Cash Holdings and Bank Loan Terms

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1 Preliminary and incomplete. Comments encouraged. Cash Holdings and Bank Loan Terms Mark Huson and Lukas Roth * January 2013 Abstract Recent evidence suggests that high cash holdings presage financial difficulties, resulting in a positive relation between cash holdings and yield spreads on corporate debt. Motivated by the strong empirical evidence that bank loans are different from bonds, we investigate whether and how a firm s cash holdings influence the terms in its bank loan agreements. Using a sample of 38,618 loans obtained by 8,342 firms over the period 1982 to 2010, we find a significant negative relation between cash balances and loan spreads that is stronger for shorter maturity loans. We also document a positive relation between cash balances and the likelihood of borrowing from a single lender and a negative relation between cash balances and (i) the likelihood that the loan is secured and (ii) the number of covenants associated with a loan. Our results suggest that high cash balances may further increase financial and operational flexibility through easing access to bank financing. Keywords: Bank Loans, Cash Holdings, Yield Spread, Credit JEL Codes: G21, G32 * Mark Huson is at the University of Alberta, [email protected]; and Lukas Roth is at the University of Alberta, [email protected]. We thank Laurant Fresard for helpful comments and suggestions. We also thank Michael Roberts and Sudheer Chava for providing us with the DealScan-Compustat Link File. Huson acknowledges support from the Pocklington Professorship in Private Enterprise.

2 A banker will lend you money only if you can prove you don't need it. Bob Hope 1. Introduction Many papers that discuss firms cash holdings describe them as negative debt. Based on this understanding of cash, lenders should view firms with a readily available means of paying down their outstanding debt as being less risky. A recent paper by Acharya, Davydenko, and Strebulaev (2012) points out that this common intuition breaks down because cash balances are endogenous. In particular, their model and empirical work suggests that managers increase cash balances when they anticipate poor outcomes in the future. This in turn generates a positive relation between cash holdings and credit spreads on corporate bonds. Motivated by theoretical work and empirical evidence suggesting that bank loans are different from corporate bonds, this paper asks What do banks think about cash? More specifically, we examine the relation between firms balance sheet liquidity and the pricing and other contract terms of bank loans. We think that looking at bank loans is important for several reasons. First, bank loans are an important source of debt financing for U.S. firms. For example, in 2011, banks lent $1.9 trillion to U.S. firms. 1 Second, it extends and complements the analysis in Acharya et al. (2012) by looking at the impact of cash on the yield spreads of a different type of debt. Several studies suggest that bank debt is special and differs from corporate bonds in several dimensions (see, e.g., James, 1987). Thus, we provide evidence on whether banks evaluation of cash differs from that of other lenders, for example, public bond investors. Third, we exploit variation in the maturity of corporate loans to examine the relation 1 Thomson Reuters Global Syndicated Loans Review, Full Year

3 between cash and credit spreads as the loan maturity changes. This is important since Acharya et al. (2012) suggest that cash is inversely related to the likelihood of default over the short-term and positively related to the likelihood of default over the long-term. Finally, using bank loans allows for the examination of the relation between cash and other terms of the loan agreement such as the number of lenders and the use of covenants. To conduct our tests we compile a sample of 38,618 bank loans obtained by 8,342 firms over the period 1982 to 2010 from the Thomson-Reuters LPC DealScan database. We start our analysis by examining the relation between cash and loan spreads. We measure cash in the year prior to any loan agreement as the ratio of cash and short-term investments to total assets. We calculate loan spreads as the yield on the bank loan over LIBOR including any fees paid to the bank (the so-called all-in spread drawn ). We find a significant negative relation between cash balances and loan spreads. In terms of economic magnitude, an inter-quartile change in the ratio of cash to assets results in an 8 to 12 basis point reduction in loan spreads, which corresponds to a 4% to 6% decrease in bank borrowing costs. This result holds when we control for a firm s credit risk, deal purpose, and loan type, and when we estimate the models with and without firm fixed effects. In further tests, we find that the relation between loan spreads and cash balances is affected by the riskiness of the borrower, market-wide credit conditions, and loan maturity. Specifically, cash balances have a significantly greater impact on loan spreads of below investment grade firms than they do on the loan spreads of investment grade firms. An interquartile change in cash balances is associated with a 19 basis point decrease in loan spreads for below investment grade firms and no significant change in loan spreads for investment grade firms. Further, we find that when credit conditions are tighter, the relation between cash and loan 2

4 spreads is stronger, suggesting that cash is more important for banks when credit markets are relatively weak. Lastly, our results show that the negative relation between cash and spreads is stronger for shorter term loans (tenors < 3.8 years) than it is for longer term loans (tenor > 3.8 years). Overall our results indicate that banks think they face less risk when lending to high cash firms. The results based on credit ratings and credit market conditions suggest that this is particularly true when firms face either idiosyncratic or market-wide difficulties accessing external financing. The weakening of the value of cash as collateral as loan maturity increases is generally consistent with the model in Acharya et al. (2012). However, the significant negative relation between spreads and cash for loans with tenors exceeding three years suggests that any negative event against which cash is held is somewhat long-dated. Our final tests examine whether cash levels are related to other loan contract terms, in addition to loan spreads. In particular, we find that firms with higher cash levels are more likely to borrow from a single lender, and that greater cash holdings are negatively associated with the number of restrictive covenants included in the loan contract. Further, cash balances are positively associated with loan maturity, and negatively related to whether the loan is secured. All of these results are consistent with cash holdings lowering the perceived risk of a loan. These results suggest other ways that cash holdings increase financial flexibility. Not only does cash provide readily available capital, it also lowers the cost of bank capital which is a substitute for balance sheet liquidity (Amihud, Bharath, and Saunders, 2007; Sufi, 2007). In addition, high cash balances are associated with fewer restrictive covenants providing both greater financial and operating flexibility. 3

5 The remainder of the paper is organized as follows. In section 2 we discuss several papers that provide different views of how banks might think about cash. In the following section we describe our sampling process and data and present descriptive statistics. Section 4 presents our empirical evidence and Section 5 concludes. 2. Banks View of Cash As mentioned above, Acharya et al. (2012) suggest that public lenders view high cash levels as portents of financial difficulties and correspondingly, yield spreads on public debt are greater for firms holding more cash. However, bank loans and public debt differ along several dimensions. For example, Fama (1985) describes bank debt as inside-debt and discusses banks superior access to information and the potential for banks participation in firms decision processes. Fama also points to the relatively short tenor of bank loans as providing banks with effective means of removing funds from a bad credit risk in a timely fashion. Bank loans also have seniority in the event of default and superior covenant protection (Chava and Roberts, 2008; Kahan and Tuckman, 1995). In this section we discuss whether and how these and other differences are likely to result in banks view of cash differing from that of public lenders. 2.1 Banks Access to Superior Information Fama (1985) discusses banks superior access to information relative to that of public debt holders. Banks can garner useful information about a firm through the provision of non-loan services (Black 1975). For example, banks might obtain information about a firm s sales by monitoring its demand deposits and through the provision of factoring services. Petersen and 4

