Aggregate Risk and the Choice Between Cash and Lines of Credit

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1 Aggregate Risk and the Choice Between Cash and Lines of Credit Viral Acharya NYU Stern School of Business, CEPR, NBER Heitor Almeida University of Illinois at Urbana Champaign, NBER Murillo Campello Cornell University, NBER

2 Introduction How do firms manage their liquidity needs? Several options to consider Future cash flows Future financing (debt, equity) Bank credit lines Cash holdings

3 Introduction How do firms manage their liquidity needs? Several options to consider Future cash flows Future financing (debt, equity) Bank credit lines Cash holdings Liquidity management requires pre- commi6ed liquidity (Holmstrom and Tirole, 1998, Acharya, Almeida and Campello, 2007) But what determines the choice between cash and credit lines? Growing empirical literature on this question (later) Is there a unique role for cash management?

4 Key insight: This paper Aggregate risk exposure is a key determinant of firms choice between cash and lines of credit Cash is king, as a hedge against aggregate liquidity risk We develop this argument in a simple model We test empirically whether greater aggregate risk increases cash relative to LCs, and find both cross- sectional (firm- level) and time series (economy- wide) evidence High aggregate risk: fewer LCs, more cash We also establish a link between corporate liquidity management and bank liquidity

5 Roadmap Simple model to show role of pre- commi`ed financing Cash versus credit lines: sketch of model in the paper Empirical implications Empirical tests Cross- sectional (Beta and liquidity management) Time- series (VIX and liquidity management) Related literature and interpretation of results More evidence on mechanism (bank liquidity) Conclusions and possible extensions

6 A simple model of liquidity management Consider a firm with the following project 1 λ 0 ρ 1 - I λ ρ Pay ρ ρ 1 Don t pay ρ 0 Key feature: uncertain investment need in the future

7 Assumptions Firm must borrow to finance the project Project is positive NPV: (1 λ) ρ 1 + λ (ρ 1 ρ) > I It is (conditionally) efficient to invest ρ in state λ: ρ 1 ρ > 0

8 Assumptions Firm must borrow to finance the project Project is positive NPV: (1 λ) ρ 1 + λ (ρ 1 ρ) > I It is (conditionally) efficient to invest ρ in state λ: ρ 1 ρ > 0 Financial friction limited pledgeability Firm can only pledge a fraction of the cash flow ρ 1 as collateral for borrowing Pledgeable income = ρ 0 < ρ 1 Pledgeable income is low enough: ρ 0 < ρ

9 Financing the project Consider two options (for now) Option 1: borrow exactly I, and wait until tomorrow to secure financing for the investment need ρ. No cash holdings Option 2: borrow more than I, and hold the balance as cash in the firm. Cash holdings = C

10 Option 1 no cash Firm has zero cash, and needs to borrow ρ if the investment need realizes Because of limited pledgeability this strategy may not work Since ρ 0 < ρ, the firm does not have enough collateral to borrow in state λ The firm is liquidated, and loses value ρ 1 ρ > 0

11 Option 2 holding cash Firm has cash = C, and can use it to fund the investment need If C + ρ 0 > ρ, the firm is not liquidated Value gained = ρ 1 ρ > 0 If this value is greater than the costs of holding cash, then it is optimal for the firm to hold cash

12 Option 2 holding cash Borrow more than I Additional payment to investors 1 - λ Cash λ Use cash to finance liquidity shock Save cash So cash is a way of transferring funds from good to bad states of the world (Acharya, Almeida and Campello, 2007)

13 Cash versus Credit Lines While the theoretical motivation for pre- commi`ed financing is known, there are multiple ways to do it Cash: borrow more today, and carry funds into the future Credit Lines: buy an option to borrow, up to a maximum pre- commi6ed amount How do firms choose between cash and credit lines?

14 Managing liquidity without cash 1 - λ Payment y to bank No cash payments λ Bank injects w in firm (1 λ)y = λw, so bank breaks even w > ρ ρ 0, so that firm can finance investment need Credit line implementation: y = credit line commitment fee w = size of the credit line

15 This paper How do firms choose/substitute between cash and LC? Key insight: Aggregate risk exposure is a key determinant of firms choice between cash and lines of credit

16 Intuition for Cash/LC substitution Managing liquidity with cash requires firm to hold liquid funds across time. This may be costly for several reasons including Liquidity premia (this paper) But also taxes, free cash flow issues inside the firm If this is case credit lines may be superior since it is a form of contingent cash (firm only arranges for cash when it is required) But this argument is overtly simplistic since it ignores the cost for the bank to fund credit line drawdowns

17 Aggregate risk and credit line provision Suppose there are many firms who contract with the bank to secure liquidity through credit lines State 1 - λ State λ Firm y Firm y BANK w Firm Firm y If liquidity risk is idiosyncratic, bank can use payments from healthy firms to fund credit line drawdowns by illiquid firms

