Cash-flow volatility and capital structure choice

Cash-flow volatility and capital structure choice

Evan Dudley Queen’s University Kingston, ON K7L 3N6 [email protected] (613) 533-6259 and Christopher James* Warrington College of Business Administration University of Florida Gainesville, FL 32611-7168 [email protected] (352) 392-3486

  First draft: August 2014 This draft: July 2015   Abstract Prior research finds a weak relation between cash-flow volatility and leverage. Using a novel measure of cash-flow volatility, we find that volatility matters more for firms that are financially constrained. Constrained firms issue debt when volatility is low, but have trouble deleveraging in response to increases in volatility. Constrained firms also hoard the proceeds from debt issues undertaken during low-volatility regimes, but invest the proceeds from debt issues when volatility is high. Overall, the observed relation between cash-flow volatility and capital structure choice is driven by financially constrained firms’ desire to ensure future financial flexibility. JEL Classification: G32 Keywords: Capital structure, volatility, leverage, product-market competition, financial constraints  

*: Corresponding author. We thank Dominique Badoer, Sean Cleary, Cem Demiroglu, Jongsub Lee, John Wald (discussant) and seminar participants at the 2015 Midwestern Finance Association meeting for valuable comments.  

 

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I. Introduction “… We’ve been managing the business and the balance sheet with the expectation of volatile oil prices. We fully expect the price cycle to include highs and lows and we have a financial strategy that’s designed to work through the entire price cycle.” -

Steve Williams, Suncor’s chief executive officer in his February 2015 conference call with analysts.

The above quote suggests that managers consider future cash-flow volatility when making capital budgeting decisions and setting their financial policy.1 Survey evidence provided in Graham and Harvey (2001) also confirms that firms consider cash-flow volatility when making financing decisions. However, despite practitioner concern with volatility, our understanding of how cash-flow volatility affects financing decisions and capital structure choice remains poorly developed. For example, even though there are strong theoretical reasons for why cash-flow volatility should matter for firms’ financial policy, existing empirical evidence shows a statistically significant but weak relation between cash-flow volatility and leverage.2 For example, Frank and Goyal (2009) and Rajan and Zingales (1995)’s list of reliably important firm-level factors includes profitability, asset tangibility, firm size and market-to-book, but cashflow volatility is notably absent from the list. Several recent studies suggest that the impact of volatility shocks on financing and investment decisions may vary with the financial constraints firms face. 3 The basic idea is that that a combination of lessening financial constraints and investor over-extrapolation during periods of relative calm may lead poorer quality firms to aggressively increase their leverage in good times, which in turn, leaves them more vulnerable to industry or macroeconomic downturns.4 Much of the research to date in on this issue has focused on the relationship between capital raising or target leverage ratios and macroeconomic conditions.5 Overall these studies

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 Transcript of Suncor’s February 5th, 2015 conference call provided by seekingalpha.com.   Bradley et al. (1984), Leland (1994), Strebulaev (2007) all predict an important role for volatility in determining corporate leverage ratios. 3 See Bloom (2014) for a review of the literature on the effects of fluctuations in uncertainty on investment, product pricing and earnings. 4 See for example Stein (2013) and Greenwood and Hanson (2013)  5 See for example Korajczyk and Levy (2003) and more recently Erel et al. (2012). 2

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find that capital raising and target leverage for financially constrained firms are highly procyclical while investment-grade firms’ leverage appears to be counter-cyclical. In this paper we examine the relationship between changes in expected future cash flow volatility and capital structure choice. We focus our analysis on whether firms react asymmetrically to cash-flow volatility increases compared to decreases, and whether the response to volatility shocks varies based on the financial constraints firms face. We explore these issues using a measure of cash-flow volatility that is forward-looking in nature. Specifically, we measure cash-flow volatility at the industry-level using quarterly accounting information with a Generalized Autoregressive Conditional Heteroskedasticity process (GARCH). The main benefit of this measure is that it is a more accurate predictor of innovations in volatility than existing measures used in the literature. In particular, most of the previous corporate finance studies on volatility use backward looking measures based on a moving average of past profitability shocks. This rolling-standard deviation approach results in estimates of volatility that change very slowly over time and weighs all past variations from average cash flows equally during the look-back period. Moreover, using equal weights leads to the unattractive assumption that recent changes in cash-flows are no more relevant than changes in cash-flows two or even 10 years in the past. Our first test estimates a leverage regression in first-differences. We show that innovations in the GARCH process are significantly negatively associated with changes in leverage. However, none of the other commonly used measures of cash-flow volatility is able to detect a significant relation between innovations in volatility and changes in leverage. This is true even though both GARCH and realized measures of volatility are negatively related to the level of leverage in cross-sectional tests typically employed in the literature. Since volatility and profitability are negatively correlated (Bloom 2014), and both are likely to be endogenous, identifying the effect of changes in volatility on leverage requires a way to identify exogenous variation in both earnings and volatility. Previous studies use exogenous shifts in industry competition to identify changes in future profitability. For example, drawing on an extensive literature relating import competition to industry profitability, Xu (2012) uses tariff changes and foreign exchange rates as instruments for changes in future profitability. The appeal of using import competition as an instrument for future profitability is that imports are likely to 3  

   

affect profitability but there is little reason to believe they are influenced by firm profitability or leverage. However as Valta (2012), Irvine and Pontiff (2009) and others have pointed out, competition also affects cash-flow volatility. The idea that the degree of product market competition and cash-flow volatility may be related is not new. For example, Hicks (1935) proposed the “quiet life” hypotheses stating “the best of all monopoly profits is a quiet life” (p. 8). More recently, Hou and Robinson (2006) and others argue that when firms are insulated from competition by high entry barriers, they engage in less innovation and are thus less risky. Regardless of the mechanism, instruments such as tariff changes and exchange rate changes may be doing double duty-- picking up the effect on leverage of both changes in expected profitability and changes in cash-flow volatility. Therefore we isolate the impact of cash-flow volatility on financing choices by using both changes in tariffs and industry-level trade-weighted foreign exchange rates as instruments for profitability and volatility. Using forward looking measures of volatility and separating the impact of innovations in volatility from profitability produces a number of interesting results. First, we find considerable heterogeneity in how firms react to volatility shocks. In particular, almost all of the observed response to volatility shocks is among financially constrained firms, where constraints are measured by whether a firm has a credit rating or pays a dividend. Unconstrained firms’ capital raising process is largely independent of observed volatility levels, while constrained firms’ issuance choice is negatively correlated to innovations in volatility. We also find that the impact of volatility on leverage for constrained firms is more pronounced during periods of easy credit conditions, measured using the Survey of Senior Lending Officers. This finding is consistent with supply-based explanations of financing choice found in Holmstrom and Tirole (1997) and He and Krishnamurthy (2013). Second, we find that financially constrained firms behave asymmetrically in their response to increases versus decreases in volatility. Innovations that reduce volatility are associated with leverage-increasing transactions on the part of these firms, mainly in the form of new debt issuance. However, positive innovations in volatility do not lead to a corresponding reduction in debt. We investigate the reason for this asymmetry and find that it is related to how these firms pay down existing debt. Unconstrained pay down existing debt with the proceeds 4  

   

from equity issues and operating cash flows, and the magnitude of these repayments is not correlated with observed volatility levels. In contrast, constrained firms repay existing debt primarily out of operating cash flows, and the extent to which they do so is negatively correlated with cash-flow volatility. Third, we find that the level of volatility is related to how firms use the proceeds from capital raising activities. Contracting-cost explanations of debt predict that debt may be costlier to issue when cash-flow volatility is high. Consistent with these explanations, conditional on being able to issue debt, constrained firms hoard the proceeds from debt issues undertaken when volatility is low, and they spend the proceeds from debt issues undertaken during high volatility regimes. Our study contributes to the literature in a couple of ways. First, we address the question that easier access to credit during periods of low cash-flow volatility leads poorer quality firms to aggressively increase leverage in good times (Greenwood & Hanson 2013, Stein 2013). We show that constrained firms’ inability to deleverage when volatility is high occurs because these firms have difficulty raising capital during high volatility regimes, and operating profits are negatively correlated with volatility. Second, we provide evidence consistent with firms attempting to preserve future financial flexibility as an important factor driving the relation between cash-flow volatility and leverage. As such, our results are more consistent with survey responses in Graham and Harvey (2001) and theories of capital structure choice based on investment such as DeAngelo et al. (2011) than on theories that emphasize the trade-off between the tax benefits of debt and expected bankruptcy costs.6 The remainder of this paper is organized as follows. Section II provides a brief review of the literature on capital structure and cash-flow volatility. Section III describes the data and our methods. In section IV, we present our empirical findings on which firms react to innovations in volatility, the use of issuance proceeds, and the extent to which financially constrained firms are able to deleverage following positive volatility shocks. Section V concludes. II. Background on the effect of volatility on leverage There are strong theoretical reasons to believe that volatility matters for capital structure. Consider Merton (1974)’s model of equity as a call option on the firm’s assets. In this model, the                                                              6

 See Fischer et al. (1989), Goldstein et al. (2001), and Danis et al. (2014). 

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firm defaults on its obligations at maturity of the debt if the value of the assets is below the face value of the firm’s debt. This assumption yields a simple expression for the likelihood of default as function of the current value of the firm’s assets relative to the amount of debt (i.e. the firm’s leverage ratio) and the volatility of the firm’s assets.7 In Leland (1994), the optimal capital structure is derived by solving a Merton-like model. Optimal leverage balances expected costs of financial distress with the tax benefits of debt. Increasing volatility increases the likelihood of occurrence of states of the world in which the firm does not benefit from debt tax shields. Overall, the effect of volatility on optimal leverage is predicted to be negative because higher volatility increases the likelihood of financial distress and reduces the present value of tax shields.8 Note that the theoretical link is between leverage and the expected future level of volatility. Empirically, leverage and past volatility will be linked only to the extent that past volatility is a good predictor of future volatility.9 More recent models of dynamic capital structure imply that the role played by cash-flow volatility is related to firms’ desire to preserve future financial flexibility. This motive dominates the expected tax benefits of debt in models in which precautionary savings facilitate investment for capital-constrained firms. In Whited and Riddick (2009) firms trade-off the tax disadvantage of holding cash with the benefits of having internal funds to fund future investments. DeAngelo et al. (2011) link firms’ debt policy to their investment policy and argue that firms’ desire to maintain future financing slack in the face of uncertainty dominates the tax-benefits of debt. The empirical capital structure literature finds a weak association between asset-, or cashflow volatility and leverage (Graham & Leary 2011). Measures of volatility used in these studies                                                              7

In Black and Cox (1976), the Merton model is generalized to allow equity holders to default before the maturity of the debt claim when the value of the firm’s assets falls below an optimal default boundary. Allowing for an endogenously determined default threshold yields an expression for the probability of default that, like in the Merton model, is a function of leverage and volatility. 8 Intuitively, debt tax shields are a concave function of firm value and therefore Jensen’s inequality implies that increasing the volatility of the firm’s cash flows reduces the present value of debt tax shields.  Smith and Stulz (1985) make a similar argument with respect to the benefits of corporate hedging. 9  Not all models however, predict a negative relation between leverage and asset volatility. Early work on this question by Bradley et al. (1984) indicates that the relation between volatility and leverage may be positive if bankruptcy costs are low. However, they conclude that, generally, leverage and volatility should be negatively related. Gorbenko and Strebulaev (2010) show that asset volatility and leverage are positively related when the importance of temporary cash flow shocks is low relative to permanent cash flow shocks. Their model highlights the importance of cash-flow volatility in capital structure choice, and suggests that asset volatilities are too low to be empirically relevant for corporate leverage ratios.  

