Heterogeneity in the Speed of Capital Structure Adjustment across Countries and over the Business Cycle

Author:Dirk C. Schilling, Wolfgang Drobetz, Henning Schröder
DOI:http://doi.org/10.1111/eufm.12048
Publication Date:01 Nov 2015
Heterogeneity in the Speed of Capital
Structure Adjustment across Countries
and over the Business Cycle
Wolfgang Drobetz
Hamburg Business School, University of Hamburg, VonMellePark 5, 20146 Hamburg, Germany
E-mail: wolfgang.drobetz@wiso.uni-hamburg.de
Dirk C. Schilling
HDIGerling Industrial Insurance Company, 10 Fenchurch Street, London EC3M 3BE, UK
E-mail: dirk.c.schilling@gmail.com
Henning Schröder
Hamburg Business School, University of Hamburg, VonMellePark 5, 20146 Hamburg, Germany
E-mail: henning.schroeder@wiso.uni-hamburg.de
Abstract
This study analyses the heterogeneity in the speed of capital structure adjustment.
Using a doublycensored Tobit estimator that accounts for mechanical mean
reversion in leverage ratios, the speed of adjustment is 25% per year in a large
international sample, supporting the economic relevance of the tradeoff theory.
Differences in the adjustment speed across nancial systems are attributable to
differences in the costs of adjustment. Macroeconomic and microlevel supplyside
constraints also affect the dynamics of leverage. Firms adjust more slowly during
recessions, and the business cycle effect on adjustment speed is most pronounced
for nancially constrained rms in marketbased countries.
Keywords: capital structure, speed of ad justment, institutional arrangem ents,
business cycle, dynamic panel methods
JEL classification: G30, G32
1. Introduction
If rms pursue target leverage ratios, how quickly do they adjust back to their target
capital structure subsequent to leverage shocks? How do institutional differences across
We thank two anonymous referees, Manuel Rocha Armada, Gonul Colak, Ettore Croci,
John Doukas (the editor), Yilmaz Guney, Ottorino Morresi, and Tatjana Puhan as well as
participants of the 2012 Financial Management Association (FMA) European Conference in
Istanbul, the 2013 Midwest Finance Association (MFA) meeting in Chicago, and the 2014
European Financial Management (EFMA) Annual Meeting in Rome for helpful comments.
Correspondence: Henning Schröder.
European Financial Management, Vol. 21, No. 5, 2015, 936973
doi: 10.1111/eufm.12048
© 2014 John Wiley & Sons Ltd
countries and nancial systems affect the speed of adjustment? What impact do
macroeconomic and microlevel supplyside constraints have on the speed of adjustment?
And how does the interaction between institutional characteristics, macroeconomic
conditions, and nancial constraints affect the dynamics of leverage?
There is still no consensus in the literature on the question how fast rms are able to
adjust back to their target capital structure subsequent to leverage shocks. Huang and
Ritter (2009, p. 239) call this question perhaps the most important issue in capital
structure research.An estimate of the speed of adjustment can help to sort out major
theories that explain the dynamics of capital structure. A positive speed of adjustment may
be interpreted as evidence for the existence of a target leverage ratio, or more generally, a
dynamic tradeoff model of capital structure. For example, Fischer et al.s (1989) dynamic
tradeoff model shows that even small adjustment costs can lead to large swings in rms
capital structures. While any variant of the dynamic tradeoff model with low or moderate
adjustment costs implies a positive adjustment speed, the pecking order theory predicts no
measurable adjustment (Fama and French, 2002). Instead, there is no target leverage ratio,
and leverage changes according to the nancing decit (Myers and Majluf, 1984).
Markettiming theories even support a negative speed of adjustment. If rms respond to
increasing stock prices by issuing equity, the measured adjustment speed will be lower
than zero (Baker and Wurgler, 2002; Dittmar and Thakor, 2007).
Timevarying estimates for the speed of capital structure adjustment may contribute to
our understanding about leverage dynamics in an even broader context. In an inuential
study, Lemmon et al. (2008) report that corporate leverage is stable and driven mostly by
timeinvariant determinants. Their results are consistent with the unexpectedly low
estimates for (average) adjustment speed in most empirical studies (see Section 2 for a
review). In contrast, DeAngelo and Roll (2013) nd that many rms have high and low
leverage at different times and that capital structure stability is the exception. Signicant
variation in the speed of adjustment estimates and in the coefcients measuring the impact
of rmlevel characteristics on target leverage over the business cycle would lend support
to these latter ndings for instability of corporate leverage.
