ON INDUSTRY MOMENTUM STRATEGIES

• DOI: http://doi.org/10.1111/jfir.12205
• Date: 01 March 2020
• Author: Klaus Grobys,James Kolari
• Published date: 01 March 2020

The Journal of Financial Research Vol. XLIII, No. 1 Pages 95–119 Spring 2020

DOI: 10.1111/jfir.12205

ON INDUSTRY MOMENTUM STRATEGIES

Klaus Grobys

University of Vaasa

James Kolari

Texas A&M University

Abstract

In this article, we investigate industry momentum strategies. We find that industry

portfolios that outperformed in the previous month generate on average significantly

higher returns in the holding period than those that underperformed. Plain and

risk‐managed strategies using this short‐run industry momentum are not subject to

optionality effects. Also, the tail risks of these strategies are uncorrelated with

traditional industry momentum strategies. The spread associated with the

risk‐managed strategy both meets necessary conditions as a risk factor and is

significantly priced in the cross‐section of U.S. industry portfolios.

JEL Classification: G12, G14

I. Introduction

Empirical asset pricing research has largely focused on exploring cross‐sectional patterns

in stock returns. Because of the failure of the capital asset pricing model (CAPM) of

Treynor (1961, 1962), Sharpe (1964), Lintner (1965), and Mossin (1966) in cross‐sectional

tests, Fama and French (1992, 1993) proposed their now‐famous three‐factor model with

market, size, and value factors. Subsequently, researchers have advanced a growing list of

factors and asset pricing models. For example, Carhart (1997) includes Jegadeesh and

Titman’s (1993) momentum portfolio as a fourth portfolio‐based risk factor to the three‐

factor model. Novy‐Marx (2013) suggests a four‐factor model containing market, value,

profitability, and momentum. Benchmarking against Carhart’sfour‐factor model, he argues

that his new model better explains more than a dozen cross‐sectional asset pricing

anomalies. Hou, Xue, and Zhang (2015) posit a new factor model consisting of market,

size, investment, and return on equity factors that improve on Carhart’s model. Relatedly,

Fama and French (2015, 2017) update their earlier research by offering a five‐factor model

that adds investment and profitability factors to their earlier three‐factor model. Fama and

French (2018) augment this model with the momentum factor to form a six‐factor model.

We are grateful for the comments received from the participants of the INFINITI Conference 2017 in

Valencia (Spain). We also appreciate the useful comments from the participants at the Accounting and Finance

seminar 2016 at the University of Vaasa. In particular, we thank Ali Anari, Julian Gaspar, Seppo Pynnönen, and

Wei Liu. We also thank an anonymous reviewer for helpful comments.

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© 2019 The Southern Finance Association and the Southwestern Finance Association

In general, as models have become more refined and complex, the ability to cross‐

sectionally price stock returns has gradually improved.

Particularly relevant to our article, a major gap in the asset pricing literature exists

with respect to industry portfolios. Lewellen, Nagel, and Shanken (2010) investigate the

usefulness of standard risk factors, such as the size and value factors, for pricing U.S.

industry portfolios. Unfortunately, “adding industry portfolios dramatically changes the

performance of the models, in terms of slope estimates, cross‐sectional R

2

s, and Tstatistics.

Compared to regressions with only size‐B/M (book‐to‐market equity) portfolios, the slopes

estimated using all 55 (industry) portfolios are almost always closer to zero and the cross‐

sectional R

2

s drop substantially”(Lewellen, Nagel, and Shanken 2010, p. 189). Given that

standard risk factors do not explain the cross‐section of industry returns, the question arises:

which factors span the returns of industry portfolios? To our knowledge, no study addresses

this question.

Filling this gap in the literature, we investigate the asset pricing implications of

different industry momentum strategies. Following standard practice, we sort U.S. industry

portfolios into quintiles. The first group comprises industries that have the lowest returns in

the previous month, and the fifth group comprises industries with the highest returns in the

previous month. A zero‐cost strategy is constructed that is long (short) the fifth (first)

portfolio group. Using a correlation analysis, this strategy is compared to traditional

industry momentum strategies. Further analyses examine whether the industry momentum

strategies can be explained by the Fama and French (2015, 2017) five‐factor model.

Motivated by recent literature on risk‐managed momentum, we also explore risk‐managed

industry momentum strategies and their associated tail risks by means of optionality

regressions. Finally, we employ stochastic discount factor model analysis to determine the

usefulness of the proposed strategies for pricing the cross‐section of U.S. industry portfolio

returns. Split‐subsample tests are employed to check the robustness of the results.

Moreover, a recently developed statistical approach is employed to investigate whether the

strategy satisfies theoretically motivated necessary conditions to qualify as a risk factor.

This article contributes to the literature in several important ways. First, we

investigate the correlation between different industry momentum strategies. In this regard,

Grobys, Ruotsalainen, and Äijö (2018) find that industry momentum is uncorrelated with

risk factors in Fama and French’s (2015) five‐factor model. Grobys (2018) proposes a

52‐week high industry momentum strategy with similar results. Also, Grundy and Martin

(2001) argue that the industry momentum strategy can largely be explained by the first‐order

autocorrelation of industry returns. Second, we extend Grobys, Ruotsalainen, and Äijö

(2018) and Grobys (2018) by employing Moreira and Muir’s (2017) risk‐managing

approach to the proposed zero‐cost portfolio based on first‐order autocorrelation. Unlike

previous studies, we explore whether the zero‐cost portfolio based on short‐run industry

momentum or its risk‐managed counterpart are subject to Daniel and Moskowitz’s (2016)

optionality effects. In doing so, we extend Moskowitz and Grinblatt (1999), who also

document the profitability of short‐run momentum. Specifically, Moskowitz and Grinblatt

explore microstructure effects of various industry momentum strategies, whereas we analyze,

among others, conditional correlations in terms of tail risks and potential optionality effects

as well as asset pricing implications of different industry momentum strategies. Third, and

last, we extend Fama and French’s (2018) study by comparing different industry momentum

96 The Journal of Financial Research