How much is too much? Debt Capacity and Financial Flexibility

Transcription

1 How much is too much? Debt Capacity and Financial Flexibility Dieter Hess and Philipp Immenkötter October 2012 Abstract This paper explores empirically the link between corporate financing decisions and the debt capacity of a firm. We estimate firm-year specific debt capacities based on target ratings and measure financial flexibility as the firm s unused debt capacity which we call the debt buffer. This measure depicts the firm s temporal access to external debt funds and is a strong predictor for financing decisions. The distribution of financing activities shows that firms target on preserving financial flexibility provided by the debt buffer. Firms issue debt if their debt buffer is large enough to settle the need for external capital. Equity issuers abstain from debt because an increase in leverage would exceed the estimated debt capacity resulting in downgrades of creditworthiness. In contrast to measuring financial flexibility as a function of cash holdings, we use the availability of external debt funds to circumvent agency problems associated with free cash flow. Our study provides new implications for prior evidence on capital structure theories. Keywords: corporate finance; credit ratings; capital structure; JEL classification: G31; G32 We are grateful for valuable comments from Darius Miller, Nagpurnanand Prabhala, Jens Martin, Oliver Pucker, Kristian Dicke, and Thomas Hartmann-Wendels. This paper was part of the Doctoral Student Consortium at the FMA Europe 2012 and was presented and benefited from the FMA European Conference 2012, Istanbul (Turkey), the DGF Conference 2012, Hannover (Germany), the Corporate Finance Research Seminar, Cologne, and Department of Bank Management Research Seminar, Cologne (Germany). University of Cologne, Corporate Finance Seminar and CFR Cologne, [email protected], tel Corresponding author, University of Cologne, Corporate Finance Seminar, [email protected], tel

2 Financial flexibility is a key aspect in the practice of corporate finance. Documented in several surveys, it outranks traditional factors such as tax benefits, default costs, and information asymmetries in their importance for capital structure decisions (Graham and Harvey (2001), Brounen, de Jong, and Koedijk (2004), and Bancel and Mittoo (2004)). A firm is considered to be financially flexible if it is unconstrained in its issuance decision, sufficiently liquid to react to cash flow shocks and able to timely pursue investment opportunities due to an easy access to funds. From an empirical point of view, the measurement of financial flexibility is challenging. A common approach is to evaluate the value of cash holdings as cash provides a buffer for unexpected cash outflows (Faulkender and Wang (2006) and Gamba and Triantis (2008)). However, there is a dark side to cash holdings. As management starts to pile up cash, agency problems arise due to shareholders limited monitoring ability towards the use of funds (Jensen (1986)). As well, cash holdings provide rather short-term than long-term liquidity and are often insufficient for large investment projects. To explore a new way of measuring financial flexibility that circumvents these problems, we focus on the company s ability to access external capital markets and quantify the degree of financial flexibility with its unused debt capacity. In this paper, we introduce a novel measure of financial flexibility. We measure financial flexibility as the company s unused debt capacity, called the debt buffer, which corresponds to the difference between its estimated debt capacity and its debt ratio. The debt buffer provides financial flexibility as it denotes the amount of debt a firm can issue without facing constraints and severely higher cost of capital. These easily accessible funds contribute to the improvement of short-term and long-term liquidity so that a firm can react to changing market conditions and productivity shocks. To analyze financing decisions and financial flexibility, we calculate the debt buffer after three different financing scenarios. First, we assume that the firm financed all investments in the past fiscal year exclusively with debt, secondly, exclusively with equity and last we calculate the debt buffer after the observed funding. These three versions of the debt buffer shed light on the possible consequences of 2

3 each financing scenario. In order to obtain the debt buffer, we first need to explore how much debt would be too much for a company by estimating its debt capacity. Following de Jong, Verbeek, and Verwijmeren (2011), we implement the debt capacity as a firm-year specific threshold for the debt ratio which is chosen in dependence of the likelihood of losing a designated target rating. The debt capacity is a function of credit ratings because a downgrade in credit worthiness leads to a shock to cost of capital which firm intend to avoid (Kisgen (2006)). We consider two different specifications of target ratings. Since the shock to cost of capital is largest for downgrades from investment-grade to speculative-grade ratings, the lowest investment-grade rating BBB serves as lower boundary for credit ratings. 1 A firm has reached its debt capacity if its debt ratio would trigger the loss of an investment-grade rating. In an alternative specification of the debt capacity, we implement target ratings that are close to the firm s current rating. An announcement of being downgraded even within the investment-grade segment is generally considered as bad news and hence a company intends to avoid such events (Kisgen (2009)). This setting puts up tighter restrictions on capital issuance decision since firms always operate close to their lower rating boundary. Our main finding is that we identify a direct link between firm s funding decision and its unused debt capacity. The debt buffer which indicates the firm s financial situation after possible financing scenarios serves as determinant for the choice between equity and debt. If equity issuing firms would have chosen debt to settle their financing deficit, their debt buffer would be substantially smaller than of those firms that have actually chosen debt. In fact, these firms would exceed their debt capacity and face high downgrading risk and financial constraints in the near future. Examining a sample of debt issuers, we find that leverage increasing financing activities are only realized if the unused debt capacity is large enough to cover the need for external funds. In our sample, the debt buffer of debt issuers amounts 17.2% of firm s assets which enables them to issue even more debt in the future. In contrast, 1 We use Standard&Poor s rating scheme, which classifies ratings from AAA to BBB as investment-grade and ratings equal to or below BB as speculative-grade. 3

4 if equity issuers would have chosen debt, they would be overleveraged due to a debt buffer of -1.5%. The comparison of reductions in debt outstanding and equity repurchases as well as capital substitutions exhibits a similar pattern. These systematic differences between firms that increase or decrease leverage show that the debt buffer serves as measure of financial flexibility. To provide further evidence on our hypothesis, we analyze the relationship between the debt buffer and future financing decisions. Firms operating at the end of their fiscal year close to or beyond their debt capacity are in the following period more likely to reduce leverage by issuing equity or repurchasing debt. Especially the frequency of debt repurchases peaks for firms with negative debt buffers indicating the importance of the debt capacity as upper boundary for the debt ratio. Moreover, debt issues are most common for firms which can afford a increase due to their large debt buffer. However, the frequency of debt issues for firms with small or negative debt buffers is not negligible. As noted in Denis and McKeon (2012), firms intentionally increase leverage even after substantial debt issues. These firms are either constrained in their ability to reduce leverage or leverage targets are less important for their financing policy as noted by Fama and French (2012). Firms with a large debt buffer exhibit a higher frequency of increasing their debt ratio in the following fiscal year. These findings indicate that on the one hand firms use their flexibility provided by the debt buffer to pursue investment but on the other firms with insufficiently large debt buffers take up actions in the near future to restore their financial flexibility. Our measure of financial flexibility yields high explanatory power for firms financing decisions even after controlling for factors that are commonly associated with capital structure decisions. Prior studies relate changes in the firm s debt ratio to firm characteristics which serve as proxies for the influence of the most predominant capital structure theories (Rajan and Zingales (1995), Frank and Goyal (2009)). Introducing the debt buffer to these regression models increases the explained variation from 23.9% to 27.3% reflecting an economical significant improvement. Firms use the information provided by the unused debt capacity 4

5 to decide on additional debt issues. This paper brings about firm-year specific debt capacity estimates that correspond to a debt ratio threshold. The debt capacity of a firm depends on the target of the financing strategy and firm characteristics. If firms intend to maintain an investment-grade rating, BBB rated firms face the toughest constraints because on average their debt ratio must not exceed 44.1% while firms with a rating of AA can use on average 73.4% of debt 2. In a second specification of the debt capacity, we assume that firms target on staying in their respective rating category and find that the debt capacity decreases with increasing ratings. On average, firms with the highest speculative grade rating (BB) can hold up to 54.9% of debt while firms in the highest investment grade rating category AAA can afford only 20.9% of debt on average. For all but one rating category, the difference between the debt capacity and the debt ratio is statistically and economically significant indicating that unused debt capacities yield important information on financing decisions. Our study contributes to the literature of capital structure theories is several ways. The dynamic trade-off model by Fischer, Heinkel, and Zechner (1989) shows that balancing costs and benefits of debt financing results under the assumption of costly adjustment in a range of optimal debt ratios that maximize the levered firm value. The upper end of this range depicts the firm s debt capacity as it serves as upper boundary for the optimal debt ratios. Kisgen (2006) introduces credit ratings into a trade-off model and shows that downgrades due to increasing leverage result in negative shocks to the levered firm value. The implementation of the debt capacity in this paper makes use of variables such as firm size, profitability and liquidity that proxy default risk. Hence, our methodology provides empirical estimates that can either serve as upper boundary for the range of optimal debt ratios or correspond to the debt ratios that trigger the downgrade in Kisgen s model. In another trade-off model, DeAngelo, DeAngelo, and Whited (2011) show that financial flexibility is the firm s option 2 We follow Baker and Wurgler (2002) and define the debt ratio as financial and non-financial liabilities over total assets in market values. Further specifications with the debt ratio in book values lead to similar results. 5