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can be classified into three categories. The most widely used measure of volatility is the rollingwindow measure used in Rajan and Zingales (1995), Opler et al. (1999), Valta (2012), and others. The rolling widow approach measures volatility as the realized standard deviation of cash-flows over a constant number of past periods. The merit of this approach is its simplicity of implementation. However its principal drawback is that it puts an equal weight on recent and past innovations in firm profitability, which produces a very persistent and slow to adapt volatility process for cash flows. It may therefore come as no surprise that innovations in these measures of volatility are weakly related to leverage. An additional drawback is that a rolling window leads to mechanical drops in the effect of past innovations in volatility when past innovations drop out of the rolling window. A second measure of volatility used in the capital structure literature is the variance of past stock returns (Frank & Goyal 2009, Faulkender & Petersen 2006). Although volatility is typically measured using de-leveraged equity returns to account for the leverage effect, leverage and asset-volatility are endogenously determined, which raises concerns about proper identification. A third approach is employed in Leary and Roberts (2005) who model cash-flow volatility as the absolute change in the prior period’s earnings. The concern with this approach is that it puts all of the weight on the most recent innovation in corporate profits without giving any weight to volatility prior to time t-1. Finally, some studies extract return volatility forecasts from exchange-traded out-of-themoney call and put option prices. The advantage of this method is that is also generates a forward-looking measure of volatility. It has two limitations, however. First, as with measures based on stock returns, this approach requires one to delever the implied volatility using the firm’s market leverage ratio, leading to endogeneity problems as volatility is also used to explain leverage. The second limitation of this approach is that it restricts the sample to large publicly traded firms with liquid option markets. We show that the effect of volatility on leverage is concentrated among firms that are financially constrained. Most of these firms do not have liquid option markets for their shares. III. Data and methodology The empirical analysis is based on COMPUSTAT’s quarterly data set. We restrict the sample to manufacturing firms (SIC codes 2000 to 3999) over the sample period beginning in 7  

   

quarter 1 of 1985 and ending in quarter 4 of 2005. We limit our sample to manufacturing firms because our identification strategy relies in part on tariff changes. Tariff data up to 2005 are available on Peter Schott’s web site and they are calculated as the ratio of duties collected to freeon-board customs value of imports times 100 (Bernard et al. 2006). Our second instrument is the industry trade-weighted foreign exchange rate. The trade-weighted index consists of a weighted average index of the real value of the U.S. dollar in each country’s foreign currency for each industry, and the construction of this index follows Bertrand (2004).10 Industry imports and exports from 1989 to 2005 are obtained through TradeStats Express at the U.S. Department of Commerce. GDP growth data is obtained from the Bureau of Economic Analysis, and credit spreads on 30Y bonds are obtained from Moody’s. Data on foreign exchange rates is obtained from the St-Louis Federal Reserve Bank. Firm level variables are defined in Table 1. The definitions of most of these variables are common to capital-structure studies with the exception of profitability and volatility. We define both of these variables at the industry-level in order to reduce endogeneity concerns and to facilitate the measurement of cash-flow volatility. Industries are defined with 3-digit historical SIC codes and industry quarters must have at least five firms to be included in the sample. Industry profitability is defined as the ratio of aggregate operating income before depreciation (EBITDA) in each quarter dividend by aggregate assets net of cash for each industry. Because we are interested in firm-level responses to innovations in volatility and profitability, we restrict firms to have a minimum of four quarters of data. These procedures yield 4,559 firms across 76 manufacturing industries with valid estimates of profit volatility between 1985 and 2005. 3.1 Volatility estimates We measure cash-flow volatility as the square root of the variance of industry operating cash flows. Measuring volatility at the industry level has several advantages. First, using industry level volatility partially alleviates endogeneity concerns because reverse causality between                                                              10

 For each NAICS industry classification we obtain industry imports and exports for the year 1989. We use 1989 as a base year to compute each industry’s import share and export share by foreign country. Each industry-currency pair’s weight is set to ½ times the sum of the import and export shares of that industry-currency pair. The tradeweighted index in a given quarter for an industry uses these weights in a weighted sum of the real exchange rate at the end of the quarter across country’s with whom the industry has a trade relation. Our measure differs slightly from Bertrand’s who only weighs the real FX rate by the import share. We incorporate the export share because industries in our sample have bilateral trade agreements that may affect exports to foreign countries. 

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industry volatility and firm capital structure is less likely than if firm-level volatility were used. Second, industry-level estimates provide a lengthy time-series to obtain accurate volatility estimates with a GARCH-type model. Because GARCH estimations require long time series, we use industry-level data from 1980 to 2005 to estimate the volatility process and earnings models for each industry. Industry cash-flow volatility is measured with the following model of quarterly earnings, which is jointly estimated along with a GARCH model of conditional volatility. 1

2

The variable net assets and

is the ratio of aggregate industry operating income to aggregate industry

is a dummy variable for calendar quarter j. Assets are measured net of cash in

order to exclude possible effects of volatility on cash holdings (Opler et al. 1999 find that cashflow volatility increases cash holdings). Equations (1) and (2) are measured jointly for each industry using the time series of quarterly earnings. The resulting volatility measure,

, is a

forecast of time-t cash-flow volatility based on information up to time t-1. The selection and estimation procedure of (1) and (2) follows Fu (2009). Specifically, each industry is assigned a GARCH(p,q) process. The parameters p and q vary between 0 and 3, and 1 and 3 respectively, and they are chosen as follows. For each industry, we estimate 12 different GARCH models by varying the possible combinations of p and q.11 We then select p and q based on the GARCH process that has the lowest Schwartz Bayesian Criterion (SBC). This process ensures that industries are not dropped out of the sample because of convergence problems with an incorrect specification. It also ensures that each industry is fit with a GARCH model proper to that industry. Using this procedure results generates the following sample. There are 127 manufacturing industries in the COMPUSTAT quarterly dataset. Restricting these industries to industry-year                                                              11

These models are the ARCH(q), and GARCH(p,q) processes with 1

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3

1

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observations with at least five firms and industries with no less than 12 quarterly observations, yields 103 industries. In order to ensure that the volatility process is heterokedastic, we restrict the set of GARCH models to those that have at least one non-zero

term in equation (2). Imposing

this restriction reduces the number of industries to 84. 12 For each of the remaining industries we estimate and select an ARCH/GARCH process and select the best-fit using the SBC criterion. Matching these industries to the tariff and trade-weighted FX index data further reduces the number of industries to 76. As shown in Table 1, the average quarterly ARCH/GARCH volatility estimate is 0.81%. The interquartile range for quarterly changes in this process (D.Volatility) is about 16 basis points per quarter, and one standard deviation in changes equals 33 basis points. Figures 1-3 plot quarterly ARCH/GARCH estimates of the variance of industry profits for select industries. The plots also report moving window (five-year) estimates of the variance of industry profits for purposes of comparison. The plots illustrate the advantage of using GARCH estimates instead of rolling window estimates of volatility. As shown, a high squared residual term causes both GARCH and the moving window estimates to increase. The moving window estimates are more persistent because the innovation in volatility remains within the window until the window moves forward a sufficient number of quarters. The moving window estimates fall sharply after 20 quarters, creating a mechanical relation between time and variance, which could lead to spurious regression results. In order to further understand the properties of the volatility process, we compare its effect on market leverage with volatility measures of cash-flow and asset volatility previously used in the literature. This regression is estimated in levels and in differences, where the levels regression has industry dummies to control for cross-sectional differences in volatility across industries. The differences specification reveals the extent to which each measure can pick up the impact of innovations in volatility on market leverage ratios. The results are reported in Table 2. As shown in Panel A, GARCH volatility is negatively related to the firms’ leverage ratios. The same holds for one measure of realized volatility (Realized2) and asset volatility, measured using the standard deviation of stock returns (see last column). However, when the regression is measured in differences in Panel B, the GARCH volatility keeps its sign and significance, but the other                                                              12

 The remaining manufacturing industries do not have sufficient variation in the volatility of their cash flow process to obtain convergence with a specification that has a heteroskedastic term in it.  

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measures lose their significance or have the wrong sign. This table corroborates the intuition obtained from Figures 1-3 that measures of volatility based on moving windows do a poor job at capturing the effect on capital structure of within-industry and within-firm variations in volatility. 3.2 Persistent vs. temporary shocks to volatility One attractive feature of GARCH volatility estimates is that, if the GARCH process is stationary, we can calculate the expected half-life of the volatility process. This enables us to examine whether the relationship between leverage and innovations in volatility varies with the expected persistence of the innovation in volatility. A significant proportion of the industries in our sample have stationary GARCH processes. For each of these industries, we compute the halflife of the volatility process. For example, the half-life of a GARCH(1,1) is obtained by solving the following equation for k. 1 , 2

1. 3

The value of k indicates the number of quarters it would take for the GARCH process to halve the distance between the current level of variance and the long-run level of variance for that industry. 13 To examine whether the relation between leverage and volatility varies with the persistence of the volatility process, we split our sample into industries with high and low halflives according to whether the half-life is greater than four quarters. Four quarters is about half the time it takes for volatility to revert back to its pre-shock level following a significant tariff cut. Table 3 reports the regression results. As shown, the effect of innovations in volatility on leverage is over twice as large for industries in the high-half-life group. The coefficient on volatility for these industries is -0.65, compared with -0.17 for industries with less persistent innovations in the GARCH process. The difference between the two coefficients is statistically significant at the 5% level of confidence. Both coefficients are statistically significant at the 5% level of confidence, indicating that volatility has a significant effect on leverage even in industries with less persistent shocks. 3.3 Instruments for volatility and profitability                                                              13

The k-step ahead forecast for higher order processes can be expressed in a similar fashion. We derive corresponding formulas in the appendix.

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One of the empirical challenges in measuring the effect of cash-flow volatility on capitalstructure choice is the negative relation between volatility shocks and profitability shocks. This relation is apparent in our data. For example, Figure 4 plots the percentiles of industry profitability in each quarter over time for our panel of manufacturing industries. The bottom panel reports the weighted median level of industry profits. The plots show that the dispersion in profitability across industries increases during period when median profits are low. During these periods, the percentiles widen out because some industries do well and others incur significant losses. Therefore, in order to identify the effect of volatility on leverage and to make causal inferences, we need instruments for both industry profits and industry volatility. This approach, if properly executed, allows for causal inference to be made because it measures the component of volatility that is correlated with a common shock, product market competition. There are several reasons to suspect that product market competition will affect both firm profitability and cash flow volatility. For example, increased competition may lead to predatory market pricing and a greater likelihood of financial distress (Bolton and Sharfstein (1990) model leverage as a function of product market competition). Product market competition may also reduce pledgeable income, making financial distress more likely and reducing the value of the firm’s collateral (Hart & Moore 1994). Researchers have exploited these relations and used measures of competition based on trade barriers and import competition to study corporate cash holdings (Fresard 2010) and syndicated bank-loan spreads (Valta 2012). Both authors argue that tariffs make an appealing instrument because they are a source of exogenous variation in the firm’s competitive environment. We adopt a similar identification strategy and instrument changes in volatility with changes in tariffs. Figure 5 plots weighted-average ARCH/GARCH volatility estimates in event time around tariff cuts, where the weights are proportional to the number of firms in each industry-quarter.14 As shown, the variance of industry profits rises significantly around tariff reductions, and the effect of the tariff reduction on leverage persists up to two years following the tariff cut. This graph thus provides visual evidence that the GARCH measure of volatility is sensitive to industry-level changes in the firms’ competitive environment.

