financial leadershipETHICALC O N D U C TWHAT FINANC.docx

financial leadership'
ETHICAL
C O N D U C T
WHAT FINANCIAL EXECUTIVES
Do To LEAD
BY FREDERICK MILITELLO AND MICHAEL
SCHWALBERG
T
oday's so-called "crisis" of
accountability or financial
integrity has been met with a
flurry of laws and regulations
designed to restore public confidence
in corporations. Likewise, many
financial institutions and some corpo-
rations seem to be trying to outdo one
another in announcing new policies
demonstrating their commitment to
integrity and ethical behavior.
Yet, a new Executive Report by the
Financial Executives Research Founda-
tion finds that the vast majority of cor-
porations and financial executives
express strong beliefs that ethical
behavior and financial integrity
remain the rule of the day. Rather than

believing that investor confidence can
be restored by external regulation,
they see the importance of "staying
the course" and "walking the walk" as
both the ethical gatekeeper and con-
science of their organizations.
In the study, Integrity-Based Finan-
cial Leadership and Ethical Behavior: A
Professional Response to Meeting the
Challenges and Responsibilities, finan-
cial executives from a wide range of
companies openly share thoughts,
insights and practices that relate to
the "crisis" of financial integrity.
While the findings are vast, and at
times controversial, two ideal por-
traits of ethical behavior emerged; the
"Ethically Intelligent Financial Execu-
tive" (EIFE) and the "Ethically Intelli-
gent Finance Organization" (EIFO).
The Ethically Intelligent
Financial Executive
He or she is aware of the multiple
pressures that may potentially
impinge upon the maintenance of
X, /
one's integrity, and is further aware of
the ubiquitous presence of ethical
dilemmas faced by leaders in daily
business life, and takes the time to

reflect upon these dilemmas.
George Boyadjis, EVP, CFO and
Treasurer of American TeleCare Inc.,
speaks for many in the study when he
observes, "So much of what we do is
driven by the creation of value
through increasing the speed of busi-
ness — shortening time to market,
accelerating growth rates, cutting
cycle times, etc. But, if we as financial
executives are truly focused on value
creation for the enterprise, then we
must also reflect on the ethics and
transparency of transactions and rela-
tionships."
The EIFE is a valued business part-
ner who actively assists the business-
es in planning, development and
T w o IDEAL PORTRAITS OF ETHICAL BEHAVIOR
EMERGED FROM A NEW
REPORT BY THE FINANCIAL EXECUTIVES RESEARCH
FOUNDATION: THE
"ETHICALLY INTELLIGENT FINANCIAL EXECUTIVE"
(EIFE) A N D THE
"ETHICAL INTELLIGENT FirviANCE ORGANIZATION" ( E I
F O ) .
www.fei.org January/February 2003 49

Arnold l-ldnish,
Executive Director, Finance and Chief
Accounting Officer, Eli Lilly and Co.
George Boyadjis,
EVP, CFO and Treasurer,
American TeleCare Inc,
Gary L. Ellis,
VP, Corporate Controller and Treasurer,
Medtronic Inc.
implementation of projects and goals,
and as the conscience of the organiza-
tion, he or she at times must say "no."
This executive also sets the tone at the
top and leads by example.
A highly visible role model, thc
EIFE uses every opportunity to articu-
late and demonstrate high-integrity
behavior to the finance organization
and to the organization as a whole.
This is the true meaning of integrity-
based leadership.
Finally, the EIFE typically has
received some training in the area of
ethics. When playing a leadership
role, he or she provides ample oppor-
timities to others to experience ethical
dilemmas through situational train-
ing opportunities, bringing financial
people together with busmess associ-
ates from a wide spectrum of back-

grounds.
Nick Cyprus, VP and Controller of
AT&T Corp. notes, "In addition to
standard controls, such as a code of
conduct and good background checks,
other controls could also be used. For
example, financial people should be
trained to identify and understand
ethical/unethical behaviors and situa-
tions. 1 like to get all my controllers
and their key leaders together to do
just that. We break into teams, where
each team gets a different business sit-
uation to deal with. They then have to
come back to the broader group and
discuss how they decided to resolve it.
"Basically, I give them the interest-
ing situations 1 see on a day-to-day
basis — not the headliners, but the
stuff you really tend to confront all of
the time. What's important is that the
training gives them an opportunity,
[so] that when they actually see that
situation, they know what to do. You
liave to set the right tone, and ethics
training helps in that regard."
The Ethically Intelligent
Finance Organization
The authors suggest that there is an
"ideal" finance organization for ethi-
cal conduct. They found it quite
extraordinary how much agreement
existed among financial executives

when it came to the "operationaliza-
tion" of ethical conduct through the
structure and support mechanisms
inherent in the structure of finance
organizations.
The EIFO "walks the talk of
integrity." Structurally, it strikes a bal-
ance between centralization and
decentralization. On the one hand,
concentrating too much authority in a
few hands may encourage the abuse
of power. On the other hand, a clear
functional chain of command, espe-
cially with a strong finance organiza-
tion at the top, can be an important
source of support for those financial
executives working closely with
and/or reporting to operations.
Eli Lilly and Co.'s Executive Direc-
tor, Finance and Chief Accounting
Officer, Arnold Hanish, reiterated the
importance of financial executives
being accountable to, and having the
support of, corporate finance: "Our
operations management understand
that their finance colleagues have a
certain amount of latitude and some
degree of judgment as it relates to
interpreting accounting rules and
financial matters, but ultimately must
follow the rules and behave in an ethi-
cal manner."

Given the plethora of rules and
regulations in the pharmaceutical
industry, they can certainly relate to
the policies and practices within
finance, maintains Hanish. If there are
any questions or grey areas, the
finance member of the operations
team generally contacts corporate for
advice and direction.
The EIEO embraces corporate
codes of ethics and mission state-
ments — formal training on such doc-
uments are imperative. Most impor-
tantly, EIFOs incorporate such codes
of conduct into their financial deci-
sion-making processes. Such rules of
conduct are more than just compii-
ance statements of behavior; they are
action statements.
At Medtronic Inc., VP, Corporate
Controller and Treasurer Gary L. Ellis
says, "There are two reference points
regarding ethics — the company mis-
sion statement and its code of con-
duct. When looking at any major
transaction or acquisition, these two
pillars become our guiding lights. If
50 FINANCIAL EXECUTIVE January/February 2003
PRESSURES ON
ETHICAL CONDUCT

Why do good people do bad
thlng5? Pressures on financial
professionals might tempt them
to stray from ethical behavior.
Cognizance of the pressures can
be an important preventive
measure.
• Emphasis on Short-Term
Results
Executives stressed as a criti-
cal concern the importance of
"making the numbers" in the
short term. This can lead to a
shortsighted approach to push
the envelope on both the
accounting and the business
side.
• Sweat the Small Stuff
Corporate misdeeds are often
the culmination of a series of
small steps. These are not the
notorious crimes that make
headlines, but rather the rela-
tively mundane issues faced on a
daily basis. The first step may
seem immaterial and unimpor-
tant, but can set in motion a
series of events leading to a
major ethical breach.
• Economic Downturns

Some executives felt that the
current ethical crisis is essentially
a cyclical event — "a down mar-
ket reveals what an up market
conceals." Clearly, pressure to
meet short-term results is exac-
erbated in a cyclical downturn.
• Accounting Rules
Accounting rules and the
transactions that they reflect
have become increasingly com-
plex and less intuitive — making
it easier to abuse the rules or
commit outright fraud. To com-
pensate, many are moving
toward greater disclosure —
which does not always mean
better understanding; it can cre-
ate more confusion for "typical"
shareholders.
we are uncomfortable with
the management or the cul-
ture of the target organiza-
tion, relevant to our mission
statement or code of con-
duct, then we won't do the
deal, no matter bow attrac-
tive it may be."
Moreover, the research
indicates that in EIFOs,
rewarding integrity and

integrity-based leadersiiip is
critical. Tying performance
appraisals to living a com-
pany's values and demon-
strated integrity is one way
of doing that.
Some finance organiza-
tions set their ethical tone at
the bottom as well as the
top, working with profes-
sionals (internal or external)
who assist tbem in selecting
and orienting bigh-integrity
new hires. Ethical profiling
is becoming a common and
expected practice. Several
executives said their organi-
zations bave hiring practices
that attempt to select for qualities
related to ethical behavior.
Tbe financial executives seem to
agree that there should be no differ-
ence between one's personal and
business ethics. In today's atmos-
phere, it is no longer acceptable to
rationalize bad behavior by deferring
to the notion that "business is busi-
ness." A business model paradigm
shift — perhaps driven by growing
ethical awareness and social responsi-
bility — is occurring. The executives
in this study welcome a vision of
business in wbich integrity is not
something to be checked at the corpo-

rate door, but is an integral part of life
in and out of business, and a central
aspect of all financial decision-making
processes.
Medtronic's Ellis points out, "For
the most part, being involved in the
businesses is the right thing. 1 just
don't see any negatives to a business
partnering relationship. Where it
migbt have gotten a little out of kilter
is where the finance organization
came up with the deals that saved the
/ RATHER THAN
BELIEVING THAT INVESTOR
CONFIDENCE CAN
BE RESTORED BY EXTERNAL
REGULATION. THE
EXECUTIVES SEE THE
IMPORTANCE OF "STAYING
THE COURSE" AND "WALKING
THE WALK" AS THEIR
ORGANIZATIONS' ETHICAL
GATEKEEPER AND y

CONSCIENCE. _ ' .
quarter. If you look at the companies
that got in trouble, the 'deal-makers'
— the ones that were really coming
up with the "fiavor of the month
ideas" — were not assisting the busi-
nesses, but in almost all cases, were
creating a completely different busi-
ness. This is not business partnering."
Ellis asserts that the last thing
financial executives should do is
climb back into their financial silos.
"We will be much more successftil as
a profession in bringing back corpo-
rate America by being good business
partners, but doing so with integrity."
Fred Militello Jr. is a Senior Partner with
FinQuest Partners LLC, a Wall Street-
based financial consultancy practice, and
adjunct professor of finance and interna-
tional business at New York University's
Leonard N. Stern School of Business.
Dr. Michael Schwalbert is associated
with Hudson Vailey Psychology Associates
PLC. The complete study can be pur-
chased from the Financial Executives
Research Foundation at: www.fei.org/
rfbookstore, or 973.765.1033.
vwvw.fef.org January/February 2003 51

Research the aspects of company targets and define categories
such as industries, locations, specialties, job-related
opportunities, sources, and other characteristics that will help
identify company targets for you to promote your job interest.
Your company categories should provide details on where to
find additional targets.
Present your response as a mind map using MS Word or
www.MindMeister.com.
MindMeister is a free service that allows the creation of up to
six mind maps. To use this tool, complete the following steps:
1. Establish an account by choosing a user name and password.
2. Begin a mind map by clicking the Create a New Map button
on the home page.
3. Once inside the map screen, click the Help button to view a
short tutorial on using the interactive features of MindMeister.
4. Start with placing the main idea in the center.
5. Identify the ideas.
6. Develop each idea, adding more details to the mind map as
necessary.
7. You may invite your course instructor to view your map
online by entering his or her NCU e-mail address into the
invitation screen. She/he might make suggestions or changes in
your map to further guide your thinking.
8. To save a copy of your map, click the Export button at the
top of the work screen, and choose PDF or GIF as the export
version.
Then, prepare a written response that details and explains your
mind map. Include the following in your paper:
1. Determine which companies appear to provide the best
opportunities for your job interest.

