How to build a factor portfolio: Does the allocation strategy matter?

• Published date: 01 January 2021
• Author: Hubert Dichtl,Wolfgang Drobetz,Viktoria‐Sophie Wendt
• Date: 01 January 2021
• DOI: http://doi.org/10.1111/eufm.12264

Eur Financ Manag. 2021;27:20–58.20

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wileyonlinelibrary.com/journal/eufm

DOI: 10.1111/eufm.12264

ORIGINAL ARTICLE

How to build a factor portfolio: Does the

allocation strategy matter?

Hubert Dichtl

1

|Wolfgang Drobetz

1

|Viktoria‐Sophie Wendt

2

1

Faculty of Business, University of

Hamburg, Hamburg, Germany

2

BlackRock Investment Management

Limited, London, UK

Correspondence

Wolfgang Drobetz, Faculty of Business,

University of Hamburg, Moorweidenstr,

18, 20148 Hamburg, Germany.

Email: wolfgang.drobetz@uni-hamburg.de

Abstract

Factor‐based allocation embraces the idea of factors, as

opposed to asset classes, as the ultimate building blocks

of investment portfolios. We examine whether there is a

superior way of combining factors in a portfolio and

provide a comparison of factor‐based allocation strategies

within a multiple testing framework. Factor‐based allo-

cation is profitable beyond exploiting genuine risk pre-

mia, even when applying multiple testing corrections.

Investment portfolios can be efficiently diversified using

factor‐based allocation strategies, as demonstrated by

robust economic performance over various economic

scenarios. The naïve equally weighted factor portfolio,

albeit simple and cost‐efficient, cannot be outperformed

by more sophisticated allocation strategies.

KEYWORDS

factor‐based allocation, multiple testing

JEL CLASSIFICATION

G11; G23

EUROPEAN

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This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and

reproduction in any medium, provided the original work is properly cited.

© 2020 The Authors. European Financial Management published by John Wiley & Sons Ltd.

We thank two anonymous referees, Geert Bekaert, John Doukas (editor), Tizian Otto, Tatjana Puhan, and Henning

Schröder for helpful comments.

1|INTRODUCTION

Building on the seminal work of Markowitz (1952), investors have traditionally focused on diversi-

fying across broad asset classes, such as equities and bonds, when building their investment portfolios

in order to balance risks and rewards. The premise underlying the asset allocation decision is that,

while efficiently diversifying away unrewarded (idiosyncratic) risks, the asset classes considered are

still subject to an inherent return premium that can be explained by some underlying common

factors. The capital asset pricing model (Lintner, 1965; Mossin, 1966;Sharpe,1964)promotedthe

“market”as the only explanatory factor. However, ever since the introduction of the arbitrage pricing

theory (Ross, 1976), assets have been thought of as a bundle of multiple factors that reflect different

risks and rewards (Ang, 2014; Ang, Goetzmann, & Schaefer, 2009; Ferson & Harvey, 1993).

Koedijk, Slager, and Stork (2016a) contrast three approaches to integrating the concept of factor

bundles into the investment management process. The first, so‐called risk due diligence approach,

simply uses the factor methodology to gain a better understanding of the factor exposures within a

previously determined investment portfolio, and to potentially revise the asset allocation accordingly.

The second approach, factor tilting, is currently the most widely used approach and refers to actively

tweaking a portfolio's exposure toward certain factors based on the existing asset allocation by either

introducing factors biases within the assets or complementing the assets with “pure”factors (Amenc,

Deguest, Goltz, Lodh, & Martellini, 2014; Dichtl, Drobetz, Lohre, & Rother, 2020). Lastly, factor

optimization uses the factors, instead of asset classes, as the ultimate building blocks of investment

portfolios, thus deciding on a factor‐based as opposed to asset‐based allocation. Scarred by the last

financial crisis, when presumably “well‐diversified”asset‐based portfolios declined substantially,

factor‐based allocation is becoming increasingly popular with institutional investors seeking to ef-

fectively diversify their portfolios (Koedijk, Slager, & Stork, 2016b;Ung&Kang,2015).

While the advantages and disadvantages of factor‐based allocation are fiercely debated (Idzorek

&Kowara,2013; Ilmanen & Kizer, 2012), the question how to optimally combine factors in a

portfolio has remained largely unanswered. Some recent studies use a heuristic approach such as a

portfolio of equally weighted or equal‐volatility weighted factors (Bender, Briand, Nielsen, &

Stefek, 2010; Ilmanen & Kizer, 2012). Other academic studies revert to risk‐minimization strategies

such as the global minimum variance portfolio (Amenc et al., 2014), or analyze efficient frontiers

from mean–variance optimizations (Brière & Szafarz, 2017; Clarke, de Silva, & Murdock, 2005;

Idzorek & Kowara, 2013). While each approach has its pros and cons, a thorough comparison of

these allocation strategies against the backdrop of factor‐based portfolio formation is pending.