6 Rajan (1994) report evidence consistent with this non-loan information affecting banks lending decisions. To the extent that banks have access to superior information, information contained in a firm s cash balance is likely to be less useful in determining lending terms. Two firms with identical cash balances may have completely different risk profiles based on the non-cash signals that banks observe. Thus, if a firm s cash balance is uncorrelated with the signals bankers glean from their soft information, cash balances should be unrelated to loan pricing and lending terms. Alternatively, if cash balances correlate positively with soft information about a firm s financial health, then the relation between cash balances and bank-loan terms should be similar to that observed between cash balances and credit spreads on public debt. Finally, bankers superior information might inform them as to why a firm s cash level is high. To the extent we only observe loans to high cash firms when bankers know that the cash is not being held to stave off distress, we might expect to see a negative relation between cash balances and loan spreads. 2.2 Covenants, Acceleration, Seniority, and Horizon Private debt, such as bank loans, typically contains both more and more stringent covenants than public debt (see, e.g., DeAngelo, DeAngelo, and Skinner, 1994; Kahan and Tuckman, 1995). The larger number of covenants and the closeness of the thresholds to a borrowing firm s current accounting measures provide banks with more opportunities to intervene over the life of the loan. One action a bank can take in the event of a covenant violation is acceleration of loan repayment. To the extent that banks can shorten the horizon of the loan, cash is more valuable collateral because managers ability to waste cash increases with time. Even if bankers do not accelerate the loan they can control managers use of the cash. Chava and 5

7 Roberts (2008) report that technical default of loan covenants results in significant reductions in capital investments at borrowing firms. Even without acceleration, bank loans typically have shorter tenor than public debts. Acharya et al. (2012) point out that high cash balances are related to a higher likelihood of default at horizons greater than one year. For shorter horizons, high cash balances are related to lower levels of default. 2 Firms pre-distress cash build-ups could be made to meet the short term needs of covering bank loans. Banks relative seniority would give them a claim to these liquid assets. Thus, banks ability to oversee operations and having access to a firms cash over short horizons suggests that banks may view cash positively, which results in a negative relation between loan spreads and cash levels. 2.3 Bank Loans and Cash Policy Access to bank debt can substitute for cash balances. Amihud, Bharath, and Saunders (2007) report that firms that obtained at least one bank loan in the period (t-1, t) hold less cash in period t than firms that did not obtain bank loans over the same window. Sufi (2007) indicates that bank lines of credit are a substitute for cash holdings. The cost of bank funds influences a firm s choice to hold cash rather than utilize bank capital to manage its liquidity. To the extent that a firm s choice to maintain high cash balances reflects a firm s high cost of bank funds, we expect to find a positive relation between the level of cash holdings and loan spreads. 2.4 Cash and Bank Loans The preceding discussion suggests there is no clear prediction as to the relation between firms cash holdings and the spread charged in their bank loans. Banks soft information could 2 See also Davydenko (2011). 6

8 either reinforce or contradict the signal in cash balances, making the relation between loan spreads and cash unpredictable. Alternatively, banks superior monitoring ability and earlier access to firms assets could make cash valuable collateral resulting in lower loan spreads for firms with high cash holdings. Finally, firms with higher borrowing costs may choose to rely more heavily on internal liquidity resulting in a positive relation between loan spreads and cash. We empirically assess the relation between cash holdings and loan spreads. In addition to affecting the cost of bank funds cash holdings can affect non-price attributes of bank loans. Qian and Strahan (2007) argue that stronger creditor protection substitutes for actions that banks can take to mitigate the riskiness of the borrower. For example, banks can lower risk by increasing the number of lenders in the syndicate. This both diversifies the risk and lowers the benefits of strategic default (Bolton and Scharfstein, 1996). Banks can also shorten loan tenor to manage risk (Strahan, 1999). Further, Qian and Strahan (2007) show that bank loans made in countries with stronger creditor protection not only have lower interest rates but also involve fewer lenders and have longer maturities. To the extent that a firm s cash holdings alter lenders risks we expect cash holdings to be related to these non-price attributes in a manner consistent with the relation between cash and loan spreads. Following Qian and Strahan (2007) we consider the number of lenders and loan maturity. We also consider whether the loan is secured, and the number of restrictive covenants included in the loan. If cash provides banks incremental protection, there should be less need for specific collateral requirements and restrictive covenants. The relations between cash holdings and these non-price attributes provide additional evidence on banks view of cash. 7

9 3. Data We compile bank loan data, measures of cash holdings, and data on firm characteristics from various sources. In this section, we describe our main variables and the selection of controls. 3.1 Bank Loan Data and Measures We obtain a sample of bank loans from the Thomson-Reuters LPC DealScan database. DealScan provides detailed data on loan characteristics, including loan pricing, loan covenants, and other contract terms at loan origination. 3 Carey and Hrycray (1999) estimate that DealScan loans cover between half and three-quarters of the volume for outstanding commercial and industrial loans in the U.S., and Chava and Roberts (2008) report that around 60% of DealScan data comes from SEC filings. Loan data coverage starts in 1982, and our sample covers the period from June 1982 to April In our sample, we include only senior bank loans denominated in US$, and we exclude financial firms and public administration firms (SICs 6 and 9), as well as foreign issuers. The main dependent variable in our analysis is the bank loan yield spread measured as the all-in spread drawn reported in DealScan. This variable is calculated as the difference between the yield on the bank loan over LIBOR for each dollar drawn, including any fees paid to the bank, and is reported in basis points (bps). In all of our tests involving loan spreads, we use the natural logarithm of the loan spread as our dependent variable. In addition to loan pricing data, we obtain information on other loan contract terms. Maturity is the time to maturity in years; loan size is the loan commitment in millions of US$; 3 Carey and Hrycray (1999) and Chava and Roberts (2008) provide an overview of the DealScan database. 4 Coverage before the late 1980s is thin; however, when we exclude bank loans originated before 1988, our results are unchanged. 8

10 secured is a dummy variable that equals one if the loan is secured, and zero otherwise; number of lenders is the number of banks involved in the deal; syndication is a dummy variable that equals one if the loan is syndicated, that is, multiple lenders are involved, and zero otherwise. 5 We construct a bank loan covenant index based on the covenants included in the loan agreement. DealScan reports various types of financial ratio and net worth covenants, as well as dividend and prepayment restrictions ( sweep restrictions). Since various covenants are designed to limit similar types of activities, we group covenants into twelve different categories: general debt ratio, liquidity ratio, senior debt ratio, coverage ratio, investment restrictions, profitability restrictions, net worth restrictions, dividend restrictions, and prepayment restrictions related to equity issuances, debt issuances, asset sales, or excess cash flows. For each of these categories, we create a dummy variable that is equal to one if a particular activity is restricted, and zero otherwise. Finally, we create an overall bank loan covenant index by adding up these dummy variables. The covenant index ranges from 0 to 12, with greater values indicating more covenants included in a bank loan. Appendix A provides a description of loan covenants (see, e.g., Billett, King, and Mauer, 2007; Mansi, Qi, and Wald, 2012). DealScan also provides information on the loan type (e.g., line of credit, bridge loan, term loan, etc.) as well as on the purpose of the loan (e.g., working capital, corporate purposes, takeover, etc.). Since loan pricing is likely being affected by the type of the loan and the loan purpose, we include loan type and loan purpose dummy variables in our regressions. 5 In our spread regressions, we decide not to use these loan term variables as controls, since these are likely to be endogenous to bank loan spreads. In practice, when we include loan terms as controls, our results do not change. 9