2. Identify the best sources to find more of these types of prime
companies.
3. Describe the best features and supply examples of the type of
company that fits your job search.
Support your paper with at least one scholarly resource. In
addition to these specified resources, other appropriate
scholarly resources, including older articles, may be included.
Length: 3-5pages, not including title and reference page (but
including your mind map)
Your paper should demonstrate thoughtful consideration of the
ideas and concepts presented in the course by providing new
thoughts and insights relating directly to this topic. Your
response should reflect scholarly writing and current APA
standards where appropriate.
The effect of financial constraints, investment policy,
product market competition and corporate governance
on the value of cash holdings
Howard W. H. Chan
a
, Yufei Lu
b
, Hong F. Zhang
b
a
Department of Finance, Faculty of Business and Economics,

University of Melbourne, Parkville,
VIC, Australia
b
School of Accounting, Economics and Finance, Faculty of
Business and Law, Deakin University,
Burwood, VIC, Australia
Abstract
This study empirically investigates the value shareholders place
on excess cash
holdings and how shareholders’ valuation of cash holdings is
associated with finan-
cial constraints, firm growth, cash-flow uncertainty and product
market competi-
tion for Australian firms from 1990 to 2007. Our results
indicate that the marginal
value of cash holdings to shareholders declines with larger cash
holdings and higher
leverage. However, firms that are more financially constrained,
that have higher
growth rates and that face greater uncertainty exhibit a higher
marginal value of
cash holdings. These findings are consistent with the
explanation that excess cash
holdings are not necessarily detrimental to firm value. Firms
with costly external
financing and that also save more cash for current operating and
future investing
needs find that the market values these cash hoarding policies
favourably. Finally,
there is limited evidence of an association between various
corporate governance
measures and the value of cash holdings for a shorter sample

period.
Key words: Financial constraints; Cash policy; Australian firms
JEL classification: G31, G32
doi: 10.1111/j.1467-629X.2011.00463.x
We thank Jim Psaros for the use of the Horwath Corporate
Governance data. Also, we
thank an anonymous Accounting and Finance reviewer, Robert
Faff (the editor), confer-
ence participants at the AsianFA 2011 annual meeting and the
AFAANZ 2011, and semi-
nar participants at the University of Newcastle and the
University of Queensland for their
helpful comments and suggestions.
Received 30 November 2010; accepted 19 November 2011 by
Robert Faff (Editor).
� 2011 The Authors
Accounting and Finance � 2011 AFAANZ
Accounting and Finance 53 (2013) 339–366
1. Introduction
Areexcess cash holdingsgood or bad? Inthe real world, excess
cash holdingsplay
a vital role as a cash buffer in corporate investment and
financing decisions. How-
ever,Jensen(1986)holdstheviewthatexcesscashholdingsaredetrim
entaltoshare-

holder value because managers waste the excess cash through
over-investment and
value-
destroyingacquisitions.Incontrast,havinglargecashholdingsprovi
desfirms
with flexibility in making investment decisions, as it avoids the
need to raise more
costlyexternalfinancing(Opleret
al.,1999;MikkelsonandPartch,2003).
A number of empirical studies, such as Mikkelson and Partch
(2003), Pinko-
witz and Williamson (2004), and Faulkender and Wang (2006),
investigate the
value of corporate cash holdings and how excess cash holdings
are related to
stock returns in US markets. In Australia, Lee and Powell
(2011) examine the
value of excess cash holdings. Their findings support the
agency cost argument
of excess cash holdings by showing that firms with a longer
duration of excess
cash holdings have a lower marginal value of cash. However,
Lee and Powell
(2011) only show how the marginal value of cash is related to
the persistence of
excess cash holdings.
Given the limited Australian evidence, the focus of this study is
on how share-
holders value excess cash holdings associated with financial
constraints, firm
growth opportunities, uncertainty in cash flows, product market
competition
and corporate governance.

1
We first estimate the marginal value of cash holdings by
following the method-
ology in Faulkender and Wang (2006). In particular, we use
excess stock returns
as the dependent variable and unexpected changes in cash
holdings and firm
characteristics that are related to cash as the explanatory
variables. Excess stock
returns are calculated using returns from the 25 Fama and
French portfolios
formed on market capitalization (size) and book-to-market as
benchmark
returns.
2
Consistent with the results of Faulkender and Wang (2006),
firms with
larger cash holdings and higher leverage ratios generate a lower
marginal value
of cash holdings. When we employ dividend-paying ability and
book value of
total assets to partition our sample according to the degree of
financial con-
straints, we find that more financially constrained firms have a
significantly
higher marginal value of cash holdings. This indicates that
investors place greater
1
The Australian market differs from the US market. There are
more natural resources
firms and fewer technology-related firms, in terms of both

number and size. Natural
resource firms have more tangible assets in place as compared
to technology and internet-
related firms. Finally, Australian firms are generally smaller in
size and the Australian
market has a much smaller and less liquid corporate bond
market.
2
This approach follows Faulkender and Wang (2006). These
benchmark portfolios
formed on size and book-to-market account for the common risk
factors that affect stock
returns. In contrast, Lee and Powell (2011) use excess returns
adjusted by market or
industry returns instead of the 25 Fama and French portfolio
returns.
340 H. W. H. Chan et al./Accounting and Finance 53 (2013)
339–366
value on excess cash holdings in financially constrained firms,
as these firms are
less likely than unconstrained firms to gain access to external
capital.
To shed light on how the value of cash holdings is associated
with firms’
investment decisions, uncertainty in cash flows, product market
competition and

corporate governance, we further partition our sample using the
following vari-
ables: book-to-market for growth opportunities; average
volatility in earnings;
Herfindahl index constructed using the market share of a firm’s
sales within its
industry; and, finally, various corporate governance proxies. We
find that firms
with higher growth rates and higher levels of uncertainty in
their cash flows exhi-
bit a higher marginal value of cash holdings. However, product
market competi-
tion has little impact on firms’ value of cash holdings. Our
findings indicate that
internal financing has clear cost advantages over external
financing for firms with
high growth potential and those facing uncertain prospects.
Costly external
financing would force firms that are at a disadvantage to save
more cash for cur-
rent operating and future investing needs. Investors are aware of
these cash
hoarding policies and view them quite favourably. However,
within industry
product markets, competition seems to have little influence on a
firm’s cash
hoarding policy; for corporate governance, we find limited
evidence for its asso-
ciation with the value of cash holdings. Overall, our findings
are mainly consis-
tent with increased cash holdings being dependent on the firm’s
ability to access
external capital, as in the study of Hennessy and Whited (2005).
The remainder of this study is organized as follows. Section 2
examines the exist-

ingliterature,whichaimstodiscussthestudiessurroundingcorporate
cashholdings
and to link the cash-holding behaviour to various firm-specific
factors in Australia.
Section 3 discusses our empirical methodology and outlines our
main hypotheses.
The sample and summary statistics are described in Section 4.
Section 5 presents
ourempiricalresultsandrobustnesschecks.Section
6concludesthestudy.
2. Literature review
Opler et al. (1999) examine the determinants and implications
of cash holdings
and cash equivalents based on data from 1048 publicly traded
US firms during
the period 1971–1994. Their findings show that the level of
corporate cash hold-
ings is positively correlated with future investment
opportunities, cash-flow-to-
assets ratios, capital investment, industry volatility and
investments in fixed
assets and is negatively correlated with firm size, leverage, and
networking capi-
tal and dividend payments.
In a more recent paper, Faulkender and Wang (2006) extend this
line of
research by analysing the value that shareholders placed on the
cash held by a
firm.
3
These authors argue that the value of one additional dollar of
cash

3
The focus of their paper is not on how much cash is saved out
of cash flow but on how
the shareholders would value this by examining their excess
stock returns.
H. W. H. Chan et al./Accounting and Finance 53 (2013) 339–
366 341
reserves should decline with larger cash holdings, higher
leverage and better
access to capital markets. Their empirical findings support all
of these argu-
ments, including the idea that excess cash holdings are more
valuable for share-
holders in financially constrained firms. On the other hand,
Pinkowitz and
Williamson (2004) examine the value placed on a firm’s cash
holdings under dif-
ferent growth scenarios. These authors find that investors
placed a higher value
on cash holdings of firms that had higher growth opportunities.
More recently, MacKay and Phillips (2005) investigate the
effect of product
market competition on a firm’s financial choices, in particular,
leverage. These
authors find that industry-related factors and financial choices
such as leverage

are jointly determined. In addition, they find that firms in
competitive industries
used less financial leverage than those in less competitive
industries. Fresard
(2010) examines directly the role of cash holdings in a firm’s
product market
decision-making. This author finds that cash holdings can be
used to support
competitive strategies against industry rivals. However, he does
not directly
investigate whether firms in competitive industries will retain
greater cash hold-
ings than those in less competitive industries, and he does not
examine whether
investors place a different value on excess cash holdings.
How corporate governance is associated with the value of cash
holdings has
also been explored both internationally and in the United States
Pinkowitz et al.
(2006) investigate how minority shareholders in countries with
poorer investor
protection value a firm’s cash holdings. These authors find that
a firm’s cash is
valued at a discount in countries with weaker investor rights,
because controlling
shareholders might use their position to extract private benefits
from cash hold-
ings. Dittmar and Mahrt-Smith (2007) document that
shareholders in the United
States assign a lower value to an additional dollar of cash
reserves when a firm
has a more entrenched management team or lower institutional
ownership.
These investigators argue that investors discount more heavily
the cash holdings

of a firm with a higher likelihood of having agency problems.
Finally, the main focus of Lee and Powell (2011) is to examine
the determi-
nants of the level of cash holdings in Australia. These authors
find results similar
to Opler et al. (1999). In addition, they investigate the value of
excess cash hold-
ings in Australia. They show that firms with a longer duration
of excess cash
holdings have a lower marginal value of cash. However, they do
not examine
how shareholders value cash holdings with respect to firm
growth, cash-flow
uncertainty and corporate governance.
3. Empirical methodology
This section outlines the baseline empirical model involved in
examining the
value of cash holdings for Australian firms, followed by a
discussion of the
impact of financial constraints, firm growth opportunities,
uncertainty in cash
flows, product market competition and corporate governance on
the marginal
value of cash holdings.
339–366

3.1. Baseline empirical model
The primary goal of this study is to investigate the value
investors place on an
extra dollar of cash held by Australian firms and how various
factors that affect
a firm’s external financing conditions would alter this value.
Following Faulk-
ender and Wang (2006), we employ the following baseline
model to regress
excess stock returns over a fiscal year on the unexpected change
in various firm-
specific characteristics that affect cash positions in that fiscal
year. This is repre-
sented by the following:
ri;t � RBPi;t ¼ a0 þ b1
DCi;t
Mi;t�1
þ b2
DEi;t
Mi;t�1
þ b3
DNAi;t
Mi;t�1
þ b4
DRNDi;t
Mi;t�1
þ b5
DIi;t
Mi;t�1