6 to deviate from target leverage by issuing transitory debt to pursue new investments. This option comes at the opportunity costs of being unable to borrow in the future because the amount that firms can borrow is constrained by the firm s debt capacity. In contrast to our approach, lenders ration credit due to adverse selection costs and assets substitution problems which constrains the company-specific credit supply. We confirm these results by explicitly estimating the size of the possible future debt issues. Our debt buffer indicates whether and to which extend this option exists. Empirical tests of the trade-off theory often involve target-adjustment regressions to estimate the speed of the adjustment behavior of the firm s observed debt ratio to its target debt ratio. Our methodology of using the distance to the debt capacity has some distinct differences and advantages over the target-adjustment approach. First of all, we estimate a boundary that the debt ratio should not exceed. In contrast to target debt ratios, the choice of the boundary is not based on predictions of a linear regression of historical values but on evidence taken from the surveys mentioned above. The surveys represent managers motivation for funding decisions and enter our data as exogenous information. Secondly, the problems of mechanical mean reversion (Chang and Dasgupta (2009)) and bounded dependent variables do not arise because we do not need to assume that debt ratios revert or converge to a given target. Fama and French (2012) indicate that adjustment behavior is not a first-order consideration for funding decisions, but the surveys strengthen the importance of the debt capacity. Our empirical results show the importance of the unused debt capacity for funding decisions even after controlling for target adjustment behavior. Our study contributes to the interpretation of the pecking order theory (Myers and Majluf (1984)) in which firms have a hierarchical order of preferences for financial sources driven by adverse selection. The debt capacity plays an important role as it indicates when firms should switch from debt to equity financing. While the traditional test of the pecking order by Frank and Goyal (2003) does not account for the debt capacity, Lemmon and Zender (2010) introduce a debt capacity measure into such a pecking order test. They measure 6

7 firms debt capacities through a sample split that depends on the firms bond market access. Our debt capacity estimates are build on a more general approach because we account for firm characteristics and require the bond market access so that financing decisions are not driven by the availability of external capital. Leary and Roberts (2010) and de Jong, Verbeek, and Verwijmeren (2011) test hypotheses on the pecking order theory and estimate the debt capacity in different specifications. We extend their methodologies by introducing further debt capacity specifications and using the difference between the debt buffer and the debt ratio as measure for financial flexibility to explain capital issues and repurchases. To answer the question how much is too much? and to show the importance of the debt buffer for financing decisions we proceed as follows: After deriving firm-specific measurements for the debt capacity as a function of target ratings and firm characteristics in section I, we use the debt capacity estimates to measure financial flexibility as the debt buffer that depicts the unused debt capacity and show that the debt buffer has explanatory power for changes in the debt ratio in section II. The last section concludes and all tables are reported in the appendix. I Estimating the debt capacity The empirical measurement of the debt capacity is of special relevance to examine how much debt a firm can bear. The survey of Graham and Harvey (2001) reveals that financing strategies are driven by the ambition of preserving unused debt capacity because unused debt capacities provide firms with the option of financing future investments with debt. Additionally, the survey indicates that maintaining target credit ratings is a primary concern for CFOs. Credit ratings have information content on the quality of the firm beyond publicly available information as rating agencies are specialized in information gathering. Moreover, cost of capital are directly influenced by ratings because ratings predict default probabilities. Kisgen (2006) documents a close relation between credit ratings and capital structure 7

8 decisions because a change in the credit rating results in a shock to cost of capital. Building on these empirical findings, we define the debt capacity of a firm as the critical debt ratio that causes a firm to lose its target rating. Since target ratings are not observable, we will discuss different measures of the debt capacity that are based on two different target rating definitions. Our first measure is based on the boundary between investment-grade and speculative-grade ratings. Due to the reduced liquidity of securities in the speculative-grade category, the jump in cost of capital from an investment-grade to a speculative-grade rating is larger than between all other ratings (Kisgen and Strahan (2010)). Consequently, we use the smallest investmentgrade credit rating (BBB) as a target rating for investment-grade firms. In our second specification of the debt capacity, we follow Kisgen (2009) and argue that firms do not intend to maximize their rating, but rather have individual lower boundaries for their credit rating. Investment-grade firms want to keep an investment-grade rating while at the same time not losing more than one rating class. Firms rated BB or worse avoid downgrades to prevent increasing default probabilities. This specification holds for all rated firms but puts up tighter restrictions on firms capital issuance and repurchase decisions. In the following sections, we will provide the empirical framework to implement these debt capacity definitions. I.1 The data set To apply our debt capacity measures and test hypotheses about financing decisions, we need a sample of firms that are unconstrained in their access to financial markets. If firms are constrained in their access to financial markets, their financing decisions might rather be determined by market entrance constraints instead of debt capacity concerns. Publicly traded firms with credit ratings have access to both the stock and the bond market and are therefore not constrained in their access to these markets. Nevertheless, there are other constraints in their financing decision if they have target credit ratings (Kisgen (2009)). Our sample is a panel data set of US-American firms with Standard&Poor s (S&P) Long 8

9 Term Credit Issuer ratings during the period of 1985 up to 2011 listed in the COMPUSTAT annual file. To each firm-year observation we add the monthly S&P credit rating from one month after the report date of the annual financial statements. We add this additional month so that rating agencies have time to incorporate new information and adjust the rating. Report dates are taken from the COMPUSTAT quarterly file and missing report dates are replaced with the median time between the end of the fiscal year and the report date in the sample. Credit ratings are transformed into a discrete variable on an ordinary scale from 1 to 10, where 10 refers to a AAA rating, 9 to AA, 8 to A and so on, and do not distinguish between microratings (i.e. AA+ or AA-). Financial firms and utilities (Standard Industrial Classification Code (SIC) and ) are excluded from the sample because their capital structure is subject to regulation. We drop observations with negative sales or assets, debt ratios above one or below zero, and firms with missing information in relevant variables. Variables with extreme outliers are trimmed at the upper 0.1% level. In contrast to prior studies, we do not require a minimum number of years of continuous balance sheet data to avoid survivor biases in the results. Our definition of the debt ratio in market and in book values is based on Fama and French (2002), Baker and Wurgler (2002), Chang and Dasgupta (2009), and equals book debt over book assets. We calculate book debt as total liabilities plus preferred stock less deferred taxes and convertible debt. Preferred stock is replaced with the redemption value of preferred stock if it is missing. Book equity is the residual component and calculated as total assets less book debt. To obtain the debt ratio in market values, we make the usual assumption that the market value of debt is equal to its book value. We calculate the market value of equity as stock price times number of shares outstanding. Then, the market value of assets is defined as book assets less book equity plus market equity and consequently, the debt ratio in market values equals book liabilities over market assets. This definition of the debt ratio classifies financial and non-financial debt, for example accounts payable, as debt and represents a conservative view on the capital structure. We explicitly include non- 9

10 financial liabilities in the debt ratio because we are interested in the maximum amount of liabilities that a company can bear. As pointed out by Welch (2011), excluding non-financial liabilities from debt ratio can result in biased implications 3. Following Chang and Dasgupta (2009) and the accounting identity that book equity is equal to balance sheet retained earnings plus paid-in capital, equity issues ( equity) are defined as the change in book equity less the change in retained earnings. Net debt issues ( debt) correspond to the change in book assets less equity and the change in retained earnings. The sum of debt and equity is the firm s financing deficit def and indicates how much external capital the firm needs to raise from external capital markets. If the financing deficit def is negative, then the firm has a financial surplus and can repurchase debt or equity. 4 All variables are scaled by total assets. Table 1 shows the summary statistics of our sample. Our final sample consists of 17,192 observations of 2,489 firms, on average seven observations per firm. The median of firm-year observations per firm is 4 while only 25% of the firms stay more than 10 years in the sample. 96% of the firms have a rating between AA and B, while BBB and BB ratings are the most common ones. In comparison to prior studies, we have a larger data set over a longer period of time. The average firm has a debt ratio of 44.3% in market values and the debt ratio is increasing with declining ratings. AAA rated firms have 20.1% debt while AA firms carry 27.5% debt. With decreasing ratings, the debt ratio increases up to 74.1% for firms close to default. The order of the mean values indicates that target ratings go along with target debt ratios, 3 Our results are robust to alternative definitions of the debt ratio. For example, following Rajan and Zingales (1995), the debt ratio in market values equals as short-term debt plus long-term debt divided by capital. Capital is defined as the sum of long-term debt, short-term debt and the share price at the end of the fiscal year times the number of shares outstanding. This definition of the debt ratio captures the primary sources of external capital and does not include other use of debt, such as account payable and convertibles. 4 We use balance sheet information to identify capital issues, repurchases, and the financing deficit because, due to differences in cash flow accounting and accrual accounting, the information on capital issues from the statement of cash flows does not necessarily coincide with the change of the debt ratio in the balance sheet. Moreover, the change of the debt ratio obtained from the balance sheet includes non-financial liabilities and the COMPUSTAT database contains several missing values in the statement of cash flows. Nevertheless, using the financing deficit definition of Frank and Goyal (2003) and the statement of cash flows to identify capital issues does not change the tone of our results. 10

