How and why do small firms manage interest rate risk? a

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1 How and why do small firms manage interest rate risk? a James Vickery * Banking Studies, Federal Reserve Bank of New York, 33 Liberty Street, New York, NY, 10045, USA Received 14 October 2005; received in revised format 26 July 2006 accepted 6 September 2006 Abstract Although small firms are particularly sensitive to interest rates and other shocks, empirical work on corporate risk management has focused instead on large public companies. This paper studies fixed-rate and adjustable-rate loans to see how small firms manage their exposure to interest rate risk. Credit-constrained firms are found to match significantly more often with fixed-rate loans, consistent with prior research that shows the supply of credit shrinks during periods of rising interest rates. Banks originate a higher share of adjustable-rate loans than other lenders, ameliorating maturity mismatch and exposure to the lending channel of monetary policy. Time-series patterns in the fixedrate share are consistent with recent evidence on debt market timing. JEL Classifications: G21; G30; G32 Keywords: Risk management; Interest rate risk; Loans; Small firms a This paper is based on part of my PhD thesis at MIT. I thank my thesis advisers Ricardo Caballero, David Scharfstein and Robert Townsend for their generous guidance, as well as Allen Berger, Olivier Blanchard, Bengt Holmstrom, Don Morgan, Jeremy Stein, David Smith, John Wolken and seminar participants at MIT, London Business School, Notre Dame Mendoza, Columbia Business School, Chicago GSB, Yale SOM, Harvard Business School, New York Fed, Fed Board of Governors, NYU Stern and the Richmond Fed for their insightful comments. The views expressed in this paper are the author s and should not be attributed to the Federal Reserve Bank of New York or the Federal Reserve System. * Tel.: ; fax: address: james.vickery@ny.frb.org

2 1. Introduction Empirical research on corporate risk management has generally focused on large public companies, most often studying firms use of financial derivatives. 1 This paper instead examines fixed-rate and adjustable-rate commercial loan contracts to study how small firms adjust their exposure to interest rate risk. Small and medium-size firms are important to the US economy; firms with fewer than five hundred employees generate half of non-farm private gross domestic product 2. Small firms are often financially constrained, which is considered a key theoretical rationale for why firms engage in risk management (e.g., Froot, Scharfstein, and Stein, 1993, hereafter FSS). Moreover, work on the credit channel of monetary policy shows directly that small firms are sensitive to interest rate shocks (e.g., Gertler and Gilchrist, 1994; Ehrmann 2000). Although small and medium-size firms make little use of derivatives, they do borrow extensively from financial institutions. In some cases the interest rate on these loans is fixed, while in other cases it adjusts with market interest rates. I study this variation in fixed-versus-adjustable outcomes as a window into how small firms adjust their exposure to interest rate risk. I firstly examine the relationship between fixed-versus-adjustable outcomes and firm financial constraints. FSS show that optimal risk management policy should aim to generate cash in states of nature in which an additional dollar of internal funds is most valuable. Empirically, research 1 Cross-industry studies of the determinants of firms use of derivatives include Purnanandam (2007), Covitz and Sharpe (2005), Lin and Smith (2003), Rogers (2002), Graham and Rogers (2002), Géczy, Minton, and Schrand (1997), Mian (1996) and Fenn, Post, and Sharpe (1996). Some studies focus on particular types of derivatives, e.g., Géczy, Minton, and Schrand (1997) concentrate on foreign currency derivatives, while Covitz and Sharpe study interest rate contracts. Allayanis and Weston (1998) study the relationship between derivatives use and firm value. Guay (1999) examines how derivatives hedging affects firm risk. Guay and Kothari (2003) examine the quantitative relevance of firms derivatives holdings. Other papers take an industry-specific approach. Faulkender (2005) studies chemicals firms, Haushalter (2000) focuses on oil and gas, and Tufano (1996) studies gold mining firms. The literature is also broadening to consider other dimensions of risk management. For example, Bartram, Brown, and Minton (2006) and Pantzalis, Simkins, and Laux (2001) present evidence that firms use operational hedging (e.g., matching foreign sales to foreign production) to manage exchange rate risk. Petersen and Thiagarajan (2000) study two gold mining firms that achieve a similar reduction in exposure to gold price risk, one using derivatives, and the other using a combination of operating, financial, and accounting decisions. 2 Source is the Small Business Administration. See 1