þ b6
DDi;t
Mi;t�1
þ b7
Ci;t�1
Mi;t�1
þ b8Li;t þ b9
NFi;t
Mi;t�1
þ b10
Ci;t�1
Mi;t�1
�
DCi;t
Mi;t�1
þ b11Li;t
DCi;t
Mi;t�1
þ ei;t
ð1Þ
where D represents the change in the variable X of firm i
between fiscal year t
and t ) 1.
The dependent variable is excess stock return, where ri,t is
stock i’s annualized
return in fiscal year t and RBPi;t is annualized return of stock
i’s benchmark port-

folio during fiscal year t. The benchmark portfolio is based on
the whole sample
of the Australian Graduate School of Management (AGSM)
Share Price and
Price Relative (SPPR) database. We use the 25 Fama and French
(1993) portfo-
lios formed on market capitalization (size) and book-to-market
equity ratios as
benchmark portfolios. This is because Fama and French (1993)
find that size
and book-to-market proxy for common risk factors. By
controlling for these two
common risk proxies, any relationship found between excess
stock return and
cash holdings would not be attributed to common risk factors.
Ct is cash includ-
ing short-term deposits, Et is earnings before interest and tax
(EBIT), and NAt is
total book assets minus Ct. RNDt is capitalized research and
development
expenses, It is net interest expense, Dt is total common dividend
paid, Lt is total
debt divided by total book assets, and NFt is net changes in
total financing cash
flow. D is notation for the change of variables from fiscal year t
) 1 to t. All vari-
ables except Lt (leverage) and excess stock return are deflated
by the lagged mar-
ket value of equity (Mt)1). As indicated by Faulkender and
Wang (2006), this
methodology is a type of long-term event study that exploits
unexpected change
in firm-specific factors to explain abnormal returns.
Specifically, the change in
the value of cash reserves is the event, while the entire fiscal
year is defined as the

event window.
Our benchmark portfolios formed on size and book-to-market
equity ratios
may not be free from industry bias, because the US market is
quite different
366 343
from the Australian stock market, which is characterized by a
high proportion
of resources firms the ASX. These resources firms are rich in
cash reserves, and
the time-series association between cash holdings and stock
returns is likely to be
volatile owing to the structural influence of the resources
sector. In addition,
Masulis et al. (2009) argue that the Faulkender and Wang
(2006) approach suf-
fers a potential endogeneity problem because a firm’s book-to-
market equity
ratio is endogenous. Therefore, we also use RBIi;t , industry
value-weighted annual-
ized benchmark returns, as in the study of Masulis et al. (2009),
where industries
are defined based on the AGSM Centre for Research in Finance
(CRIF) 26
industry classifications.
For our baseline regression, we estimate the pooled ordinary
least square

(OLS) regression model given by Equation (1) with year
dummies. We allow
residuals to be correlated within years by using the Huber–
White variance/
covariance matrix estimator to correct for potential
heteroscedasticity. Petersen
(2009) argues that both time effect and firm effect should be
properly dealt with
in a panel data set to mitigate estimation biases. Industry effects
are also impor-
tant for an Australian sample given that cash holdings are
particularly skewed
across resources industries. In robustness checks, we also re-
estimate our baseline
model using the industry fixed-effects model and the firm fixed-
effects model.
Our main results are qualitatively similar. For comparison with
the results
obtained in Faulkender and Wang (2006) and Lee and Powell
(2011), we use
pooled OLS in our baseline regressions.
3.2. Financial constraints and the value of cash
To examine the impact of financial constraints on the marginal
value of cash
holdings, we need to classify our sample into financially
constrained (FC) and
non-financially constrained (NFC) firms.
4
A firm is said to be FC if its cost of
external capital exceeds the cost of internal funds. However,
this definition does

not provide us with clear-cut guidance on identifying
constrained firms. Chang
et al. (2007) suggest that FC firms generally tend to have one or
more of the fol-
lowing characteristics: small or unprofitable, high growth
potential, high leverage
and low debt capacity. Standard corporate finance theory
suggests that smaller
firms are more financially constrained than larger firms. As a
result, we use an
approach similar to Chang et al. (2007). Our sample of firms is
evenly divided
into two groups according to their median book value of total
assets. We define
4
Some studies have used the KZ index (Kaplan and Zingales,
1997) to classify firms as
financially constrained (above-median KZ index) or
unconstrained (below-median KZ
index). The KZ index is constructed and based on a small
sample of 49 US firms; it may
not be appropriate for Australian firms. Nevertheless, and as a
robustness check, we use
the methodology from Kaplan and Zingales (1997) to partition
our sample into finan-
cially constrained and unconstrained firms. We find
qualitatively similar results to those
reported in this study. The results are not reported here but are
available upon request.
339–366

below-median firms as FC firms and above-median firms as
NFC firms.
5
Smaller
firms are more likely to be financially constrained than larger
firms because they
are typically young and less known to the market. Smaller firms
are more likely
to encounter information asymmetry and agency problems,
which will make
their external financing more expensive.
Fazzari et al. (1988) used the dividend payout approach to
classify firms into
FC and NFC firms. They argue that owing to information
asymmetries in capi-
tal markets, FC firms have limited access to external financing.
As a result, FC
firms tend to retain most of their income. We follow Chang et
al. (2007) to parti-
tion our sample of firms into two groups according to their
dividend payout
ratios (as measured by dividend/EBIT). Non-dividend payers are
firms that do
not pay dividends and are more likely to be financially
constrained. Dividend
payers are those firms paying dividends in a particular year and
are viewed as
unconstrained.
6

We first follow Faulkender and Wang (2006) to test the follow-
ing hypothesis.
Hypothesis 1: The marginal value of cash holdings is negatively
associated with
the level of a firm’s cash position and the level of a firm’s
leverage. Investors in
financially constrained firms value excess cash holdings more
than those in non-
financially constrained firms.
3.3. Growth opportunities, uncertainty, product market
competition and the value
of cash
Firms with strong growth opportunities and investment needs
are likely to
hold more cash (Opler et al., 1999). As a result, investors value
cash holdings by
firms with good growth opportunities at a premium to those
with poor growth
opportunities (Pinkowitz and Williamson, 2004). We use book-
to-market ratios
(BTMs) as a proxy for investment opportunities to partition our
sample of firms
into two groups: a low BTM group and a high BTM group.
7
Hypothesis 2: Investors value excess cash holdings of firms
with higher growth
opportunities more than excess cash holdings of firms with
lower growth opportuni-
ties.

Firms facing higher uncertainty in their cash flows are likely to
hoard excess
cash in fear of future cash shortfalls. Firms with highly
uncertain cash flows
5
We also partition our sample of firms into tertiles (quintiles)
and then compare the low-
est tertile (quintile) with the highest tertile (quintile). Our
results still hold.
6
We do not use debt ratings as less than 5 per cent of firms in
our sample have debt rat-
ings. Also, during the majority of our sample period, there were
very few corporate debt
issues.
7
Using alternative proxies, such as growth in total assets, yield
quite similar results.
366 345
face higher external funding costs when compared to internally
generated
funds. As a result, firms with a higher level of uncertainty are
expected to rely

more on internal funds and to save more cash. Opler et al.
(1999) find that
firms with high-risk cash flows generally hold relatively high
levels of cash. We
use the standard deviation of earnings ratios (as measured by
EBIT/total book
assets) for the past 5 years to proxy for uncertainty in cash
flows. Our sample
is partitioned into high-uncertainty and low-uncertainty groups
according to
their standard deviations of earnings ratios. We propose the
following
hypothesis.
with higher cash-flow
uncertainty more than excess cash flows of firms with lower
cash-flow uncertainty.
Finally, it has been argued in the literature by Phillips (1995),
MacKay and
Phillips (2005) and Fresard (2010) that intense product market
competition
affects firm financial choices. Firms in highly competitive
industries are more
likely to hoard more cash reserves as a buffer for their future
liquidity needs.
Therefore, firms with excess cash holdings are viewed
positively by stock mar-
ket investors. We use the Herfindahl–Hirschman index (HHI) as
a proxy for
product market competition. HHI is calculated by summing up
the squares of
the individual market shares by sales for firms in a specific
industry. In this
study, we use the CRIF 26 industry classifications to calculate

HHIs. Our sam-
ple is partitioned into high industry competition (with low
HHIs) and low
industry competition (with high HHIs) groups according to HHI
at the indus-
try level.
8
Hypothesis 4: Investors value excess cash holdings of firms in
highly competitive
industries more than excess cash holdings of firms in industries
with less competi-
tion.
3.4. Corporate governance and the value of cash
Firms with higher agency costs are likely to misuse their cash
reserves. So,
investors would value cash holdings less when controlling
shareholders might
have the opportunity to expropriate minority shareholders in
countries with
poorer shareholder protection (Pinkowitz et al., 2006). Even in
countries with
strong shareholder protection, shareholders are still concerned
about whether
managers will waste cash reserves, especially for firms with a
high level of
managerial entrenchment or for firms that lack investor
oversight by large
institutional shareholders (Dittmar and Mahrt-Smith, 2007). We
use two
8
We obtain similar results for Tables 4, 5, 6 and 7 when

comparing the lowest tertile
(quintile) with the highest tertile (quintile) by using tertile or
quintile partition. The results
are not reported but are available upon request.
339–366
corporate governance measures to investigate their association
with the value of
cash holdings. These are the Horwath Corporate Governance
index (HCG index
hereafter) of Australian firms and the ownership of large
shareholders (block-
holders) in the firm.
9
3.4.1. HCG index
The Horwath Corporate Governance Report is a review of
Australian cor-
porate governance that was published annually from 2002 to
2006. The report
is based on annual report disclosures of Australia’s top 250
firms based on
market capitalization. The report intends to provide an overall
assessment of
each firm’s corporate governance structures and constructs a
star rating with a

maximum value of 5 and a relative corporate governance
ranking from 1 to
250. The fundamental focus of the report is to assess the
independence of a
firm’s board of directors and associated committees, including
audit commit-
tees, nomination committees and remuneration committees. The
report also
measures ‘the level of perceived independence of the firm from
the external
auditors, and disclosures relating to the existence of a code of
conduct, risk
management and share trading policy’. If a firm has a 5-star
rating, this indi-
cates the firm has outstanding corporate governance structures
and ‘the struc-
tures met all best practise standards and could not be faulted’. If
a firm is
assigned a 1-star rating, its corporate governance structures are
in very poor
condition and ‘almost without exception the board of directors
and the associ-
ated committees (where they existed) contained no independent
members’. We
employ both the Horwath star and ranking (from number 1, the
highest gover-
nance ranking, to 250, the lowest) measures in the value of cash
holdings
regressions.
3.4.2. Block holdings
We follow the study of Dittmar and Mahrt-Smith (2007) and use
the sum of
all ownership positions >5 per cent held by large investors as
our measure for

large shareholder monitoring. Prior research, such as the study
of Dlugosz et al.
(2004), indicates that large shareholders have more incentive to
monitor and
influence managers’ decisions, because they have a large capital
stake in the firm.
However, Pagano and Röell (1998) suggest that large
shareholders could end up
9
The Horwarth Governance index is published by the University
of Newcastle and is
viewed as an independent and reputable measure of corporate
governance by the media
and sections of the investment community. The index is publicly
available only for the
period 2002–2006. Blockholders are shareholders who own
more than 5 per cent of a
firm’s issued capital. These data are hand-collected from annual
reports with all nominee
holdings excluded.
366 347
using their position for ex post opportunism and to expropriate
wealth from
minority shareholders.
Our regression model for the association between the value of
cash holdings

and corporate governance is
ri;t � RBi;t ¼ a0 þ b1
DCi;t
Mi;t�1
þ b2
DEi;t
Mi;t�1
þ b3
DNAi;t
Mi;t�1
þ b4
DRNDi;t
Mi;t�1
þ b5
DIi;t
Mi;t�1
þ b6
DDi;t
Mi;t�1
þ b7
Ci;t�1
Mi;t�1
þ b8Li;t þ b9
NFi;t
Mi;t�1
þ b10

Ci;t�1
Mi;t�1
�
DCi;t
Mi;t�1
þ b11Li;t �
DCi;t
Mi;t�1
þ b12Govi;t
þ b13Govi;t �
DCi;t
Mi;t�1
þ ei;t
ð2Þ
where Govi,t represents corporate governance measures such as
the Horwath gov-
ernance star and ranking, or the ownership of blockholders. We
also follow Ditt-
mar and Mahrt-Smith (2007) to include corporate governance in
our analysis as
a binary dummy by splitting the sample into subgroups. For the
HCG index, a
firm with more than three stars is coded one (strong
governance) and less than
three stars is coded zero (weak governance), while firms with a
top 100 gover-
nance ranking are coded one and firms in the bottom 100 are
coded zero. For
block ownership, the highest tercile of ownership is coded one