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 The majority of major tariff cuts occur between 1980 and 1995, with much fewer cuts occurring after 1995. 

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Tariff changes may also affect profitability levels, potentially confounding the interpretation of the effect of tariff reductions on leverage. Valta (2012) leaves open to interpretation whether competition increases loan spreads because of greater volatility or reduced profitability. We further investigate the relation between tariffs, profitability and volatility in Figure 6, which plots industry profits and volatility against tariffs. As shown, industries with higher tariffs tend to have higher average profits. Taken together, Figures 5-6 suggest that import competition may affect capital structure by changing future profitability, future volatility or both, thereby making any conclusions about the channel through which changes in competition affect leverage difficult to identify.15 Because of these concerns, we identify an additional instrument for volatility and profitability. Exchange rates may affect industry profits and volatility by increasing productmarket competition. For example, an appreciation of an industry’s local currency may decrease profits and increase cash-flow volatility by making exports more expensive and increasing the import share of foreign goods in that industry. However, exchange rate levels have a more complex effect on industry profits than implied by this simple example. Import industries may benefit from an appreciation of the local currency because their raw materials are cheaper. For example, Griffin and Stulz (2001) find that the integrated oil industry in Japan responds positively to local currency appreciations. Furthermore, Knetter (1989) and Froot and Klemperer (1989) show that firms differ in their ability to pass-through changes in exchange rates into prices, which affects profit margins and leads to deviations in the law-of-one-price. Finally, some firms may engage in hedging activities that offset the effects of exchange rate fluctuations on corporate profits (Griffin & Stulz 2001). Following Bertrand (2004), we use the industry trade-weighted foreign exchange rate as a source of exogenous variation in industry import and export shares. By using a trade-weighted measure, the effect of a change in exchange rates will vary across industries based on import penetration and industries’ focus on exports. The attraction of the trade-weighted index as an instrument is that it is less likely to be correlated with firm leverage than industry import or export

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 Xu (2012) finds that import competition increases future leverage and concludes that this is evidence of the positive effect of profitability on leverage.

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shares because foreign exchange rates are determined by factors that are outside the scope of the firm’s product environment. We establish the empirical validity of our instruments by measuring univariate and partial correlations of the two instruments with volatility and profitability. Table 4 reports pairwise correlations in quarterly differences at the industry level. As shown, industry profits and volatility are significantly negatively correlated, indicating that periods of high volatility are accompanied by episodes of low profitability. Consistent with Figures 5-6, tariff changes are significantly negatively correlated with industry volatility and significantly positively correlated with industry profits. Changes in the trade-weighted index are positively associated with volatility and profitability. However, lagged innovations in the trade-weighted FX rate are negatively correlated with profitability, suggesting that the relation between foreign exchange rates and profitability may be dynamic and more complex than implied by univariate correlations. Table 5 reports first-stage instrumental variable regression estimates in levels (Panel A) and in differences (Panel B). We use time t-1 levels of tariffs and the trade-weighted FX rate as instruments in the levels equations, and we use time-t differences in tariffs and the trade-weighted FX rate as instruments in the differences equations. The levels equations include industry fixed effects in order to control for cross-sectional differences across industries in the average level of volatility or profitability. Because the industry-level variables are repeated across firms, we also estimate the first-stage regressions at the industry level, where observations are weighted by the number of firms in the industry. These regression are reported in columns (3) and (4) of each panel. As shown, tariffs are negatively related to industry volatility and positively related to industry profits, consistent with earlier graphical evidence. The trade-weighted index is positively related to profits and volatility in all specifications. We further investigate the relevance of these instruments by testing for the joint significance of these variables in each of the first-stage regressions. Corresponding F-tests reject the null hypothesis, and these statistics are greater than 10, the commonly used benchmark for instrumental variable relevance. We also report the Stock and Yogo (2005) minimum Eigenvalue statistic at the bottom of each panel. This tests rejects the null of weak instruments in all cases.16                                                              16

 Table 2 of Stock and Yogo (2005) reports the critical values for the test of weak instruments. In the case of two endogenous variables and two instruments, the critical value is 7.03.

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IV. How does uncertainty affect capital structure? How does uncertainty affect capital structure? Our analysis proceeds in two steps. We first examine the effect of volatility on market and book leverage ratios. In the second step, we measure the effect of volatility on security issuance and repurchase activity. The rationale for these tests is that increases in volatility may be associated with an increase in the value of market equity and, since leverage is measured using book values of debt, this may lead to mechanical declines in market leverage. Therefore, we adopt an approach similar to Leary and Roberts (2005) and use financing spikes to identify capital structure adjustments. 4.1 Leverage equation estimates Table 6 reports estimates of the effect of volatility on book leverage and market leverage. The regression specification is estimated in both levels (Panel A) and in differences (Panel B). The levels equation includes industry fixed effect in order to control for cross-sectional differences in average leverage across industries. Industry effects are differenced out from the specification in differences. The IV levels regressions instrument industry volatility and profitability with time t1 tariffs and the trade-weighted FX index. The IV differences regressions instrument volatility and profitability with time-t differences in the two instruments. The third row for each variable reports bootstrapped standard errors. Ordinary Least Squares estimates reported in Panel A indicate that volatility is significantly negatively related to market leverage but not book leverage. However, the IV regressions indicate that volatility, when instrumented, is negatively related to both market and book leverage levels. Moreover, the economic significance of volatility increases when instrumented, implying that (assuming our instruments are valid) OLS estimates understate the effect of volatility on leverage. For example, multiplying the coefficient on volatility by the interquartile range of quarterly differences in volatility implies a 4.40% decline in market leverage based on IV estimates for market leverage, and a 3.8% decline, based on IV estimates for the book leverage regression. In comparison, the corresponding change implied by the OLS estimate in column (1) is only 13 basis

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points.17 Bootstrapped standard errors are very similar in magnitude to the asymptotic standard errors, and we use conventional standard errors for the remainder of the empirical tests. Because our focus is on the effects of innovations in volatility, we also estimate the leverage regression in differences. Innovations in volatility significantly affect market leverage, but not book leverage ratios. The effect of volatility, when measured in differences, is also economically significant. Multiplying the coefficient on volatility reported in column (2) of Panel B by the interquartile range for volatility implies a 3.0% change in market leverage, which is comparable with the results obtained for the levels equation. Overall, the IV-based regressions indicate that volatility has a significant negative effect on capital structure choice after controlling for the contemporaneous effect of profitability on leverage. 4.2 Which firms respond to changes in volatility? A largely unexplored question in the literature is whether some firms respond more to innovations in volatility than others. Financial theory predicts that financially constrained firms are more sensitive to volatility shocks than unconstrained firms. For example, Holmstrom and Tirole (1997) show that such firms are more sensitive to contractions in intermediary capital during recessions. Leary (2009) provides empirical evidence that these firms’ leverage ratios decline following a contraction in bank lending. In addition, Bloom (2014) shows that declines in economic growth are generally accompanied by increases in profit volatility. Put together, these studies imply a greater effect of cash-flow volatility on constrained firms than unconstrained firms. Following the literature, we identify constrained firms by whether they pay a dividend or have a credit rating (see Faulkender & Wang 2006). One potential concern is that leverage regressions capture the effect of passive changes in leverage as opposed to changes due to managerial activity. We address this issue by defining basic financing events as in Leary and Roberts (2005) and Korajczyk and Levy (2003). This approach allows us to isolate changes in leverage that arise from actions as opposed to passive changes in                                                              17

 As James Stock and Mark Watson Introduction to Econometrics 3rd edition 2011 pp. 464-465 explain, the ratio of the bias in OLS coefficient estimates to the bias in IV coefficient estimates is approximately equal to the F statistic associated with the instruments in the first stage regression minus 1. Given that the F statistics reported in Table 4, the difference in the OLS and IV estimates for volatility are consistent with a significant bias in OLS estimates. Intuitively, exogenous shock to volatility arising from tariff changes may have a greater impact on leverage than endogenous changes in industry volatility due to, for example, changes in firm operating leverage.  

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leverage caused by a change in the value of the firm’s liabilities (Welch 2004). A security issue is defined as having occurred in a given quarter if the net change in debt or equity divided by the value of book assets at the end of the prior quarter exceeds 5%. A similar definition holds for equity repurchases and debt reductions. This yields four basic financing events: equity issues, debt issues, debt reductions and equity repurchases. With the exception of equity repurchases, all events use a 5% cutoff. Following Leary and Roberts (2005) we use a 1.25% cutoff for equity repurchases because of their more frequent nature and smaller size. To further isolate issuance decisions that impact leverage, we also define leverage increases as occurring whenever net debt issuance minus net equity issuance exceeds 5% of prior quarter book assets. Leverage decreases occur whenever net equity issuance minus net debt issuance exceeds 5% of prior quarter book assets. Each of the four financing spikes and the leverage increases and decreases take the form of a binary variable in our regressions.18 Table 7 reports the frequency of each type of event in the sample. The impact of cash-flow volatility on security issuance is measured with the following PROBIT model of issuance. Pr









4 where j indexes the industry, i indexes the firm and t indexes the quarter. The variable

is

a binary variable that designates a basic financing spike, or a leverage increase or decrease. Because the GARCH model measures expected time-t volatility, innovations in industry levels of volatility and profitability are measured at time t. We also include year and industry fixed effects

. The industry fixed effects control for cross-sectional differences in volatility and

profitability across industries. This ensures that we are capturing the effect of within-industry                                                              18  An alternative approach would be to follow Denis and McKeon (2012) and define proactive changes in leverage as the increase in leverage resulting from changes in the firm’s debt that is normalized by the change in value of market assets. See their equation (4). The appeal of this procedure is that it identifies large increases in debt that lead to increases in leverage. However, it also has several drawbacks. Their analysis conditions on deviating from target leverage, which is unobserved. Misspecification of the firm’s target leverage ratio may yield incorrect inferences as to whether the firm is deviating from its current target or moving to a new target. An alternative interpretation of Denis and McKeon (2012) is that target leverage ratios changes along an unmeasured dimension that is not included in their specification. Second, proactive increases are, by definition, infrequent events. Depending on the nature of their adjustment costs, leverage adjustments may be more frequent in nature, especially for debt issues, which typically have lower issuance costs than equity issues.  