11 3.2 Measures of Cash and Firm-level Controls We augment the bank loan sample with firm characteristics obtained from Compustat. We use the DealScan-Compustat Link from Chava and Roberts (2008) to merge the two databases. We measure firm-level variables at the end of the most recent fiscal year prior to the loan origination date. Our main variable of interest is the amount of cash firms hold on their balance sheet. Cash is measured with cash and short-term investments (Compustat item: che) scaled by total assets (at). Following the literature, we obtain additional control variables likely to be important for loan pricing. Firm size is measured with total assets in millions of US$, and we use the natural logarithm of total assets in our regressions; the ratio of property, plant, and equipment (ppent) to total assets measures the tangibility of a firm s assets; leverage is debt (dltt) divided by total assets; and profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. In our analysis, we expect that firm size, tangibility, and profitability are all negatively associated with yield spreads, whereas leverage is positively related to spreads. To control for a borrower s risk, we use the issuer s credit rating obtained from Compustat. We use the S&P long-term issuer credit rating prior to loan origination. Following Qian and Strahan (2007), we create a rating index from one to six, with one indicating a rating of AAA, two a rating of AA+, AA, or AA-, etc. If a borrower is not rated, we assign a zero, and include a separate dummy variable that equals one if a firm is unrated, and zero otherwise. In our regressions, we control for borrower risk by either using these two rating variables, or alternatively, by including separate rating dummy variables for each credit rating (i.e., for AAA, AA+, AA, etc.). 10

12 Our final dataset consists of 38,618 bank loans issued by 8,342 firms over the period of 1982 to We winsorize all variables at the 1% and 99% level to mitigate the impact of outliers. In all our regressions we include two-digit SIC industry dummies as well as year dummies. Since we have multiple bank loan observations by firm, we report t-statistics computed using standard errors corrected for heteroskedasticity and clustered at the issuer level. 4. Empirical Results 4.1 Descriptive Statistics Panel A of Table 1 presents descriptive statistics for the 38,618 bank loans in our sample. The average (median) loan amount in our sample is $263 ($85) million dollars. Sample loans have an average maturity of 3.7 years, are typically secured (79%), have 2 to 3 covenants, and are syndicated (84%) with an average syndicate size of 7 lenders. The average (median) all-in spread on the sample loans is 210 (200) basis points. In the year prior to taking out a loan, the average borrower holds 8% of its assets in cash and there is substantial variation in the cash-to-assets ratio. The cash holdings in our sample of bank-borrowers is similar to the cash holdings of firms that access public debt used in Acharya, Davydenko, and Strebulaev (2012) who report average cash holdings of 7.1%. It is slightly lower than the average cash holdings of all COMPUSTAT firms over the sample period (12.2%) and of the COMPUSTAT firms that do not obtain bank loans over the sample period (16.4%). 6 Like loan size, firm size is also skewed with an average (median) firm having approximately $3.26 ($0.46) billion in total assets in the year prior to taking out a loan. On average property, plant, and equipment (tangibility) makes up 34% of a borrowing firm s assets 6 Later in the paper we provide supplementary analysis using sub-samples of firms with cash-asset ratios in excess of 10% and 15% with sub-sample average cash-to-asset ratios of 24.7% and 30.6% respectively. 11

13 and debt 29% of assets. Finally, the typical borrowing firm is profitable with an EBITDA to assets ratio of 12%. Panel B of Table 1 breaks down the averages of our key variables, spread and cash, by firms long-term issuer credit rating in the year prior to loan origination. Looking first at the average spreads of rated firms we see that the spreads are decreasing in credit rating. The difference in spreads between AAA and AA rated firms is not statistically significant but the difference in spreads between each successive pair of rating categories is statistically significant (with p-values less than 5%). Loan spreads for firms without a credit rating are higher than spreads for BB rated firms and not significantly different than spreads for firms rated below BB-. Looking at the cash-to-asset ratios across the credit rating bins we see both highly rated and non-rated firms have the highest levels of cash holdings. The difference in cash holdings between the firms in these extreme credit quality groups is not statistically significant. We also see that cash holdings fall as ratings move from AAA to BBB and then increase as credit quality further deteriorates. The mean cash holdings of the firms in the intermediate credit quality groups are significantly different from the mean cash holdings of the firms in the extreme credit quality groups. This U-shaped pattern in cash holdings across the credit spectrum of corporate debt is consistent with the results reported in Acharya et al. (2012). Table 2 presents correlations between key variables used in our analysis. The first thing to notice is the positive correlation between loan spreads and cash. While small in magnitude (0.10) the correlation is statistically significant. The positive association between spreads and cash is consistent with the results reported in Acharya et al. (2012). However, this is a univariate measure, and we must consider the correlations between spreads, cash, and other firm and loan attributes. 12

14 Focusing on the relations between loan spreads and firm attributes we see that the loan spread is decreasing in firm size, asset tangibility, credit ratings, and profitability and increasing in leverage. We also see that spreads are decreasing in loan size and the number of lenders. All of these relations are consistent with spreads being lower when banks face less risk. With the exception of leverage the correlations between cash and firm (loan) attributes mirror the correlations between loan spreads and firm (loan) attributes. Specifically cash is also negatively related to firm size, asset tangibility, credit ratings, profitability, loan size, and the number of lenders. The similarity in the correlation matrices of loan spreads and cash holdings suggests that firm or loan characteristics play a role in the univariate relation between the two. For example large firms both have lower loan spreads and hold relatively low amounts of cash. We address this in the next section where we investigate the relation between loan spreads and cash in a multivariate setting. 4.2 Multivariate Evidence on the Relation between Cash and Loan Spreads Table 3 presents a multivariate analysis of the relation between cash holdings and spreads on corporate loans. The independent variable in all six models is the natural logarithm of the loan spread. The first three models control for firm attributes such as size, asset tangibility, leverage, and profitability. These models also contain industry and year fixed effects. In model 1 we do not control for any loan attributes and observe a significantly negative relation between loan spreads and cash. The point estimate (-0.196) indicates that an inter-quartile change in the ratio of cash to assets (0.187) results in a 3.7% reduction in the loan spread (calculated as ). With an average loan spread of 210, this corresponds to a decrease in borrowing costs of 8 basis points. Models 2 and 3 include information about firms long-term credit ratings and information 13

15 about the loan itself. 7 Inclusion of this additional information about the loan does not change the significantly negative relation between cash and loan spreads. Banks provide lower costs funds to high cash firms. Looking at the control variables in Table 3 we see that they confirm the univariate correlations shown in Table 2. Loan spreads are negatively and significantly related to firm size, asset tangibility, profitability, and credit quality. We also observe a positive and significant relation between loan spreads and leverage. Controlling for other firm attributes that affect loan spreads shows the incremental role played by cash in determining loan spreads: higher cash holdings are associated with lower costs of bank funds. To ensure that we are estimating variation in spreads related to variation in cash holdings at the firm level rather than picking up cross-firm variation in both cash holdings and loan spreads we re-estimate models 1 to 3 including firm fixed effects. Models 4 to 6 show these results when focusing on within firm variation only. After including firm fixed effects we still see a negative relation between loan spreads and cash holdings. The results in models 4 to 6 suggest an even stronger relation between spreads and cash. The point estimate in model 6 ( ) indicates that an inter-quartile increase in the ratio of cash to assets results in a 12 bps reduction in loan spreads, corresponding to a reduction in borrowing costs of almost 6%. As we mentioned in our data description the average cash holdings of firms obtaining bank loans is lower than that of the typical firm not obtaining bank loans. To address any concerns that our results reflect the relatively low cash holdings of some of our sample firms we re-estimate model 6 for two sub-samples created on the basis of cash holdings. In the first subsample we include all firm years where the cash to asset ratio is in excess of 10% resulting in 7 The negative relation between loan spreads and cash holds when we estimate the model separately for various loan types. No one type of loan drives the results. 14