3 on the credit channel of monetary policy finds that the availability of finance to bank dependent firms becomes scarcer relative to investment opportunities during periods of rising interest rates, causing lower investment and output amongst credit-constrained firms. (Section 2 reviews this literature in detail.) Correspondingly, I test the hypothesis that credit-constrained firms match with fixed-rate debt, thereby maximizing net cash flows during periods of rising interest rates when the shadow value of internal funds is high. 3 A related implication of FSS is that risk management outcomes should reflect variation across firms in the correlation between interest rates and pre-interest firm cash flows. In sectors in which industry output or cash flows covary positively with interest rates, firms have a partial or complete natural hedge against interest rate risk, and thus fixed-rate debt is less likely to be optimal. I test the hypothesis that the share of adjustable-rate loans is higher in such industries, using an estimated index of industry interest rate procyclicality. Although plausible, there are several reasons for why these two FSS hedging hypotheses might fail to hold empirically. One alternative hypothesis is that fixed-versus-adjustable outcomes are set by the firm s banks (e.g., perhaps the firm s relationship lender originates only fixed-rate loans or only adjustable-rate loans, so the firm does not have a choice between contracts). Another possibility is that small firms prefer to amplify volatility in the shadow value of internal funds. Adam, Dasgupta and Titman (2007) presents a model in which such behavior could be optimal in an imperfectly competitive industry setting. A third possibility is that small firms are financially unsophisticated or the fixed-versus-adjustable margin is unimportant, so no systematic correlations exist in the data. Using data from the Federal Reserve Board s Survey of Small Business Finance (SBF) I find evidence consistent with the two FSS hedging hypotheses. First, as predicted, matching with a fixed- 3 In the FSS framework, a non-credit-constrained firm would be simply indifferent between fixed and adjustable-rate loans, in line with the Modigliani and Miller theorem. However, I argue in the body of the paper that when lenders are also exposed to interest rate risk (consistent with a large body of empirical evidence) unconstrained firms could strictly prefer adjustable-rate debt. 2

4 rate loan is positively correlated with several different proxies for financial constraints. Fixed-rate debt is most popular amongst smaller firms, younger firms, firms switching from their primary lender, and firms with low cash flows (measured by current profits) or high investment opportunities (measured by sales growth). These results are economically as well as statistically significant. For example young, small firms in the SBF are about twice as likely to match with fixed-rate debt as old, large firms (69% compared with 38%). Second, fixed-rate debt is less prevalent in two-digit standard industrial classification (SIC) sectors in which industry output comoves most positively with interest rates, and thus when firms have a partial natural hedge against interest rate risk. Next, I study how lender characteristics influence fixed-versus-adjustable outcomes. Several theoretical papers on loan contract design and bank risk management suggest that the share of interest rate risk in a loan borne by the borrower should depend in part on the lender s interest rate risk profile (Arvan and Brueckner, 1986; Edelstein and Urosevic, 2003; Froot and Stein, 1998). These models predict that lenders who are exposed ex ante to rising interest rates optimally originate a smaller share of fixed-rate loans, because the present value of such loans declines by comparison with adjustable-rate loans when interest rates rise. I test this prediction by comparing bank loans with loans from nonbank institutions. Banks are exposed to rising interest rates in two ways that are specifically tied to their reliance on deposit finance. First, banks are affected by the lending channel of monetary policy (Stein, 1998; Kashyap and Stein, 2000; Ashcraft, 2006), in which tight monetary policy reduces the insured deposit base, raising banks cost of funds. Second, banks are subject to maturity mismatch, in which demand deposits and short-term time deposits fund long-duration assets such as mortgages. Correspondingly, I test the hypothesis that bank loans are more likely to involve an adjustable interest rate than loans from other lender types. This lender risk management hypothesis receives strong support in the data. I find that a loan from a commercial or savings bank is 14 3