11 namely an increase in rating is correlated with a decrease in the debt ratio. If the debt ratio is defined in book values, then 55.2% of the assets are classified as debt. The increase in the debt ratio with declining ratings is less pronounced in book values. For highly rated firms the difference between the debt ratio in market and in book values is larger than for low rated firms. This is due to the fact that the value of future investments of highly rated firms is higher due to a higher success probability which lead to a higher stock price (Damodaran (2010)). The financing deficit denotes the firm s need or surplus of external capital. The average deficit is close to zero for ratings higher than BBB and slightly declining in ratings. The dispersion of the deficit is rather small for the top rating categories while speculative-grade firms exhibit a standard deviation of more than 20%. The standard deviation of the financing deficit guarantees that for each rating category there are various firms with positive or negative financing deficits so that in each rating category there are enough firms that either issue or repurchase capital. The financing deficit can be split up into net equity issues and net debt issues that include the change in non-financial liabilities. The net debt and net equity issues indicate that firms with low ratings are in need for more external finance and the net debt issues are on average larger than the equity issues. Again, the standard deviation of the variables indicates a large dispersion in all rating categories. In order to analyze financing decisions empirically, we need to distinguish between capital issues, reductions, and substitutions, and differentiate each of these categories further into debt, equity, or dual financing activities. Since firms usually issue or repurchase at least a small amount of both securities each fiscal year, a more precise definition of these financing activities is necessary. Similar to Korajczyk and Levy (2003) and Hovakimian (2006), debt issues are observations where the net debt issues are larger than 5%, absolute net equity issues are smaller than 5% but the sum of both is larger than 5% of the asset value. Observations that are identified as dual issues, are firms that issue more than 5% of net debt and of net equity and the sum is larger, or smaller than 5%. As well, dual issues correspond to 11

12 cases when firms issue less than 5% of each security but in sum more than 5%. We call an observation a debt-to-equity substitution if net debt issues are smaller than -5% but net equity issues exceed +5% and the absolute value of the sum is smaller than 5%. Debt reductions, equity issues and repurchases, dual repurchases and equity-to-debt substitutions are defined analogously. Capital issues always correspond to a financing deficit (def > 0) while capital repurchases correspond to a financial surplus (def < 0). To address issues of sample selection, we point out the major differences between firms that have a credit rating and firms without credit rating. Table 2 reports the summary statistics, frequency of financing activities and selected balance sheet items for firms with and without credit ratings. In detail, the first column represents all COMPUSTAT firms that do not have a credit rating throughout their existence. The second and third column show firms that have a rating at any point in time. The second column reports these firms before their initial rating and the third column after their initial rating. Our final data used for the remainder of the study consists of all observations from the third column. Not surprisingly, the debt ratios of rated firms are substantially higher in both market and book values than debt ratios of firms without credit rating. However, the asset growth of rated firms is much larger before the initial rating, indicating that the firms in our final sample are mature firms without excessive growth options. Exploring the frequency of capital issues, the second panel of table 2 indicates no substantial differences in the frequency of debt issues between firms after their initial rating and unrated firms. In contrast, firms with credit ratings issue equity only on rare occasions. Similarly, the dual issues and equity-todebt substitutions are less common. The issuance behavior indicates that firms with credit ratings follow a pecking order more closely than firms without a rating. This finding is of substantial importance because the debt capacity of a firm is of higher relevance for firms that issue debt more often. The third panel of the table explore selected items of the balance sheet for the three categories of firms. The high debt ratio of rated firms is not driven by short-term debt and trade credit, but rather by long-term liabilities such as long-term debt. 12

13 In summary, the sample of firms with credit ratings depicts a promising subsample of firms to test prediction on the corporate debt capacity. Due to high leverage and frequent debt issues, the debt capacity is an important factor for their financing decisions. I.2 The credit score regression We use a credit score regression (Altman (1968), Kaplan and Urwitz (1979), and de Jong, Verbeek, and Verwijmeren (2011)) to estimate firm-specific credit ratings as a function of the firm s debt ratio and other characteristics. The results of the regression will later be used to define the debt capacity of a firm. The ordered logit regression to estimate credit ratings as a function of the debt ratio and other firm characteristics reads: credit score it = αdr it + β 1 x it + β 2 z it + ε it rating it = j, if µ j 1 < credit score it µ j, j = 1,..., 10. (1) The left hand variable credit score it is the unobserved latent variable and µ j, j = 1,..., 9, denote the estimated thresholds that separate the credit scores into the rating categories. Note that j = 10 indicates a rating of AAA and j = 1 a rating of D. We set µ 0 = and µ 10 = +. The debt ratio is denoted by dr it and x it is a vector of observable firm characteristics. Besides the debt ratio, Standard & Poor s (2008) name firm size, profitability, liquidity, age, characteristics of assets, and industry specific effects as the most important rating determinants. We measure these factors using the following proxy variables: firm size it = log(sales it ) (2) profitability it = ebitda it / assets it (3) liquidity ratio 1,it = working capital it / assets it (4) liquidity ratio 2,it = retained earnings it / assets it (5) tangibility it = property, plant & equipment it / assets it (6) 13

14 where tangibility is our measure for asset characteristics. The measurement of firm size, profitability, and tangibility correspond to the commonly used determinants in capital structure research (Rajan and Zingales (1995), Frank and Goyal (2009)) and the liquidity measures to the standard proxies in the rating literature Altman and Rijken (2004). The summary statistics in table 1 implicitly indicates the correlation of ratings and the explanatory variables. Highly rated firms are on average larger, more profitable, and have a higher portion of liquid assets. Since we are interested in a ceteris-paribus-analysis of debt ratios and credit ratings but all five proxy variables are correlated with the debt ratio, we need to substitute all the five proxy variables with their orthogonal values to the debt ratio. Orthogonal values of the variables correspond to the residuals of an univariate OLS-regression of the variable on the debt ratio and are uncorrelated with debt ratio by construction of the OLS-estimator. Mählmann (2011) finds the age of the rating to influence the rating agency s decision independent of other observable firm characteristics because of the companies ability to control their information flow to the rating agency. To capture this rating-age-specific effect, we include dummy variables for the age of the firm s rating, limited to a maximum of 10 and using the first year as base category. We use the Fama-French 38 industry classification (Fama and French (1997)) to account for differences across industries that are not captured in the explanatory variables above. We include dummy variables for each of the 38 Fama- French industries, which reduce to 34 dummy variables due to the exclusion of financial firms and utilities and using one category as base case. Moreover, we include dummy variables for each observation year to capture time-specific effects such as the change of the rating agency s standards over time (Blume, Lim, and Mackinlay (1998)). All dummy variables are contained in the explanatory variable z it. Panel (a) of table 3 shows the results of the credit score regression for the debt ratio in market and book values using maximum likelihood estimation to determine all coefficients and parameters. Both specifications yield similar results and all but one explanatory 14

15 variables are significant at the 1% level. A negative sign of a coefficient indicates that the variable decreases the latent credit score and hence reduces the likelihood of a high rating, or likewise increases the probability of being downgraded. Positive signs work in the opposite direction. The negative coefficient of the debt ratio shows that with an increasing amount of debt ratings decrease or it becomes more likely that a firm loses its current rating. Similarly, large and profitable firms tend to have higher ratings. The liquidity ratio 1, defined as retained earnings scaled by total assets, increases the credit score, but in contrast, liquidity ratio 2, defined as working capital over total assets, decreases the credit score which corresponds to the findings of de Jong, Verbeek, and Verwijmeren (2011). The combined hypothesis that the sum of both coefficients is smaller zero can be rejected at the 1% level (not reported). All together, we find that liquidity has a positive effect on the firm s credit rating as predicted by theory. We call a dummy variable significant if its p-value is below In both specifications, 8 (9 in book values) out of 9 age dummy variables are significant and indicate that the age of the rating yields information about the rating agency s decision. 23 (21 in book values) out of the 25 year dummies capture time-specific effects, such as the change of the rating agency s standards. The significance of 29 (28 in book values) out of 33 industry dummies shows that there are different industry characteristics that rating agencies take into account and which are not captured in other explanatory variables. In panel (b) of table 3, we report the precision of the estimation results. We use the debt ratio, firm characteristics, and dummy variables to estimate the firm s credit rating and then calculate the difference between the actual and the estimated rating. Using the debt ratio in market values, our results show that 55.4% of the firms are accurately classified into the rating categories. 95.6% of the observations are estimated exactly or one category off resulting in 4.4% that deviate more than one category. We are able to classify 87.4% of the investment-grade firms correctly as firms with an estimated investment-grade rating. We find similar results using the debt ratio in book values. 15