16 9,242 loans at 3,903 firms. The second sub-sample includes all firm years where the cash-toasset ratio is in excess of 15% and is comprised of 6,373 loans at 2,919 firms. The average cashto-asset ratios in the two sub-samples are 24.7% and 30.6% respectively. In both high-cash subsamples cash has a greater impact on loan spreads than in the entire sample. In the greater than 10% sub-sample the point estimate on cash is (t=-3.51). This implies that an inter-quartile change in cash holdings results in an 11.5% reduction in borrowing costs. The corresponding numbers for the greater than 15% sub-sample are (t=-3.52) and -14.8%. These results indicate that cash balances lower borrowing costs even among firms with relatively high cash holdings. 4.3 Variation in the Relation between Cash and Loan Spreads The results in Table 3 show that high cash holdings are associated with lower loan spreads. To the extent that cash mitigates the riskiness of loans the relation between cash and spreads should vary with firm attributes, market conditions, and loan attributes. In this section we examine the impact of firms credit ratings, tightness in the credit market, and loan maturity on the relation between loan spreads and cash holdings Credit Ratings and the Relation between Cash and Loan Spreads We begin by separating our sample into groups based on the firms credit ratings. We do this for two reasons. First, firms with higher credit ratings have better access to financing. To the extent that firms can readily access the credit market, banks may be less concerned about actual cash on hand. The second reason is that a firm s credit rating incorporates information about the 15

17 likelihood of default. If cash provides a safety-net for banks when dealing with risky firms, cash should have a greater influence on credit spreads for lower rated firms. We group borrowing firms into investment grade (rated BBB- and above) and below investment grade (below BBB-) issuers. We also create a sample of below investment grade issuers that includes non-rated firms. We then estimate models 3 and 6 of Table 3 for each of these sub-samples. Table 4 presents the results. Models 1 to 3 and models 4 to 6 in Table 4 are similar except that the latter models contain firms fixed effects. Since the results are similar across the models we restrict our discussion to models 4 to 6. Model 4 shows that for investment grade borrowers cash holdings do not significantly affect loan spreads. This is consistent with both easy access to external finance and lower likelihood of default, making cash on hand less important in lending decisions. For below investment grade borrowers, the estimated relation between cash holdings and loan spreads is significantly negative. To test whether the coefficients on cash in the different subsamples are significantly different, we estimate a fully-interacted model where we interact all the dependent variables with a dummy variable that equals one if the issuer has an investment grade rating, and zero otherwise. These tests show that the coefficients on cash are significantly different across the various sub-samples at customary significance levels. 8 The difference is also economically significant, the point estimate in model 6 (-0.377) indicates that an inter-quartile increase in the ratio of cash to assets of a below investment grade borrower (0.211) results in a 19 bps reduction in the loan spread (calculated as ; the average loan spread for below investment grade borrowers is 242 bps). This result indicates that banks care about cash when firms are likely to have trouble accessing external finance or when default likelihoods 8 In all of the tests conducted the highest p-value observed is

18 are higher. This significant reduction in borrowing costs provides further support for the supposition that firms with cash have an easier time borrowing money. The difference between the two groups in the impact of the control variables on loan spreads supports the idea that cash is a determinant of loan spreads and matters differentially for investment and non-investment grade borrowers. In particular we observe that firm size reduces loan spreads only for non-investment grade issuers. Likewise leverage only increases loan spreads for non-investment grade issuers Credit Market Conditions and the Relation between Cash and Loan Spreads We next investigate whether the tightness of credit conditions affects the relation between cash and loan spreads. If we think that banks take more care in their lending decisions when credit is dear, firm attributes such as cash holdings should play a larger part in lending decisions at such times. Bates, Chang, and Chi (2011) show that the value of cash changes through time as credit conditions change. They show that financial constraints and credit market conditions are associated with the observed intertemporal variation in the value of cash. Basically, when credit conditions are tight (loose) and external cash is expensive (inexpensive) cash is more (less) valuable. We obtain the value of cash for the years 1980 to 2009 from Bates, Chang, and Chi (2011). We split our sample into time periods when cash is more valuable and periods when cash is less valuable using the sample median as the breakpoint and estimate the relation between cash and loan spreads during these periods. Table 5 reports the results of these estimations. Focusing on the models with firm fixed effects (models 3 and 4) we see that cash has no significant relation to loan spreads during periods when the value of cash is low. The relation 9 When we drop the rating dummies, total assets and leverage are significant for investment grade borrowers, but tangibility is still positive for non-investment grade borrowers, however, not significant. 17

19 between cash holdings and loan spreads when the value of cash is high is negative and significant. Again, we find that the difference between the cash coefficient estimates across the sub-samples in each model is statistically significant at conventional levels. 10 The point estimate on cash holdings during periods of highly valued cash is indicating that an inter-quartile increase in cash holdings results in a 17 bps decrease in loan spreads during periods when cash has high value. Thus, our findings suggest that banks provide better terms to cash heavy firms when credit conditions are tight. This is consistent with the oft stated aphorism that banks like to lend money to people who do not need it. In addition to using the value-of-cash measure from Bates et al. (2012), we use a more direct measure of credit market tightness. We obtain the net fraction of loan officers tightening credit terms from the Federal Reserve Board s Senior Loan Officer Opinion Survey. 11 We use these data to split our sample loans into those made during periods of tight and loose credit conditions. We first define tight (loose) conditions based on the median value of the net percentage of loan officers tightening lending standards. In separate analysis, we define tight and loose conditions based on the net percentage of loan officers tightening lending standards being above the third quartile and below the first quartile, respectively. Panel A of Table 6 presents evidence on the relation between cash and loan spreads in the two regimes based on the median split. In both the OLS and the firm fixed effects models we see that cash holdings are negatively related to credit spreads in both tight and loose market environments. More interestingly, we observe that cash holdings have a more pronounced impact on loan spreads when credit conditions are tight. In the OLS models the difference in the 10 As in Table 4, we use a fully-interacted model to test for statistical differences across the sub-samples. 11 Lown, Morgan, and Rohatgi (2000) report that the spread between rates on commercial and industrial loans and the federal funds rate is highly correlated with the degree of credit tightening reflected in the senior loan officer survey. 18

20 estimated relation between cash and loan spreads is statistically significant. The difference in the impact of cash in the firm fixed effects models is consistent with that in the OLS model but does not achieve statistical significance. Panel B presents the results when we classify credit market conditions using the first and third quartile as cutoffs and discard loans made when the observed net fraction of loan officers tightening credit terms falls in the inter-quartile range. This classification provides a more dramatic split between tight and loose credit market conditions. The results reflect this. In Panel B the point estimates on the cash variable when credit markets are tight are in model 1 and in model 3. These are 36% and 79% larger in magnitude than the point estimates obtained using the median split. Comparing the impact of cash on loan spreads between tight and loose market conditions using the quartile splits shows results consistent with those using the median split. However, the larger point estimates on cash during tight credit conditions using the quartile split results in the difference in the estimated relation between cash and loan spreads being statistically significant in both the OLS and firm fixed effect models. The results in Table 6 indicate that cash mitigates the general increase in loan spreads that is associated with the degree of credit tightening reflected in the senior loan officer opinion surveys. Overall the results in Tables 5 and 6 indicate that at times when banks are reluctant to make loans, having large cash balances in place increases firms ability to access external funds by lowering bank borrowing costs Loan Maturity and the Relation between Cash and Loan Spreads Our next tests investigate whether and how the maturity of bank loans affects the relation between cash levels and loan spreads. Acharya et al. (2012) show that high cash is related to 19