17  

   

innovations in volatility and profitability on security issuance. Equation (4) is estimated with an instrumental-variable specification based on the Instrumental Variables Probit (IVPROBIT) model implemented as described in Wooldridge (2010) and Rivers and Vuong (1988). Time-t volatility and profitability are instrumented with tariffs and the trade weighted FX index, both measured in levels at time t-1. IVPROBIT estimates are reported in Table 8. As shown, the Chi-square statistics for exogeneity of the independent variables rejects the null hypothesis of exogeneity at the 5% level of confidence in the leverage increases, debt issuance and share repurchase regressions. The IVPROBIT estimates of the coefficient on volatility are significantly negative and economically significant for leverage increases, debt issues, and share repurchases. These results indicate that firms increase their leverage in response to a decrease in volatility. For example, a one standard deviation in the quarterly change in volatility reduces the probability of a leverage increasing transaction by 5.4% (based on column (1) estimates). Examination of the other column estimates indicates that the effect of volatility is economically significant in both the debt and share repurchase regressions. Measured the same way, the economic significance of volatility is 7.8% and 14.1% for each of these two variables, respectively (see Panel B). We next sort the sample by whether a firm pays a dividend or has a credit rating. These results are reported in Panel C. As shown, the magnitude of the coefficient on volatility for leverage increasing transactions for constrained firms is different in sign and larger in magnitude than for unconstrained firms regardless of the classification scheme. For example, non-dividend paying firms are more likely to undertake leverage increasing transactions when volatility is low than dividend-paying firms, as shown by the propensity to issue debt and repurchase shares. Similarly, unrated firms are less likely to issue debt and repurchase shares when volatility is high than rated firms. Overall, these results indicate that decreases in volatility significantly affect the likelihood of leverage increasing transactions by firms that face financing frictions when raising capital. This result is consistent with Harford et al. (2014) who show that financially constrained firms take advantage of mis-valued credit markets by issuing more debt and increasing leverage. Our results are complimentary in that we provide an explicit mechanism through which these firms are able to increase their debt. 18  

   

Furthermore, the asymmetrical effect of volatility on leverage increasing transactions (debt issues and share repurchases) compared with leverage decreasing transactions (debt repurchases and equity issues) suggests that financing frictions may prevent firms from reducing leverage in response to large increases in volatility. We further explore this question in the next section by examining the effect of credit market conditions and volatility on the capital-raising process. 4.3 Effect of credit market conditions on leverage It is well known that credit market and macro-economic conditions are important determinants of security issuance (see Korajczyk & Levy 2003, Greenwood & Hanson 2013, and Erel et al. 2012 for evidence that leverage ratios and capital raising vary over the business cycle). What is less well understood is why these factors affect capital raising activity. Part of the reason is that empirical studies have a difficult time distinguishing between demand and supply based explanations (Ivashina & Sharfstein 2010, Kahle & Stulz 2013, and Baker 2009). In this section, we explore the role cash-flow volatility plays across different times in the credit-market cycle and attempt to identify when volatility matters most for capital structure choice. We measure the availability of credit using the Federal Reserve Boards’ survey of senior lending officers. This data is obtained at a quarterly frequency beginning in 1990 and summarizes lending officers’ views about the availability of credit in the economy. The survey reports this data as the net percentage of senior loan officers reporting a tightening of lending standards (the value of this variable, Tighten, lies between -100 and 100). We also code a binary variable Dummy: Tighten>0 equal to one whenever the net percentage of senior loan officers reporting tightening of lending standards is positive. The interpretation of our results depends on whether tight credit conditions increase the level of cash-flow volatility, or whether a given level of cash-flow volatility has a greater effect on financing decisions when credit conditions are tight. We address this question by estimating an industry-level regression of volatility and profitability on Dummy: Tighten>0, the two instruments and control variables averaged across firms in each industry. These regressions are reported in Table 9 using Weighted Least Squares in which the weights are proportional to the number of firms in each industry-quarter. As shown, the relation between credit conditions and cash-flow volatility is weak: tight credit conditions are associated with only a 3 bp increase in cash-flow volatility. In comparison, the association between credit conditions and industry profits is both 19  

   

statistically and economically significant: tight credit conditions are associated with a 37 bp drop in industry profits. In order to determine whether the effect of cash-flow volatility varies with credit conditions, we interact Tighten with both cash-flow volatility and profitability. Pr







5

The interaction terms are instrumented with the interaction of Tighten with tariffs and the trade-weighted index, both measured at time t-1. The results reported in Table 10 can be summarized as follows. First, as in the previous tables, volatility is significantly negatively related to debt issues. Second, the effect of volatility on debt issues is larger when credit conditions are easy (i.e. when Tighten is negative), as shown in Panel A. Third, the effect of volatility on equity issues is negative and significant, but not related to credit conditions. For all three findings, the effect of volatility on security issuance (debt or equity) is statistically significant only for firms that are financially constrained. These findings suggest two potential channels through which cash-flow volatility affects financing decisions. First, the effect of cash-flow volatility on debt issues appears to be more important when lending standards are loose (perhaps because the supply of financial intermediary capital is plentiful). This result is consistent with general concerns described in the introduction that periods of over-confidence lead to excessive debt issuance. Our results indicate that these periods coincide with easy credit conditions and low cash-flow volatility. Second, our findings are consistent with predictions about the importance of financial intermediaries in regulating access to credit markets for financially constrained firms. He and Krishnamurthy (2013) show that financial intermediaries play a role in propagating cash-flow shocks across the economy. Holmstrom and Tirole (1997) show that credit crunches (measured with the credit conditions measure) and collateral squeezes (measured with the volatility shock) impact financially constrained firms the most. Finally, we note that constrained firms may wish to issue equity in order to reduce their debt burden in response to increases in cash-flow volatility. Panel C of Table 10 suggests that this rarely (if ever) occurs: equity issues are negatively related to cash-flow volatility for all types of firms.

20  

   

4.4 Reduction in total debt and volatility shocks How easy is it for firms to deleverage in response to increases in cash-flow volatility? What sources of capital are used to reduce their debt burden in response to these shocks, and does the source of capital vary with financial constraints? This section explores these questions by looking at changes in total debt outstanding following quarters in which firms either issue equity or have positive operating cash flow realizations greater than 1% of total assets. To address these questions we examine the relationship between volatility and the use of issuance proceeds using an empirical models similar to the one used by Kim and Weisbach (2008). This specification examines the impact of equity issuance and operating cash flows on reductions in total debt, and it allows equity issues and operating cash flows (OCF) to enter the specification separately. Operating cash flows are defined as operating income before depreciation minus interest and taxes. Each source of funds is normalized by total assets at the end of the fiscal quarter prior to issuance, and we take the log of one plus the normalized variable. Each source of funds is also interacted with the GARCH estimate of cash-flow volatility, where volatility is net of the time-series mean for each industry. Volatility is instrumented with tariffs and the trade-weighted FX rate, also net of the time-series mean for each industry. The regression specification also include the log of total assets and industry fixed effects. Specifically,

ln

1

ln

ln



ln



ln

1





6

ln

∑



1 6



Table 11 presents the results estimated up to 8 quarters after the issuing quarter. Comparing Panel A and B (and C and D) reveals that unconstrained firms (i.e. dividend-paying and rated firms) are more likely to use the proceeds from equity issues to pay down their debt than their 21  

   

financially constrained counterparts. For example, the coefficients on equity proceeds vary between 0.06 and 0.14 for dividend-paying firms, compared with 0.001 to 0.01 for non-dividend paying firms. The latter type of firm is also more likely to pay down debt out of operating cashflows than dividend paying firms. The coefficients on OCF are negative for dividend-paying firms, but significantly positive for non-dividend paying firms (between 0.04 and 0.24). Similar results hold for rated and unrated firms. What happens when volatility is high? High levels of volatility do not have a significant effect on the use of equity proceeds across all four classifications. However, higher levels of volatility reduce the proportion of operating cash flow used to pay down debt. The coefficients on the interaction terms between OCF and volatility are significantly negative in three of the four panels (the exception being rated firms in Panel D). In order to further understand this relationship, we quantify in Table 12 the dollar change in debt in response to a dollar increase in equity or operating cash flows. We first define a high (or low) volatility regime as having industry volatility one standard deviation above (below) the industry mean. Using the method described in Kim and Weisbach (2008), a dollar increase in OCF during low volatility regimes by dividend paying firms reduces total debt by 0.28, compared with 0.15 for non-dividend paying firms.19 A dollar increase in OCF during low volatility regimes by rated firms leads to a 0.03 reduction in debt, compared with a 0.19 reduction in debt for unrated firms. The corresponding debt changes for medium and high volatility regimes are small or negative for all four types of firm, indicating that operating cash flows are used to pay down debt when volatility is low, but not when volatility is high. Constrained and unconstrained firms also differ in the use of proceeds from equity issues across different volatility regimes. As shown, dividend paying firms pay down 0.139 in long-term debt for every dollar of equity raised during low volatility regimes, whereas for non-dividend paying firms, this number is zero. However, when volatility is high, dividend paying firms do not pay down their long-term debt with the proceeds from equity issues, yet non-dividend paying firms                                                              19

 The calculation is based on the median-sized firm in the sample. For example, the dollar change in debt reduction for t=1 by the median dividend paying firm is calculated as follows. Median total assets are 95, median net equity issue is 0.18, and median operating cash flow is 1.9. The coefficients from table 11 yield 0.012 as the predicted value of the log transformation, implying a predicted debt reduction of 1.12. We then add one to median operating cash flows and repeat the above procedure, which results in a predicted change of 1.18. The difference in the two predicted changes represents the dollar change in debt reduction for a one-unit increase in operating cash flows, which equals 0.06.

22  

   

pay down 0.148 for every dollar of equity raised. We find similar results when constraints are defined by the lack of a credit rating. Thus, conditional on being able to raise equity during periods of high uncertainty, constrained firms have a higher propensity to use the proceeds to pay down existing debt than unconstrained firms These findings explain why debt reductions by constrained firms are insensitive to volatility (see Table 8, Panel A). Specifically, our results indicate that constrained firms rely on operating cash flows to pay down their debt during low and normal levels of volatility. However, when volatility is high, these firms do not use operating cash flows to pay down debt. Part of the reason is that operating cash flows and volatility tend to be negatively correlated (extant empirical evidence by Bloom (2014) indicates that this is the norm rather than the exception). Another reason is that operating cash flows may be used to finance investment during high volatility regimes. Given that access to capital markets is likely to be restricted when volatility is high, constrained firms may use operating cash flows to fund capital expenditures instead of reducing long-term during high-volatility regimes. We examine this issue in more detail in the next section. 4.5 Financing proceeds and cash holdings In order to gain some insights into why periods of accommodative credit conditions and low cash-flow volatility are associated with excessive debt financing, we examine what firms do with the proceeds raised from external capital raised during high- and low-volatility regimes. Specifically, we test whether the propensity to save debt proceeds as cash is greater when cashflow volatility is low. There are three reasons why this might occur. First, the cost of raising debt in low volatility regimes is relatively lower, making financing cash reserves relatively less costly. Debt is costly to raise when volatility is high because of higher default risk, and firms may then only issue debt if they have valuable investment opportunities that they do not wish to forego. A second reason is that current cash flows are less informative about future investment opportunities when volatility is high. This mechanism is at work in Whited and Riddick (2009) who find that the cash-flow sensitivity of cash is lower in absolute value when cash-flow volatility is high. The third reason is that constrained firms may behave opportunistically when credit conditions are favorable by raising debt and keeping the proceeds for a rainy day. Extant evidence by Greenwood and Hanson (2013) indicates that debt issued by low-investment grade firms during accommodative credit conditions earns negative returns in the following years. 23  

   

We test this prediction with the following specification. The dependent variable is the cumulative change in cash and marketable securities since the quarter of issuance, scaled by total assets in the fiscal quarter prior to issuance. ln



1 7

The independent variables include the total capital raised, the interaction of this variable with volatility and the log of total assets. As before, volatility is measured relative to the timeseries mean in each industry, and volatility is instrumented with tariffs and the trade-weighted FX index, both lagged one quarter. The type of capital raised (e.g. debt or equity) further splits the sample. Results are reported in Table 13. As shown, a sizable portion of equity proceeds are kept as cash when capital is issued during low to normal volatility regimes. Both dividend and nondividend paying firms save part of the proceeds raised from equity raised during low-volatility regimes as cash, and non-dividend paying firms save a lot more during these times than dividend paying firms. Table 12 shows that the dollar change in cash for dividend paying firms that raise equity capital is 0.17, compared with 0.64 for non-dividend paying firms. Similarly, one extra dollar of equity implies a 0.40 increase in cash for rated firms, compared with a 0.63 increase in cash for unrated firms. Constrained and unconstrained firms also differ in their use of debt proceeds raised when volatility is high. Forty three cents of every dollar of debt issued when volatility is high by dividend-paying firms is saved as cash. In contrast, debt raised during high volatility regimes by non-dividend paying firms is associated with a decrease in cash in the next quarter, indicating that these firms use the entire proceeds to fund investment activities. Similarly, rated firms save 0.64 of every dollar of debt raised during high volatility regimes, compared with -0.32 for unrated firms. Debt raised during low volatility regimes by constrained firms is saved as cash. For each dollar of debt raised during low volatility regimes, 0.89 is kept as cash in the first quarter following the debt issue by non-dividend paying firms. The corresponding amount for dividend-paying firms is negative. Similarly, unrated firms save 0.76 of every dollar of debt raised during low volatility regimes, compared with 0.20 for unrated firms.

