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For example, Google boosted its cash holdings to $7.1
billion during 2005.1Why do firms hold so much cash? Why do nonfinancial firms
invest in zero net present value investment while there are more profitable projects?2
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Keynes (1936)
 Suffix

suggests two main reasons that nonfinancial firms maintain a positive
level of liquid assets. First, firms hold liquid assets to reduce transaction costs. Second,
a stock of cash provides a buffer to meet unexpected contingencies.
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Kim and Sherman (1998) develop a tradeoff model of
optimal cash holdings where a firm’s cash stock depends on the expected returns on
current investment opportunities.
Asymmetric information concerning the ability of raising external financing constitutes theprecautionary motivefor holding cash.
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Myers and Majluf (1984)
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define cash on
hand and marketable securities as financial slack which could be used to overcome the
problem of financial constraints. Furthermore, managers can increase firm value by managing their cash balances.
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Furthermore, managers can increase firm value by managing their cash balances. The cash buffer allows the company to maintain the ability
to invest when the company does not have sufficient current cash flows to meet capital
investment demands. In their recent study
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Almeida, Campello and Weisbach (2004)
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1http://www.busrep.co.za/index.php?fArticleId=2896664
2In the seminal paper of Modigliani and Miller (1958) cash is considered as a zero net present value
investment. There are no benefits from holding cash in a world of perfect capital markets lacking
information asymmetries, transaction costs or taxes.
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The cash buffer allows the company to maintain the ability
to invest when the company does not have sufficient current cash flows to meet capital
investment demands. In their recent study Almeida, Campello and Weisbach (2004) 1http://www.busrep.co.za/index.php?fArticleId=2896664
2In the seminal paper of
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Modigliani and Miller (1958)
 Suffix

cash is considered as a zero net present value
investment. There are no benefits from holding cash in a world of perfect capital markets lacking
information asymmetries, transaction costs or taxes. Even in the absence of perfect capital markets
firms appear to hold far more cash than any transactionsbased model would imply.
3
investigate how macroeconomic shocks affect firms’ cash flow sensitivi
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They find that financially constrained firms’ cash flow sensitivity increases during
recessions, while financially unconstrained firms’ cash flow sensitivity is unaffected by
the business cycle. The idea of a precautionary demand for cash is further explored in
recent literature.
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Baum, Caglayan, Ozkan and Talavera (2006)
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develop a static model of
cash management with a signal extraction mechanism. Their model shows a positive relationship between cash holdings, the interest rate on loans and the level of uncertainty.
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The results differ
across different categories of firms.
The rest of the paper is organized as follows. Section 2 discusses nonfinancial firms’
motives for cash holdings and reviews the related literature. Section 3 presents our
3
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Bo and Lensink (2005)
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suggests that the presence of uncertainty factors changes the structural
parameters of the Qmodel of investment.
4
measure of industrylevel uncertainty, while Section 4 overviews data and discusses our
empirical results.
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Finally, Section 5 concludes.
2 TheQModel of Firm Value Optimization
The theoretical model proposed in this paper is based on the firm value optimization
problem and represents a generalization of the standardQmodels of investment by
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Whited (1992) and Hubbard and Kashyap (1992).
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The present value of the firm is
equated to the expected discounted stream ofDt, dividends paid to shareholders, where
βis the discount factor.
Vt(Kt) = max
{It+s,Bt+s}∞s=0
Dt+Et
[∞
∑
s=1
βt+s−1Dt+s
]
,(1)
Kt= (1−δ)Kt−1+It,(2)
Dt= Π(Kt−1, Nt)−wtNt−C(It, Kt−1)−It+Bt−Bt−1R(Bt−1, Kt−1),(3)
Dt≥0,(4)
lim
T→∞
T−1∏
j=t
βj
BT= 0,∀t(5)
The firm maximizes equation (1) subject to three constraints.
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The real cost of
adjustingItunits of capital is denoted asC(It, Kt−1).
The price of external financing is equal to the base gross interest rate,R(Bt−1, Kt−1)
which depends on firmspecific characteristics such as debt and capital stock. Similar
to
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Gilchrist and Himmelberg (1998),
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we also assumeRB,t>0: i.e., highly indebted
firms must pay an additional premium to compensate debtholders for additional costs
5
because of monitoring or hazard problems. Moreover,RK,t<0: i.e., large firms enjoy a
lower risk premium.
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Assuming a Cobb–Douglas production functionYt+1=At+1KαktNαnt+1we rewrite the
marginal product of capital∂Yt+1/∂Ktas4
Et[ΠK,t+1] =Et
[
Pt+1
μ
αkYt+1
Kt
]
=Et
[
Pt+1(Rt−1)
μwt+1
αnYt+1
N+1
]
(9)
IfEt[wt+1] is an increasing function of input price volatility,τ2t+1(See
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Hartman (1976), Sandmo (1971))
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then we derive our main theoretical prediction
∂Θt
∂τ2t+1
=
∂Θt
∂Πt+1
∂Πt+1
∂wt+1
∂wt+1
∂τ2t+1
>0(10)
Compared to a certainty equivalent economy, the firm facing higher costs of external
financing caused by an increase in industrylevel uncertainty increases its level of cash
holdings.
3 Uncertainty Measures
The industrylevel uncertainty identification approach resembles that of Baum et al.
(200
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Sandmo (1971)) then we derive our main theoretical prediction
∂Θt
∂τ2t+1
=
∂Θt
∂Πt+1
∂Πt+1
∂wt+1
∂wt+1
∂τ2t+1
>0(10)
Compared to a certainty equivalent economy, the firm facing higher costs of external
financing caused by an increase in industrylevel uncertainty increases its level of cash
holdings.
3 Uncertainty Measures
The industrylevel uncertainty identification approach resembles that of
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Baum et al. (2006).
 Suffix