21 higher default likelihood over longer windows. To the extent that cash is related to longer term financial problems, cash should be less valuable as collateral for longer term loans. This logic is partially tempered by banks increased ability to monitor firms and the tighter covenants included in bank loans. We separate our sample of bank loans into two groups based on loan maturity using the median maturity of 3.8 years as the breakpoint. We then estimate models 3 and 6 of Table 3 for the two sub-samples. Table 6 presents the results of these estimations. The first two models are for short and long maturity loans and do not include firm fixed effects. Models 3 and 4 include firm fixed effects and we will limit our discussion to these. In both short and long-term loans we see that higher levels of cash result in lower loan spreads. The point estimate in model 3 (-0.391) indicates that an inter-quartile increase in cash holdings results in a 19 bps reduction in loan spreads for short-term loans. The corresponding point estimate for long-term loans (-0.188) shows that an inter-quartile increase in cash holdings results in a smaller reduction in loan spreads of only 6 bps. 12 The reduction in the collateral value of cash as loan maturity increases is consistent with the result in Acharya et al. (2012) that high levels of cash are associated with higher default likelihoods in the longer term. However, we still observe a significantly negative relation between cash levels and loan spreads in the sample of loans with maturities in excess of 3.8 years. Acharya et al. (2012) show that the increase default likelihood associated with high cash levels begins as soon as one year after the measurement of cash levels. That banks still find cash valuable collateral for loans with maturities exceeding 3.8 years is consistent with banks ability to monitor and achieve better recoveries than non-bank lenders. 12 In each model the difference between the estimates of coefficients on cash across the sub-samples is statistically significant at conventional levels. 20

22 4.4 Multivariate Evidence on the Relation between Cash and Non-Price Loan Attributes The evidence presented above is consistent with cash lowering the riskiness of bank loans. If cash does indeed lower banks risk in lending, cash should also alter non-price loan attributes. In this section we present evidence on the relations between cash holdings and syndicate size (the number of lenders involved in a deal), the maturity of the loan, whether the loan is secured, and the number of covenants included in the loan. Table 8 presents the results. Model 1 shows that firms with high cash holdings tend to borrow from fewer lenders. This is consistent with the negative relation between creditor rights and the number of lenders reported in Qian and Strahan (2007). The negative relation between cash and the number of lenders suggests that cash holdings lower the need to diversify the riskiness of the borrower and reduce the risk of opportunistic default. Shorter loan maturity increases a banks control of the borrowing firm and lowers the bank s exposure to the firms risk because they can refuse to refinance a loan. To the extent that cash lowers the riskiness of a borrower we should see a positive relation between cash holding and loan maturity. Consistent with this idea, model 2 shows that firms with higher cash holdings tend to obtain longer maturity loans. Models 3 and 4 examine the effect of cash on the likelihood that a loan is secured and on the number of covenants contained in the loan, respectively. Both of these variables provide explicit protection to banks. Assuming that cash itself is adequate collateral, loans to firms with high cash holdings are less likely to be secured. If cash lowers the riskiness of a borrower, loans to cash rich borrowers should contain fewer restrictive covenants. Using a probit regression, model 3 shows that high levels of cash lower the likelihood that a loan is secured. Model 4 21

23 employs Poisson estimation and shows that high cash levels are associated with fewer restrictive covenants included in the loan agreement. These two models suggest that cash is a substitute for explicit loan security and for restrictive covenants. The results in Table 8 are consistent with the idea that cash holdings lower the perceived riskiness of bank loans. These results compliment and reinforce the earlier results showing that cash holdings lower loan spreads. Overall, cash holdings ease bank borrowing by lowering the cost of funds and providing better non-price loan terms. 5. Conclusion In this paper we document another difference between public lenders and banks. Recent evidence suggests that cash holdings are viewed negatively by public creditors who see them as a signal of pending financial difficulties. We find that banks like cash. In particular, higher cash holdings result in lower loan spreads. The impact of cash on spreads is most pronounced for creditors with below investment grade ratings on their long-term debt suggesting that banks care about cash when firms are likely to experience financial difficulty. Cash also has a greater impact on loan spreads during periods when credit is tight and when loans have shorter maturity. We also find evidence indicating that cash holdings substitute for explicit loan security and for restrictive covenants. These results suggest other ways in which cash holdings increase financial flexibility. In addition to providing readily available capital, cash balances lower the cost of bank capital which is a substitute for balance sheet liquidity. Not only do cash balances lower the cost of funds but they also reduce the number of restrictive covenants. Overall, lower borrowing costs and fewer covenants provide both greater financial and operating flexibility. 22

24 Finally we would like to point out that our results complement those of Acharya et al. (2012) who find that the positive association between the yield on long-term debt and cash is driven by firms building cash reserves to cover expenses during anticipated periods of low cash flow. To the extent that bank loans are one of the expenses that the cash build up is intended to cover, our results are consistent with those in Acharya et al. (2012). 23

25 References Acharya, V. V., S. A. Davydenko, and I. A. Strebulaev, 2012, Cash holdings and credit risk, Review of Financial Studies, forthcoming. Amihud, Y., S. Bharath, and A. Saunders, 2007, How does the source of capital affect corporate liquidity?, Working paper, Stern School, New York University. Bates, T.W., C. Ching-Hung, and C. Jianxin, 2011, Why has the value of cash increased over time?, Working paper, Arizona State University. Billett, M., King, D., Mauer, D., Growth opportunities and the choice of leverage, debt, and covenants, Journal of Finance 62, Black, F., 1975, Bank funds management in an efficient market, Journal of Financial Economics, 2, Bolton, P., and D. S. Scharfstein, 1996, Optimal debt structure and the number of creditors, Journal of Political Economy, 104, Carey, M., and M. Hrycray, 1999, Credit flow, risk, and the role of private debt in capital structure, Working paper, Federal Reserve Board. Chava, S., and M. R. Roberts, 2008, How does financing affect investment: the role of debt covenant violations, Journal of Finance, 63, Davydenko, S. A., 2011, What triggers default? A Study of the default boundary, Working paper, University of Toronto. DeAngelo, H., L. DeAngelo, and D. J. Skinner, 1994, Accounting choice in troubled companies, Journal of Accounting and Economics, 17, Fama, E. F., 1985, What s different about banks?, Journal of Monetary Economics, 15, James, C., 1987, Some evidence on the uniqueness of bank loans, Journal of Financial Economics, 19, Kahan, M., and B. Tuckman, 1995, Private versus public lending: evidence from covenants, J. D. Finnerty and M. S. Fridson eds., The Yearbook of Fixed Income Investing (Irwin, NY). Lown, C., Morgan, D., Rohatgi, S., July Listening to loan officers: The impact of commercial credit standards on lending and output. FRBNY Economic Policy Review. Mansi, S., Qi, Y., Wald, J. K., Debt Covenants and bankruptcy risk, Working paper, Virginia Tech. Petersen, M. A., and R. G. Rajan, 1994, The benefits of lending relationships: Evidence from small business data, Journal of Finance, 49,

26 Qian J., and P. E. Strahan, 2007, How laws and institutions shape financial contracts: the case of bank loans, Journal of Finance, 62, Strahan, P. E., 1999, Borrower risk and the price and nonprice terms of bank loans, Working paper, Federal Reserve Bank of New York. Sufi, A., 2007, Bank lines of credit in corporate finance: an empirical analysis, Review of Financial Studies, 22,