To the best of our knowledge, our study is the first to jointly compare the performance of a

comprehensive set of tradable factor‐based allocation strategies in order to address two research

questions. First, is factor‐based allocation able to provide superior portfolio returns? And sec-

ond, does the specific allocation strategy applied matter much, that is, is there a superior way of

combining factors in a portfolio?

1

Our analysis assumes an investor who allocates to a com-

prehensive set of global equity and fixed income factors and applies a variety of factor opti-

mization strategies that are either estimation‐free or require the estimation of some factor

moments. In addition, we use Hansen's (2005) test for superior predictive ability (SPA test) and

its extensions to avoid a common bias in statistical inference referred to as “data snooping”, that

1

Our study examines how to optimally combine factors in an investment portfolio. We do not address the broader

question whether factor‐based allocation is really superior to asset allocation. For example, it is conceivable that factors

are simply a rotation of the underlying asset classes (Idzorek & Kowara, 2013).

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is, the fact that when many portfolios are evaluated individually on the same data set, some are

bound to show superior performance by chance alone.

By applying a multiple testing design, our study is related to Arnott, Harvey, Kalesnik, and

Linnainmaa (2019), who suggest that many investors develop exaggerated expectations about

factor performance as a result of data snooping. Furthermore, we contribute to the literature on

factor investing by asserting that factor‐based allocation is relatively easy to implement using

tradable instruments; by showing that such strategies earn significant risk‐adjusted excess

returns relative to the market; and by demonstrating that it is difficult to beat an equally

weighted factor portfolio, thereby providing guidance on the best method(s) to choose when

constructing factor‐based portfolios (Koedijk et al., 2016a). Finally, our analysis is closely re-

lated to the recent literature on optimization methods (Bessler & Wolff, 2015; De Miguel,

Garlappi, & Uppal, 2009; Hsu, Han, Wu, & Cao, 2018; Kritzman, Page, & Turkington, 2010) and

applies previously researched methods to a new data set, that is, global equity and fixed income

factor premia that most investors can easily invest in. Our approach is straightforward to

replicate for almost any investor and, for practical applications, can be extended in‐house.

Our results confirm that factor investing, or rather factor‐based allocation, is profitable, as we find

positive returns in excess of cash that are robust to data‐snooping corrections for some strategies.

Although cash is technically speaking the correct benchmark for long‐short strategies, we also apply

the equity market portfolio as an alternative benchmark. Again, accounting for data‐snooping biases,

at least the naïve equally weighted factor portfolio is able to beat the equity market benchmark on a

risk‐adjusted basis. Perhaps even more important, our results ascertain that investment portfolios can

be efficiently diversified using factor‐based allocation because most factor portfolios deliver robust

economic performance across a variety of macroeconomic states. Finally, we establish that a naïve

equal weighting of factors exhibits comparatively good economic performance that cannot be out-

performed by more sophisticated allocation strategies within Hansen's (2005) multiple testing fra-

mework, thus mirroring previous results for traditional asset‐based allocation strategies (De Miguel

et al., 2009;Hsuetal.,2018). Our main finding that the particular portfolio allocation technique does

not matter much, and thus the equally weighted factor strategy is hard to beat, provides guidance for

investors who are interested in implementing factor optimization (Koedijk et al., 2016a).

Our findings also reinforce the notion that factor timing is difficult, and strategic diversification

across factors tends to outdo any attempts to actively time them. Both Asness (2016) and Asness,

Chandra, Ilmanen, and Israel (2017) argue that factor timing strategies are too correlated with the

basic factor strategies themselves to have a great impactonaportfoliothatalreadyincludesastrategic

allocation to factors. Such strategies may result in large bets on some of the factors, thereby weak-

ening performance due to forgone diversification. In brief, lost diversification is not overcome by

potential timing benefits.

2

Most recently, Dichtl, Drobetz, Lohre, Rother, and Vosskamp (2019)show

evidence that equity factors are predictably related to fundamental and technical time‐series indicators

as well as factor characteristics such as factor momentum and crowding, but they conclude that such

predictability is hard to take advantage of after transaction costs.

The remainder of our study is as follows. Section 2describes the factors and factor allocation

strategies we apply. Section 3contains an empirical analysis of factor premia. Section 4presents

2

Similarly, Lee (2017) suggests that factor timers must accept some degree of lost diversification in a portfolio as

potential opportunity costs of factor timing. Other authors such as Arnott, Beck, Kalesnik, and West (2016) and Bender,

Sun, Thomas, and Zdorovtsov (2018) also recognize that factor prediction based on market, sentimental, and macro-

economic indicators is not an easy task. However, they do not go as far to believe it is completely futile provided that an

investor has a long horizon and a good understanding of the investment rationale behind the factors.

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DICHTL ET AL.