(strong gover-
nance), while the lowest tercile of ownership is coded zero
(weak governance).
Dittmar and Mahrt-Smith (2007) argue that using a dummy
variable could allow
for more intuitive interpretation of the coefficients on the
interaction terms. This
also helps us to avoid difficulties in interpreting how changes in
governance
measures could lead to very different management independence
or investor
monitoring.
with strong corporate
governance mechanisms more than those of firms with weak
corporate governance
mechanisms.
4. Data
4.1. Sample selection
We start from a merged sample of firms listed on the ASX over
the
1990–2007 period with accounting data and stock return data
available from
the Aspect Financial Database and the Australian Graduate
School of Man-
agement Share Price and Price Relative (AGSM_SPPR)
database. Firms are
required to have no missing data for any of the following key
variables: cash,
339–366

earnings before interest and tax, total book assets, net interest
expense, total
common dividend paid, total debt, monthly stock returns and
book value of
equity from fiscal year t ) 1 to t. We limit our sample to the 500
largest firms
listed on the ASX according to their market capitalizations in
the fiscal year
period. We also exclude firms in the financial and utilities
sector (with CRIF
industry code ‘18’, banks ‘20’, insurance ‘22’, real estate
investment trusts and
utilities ‘26’) owing to the relatively low physical capital
investment for finan-
cials and the regulated nature of utilities. Our sample consists
of 1108 individ-
ual firms and 6412 firm-year observations from 1990 to 2007.
10
All variables
have been winsorized at the 1st and 99th percentiles. This
approach reduces
the impact of extreme observations by assigning the cut-off
value to values
beyond the cut-off point.
11
The dependent variable in the baseline regression is excess

stock return.
Monthly stock returns are required to calculate individual stock
returns and
benchmark portfolio returns. Following Faulkender and Wang
(2006), we
use the 25 Fama and French (1993) portfolios formed on market
capitaliza-
tion (size) and BTM. In particular, for each fiscal year, we sort
firms into
25 size and BTM portfolios based on their market capitalization
and BTMs.
Then, excess stock returns are calculated by subtracting
annualized bench-
mark portfolio returns from annualized stock raw returns. We
also use the
industry value-weighted annualized benchmark return as in the
study of
Masulis et al. (2009) to examine whether our main results suffer
industry
bias.
In addition, we use two measures of corporate governance, the
independence
of corporate governance structures (the HCG) and the presence
of large share-
holders who monitor the firm. We sum all ownership positions
>5 per cent held
by blockholders. The HCG star and ranking measures are
manually collected
from the Horwath Corporate Governance Report. The original
Horwath Corpo-
rate Governance Report includes 1250 observations. Our final
sample drops to
557 observations after excluding firms in the financial and
utilities sectors. The
percentage ownership of blockholders is manually collected

from annual report
disclosures from Australia’s top 500 firms based on market
capitalization from
2000 to 2007. Our final blockholder sample has 1798
observations after exclud-
ing firms in the financial and utilities sectors and observations
with missing
values.
10
The minimum number of firms in any year is 221 (1990), and
the maximum is 404
(2002, 2007). The average (median) over this period is
approximately 356 (389) firms in a
year. The number of firm-year observations compares
favourably with the 5876 firm-year
observations over the same sample period for the study of Lee
and Powell (2011).
11
Our results are qualitatively very similar when we truncate the
distribution instead of
winsorizing it.
366 349
4.2. Summary statistics

The summary statistics of the main variables are reported in
Table 1. As all
the explanatory variables except leverage ratio are scaled by the
1-year lagged
market value of equity, we interpret our variables as changes in
dollar value.
Table 1 indicates that, on average, earnings before interest and
taxes (Et), non-
cash book assets (NAt) and total common dividends paid (Dt)
increase over the
sample period, as both their mean and median values are
positive.
Panel A of Table 2 reports summary statistics of firms in our
sample after
we classify them into as constrained (FC) or unconstrained
(NFC). Accord-
ing to size and dividend payout, a FC firm has on average a
higher annual-
ized excess return, a higher change in cash holdings, a higher
level of cash
holdings, higher leverage and higher change in financing cash
flow than does
a NFC firm. Similarly, Panel B of Table 2 indicates that firms
with lower
BTM (higher growth opportunities) and higher level of cash-
flow uncertainty
have higher annualized excess returns, higher changes in cash
holdings and
higher changes in financing cash flow. However, firms in highly
competitive
industries have lower annualized excess returns, lower changes
in cash
Table 1

Sample summary statistics (1990–2007)
Variable Observations Mean Median SD Min Max
Ri;t � RBPi;t 6412 0.0476 )0.0416 0.7021 )1.9737 3.4540
Ri;t � RBIi;t 6412 0.2138 0.0278 0.7796 )0.9353 4.2872
DCt 6412 0.0390 0.0033 0.2166 )0.8363 1.5223
Ct)1 6412 0.1177 0.0547 0.2127 0 2.0023
DEt 6412 0.0246 0.0107 0.1800 )1.0969 1.6511
DNAt 6412 0.2371 0.0907 0.8052 )3.6493 5.8598
DIt 6412 0.0002 )0.0001 0.0335 )0.1676 0.2463
DDt 6412 0.0078 0.0015 0.029 )0.1025 0.1342
Lt 6412 0.1939 0.1862 0.1658 0 0.8669
NFt 6412 0.0911 )0.0012 0.4551 )1.1248 3.3568
Accounting data are obtained from the Aspect Financial
Database, and stock returns are obtained
from the AGSM_SPPR Database for fiscal years 1990–2007.
Firms are required to have available
information for all key variables needed in the study and to be
among the top 500 largest firms listed
in the ASX according to market capitalization in the study
period. Ri;t �RBPi;t and Ri;t � R
BI
i;t are
excess stock returns, where Ri,t is the annualized stock return
of firm i in fiscal year t, Ri;t �RBPi;t is
stock i’s annualized benchmark portfolio return in fiscal year t
calculated on value-weighted returns

of 25 portfolios formed on size and book-to-market (5 · 5) as in
the study of Fama and French
(1993), and Ri;t � RBIi;t is industry value-weighted annualized
benchmark return as in the study of
Masulis et al. (2009). All variables except Lt (leverage) and
excess stock return are deflated by the
lagged market value of equity (Mt)1). Ct is cash including
short-term deposits; Et is earnings before
interest and tax; and NAt is total book assets minus Ct; It is net
interest expense; Dt is total common
dividend paid; Lt is total debt divided by total book assets, and
NFt is net changes in total financing
cash flow. D is notation for the change of variables from fiscal
year t ) 1 to t.
339–366
T
a
b
le
2
F
in

a
n
ci
a
l
co
n
st
ra
in
ts
,
g
ro
w
th
o
p
p
o
rt
u
n
it
ie
s,
ca
sh
-fl
o
w
u
n
ce
rt

a
in
ty
a
n
d
co
rp
o
ra
te
g
o
v
er
n
a
n
ce
P
a
n
el
A
:
F
in
a
n
ci
a
l
co

n
st
ra
in
ts
V
a
ri
a
b
le
D
iv
id
en
d
p
a
y
in
g
S
iz
e
F
C
N
F
C
F

C
)
N
F
C
F
C
N
F
C
F
C
)
N
F
C
R
i;
t
�
R
B
P
i;
t
0
.0

9
9
0
.0
2
3
0
.0
7
6
*
*
*
0
.0
8
3
0
.0
1
2
0
.0
7
1
*
*
*
R
i;

t
�
R
B
I
i;
t
0
.2
3
1
0
.0
4
8
0
.1
8
3
*
*
*
0
.2
2
7
7
)
0

.0
1
3
0
.2
4
1
*
*
*
D
C
t
0
.0
8
2
0
.0
1
8
0
.0
6
4
*
*
*
0
.0

6
0
.0
1
8
0
.0
4
2
*
*
*
C
t)
1
0
.1
6
9
0
.0
9
3
0
.0
7
6
*
*
*

0
.1
2
0
.1
1
6
0
.0
0
4
D
E
t
0
.0
0
4
0
.0
3
4
)
0
.0
3
*
*
*

0
.0
2
8
0
.0
2
1
0
.0
0
7
*
D
D
t
0
.0
0
3
0
.0
1
)
0
.0
0
7
*

*
*
0
.0
0
8
0
.0
0
8
0
L
t
0
.1
7
0
.2
0
6
)
0
.0
3
6
*
*
*

0
.1
3
5
0
.2
5
3
)
0
.1
1
8
*
*
*
N
F
t
0
.2
1
8
0
.0
3
0
.1
8
8

*
*
*
0
.1
3
6
0
.0
4
7
0
.0
8
9
*
*
*
P
a
n
el
B
:
G
ro
w
th
,
u
n
ce

rt
a
in
ty
a
n
d
co
m
p
et
it
io
n
V
a
ri
a
b
le
B
o
o
k
-t
o
-m
a
rk
et
ra

ti
o
C
a
sh
-fl
o
w
u
n
ce
rt
a
in
ty
In
d
u
st
ry
co
m
p
et
it
io
n
H
ig
h
L

o
w
L
o
w
)
H
ig
h
L
o
w
H
ig
h
H
ig
h
)
L
o
w
L
o
w
H
ig
h

H
ig
h
)
L
o
w
R
i;
t
�
R
B
P
i;
t
)
0
.0
2
2
0
.1
1
8
0
.1
3

9
*
*
*
0
.0
2
5
0
.0
7
1
0
.0
4
6
*
*
*
0
.0
8
3
0
.0
1
2
)
0
.0

7
1
*
*
*
R
i;
t
�
R
B
I
i;
t
)
0
.0
7
0
0
.2
8
5
0
.3
5
6
*
*
*

0
.0
1
6
0
.1
9
9
0
.1
8
3
*
*
*
0
.1
5
1
0
.0
6
3
)
0
.0
8
8
*
*

*
D
C
t
0
.0
3
0
0
.0
4
8
0
.0
1
8
*
*
*
0
.0
1
9
0
.0
5
9
0
.0

4
*
*
*
0
.0
5
0
.0
2
9
)
0
.0
2
1
*
*
*
C
t)
1
0
.1
3
4
0
.1
0
1

)
0
.0
3
3
*
*
*
0
.1
0
8
0
.1
2
7
0
.0
1
9
*
*
*
0
.1
2
1
0
.1
1

4
)
0
.0
0
7
D
E
t
0
.0
2
3
0
.0
2
6
0
.0
0
2
0
.0
1
3
0
.0
3
6

0
.0
2
3
*
*
*
0
.0
2
4
0
.0
2
5
0
.0
0
1
D
D
t
0
.0
0
8
0
.0
0

7
)
0
.0
0
1
0
.0
0
8
0
.0
0
8
0
0
.0
0
7
0
.0
0
9
0
.0
0
2
*
*
*

L
t
0
.2
0
9
0
.1
7
9
)
0
.0
3
0
*
*
*
0
.2
2
2
0
.1
6
5
)
0
.0

5
7
*
*
*
0
.1
9
7
0
.1
9
1
)
0
.0
0
6
N
F
t
0
.0
7
2
0
.1
1
1

0
.0
3
9
*
*
*
0
.0
2
9
0
.1
5
4
0
.1
2
5
*
*
*
0
.1
1
4
0
.0
6
9

)
0
.0
4
5
*
*
*
366 351
T
a
b
le
2
(c
o
n
ti
n
u
ed
)
P
a
n
el

C
:
C
o
rp
o
ra
te
g
o
v
er
n
a
n
ce
V
a
ri
a
b
le
H
C
G
st
a
r
H
C
G

ra
n
k
in
g
B
lo
ck
h
o
ld
er
o
w
n
er
sh
ip
S
tr
o
n
g
W
ea
k
S
tr
o
n

g
)
W
ea
k
S
tr
o
n
g
W
ea
k
S
tr
o
n
g
)
W
ea
k
S
tr
o
n
g
W
ea

k
S
tr
o
n
g
)
W
ea
k
R
i;
t
�
R
B
P
i;
t
)
0
.0
3
1
)
0
.0
2
7

)
0
.0
0
4
)
0
.0
3
1
)
0
.0
3
8
0
.0
0
6
0
.1
3
1
0
.1
6
8
)
0

.0
3
7
R
i;
t
�
R
B
I
i;
t
)
0
.0
3
2
)
0
.0
3
8
0
.0
0
7
)
0
.0

2
7
)
0
.0
5
1
0
.0
2
4
0
.0
7
8
0
.1
0
9
)
0
.0
3
0
D
C
t
0
.0

1
6
0
.0
3
1
)
0
.0
1
5
0
.0
1
5
0
.0
3
5
)
0
.0
2
0
0
.0
2
2
0

.0
5
9
)
0
.0
3
8
*
*
*
C
t)
1
0
.0
9
2
0
.0
8
9
0
.0
0
3
0
.0
8
0

0
.0
8
8
)
0
.0
0
8
0
.1
4
5
0
.1
2
4
0
.0
2
1
D
E
t
0
.0
1
1

0
.0
2
2
)
0
.0
1
1
0
.0
1
2
0
.0
2
1
)
0
.0
1
0
0
.0
2
6
0
.0
2
2

0
.0
0
4
D
D
t
0
.0
0
6
0
.0
0
5
0
.0
0
1
0
.0
0
5
0
.0
0
6
)