27 Appendix A: Loan Covenants The table shows the construction of the bank loan covenant index. We group covenants that are designed to limit similar types of activities into twelve different categories. For each of these categories, we create a dummy variable that is equal to one if a particular activity is restricted, and zero otherwise. We create an overall bank loan covenant index by adding up these dummy variables. The covenant index ranges from 0 to 12, with greater values indicating more covenants included in a bank loan. Covenant Categories General debt ratio Liquidity ratio Senior debt ratio DealScan Variables or Covenant Description Types Max. Debt to EBITDA Max. Debt to Equity Max. Debt to Tangible Net Worth Max. Leverage ratio Max. Loan to Value Min. Debt Service Coverage Min. Current Ratio Min. Quick Ratio Min. Cash Interest Coverage Max. Senior Debt to EBITDA Max. Senior Leverage Min. Interest Coverage An indicator variable that equals one if a particular debt ratio covenant exists, and zero otherwise. An indicator variable that equals one if a particular liquidity ratio covenant exists, and zero otherwise. An indicator variable that equals one if a particular senior debt ratio covenant exists, and zero otherwise. Coverage ratio Min. Fixed Charge Coverage An indicator variable that equals one if a coverage ratio covenant exists, and zero otherwise. Investment restrictions Max. Capex An indicator variable that equals one if an investment restriction exists, and zero otherwise. Profitability restrictions Min. EBITDA An indicator variable that equals one if a profitability restriction exists, and zero otherwise. Net worth restrictions Net Worth Tangible Net Worth An indicator variable that equals one if a particular net worth covenant exists, and zero otherwise. Dividend restrictions DividendRestrictions An indicator variable that equals one if the borrower is restricted from paying dividends to shareholders, and zero otherwise. PercentageofExcessCF Percentage of excess cash flow the borrower is allowed to use towards dividends. PercentageofNetIncome Percentage of net income the borrower is allowed to use towards dividends. Equity issuance sweep EquityIssuanceSweep The percentage of net proceeds a company receives from the issuance of equity that must be used to pay down any outstanding loan balance. Debt issuance sweep DebtIssuanceSweep The percentage of net proceeds a company receives from the issuance of debt that must be used to pay down any outstanding loan balance. Asset sales sweep AssetSalesSweep The percentage of net proceeds a company receives from an asset sale that must be used to pay down any outstanding loan balance. Excess cash flow sweep ExcessCFSweep The percentage of excess cash flows the company must use to pay down any outstanding loan balance. 26

28 Table 1 Descriptive Statistics The table shows descriptive statistics. Data on bank loans are obtained from DealScan. Spread is the all-in spread drawn calculated as the yield on the bank loan over LIBOR for each dollar drawn including any fees paid to the bank (in basis points). Maturity is the time to maturity in years; Loan Size is the loan commitment in US$ million; Secured is a dummy variable that equals one if the loan is secured, and zero otherwise; Number of Lenders is the number of banks involved in the deal; Syndication is a dummy variable that equals one if the loan is syndicated, and zero otherwise; and Covenant Index is the number of covenants included in the loan (see Appendix A for a detailed description of loan covenants). The data on firm characteristics are obtained from Compustat. Cash is cash and short-term investments (che) divided by total assets (at); Total Assets is total assets in US$ million; Tangibility is property, plan, and equipment (ppent) to total assets; Leverage is debt (dltt) divided by total assets; and Profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. Rating information is obtained from Compustat; we use the S&P long-term issuer credit rating prior to loan origination. All variables are winsorized at the 1% and 99% level. We include only senior bank loans denominated in US$. Financial firms and public administration firms (SICs 6 and 9), and non-u.s. firms are excluded. The sample period is 1982 to Panel A: Summary Statistics Variables Average Standard Median Observations Deviation Spread ,618 Log Spread ,618 Maturity ,012 Loan Size ,618 Secured ,137 Number of Lenders ,599 Syndication ,618 Covenant Index ,618 Cash ,618 Total Assets 3, , ,618 Log Total Assets ,618 Tangibility ,618 Leverage ,618 Profitability ,618 Panel B: Average by Rating of Key Variables Rating Spread Cash Observations AAA AA A ,652 BBB ,238 BB ,834 Less than BB ,640 Not-Rated ,665 27

29 Table 2 Correlations The table reports pairwise correlation coefficients for key variables. Spread is the all-in spread drawn calculated as the yield on the bank loan over LIBOR for each dollar drawn including any fees paid to the bank (in basis points). Cash is cash and short-term investments (che) divided by total assets (at); Total Assets is total assets in US$ million; Tangibility is property, plan, and equipment (ppent) to total assets; Leverage is debt (dltt) divided by total assets; and Profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. Rating is an index variable that equals one if the rating is AAA, two for AA+/AA/AA- ratings, three for A+/A/A- ratings, four for BBB+/BBB/BBB- ratings, five for BB+/BB/BB- ratings, and six for ratings lower than BB-. Rating is set to missing for non-rated firms. Maturity is the time to maturity in years; Loan Size is the loan commitment in US$ million; and Number of Lenders is the number of banks involved in the deal. Data on bank loans are obtained from DealScan, and data on firm characteristics is from Compustat. All variables are winsorized at the 1% and 99% level. We include only senior bank loans denominated in US$. Financial firms and public administration firms (SICs 6 and 9), and non-u.s. firms are excluded. The sample period is 1982 to Log Spread Cash Log Total Assets Tangibility Leverage Profitability Rating Log Maturity Log Loan Size Cash 0.10 Log Total Assets Tangibility Leverage Profitability Rating Log Maturity Log Loan Size Log Number of Lenders

30 Table 3 Bank Loan Spreads and Cash Holdings The table shows regression estimates of bank loan spreads on cash holdings and firm and loan control variables. The dependent variable, Log Spread, is the logarithm of the yield spread on the bank loan. Data on bank loans are obtained from DealScan. We measure loan spreads with the all-in spread drawn calculated as the yield on the bank loan over LIBOR for each dollar drawn including any fees paid to the bank (in basis points). The data on firm characteristics are obtained from Compustat. Cash is cash and short-term investments (che) divided by total assets (at); Total Assets is total assets in US$ million; Tangibility is property, plan, and equipment (ppent) to total assets; Leverage is debt (dltt) divided by total assets; and Profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. All firm-level variables are measured at the end of the fiscal year prior to the loan origination date. Rating information is obtained from Compustat; we use the S&P long-term issuer credit rating prior to loan origination. Rating Index is an index variable that equals one if the rating is AAA, two for AA+/AA/AA- ratings, three for A+/A/A- ratings, four for BBB+/BBB/BBB- ratings, five for BB+/BB/BB- ratings, and six for ratings lower than BB-. Rating Index is set to zero for non-rated firms. Non-Rated Dummy is a dummy variable that equals one if the firm has no credit rating, and zero otherwise. We create rating indicator variables for each credit rating ranging from AAA, AA+ to not-rated. We also create loan indicator dummy variables for the deal purpose and the loan type. All variables are winsorized at the 1% and 99% level. We include only senior bank loans denominated in US$. Financial firms and public administration firms (SICs 6 and 9), and non-u.s. firms are excluded. The sample period is 1982 to All models include industry and year dummies; loan indicator variables are included in some of the models. For each coefficient, the t-statistic (computed using standard errors corrected for heteroskedasticity and clustered at the firm level) is reported in parentheses. Log Spread (1) (2) (3) (4) (5) (6) Cash (-4.10) *** (-5.81) *** (-6.07) *** (-3.52) *** (-5.28) *** (-5.98) *** Log Total Assets (-50.10) *** (-38.57) *** (-38.11) *** (-18.10) *** (-16.64) *** (-16.65) *** Tangibility (-5.71) *** (-4.05) *** (-3.79) *** (-1.61) (-2.48) ** (-2.96) *** Leverage (31.10) *** (14.41) *** (13.42) *** (9.60) *** (8.46) *** (7.15) *** Profitability (-24.27) *** (-29.92) *** (-30.35) *** (-18.67) *** (-19.06) *** (-18.95) *** Rating Index (31.95) *** (17.74) *** Non-Rated Dummy (28.92) *** (15.81) *** Loan Indicator Variables Rating No No Yes No No Yes Deal Purpose No Yes Yes No Yes Yes Loan Type No Yes Yes No Yes Yes Fixed Effects Firm No No No Yes Yes Yes Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 38,618 38,616 38,616 38,618 38,616 38,616 Adjusted R

