.pdf（525KB）	智库出版物		限制开放	CC BY-NC-SA	浏览