24  

   

Overall these results show that the sensitivity of the use of proceeds to cash-flow volatility is strongest among financially constrained firms. These firms hold more of the proceeds from debt issued during low volatility regimes in cash, and they are more likely to use the proceeds from debt issued during high volatility regimes to finance investment. These results are consistent with contracting cost explanations of debt issuance that predict that debt issuance costs are higher when volatility is high. Conditional on raising debt in a high volatility environment, constrained firms are more likely to use the proceeds to finance a high NPV project instead of holding the proceeds as cash. Financially constrained firms are also more likely to pay down their debt out of operating cash flows than unconstrained firms, making deleveraging difficult for firms in the former group when profits are negatively correlated with cash-flow volatility. Our findings suggest that financially constrained firms behave aggressively by issuing debt when cash-flow volatility is low, leaving them vulnerable to increases in cash-flow volatility in the future. V. Conclusion Most theories of capital structure predict a negative relation between volatility and leverage, yet prior studies find at best a weak relation between these two variables. We argue that the lack of significance can be explained by measurement error in empirical estimates of the volatility process and the fact that there is considerable heterogeneity in the ability of firms to adjust to volatility shocks. Specifically, the use of rolling windows to estimate cash-flow or asset volatility produces a volatility process that is slow to reflect new information and that puts equal weight on recent observations and past observations. A further difficulty is that positive innovations in volatility tend to coincide with negative innovations in profitability, making inferences about the effect of volatility on leverage difficult to make. We make two contributions to the literature. First, we show that GARCH estimates do a much better job at detecting the impact of innovations in volatility than more commonly used measures. While GARCH and moving-window measures are able to detect cross-sectional variations in leverage with volatility, only the GARCH process is able to detect firms’ responses to time-series changes in volatility. Second, we find that volatility innovations are more important for financially constrained firms. This is an important result since in prior studies, leverage regressions that test for the effect of volatility group all firms together. We find that the

25  

   

issuance of debt by financially constrained firms is negatively correlated with cash-flow volatility, and this effect is strongest when credit market conditions are accommodative. Constrained firms react to volatility shocks in an asymmetric fashion: leverage reductions by these firms are uncorrelated with cash-flow volatility, but leverage increases are negatively correlated with cash-flow volatility. The reason is that financially constrained firms differ in their ability and in the way they deleverage in response to positive shocks to cash-flow volatility. These firms use operating cash flows instead of equity to pay down debt when volatility is high. Leverage reductions therefore become difficult for these firms if profitability is negatively correlated with cash-flow volatility, which is an empirical regularity in our data. Summarizing, these firms aggressively issue debt when cash-flow volatility is low, but have trouble deleveraging when volatility is high, leaving them vulnerable to industry or macro-economic downturns.

26  

Appendix A: Derivation of k-step ahead forecasts for higher-order volatility processes The k-step ahead forecast for the GARCH(1,1) process is derived in Campbell et al. (1997). The k-step ahead forecasts for higher-order processes involve an approximation in order to obtain the expression for the half-life. We derive forecast approximations for the ARCH(2), and GARCH(1,2) processes. The ARCH(2) process can be written as , , 1 Using the law of iterated expectations and the fact that the long-run variance

For

1

,

2,

For a stationary process, the third and fourth terms approach zero because the differences in the expectations are small. Therefore the half-life can be approximated with the following expression. ≅ The GARH(1,2) process can be re-written in terms of the error

. , 1



. The law of iterated 1 expectations implies that the k-step ahead forecast can be written as follows. The long-run variance level of this process is

For

2,

Because the differences in expectations on the right-hand side are small, the third and fourth terms vanish and . ≅

27   

Figure 1: GARCH estimates of industry variance for SIC code 371 (Motor Vehicles and Motor Equipment) of industry profits (percent) for SIC code 371. The This figure plots ARCH/GARCH estimates of the variance ( moving window estimate is a rolling five-year moving average of the variance of industry profits (percent). The grey vertical line indicates a tariff cut. This industry experienced a tariff cut in 1982.

28   

   

Figure 2: GARCH estimates of industry variance for SIC code 331 (Primary Metals and Basic Steel Products) of industry profits (percent) for SIC code 331. The This figure plots ARCH/GARCH estimates of the variance ( moving window estimate is a rolling five-year moving average of the variance of industry profits (percent). The grey vertical line indicates a tariff cut. This industry experienced a tariff cut in 2004.

29  

   

  Figure 3: GARCH estimates of industry variance for SIC code 286 (Inorganic chemicals) This figure plots of industry profits (percent) for SIC code 286. The moving window ARCH/GARCH estimates of the variance ( estimate is a rolling five-year moving average of the variance of industry profits (percent). ). The grey vertical line indicates a tariff cut. This industry experienced a tariff cut in 1995.

30  

 

0

2

4

6

8

 

1985q1

1990q1

1995q1 DATE

2005q1

90th percentile 50th percentile 10th percentile

3

Median industry profitability 4 5

6

95th percentile 75th percentile 25th percentile 5th percentile

2000q1

1985q1

1990q1

1995q1 DATE

2000q1

2005q1

Figure 4: The top panel of this figure shows the 5th, 10th, 25th, 50th, 75th, 90th and 95th of industry profitability in each quarter between 1986Q1 and 2005Q4. The bottom panel shows the weighted median industry profitability over the same period.

31  

 

.8

GARCH estimate of volatility(%) .85 .9 .95

1

 

-10

-5

0 Event time (quarters)

5

10

Figure 5: ARCH/GARCH estimates of quarterly variance in industry profitability This figure reports the weighted average measure of the GARCH measure of the variance of industry profits around tariff cuts. Industryyear observations are weighted by the number of firms in each industry and tariff cuts are defined as in Valta (2012).

32  

 

0

5

Industry ROA(%) 10

15

 

0

5

10

15

20

25

15

20

25

0

2

GARCH volatility(%) 4 6

8

Tariff(%)

0

5

10 Tariff(%)

Figure 6: This figure plots quarterly industry profitability (measured as aggregate industry return on net assets) and the ARCH/GARCH variance of industry profits against tariffs. Tariffs equal the ratio of duties to free-on-board customers value of imports in percentage.

33  

    Table 1: Summary statistics on sample of firms The sample is comprised of quarterly data on U.S. industrial firms from COMPUSTAT from 1980 to 2005. There are 4,559 firms in 76 industries defined at the 3-digit SIC code level. TANG equals the ratio of property, plant and equipment over assets. MB equals the market-to-book ratio of assets. Profitability equals industry profitability, defined as aggregate industry operating income over aggregate net assets in each 3-digit SIC code category. RD indicates research and development expense over book assets (set to zero if missing). RDD is a dummy variable for missing research and development expenses. Credit is the difference between the 30-year BBB bond yield and the 30-year Treasury bond yield. Term is the difference between 10Y Treasury bond yields and 6 month Treasury Bills. GRGDP is the growth in GDP over the past 4 quarters. ML equals market leverage, defined as the ratio of short- and long-term debt to the market value of assets. 5Y Window is the 5-year rolling window standard deviation of past abnormal earnings ( . 10Y Window is the 10-year rolling window standard deviation of past abnormal earnings ( . Volatility is the Generalized Autoregressive Conditional Heteroskedasticity estimate of volatility using a GARCH(p,q) process, where the parameters p and q are determined by ranking with the AIC criterion.

34  

    Variable

Unit

Description

mean

sd

p25

p50

p75

min

max

N

ML BL Profitability D.Profitability 5YWindow D.5YWindow 10YWindow D.10YWindow Volatility D.Volatility Tariff TWFX TANG MB ATDEF LATDEF RD RDD GRGDP Credit Term

Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent

Market leverage Market leverage Return on net assets industry Change in return on net assets 5-year rolling window stdev. Qtr. change in 5Y window 10-year rolling window stdev. Qtr. change in 10Y window GARCH volatility estimate Quarterly change in volatility Tariff Trade-weighted value of US dollar Asset tangibility Market to book ratio Total assets (deflated) Log total assets (deflated) R&D expense Missing R&D expense Annual growth in GDP Moody's 30Y credit spread Term structure spread

19.55 20.47 4.61 0.00 0.77 0.00 0.82 0.00 0.81 0.00 2.03 1.92 0.25 2.25 1244 4.61 0.02 0.49 0.03 0.87 1.51

21.80 21.19 1.73 1.17 0.32 0.06 0.30 0.03 0.38 0.33 2.38 0.48 0.18 3.57 5955 2.15 0.04 0.50 0.01 0.24 1.11

0.92 2.10 3.46 -0.56 0.56 -0.01 0.61 -0.01 0.60 -0.08 0.41 1.62 0.11 0.81 23 3.13 0.00 0.00 0.02 0.68 0.51

11.60 16.22 4.48 0.01 0.71 0.00 0.78 0.00 0.77 0.00 1.38 1.99 0.21 1.23 85 4.44 0.00 0.00 0.03 0.81 1.38

31.83 32.34 5.64 0.57 0.94 0.01 0.96 0.00 0.93 0.08 2.97 2.26 0.35 2.25 389 5.96 0.03 1.00 0.04 1.06 2.39

0.00 0.00 0.13 -7.22 0.16 -0.83 0.25 -0.49 0.13 -4.30 0.00 0.37 0.00 0.27 0 -6.79 0.00 0.00 -0.01 0.55 -0.68

91.29 257.66 9.59 5.52 3.25 0.61 3.13 0.38 6.83 3.20 21.42 3.83 0.78 37.89 225213 12.32 0.35 1.00 0.05 1.50 3.51

118206 118206 118206 118206 118206 118206 118206 118206 118206 118206 113463 118206 118206 118206 118206 118206 118206 118206 118206 118206 118206

Fraction Ratio $M 2009 Fraction Binary Decimal Percent Percent

35  

    Table 2: Comparison of different measures of volatility The dependent variable equals levels (Panel A) or the first difference (Panel B) in market leverage (ML). Standard errors (in parentheses) are clustered by firm. Independent variables are in first differences. All independent variables are lagged one quarter with the exception of Volatility and Profitability whose differences are measured at time t. Industry volatility is based on the following model of quarterly aggregate industry profits.