31 Table 4 Bank Loan Spreads, Cash Holdings, and Access to External Finance The table shows regression estimates of bank loan spreads on cash holdings for rating sub-samples. The dependent variable, Log Spread, is the logarithm of the yield spread on the bank loan. Data on bank loans are obtained from DealScan. We measure loan spreads with the all-in spread drawn calculated as the yield on the bank loan over LIBOR for each dollar drawn including any fees paid to the bank (in basis points). The data on firm characteristics are obtained from Compustat. Cash is cash and short-term investments (che) divided by total assets (at); Total Assets is total assets in US$ million; Tangibility is property, plan, and equipment (ppent) to total assets; Leverage is debt (dltt) divided by total assets); and Profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. All firm-level variables are measured at the end of the fiscal year prior to the loan origination date. Rating information is obtained from Compustat; we use the S&P long-term issuer credit rating prior to loan origination. We create rating indicator variables for each rating ranging from AAA, AA+ to not-rated. We also create loan indicator dummy variables for the deal purpose and the loan type. All variables are winsorized at the 1% and 99% level. We include only senior bank loans denominated in US$. Financial firms and public administration firms (SICs 6 and 9), and non-u.s. firms are excluded. The sample period is 1982 to All models include industry dummies, year dummies, and loan indicator variables. For each coefficient, the t-statistic (computed using standard errors corrected for heteroskedasticity and clustered at the firm level) is reported in parentheses. Investment Grade AAA to BBB- Below Investment Grade Less than BBB- Less than BBB- or Unrated Log Spread Investment Grade AAA to BBB- Below Investment Grade Less than BBB- Less than BBB- or Unrated (1) (2) (3) (4) (5) (6) Cash (0.84) (-2.28) ** (-6.32) *** (0.91) (-3.44) *** (-7.24) *** Log Total Assets (-5.43) *** (-6.50) *** (-38.46) *** (-1.29) (-2.85) *** (-15.93) *** Tangibility (-1.99) ** (0.65) (-2.70) *** (-2.02) ** (2.13) ** (-1.97) ** Leverage (3.39) *** (5.59) *** (13.11) *** (0.02) (2.60) *** (6.81) *** Profitability (-5.76) *** (-9.34) *** (-28.75) *** (-4.50) *** (-7.42) *** (-17.93) *** Loan Indicator Variables Rating Yes Yes Yes Yes Yes Yes Loan Purpose Yes Yes Yes Yes Yes Yes Loan Type Yes Yes Yes Yes Yes Yes Fixed Effects Firm No No No Yes Yes Yes Industry Yes Yes Yes Yes Yes Yes Year Yes Yes Yes Yes Yes Yes Observations 7,479 10,474 31,137 7,479 10,474 31,137 Adjusted R

32 Table 5 Bank Loan Spreads, Cash Holdings, and Value of Cash The table shows regression estimates of bank loan spreads on cash holdings for value-of-cash sub-samples. The subsample Low Value of Cash is comprised of firm-year observations when the value of cash is less than or equal to the sample median, whereas the sub-sample Low Value of Cash includes firm-year observations when the value of cash is greater than the sample median. The data on the value of cash is obtained from Bates, Chang, and Chi (2011). The dependent variable, Log Spread, is the logarithm of the yield spread on the bank loan. Data on bank loans are obtained from DealScan. We measure loan spreads with the all-in spread drawn calculated as the yield on the bank loan over LIBOR for each dollar drawn including any fees paid to the bank (in basis points). The data on firm characteristics are obtained from Compustat. Cash is cash and short-term investments (che) divided by total assets (at); Total Assets is total assets in US$ million; Tangibility is property, plan, and equipment (ppent) to total assets; Leverage is debt (dltt) divided by total assets; and Profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. All firm-level variables are measured at the end of the fiscal year prior to the loan origination date. Rating information is obtained from Compustat; we use the S&P long-term issuer credit rating prior to loan origination. We create rating indicator variables for each rating ranging from AAA, AA+ to not-rated. We also create loan indicator dummy variables for the deal purpose and the loan type. All variables are winsorized at the 1% and 99% level. We include only senior bank loans denominated in US$. Financial firms and public administration firms (SICs 6 and 9), and non-u.s. firms are excluded. The sample period is 1982 to All models include industry dummies, year dummies, and loan indicator variables. For each coefficient, the t- statistic (computed using standard errors corrected for heteroskedasticity and clustered at the firm level) is reported in parentheses. Log Spread Value of Cash Low High Low High (1) (2) (3) (4) Cash (-1.96) ** (-7.56) *** (-3.00) *** (-5.10) *** Log Total Assets (-34.73) *** (-26.31) *** (-12.85) *** (-9.12) *** Tangibility (-1.56) (-4.05) *** (-1.73) * (-2.20) ** Leverage (11.54) *** (8.72) *** (5.63) *** (4.80) *** Profitability (-22.00) *** (-24.62) *** (-10.61) *** (-12.78) *** Loan Indicator Variables Rating Yes Yes Yes Yes Loan Purpose Yes Yes Yes Yes Loan Type Yes Yes Yes Yes Fixed Effects Firm No No Yes Yes Industry Yes Yes Yes Yes Year Yes Yes Yes Yes Observations 20,212 18,404 20,212 18,404 Adjusted R