18 Aggregate risk and credit line provision But, if liquidity risk is systematic, firms will all demand credit lines at same time and bank liquidity may disappear! State 1 - λ State λ No firms! BANK w w Firm Firm w Firm Thus, aggregate risk limits the liquidity- creation role of bank credit lines

19 Model key ingredients Semi- dynamic framework in which firms must fund both today s and tomorrow s (uncertain) investment need Limited pledgeability of cash flows arising from moral hazard A set of firms that are identical except for their exposure to systematic liquidity risk A fraction θ of firms have perfectly correlated liquidity shocks

20 Figure 1: Timeline of the model t = 0 t =1 t = 2 θ 1 λ ρ = 0 1 λ λ ρ = 0 ρ > 0 Continuation subject to moral hazard Liquidations I θ λ ρ > 0 1 λ λ ρ = 0 ρ > 0 Continuation subject to moral hazard Liquidations Aggregate shock to fraction θ of firms Idiosyncratic shock to other firms

21 Model key ingredients (cont.) One intermediary ( bank ) who writes credit line contracts with firms Bank collects payments from healthy firms to fund drawdowns by distressed firms. It also finances initial investment needs. Bank can observe firm type and write different credit line contracts for systematic firms Firms can hold cash in the form of treasury bonds (safe and liquid) Firm buys bonds at the initial date at price q (determined in equilibrium). If q > 1, holding liquid funds is costly (risk- neutrality, discount factor = 1) Bond supply = L s, determined exogenously (by the government)

22 Model main mechanism The key quantity of the model is the maximum credit line drawdown that is available to the fraction θ of systematic firms w max (1 θ)( ρ0 ( θ) = θ λρ) If θ is low enough, the bank has enough liquidity, even in the worst possible state of the world (when all θ firms need liquidity) If θ is high, then the bank must force some systematic firms to hold cash rather than opening credit lines. It does so by rationing credit lines to systematic firms The credit line w max allows systematic firms to hedge only a fraction of its future liquidity need. These firms buy more liquidity by holding treasury bonds (e.g., cash) The equilibrium liquidity premium is determined such that the marginal benefit of additional liquidity equals the marginal cost of holding cash

23 EMPIRICS

24 Testable implications Firms with higher aggregate risk exposure (e.g., beta) are more likely to hold cash reserves than lines of credit Tradeoff is more relevant for financially constrained firms Effect of beta on liquidity is stronger for firms with high beta LC s more expensive for firms with higher aggregate risk During periods with higher aggregate risk in the economy (e.g., high VIX periods) LC availability is lower (LC maturity is shorter, spreads higher) More reliance on cash, lower credit line initiations

25 Line of Credit and Compustat Data Sample A: We measure line of credit availability using LPC DealScan Drop financials, utilities and quasi- public firms Drop term loans, use only short and long term credit lines Sample has deals between 1987 and 2008 Sample B: Sufi (2009) sample of 300 random firms in 1996 to 2003 Complete data on LCs and on usage of LCs Match to Compustat Annual Keep in sample all firms that had LCs at some point (when using LPC DealScan) Winsorize all data at 5%

26 Measuring Line of Credit Availability LPC does not tell us whether a credit facility has been used We measure credit line availability by summing all existing credit lines that have not yet matured. This assumes that LCs remain open until they mature We will show that we can replicate results in Sufi (2009), who has drawdown information so that we can computed Unused LC For each firm- quarter (i, t), we sum total existing facilities LC: Total LC i, t = LC i, t s if the maturity of the facility LC t- s is greater than s (potentially still open)

27 Other Variable Definitions Assets are defined net of cash balances Cash flow is measured by EBITDA Some variable definitions (others are standard) LC-to-cash = Total LC Total LC + Cash Industry sales volatility is a measure of seasonal variation in sales by 3- digit SIC industry EBTIDA volatility is a measure of firm- level cash flow volatility

28 Data on Asset Betas and Total Asset Volatility Equity betas and equity volatility are mechanically related to leverage, so we unlever them in different ways; also look at different betas 1. KMV- type model a. Yearly equity betas/vol estimated using past 12 month returns (beta KMV) b. Face value of debt = ST debt * LT debt (MKMV rule of thumb) 2. Data on asset returns from Choi (2009), Choi and Richardson (2009) a. Uses data on market values of the firm s loans and bonds b. Directly compute asset beta relative to aggregate equity market (beta asset). Similar results when using aggregate asset market 3. Cash- adjusted (net debt to un- lever) median industry beta 4. Bank beta (beta with respect to index of bank stock returns) 5. Tail beta (Acharya et al., 2010). Exposure to negative shocks. 6. Financing gap and cash flow beta We instrument firm- level beta with two lags (measurement error)

29 Systematic volatility Besides using asset beta proxies directly in empirical models, we use them to decompose total risk into systematic and idiosyncratic components 2 i, t i,t * VarMarket t SysVar = Beta Systematic risk exposure increases demand for cash

