Fama–French factor timing: The long‐only integrated approach

• Published date: 01 September 2021
• Author: Markus Leippold,Roger Rueegg
• Date: 01 September 2021
• DOI: http://doi.org/10.1111/eufm.12285

DOI: 10.1111/eufm.12285

ORIGINAL ARTICLE

Fama–French factor timing: The long‐only

integrated approach

Markus Leippold

1

|Roger Rueegg

2

1

Department of Banking and Finance,

University of Zurich, Plattenstrasse 14,

Zurich, Switzerland

2

Department of Banking and Finance,

University of Zurich and Zurich

Cantonal Bank, Hardstrasse 201, Zurich,

Switzerland

Correspondence

Markus Leippold, Department of

Banking and Finance, University of

Zurich, Plattenstrasse 14, 8032 Zurich,

Switzerland.

Email: markus.leippold@bf.uzh.ch

Abstract

There is ample evidence that factor momentum exists

in the standard long–short mixed approach to factor

investing. However, the excess returns are put under

scrutiny due to the high implementation costs. We

present a novel real‐life approach that relies on the

long‐only integrated approach to factor investing.

Instead of exploiting the potential momentum in factor

portfolios, our strategy builds on the momentum of the

optimal factor score weights in the integrated ap-

proach, which allows us to additionally profit from the

serial dependence in the factors' interaction effects.

One limitation of short‐term timing strategies is their

high turnover. By including the information of the

covariance matrix and minimising the strategy's risk to

the market portfolio, we can substantially reduce

turnover. The resulting timing alpha remains sig-

nificant even after transaction costs in a robust statis-

tical test framework across the major stock markets.

KEYWORDS

equity style timing, factor timing, integrated approach,

momentum

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© 2020 John Wiley & Sons Ltd.

We thank John Doukas (the editor) and two anonymous referees for their thoughtful comments. We are also indebted

to Fabian Ackermann, Michael Bretscher, Martin Gillholm, Andreas Kappler, Istvan Redl and Jue Ren for helpful

discussions on the subject.

1|INTRODUCTION

Factor timing has gained much popularity recently. Arnott et al. (2018) suggests that industry

momentum may be explained by factor momentum. Other recent works that document factor

momentum are Gupta and Kelly (2019) and Ehsani and Linnainmaa (2019), who find strong

autocorrelations among a broad set of factors that lead to high returns of cross‐and time‐series

momentum strategies. Their results indicate that the market prices of risk only adjust slowly

over time. In retrospect, this factor persistence represents a source of alpha, which could

potentially be exploited with an appropriate factor timing strategy.

The recent papers by Novy‐Marx and Velikov (2016) and Patton and Weller (2020) show

that there is a gap between the profitability of trading strategies ‘on paper’and the returns

exploited in practice. An open debate goes back to the work of Shleifer and Vishny (1997). They

state that even the simplest arbitrage strategies are difficult to implement profitably. In parti-

cular, Novy‐Marx and Velikov (2016) and Patton and Weller (2020) state that accounting for

implementation costs diminishes the returns of momentum strategies. Since existing literature

sees factors as returns of long–short portfolios, the turnover of the single securities and im-

plementation costs are often neglected. Hence, there is the open question regarding whether

recently documented factor momentum returns are robust in a realistic setting. In particular,

we consider the situation in which the investor is restricted to a long‐only portfolio.

1

To answer this question, we introduce the integrated approach to long‐only factor timing.

2

As shown in Figure 1, the standard approach to factor timing in literature considers a broad set

of long–short strategies and analyses performance on portfolio‐level data.

3

We study factor

timing using equity‐level data in a pure long‐only context. Since the mix of individual

long–short portfolios can only be efficient by chance, our approach offers two advantages over

the standard approach. On the one hand, it allows us to exploit factors' interaction effects in the

factor momentum portfolios.

4

On the other hand, we can extend the analysis of the simple

portfolio sorts and include the information of the covariance matrix of equity returns in the

construction of the long‐only factor timing portfolio, which gives us additional robustness for

the alpha, lower turnover and a significant improvement in the Sharpe ratio. Moreover, by

applying realistic transaction costs in our stock‐level analysis, we can explore most directly the

unresolved issue of whether auspicious factor timing is practically feasible or not.

