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

//, Factor Investing, Basilico and Johnsen, Academic Research Insight/Constructing Long-Only Multifactor Strategies: Portfolio Blending vs. Signal Blending

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

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

  • Khalid Ghayur, CFA, Ronan Heaney, and Stephen Platt, CFA
  • Financial Analysts Journal
  • A version of this paper can be found here
  • Want to read our summaries of academic finance papers? Check out our Academic Research Insight category

What are the research questions?

The heightened interest in factor investing has been accompanied by a corresponding focus on the nuts and bolts of constructing multifactor portfolios. There are essentially two ways to go: In a one-step process, single factor signals are blended into a composite signal and one multifactor portfolio is created from the individual stock composites. Or in a two-step process, single factor portfolios are created from single-factor signals and then blended into one multifactor portfolio. Either way, a multifactor portfolio is the result. The question as to which process is superior has been debated in the academic literature and in practice. The weight of the evidence to-date falls on the side of signal blending. However, the results of this study challenge that conclusion. (AA finds similar results here).

  1. Do factor portfolios built using “signal blending” dominate portfolios built using a “portfolio blending” approach?

What are the Academic Insights

  1. YES and NO. It depends on the level of active risk and the amount of factor exposure exhibited by each type of portfolio.  Portfolio blending produced higher IRs at low/moderate levels of factor exposure and active risk. Signal blending produced higher IRs at high levels of factor exposure and active risk.  Results are presented in the chart below.  As average exposure increases, portfolio blending produces higher IRs until the average exposure increases beyond approximately .9. For average exposures beyond .9, signal blending is superior.  However, signal blending exhibits higher active risk as average exposure increases until approximately .9, at which point portfolio blending exhibits exponential increases in active risk.  Portfolio blending remains superior at levels of active risk of approximately 8.0 or lower, at which point the two processes appear to converge.

Why does it matter?

The authors create (average) exposure-matched portfolios at varying levels of factor exposure using each multifactor construction process. The analysis is completed for one, two, three and four-factor portfolios. The US results were validated using international and emerging market data over the periods 1995/1998 to 2016.  The cross-validation and long time series both lend a pretty fair amount of credibility to the results.

The conventional wisdom holds that investors are penalized in terms of efficiency when they adopt a portfolio blending (or signal blending for that matter) approach if other issues such as transparency in construction, transparency in return attribution, implementation costs, factor representation and capacity are overriding or conflicting objectives. Defining these tradeoffs should influence the decision as to whether blending signals or blending portfolios is optimal for the investor interested in smart beta or multifactor strategies.

The authors say it best:

…..the focus of the current debate is misplaced. The existing literature makes an attempt to reach a general conclusion about whether portfolio blending is a superior approach to signal blending or vice versa. We argue that such an assessment is more appropriately made in the context of a given investment process.

The most important chart from the paper

The results are hypothetical results and are NOT an indicator of future results and do NOT represent returns that any investor actually attained. Indexes are unmanaged and do not reflect management or trading fees, and one cannot invest directly in an index.


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-tomoderate 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.


  • The views and opinions expressed herein are those of the author and do not necessarily reflect the views of Alpha Architect, its affiliates or its employees. Our full disclosures are available here. Definitions of common statistics used in our analysis are available here (towards the bottom).
  • Join thousands of other readers and subscribe to our blog.
  • This site provides NO information on our value ETFs or our momentum ETFs. Please refer to this site.

About the Author:

Tommi Johnsen, PhD
Dr. Tommi Johnsen, until retirement in 2017, was the Director of the Reiman School of Finance and a tenured faculty at the Daniels College of Business at the University of Denver.She has worked extensively as a consultant and investment advisor in the areas of quantitative methods and portfolio construction. She taught at the graduate and undergraduate level and published research in several areas including: capital markets, portfolio management and performance analysis, financial applications of econemetrics and the analysis of equity securities. Her publications have appeared in numerous peer-reviewed journals.
Yes No
This website uses cookies and third party services. Settings Ok


We use “cookies” on this site. A cookie is a piece of data stored on a site visitor’s hard drive to help us improve your access to our site and identify repeat visitors to our site. For instance, when we use a cookie to identify you, you would not have to log in a password more than once, thereby saving time while on our site. Cookies can also enable us to track and target the interests of our users to enhance the experience on our site. Usage of a cookie is in no way linked to any personally identifiable information on our site. Some of our business partners may use cookies on our site (for example, advertisers). However, we have no access to or control over these cookies.

Embedded Content

Articles on this Site may include embedded content (e.g. videos, images, articles, etc.). Embedded content from other websites behaves in the exact same way as if the visitor has visited the other website.These websites may collect data about you, use cookies, embed additional third-party tracking, and monitor your interaction with that embedded content, including tracking your interaction with the embedded content if you have an account and are logged in to that website.