5 percentage points more likely to involve an adjustable interest rate compared with a loan from a nonbank financial institution. Because many small bank-dependent enterprises are closely held and owner-managed, it also seems plausible that owner characteristics play a significant role in fixed-versus-adjustable outcomes. Somewhat surprisingly, I find that variables such as the owner s age and the concentration of ownership are nearly uncorrelated with the loan type chosen. I do find some evidence that adjustable-rate loans are more common amongst firms with wealthier owners, consistent with the view that risk aversion is declining in wealth. The last part of the paper studies time-series patterns in the aggregate share of fixed-rate loans. Using data from the Survey of Terms of Business Lending (STBL), I construct and study a 28- year quarterly time series of the fixed-rate share for business loans originated by commercial banks. I find that high real interest rates and a steep yield curve are correlated with a lower proportion of fixed-rate loans, consistent with previous work on debt market timing by Faulkender (2005) and Baker, Greenwood, and Wurgler (2003). To my knowledge, this paper is the first to show these results also extend to small, bank dependent firms. Implications of these findings for theoretical explanations of market timing patterns are discussed. The rest of this paper proceeds as follows. Section 2 reviews existing literature on the sensitivity of small firms to interest rate shocks. Section 3 describes the Survey of Small Business Finance and discusses the measures of financial constraints I use. Section 4 presents cross-sectional empirical evidence from the SBF. Section 5 presents time-series evidence on the share of fixed-rate commercial loans. Section 6 presents cross-sectional evidence from the STBL. Section 7 concludes. 2. Small firms and interest rate shocks Research on the credit channel of monetary policy argues that higher interest rates lead to a decline in the availability of internal and external finance relative to investment opportunities, resulting in lower investment and output amongst credit-constrained firms. This channel is considered to be most 4

6 important for small, informationally opaque, bank-dependent firms, which are most likely to be constrained in their access to finance. Consistent with this view, Gertler and Gilchrist (1994) show that small US manufacturing firms are disproportionately affected during periods of rising interest rates. Small firms reduce external borrowing, shed inventories, and experience sharp falls in sales growth. In contrast, larger firms maintain debt levels, increase inventories, and experience a substantially smaller decline in sales growth. Ehrmann (2000) finds similar evidence using data on German firms. The broad credit channel can be decomposed into balance sheet and lending effects. The balance sheet channel is that higher interest rates weaken firm balance sheets, partially by reducing expected future profits and partially because small firms have long-lived physical assets but mainly short term or adjustable-rate liabilities (bank loans, credit lines, etc.). This maturity mismatch implies that net current cash flows decline when interest rates increase, and also that the present value of assets declines relative to the present value of liabilities. The latter makes the firm less creditworthy, reducing its ability to raise external finance. Consistent with the balance sheet channel, Bernanke and Gertler (1995) show that firms balance sheet strength, proxied by interest coverage, declines during periods of high interest rates. Greenwood (2003) finds that firm investment is most sensitive to interest rates when maturity mismatch is high and that this relation is most pronounced for financially constrained firms. Ashcraft and Campello (2007) find commercial lending is more sensitive to monetary policy in geographic regions where firm balance sheets are weak. The lending channel is that contractionary monetary policy reduces the ability of banks to lend by shrinking the supply of bank deposits. Stein (1998) presents a formal model of the lending channel. Kashyap, Stein, and Wilcox (1993) show the bank lending as a share of total debt finance falls during periods of contractionary monetary policy. Kashyap and Stein (1995, 2000) present further empirical evidence on the lending channel for the US. 5