We study factor timing in a realistic setting, but we also minimise the common pitfalls of

out‐of‐sample backtesting. In particular, we apply a robust statistical testing framework that

adjusts for multiple tries and respects the time‐series nature of the returns by taking into

account serial and cross dependence. We jointly test three different equity universes: for the

1

Most institutional investors are long‐only investors (Cao et al., 2017; Molk & Partnoy, 2019). Moreover, short‐selling costs are still substantial, leading to an

asymmetry in arbitrage (Nagel, 2005; Stambaugh et al., 2015). Lastly, during market turmoil, regulators tend to impose short‐selling bans, e.g. during the 2007‐

09 crisis (Beber & Pagano, 2013) and also in the wake of the COVID‐19 pandemic, as numerous European jurisdictions, including France, Italy, Spain, Greece

and Belgium, have enacted short‐sale bans in an attempt to stabilise financial markets and maintain investor confidence. Therefore, we believe that the analysis

of a long‐only investor is a relevant one.

2

There are (at least) two diverging views on how to build multi‐factor portfolios. The first approach, the mixed approach, is to mix where a portfolio is formed

by combining stand‐alone factor portfolios. In the second approach, the integrated approach, a portfolio is created by selecting securities that have

simultaneously strong exposure to multiple factors at once. For our long‐only factor timing, we rely on the integrated approach. See Bender and Wang (2016)

for a comparison of the two approaches.

3

See Arnott et al. (2018), Gupta and Kelly (2019) and Ehsani and Linnainmaa (2019).

4

Our observation that factor returns play a crucial role in generating performance is an indication of the benefit of assuming that factors are not only time

varying but also correlated. As Haddad et al. (2020) remark, an optimal factor timing portfolio is equivalent to a stochastic discount factor, and the maximum

conditional Sharpe ratio return depends on the inverse of the covariancematrix of factor returns. Hence, including the off‐diagonal terms of the factor returns'

covariance matrix is crucial to the formation of such an optimal factor timing strategy.

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US, developed markets and emerging markets. We also avoid the common pitfalls of look‐

ahead biases with survivorship‐free universes, and a data lag of two days for international

portfolios, which require trading in different time zones. Since this paper focusses on whether

we can capture factors outside the sample over time, we do not consider the entire ‘zoo of

factors’.

5

Instead, for our analysis, we rely on the most well‐known factor model specifications

that have the most extended out‐of‐sample history.

6

In particular, we use the classical

Fama–French three‐factor model presented in Fama and French (1992) and the Fama–French

five‐factor model introduced in Fama and French (2015). As the third factor model, we add the

momentum factor of Jegadeesh and Titman (1993) to the five‐factor model. These models are

widely accepted in both industry and academic literature.

For the US market, our approach allows us to generate significant out‐of‐sample alphas relative to

the multi‐factor benchmark that are robust to multiple hypothesis testing, ranging between 0.36%

and 0.42% per month, depending on the factor model. As a result, we can almost double the market's

Sharpe ratio with our long‐only factor timing strategies. Our strategy also remains significant for

developed markets for all factor models. The alphas range between 0.29% and 0.47% per month. For

the emerging market, only the three‐factor model remains significant, with an alpha of 0.44%.

We remark that the alpha arises solely because of the timing ability, since we adjust for the

traditional long–short factor exposures in our regression analysis. We emphasise this finding

because our long‐only integrated approach without timing does not survive the adjustment in

FIGURE 1 Long‐only mixed and integrated approach to factor timing. The mixed approach to factor

timing that is standard in literature compared with our long‐only integrated approach to factor timing. We

illustrate the different steps that lead to the optimal long‐only portfolio with

N

=10

stocks and two factors in

addition to the market factor

5

As of February 2019, Harvey and Liu (2019) document over 400 factors published in top journals. However, new factors must clear a much higher hurdle, and

there is little evidence about their post‐publication returns.

6

Also, as shown in Bauer et al. (2010), there arise regional differences as to whether a premium is positive or not. Since we analyse different regions, we are

well advised in focussing on the established factors that have been tested in worldwide portfolios.

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