Adjustment speed depends on (i) the costs of deviating from the target capital structure
and (ii) the costs of adjusting back to the target. Financial managers must assess the trade
off between the costs of being off the target leverage ratio and the costs of adjustment. On
the one hand, the nancial status of a rm, such as the degree of target deviation or the
magnitude of the nancing decit, has an impact on the speed of adjustment (Faulkender
et al., 2012). On the other hand, both the costs of deviating from the target leverage ratio
and the costs of adjustment are affected by a rms institutional, legal, and nancial
environment (Antoniou et al., 2008; Öztekin and Flannery, 2012). Finally, macroeco-
nomic conditions may have an impact on rmstimevarying abilities to readjust
subsequent to a leverage shock, as recession periods are often accompanied by a shortage
of capital supply (Cook and Tang, 2010; Halling et al., 2012). Moreover, during
recessions most of the traditional capital structure variables experience signicant shocks,
thus changes in the macroeconomic state will affect leverage dynamics. Overall, rm
level, countrylevel, and macroeconomic factors are likely to be responsible for the
observed heterogeneity in the speed of adjustment.
In our study, we use a comprehensive sample of rms from the G7 countries and
explore the heterogeneity in the speed of adjustment in two ways. First, we make cross
country comparisons to determine whether there are differences between bankbased and
marketbased nancial systems with respect to adjustment speed. For example, rms in
© 2014 John Wiley & Sons Ltd
Heterogeneity in the Speed of Capital Structure Adjustment 937
countries with a bankoriented nancial system tend to suffer from less liquid capital
markets, making it more expensive for them to issue new or to retire outstanding securities
and to rebalance after a leverage shock. Second, we contribute to the literature by
comparing the speed of adjustment in different macroeconomic states. On the one hand,
adverse selection costs vary over the business cycle, implying that issuing (or retiring)
securities becomes more expensive and that external relative to internal nancing costs
increase during economic downturns. On the other hand, the business cycle may inuence
the aggregate supply of capital, thereby affecting nancing choices at the economy level.
To the extent that rms depend on external nancing to adjust their capital structure, these
effects will increase rmsrebalancing costs and slow down their speed of adjustment
particularly during nancial crisis episodes.
In addition to measuring heterogeneity in the speed of adjustment, we also provide a
methodological contribution. By imposing a large set of different dynamic panel
estimators on a regimeswitching partial adjustment model for international data, our
results can be interpreted as an outofsample test given that these estimators have been
tested mostly on US data. To mitigate the biases inherent in virtually all estimators for the
speed of adjustment (Chang and Dasgupta, 2009; Iliev and Welch, 2010), we employ a
new estimator for dynamic panel models introduced by Elsas and Florsysiak (2014) and
compare it to the more standard estimators used in other recent studies. This fractional
dependent variables estimator (DPFestimator) exhibits the smallest bias in their US
sample and delivers adjustment speed estimates that support the tradeoff theory of capital
structure. Comparing the estimates from all commonly used dynamic panel estimators, we
nd that that the mean speed of adjustment is closest in magnitude to the DPFestimator.
The empirical results enhance our understanding of rmscapital structure dynamics.
Based on our full sample of rms from the G7 countries and estimating the regime
switching partial adjustment model using the DPFestimator, the estimated speed of
adjustment in our regimeswitching partial adjustment model is approximately 25% per
year. The implied halflife of the average shock of roughly 2.5 years supports the
economic relevance of the tradeoff theory. As expected, the speed of adjustment is
signicantly faster in marketbased countries than in bankbased countries. The
differences in adjustment speed across countries are attributable to differences in both
the costs of deviation from target and the costs of adjustment back to the target, although
the latter effect seems to be much stronger. Furthermore, the macroeconomic environment
has an impact on the speed of adjustment. Firms adjust more slowly during bad
macroeconomic states, and the business cycle effect is most pronounced for nancially
constrained rms in countries with a marketbased nancial system.
The remainder of our study is as follows: Section 2 provides a brief literature overview.
Section 2 discusses the econometric problems involved in estimating adjustment speeds
in the framework of dynamic panel models. Section 4 describes the data. Section 5
compares the speed of adjustment across countries and explores the inuence of different
institutional arrangements. Section 6 analyses the heterogeneity in the speed of
adjustment over the business cycle. Finally, Section 7 concludes and provides an outlook
for future research.
2. Review of the Literature
Modigliani and Miller (1958) conclude that a companys capital structure is irrelevant for
its valuation. Their original framework is very restrictive and implies no adjustment to any
© 2014 John Wiley & Sons Ltd
938 Wolfgang Drobetz, Dirk C. Schilling and Henning Schröder

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