30

31 Empirical Evidence (cross- section) Firm- level regressions of LC- to- cash on aggregate risk proxies and controls, including variables in Sufi (2009) Does aggregate risk ma`er beyond total risk and other determinants of liquidity policy? LC Controls from Sufi (2009) + Year + to Cashi, t = α + β1betai, t + β2controls t εi, t t

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37 Empirical evidence LC pricing Mechanism in the model: high beta firms switch to cash because of costs of opening bank credit line Do high beta firms pay high credit line spreads? Spread i Beta, t = µ 0 + µ 1 i, t + µ 2Controls + ωi, t

38

39 Empirical evidence time series We examine aggregate LC initiations and aggregate change in cash holdings (both scaled by aggregate assets). Regress them in lagged macro variables ( ). Besides VIX, GDP growth and CP- Treas. Spread (Gatev and Strahan, 2005). Time trend also included Replace VIX with Bank VIX (GARCH (1,1) model) in some specifications LC Initiation = a + b VIX + b GDP growth + b Change in Cash t t 1 1 t-1 t-1 2 = a + b VIX + b GDP growth + b 2 t-1 t-1 3 CP -Treasury Spread 3 t-1 CP -Treasury Spread t-1 SUR (seemingly- unrelated regressions) Hypotheses: b 1 < 0, β 1 > 0

40 Aggregate LC Initiations, Cash changes and Lagged VIX Lvix ChgCash LC Init

41

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43 Other interpretations Interpretation Demand effects. Less investment opportunities in bad times, less demand for credit lines Increase in overall cost of debt in bad times We try to rule these out using two additional tests Price effects: if LCs become more expensive, some evidence of supply effect Use total change in debt rather than LC initiations

44 Empirical evidence time series (cont.) We look at average spreads and maturities on LCs initiated in a given year Regress them in lagged macro variables ( ). Besides VIX, GDP growth and CP- Treas. Spread (Gatev and Strahan, 2005). Time trend also included Average LC Spread = υ + υ1vix+ υ2gdp growth + υ3cp - Treasury Spread Average LC Maturity = ς + ς1vix+ ς 2GDP growth + ς 3CP - Treasury Spread SUR (seemingly- unrelated regressions) Hypotheses: ν 1 > 0, ξ 1 < 0

45 LC contractual terms and lagged VIX AvgMaturity AvgSpread Lvix year

46

47 Theory: Related literature Holmstrom and Tirole (1998), Tirole (2007) Consider the role of aggregate risk in affecting cash and separately LC provision Acharya, Almeida and Campello (2007) Consider the relative choice between holding cash and saving debt capacity Bolton, Chen and Wang (2009) Dynamic investment, exogenous size of credit line Banks and provision of lines of credit Kashyap, Rajan, Stein (2002), Gatev and Strahan (2005), Pennacchi (2006) We control for the flow of deposits into banks by including the CP- Treasury spread in our empirical models

48 Related literature Empirics Growing literature on trade- off between cash and LCs, not focusing on aggregate risk exposure Campello et al. (2010), Lins, Servaes, and Tufano (2010), Disatnik, Duchin, and Schmidt (2010) Sufi (2009): Analyzes the determinants of LC / (LC + Cash) Low profitability triggers covenant violations and decreases credit line availability Firms that are at greater risk of violating covenants (low and risky profits) are less likely to have credit lines Can our results be due to the risk of covenant violations?

49 Covenant violations or bank liquidity? Two complementary tests Bank- level evidence on the link between credit line exposure and aggregate risk Covenant violations, credit line revocation and aggregate risk Evidence is consistent with the bank liquidity channel Exposure to undrawn corporate credit lines increases bank risk, but only when aggregate risk (VIX) is high Covenant violations/revocations are unrelated to Beta/ VIX after controlling for firm characteristics

50 Bank risk and credit line exposure We estimate the following model (Gatev, Schuermann and Strahan, 2009) Data on bank credit line exposure come from Call Reports ( ) We eliminate non- corporate credit lines such as credit cards and home equity lines We run the regression above in periods of high and low VIX (potential non- linearities)

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52 Covenants and aggregate risk We follow Sufi (2009), including data, and add Beta and VIX to his empirical models of covenant violations and credit line revocations

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55 What explains the absence of link between covenant violations and aggregate risk? We extended the model to endogenize credit line revocations as a solution to a moral hazard problem Intuition: full insurance leads to excessive risk- taking Firms that have high liquidity risk have greater monitoring cost and greater likelihood of credit line revocation (if they use credit lines). They switch to cash instead But the probability of revocation is independent of the economy s aggregate state Intuition: bank should not punish firm for an aggregate liquidity shock that is clearly not due to firm- level moral hazard Systematic risk should be priced ex- ante (commitment fees) and not ex- post (credit line revocations)

56 Conclusion Aggregate risk affects firms choice between cash and LC High beta firms hold more cash relative to credit lines, and economy- wide demand for cash is higher when aggregate risk goes up (and LC initiations decrease) Mechanism is the impact of correlated liquidity shocks in banks liquidity constraints Cash is king has some ring of truth to it. Cash has a unique role as a hedge against aggregate risk

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