Market operating leverage and expected stock returns

• Published date: 01 October 2022
• Author: Roi D. Taussig
• Date: 01 October 2022
• DOI: http://doi.org/10.1002/jcaf.22574

Received: 3 March 2022 Accepted: 12 May 2022

DOI: 10.1002/jcaf.22574

RESEARCH ARTICLE

Market operating leverage and expected stock returns

Roi D. Taussig

Department of Economics and Business

Administration, Ariel University, Ariel,

Israel

Correspondence

Roi D. Taussig,Department of Economics

and Business Administration, Ariel

University, Ariel, Israel40700.

Email: roit@ariel.ac.il

Abstract

This article’s main goal is to analyze the impact of market operating leverage

(OL) on expected stock returns. While OL has been analyzed substantially in

the literature, the analysis of an aggregate measure is new. The article begins

with a cross-section analysis of 184,280 firm-year US firms, after which a former

literature analysis is confirmed with current data. Next, an Aggregate Operating

Leverage (AOL) is constructed. AOL predicts market returns both in and out of

sample. Moreover, AOLsignificantly predicts the S&P500 Index, which is also of

great consequence to practitioners.

KEYWORDS

asset pricing, market returns, operating leverage, stock returns

JEL CLASSIFICATION

G11, G12, G13, G14, G17

1 INTRODUCTION

The analysis of Operating Leverage (OL) began with Lev

(1974), and since then has attracted both researchers and

practitioners alike. The literature primally inspects the

impact of OL on the cross-section of stock returns (Novy-

Marx, 2011). This article begins by analyzing the influence

of OL on the up-to-date data. OL continues to be an

important determinant of stock returns, when analyzing

the data up to December 2021. Then, the cardinal part of

this article begins with the analysis of market OL. While

the influence of OL on the cross-section of stock returns

has been studied, the analysis of an aggregate measure is

new. The analysis opens by defining a new measure for

Aggregate Operating Leverage (AOL). Then, the analysis

reveals the importance of AOL in a time-series framework.

AOL significantly predicts market returns both in and out

of sample, and the predictions are robust to a variety of

empirical tests. The predications are evaluated on three

major market portfolio returns, which include the S&P500

Index. Practitioners can also benefit from the results of this

index’s analysis.

The current article proceeds with a literature review in

Section 2; Section 3presents the methodology, data, and

results of the cross-section analysis; Section 4presents

the main part of the time-series analysis; and Section 5

concludes.

2LITERATURE REVIEW

The most common model to assess financial asset pric-

ing is the Capital Asset Pricing Model (CAPM; Black,

1972; Lintner, 1965; Sharpe, 1964), which explains the

cross-section of stock returns by employing the system-

atic risk coefficient (Beta). However, Fama and French

(1992) reveal that Beta becomes insignificant when con-

trolling for firms’ market capitalization (Size). Moreover,

Momentum in stock returns has also been argued to

explain expected stock returns (Jegadeesh& Titman, 1993).

Page et al . (2021)showhowMomentum impacts stock

returns in the Johannesburg Stock Exchange and find sig-

nificant results. An additional asset pricing model, the

3-factor-model, was suggested by Fama and French (1993).

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