7 For the purposes of this paper, the key implication of both the lending and balance sheet channels is that for the average small firm, the supply of internal finance plus external finance declines relative to investment opportunities during periods of rising interest rates. This fall in credit availability has no real effects if credit constraints are not binding. For firms that are creditconstrained, such a shrinkage in the availability of finance induced by rising interest rates reduces investment and output and raises the shadow value of internal funds. Correspondingly, I test the hypothesis that credit-constrained firms match with fixed-rate loans instead of adjustable-rate loans, maximizing cash flows during periods of rising interest rates, when internal funds are most valuable. What about financially unconstrained firms? In FSS, such firms would be indifferent between the two loan types, in line with the Modigliani and Miller theorem. However, a substantial amount of evidence suggests lenders, too, are exposed to interest rate risk, via the bank lending channel as well as maturity mismatch (Sierra and Yaeger, 2004). In such an environment, the optimal contract involves a financially unconstrained firm bearing the interest rate risk of the loan (Arvan and Brueckner, 1986; Edelstein and Urosevic, 2003; Froot and Stein, 1998). In subsection 4.2 I present direct evidence that lender types exposed to rising interest rates originate a higher fraction of adjustable-rate loans, consistent with this view. Alternatively, unconstrained firms could match with adjustable-rate debt to signal firm quality, in line with the model of Guedes and Thompson (1995). Even without these effects, it still holds that unconstrained firms have no explicit incentive to protect cash flows against rising interest rates, unlike constrained firms. 3. Survey of Small Business Finance data The SBF is a cross-sectional survey conducted approximately every five years by the Federal Reserve Board, containing detailed microeconomic information on firm characteristics and financing behavior for a representative sample of US small and medium-size enterprises, defined as firms with fewer than five hundred employees at the end of the reference year. The SBF provides detailed information on the firm s most recent loan, including the size of the loan, interest rate and fees paid, 6

8 category of loan (e.g., line of credit, business mortgage, etc.), maturity, and collateral posted against the loan. Most important for this paper, the SBF also records whether the most recent loan was issued at a fixed or adjustable interest rate. I pool data from the 1987, 1993 and 1998 SBF surveys, for a total of 11,422 firm observations. Of these, 4,000 firms had received a loan within three years of the end of the survey reference year. (I use three years as a cutoff because the 1993 and 1998 surveys do not collect information on the most recent loan if it was originated more than three years ago.) I then drop firms when data are missing for one or more key variables: total assets, firm age, profits, total debt, sales growth, years with primary lender, or the amount, maturity or fixed-versus-adjustable status of the most recent loan. This yields a final sample of 3248 loans matched with firm characteristics. (The SBF is not a panel dataset, so each of these observations relates to a different firm.) [INSERT TABLE 1 NEAR HERE] Table 1 presents descriptive statistics for this final sample of 3248 observations. Because the survey oversamples large firms and minority-owned firms, I present weighted averages based on the SBF sampling weights, as well as unweighted statistics for comparison. Seventy percent of unweighted observations are S- or C-corporations. Average assets are $939 thousand ($2.7 million on an unweighted basis). Comparing the last two columns of the table, firms in the final sample are of similar age although substantially larger than the overall SBF sample. Relatively fewer observations in the final sample are from the 1998 survey. This is partially due to a change in survey design; in 1998 the survey does not consider renewals of existing credit lines to be new loans. Thirty-eight percent of loans are lines of credit (43% on an unweighted basis). The distinction between credit lines and other loan types is important for the fixed-versus-adjustable dimension of the loan contract. A fixed-rate credit line creates a potential arbitrage opportunity, because any change in market interest rates affects the wedge between the market rate and the rate on the commitment. For example, if interest rates rise sharply, the firm could aggressively draw down 7

9 Table 4 Interest rate sensitivity of debt fundings from banks and nonbanks. Coefficients in the table represent the change in probability that the debt contract involves a fixed interest rate if funds are provided by a bank rather than the comparison source of finance listed. The estimate in Column 2 is from Table 1 of Faulkender (2005). Because this is a table of summary statistics, no standard error is available. Estimates in Column 3 are from Table 2 of Vickery (2007). ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. Feature of the dataset Table 3 Faulkender (2005) Vickery (2007) (1) (2) (3) Type of debt contract Loans to small and medium-size firms Debt fundings by public firms Residential mortgage originations Type of banks Comparison source of finance Commercial banks and savings banks Loan is from nonbank financial institution Commercial banks Funding is a corporate bond Commercial banks and savings banks Loan is from a finance company Change in probability of fixed-rate loan if source of loan is a bank (0.042) *** (n/a) (Commercial (0.037) *** bank dummy) (Savings bank (0.020) *** dummy) 44