Firms’ liquidity decisions depend on anticipation of future profits and capital
investment needs. The manager’s problem of determining the appropriate level of cash
4We use (∂Yt+1/∂Kt)/(∂Yt+1/∂Nt) = (Rt−1)/wt+1.
7
holdings becomes more difficult at higher levels of uncertainty about the industry’s
prospects.
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The manager’s problem of determining the appropriate level of cash
4We use (∂Yt+1/∂Kt)/(∂Yt+1/∂Nt) = (Rt−1)/wt+1.
7
holdings becomes more difficult at higher levels of uncertainty about the industry’s
prospects.
The literature suggests candidates for uncertainty proxies such as a moving standard
deviation (see
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Ghosal and Loungani (2000)),
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the standard deviation across 12 forecast
periods of output growth and the inflation rate in the next 12 months (see Driver and
Moreton (1991)). However, as in Driver, Temple and Urga (2005) and Byrne and Davis
(2002) we use a GARCH model for measuring industrylevel uncertainty.
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The literature suggests candidates for uncertainty proxies such as a moving standard
deviation (see Ghosal and Loungani (2000)), the standard deviation across 12 forecast
periods of output growth and the inflation rate in the next 12 months (see
 Exact

Driver and Moreton (1991)).
 Suffix

However, as in Driver, Temple and Urga (2005) and Byrne and Davis
(2002) we use a GARCH model for measuring industrylevel uncertainty. We argue that
this approach is better suited in our case for two reasons.
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The literature suggests candidates for uncertainty proxies such as a moving standard
deviation (see Ghosal and Loungani (2000)), the standard deviation across 12 forecast
periods of output growth and the inflation rate in the next 12 months (see Driver and Moreton (1991)). However, as in
 Exact

Driver, Temple and Urga (2005) and Byrne and Davis (2002)
 Suffix

we use a GARCH model for measuring industrylevel uncertainty. We argue that
this approach is better suited in our case for two reasons. First, industrylevel forecasts
are not as generally available as are macroeconomic forecasts.
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Table 2 presents descriptive statistics for (Cash/T A)it, (S/T A)it, (I/T A)itandτ2it
variables for the pooled timeseries crosssectional data, 1987–2000. The median for
(Cash/T A)tis 2% while the mean is 6%. This ratio is considerably lower in Germany
than in the USA. Based onCOMPUSTATdata,
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Baum et al. (2006)
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report that US
corporations hold over 10% of their total assets in cash.
The empirical literature investigating firms’ capital structure behavior has identi6We excluded firms with Opening Balance Sheet (Er ̈offnungsbilanz) or Carcass Balance Sheet
(Rumpfbilanz).
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A firm is classified as part
of a business group if it reports a business group identification number.
4.2 Econometric Results
The research design to be used in the current paper is similar to recent papers in this
area (e.g.,
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Opler, Pinkowitz, Stulz and Williamson (1999), Alfonsina, Leonida and Ozkan (2004), Bruinshoofd and Kool (2004)).
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We derive our econometric model specification for firmiat timet:
Cashit
T Ait
=φ0+φ1
Cashit−1
T Ait−1
+φ2
Iit
T Ait
+φ3
Sit
T Ait
+φ4τ2t+κt+ωi+νit(11)
The key coefficient of interest isφ4. Our theoretical framework predicts a positive sign
indicating that a higher level of the liquidity ratio is associated with a higher level of
industryspecific uncertainty.
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Our theoretical framework predicts a positive sign
indicating that a higher level of the liquidity ratio is associated with a higher level of
industryspecific uncertainty.
Estimates of optimal corporate behavior often suffer from endogeneity problems, and
the use of instrumental variables may be considered as a possible solution. We estimate
7See
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Ozkan and Ozkan (2004).
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10
our econometric models using the system dynamic panel data (DPD) estimator. DPD
combines equations in differences of the variables with equations in levels of the variables.
In this system GMM approach (see Blundell and Bond (1998)), lagged levels are used
as instruments for differenced equations and lagged differences are used as instruments
for level equations.
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We estimate
7See Ozkan and Ozkan (2004). 10
our econometric models using the system dynamic panel data (DPD) estimator. DPD
combines equations in differences of the variables with equations in levels of the variables.
In this system GMM approach (see
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Blundell and Bond (1998)),
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lagged levels are used
as instruments for differenced equations and lagged differences are used as instruments
for level equations. We report twostep estimates computed with Windmeijercorrected
standard errors from Stata’sxtabond2package.
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Large firms, firms
outside business groups, low leverage firms and high investment firms exhibit a much
greater sensitivity of their liquid assets ratio to changes in industrylevel uncertainty.
Our results should be considered in conjunction with those of
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Baum et al. (2006)
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who
predict that during periods of higher uncertainty firms behave more similarly in terms
of their cashtoasset ratios. Taken together, these studies allow us to conjecture that as
industry level uncertainty increases the total amount of cash held by nonfinancial firms
12
will increase significantly, with negative effects on the economy.
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