16 Molina (2005) documents an endogenous relationship between the debt ratio and credit ratings because a sudden reduction in operating risk leads to an increase in creditworthiness but at the same time it encourages the firm to take on more debt. Molina proposes instrumenting the debt ratio with the history of firms past market valuations (Baker and Wurgler (2002)) and the firms marginal tax rates (Graham and Mills (2008)). In our study, the tenor of the results remains the same after introducing these instruments to control for endogeneity. Introducing the instruments comes at the cost of a reduced sample size due to the need of various lags of the market-to-book ratio or missing marginal tax rates to construct the instruments. Hence, we continue our analysis without using instrumental variable regressions. Using an ordered probit regression instead of an ordered logit regression does not change the tenor of the results. In line with prior research, our evidence shows that the cross-sectional variation of credit ratings can partly be explained with the given variables. In particular, the debt ratio is of special importance because of its large significant coefficient which motivates a definition of the debt capacity with help of the credit score regression. I.3 Debt capacity estimates Using the functional form of the credit score regression (1), we derive two measures of the debt capacity. For the ease of notation, we suppress the argument i for the remainder of the paper (y t = y it ). For each credit score t larger than µ j, the probability to be currently downgraded to a rating of j or smaller is given by the logit distribution: P(rating t j) = exp( µ j + αdr t + β 1 x t + β 2 z t ), j = 2,..., 9. (7) The first measure of debt capacity follows de Jong, Verbeek, and Verwijmeren (2011) and defines the debt capacity as the critical debt ratio where the probability of losing the investment-grade rating equals a given constant p. Solving equation (7) for dr t and set- 16

17 ting j = 6, because BB (rating = 6) is the highest non-investment-grade rating, yields DC t = log(1/p 1) + µ 6 β 1 x t β 2 z t α. (8) The resulting value DC t is the firm s debt capacity. For every debt ratio larger than the debt capacity DC t the firm s probability of being downgraded to a non-investment-grade rating is larger than p. If the firm wants to avoid a high downgrading probability, it has to keep its debt ratio below its debt capacity. The results of this debt capacity specification hold by definition for investment-grade firms only and all firms have a common lower boundary for their target rating. We call this specification the investment-grade debt capacity. In our second specification of the debt capacity, we follow Kisgen (2009) and assume that all firms in the same rating class have a common lower boundary for their target rating. In this manner, firms do not aim at maximizing their rating but rather target at staying above a given boundary. In detail, investment-grade firms intend to lose not more than one rating class but aim to stay in the investment-grade category. For example, AAA and AA aim to maintain a rating of AA or A, respectively, while A and BBB rated firms target an rating of BBB. All other firms (ratings BB through C) intend to maintain their current ratings. In this manner all investment-grade firms want to preserve an investment-grade rating and avoid a significant increase in cost of capital. All other firms intend to avoid being downgraded further and to stay close to the investment-grade region. For each rating category j, we denote the lower boundary for the target rating as k j. From equation (7) we receive DC t = log(1/p 1) + µ k j β 1 x t β 2 z t α, j = 2,..., 10. (9) We call this specification the target rating debt capacity. It is more general than (8) as it holds for all firms that have a credit rating while the upper specification holds for investment-grade firms only. The investment-grade specification is nested in the target rating specification if one chooses BBB as target rating for all investment grade firms. 17

18 All debt capacity estimates are functions of the exogenous probability p. A variation in p changes the size of the debt capacity, but it never changes the cross sectional order of debt capacities. For example, if two firms A and B have the same debt ratio, but firm A has a higher credit score than firm B, then independent of the choice of p, firm A will have a higher debt capacity. The empirical rating transition rates of losing the current rating within the next three years as reported by Standard&Poor s (2010) varies from 18.47% to 48.1% for firms rated between AAA and A. To fit this data with a uniform probability for all rating classes, we chose p = 30%. Since both specifications of the debt capacity are given in terms of the debt ratio, we need to limit the value for DC t to the interval [0, 1]. Estimates of DC t that exceed the boundaries are replaced with 1 or 0, respectively. Robustness of our main results with respect to the exogenous variable p are discussed in section II.2. Table 4 reports the results of the debt capacity estimation. The debt capacity is calculated in four different settings. We have two specifications of the debt capacity and for each specification we use the debt ratio in market and in book values. The results on the investment-grade debt capacity are shown in panel (a) and panel (b) reports the results on the target rating debt capacity. In panel (a) the average debt capacity is while the average debt ratio in market values is only The difference between the debt capacity and the debt ratio is statistically and economically significant which indicates that firms operate on average below their debt capacity and could take on additional debt. The debt capacity of BBB rated firms is the smallest (0.441) and increases in ratings. Firms with a rating of A can afford to take on another more debt than BBB firms because their current probability of being downgraded is smaller due to their higher credit score. The debt capacity of firms in the top rating category (AAA) is fairly large (0.854) because on the one hand their firm characteristics allow them to take on much debt and on the other their current rating is far a away from the target rating BBB. Using the debt ratio in book values, we find similar results. The average debt capacity and debt ratio are higher than in market values due to the average positive growth of market values. If the financial target of the firm is 18

19 to maintain a rating of BBB, then firms in the upper rating categories can take on more debt than firms close to the BBB threshold. Firm s liquidity, profitability, size, and asset characteristics allow them to carry more debt than firms with lower ratings. In the target rating specification of the debt capacity (panel (b)), the average debt capacity is while the average debt ratio is measured in market values. On average, firms can issue additional 14.9% of debt. Both figures are higher than in the first specification, because the sample includes highly levered firms in the speculative-grade segment. Taking a look at the debt capacity across different ratings, we observe that AAA firms face the hardest financing restrictions. On average only 20.9% of their assets can be financed with debt if they want their probability of losing the rating to stay below p = 30%. Even though AAA firms have the largest fraction of liquid assets, are bigger and more profitable than the other firms, they cannot take on more debt if they aim to preserve a high rating. Having the second best firm characteristics, AA firms can afford a debt ratio of before exceeding their debt capacity. Intuitively, speculative-grade firms can use more debt than investmentgrade rated firms. BB firms have a debt capacity of and firms with high default risk can finance more than 93.0% of their assets with debt. The standard deviation of the debt capacity across different ratings increases with declining ratings. This finding supports that financing constraints are stronger for highly rated firms. Using the debt ratio in book values results in similar findings. Again, the debt capacity is on average higher due to the definition of the debt ratio, and the debt capacity and its standard deviation increase with declining ratings. The cross-sectional variation of the debt capacity within each rating category can be inferred from the regression results in table 3. Size and profitability increase the amount of debt a firm can take on, as well as the tangibility of assets and firms liquidity. The significance of the year and industry dummies indicates that debt capacities change over time and across industries. All these explanatory factors are incorporated into the debt capacity estimates in equation (9). 19

20 Our results emphasize the heterogeneity of the debt capacity across different rating categories. If firms want to maintain an investment-grade rating, then highly rated firms can take on more debt, but if firms strive for a minimum rating close to their current rating, then highly rated firms face the tightest financing constraints. The comparison of the debt capacity estimates and the debt ratios indicate that firms can take on additional debt to fund future investments. II Financial flexibility and financing decisions Surveys on the practice of corporate finance by Graham and Harvey (2002), Bancel and Mittoo (2004), and Brounen, de Jong, and Koedijk (2004) show that a primary concern of financing decisions is to preserve financial flexibility. Following DeAngelo, DeAngelo, and Whited (2011), a firm constrained by its debt capacity has the option to issue debt comes with the opportunity costs of being unable to borrow in the future. A firm is financially flexible if it is able to exercise this option without exceeding its debt capacity to fund future projects with debt as they come along. We assume that a firm does not want to exhaust its debt capacity completely but rather retain a buffer of debt left, so that the debt ratio is neither close to nor exceeds the debt capacity. Using for each firm-year observation the debt capacity estimates of section I, we will calculate a debt buffer as the difference between the debt capacity and the debt ratio. This buffer measures financial flexibility as it indicates how much additional debt a firm can issue. To test if firms actively issue and repurchase capital to preserve financial flexibility, we will consider hypothetical alternative financing scenarios and compare the resulting debt buffer across the different financing activities. In a second step, we include this new variable into standard regression of capital structure to show its additional explanatory power for capital issuance decisions. 20