The variable is the ratio of aggregate industry operating income to aggregate industry net assets and is a dummy variable for calendar quarter i. Volatility measures are based on the residual term . 5Y Window is the 5-year rolling window standard deviation of past . 10Y Window is the 10-year rolling window standard deviation of past . Abs change is the absolute value of . GARCH is the Generalized Autoregressive Conditional Heteroskedasticity estimate of volatility using a GARCH(p,q) process. estimate of volatility. Realized1 is the standard deviation of operating cash flows over the past 8 quarters divided by average book assets over the past 8 quarters. Realized2 is the standard deviation of return on assets over the past 8 quarters. Assetvol is asset volatility estimated from the delevered stock return volatility over the past 12 months. Standard are errors clustered by firm are in parentheses.

Panel A: Levels regressions Industry-level measures of vol.

Volatility(t) Profitability(t) TANG(t-1) MB(t-1) Ln(ATDEF)(t-1) RD(t-1) RDD(t-1) Credit(t-1) GRGDP(t-1) Term(t-1)

AdjRSq N Nfirms

5Y window (1)

10Y window (2)

GARCH (3)

Realized1 (4)

Realized2 (5)

Assetvol (6)

0.277 (0.762) -0.666*** (0.089) 20.408*** (1.701) -1.157*** (0.057) 0.055 (0.124) -9.109*** (3.202) 6.363*** (0.540) 2.312*** (0.619) -63.427*** (8.127) -1.134*** (0.094)

1.433 (1.113) -0.660*** (0.088) 20.337*** (1.703) -1.156*** (0.057) 0.061 (0.124) -8.864*** (3.209) 6.286*** (0.546) 2.096*** (0.601) -63.026*** (8.065) -1.140*** (0.093)

-0.795** (0.355) -0.679*** (0.088) 20.400*** (1.702) -1.157*** (0.057) 0.054 (0.124) -9.185*** (3.200) 6.389*** (0.537) 2.458*** (0.586) -63.701*** (8.079) -1.154*** (0.093)

0.062 (0.115) -0.700*** (0.097) 20.374*** (1.907) -1.336*** (0.082) -0.181 (0.135) -10.667*** (3.790) 6.212*** (0.591) 2.036*** (0.643) -69.598*** (8.902) -1.114*** (0.101)

-0.017* (0.010) -0.683*** (0.089) 20.163*** (1.775) -1.251*** (0.064) 0.025 (0.128) -6.622* (3.416) 6.327*** (0.545) 2.374*** (0.594) -62.882*** (8.163) -1.162*** (0.094)

-0.256*** (0.008) -0.899*** (0.088) 13.583*** (1.847) -1.296*** (0.092) -0.827*** (0.139) 4.302 (3.727) 4.268*** (0.543) 3.258*** (0.621) -66.067*** (8.270) -0.929*** (0.096)

0.249 118206 4559

0.249 118206 4559

0.249 118206 4559

0.241 99833 4115

0.248 116083 4386

0.326 93961 3837

36  

Firm-level measures of vol.

   

Panel B: Differences regression Industry-level measures of vol.

Volatility(t) Profitability(t) TANG(t-1) MB(t-1) Ln(ATDEF)(t-1) RD(t-1) RDD(t-1) Credit(t-1) GRGDP(t-1) Term(t-1) Constant

AdjRSq N Nfirms

5Y window (1)

10Y window (2)

GARCH (3)

Realized1 (4)

Realized2 (5)

Assetvol (6)

0.289 (0.312) -0.025 (0.019) 7.024*** (0.759) -0.039*** (0.006) -0.818*** (0.124) -2.518*** (0.629) -0.159** (0.069) -1.258*** (0.140) 14.764*** (1.305) 0.166*** (0.038) -0.156*** (0.044)

0.091 (0.595) -0.026 (0.019) 6.905*** (0.749) -0.038*** (0.006) -0.816*** (0.124) -2.469*** (0.628) -0.162** (0.069) -1.264*** (0.140) 14.689*** (1.301) 0.197*** (0.038) -0.160*** (0.044)

-0.190*** (0.063) -0.032* (0.019) 6.929*** (0.749) -0.039*** (0.006) -0.814*** (0.124) -2.480*** (0.629) -0.160** (0.069) -1.274*** (0.140) 14.573*** (1.302) 0.195*** (0.037) -0.158*** (0.043)

0.006** (0.003) -0.029 (0.022) 7.725*** (0.949) -0.037*** (0.008) -0.953*** (0.159) -2.197*** (0.815) -0.133* (0.075) -1.185*** (0.152) 14.378*** (1.436) 0.159*** (0.040) -0.244*** (0.048)

0.004 (0.003) -0.025 (0.019) 6.909*** (0.812) -0.037*** (0.006) -0.957*** (0.141) -2.726*** (0.689) -0.173** (0.070) -1.242*** (0.141) 15.095*** (1.314) 0.205*** (0.038) -0.183*** (0.044)

0.005*** (0.002) -0.040* (0.022) 9.604*** (0.907) -0.055*** (0.008) -1.050*** (0.182) -1.224 (0.816) -0.175** (0.076) -1.202*** (0.153) 18.149*** (1.449) 0.263*** (0.041) -0.334*** (0.048)

0.005 117209 4556

0.005 118127 4559

0.005 118206 4559

0.005 97193 4018

0.005 115910 4380

0.007 88804 3725

37  

Firm-level measures of vol.

    Table 3 Leverage and persistence in innovations in volatility The dependent variable equals first difference in market leverage (ML). Standard errors (in parentheses) are clustered by firm. Independent variables are in first differences. All independent variables are lagged one quarter with the exception of Volatility and Profitability whose differences are measured at time t. Industry volatility is based on the following model of quarterly aggregate industry profits.

The variable is the ratio of aggregate industry operating income to aggregate industry net assets and is a dummy variable for calendar quarter i. Volatility measures are based on the residual term . The sample is restricted to industries with stationary GARCH processes. The column High reports regression estimates for the sample of firms belonging to industries with a half-life for the GARCH volatility process that is greater than 2 quarters (the sample 67th percentile). The column Low reports regression estimates for the sample of firms belonging to industries with a half-life for the GARCH volatility process that is less than or equal to 2 quarters. The column labeled P-value reports the p-value for the test in the difference in coefficients across the High and Low regressions. Standard errors clustered by firm are in parentheses. Halflife: Volatility(t) Profitability(t) TANG(t-1) MB(t-1) Ln(ATDEF)(t-1) RD(t-1) RDD(t-1) Credit(t-1) GRGDP(t-1) Term(t-1) Constant

AdjRSq N Nfirms

High

Low

p-value

-0.656*** (0.236) -0.168*** (0.064) 1.256 (3.715) -0.059* (0.033) -2.006*** (0.588) 4.365 (6.705) -0.325* (0.177) -1.047** (0.524) 15.113*** (4.569) 0.402*** (0.136) -0.265* (0.147)

-0.168** (0.080) -0.029 (0.021) 7.229*** (0.764) -0.036*** (0.007) -0.589*** (0.127) -1.951*** (0.612) -0.089 (0.088) -1.250*** (0.160) 14.118*** (1.518) 0.147*** (0.043) -0.127** (0.051)

0.026

0.006 10926 385

0.005 86223 3393

38  

0.020

    Table 4 Industry-level correlations This table reports industry-level correlations at a quarterly frequency. The variables are in quarterly first differences and each industry-quarter observation is weighed by the number of firms belonging to the industry-quarter. P-values are in parentheses. Variables are defined in Table 1. Volatility Volatility Profitability Tariff Tariff(t-1) TWFX TWFX(t-1) GRGDP Credit Term

Profitability

Tariff

Tariff(t-1)

TWFX(t-1)

GRGDP

Credit

Term

1.000 -0.0913 (0.000) -0.0107 (0.000) -0.009 (0.002) 0.0169 (0.000) -0.009 (0.002) -0.0155 (0.000) -0.0204 (0.000) 0.045 (0.000)

1.000 0.0487 (0.000) -0.0226 (0.000) 0.0869 (0.000) -0.0642 (0.000) 0.0521 (0.000) 0.0341 (0.000) -0.0847 (0.000)

1 -0.0192 (0.000) 0.0356 (0.000) -0.0179 (0.000) -0.0013 (0.664) 0.0518 (0.000) -0.0066 (0.023)

1 0.0061 (0.035) 0.0346 (0.000) -0.0078 (0.007) 0.0418 (0.000) -0.0016 (0.585)

1 -0.0069 (0.017) -0.0683 (0.000) 0.0299 (0.000) -0.1239 (0.000)

39  

TWFX

1 -0.0646 (0.000) 0.1344 (0.000) -0.1335 (0.000)

1 -0.1137 (0.000) -0.3182 (0.000)

1 0.0735 (0.000)

1

    Table 5 First-stage estimates The dependent variables are industry volatility (Volatility) and industry profitability (Profitability). Independent variables are defined in Table 1. All variables are in quarterly differences. Independent variables are lagged one quarter, except for volatility and profitability which are measured at time t. Industry volatility is based on the following model of quarterly aggregate industry profits.

The variable is the ratio of aggregate industry operating income to aggregate industry net assets and is a dummy variable for calendar quarter j. Volatility measures are based on the residual term . Volatility denotes a GARCH estimate of the volatility of the residual term. Columns (1) to (4) report firm-level regressions where each observation corresponds to a firm-quarter. Columns (5) to (8) report industry-level weighted-least squares regressions, where each observation corresponds to an industry-quarter. Industry-level values are obtained by averaging across firms in an industry-quarter. Each industry-quarter is weighed by the number of firms in that industry-quarter. Standard errors (in parentheses) are clustered by firm (columns (1) to (4)), or are heteroskedasticity robust (5) to (8). Panel A: Regressions in levels Firm regressions

Tariff(t-1) TWFX(t-1) TANG(t-1) MB(t-1) Ln(ATDEF)(t-1) RD(t-1) RDD(t-1) Credit(t-1) GRGDP(t-1) Term(t-1) Constant

AdjRSq

Volatility (1)

Profitability (2)

Volatility (3)

Profitability (4)

-0.014*** (0.002) 0.119*** (0.008) -0.019** (0.009) 0.000 (0.000) -0.033** (0.013) -0.001* (0.001) 0.014*** (0.003) -0.084*** (0.025) 0.070*** (0.007) -1.410*** (0.119) -0.010*** (0.001)

0.078*** (0.009) 0.112*** (0.033) 0.094** (0.046) 0.023*** (0.002) 1.047*** (0.077) -0.016*** (0.003) 0.079*** (0.017) 0.540*** (0.161) -0.911*** (0.040) 20.802*** (0.532) -0.083*** (0.006)

-0.021*** (0.001) 0.128*** (0.004) -0.005*** (0.001) -0.561*** (0.033) 0.004*** (0.001) -0.065*** (0.003) -0.023 (0.150) 0.096*** (0.009) 0.027*** (0.005) -1.755*** (0.090) -0.006*** (0.001)

0.044*** (0.004) 0.110*** (0.018) 0.009*** (0.003) 2.101*** (0.139) 0.223*** (0.005) -0.146*** (0.012) -4.806*** (0.545) 0.133*** (0.033) -1.122*** (0.021) 18.037*** (0.353) -0.059*** (0.004)