0
.0
0
2
0
.0
0
6
0
.0
0
6
0
.0
0
0
L
t
0
.2
3
5
0
.1
7
8
0
.0
5

7
*
*
*
0
.2
3
3
0
.1
8
1
0
.0
5
2
*
*
*
0
.1
7
1
0
.1
9
0
)
0
.0

1
9
*
*
N
F
t
0
.0
2
7
0
.0
3
3
)
0
.0
0
6
0
.0
1
3
0
.0
4
7
)

0
.0
3
4
0
.0
4
0
0
.1
2
9
)
0
.0
8
9
*
*
*
A
cc
o
u
n
ti
n
g
d
a
ta

a
re
o
b
ta
in
ed
fr
o
m
th
e
A
sp
ec
t
F
in
a
n
ci
a
l
D
a
ta
b
a
se
,
a
n
d

st
o
ck
re
tu
rn
s
a
re
o
b
ta
in
ed
fr
o
m
th
e
A
G
S
M
_
S
P
P
R
D
a
ta

b
a
se
fo
r
fi
sc
a
l
y
ea
rs
1
9
9
0
–
2
0
0
7
.
F
ir
m
s
a
re
re
q
u
ir
ed

to
h
a
v
e
a
v
a
il
a
b
le
in
fo
rm
a
ti
o
n
fo
r
a
ll
k
ey
v
a
ri
a
b
le
s
n

ee
d
ed
in
th
e
st
u
d
y
a
n
d
to
b
e
a
m
o
n
g
th
e
to
p
5
0
0
la
rg
es
t
fi
rm

s
li
st
ed
o
n
th
e
A
S
X
a
cc
o
rd
in
g
to
m
a
rk
et
ca
p
it
a
li
za
ti
o

n
in
th
e
st
u
d
y
p
er
io
d
.
R
i;
t
�
R
B
P
i;
t
a
n
d
R
i;
t
�
R

B
I
i;
t
a
re
th
e
ex
ce
ss
st
o
ck
re
tu
rn
s,
w
h
er
e
R
i,
t
is
th
e
a
n
n
u

a
li
ze
d
st
o
ck
re
tu
rn
o
f
fi
rm
i
in
fi
sc
a
l
y
ea
r
t,
R
i;
t
�
R
B

P
i;
t
is
st
o
ck
i’
s
a
n
n
u
a
li
ze
d
b
en
ch
m
a
rk
p
o
rt
fo
li
o
re
tu
rn

in
fi
sc
a
l
y
ea
r
t
ca
lc
u
la
te
d
o
n
v
a
lu
e-
w
ei
g
h
te
d
re
tu
rn
s
o
f
2
5
p

o
rt
fo
li
o
s,
w
h
ic
h
a
re
fo
rm
ed
o
n
si
ze
a
n
d
b
o
o
k
-t
o
-m
a
rk

et
(5
·
5
),
a
s
in
th
e
st
u
d
y
o
f
F
a
m
a
a
n
d
F
re
n
ch
(1
9
9
3
),
a

n
d
R
i;
t
�
R
B
I
i;
t
is
in
d
u
st
ry
v
a
lu
e-
w
ei
g
h
te
d
a
n
n
u

a
li
ze
d
b
en
ch
m
a
rk
re
tu
rn
a
s
in
th
e
st
u
d
y
o
f
M
a
su
li
s
et
a

l.
(2
0
0
9
).
A
ll
v
a
ri
a
b
le
s
ex
ce
p
t
L
t
(l
ev
er
a
g
e)
a
n
d
ex
ce
ss
st

o
ck
re
tu
rn
a
re
d
efl
a
te
d
b
y
th
e
la
g
g
ed
m
a
rk
et
v
a
lu
e
o
f

eq
u
it
y
(M
t)
1
).
C
t
is
ca
sh
in
cl
u
d
in
g
sh
o
rt
-t
er
m
d
ep
o
si
ts
;
E
t

is
ea
rn
in
g
s
b
ef
o
re
in
te
re
st
a
n
d
ta
x
;
N
A
t
is
to
ta
l
b
o
o
k
a
ss
et

s
m
in
u
s
C
t;
I t
is
n
et
in
te
re
st
ex
p
en
se
;
D
t
is
to
ta
l
co
m
m
o
n
d

iv
id
en
d
p
a
id
;
L
t
is
to
ta
l
d
eb
t
d
iv
id
ed
b
y
to
ta
l
b
o
o
k
a
ss
et
s;
a

n
d
N
F
t
is
n
et
ch
a
n
g
e
in
to
ta
l
fi
n
a
n
ci
n
g
ca
sh
fl
o
w
.
D
is

n
o
ta
ti
o
n
fo
r
th
e
ch
a
n
g
e
o
f
v
a
ri
a
b
le
s
fr
o
m
fi
sc
a
l
y
ea
r

t-
1
to
t.
F
ir
m
s
a
re
ca
te
g
o
ri
ze
d
a
s
b
ei
n
g
fi
n
a
n
ci
a
ll
y
co
n
st

ra
in
ed
(F
C
)
a
n
d
u
n
co
n
st
ra
in
ed
(N
F
C
)
a
cc
o
rd
in
g
to
th
ei
r
b
o

o
k
v
a
lu
e
o
f
a
ss
et
s
a
n
d
d
iv
id
en
d
p
a
y
o
u
t
ra
ti
o
.
t-
st
a
ti
st

ic
s
si
g
n
ifi
ca
n
t
a
t
th
e
1
0
,
5
a
n
d
1
p
er
ce
n
t
le
v
el
s
a
re
d
es

ig
n
a
te
d
w
it
h
*
,
*
*
,
a
n
d
*
*
*
,
re
sp
ec
ti
v
el
y
.
339–366

holdings and slightly lower level of cash holdings.
12
Panel C of Table 2 indi-
cates that firms with strong corporate governance measures have
higher
changes in cash holdings. However, the differences between
strong and weak
corporate governance firms are statistically significant only for
the block-
holder ownership measure and not for the HCG star and ranking
measures.
To summarize, summary statistics and univariate analyses
indicate that
financially constrained firms, firms with higher growth
opportunities, firms
with higher uncertainty and firms with lower industry
competition exhibit
higher annualized excess returns and hold more excess cash.
Firms with
higher blockholder ownership are associated with lower
annualized excess
returns and hold less excess cash. This evidence provides initial
support for
our hypotheses.
5. Empirical results
5.1. The marginal value of cash holdings
Table 3 presents estimates from the baseline model for the
entire sample using
the pooled OLS regression. The initial coefficient estimates on
the changes in
cash holdings are statistically significant and positive at the 1

per cent level. This
suggests that an additional dollar of cash corresponds to
AU$0.726 as valued by
shareholders (Column (1)). The estimated coefficients on the
other control vari-
ables have the expected signs and are largely consistent with
those reported in
Faulkender and Wang (2006) and Lee and Powell (2011). In
particular, the coef-
ficients on changes in earnings, changes in dividends and level
of cash holdings
are positive and significant in all four columns. Further, the
estimated coeffi-
cients are qualitatively similar when the following interaction
terms are both
included in the estimation: change in cash with the level of cash
holdings
(Ct)1 * DCt) and the leverage ratio with the level of cash
holdings (Lt * DCt).
Based on the estimation in Column (2), the marginal value of
cash to investors
in the mean firm is equal to AU$0.867 (= AU$1.095 + ()0.471
* 0.1177) + ()0.891 * 0.1939)) for a firm with a 11.77 per cent
level of cash to
market value of equity and a 19.39 per cent leverage ratio. This
result is consis-
tent with Hypothesis 1 that the marginal value of cash holdings
is negatively
associated with the level of a firm’s cash position and the level
of a firm’s lever-
age ratio. Columns (3) and (4) use alternative excess stock
returns, the annual-
ized industry-adjusted excess returns. The results are very
similar to those in
12

In the literature, there is no direct empirical evidence on
whether firms in highly com-
petitive industries would hold more or less cash. MacKay and
Phillips (2005) investigate
only the association between industry competition and leverage.
In Panel B, the leverage
ratios for highly competitive industries are slightly lower than
those for less competitive
industries. This is consistent with MacKay and Phillips (2005).
366 353
Columns (1) and (2) using the 25 Fama and French (1993)
portfolios’ adjusted
excess returns.
5.2. Financial constraints and the value of cash holdings
Table 4 presents the estimated results for the association
between financial
constraints and the value of cash holdings. Columns (1) and (2)
of Table 4 con-
tain the regression results for the dividend payout groupings.
Non-dividend pay-
ers (FC) exhibit a higher value of cash holdings than dividend
payers (NFC).
Columns (4) and (5) report results obtained by using firm book
value of assets as
the measure of financial constraints. Similarly, small firms (FC)

exhibit higher
Table 3
Regression results for the marginal value of cash holdings
Variables
(1) (2) (3) (4)
Ri;t �RBPi;t Ri;t � RBPi;t Ri;t �RBIi;t Ri;t � RBIi;t
DCt 0.726*** (8.50) 1.095*** (8.96) 0.735*** (8.12) 1.108***
(8.47)
DEt 0.449*** (4.48) 0.450*** (4.86) 0.479*** (4.64) 0.480***
(4.99)
DNAt 0.041 (1.50) 0.040 (1.54) 0.066** (2.31) 0.064** (2.43)
DRNDt )0.526 ()0.44) )0.648 ()0.56) )0.381 ()0.32) )0.505
()0.43)
DIt 1.351*** (3.11) 1.059*** (2.63) 1.867*** (4.36) 1.569***
(3.97)
DDt 0.823** (2.56) 0.738** (2.38) 1.040*** (3.01) 0.954***
(2.86)
Ct)1 0.466*** (6.63) 0.416*** (6.60) 0.393*** (5.33) 0.342***
(5.26)
Lt )0.272*** ()4.88) )0.240*** ()4.43) )0.415*** ()7.22)
)0.383*** ()6.86)
NFt 0.094* (1.88) 0.065 (1.34) 0.206*** (3.98) 0.176*** (3.50)
Ct)1 * DCt )0.471*** ()2.98) )0.471*** ()3.13)
Lt * DCt )0.891** ()2.53) )0.916** ()2.44)
Intercept )0.098** ()2.26) )0.106** ()2.47) 0.106** (2.43)
0.098** (2.33)
Year dummy

included
Yes Yes Yes Yes
Observations 6412 6412 6412 6412
R
2
0.13 0.14 0.18 0.19
This table presents the results of regressing the excess stock
return on changes in firm characteristics,
including interaction terms, between cash and leverage over the
fiscal year. Ri;t � RBPi;t and Ri;t �RBIi;t
are the excess stock returns, where Ri,t is the annualized stock
return of firm i in fiscal year t;
Ri;t � RBPi;t is stock i’s annualized benchmark portfolio return
in fiscal year t calculated on value-
weighted returns of 25 portfolios formed on size and book-to-
market (5 · 5) as in the study of Fama
and French (1993); and Ri;t � RBIi;t is industry value-weighted
annualized benchmark return as in the
study of Masulis et al. (2009). All variables except Lt
(leverage) and excess stock return are deflated
by the lagged market value of equity (Mt)1). Ct is cash
including short-term deposits; Et is earnings
before interest and tax; NAt is total book assets minus Ct; RNDt
are capitalized research and develop-
ment expenses; It is net interest expense; Dt is total common
dividends paid; Lt is total debt divided

by total book assets; and NFt is net changes in total financing
cash flow. All variables are winsorized
at the 1st and 99th percentiles. This approach reduces the
impact of extreme observations by assign-
ing the cut-off value to values beyond the cut-off point. t-
statistics significant at the 10, 5 and 1 per
cent levels are designated with *, ** and ***, respectively.
339–366
value of cash holdings than large firms (NFC). On average, the
marginal value
of cash holdings for small firms is AU$1.056 (= AU$1.213 +
()0.765 *
0.120) + ()0.479 * 0.135)), while the marginal value of cash
holdings for large
firms is AU$0.501 (= AU$0.701 + ()0.178 * 0.116) + ()0.708 *
0.253)). The
value difference for an additional dollar of cash holdings is
AU$0.555. The
coefficients on the change in cash are statistically different
across FC and NFC
Table 4
Regression results for financial constraints