21 II.1 The debt buffer Our measure of financial flexibility indicates how much additional debt the firm can issue before it exceeds its debt capacity. If the debt buffer is sufficiently large, then a firm is able to issue debt without facing constraints if it is in need for external capital, while a firm with only a small positive debt buffer has to use equity to maintain its target rating. If the debt buffer is even negative, then the debt ratio already exceeds the debt capacity and the probability for the firm to lose the target rating exceeds the tolerated probability p. If the firm reduces its debt ratio by issuing equity, repurchasing debt, or substituting debt with equity, it can increase its financial flexibility. We will consider three different versions of the debt buffer that result from different financing scenarios to obtain information on the firm s financial situation after possible funding decision. First, we define the debt buffer after the observed financial decision DB after,t as the difference between the estimated debt capacity and the debt ratio at the end of the firm s fiscal year: DB after,t = DC t dr t. (10) This variable shows the firm s unused debt capacity at the end of the fiscal year and indicates whether the firm can take on additional debt to finance future projects as they arise. The second informative figure is the debt buffer that would result if the firm had used equity as the sole external financing source during the last fiscal year. The debt buffer DB equity,t corresponds to the debt buffer after financing corrected by the firm s net debt issues debt t in the same period. DB equity,t = DC t dr t + debt t (11) This variable contains information about the firm s financial situation if it had chosen equity 21

22 to fund all its projects. The third important figure is the debt buffer that results from using exclusively debt to settle the financing deficit. The debt buffer after debt financing DB debt,t corresponds to the debt buffer after equity financing less the financing deficit: DB debt,t = DC t dr t + debt t def. (12) This variable indicates whether the firm has enough unused debt capacity to settle the need for external capital completely with debt. If DB debt,t is close to zero, then the firm faces potential financial constraints after issuing debt because it will no longer be able to fund future investments with debt. If DB debt,t is negative, then the firm exceeds its debt capacity and might lose the target rating. Hence, the firm should only rely solely on debt if DB debt,t is sufficiently large. All three versions of the debt buffer serve as a management information tool that can be used for making capital issue and repurchase decisions. If the management has sufficient information on future investments and characteristics of the new assets, then it can determine all three versions of the debt buffer before financing and investment decisions to analyze the situation. We do not report the results on the debt buffer for each single rating category as the number of observations is too small in several categories but nevertheless the distribution of the financing choices is similar across all rating groups. We can identify 8,815 firm-year observations that either are classified as debt, equity, or dual financing or capital substitutions. 4,232 of these stem from investment-grade firms. Since we employ rating specific targets and control for firm characteristics, we pool all observations into one sample and split the sample according to the issuance and repurchase type. If we would use the change in the debt ratio instead of the change in debt and equity to identify financing activities, then we would not be able to separate debt issues from equity repurchases and capital substitutions. The debt buffer after debt financing does not always coincide with the debt buffer after the 22

23 actual financing decision even though the firm chose debt because firms issue and repurchase a small amount equity in almost every observation. The same holds for firms that issue equity and at the same time a small proportion of debt. If firms intend to preserve financial flexibility, we expect firms to issue debt if they can settle their need for external capital with debt without facing financial constraints due to a small debt buffer DB debt,t. Equity issuers are expected to have a small or negative debt buffer DB debt,t so that they avoid debt issues to maintain their target rating. As a result, the debt buffer after financing should be non-negative for all issuance types. Firms that have a financial surplus are expected to repurchase debt if their debt buffer DB equity,t is low and to repurchase equity if it is high. For firms that engage in capital substitutions the debt buffer DB equity,t depicts the financial situation before the capital substitution. We expect firms that substitute debt with equity to have on average a lower debt buffer DB equity,t than firms with opposing capital substitutions. Table 5 reports mean values of the debt buffer with exclusive debt financing DB debt, with exclusive equity financing DB equity, and after the actual financing decision DB after for each issuance and repurchase category separately. The left hand side of panel (a) shows the results for the investment-grade debt capacity and the debt ratio in market values while the right hand side illustrates the results for the debt ratio in book values. To test the statistical significance of our results, we apply a t-test of equality of mean values of the debt buffer DB debt across the different issuance groups 5. Using the investment-grade debt capacity and the debt ratio in market values, we find that debt issuing firms have a debt buffer DB debt of that enables the use of debt to settle the need for external capital without facing a potential loss of the credit rating. At the same time, the debt buffer after equity financing DB equity is large for this issuance group indicating that the firm misses potential benefits of debt financing. If equity issuing firms would fund their deficit exclusively with debt, their debt buffer DB debt is negative and 5 The estimated mean values and significances are identical to the inference of an OLS regression of DB debt on dummy variables for each issuance category without a constant. 23

24 indicates that these financing activities would exceed the firm s debt capacity. To avoid costs of exceeding the debt capacity these firms choose dual or equity funding. The debt buffer after investments and financing is comparable for all three issuance types, showing that the designated target of their financing decision has common factors. Firms avoid to exceed their debt capacity and try to stay in the investment-grade category. In this manner, firms preserve financial flexibility because they make their financing decision to preserve unused debt capacities. The choice between debt and equity financing is not driven by the firms investment opportunities. As shown in the seventh column of table 5, the financing deficit of debt and equity issuers is economically comparable. Only dual issuers have a significant larger deficit indicating that the issuance of only one kind of security would either exceed the debt capacity or reduce leverage too drastically. Panel (a) in table 5 reports the results of the debt buffer for firms that have a financial surplus and engage in capital repurchases. For debt repurchases the debt buffer DB equity indicates that firms choose to repurchase debt when equity repurchases exceed their debt capacity. The results on the debt buffer DB debt for dual and equity repurchases reveal that the debt buffer becomes excessively high if only debt would be repurchased. Hence, firms choose to repurchase equity or both. Even though there is an economically and statistically significant difference in the debt buffer after debt financing DB debt across the repurchasing groups, the debt buffer after financing is similar for the subgroups. The size of the financial surplus does not provide information on the repurchasing decision between debt and equity because economically the two figures are not different from each other. The results on capital repurchases indicate that firms do not repurchase capital randomly, but try to restore financial flexibility by increasing their debt buffer if the unused debt capacity is too low. The results on capital substitutions are shown in lower part of panel (a) in table 5. DB equity explores the financial situation before the capital substitution because debt buffer is corrected for the debt issue or reductions in the current fiscal year. We do not report the financing deficit def and DB debt because these variables do not contain information since the 24

25 deficit of these observations is close to zero by definition. The economic difference of the debt buffer before substitutions between firms that substitute debt with equity and those that do vice versa is large. For firms that engage in an debt-to-equity substitution operate close to their debt capacity before the substitution which motivates these firms to reduce their debt outstanding to improve their financial situation. In contrast, a large debt buffer can be reduced through equity-to-debt substitutions to profit from benefits to leverage. Again, our results indicate that preserving financial flexibility is the goal of financing decisions because firms try to restore or use their debt buffer. Using the target rating debt capacity specification, we find similar results for both definitions of the debt ratio. In economic terms, the order of the estimated mean values does not vary and we come to the same conclusion as for the investment-grade debt capacity. Panel (b) of table 5 lists the results. The number of observations is larger than in panel (a) because we include all rated firms while panel (a) is only valid for firms with investment-grade ratings. II.2 Robustness to the choice of p The debt capacity and debt buffer are functions of the exogenous probability p of losing the target credit rating. Table 6 shows that a variation in p changes the size of the estimated debt buffer but does not change the order of the results in the different financing activities. Examining equations (8) and (9), we find that the debt capacity is a strictly increasing function in p. Firms can take on more debt if they are willing to tolerate a higher probability of being downgraded. Choosing a lower value for p reduces the debt capacity and puts up tighter restrictions on the firms for keeping their target rating. Equations (10), (11), and (12) show that the debt buffer is strictly increasing in p because it is a linear function of the debt capacity. The debt ratio itself dr t is not influenced by the choice of p. The marginal influence of p on the different versions of the debt buffer is hence identical. Independent of the choice of p, the debt buffer DB debt is the largest for debt issuers and 25

26 significantly smaller for equity and dual issuers. If p equals 0.20, debt issuers still have a positive debt buffer after issuing debt while dual and equity issuers would exceed their debt capacity through debt issues. If the firms are willing to tolerate a downgrading probability of 40%, then the debt buffer estimates are somewhat larger and slighty above zero for equity issuers. Still, equity issuers are constrainted by their debt capacity because their debt buffer amounts only Hence, we come to the conclusion that the economic implications remain the same if we use different values for p. We set p to 0.30 because the average 3-year rating transition rate as reported by Standard&Poor s is roughly 30% 6. II.3 Debt buffer and future financing decisions So far, we have documented that the debt buffer serves as measure for financial flexibility because firms can access bond markets without facing constraints if they have sufficient unused debt capacities. To provide more evidence on the validity of this measure, we will analyze the relation between the debt buffer at the end of the fiscal year and the financing decision in the following period. To analyze if the conditional distribution financing activities depends on the degree of financial flexibility, we divide our sample into terciles of the estimates of the debt buffer after financing DB after. The first tercile depicts firms with the lowest debt buffer in our sample. These firms have on average a negative debt buffer and hence low financial flexibility as they face financing constraints in form of high downgrading risk. In the second and third tercile firms are financially flexible as they can issue debt more easily without being constrained by their debt capacity. The separation only accounts for firm characteristics and financing activities up to the end of the respective fiscal year. For each tercile, we calculate the distribution of financing decisions in the following fiscal year. According to our hypothesis, the frequency of financing activities should vary across the terciles because firms in the lower tercile are more constrained than in the upper tercile. 6 de Jong, Verbeek, and Verwijmeren (2011) estimate firm specific debt capacities in similar setting and use p = We choose a more conservative setting by fitting the 3-year S&P rating transition probabilities 26