0.399

0.507

0.414

0.525

40  

Industry regressions

    N Nfirms Fstat (all) Fstat (IV) Min. Eigenvalue 2SLS Wald test (10% size)

113463 4552 2786.0 115.8 413.8 7.0

113463 4552 1194.9 95.0

4967

4967

2816.298 500.0 142.1 7.0

1951.661 130.0

Panel B: Regression in differences Firm regressions

Tariff(t) TWFX(t) TANG(t-1) MB(t-1) Ln(ATDEF)(t-1) RD(t-1) RDD(t-1) Credit(t-1) GRGDP(t-1) Term(t-1) Constant

AdjRSq N Nfirms Fstat (all) Fstat (IV) Min. Eigenvalue 2SLS Wald test (10% size)

Profitability (2)

Volatility (3)

Profitability (4)

-0.011*** (0.003) 0.066*** (0.011) 0.079*** (0.026) -0.001*** (0.000) 0.009 (0.006) -0.06 (0.036) -0.001 (0.005) -0.015* (0.008) -0.530*** (0.054) -0.016*** (0.002) 0.013*** (0.002)

0.158*** (0.017) 1.362*** (0.049) -0.226** (0.113) 0.013*** (0.002) 0.026 (0.020) -0.399** (0.171) 0.162*** (0.024) -0.315*** (0.034) 4.680*** (0.164) 0.126*** (0.007) -0.126*** (0.005)

-0.011*** (0.003) 0.066*** (0.011) 1.444*** (0.231) -0.024*** (0.002) 0.214*** (0.049) -0.599*** (0.079) -3.153*** (0.321) -0.072*** (0.013) -0.018* (0.010) -0.016*** (0.002) 0.011*** (0.003)

0.118*** (0.015) 1.354*** (0.052) -2.741*** (0.638) 0.167*** (0.007) -0.667*** (0.128) 4.707*** (0.284) -20.463*** (0.828) 0.828*** (0.038) -0.027 (0.031) 0.081*** (0.008) -0.104*** (0.009)

0.001 118206 4559 23.1 29.7 11.8 7.0

0.017 118206 4559 168.4 400.9

0.006 4967

0.042 4967

52.9 29.6 10.8 7.0

491.3 374.9

41  

Industry regressions

Volatility (1)

    Table 6 The effect of industry volatility and profitability on leverage The dependent variable equals market leverage (ML) or book leverage (BL). All independent variables are lagged one quarter with the exception of Volatility and Profitability which are measured at time t. All regressions include industry fixed effects. Industry volatility is based on the following model of quarterly aggregate industry profits.

The variable is the ratio of aggregate industry operating income to aggregate industry net assets and is a dummy variable for calendar quarter j. Volatility measures are based on the residual term and volatility denotes a GARCH estimate of the volatility of the residual term. IV indicates that Volatility and Profitability are instrumented with timet-1 levels of tariffs and TWFX in panel A, and time-t differences in tariffs and TWFX (trade-weighted FX rate) in Panel B. Panel A reports leverage regressions in levels with industry fixed effects. Panel B reports the same regression in differences. Standard errors (in parentheses) are clustered by firm. The third row for each variable reports bootstrapped standard errors using 200 replications.

Panel A: Regression in levels Market leverage OLS IV (1) (2) Volatility(t)

Profitability(t)

TANG(t-1)

MB(t-1)

Ln(ATDEF)(t-1)

RDD(t-1)

RD(t-1)

Credit(t-1)

-0.795** (0.355) 0.196 -0.679*** (0.088) 0.047 20.400*** (1.702) 0.394 -1.157*** (0.057) 0.018 0.054 (0.124) 0.029 6.389*** (0.537) 0.147 -9.185*** (3.200) 1.270 2.458*** (0.586) 0.277

Book leverage

-28.587*** (6.577) 2.386 -1.491 (1.524) 0.507 19.909*** (1.782) 0.489 -1.112*** (0.059) 0.023 0.015 (0.127) 0.033 7.047*** (0.600) 0.194 -10.957*** (3.402) 1.369 3.531** (1.691) 0.648

42  

OLS (3)

IV (4)

0.015 (0.341) 0.200 -0.450*** (0.089) 0.050 26.827*** (1.988) 0.499 -0.200*** (0.064) 0.028 -0.184 (0.130) 0.036 4.323*** (0.530) 0.172 -7.867 (4.904) 2.218 1.862*** (0.586) 0.284

-24.694*** (6.837) 2.619 -2.783* (1.558) 0.553 26.621*** (2.108) 0.641 -0.152** (0.069) 0.032 -0.224* (0.135) 0.038 5.094*** (0.589) 0.178 -10.566** (5.158) 2.350 1.327 (1.695) 0.663

    GRGDP(t-1)

Term(t-1)

Constant

-63.701*** (8.079) 4.986 -1.154*** (0.093) 0.055 21.900*** (3.960) 0.842

-79.246** (34.993) 12.853 -1.546*** (0.186) 0.071 40.368*** (7.981) 2.226

-6.581 (7.787) 4.428 -0.811*** (0.094) 0.053 15.840*** (3.270) 0.810

12.797 (34.880) 13.101 -1.292*** (0.187) 0.078 39.272*** (8.045) 2.550

0.254 135050 5232

0.053 135050 5232

0.118 118206 4559

113463 4552

AdjRSq N Nfirms

Panel B: Regression in differences Market leverage OLS IV (1) (2) Volatility(t)

Profitability (t)

TANG(t-1)

MB(t-1)

Ln(ATDEF)(t-1)

RDD(t-1)

RD(t-1)

Credit(t-1)

-0.190*** (0.063) 0.058 -0.032* (0.019) 0.016 6.929*** (0.749) 0.720 -0.039*** (0.006) 0.007 -0.814*** (0.124) 0.122 -2.480*** (0.629) 0.603 -0.160** (0.069) 0.057 -1.274*** (0.140) 0.139

-19.370*** (4.181) 4.534 0.411* (0.241) 0.236 8.552*** (0.949) 0.873 -0.069*** (0.014) 0.015 -0.650*** (0.180) 0.169 -3.445*** (0.990) 0.820 -0.263** (0.125) 0.087 -1.462*** (0.245) 0.263

43  

Book leverage OLS IV (3) (4) 0.033 (0.046) 0.047 -0.041*** (0.014) 0.012 5.059*** (0.638) 0.582 -0.108*** (0.011) 0.010 -1.055*** (0.113) 0.109 -0.705 (0.713) 0.755 -0.202*** (0.053) 0.045 0.208* (0.109) 0.101

-0.905 (2.834) 2.772 -0.298* (0.152) 0.152 5.071*** (0.680) 0.646 -0.105*** (0.012) 0.012 -1.036*** (0.116) 0.110 -0.861 (0.731) 0.785 -0.160*** (0.060) 0.054 0.064 (0.128) 0.114

    GRGDP(t-1)

Term(t-1)

Constant

AdjRSq N Nfirms

14.573*** (1.302) 1.396 0.195*** (0.037) 0.040 -0.158*** (0.043) 0.048

1.994 (3.343) 3.931 -0.192* (0.102) 0.113 0.161* (0.093) 0.115

3.508*** (1.098) 0.985 -0.035 (0.029) 0.028 0.197*** (0.038) 0.034

4.016* (2.251) 2.167 -0.018 (0.067) 0.071 0.177*** (0.064) 0.063

0.254 135050 5232

0.053 135050 5232

0.005 118206 4559

118206 4559

44  

    Table 7: Summary counts on basic financing spikes This table counts the number and measures the frequency of basic financing. A leverage increase occurs whenever the net debt issuance minus net equity issuance over last quarter’s book assets exceeds 5%. A leverage decrease occurs whenever net equity issuance minus net debt issuance exceeds 5% of last quarter’s book assets. Debt and equity issuance occur whenever net debt or net equity proceeds exceed last quarter’s book assets by 5%. Debt reductions occur when net debt reductions exceed last quarter’s book assets by 5%. Equity repurchases occur whenever net equity issuance is less than -1.25% of last quarter’s book assets.   Number of adjustments per firm

Adjustment type No adjustment Debt issue Debt retirement Equity issue Equity repurchase Leverage increase Leverage decrease

Number of adjustments

Percent of obs.

Mean

Min

Median

Max

82,540 11,405 7,618 12,866 8,451 10,687 18,369

69.83 9.65 6.44 10.88 7.15 9.04 15.54

2.50 1.67 2.82 1.85 2.34 4.03

0.00 0.00 0.00 0.00 0.00 0.00

2.00 1.00 1.00 0.00 1.00 3.00

42.00 23.00 45.00 68.00 42.00 43.00

   

45  

    Table 8: Issuance activity and basic financing spikes This table reports a regression model of the probability of undertaking a leverage increasing or leverage decreasing transaction at time t. A leverage increase occurs whenever the net debt issuance minus net equity issuance over last quarter’s book assets exceeds 5%. A leverage decrease occurs whenever net equity issuance minus net debt issuance exceeds 5% of last quarter’s book assets. Debt and equity issuance occur whenever net debt or net equity proceeds exceed last quarter’s book assets by 5%. Debt reductions occur when net debt reductions exceed last quarter’s book assets by 5%. Equity repurchases occur whenever net equity issuance is less than -1.25% of last quarter’s book assets. All independent variables are in levels and are measured at time t-1, except for Volatility and Profitability which are measured at time t. Each regression includes industry and year fixed effects. Panel A reports reports coefficient estimates from the IVPROBIT model of Rivers and Vuong (1988). Volatility and Profitability are instrumented with tariffs and the trade-weighted FX rate (TWFX). Standard errors are in parentheses. Chi2 is the Chi-squared test for exogeneity of the instrumented variables. Panel B reports measures of economic significance derived from the coefficients from estimating a 2SLS linear probability model (LPM). Panel C reports separate estimates by type of firm. Control variables are included but omitted from the table for the sake of brevity. Panel A: IVPROBIT estimates Dep. Variable:

Lev. Incr. (1)

Lev. Decr. (2)

Debt iss. (3)

Debt reduc. (4)

Equ. Iss. (5)

Equ. Rep. (6)

Volatility(t)

-1.025* (0.551) -0.028 (0.103) 0.371*** (0.038) -0.005** (0.002) -0.044*** (0.003) -0.362* (0.185) 0.078*** (0.016) 0.061 (0.071) -1.591 (1.407) 0.01 (0.016) 0.049 (0.039)

0.32 (0.515) -0.03 (0.096) -0.275*** (0.034) 0.074*** (0.002) -0.069*** (0.003) 2.338*** (0.138) -0.043*** (0.015) -0.152** (0.062) 4.058*** (1.241) -0.004 (0.014) 0.720*** (0.036)

-1.506*** (0.560) -0.177* (0.104) 0.550*** (0.038) 0.007*** (0.002) -0.073*** (0.003) 0.505*** (0.170) 0.115*** (0.016) 0.095 (0.072) 0.825 (1.414) 0.018 (0.016) 0.158*** (0.040)

-0.462 (0.627) -0.106 (0.116) -0.361*** (0.044) 0.013*** (0.002) -0.050*** (0.003) -0.482** (0.206) 0.053*** (0.018) -0.216*** (0.078) 0.329 (1.575) 0.02 (0.017) 1.416*** (0.042)

0.26 (0.638) 0.054 (0.121) 0.116*** (0.039) 0.077*** (0.002) -0.091*** (0.003) 2.391*** (0.152) -0.121*** (0.018) -0.045 (0.073) 3.396** (1.471) -0.002 (0.016) -0.518*** (0.047)

-2.874*** (0.727) -0.575*** (0.140) 0.323*** (0.048) 0.017*** (0.003) 0.094*** (0.004) -3.003*** (0.263) 0.097*** (0.020) -0.340*** (0.090) 4.129** (1.777) 0.002 (0.019) -1.086*** (0.054)

113463 10.61 2 0.005

113463 4.27 2 0.118

113463 10.16 2 0.006

113463 1.18 2 0.555

113463 0.10 2 0.952

113408 22.10 2 0.000

Profitability(t) TANG(t-1) MB(t-1) Ln(ATDEF(t-1)) RD(t-1) RDD(t-1) Credit(t-1) GRGDP(t-1) Term(t-1) ML(t-1)

N Chi2 Chi2df Chi2 pvalue

46  

   

Panel B: Economic significance of IVPROBIT estimates Lev. Incr.