10 Table 5 Industry interest rate sensitivity and the share of fixed-rate loans. The dependent variable equals one if the firm s most recent loan is a fixed-rate loan, and equals zero if it is an adjustable-rate loan. A weighted probit regression is estimated, using data from the 1987, 1993, and 1998 Survey of Small Business Finance. Coefficients are normalized to display marginal effects. Standard errors in parentheses are adjusted for heteroskedasticity. Covariates are the same as the corresponding columns of Table 3, except that Columns 1-4 replace the two-digit standard industry classification (SIC) dummies with a variable that measures industry interest rate sensitivity. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. Explanatory variable Industry interest rate sensitivity (two-digit SIC) Full sample Dependent variable equals one if fixed-rate loan, zero otherwise Exclude credit lines Full sample Exclude credit lines (1) (2) (3) (4) (5) (0.623) ** (0.548) ** (0.665) * (0.573) ** Full sample Log(assets) x industry sensitivity (0.357) Endogenous loan and lender controls Two digit SIC industry dummies Other covariates No No Yes Yes No No No No No Yes As Column 1 of Table 3 As Column 2 of Table 3 As Column 3 of Table 3 As Column 4 of Table 3 As Column 1 of Table 3 Pseudo-r Number of observations 3,248 1,867 3,248 1,867 3,248 45

11 Table 6 Sensitivity of results to inclusion of owner wealth controls. The dependent variable equals one if the firm s most recent loan is a fixed-rate loan, and equals zero if it is an adjustable-rate loan. Regressions are based on data from the 1998 Survey of Small Business Finance only. A weighted probit regression is estimated. Coefficients are normalized to display marginal effects. Standard errors in parentheses are adjusted for heteroskedasticity. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. Explanatory variable Endogenous loan and lender controls excluded No owner Owner wealth wealth Dependent variable equals one if fixed-rate loan, zero otherwise Endogenous loan and lender controls included No owner Owner wealth wealth Parsimonious model. Endogenous loan and lender controls excluded No owner Owner Owner wealth wealth wealth (1) (2) (3) (4) (5) (6) (7) Firm size and owner wealth Log(assets) (0.013) ** (0.014) * (0.015) *** (0.016) *** (0.011) *** (0.013) ** (0.013) ** Log(total owner wealth) (0.015) (0.016) (0.015) * (0.016) * Owner firm equity / total wealth (0.004) Other controls Same as Column 1 of Table 3 Same as Column 3 of Table 3 Log(firm age + 1) Book debt / book assets Dummies for loan type One-digit SIC dummies Pseudo-r Number of observations

12 Table 7 Robustness checks for the Survey of Small Business Finance regressions. The table shows p-values from hypothesis tests on interaction terms added to the probit model from Column 1 of Table 3. These terms interact each measure of credit constraints in turn with various dummy variables as listed below. Contents of the cell are p- values from hypothesis test that the coefficients on the interaction term or terms are equal to zero. ***, **, and * represent statistical significance at the 1%, 5%, and 10% levels, respectively. Explanatory variable Dummy, firm not recently solicited by financial institutions Interact measure of credit constraints with Proxies that lender has captured firm Dummy for single lending relationship Dummy for HHI>1,800 Lender type dummies (lender is bank, lender is nonfinancial institution) Loan type dummies (credit line, vehicle loan, etc.) p-value, F-test for interaction term on: Log(assets) ** Log(firm age + 1) Return on assets Sales growth * Switch dummy ** * Log(years with primary institution + 1) Number of financial institutions