27 Table 7 takes a direct look at the distribution of financing activities in each tercile by reporting the frequency of each financing activity in the subsequent fiscal year as percentage of all observations in the respective tercile. Debt issues are most common for firms in the 3rd tercile. 25.7% of firms with large debt buffers issue debt in the subsequent period while only 20.5% of the firms from the first tercile use debt. The difference in debt issues shows that firms with a large debt buffer use the option to issue debt more often indicating higher flexibility while the debt capacity of firms in the first tercile constrains their ability to issue debt. However, the frequency of debt issues in the first tercile indicates that some firms issue debt even though they operate close to or beyond their debt capacity. Denis and McKeon (2012) document similar financing behavior of firms that intentionally deviate from their target debt ratios. These firms do not return to their old leverage levels and keep operating at high downgrading risk. We find similar results for other leverage increasing actions such as equity repurchases and equity-to-debt substitutions. The analysis of the terciles highlights another finding. Firms operating close to their debt capacity take up actions to reduce leverage more often to restore their debt buffer. In the first tercile, 12.8% of the firms repurchase debt while only 3.7% of firms in the third tercile do so. The results for equity issues and debt-to-equity substitutions are similar but less pronounced. Moreover, the fraction of firms that do not use external financing and hence do not actively change their debt buffer is the smallest in the first tercile. These findings highlight that firms take up immediate actions to restore their debt buffer and intend to avoid exceeding their debt capacity. In unreported results, we could document that the distribution of financing activities in the next two and three following years does not exhibit these characteristic patterns. Therefore, we conclude that the distribution of financing activities yields important information on the relevance of the debt buffer for future financing activities. 27

28 II.4 The debt buffer and leverage regressions In this section, we show that our measure of financial flexibility, the debt buffer, has explanatory power for firm s financing decisions even after controlling for factors that are commonly associated with capital structure decisions. Prior studies relate changes in the firm s debt ratio to firm characteristics which serve as proxies for the influence of the most predominant capital structure theories. Frank and Goyal (2009) propose a set of control variables with high explanatory power for capital issuance decisions. Among these variables are firm size, profitability, characteristics of assets, market-timing, tax shield substitutes, target debt ratios, and macroeconomic conditions. Firm size, profitability, and the characteristics of assets, are measured with the variables defined in equations (2), (3), and (6). In the market-timing theory (Baker and Wurgler (2002)), firms use windows of opportunities and issue equity when their market values are high relative to their book values. The market-to-book ratio, defined as the market value of assets divided by the book value of assets captures this effect. DeAngelo and Masulis (1980) show that firms with high non-debt tax shields, such as depreciation expenses, are less levered because depreciation expenses reduce tax payments in the same manner as interest payments. Tax shield substitutes are measured by the ratio of depreciation over total assets. Empirical evidence indicates that firms follow a dynamic trade-off theory and actively adjust their debt ratio to an optimal or target debt ratio (Flannery and Rangan (2006)). We use the median debt ratio within each Fama-French industry as target debt ratio and the distance to the target debt ratio as an explanatory variable to measure target adjustment behavior. The debt ratio is bounded between zero and one which implies a technical reversion if the debt ratio is near these boundaries. The deviation from the industry median always indicates the direction in which the debt ratio is more likely to change due to this mechanical relation. Macroeconomic conditions are measured by the term spread defined as the difference in interests of a ten year and a one year US-government bond. Optimal leverage choice varies pro-cyclically across the business cycle (Bhamra, Kuehn, and Strebulaev (2010), Hess and Immenkötter (2012)) 28

29 while observed debt ratio exhibit counter-cyclical patterns (Korajczyk and Levy (2003)). The three versions of the debt buffer imply different actions across the possible external financing activities. For example, a large debt buffer DB debt favors debt issues and implies an increase in leverage, while a small but still positive debt buffer DB debt favors equity or dual issues. To capture the different effect across the different financing activities, we let the debt buffer variables interact with eight indicator variables so that each represent one of the external financing activities (debt/dual/equity issues/repurchases/substitutions). All firm-year observations with no external financing activities are excluded from the regression. Since we are interested in explaining financing decisions but not the level of the debt ratio, the dependent variable of our regression approach is the change in the debt ratio over time dr t = dr t dr t 1. As well, all control variables are denoted in yearly changes. The estimated coefficients of an fixed effects regression are presented in table 8. To control for heteroskedasticity such as clustering of standard errors over time and across firms, we apply two-dimensional clustering as proposed by Petersen (2009). Panel (a) of table 8 explores the results for the investment-grade debt capacity specification. In the left side of the panel, the debt ratio is defined in market values and on the right side in book values. The first column explores the regression of change of the debt ratio on the control variables in changes. Consistent with pecking order and market-timing theory, profitability and the market-to-book ratio decrease the debt ratio while firm size and tangibility favor an incline. The significant coefficient of the deviation from the industry median indicates target adjustment behavior as predicted by the trade-off theory. However, we do not detect a significant impact of tax shield substitutes and macroeconomic conditions. The control variables account for about 23.9% of the variation of the change in the debt ratio corresponding to magnitutes of related studies. In the second model, we add the cross terms of DB debt and the financing activities indicators to the regression. For equity issues as well as for debt and dual repurchases the coefficient of the debt buffer is negative as expected. The debt buffer explains part of the 29

30 change in the debt ratio as it favors a decline in leverage due to the constraints given by the debt capacity. The size of the estimated effect is of significant relevance as the debt buffer after debt financing explains 4.0% of the change in the debt ratio. 7 As it turns out, the coefficient of DB debt is negative but insignificant for debt issues and does not yield explanatory power. Still, the adjusted R 2 of the regression including the debt buffer amounts substantially higher than in the model without the debt buffer. Columns 3 and 4 show the results for the regressions including the debt buffer after equity financing and after the observed financing decision. DB equity serves well in explaining capital substitutions well as it predicts the change in the debt ratio. DB after in column 4 contributes to explanation of the change in the debt ratio as the coefficients coincide with the change in the debt ratio. The results for the debt ratio in book values (right hand side of panel (a), table 8) and for the target rating debt capacity (panel (b), table 8) indicate similar findings. Combining the results of the regressions, we find that firms target on preserving unused debt capacities. DB debt and DB equity are important indicators for the funding choice as they are able to explain the change in leverage. The significance of DB after shows the importance of the unused debt capacity after the funding decision for a firm. Unused debt capacity provides financial flexibility as it enables future funding of investments. If the unused debt capacity is sufficiently large to cover the financing deficit, firms choose debt but if the unused debt capacity is too small, firms prefer equity and dual issues over debt to restore their flexibility. Our results are significant in economical and statistical terms. After controlling for other factors that are correlated with financing decisions, our measure of financial flexibility yields high explanatory power for funding decisions. 7 We estimate the economic size of the effect by multiplying the estimated coefficient with the standard deviation of the variable in the respective financing category: β DBdebt std(db debt ) debt repurchase = =

31 III Conclusion On a broad sample of firms with bond and stock market access, we show that firms issue debt or repurchase equity if their unused debt capacity, i.e. the amount of debt a firm can issue before they are threatened to be downgraded in creditworthiness, is sufficiently large. On the other hand, they repurchase debt or issue equity if other financing activities would exceed their debt capacity. Capital issuance and repurchase decisions are driven by the target of preserving financial flexibility which corresponds to maintaining unused debt capacity for possible future financing activities. Our results are robust to different specifications of the debt capacity, various definitions of debt ratio, and estimation techniques. The study highlights the importance of the debt capacity and financial flexibility as determinants in empirical and theoretical research. We argue that the commonly used factors such as size, growth, profitability, and tangibility are insufficient to capture manager s financing decisions. Tests and models of the trade-off theory, market-timing, or the pecking order can be enhanced using the debt buffer as explanatory variables. The debt buffer provides risk managers and investors with information on future capital issues and investments. As well, company valuation models can benefit from using this tool to obtain a more precise picture of the distribution of future financing activities. In summary, we answer the question how much is too much? as follows: Firm have too much debt on their balance sheet if downgrading risk threatens the firm s target rating and diminishes a company s financial flexibility for future investments and acquisitions. 31

