An additional analysis of estimation techniques for the degree of financial leverage

• Author: Simon Medcalfe,Michael T. Dugan,Steven Stelk,Sang‐Hyun Park
• DOI: http://doi.org/10.1016/j.rfe.2017.03.005
• Date: 01 July 2018
• Published date: 01 July 2018

ORIGINAL ARTICLE

An additional analysis of estimation techniques for the degree of

financial leverage

Steven Stelk

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Sang-Hyun Park

2

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Simon Medcalfe

3

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Michael T. Dugan

3

1

Department of Finance, Real Estate, and

Business Law, College of Business, The

University of Southern Mississippi, 118

College Dr, Box 5076, Hattiesburg, MS

39406, USA

2

Knox School of Accountancy, Hull

College of Business, Augusta University,

2500 Walton Way, Augusta, GA 30904,

USA

3

Hull College of Business, Augusta

University, 2500 Walton Way, Augusta,

GA 30904, USA

Correspondence

Steven Stelk, Department of Finance, Real

Estate, and Business Law, College of

Business, The University of Southern

Mississippi, 118 College Dr, Box 5076,

Hattiesburg, MS 39406, USA.

E-mail: steven.stelk@usm.edu

Abstract

This study compares three different empirical proxies for the financial leverage

component of a systematic risk-composition model employed in prior financial

research. We consider one static accounting measure and two elasticity-based

measures. We find that the traditional static accounting measure of financial lever-

age provides statistically different estimates of financial leverage when compared

to estimates from elasticity-based measures of the degree of financial leverage.

The findings are important because the elasticity-based models for the degree of

financial leverage have clear theoretical links to market-based models of system-

atic risk, while the static accounting measure of financial leverage does not. Prac-

titioners and researchers should carefully consider why they are estimating

financial leverage and choose the appropriate method for doing so given the goals

and potential consequences for biased estimation.

JEL CLASSIFICATION

G30

KEYWORDS

Degree of financial leverage, DFL, Empirical measurement, Financial structure, Systematic risk

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INTRODUCTION

Estimating a firm’s sensitivity to systematic risk is important to regulators, securities analysts, and corporate financial man-

agers. Regulators use beta to estimate a fair return for utility companies and set rates that consumers will pay. Securities

analysts use beta to calculate required rates of return. Corporate financial managers use the weighted average cost of capi-

tal, which is partly determined by beta, to evaluate investment opportunities. Corporate financial managers also need to

know how changes in capital structure affect the company’s systematic risk, and therefore change the required rate of return

for investment opportunities.

In the traditional Capital Asset Pricing Model (CAPM), beta is a function of only the return characteristics of the market

portfolio and an individual stock. Firm-level characteristics that regulators and securities analysts can observe and over

which mangers have control do not expressly appear in this model. Other theories of systematic risk that account for some

part of a firm’s structure-like the Fama-French factor models –also are dependent on market measures such as the adjusted

book-to-market ratio. These models do not provide a direct way for managers to determine how the risk premium is

affected by strategic, operating, and financing decisions they make.

A model that explains beta through a firm’s operating and financial leverage using readily observable accounting vari-

ables offers a number of advantages. It provides a method for securities analysts to value public and private companies

First published online by Elsevier on behalf of The University of New Orleans, 30 March, 2017, https://doi.org/10.1016/j.rfe.2017.03.005

Received: 2 September 2016

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Revised: 28 February 2017

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Accepted: 15 March 2017

DOI: 10.1016/j.rfe.2017.03.005

220

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©2017 The University of New Orleans wileyonlinelibrary.com/journal/rfe Rev Financ Econ. 2018;36:220–231.