13 Table 8 Time-series determinants of the share of fixed-rate loans. Regressions are based on quarterly time-series data on business loans from commercial banks drawn from the Survey of Terms of Business Lending. The sample period is 1977:Q1 to 2004:Q3. Results presented below are long-run coefficients from a linear regression that includes a lagged dependent variable [i.e., the regression is of the form y t = a.x t + by t-1 + e t, and thus the long-run coefficient on x t is a/(1-b)]. Standard errors on the long-run coefficients are calculated using the procedure outlined in Bewley (1979), and are also adjusted for heteroskedasticity. ***, **, and * represents significance at 1%, 5%, and 10% levels, respectively. Explanatory variable Dependent variable = fixed-rate loans / [fixed-rate loans + adjustable-rate loans] Volumeweighted fixedrate share Value-weighted fixed-rate share Volumeweighted fixedrate share (1) (2) (3) (4) Value-weighted fixed-rate share Debt market variables Real federal funds rate (0.005) *** (0.008) *** (0.005) *** (0.005) *** 10-1 year interest rate spread (0.018) *** (0.014) * (0.015) *** (0.014) *** Inflation rate (0.010) (0.010) * (0.010) (0.010) Controls log(real loan size) (0.184) ** (0.019) (0.216) *** (0.186) *** log(maturity) (0.241) (0.076) (0.264) *** (0.158) Time trend (measured in quarters) (0.004) (0.003) *** (0.003) *** (0.003) Lagged dependent variable (0.087) *** (0.077) (0.080) *** (0.071) *** Sample All loans All loans Loans smaller than $250,000 Loans smaller than $250,000 Number of observations r Autocorrelation test (p-value)

14 Table 9 Time-series determinants of the share of fixed-rate loans, based on Survey of Small Business Finance (SBF) data. The empirical specifications are the same as the corresponding regressions in Table 3, except that the calendar year dummies have been replaced with the three debt market variables listed below. The dependent variable equals one if the firm s most recent loan is a fixed-rate loan, and equals zero if it is an adjustable-rate loan. A weighted probit regression is estimated. Coefficients are normalized to display marginal effects. Standard errors in parentheses are adjusted for heteroskedasticity. ***, **, and * represents significance at 1%, 5%, and 10% levels, respectively. Explanatory variable Dependent variable equals one if fixed-rate loan, zero otherwise Full sample Exclude credit lines Full sample Exclude credit lines (1) (2) (3) (4) Real federal funds rate (0.012) ** (0.012) (0.012) * (0.014) 10-1 year yield spread (0.018) *** (0.019) *** (0.019) ** (0.019) *** Inflation rate (0.016) (0.016) (0.017) (0.017) Endogenous loan and lender controls Other covariates No No Yes Yes As Column 1 of Table 3 As Column 2 of Table 3 As Column 3 of Table 3 As Column 4 of Table 3 Pseudo-r Number of observations 3,248 1,867 3,248 1,867 49

15 Table 10 Determinants of whether the loan involves a fixed interest rate, based on panel data from the Survey of Terms of Business Lending (STBL). The dependent variable equals one if the firm s most recent loan is a fixed-rate loan, and equals zero if it is an adjustable-rate loan. A linear probability model is estimated using least squares. Standard errors in parentheses are adjusted for heteroskedasticity, and clustered by bank. Bank fixed effects are also included in column 1. The sample period is from 1997:Q1 to 2004:Q3. Both regressions also include time dummy variables for each calendar quarter during the sample period, absorbing all time-series variation in the fixed-rate share. Explanatory variable Dependent variable equals one if loan does not reprice before maturity, zero otherwise Bank fixed effects Pooled regression (1) (2) Loan variables Log(1 + loan amount) (0.007) *** (0.012) *** Loan is collateralized (1=yes, 0=no) (0.023) ** (0.041) ** Maturity (in years) (0.021) ** (0.039) Risk rating, omitted category is rating = 3 (average risk) =1 (lowest risk) (0.043) ** (0.065) =2 (low risk) (0.020) (0.062) =4 (high risk) (0.020) (0.069) =5 (highest risk) (0.026) * (0.069) Borrower not classified (0.044) (0.052) *** Branch does not use risk ratings (0.042) * (0.091) Bank variables log(1+bank assets) (0.014) *** Liquid assets / total bank assets (0.328) Bank fixed effects Yes No Number of of observations 40,658 40,657 r 2 : Within r 2 : Between r 2 : Overall