32 References Altman, E. (1968): Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy, Journal of Finance, 23, Altman, E., and H. Rijken (2004): How Rating Agencies Achieve Rating Stability, Journal of Banking & Finance, 28, Baker, M., and J. Wurgler (2002): Market Timing and Capital Structure, Journal of Finance, 57, Bancel, F., and U. Mittoo (2004): Cross-Country Determinants of Capital Structure Choice: A Survey of European Firms, Financial Management, 33, Bhamra, H., L.-A. Kuehn, and I. Strebulaev (2010): The Aggregate Dynamics of Capital Structure and Macroeconomic Risk, Review of Financial Studies, 23, Blume, M., F. Lim, and A. Mackinlay (1998): The Declining Credit Quality of U.S. Corporate Debt: Myth or Reality?, Journal of Finance, 53, Brounen, D., A. de Jong, and K. Koedijk (2004): Corporate Finance in Europe: Confronting Theory with Practice, Financial Management, 33, Chang, X., and S. Dasgupta (2009): Target Behavior and Financing: How Conclusive Is the Evidence, Journal of Finance, 64, Damodaran, A. (2010): The Dark Side of Valuation. FT Press, 2. edn. de Jong, A., M. Verbeek, and P. Verwijmeren (2011): Firms Debt-Equity Decisions when the Stratic Tradeoff Theory and the Pecking Order Theory Disagree, Journal of Banking & Finance, 35, DeAngelo, H., L. DeAngelo, and T. Whited (2011): Capital Structure Dynamics and Transitory Debt, Journal of Financial Economics, 99, DeAngelo, H., and R. Masulis (1980): Optimal Capital Structure Under Corporate and Personal Taxation, Journal of Financial Economics, 8, Denis, D., and S. McKeon (2012): Debt Financing and Financial Flexibility, Evidence from Proactive Leverage Increases, Forthcoming in the Review of Financial Studies. Fama, E., and K. French (1997): Industry Cost of Capital, Journal of Financial Economics, 43, (2002): Testing Trade-Off and Pecking Order Predictions About Dividents and Debt, Review of Financial Studies, 15, (2012): Capital Structure Choices, Critical Finance Review, 1,

33 Faulkender, M., and R. Wang (2006): Corporate Financial Policy and the Value of Cash, Journal of Finance, 61, Fischer, E., R. Heinkel, and J. Zechner (1989): Dynamic Capital Structure Choice: Theory and Tests, Journal of Finance, 44, Flannery, M., and K. Rangan (2006): Partial Adjustment Toward Target Capital Structures, Journal of Financial Economics, 79, Frank, M., and V. Goyal (2003): Testing the Pecking Order Theory of Capital Structure, Journal of Financial Economics, 67, (2009): Capital Structure Decisions: Which Factors Are Reliably Important?, Financial Management, 38, Gamba, A., and A. Triantis (2008): The Value of Financial Flexibility, Journal of Finance, 63, Graham, J., and C. Harvey (2001): The Theory and Practice of Corporate Finance: Evidence from the Field, Journal of Financial Economics, 60, (2002): How Do CFOs Make Capital Budgeting and Capital Structure Decisions?, Journal of Applied Corporate Finance, 15, Graham, J., and L. Mills (2008): Using Tax Return Data to Simulate Corporate Marginal Tax Rates, Journal of Accounting and Economics, 46, Hess, D., and P. Immenkötter (2012): Optimal Leverage, its Benefits, and the Business Cycle, Working Paper, University of Cologne. Hovakimian, A. (2006): Are Observed Capital Structures Determined by Equity Market Timing?, Journal of Financial and Quantitative Analysis, 41, Jensen, M. (1986): Agency Costs of Free Cash Flow, Corporate Finance and Takeovers, American Economic Review, 76, Kaplan, R., and G. Urwitz (1979): Statistical Models of Bond Ratings: A Methodological Inquiry, Journal of Business, 52, Kisgen, D. (2006): Credit Ratings and Capital Structure, Journal of Finance, 61, (2009): Do Firms Target Credit Ratings or Leverage Levels?, Journal of Financial and Quantitative Analysis, 44, Kisgen, D., and P. Strahan (2010): Do Regulations Based on Credit Ratings Affect a Firm s Cost of Capital?, Review of Financial Studies, 23, Korajczyk, R., and A. Levy (2003): Capital Structure Choice: Macroeconomic Conditions and Financial Constraints, Journal of Financial Economics, 68,

34 Leary, M., and M. Roberts (2010): The Pecking Order, Debt Capacity, and Information Asymmetry, Journal of Financial Economics, 95, Lemmon, M., and J. Zender (2010): Debt Capacity and Tests of Capital Structure Theories, Journal of Quantitative Financial Analysis, 45, Mählmann, T. (2011): Is There a Relationship Benefit in Credit Ratings?, Review of Finance, 15, Molina, C. (2005): Are Firms Underleveraged? An Examination of the Effect of Leverage on Default Probabilities, Journal of Finance, 60, Myers, S., and N. Majluf (1984): Corporate Financing and Investment Decisions when Firms Have Information that Investors Do Not Have, Journal of Financial Economics, 13, Petersen, M. (2009): Estimating Standard Errors in Finance Panel Data Sets: Comparing Approaches, Review of Financial Studies, 22, Rajan, R., and L. Zingales (1995): What Do We Know about Capital Structure? Some Evidence from International Data, Journal of Finance, 50, Standard & Poor s (2008): Standard & Poor s Corporate Rating Criteria. New York. Welch, I. (2011): Two Common Problems in Capital Structure Research: The Financial- Debt-To-Assets Ratio and Issuing Activity Versus Leverage Changes, International Review of Finance, 11,

35 Table 1: Summary Statistics. The table reports mean values (standard deviation in parentheses) of our variables split up into the different rating categories. The sample consists of annual COMPUSTAT data from 1985 to 2010 of 2,489 different firms with a S&P long-term credit issuer rating excluding financial firms and utilities. The debt ratio in market values equals book debt over market assets. The debt ratio in book values equals book debt over book assets. Book debt is calculated as total liabilities plus preferred stock less deferred taxes and convertible debt. Book equity equals book assets less book debt and the market value of assets equals book assets less equity debt plus market equity. Size is measured as log(sales) and profitability is EBITDA over total assets. Liquidity ratio 1 is retained earnings scaled by total assets and liquidity ratio 2 is working capital scaled by total assets. Tangibility is defined as property, plant and equipment over total assets. The financing deficit def equals the change in book assets less the change in retained earnings and scaled by total assets. Net debt issues debt are defined as the change in book debt and net equity issues equity are the change in book equity less the change in retained earnings. Both variables are scaled by total assets. N denotes the number of observations. Ratings Variables All AAA AA A BBB BB B CCC-D market debt ratio (0.219) (0.105) (0.140) (0.150) (0.172) (0.211) (0.248) (0.223) book debt ratio (0.188) (0.100) (0.130) (0.148) (0.154) (0.188) (0.234) (0.208) size (1.526) (1.219) (1.390) (1.251) (1.175) (1.139) (1.297) (1.479) profitability (0.077) (0.062) (0.052) (0.062) (0.060) (0.068) (0.084) (0.104) liquidity ratio (0.395) (0.159) (0.207) (0.208) (0.203) (0.266) (0.560) (0.760) liquidity ratio (0.164) (0.120) (0.153) (0.142) (0.147) (0.160) (0.186) (0.234) tangibility (0.235) (0.212) (0.209) (0.211) (0.241) (0.246) (0.234) (0.247) def (0.184) (0.094) (0.097) (0.120) (0.176) (0.178) (0.253) (0.263) debt (0.149) (0.074) (0.078) (0.097) (0.137) (0.144) (0.206) (0.229) equity (0.101) (0.061) (0.057) (0.067) (0.089) (0.098) (0.141) (0.130) N 17, ,127 4,230 4,531 3,

36 Table 2: Firms with and without credit rating. The table reports summary statistics, frequency of financing activities, and selected balance sheet items for firms with and without credit ratings. The sample of firms with credit ratings is split into the observations before their initial rating (column 2) and after their initial rating (column 3). For the key figures and selected balance sheet items we report mean values for the respective category. Financing activities correspond to observations where the respective financing activity exceeds 5% of firm s assets. Key Figures Firms without firms with credit rating credit rating before rating after initial rating N 71,405 6,274 17,192 market debt ratio book debt ratio asset growth def debt equity Frequency of financing activities capital issues debt issue dual issue equity issue capital reductions debt repurchase dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt no external financing Selected balance sheet items total liabilities current liabilities debt in current liabilities accounts payable long-term liabilities long-term debt convertibles deferred taxes other liabilities stockholders equity preferred stock common equity