33 Table 6 Bank Loan Spreads, Cash Holdings, and Credit Market Conditions The table shows regression estimates of bank loan spreads on cash holdings for sub-samples based on credit market conditions. We obtain the net fraction of loan officers tightening credit terms from the Federal Reserve Board s Senior Loan Officer Opinion Survey for the period of 1990 to We use these data to split our sample loans into those made during periods of tight and loose credit conditions. In Panel A, we define tight (loose) conditions based on the median value of the net percentage of loan officers tightening lending standards. In Panel B, we define tight and loose conditions based on the net percentage of loan officers tightening lending standards being above the third quartile and below the first quartile (dropping observations of the second and third quartiles), respectively. The dependent variable, Log Spread, is the logarithm of the yield spread on the bank loan. Data on bank loans are obtained from DealScan. We measure loan spreads with the all-in spread drawn calculated as the yield on the bank loan over LIBOR for each dollar drawn including any fees paid to the bank (in basis points). The data on firm characteristics are obtained from Compustat. Cash is cash and short-term investments (che) divided by total assets (at); Total Assets is total assets in US$ million; Tangibility is property, plan, and equipment (ppent) to total assets; Leverage is debt (dltt) divided by total assets; and Profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. All firm-level variables are measured at the end of the fiscal year prior to the loan origination date. Rating information is obtained from Compustat; we use the S&P long-term issuer credit rating prior to loan origination. We create rating indicator variables for each rating ranging from AAA, AA+ to not-rated. We also create loan indicator dummy variables for the deal purpose and the loan type. All variables are winsorized at the 1% and 99% level. We include only senior bank loans denominated in US$. Financial firms and public administration firms (SICs 6 and 9), and non-u.s. firms are excluded. The sample period is 1990 to All models include industry dummies, year dummies, and loan indicator variables. For each coefficient, the t-statistic (computed using standard errors corrected for heteroskedasticity and clustered at the firm level) is reported in parentheses. Panel A: Median Splits Log Spread Credit Conditions Tight Loose Tight Loose (1) (2) (3) (4) Cash (-6.07) *** (-3.02) *** (-4.00) *** (-3.50) *** Log Total Assets (-24.37) *** (-34.15) *** (-9.12) *** (-11.30) *** Tangibility (-3.77) *** (-1.15) (-2.71) *** (-1.02) Leverage (8.64) *** (11.12) *** (3.59) *** (5.77) *** Profitability (-24.88) *** (-20.75) *** (-12.10) *** (-11.18) *** Loan Indicator Variables Rating Yes Yes Yes Yes Loan Purpose Yes Yes Yes Yes Loan Type Yes Yes Yes Yes Fixed Effects Firm No No Yes Yes Industry Yes Yes Yes Yes Year Yes Yes Yes Yes Observations 17,417 18,594 17,417 18,594 Adjusted R Panel B: Quartile Splits 32

34 Log Spread Credit Conditions Tight Loose Tight Loose (1) (2) (3) (4) Cash (-5.92) *** (-1.56) (-3.43) *** (-2.13) ** Log Total Assets (-17.42) *** (-24.20) *** (-4.73) *** (-5.34) *** Tangibility (-3.41) *** (-0.82) (-2.52) ** (-0.17) Leverage (5.37) *** (8.62) *** (0.67) (2.83) *** Profitability (-19.38) *** (-16.04) *** (-7.11) *** (-5.59) *** Loan Indicator Variables Rating Yes Yes Yes Yes Loan Purpose Yes Yes Yes Yes Loan Type Yes Yes Yes Yes Fixed Effects Firm No No Yes Yes Industry Yes Yes Yes Yes Year Yes Yes Yes Yes Observations 8,874 9,408 8,874 9,408 Adjusted R

35 Table 7 Bank Loan Spreads, Cash Holdings, and Loan Maturity The table shows regression estimates of bank loan spreads on cash holdings for loan maturity sub-samples. The subsample Short-term Loans is comprised of loans with a time-to-maturity of less or equal than the median time-tomaturity of the full sample of 3.8 years. The sub-sample Long-term Loans is comprised of loans with a time-tomaturity greater than the median time-to-maturity of the full sample of 3.8 years. The dependent variable, Log Spread, is the logarithm of the yield spread on the bank loan. Data on bank loans are obtained from DealScan. We measure loan spreads with the all-in spread drawn calculated as the yield on the bank loan over LIBOR for each dollar drawn including any fees paid to the bank (in basis points). The data on firm characteristics are obtained from Compustat. Cash is cash and short-term investments (che) divided by total assets (at); Total Assets is total assets in US$ million; Tangibility is property, plan, and equipment (ppent) to total assets; Leverage is debt (dltt) divided by total assets; and Profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. All firm-level variables are measured at the end of the fiscal year prior to the loan origination date. Rating information is obtained from Compustat; we use the S&P long-term issuer credit rating prior to loan origination. We create rating indicator variables for rating ranging from AAA, AA+ to not-rated. We also create loan indicator dummy variables for the deal purpose and the loan type. All variables are winsorized at the 1% and 99% level. We include only senior bank loans denominated in US$. Financial firms and public administration firms (SICs 6 and 9), and non-u.s. firms are excluded. The sample period is 1982 to All models include industry dummies, year dummies, and loan indicator variables. For each coefficient, the t-statistic (computed using standard errors corrected for heteroskedasticity and clustered at the firm level) is reported in parentheses. Log Spread Loan type Short-term Long-term Short-term Long-term (1) (2) (3) (4) Cash (-7.07) *** (-2.02) ** (-5.89) *** (-2.15) ** Log Total Assets (-32.28) *** (-27.11) *** (-10.75) *** (-11.71) *** Tangibility (-5.37) *** (-0.16) (-3.05) *** (-0.82) Leverage (8.51) *** (13.88) *** (5.88) *** (5.75) *** Profitability (-24.99) *** (-19.09) *** (-13.75) *** (-11.61) *** Loan Indicator Variables Rating Yes Yes Yes Yes Loan Purpose Yes Yes Yes Yes Loan Type Yes Yes Yes Yes Fixed Effects Firm No No Yes Yes Industry Yes Yes Yes Yes Year Yes Yes Yes Yes Observations 18,519 18,491 18,519 18,491 Adjusted R

36 Table 8 Non-price Loan Attributes and Cash Holdings The table shows regression estimates of non-price loan attributes on cash holdings and firm and loan control variables. Data on bank loans are obtained from DealScan. The dependent variables are: Log Number of Lenders, the logarithm of the number of banks involved in the deal; Log Maturity, the logarithm of the time to maturity in years; Secured, a dummy variable that equals one if the loan is secured, and zero otherwise; and Covenant Index, the number of covenants included in the loan (see Appendix A for a description of loan covenants). The data on firm characteristics are obtained from Compustat. Cash is cash and short-term investments (che) divided by total assets (at); Total Assets is total assets in US$ million; Tangibility is property, plan, and equipment (ppent) to total assets; Leverage is debt (dltt) divided by total assets; and Profitability is earnings before interest, tax, depreciation, and amortization (ebitda) divided by total assets. All firm-level variables are measured at the end of the fiscal year prior to the loan origination date. Rating information is obtained from Compustat; we use the S&P long-term issuer credit rating prior to loan origination. We create rating indicator variables for rating ranging from AAA, AA+ to notrated. We also create loan indicator dummy variables for the deal purpose and the loan type. All variables are winsorized at the 1% and 99% level. We include only senior bank loans denominated in US$. Financial firms and public administration firms (SICs 6 and 9), and non-u.s. firms are excluded. The sample period is 1982 to All models include industry dummies, year dummies, and loan indicator variables. For each coefficient, the t-statistic (models 1 and 2) and z-statistic (models 3 and 4) (computed using standard errors corrected for heteroskedasticity and clustered at the firm level) is reported in parentheses. Log Number of Log Maturity Secured Covenant Index Lenders (1) (2) (3) (4) Cash (-3.01) *** (2.76) *** (-4.75) *** (-5.46) *** Log Total Assets (19.63) *** (7.24) *** (-23.15) *** (-3.25) *** Tangibility (-0.71) (-0.58) (8.29) *** (2.48) ** Leverage (-0.75) (1.95) * (-3.62) *** (-2.33) ** Profitability (6.51) *** (4.73) *** (-13.33) *** (7.21) *** Loan Indicator Variables Rating Yes Yes Yes Yes Loan Purpose Yes Yes Yes Yes Loan Type Yes Yes Yes Yes Fixed Effects Firm Yes Yes No No Industry Yes Yes Yes Yes Year Yes Yes Yes Yes Estimation OLS OLS Probit Poisson Observations 38,597 37,007 27,005 38,616 Adjusted R Pseudo R ,658 35