Financial Analyst Journal, Volume74, Issue3, 2018

构建做多的多因子策略：组合混合vs信号混合

作者：Khalid (Kal) Ghayur (Goldman Sachs Asset Management)

Ronan Heaney (Goldman Sachs Asset Managemen)

Stephen Platt(Goldman Sachs Asset Management)

摘要：本文发现，可以通过将单因子组合（组合混合策略）或通过将单因子信号组合成复合信号（信号混合策略）来构建仅做多的多因子策略。为了比较这两种方法，我们提出了构建曝光匹配组合的框架。在对全球股票市场的实证检验中，我们发现，投资组合的混合策略通常会为中低水平的跟踪误差产生更高的信息比率👨🏿‍🔬。在较高水平的跟踪误差下，信号混合策略产生更好的风险调整的表现。这些结果通常适用于各种因子组合🙆‍♀️，并且它们对于投资者实施多因素智能β策略具有重要的实际意义。

Constructing Long-Only Multifactor Strategies: Portfolio Blending vs. Signal Blending

Khalid (Kal) Ghayur (Goldman Sachs Asset Management), Ronan Heaney (Goldman Sachs Asset Managemen), Stephen Platt(Goldman Sachs Asset Management)

ABSTRACT

Long-only multifactor strategies may be constructed by combining individual-factor portfolios (portfolio blending) or by combining individual-factor signals into a composite signal to construct the portfolio (signal blending). To compare these two approaches, we present a framework for building exposure-matched portfolios. In empirical tests on global equity markets, we find that, generally, portfolio blending generates higher information ratios for low-to-moderate levels of tracking error. At high levels of tracking error, signal blending delivers better risk-adjusted performance. These results generally hold for various factor combinations, and they have important practical implications for investors considering the implementation of multifactor smart-beta strategies.

原文链接：

https://www.cfapubs.org/doi/abs/10.2469/faj.v74.n3.5

翻译🎏：秦秀婷