Journal of Portfolio Management · Vol. 44, No. 1, Fall 2017

横截面和时间序列预测因子的择时因子

作者：Philip Hodges（BlackRock, Inc., in San Francisco, CA.）

Ked Hogan（BlackRock, Inc., in San Francisco, CA）

Justin R. Peterson（BlackRock, Inc., in San Francisco, CA）

Andrew Ang（BlackRock, Inc., in New York, NY）

摘要：投资者在什么时候应该使用什么样的智能贝塔策略?作者在不同的经济体制和市场条件下，寻找价值、规模👩🏿‍⚕️、动量、质量和最小波动智能贝塔因子的预测值。他们发现👶🏼，多个预测因子信息的组合（如商业周期因子🦀、价值因子🥣👨🏻‍🍳、相对强度和分散度因子）比使用单个预测因子更有效。

Factor Timing with Cross-Sectional and Time-Series Predictors

Philip Hodges（BlackRock, Inc., in San Francisco, CA.）；Ked Hogan（BlackRock, Inc., in San Francisco, CA）🧖🏻‍♀️；Justin R. Peterson（BlackRock, Inc., in San Francisco, CA）；Andrew Ang（BlackRock, Inc., in New York, NY）

ABSTRACT

What smart beta strategy should investors use and when? The authors search for predictors of value, size, momentum, quality, and minimum-volatility smart beta factors under different economic regimes and market conditions. They find that combining information from several predictors such as business cycle indicators, valuation, relative strength, and dispersion metrics is more effective than using individual predictors.

原文链接：

http://www.iijournals.com/doi/full/10.3905/jpm.2017.44.1.030

翻译🦃：黄涛