The 18 reference contexts in paper Christopher F. Baum, Dorothea Schäfer, Oleksandr Talavera (2006) “The Effects of Industry-Level Uncertainty on Cash Holdings: The Case of Germany” / RePEc:boc:bocoec:637

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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 non-financial firms invest in zero net present value investment while there are more profitable projects?2
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    Keynes (1936)
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    suggests two main reasons that non-financial 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 trade-off 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)
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    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 transactions-based 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 non-financial 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 Q-model of investment. 4 measure of industry-level 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 firm-specific 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 debt-holders 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 industry-level uncertainty increases its level of cash holdings. 3 Uncertainty Measures The industry-level 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 industry-level uncertainty increases its level of cash holdings. 3 Uncertainty Measures The industry-level uncertainty identification approach resembles that of
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    Baum et al. (2006).
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    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 industry-level 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
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    Driver and Moreton (1991)).
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    However, as in Driver, Temple and Urga (2005) and Byrne and Davis (2002) we use a GARCH model for measuring industry-level 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
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    Driver, Temple and Urga (2005) and Byrne and Davis (2002)
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    we use a GARCH model for measuring industry-level uncertainty. We argue that this approach is better suited in our case for two reasons. First, industry-level 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 time-series cross-sectional 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 industry-specific 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 industry-specific 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 two-step estimates computed with Windmeijer-corrected 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 industry-level 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 cash-to-asset ratios. Taken together, these studies allow us to conjecture that as industry level uncertainty increases the total amount of cash held by non-financial firms 12 will increase significantly, with negative effects on the economy.
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