Variables
FC NFC FC NFC
Non-dividend
payers Dividend payers Small firms Large firms
DCt 1.108*** (10.20) 0.520*** (6.78) 1.213*** (12.71)
0.701*** (8.68)
P-value
(FC ) NFC „ 0)
0.00 0.05
DEt 0.315*** (3.84) 0.992*** (15.83) 0.465*** (6.41)
0.455*** (8.10)
DNAt )0.000 ()0.01) 0.092*** (6.47) 0.035 (1.49) 0.066***
(5.32)
DRNDt )0.573 ()0.40) )0.567 ()0.59) )0.804 ()0.70) )0.519
()0.47)
DIt 0.473 (1.02) 2.239*** (7.27) 0.124 (0.25) 1.194*** (4.98)
DDt )0.852 ()1.11) 1.085*** (4.58) 1.473*** (2.95) 0.135
(0.52)
Ct)1 0.477*** (6.28) 0.213*** (4.68) 0.772*** (9.44) 0.198***
(5.99)
P-value
(FC ) NFC „ 0)
0.03 0.00
Lt )0.322*** ()2.86) )0.103** ()2.18) )0.181* ()1.80) )0.225***
()4.60)
NFt 0.203*** (3.96) )0.190*** ()6.66) 0.109** (2.49)

)0.070*** ()2.59)
Ct)1 * DCt )0.667*** ()5.77) )0.001 ()0.01) )0.765*** ()6.23)
)0.178*** ()2.79)
Lt * DCt )0.782** ()2.39) )0.022 ()0.07) )0.479 ()1.05)
)0.708*** ()3.64)
Intercept )0.199* ()1.94) )0.154 ()1.19) )0.302*** ()4.36) 0.046
(1.28)
Year dummy
included
Yes Yes Yes Yes
Observations 2081 4331 3211 3201
R
2
0.17 0.17 0.17 0.12
Difference
between FC
and NFC firms
(v2 of Chow test)
123.40*** 92.16***
return Ri,t ) RBi,t on changes in firm
characteristics for firms with and without financial constraints
over the fiscal year. The baseline
model is a pooled ordinary least square model controlling for

time effect. All variables except Lt
(leverage) and excess stock return are deflated by the lagged
market value of equity (Mt)1). Ct is cash
including short-term deposits; Et is earnings before interest and
tax; NAt is total book assets minus
Ct; RNDt is capitalized research and development expenses; It
is net interest expense; Dt is total com-
mon dividend paid; Lt is total debt divided by total book assets;
and NFt is the net changes in total
financing cash flow. All variables are winsorized at the 1st and
99th percentiles. This approach
reduces the impact of extreme observations by assigning the
cut-off value to values beyond the cut-
off point. t-statistics significant at the 10, 5 and 1 per cent
levels are designated with *, ** and ***,
respectively.
366 355
groups (P-values reported in Table 4 are <0.05).
13
This implies that investors

place a significantly higher value on an extra dollar of cash
holdings for finan-
cially constrained firms, because it is more difficult for these
firms to access exter-
nal financing. These results support Hypothesis 1.
5.3. Growth opportunities, uncertainty and product market
competition
To investigate whether growth opportunities, uncertainty in
cash flows and
product market competition affect the value of cash holdings in
a way similar to
financial constraints, we use BTMs (proxy for growth
opportunities), standard
deviation of earnings (proxy for cash-flow uncertainty) and the
HHI (proxy for
product market competition) to partition our sample. Table 5
reports results for
the association between growth opportunities and the value of
cash holdings.
Low-growth firms have a lower value of cash holdings than
high-growth firms.
On average, the marginal value of cash holdings for low-growth
firms is
AU$0.675 (= AU$0.828 + ()0.225 * 0.120) + ()0.630 * 0.200)),
while the
marginal value of cash holdings for high-growth firms is
AU$0.979 (=
AU$1.233 + ()0.741 * 0.116) + ()0.897 * 0.187)). The dollar
value difference
is AU$0.304 per additional dollar of cash holdings. The
coefficients on the
change in cash are statistically different between low-growth
and high-growth

groups (reported P-values <1 per cent). This confirms
Hypothesis 2 that inves-
tors value excess cash holdings of firms with high sales growth
rates more than
those of firms with low sales growth rates.
The results for the association between cash-flow uncertainty
and the value of
cash holdings are presented in Table 6. It is evident from the
table that firms
with high uncertainty of cash flows exhibit higher value of cash
holdings than
firms with low uncertainty. On average, the marginal value of
cash holdings for
high-uncertainty firms is AU$1.014 (= AU$1.207 + ()0.410 *
0.127) +
()0.856 * 0.165)), while the marginal value of cash holdings for
low-uncertainty
firms is AU$0.626 (= AU$0.799 + ()0.399 * 0.108) + ()0.585 *
0.222)). The
P-values for the coefficient tests on the change in cash show
that the two group
coefficients are significantly different. The results support
Hypothesis 3 that firms
with high cash-flow uncertainty have higher marginal value in
excess cash hold-
ings than firms with low cash-flow uncertainty.
Table 7 reports results for the association between product
market competi-
tion and the value of cash holdings. In contrast to the findings
for sales growth
and cash-flow uncertainty, product market competition seems to
have little influ-
ence on the value of excess cash holdings. The coefficients on
the change in cash

do not statistically differ between more competitive and less
competitive groups
13
In additional analysis, we perform a Chow test to see whether
the model estimation dif-
fers statistically across these two financial constraint groups.
The statistics in the last row
of Table 4 show that the differences between the constrained
and the non-constrained for
dividend payout ratios are statistically significant with P-values
of <1 per cent.
339–366
(P-values reported are more than 15 per cent). However, when
we calculate the
difference in the value of excess cash holdings, the marginal
value of cash hold-
ings for firms in highly competitive industries (AU$0.878 =
AU$1.167 +
()0.367 * 0.114) + ()1.292 * 0.191)) is slightly higher than the
marginal value
of cash holdings for firms in industries with less competition
(AU$0.804 =
AU$0.986 + ()0.527 * 0.121) + ()0.602 * 0.197)).
5.4. Corporate governance and value of cash holdings

To investigate whether corporate governance affects the value
of cash hold-
ings, we first employ HCG stars (HWS, from 1 star to 5 stars,
representing weak
to strong corporate governance structures) and HCG rankings
(HWR, from 1,
Table 5
Regression results for growth opportunities
Variables High book-to-market (HBM) Low book-to-market
(LBM)
DCt 0.828*** (9.75) 1.233*** (14.28)
P-value (HBM ) LBM „ 0) 0.00
DEt 0.472*** (7.47) 0.423*** (5.85)
DNAt 0.073*** (3.91) 0.019 (0.96)
DRNDt )2.487** ()2.39) 0.500 (0.41)
DIt 1.485*** (4.94) 0.945** (2.10)
DDt 1.034*** (3.17) 0.442 (0.95)
Ct)1 0.313*** (7.02) 0.598*** (8.28)
P-value (HBM ) LBM „ 0) 0.00
Lt )0.149** ()2.51) )0.302*** ()3.58)
NFt 0.050 (1.27) 0.053 (1.45)
Ct)1 * DCt )0.225*** ()2.70) )0.741*** ()6.54)
Lt * DCt )0.630** ()2.57) )0.897*** ()2.70)
Intercept )0.060 ()1.33) )0.170*** ()2.63)
Year dummy included Yes Yes
Observations 3211 3201
R
2

0.11 0.16
Difference between HBM
and LBM firms
(v2 of Chow test)
42.90***
characteristics for firms with different book-to-market ratios
over the fiscal year. The baseline model
is a pooled ordinary least square model controlling for time
effect. All variables except Lt (leverage)
and excess stock return are deflated by the lagged market value
of equity (Mt)1). Ct is cash including
short-term deposits; Et is earnings before interest and tax; NAt
is total book assets minus Ct; RNDt is
capitalized research and development expenses; It is net interest
expense; Dt is total common dividend
paid; Lt is total debt divided by total book assets; and NFt is the
net changes in total financing cash
flow. All variables are winsorized at the 1st and 99th
percentiles. This approach reduces the impact
of extreme observations by assigning the cut-off value to values
beyond the cut-off point. t-statistics

significant at the 10, 5 and 1 per cent levels are designated with
*, ** and ***, respectively.
366 357
highest, to 250, lowest). Table 8 reports results for the
association between the
HCG index and the value of cash holdings. The coefficients for
HWS, HWR
and their interactions with excess cash holdings have the
expected signs but are
not statistically significant, except for HWR * DCt, which has
marginal signifi-
cance. The negative coefficient of HWR * DCt indicates that the
stronger the
HCG (high HWR represents weak governance), the higher the
value of excess
cash holdings, which is consistent with Hypothesis 5.
Columns (1) and (2) of Table 9 report the results for the
ownership of large
shareholders and the value of cash holdings. BlockHolding is
the sum of all per-
centage ownership of blockholders. BlockDummy equals 1 if a
firm is in the high-
est tercile of ownership (strong governance) and zero if a firm
in the lowest
tercile of ownership (weak governance). As shown in Table 9,
both coefficients

on BlockHolding and BlockDummy are negative and significant,
suggesting
Table 6
Regression results for cash-flow uncertainty
Variables Low uncertainty (LUC) High uncertainty (HUC)
DCt 0.799*** (10.92) 1.207*** (13.09)
P-value (LUC ) HUC „ 0) 0.08
DEt 0.183* (1.95) 0.471*** (7.23)
DNAt 0.024 (1.58) 0.042** (2.01)
DRNDt )3.464*** ()2.98) 0.320 (0.28)
DIt 1.145*** (4.17) 0.920** (2.14)
DDt 0.274 (0.96) 1.310*** (2.79)
Ct)1 0.215*** (6.05) 0.705*** (9.23)
P-value (LUC ) HUC „ 0) 0.00
Lt )0.218*** ()4.30) )0.238*** ()2.66)
NFt 0.076*** (2.59) 0.037 (0.89)
Ct)1 * DCt )0.399*** ()5.87) )0.410*** ()3.38)
Lt * DCt )0.585*** ()2.86) )0.856** ()2.43)
Intercept )0.073** ()1.97) )0.161** ()2.31)
R
2
0.10 0.17
Difference LUC and HUC

firms (v2 of Chow test)
67.89***
characteristics for firms with different cash-flow uncertainty
over the fiscal year. The baseline model
is a pooled ordinary least square model controlling for time
effect. All variables except Lt (leverage)
and excess stock return are deflated by the lagged market value
of equity (Mt)1). Ct is cash including
short-term deposits; Et is earnings before interest and tax; NAt
is total book assets minus Ct; RNDt is
capitalized research and development expenses; It is net interest
expense; Dt is total common dividend
paid; Lt is total debt divided by total book assets; and NFt is net
change in total financing cash flow.
All variables are winsorized at the 1st and 99th percentiles. This
approach reduces the impact of
extreme observations by assigning the cut-off value to values
beyond the cut-off point. t-statistics sig-
nificant at the 10, 5 and 1 per cent levels are designated with *,
** and ***, respectively.
339–366

investors put less value on the excess cash held by firms with
higher blockholder
ownership. Our results are inconsistent with Hypothesis 5 and
results docu-
mented in the United States by Dittmar and Mahrt-Smith (2007).
14
When a firm
holds more excess cash, investors discount the value of that
cash holding. A pos-
sible reason for this is concerns over potential expropriation or
ex post oppor-
tunism by large shareholders (Pagano and Röell, 1998). As the
proportion
of blockholder ownership increases, there is a higher probability
for ex post
Table 7
Regression results for industry competition
Variables High industry competition (HIC) Low industry
competition (LIC)
DCt 1.167*** (14.73) 0.986*** (10.85)
P-value (HIC ) LIC „ 0) 0.15
DEt 0.356*** (5.96) 0.564*** (7.41)
DNAt 0.079*** (4.61) 0.012 (0.60)
DRNDt 0.548 (0.51) )1.453 ()1.21)
DIt 0.944*** (3.03) 1.424*** (3.23)
DDt 0.816** (2.34) 0.715 (1.59)