37 Table 3: Credit Score Regression. Panel (a) shows the results of the ordered logit regression (1). We use annual COMPUSTAT data from 1985 to 2010 of 2,489 different firms with a S&P long-term credit issuer rating excluding financial firms and utilities. The debt ratio in market values equals book debt over market assets. The debt ratio in book values equals book debt over book assets. Book debt is calculated as total liabilities plus preferred stock less deferred taxes and convertible debt. Book equity equals book assets less book debt and the market value of assets equals book assets less equity debt plus market equity. Size is measured as log(sales) and profitability is EBITDA over total assets. Liquidity ratio 1 is retained earnings scaled by total assets and liquidity ratio 2 is working capital scaled by total assets. Age dummies indicate the age of the rating with a maximum of 10 years. Year dummies are included for every year. Industry dummies are based on the Fama-French 38 industries which are reduced to 33 categories due to the exclusion of financial firms and utilities. Robust standard errors are reported in parentheses and indicates significance at the 1% level. For each dummy variable category, we show the number of significant dummy variables at the 10% level. Panel (b) reports the fraction of the correctly specified ratings in percent. The left side denotes the difference between the observed ratings and the estimated ratings. For example 39.5% of the observed ratings are one category smaller or higher than the estimated rating. The last line reports the fraction of investment-grade firms that are estimated to have an investment-grade rating. The smallest credit score with a rating of at least BBB is µ 6. Panel (a): Estimation results explanatory variables market debt ratio book debt ratio debt ratio (0.097) (0.097) size (0.017) (0.017) profitability (0.283) (0.302) liquidity ratio (0.143) (0.155) liquidity ratio (0.149) (0.152) tangibility (0.102) (0.098) significances of age dummies 8 out of 9 9 out of 9 significances of year dummies 23 out of out of 25 significances of industry dummies 29 out of out of 33 observations 17,192 17,192 pseudo R Panel (b): Correctly specified ratings (in %) rating estimation error market debt ratio book debt ratio error = error error > Fraction of investment-grade firms with credit score > µ

38 Table 4: Debt capacity estimates. The table shows the results for the two different debt capacity specifications and for each specification we use the debt ratio in market and in book values. We report mean and standard deviation of the debt capacity and the mean of the debt ratio for each subgroup. The investment-grade debt capacity corresponds to equation (8) and the target rating debt capacity to equation (9). p is set to 0.3. The sample consists of annual COMPUSTAT data from 1985 to 2010 of 2,489 different firms with a S&P long-term credit issuer rating excluding financial firms and utilities. The debt ratio in market values equals book debt over market assets. The debt ratio in book values equals book debt over book assets. Book debt is calculated as total liabilities plus preferred stock less deferred taxes and convertible debt. Book equity equals book assets less book debt and the market value of assets equals book assets less equity debt plus market equity.,, correspond to a significances at the 1%, 5%, or 10% level, respectively, in a t-test of mean(dc dr) > 0. Panel (a): Investment-grade debt capacity market debt ratio book debt ratio rating DC std(dc) dr DC std(dc) dr AAA BBB AAA AA A BBB Panel (b): Target rating debt capacity market debt ratio book debt ratio rating DC std(dc) dr DC std(dc) dr AAA C AAA AA A BBB BB B CCC C

39 Table 5: Debt buffer, capital issues, repurchases, and substitutions. We report mean values for the three versions of the debt buffer (10), (11), and (12) in both debt capacity specifications and two versions of the debt ratio. The investment-grade debt capacity is defined in equation (8) and the target rating debt capacity in equation (9). p is set to 0.3. The sample consists of annual COMPUSTAT data from 1985 to 2010 of 2,489 different firms with a S&P long-term credit issuer rating excluding financial firms and utilities. The debt ratio in market values equals book debt over market assets. The debt ratio in book values equals book debt over book assets. Book debt is calculated as total liabilities plus preferred stock less deferred taxes and convertible debt. Book equity equals book assets less book debt and the market value of assets equals book assets less equity debt plus market equity. The financing deficit def is the change in book assets less the change in retained earnings. Financing activities correspond to observations where the respective financing activity exceeds 5% of firm s assets. We perform t-tests to test if the mean value of DB debt differs from the rest of the sample.,, correspond to a significances at the 1%, 5%, or 10% level, respectively. N denotes the number of observations. debt ratio in market values Panel (a): Investment-grade debt capacity debt ratio in book values financing activity DB debt DB equity DB after DB debt DB equity DB after def N capital issues debt issue ,800 dual issue equity issue capital repurchases debt repurchase dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt debt ratio in market values Panel (b): Target rating debt capacity debt ratio in book values financing activity DB debt DB equity DB after DB debt DB equity DB after def N capital issues debt issue ,292 dual issue ,568 equity issue capital repurchases debt repurchase ,631 dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt

40 Table 6: Debt buffer and robustness in p. We report mean values for the three versions of the debt buffer (10), (11), and (12) for different values of the exogenous probability p. We show the results for both debt capacity specifications but only for the debt ratio in market values. The debt capacity corresponds to the investment-grade debt capacity (8) or the target rating debt capacity (9). Financing activities correspond to observations where the respective financing activity exceeds 5% of firm s assets. The sample consists of annual COMPUSTAT data from 1985 to 2010 of 2,489 different firms with a S&P long-term credit issuer rating excluding financial firms and utilities. The debt ratio in market values equals book debt over market assets. Book debt is calculated as total liabilities plus preferred stock less deferred taxes and convertible debt. Book equity equals book assets less book debt and the market value of assets equals book assets less equity debt plus market equity. The number of observations and the financing deficit are identical to table 5 and hence not reported. Panel (a): Investment-grade debt capacity DB debt DB equity DB after choice of p capital issues debt issue dual issue equity issue capital repurchases debt repurchase dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt Panel (b): Target rating debt capacity DB debt DB equity DB after choice of p capital issues debt issue dual issue equity issue capital repurchases debt repurchase dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt

41 Table 7: Frequency of financing activities. The table list the average DBafter at time t and frequencies of financing activities in t+1 split up into terciles of the debt buffer after financing at time t. Column 6 shows the frequency of financing activities at time t. All frequencies are expressed in percent of all relevant observations. In panel (a) the debt capacity corresponds to the investment-grade debt capacity (8) and to the target rating debt capacity (9) in panel (b). Financing activities correspond to observations where the respective financing activity exceeds 5% of firm s assets. The sample consists of annual COMPUSTAT data from 1985 to 2010 of 2,489 different firms with a S&P long-term credit issuer rating excluding financial firms and utilities. The debt ratio in market values equals book debt over market assets and in book values book debt over book assets. Panel (a): Investment-grade debt capacity debt ratio in market values debt ratio in book values DBafter terciles DBafter terciles full 1 st 2 nd 3 rd 1 st 2 nd 3 rd sample mean(dbafter) / capital issues debt issue dual issue equity issue capital repurchases debt repurchase dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt no external financing Panel (b): Target rating debt capacity debt ratio in market values debt ratio in book values DBafter terciles DBafter terciles full 1 st 2 nd 3 rd 1 st 2 nd 3 rd sample mean(dbafter) / capital issues debt issue dual issue equity issue capital repurchases debt repurchase dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt no external financing

42 Table 8: Debt buffer and control variables. We report the estimated coefficients of a fixed effects linear panel regression of the change in the debt ratio drt on control variables in changes and the debt buffer in the three different versions interacted with indicator variables for each observed financing activity. The three versions of the debt buffer are defined in the equations (10), (11), and (12). Financing activities correspond to observations where the respective financing activity exceeds 5% of firm s assets. The sample consists of annual COMPUSTAT data from 1985 to 2010 of 2,489 different firms with a S&P long-term credit issuer rating excluding financial firms and utilities. The debt ratio in market values equals book debt over market assets. Book debt is calculated as total liabilities plus preferred stock less deferred taxes and convertible debt. Book equity equals book assets less book debt and the market value of assets equals book assets less equity debt plus market equity. Profitability is ebitda over assets, size equals log of assets, and the market-to-book ratio is defined as market value over book value of assets. Tangibility equals proberty, plant, and equipment of total assets. Tax is depreciation and amortization over total assets and industry median is the difference between the debt ratio and the median of the respective Fama-French industry debt ratio. Term spread is measured as the difference between a long-term (10 years) US-government bond yield and a short term (1 year) treasury-bill rate. All coefficients are calculated via OLS. We control for two dimensional standard error clustering by implementing the standard errors proposed by Petersen (2009).,, correspond to a significances at the 1%, 5%, or 10% level, respectively. N denotes the number of observations. Panel (a): Investment-grade debt capacity debt ratio in market values debt ratio in book values DBdebt DBequity DBafter DBdebt DBequity DBafter (1) (2) (3) (4) (5) (6) (7) (8) capital issues debt issue dual issue equity issue capital repurchases debt repurchase dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt control variables profitability size market-to-book tangibility tax industry median term spread N 3,568 3,568 3,568 3,568 3,568 3,568 3,568 3,568 adj. R

43 Table 8 continued: Debt buffer and control variables. Panel (b): target rating debt capacity debt ratio in market values debt ratio in book values DBdebt DBequity DBafter DBdebt DBequity DBafter (1) (2) (3) (4) (5) (6) (7) (8) capital issues debt issue dual issue equity issue capital repurchases debt repurchase dual repurchase equity repurchase capital substitutions debt-to-equity equity-to-debt control variables profitability size market-to-book tangibility tax industry median term spread N 6,960 6,960 6,960 6,960 6,960 6,960 6,960 6,960 adj. R