Ct)1 0.388*** (7.90) 0.416*** (6.39)
P-value (HIC ) LIC „ 0) 0.75
Lt )0.213*** ()3.26) )0.261*** ()3.34)
NFt )0.031 ()0.94) 0.150*** (3.61)
Ct)1 * DCt )0.367*** ()4.46) )0.527*** ()4.70)
Lt * DCt )1.292*** ()4.72) )0.602** ()2.03)
Intercept )0.155*** ()3.16) )0.064 ()1.04)
R
2
0.15 0.14
Difference between HIC
and LIC firms
(v2 of Chow test)
26.07
This table presents the results of regressing excess stock return
Ri,t-RBi,t on changes in firm character-
istics for firms with different industry competition provided by
the Herfindahl index over the fiscal
year. The baseline model is a pooled ordinary least square
model controlling for time effect. All vari-
ables except Lt (leverage) and excess stock return are deflated
by the lagged market value of equity

(Mt)1). Ct is cash including short-term deposits; Et is earnings
before interest and tax; NAt is total
book assets minus Ct; RNDt is capitalized research and
development expenses; It is net interest
expense; Dt is total common dividend paid; Lt is total debt
divided by total book assets; and NFt is
the net change in total financing cash flow. All variables are
winsorized at the 1st and 99th percen-
tiles. This approach reduces the impact of extreme observations
by assigning the cut-off value to val-
ues beyond the cut-off point. t-statistics significant at the 10, 5
and 1 per cent levels are designated
with *, ** and ***, respectively.
14
Unlike in studies of the United States, the blockholding type
cannot be perfectly identi-
fied from the data that we manually collected from the annual
reports. This is one possi-
ble limitation in our blockholding analysis.
366 359

opportunism to occur. Under the imputation tax system with a
preference for
fully franked dividends, non-blockholder shareholders are more
likely to prefer
that excess cash be paid out as dividends.
We also investigate how the HCG index and blockholder
ownership
together are associated with the value of excess cash holdings in
Columns (3)
and (4) of Table 9.
15
The results are largely consistent with using the two
measures separately. Taken together, we find limited evidence
on the associa-
tion between corporate governance and the value of excess cash
holdings in
Australia.
Table 8
Regression results with Horwath Corporate Governance index
Variables (1) (2) (3) (4)
DCt 0.513*** (1.60) 1.307*** (4.04) 0.873*** (5.02) 1.289***
(5.88)
DEt 0.839*** (6.03) 0.874*** (6.27) 0.839*** (4.62) 0.847***
(4.28)
DNAt 0.129*** (3.39) 0.126*** (3.32) 0.173*** (3.08)
0.352*** (4.55)
DRNDt 1.446 (0.89) 1.444 (0.89) 0.947 (0.49) )0.358 ()0.13)
DIt 0.795 (1.06) 0.825 (1.10) 1.341 (1.39) 0.271 (0.24)

DDt 0.856 (1.54) 0.846 (1.53) 1.275* (1.82) 0.790 (1.01)
Ct)1 0.460*** (3.94) 0.467*** (3.99) 0.438*** (2.60) 0.611***
(3.06)
Lt 0.005 (0.06) 0.008 (0.09) 0.033 (0.30) 0.095 (0.77)
NFt )0.164** ()2.28) )0.163** ()2.27) )0.175* ()1.73) )0.414***
()2.95)
Ct)1 * DCt )0.149 ()0.52) )0.207 ()0.72) 0.561 (1.34) 0.477
(0.76)
Lt * DCt )0.223 ()0.44) )0.249 ()0.51) )2.451*** ()3.37)
)4.019*** ()4.16)
HWS 0.02 (1.32) 0.032 (0.90)
HWR 0.001 (1.16) 0.056 (1.34)
HWS * DCt 0.111 (1.01) 0.322 (1.18)
HWR * DCt )0.003* ()1.70) )0.043 ()0.11)
Observations 938 938 557 427
R
2
0.14 0.15 0.16 0.21
This table presents the results of regressing excess stock return
Ri,t-RBi,t on changes in firm character-
istics for firms with strong and weak corporate governance
measures over the fiscal year. The baseline
model is a pooled ordinary least square model controlling for
time effect. All variables except Lt
(leverage) and excess stock return are deflated by the lagged
market value of equity (Mt)1). Ct is cash

including short-term deposits; Et is earnings before interest and
tax; NAt is total book assets minus
Ct; RNDt is capitalized research and development expenses; It
is net interest expense; Dt is total com-
mon dividend paid; Lt is total debt divided by total book assets;
NFt is net changes in total financing
cash flow; HWS is the Horwath star rating; and HWR is
Horwath ranking of corporate governance.
All variables are winsorized at the 1st and 99th percentiles. This
approach reduces the impact of
extreme observations by assigning the cut-off value to values
beyond the cut-off point. t-statistics sig-
nificant at the 10, 5 and 1 per cent levels are designated with *,
** and ***, respectively.
15
For this analysis, we restrict the blockholder ownership data to
the period 2002–2006.
339–366
5.5. Robustness checks
The results reported in the preceding sections are obtained using

pooled OLS
regressions corrected for heteroscedasticity. Petersen (2009)
shows that in the
presence of time effects and firm effects, the pooled OLS
regressions in a panel
Table 9
Regression results with blockholdings
Variables (1) (2) (3) (4)
DCt 1.141*** (7.83) 0.948*** (6.20) 1.005*** (4.52) 1.320***
(4.82)
DEt 0.774*** (8.12) 0.711*** (5.67) 1.101*** (4.71) 0.967***
(3.65)
DNAt 0.049* (1.76) 0.071** (1.97) 0.161** (2.20) 0.366***
(3.38)
DRNDt )2.011 ()1.12) )2.484 ()1.00) )0.703 ()0.24) )6.062
()1.47)
DIt 0.991 (1.57) 0.886 (1.16) 2.146* (1.67) 0.897 (0.56)
DDt 1.095** (1.98) 0.762 (1.18) 0.907 (1.17) 0.153 (0.17)
Ct)1 0.379*** (5.33) 0.391*** (4.36) 0.451** (2.32) 0.508**
(2.28)
Lt )0.238** ()2.48) )0.355*** ()2.88) 0.134 (0.88) 0.108 (0.63)
NFt 0.104** (1.97) 0.035 (0.52) )0.267** ()2.22) )0.501***
()2.92)
Ct)1 * DCt )0.261** ()2.12) 0.091 (0.49) 0.771 (1.31) )0.210
()0.28)
Lt * DCt )1.585*** ()4.14) )1.811*** ()3.56) )3.224*** ()2.74)
)3.160** ()2.57)
BlockHolding )0.003 ()0.04)
BlockDummy )0.005 ()0.13) 0.046 (0.99) 0.006 (0.12)
HWS 0.028 (0.62)

HWR 0.053 (0.98)
HWSD * DCt 0.341 (1.13)
HWRD * DCt )0.304 ()0.69)
BlockHolding * DCt )0.610* ()1.91)
BlockDummy * DCt )0.327* ()1.65) )0.826*** ()2.66) )0.616
()1.57)
Observations 1798 1201 375 288
R
2
0.16 0.12 0.18 0.21
return Ri,t-RBi,t on changes in firm char-
acteristics for firms with strong and weak corporate governance
measures over the fiscal year. The
baseline model is a pooled ordinary least square model
controlling for time effect. All variables except
Lt (leverage) and excess stock return are deflated by the lagged
market value of equity (Mt)1). Ct is
cash including short-term deposits; Et is earnings before
interest and tax; NAt is total book assets
minus Ct; RNDt is capitalized research and development
expenses; It is net interest expense; Dt is total
common dividend paid; Lt is total debt divided by total book
assets; and NFt is net changes in total
financing cash flow. HWSD is a dummy variable for the

Horwath star rating. A firm with more than
three stars is coded as 1 (strong governance) and less than three
stars as zero (weak governance).
HWRD is a dummy variable for the Horwath ranking, while
firms with top 100 governance ranking
are coded as 1 and those in the bottom 100 are coded as zero.
BlockHolding is the sum of all owner-
ship positions >5 per cent held by large shareholders.
BlockDummy is a dummy variable where the
highest tercile of ownership is coded as 1 (strong governance),
and the lowest tercile of ownership is
coded as zero (weak governance). All variables are winsorized
at the 1st and 99th percentiles. This
approach reduces the impact of extreme observations by
assigning the cut-off value to values beyond
the cut-off point. t-statistics significant at the 10, 5 and 1 per
cent levels are designated with *, ** and
***, respectively.
366 361

data set are biased. The literature advocates the firm fixed-
effects model to con-
trol for unobservable time-invariant firm heterogeneity (for
example, Himmel-
berg et al., 1999). It is possible that the effect of changes in
cash on excess stock
returns is caused by some unobserved firm-specific factor(s). In
addition, indus-
try effects are important for an Australian sample given that
resources industries
are likely to harbour more cash. We employ both the firm fixed-
effects model
and the industry fixed-effects model to check the robustness of
our findings using
the pooled OLS regressions in Table 3. The results are reported
in Table 10.
Consistent with the results estimated using the pooled OLS
approach, the main
estimated coefficients have the same predicted signs and
magnitudes. This sug-
gests that using alternative estimation approaches would
generate qualitatively
similar results.
In all our excess-return model specifications, the dependent
variable is defined
as excess stock returns, while the other variables are scaled by
the lagged market
value of equity. This allows us to interpret our findings as to
how investors value
Table 10
Robustness checks
Variables Industry fixed effects Industry fixed effects Firm

fixed effects Firm fixed effects
DCt 0.720*** (15.76) 1.085*** (18.11) 0.807*** (15.78)
1.074*** (16.15)
DEt 0.440*** (9.18) 0.442*** (9.23) 0.477*** (8.97) 0.496***
(9.32)
DNAt 0.044*** (3.28) 0.042*** (3.16) 0.046*** (3.06)
0.042*** (2.81)
DRNDt )0.808 ()0.99) )0.910 ()1.12) )1.616* ()1.92) )1.752**
()2.08)
DIt 1.398*** (5.33) 1.112*** (4.23) 1.178*** (4.11) 0.938***
(3.25)
DDt 0.863*** (3.03) 0.774*** (2.74) 0.861*** (2.97) 0.815***
(2.82)
Ct)1 0.462*** (11.45) 0.414*** (10.23) 0.913*** (15.49)
0.847*** (14.11)
Lt )0.322*** ()5.78) )0.291*** ()5.20) )0.460*** ()5.24)
)0.415*** ()4.72)
NFt 0.090*** (3.44) 0.062** (2.35) 0.079** (2.54) 0.063**
(2.02)
Ct)1 * DCt )0.466*** ()6.89) )0.148* ()1.81)
Lt * DCt )0.879*** ()4.34) )1.143*** ()5.20)
Intercept )0.094** ()2.36) )0.082* ()1.75) )0.161*** ()3.84)
)0.167*** ()3.98)
Year dummy
included
Yes Yes Yes Yes
Observations 6412 6412 6412 6412
R
2

0.14 0.15 0.37 0.38
return Ri,t ) R_BPi,t on changes in firm
characteristics using an industry fixed-effects model and a firm
fixed-effects model over the fiscal year.
All variables except Lt (leverage) and excess stock return are
deflated by the lagged market value of
equity (Mt)1). Ct is cash including short-term deposits; Et is
earnings before interest and tax; NAt is
total book assets minus Ct; RNDt is capitalized research and
development expenses; It is net interest
expense; Dt is total common dividend paid; Lt is total debt
divided by total book assets; and NFt is
net changes in total financing cash flow. All variables are
winsorized at the 1st and 99th percentiles.
This approach reduces the impact of extreme observations by
assigning the cut-off value to values
beyond the cut-off point. t-statistics significant at the 10, 5 and
1 per cent levels are designated with
*, ** and ***, respectively.
339–366

excess cash holdings in dollar terms. In the empirical literature,
an alternative
approach is to scale by the total book value of assets. To check
the robustness of
our findings, we next employ a value regression as outlined in
Fama and French
(1998). We use changes in market value over total book value of
assets as the
dependent variable and variables likely to affect the firms’
future cash flows
(scaled by total book value of assets) as the explanatory
variables. Specifically,
we estimate the following regression for each measure of our
financial constraint
proxies, BTMs, cash-flow volatility, industry competition and
corporate gover-
nance measures:
Mi;t � Ai;t
Ai;t
¼ a0 þb1
Ei;t
Ai;t
þ b2
dEi;t
Ai;t
þ b3
dEi;tþ2
Ai;t

þ b4
dAi;t
Ai;t
þ b5
dAi;tþ2
Ai;t
þ b6
RNDi;t
Ai;t
þb7
dRNDi;t
Ai;t
þb8
dRNDi;tþ2
Ai;t
þb9
Ii;t
Ai;t
þ b10
dIi;t
Ai;t
þb11
dIi;tþ2
Ai;t
þb12
Di;t
Ai;t

þ b13
dDi;t
Ai;t
þ b14
dDi;tþ2
Ai;t
þ b15
dMi;tþ2
Ai;t
þ b16
DCi;t
Ai;t
þ ci;t
ð3Þ
where d Xt represents the 2-year change in the variable X, Xt )
Xt)2; Mt is total
market value of assets; At is total book value of assets; Et is
earnings before
interest and tax (EBIT); RNDt is capitalized research and
development expenses;
It is net interest expense; Dt is total common dividend paid; and
Ct is cash includ-
ing short-term deposits. All variables are deflated by the total
book value of
assets (At) as in the study of Fama and French (1998). For
brevity, we report
only the estimated coefficients on DCt (b16) for the whole
sample and for each
measure of our financial constraints proxies, BTMs, cash-flow
volatility, industry

competition and corporate governance measures. Consistent
with the results esti-
mated using the stock return specification, the main estimated
coefficients have
the same predicted signs and magnitudes. This further confirms
our main
hypotheses that more financially constrained firms and firms
with higher growth
opportunities, higher product market competition, higher levels
of uncertainty in
cash flows, stronger HCG measures, and lower levels of
blockholder ownership
exhibit significantly higher market value (Table 11).
6. Conclusions
In this study, we investigate whether financial constraints,
firms’ growth
opportunities, uncertainty in cash flows and product market
competition affect
the value of cash holdings in the Australian context. We find
that more finan-
cially constrained firms have significantly higher marginal
value of cash holdings.
This indicates that investors value excess cash holdings of
financially constrained
366 363
firms more than unconstrained firms. Further, firms with higher

growth rates
and with higher levels of uncertainty in their cash flows exhibit
a higher marginal
value of cash holdings. However, product market competition
has little impact
Table 11
Regression results for the market value of assets
DCt Observations R
2
Panel A: Ordinary least square regressions
Whole sample 1.520*** (5.43) 4941 0.31
Non-dividend payers 0.859** (2.44) 1577 0.45
Dividend payers 0.765** (2.45) 3364 0.54
Small firms 1.248*** (3.85) 2476 0.36
Large firms 0.925** (2.37) 2465 0.35
Low BTM ratio 1.056*** (3.37) 2516 0.41
High BTM ratio 0.232*** (2.93) 2425 0.33
High cash-flow volatility 1.241*** (2.77) 2503 0.18
Low cash-flow volatility 1.157*** (3.57) 2438 0.38
High industry competition 2.318*** (5.05) 2438 0.34
Low industry competition 0.831** (2.44) 2503 0.37

High Horwath ranking 0.176 (0.09) 124 0.60
Low Horwath ranking 1.214 (0.96) 189 0.52
High block ownership 0.002 (0.00) 402 0.36
Low block ownership 2.207* (1.88) 395 0.50
Panel B: Industry fixed-effects regression
Whole sample 1.210*** (7.35) 4941 0.43
Non-dividend payers 0.800*** (2.98) 1577 0.50
Dividend payers 0.688*** (3.90) 3364 0.59
Small firms 1.058*** (4.59) 2476 0.44
Large firms 0.737*** (3.82) 2465 0.46
Low BTM ratio 0.952*** (4.23) 2516 0.46
High BTM ratio 0.187*** (3.29) 2425 0.37
High cash-flow volatility 1.056*** (5.38) 2503 0.33
Low cash-flow volatility 0.995*** (4.27) 2438 0.46
High industry competition 1.737*** (7.27) 2438 0.46
Low industry competition 0.852*** (3.78) 2503 0.43
High Horwath ranking 0.812 (0.82) 124 0.79
Low Horwath ranking 1.268 (1.40) 189 0.63

High block ownership 0.096 (0.21) 402 0.49
Low block ownership 1.330* (1.80) 395 0.58
This table presents the results of regressing the 2-year change in
market value of assets on changes in
firm characteristics, scaled by total book value of assets. Firms
are categorized as being financially
constrained (FC) and unconstrained (NFC) according to their
book value of assets and dividend
payout ratio. DCt column reports the estimated coefficients on
DCt in Equation (2). All explanatory
variables are deflated by the total book value of assets. All
variables are winsorized at the 1st and
99th percentiles. This approach reduces the impact of extreme
observations by assigning the cut-off
value to values beyond the cut-off point. t-statistics (reported in
parentheses) significant at the 10, 5
and 1 per cent levels are designated with *, ** and ***,
respectively.
339–366

on firms’ value of cash holdings. As to the role of corporate
governance, we find
limited evidence of its association with the value of cash
holdings. Our findings
indicate that internal financing has clear cost advantages over
external financing
for firms with high growth potential and facing uncertain
prospects. Costly exter-
nal financing would force firms to save more cash for current
operating and
future investing needs. Investors are aware of these cash
hoarding policies and
view them favourably. Our results are robust to using
alternative model specifi-
cations (the market value regressions) and alternative estimation
models, such as
the industry fixed-effects model and the firm fixed-effects
model. Overall, our
findings are mainly consistent with the cash regime of raising
cash holdings
based on the ability to access external capital, as in the study of
Hennessy and
Whited (2005).
References
Chang, X., T. J. Tan, G. Wong, and H. F. Zhang, 2007, The
effects of financial con-
straints on corporate policies in Australia, Accounting and
Finance 47, 85–108.
Dittmar, A., and J. Mahrt-Smith, 2007, Corporate governance
and the value of cash
holdings, Journal of Financial Economics 83, 599–634.
Dlugosz, J., R. Fahlenbrach, P. Gompers, and A. Metrick, 2004,

Large blocks of stock:
prevalence, size, and measurement, Journal of Corporate
Finance 12, 594–618.
Fama, E., and K. French, 1993, Common risk factors in the
returns on stocks and bonds,
Journal of Financial Economics 33, 3–56.
Fama, E., and K. French, 1998, Taxes, financing decisions and
firm value, The Journal of
Finance 53, 819–843.
Faulkender, M., and R. Wang, 2006, Corporate financial policy
and the value of cash,
The Journal of Finance 61, 1957–1990.
Fazzari, S., R. G. Hubbard, and B. Petersen, 1988, Financing
constraints and corporate
investment, Brookings Papers on Economic Activity 1, 141–
195.
Fresard, L., 2010, Financial strength and product market
behavior: the real effects of cor-
porate cash holdings, The Journal of Finance 65, 1097–1122.
Hennessy, C., and T. Whited, 2005, Debt dynamics, The Journal
of Finance 60,
1129–1165.
Himmelberg, C. P., R. G. Hubbard, and D. Palia, 1999,
Understanding the determinants
of managerial ownership and the link between ownership and
performance, Journal of
Financial Economics 53, 353–384.
Jensen, M. C., 1986, Agency costs of the free cash flow,

corporate finance and takeovers,
The American Economic Review 76, 323–329.
Kaplan, S., and L. Zingales, 1997, Do Investment-cash flow
sensitivities provide useful
measures of financing constraints?, Quarterly Journal of
Economics 112, 169–215.
Lee, E., and R. Powell, 2011, Excess cash holdings and
shareholder value, Accounting and
Finance 51, 549–574.
MacKay, P., and G. Phillips, 2005, How does industry affect
firm financial structure,
Review of Financial Studies 18, 1433–1466.
Masulis, R. W., C. Wang, and F. Xie, 2009, Agency problems at
dual-class companies,
The Journal of Finance 64, 1851–1889.
Mikkelson, W. H., and M. M. Partch, 2003, Do persistent large
cash reserves hinder per-
formance?, Journal of Financial and Quantitative Analysis 38,
275–294.
366 365
Opler, T., L. Pinkowitz, R. Stulz, and R. Williamson, 1999, The
determinants and impli-
cations of corporate cash holdings, Journal of Financial

Economics 52, 3–46.
Pagano, M., and A. Röell, 1998, The choice of stock ownership
structure: agency costs,
monitoring, and the decision to go public, Quarterly Journal of
Economics 113,
187–225.
Petersen, M. A., 2009, Estimating standard errors in finance
panel data sets: comparing
approaches, Review of Financial Studies 22, 435–480.
Phillips, G. M., 1995, Increased debt and industry product
markets: an empirical analysis,
Journal of Financial Economics 37, 189–238.
Pinkowitz, L., and R. Williamson, 2004, What is a dollar worth?
The market value of
cash holdings, Working paper (Georgetown University).
Pinkowitz, L., R. Stulz, and R. Williamson, 2006, Does the
contribution of corporate
cash holdings and dividends to firm value depend on
governance? A cross-country anal-
ysis, The Journal of Finance 61, 2725–2751.
339–366
Copyright of Accounting & Finance is the property of Wiley-
Blackwell and its content may not be copied or

emailed to multiple sites or posted to a listserv without the
copyright holder's express written permission.
However, users may print, download, or email articles for
individual use.

financial leadershipETHICALC O N D U C TWHAT FINANC.docx

Recommended

Recommended

More Related Content

Similar to financial leadershipETHICALC O N D U C TWHAT FINANC.docx

Similar to financial leadershipETHICALC O N D U C TWHAT FINANC.docx (20)

More from voversbyobersby

More from voversbyobersby (20)

Recently uploaded

Recently uploaded (20)

financial leadershipETHICALC O N D U C TWHAT FINANC.docx