Lev. Decr.

Debt iss.

Debt reduc.

Equ. Iss.

Equ. Rep.

-0.166 (0.101) -0.007 (0.019)

0.092 (0.109) 0.006 (0.021)

-0.240 (0.104) -0.032 (0.020)

-0.069 (0.085) -0.021 (0.016)

0.125 (0.085) 0.032 (0.016)

-0.432 (0.096) -0.082 (0.018)

Econ. Significance Volatility(t)

-0.054

0.030

-0.078

-0.022

0.041

-0.141

Profitability(t)

-0.008

0.007

-0.037

-0.025

0.037

-0.096

Average partial effects Volatility(t) Profitability(t)

Panel C: Sorts by financial constraints Dep. Variable: Dividend payers Volatility(t) Profitability(t) N Non-dividend payers Volatility(t) Profitability(t) N Rated firms Volatility(t) Profitability(t) N Unrated firms Volatility(t) Profitability(t) N

Lev. Incr. (1)

Lev. Decr. (2)

Debt iss. (3)

Debt reduc. (4)

Equ. Iss. (5)

Equ. Rep. (6)

0.63 (0.749) 1.344 (1.218) 25324

0.086 (0.757) -1.08 (1.239) 25259

0.741 (0.900) 1.892 (1.482) 25338

0.434 (0.722) -0.119 (1.178) 25245

-3.343 (8.137) 4.915 (14.756) 24340

1.212 (0.972) 2.174 (1.593) 25287

-0.859* (0.467) 0.093 (0.069) 88122

-0.029 (0.421) -0.066 (0.062) 88122

-1.222*** (0.463) -0.024 (0.068) 88122

-0.591 (0.521) -0.062 (0.076) 88072

0.055 (0.508) 0.011 (0.076) 88070

-2.036*** (0.600) -0.226** (0.092) 88052

1.236* (0.688) -0.393 (0.432) 17164

-0.682 (0.802) 0.925* (0.515) 17262

1.054 (0.679) -0.262 (0.433) 17189

-1.005 (0.935) 1.204** (0.601) 17229

-0.905 (0.936) 0.367 (0.567) 16745

1.997** (0.885) -0.747 (0.535) 16909

-0.529 (0.442) 0.134** (0.066) 96191

-0.267 (0.410) -0.074 (0.061) 96198

-1.057** (0.441) 0.007 (0.066) 96191

-0.983* (0.508) -0.08 (0.075) 96099

-0.038 (0.504) -0.004 (0.075) 96190

-2.010*** (0.561) -0.258*** (0.086) 96183

47  

    Table 9 Relation between cash-flow volatility, profitability and credit conditions This table reports the regression estimates of industry-level volatility and profitability on a dummy variable for credit conditions (Dummy: Tighten(t)>0). This variable is equals one if the Survey of Senior Lenders reports a positive net fraction of respondents reporting tightening conditions. The table reports industry-level weighted-least squares regressions, where each observation corresponds to an industry-quarter. Industry-level values are obtained by averaging across firms in an industry-quarter. Each industry-quarter is weighed by the number of firms in that industryquarter. Standard errors (in parentheses) heteroskedasticity robust. Volatility (1) Dummy: Tighten(t)>0 Tariff(t-1) TWFX(t-1) TANG(t-1) MB(t-1) Ln(ATDEF)(t-1) RD(t-1) RDD(t-1) Credit(t-1) GRGDP(t-1) Term(t-1)

AdjRSq N Fstat

0.031*** (0.003) -0.031*** (0.001) 0.102*** (0.004) -0.490*** (0.035) 0.000 (0.001) -0.083*** (0.003) -1.162*** (0.191) -0.080*** (0.014) -0.100*** (0.008) -2.430*** (0.095) 0.002** (0.001)

-0.379*** (0.010) 0.112*** (0.005) -0.032* (0.018) 4.453*** (0.147) 0.284*** (0.005) -0.202*** (0.012) -12.503*** (0.633) 1.099*** (0.045) -0.063** (0.029) 20.600*** (0.347) -0.158*** (0.004)

0.434 4967 3031

0.572 4967 2122

48  

Profitability (2)

    Table 10 Financial constraints and issuance activity This table reports a regression model of the probability of undertaking a leverage increasing or leverage decreasing transaction at time t. Tighten indicates the value reported by the Survey of Senior Lending Officers at time t. Firms are divided into groups according to whether they paid a dividend at time t or had a credit rating at time t. A leverage increase occurs whenever the net debt issuance minus net equity issuance over last quarter’s book assets exceeds 5%. A leverage decrease occurs whenever net equity issuance minus net debt issuance exceeds 5% of last quarter’s book assets. Debt and equity issuance occur whenever net debt or net equity proceeds exceed last quarter’s book assets by 5%. Debt reductions occur when net debt reductions exceed last quarter’s book assets by 5%. Equity repurchases occur whenever net equity issuance is less than -1.25% of last quarter’s book assets. All independent variables are in levels and are measured at time t-1 except for Volatility and Profitability, which are measured at time t. Each regression includes control variables from Table 7 as well as industry fixed effects. Each column reports coefficient estimates from the IVPROBIT model of Rivers and Vuong (1988). Volatility and Profitability are instrumented with tariffs and the trade-weighted FX rate measured at time t-1. Two-step standard errors are in parentheses. Chi2 is the Chi-squared test for exogeneity of the instrumented variables.

Panel A: Net debt issuance

Volatility(t) VolatilityXTighten(t) Profitability(t) ProfitabilityXTighten(t) TANG(t-1) MB(t-1) Ln(ATDEF(t-1)) RD(t-1) RDD(t-1) Credit(t-1) GRGDP(t-1) Term(t-1) ML(t-1)

All (1)

No dividend (2)

Dividend (3)

Unrated (4)

Rated (5)

-2.179** (0.943) 0.046** (0.019) 0.281*** (0.107) -0.008** (0.003) 0.564*** (0.043) 0.003 (0.002) -0.077*** (0.004) 0.615*** (0.168) 0.100*** (0.017) 0.339** (0.173) 1.951*** (0.661) -0.067*** (0.015) 0.212*** (0.034)

-2.808** (1.181) 0.057** (0.025) 0.290** (0.137) -0.010** (0.004) 0.641*** (0.049) 0.004* (0.002) -0.075*** (0.004) 0.630*** (0.181) 0.136*** (0.020) 0.432* (0.246) 1.926** (0.793) -0.086*** (0.021) 0.148*** (0.037)

0.595 (3.159) 0.004 (0.036) 0.263 (0.231) 0.000 (0.006) 0.173 (0.118) 0.008 (0.024) -0.056*** (0.013) 3.001** (1.365) 0.024 (0.043) -0.014 (0.292) 2.541* (1.509) -0.016 (0.039) 0.364* (0.215)

-1.790** (0.812) 0.032* (0.017) 0.177* (0.096) -0.006** (0.003) 0.624*** (0.044) 0.002 (0.002) -0.096*** (0.005) 0.768*** (0.164) 0.107*** (0.018) 0.176 (0.169) 1.566** (0.689) -0.059*** (0.014) 0.253*** (0.034)

7.892 (29.572) -0.066 (0.479) -0.223 (3.253) 0.011 (0.078) 0.366 (1.522) -0.024 (0.080) -0.107 (0.242) 1.542 (11.389) 0.139 (0.631) -0.279 (3.171) 1.533 (12.211) 0.032 (0.208) -0.513* (0.310)

49  

   

N Chi2 Chi2df

100675 9.52 4

78790 8.21 4

21884 2.79 4

85431 8.40 4

15174 6.77 4

Panel B: Other financing spikes All (1) Debt reduction Volatility(t) VolatilityXTighten(t) Profitability(t) ProfitabilityXTighten(t) Controls N Leverage increase Volatility(t) VolatilityXTighten(t) Profitability(t) ProfitabilityXTighten(t) Controls N Equity issuance Volatility(t) VolatilityXTighten(t) Profitability(t) ProfitabilityXTighten(t) Controls N Equity repurchase

No dividend (2)

Dividend (3)

Unrated (4)

Rated (5)

0.304 (0.992) -0.004 (0.019) -0.002 (0.113) 0.001 (0.003) Yes 100675

0.631 (1.183) -0.016 (0.025) -0.090 (0.139) 0.003 (0.004) Yes 78740

0.112 (3.853) 0.035 (0.045) 0.339 (0.281) -0.006 (0.008) Yes 21791

-0.141 (0.877) -0.003 (0.018) -0.049 (0.106) 0.000 (0.003) Yes 85332

-28.428 (72.140) 0.501 (1.184) 3.701 (8.181) -0.081 (0.193) Yes 15148

-1.101 (0.909) 0.029 (0.018) 0.240** (0.104) -0.005* (0.003) Yes 100675

-1.788 (1.136) 0.039 (0.024) 0.238* (0.134) -0.007* (0.004) Yes 78790

1.668 (3.092) -0.014 (0.036) 0.140 (0.227) 0.003 (0.006) Yes 21870

-0.838 (0.796) 0.017 (0.016) 0.138 (0.096) -0.003 (0.003) Yes 85431

4.852 (25.037) -0.032 (0.407) 0.028 (2.759) 0.005 (0.066) Yes 15149

-2.408** (1.150) 0.021 (0.023) -0.247** (0.122) -0.005 (0.004) Yes 100675

-1.984 (1.300) 0.014 (0.028) -0.252* (0.141) -0.003 (0.005) Yes 78738

-14.463 (39.949) 0.185 (0.485) 1.195 (3.593) -0.031 (0.079) Yes 20640

-1.970** (0.998) 0.012 (0.021) -0.297*** (0.107) -0.003 (0.004) Yes 85423

-36.953 (524.773) 0.700 (9.615) 5.153 (70.720) -0.112 (1.542) Yes 14731

50  

    Volatility(t) VolatilityXTighten(t) Profitability(t) ProfitabilityXTighten(t) Controls N Leverage decrease Volatility(t) VolatilityXTighten(t) Profitability(t) ProfitabilityXTighten(t) Controls N

1.316 (1.100) -0.043* (0.022) -0.367*** (0.124) 0.008** (0.004) Yes 100647

2.363 (1.465) -0.061* (0.031) -0.417** (0.169) 0.011** (0.006) Yes 78720

-2.096 (3.071) -0.008 (0.036) -0.305 (0.228) 0.001 (0.006) Yes 21859

1.291 (0.975) -0.035* (0.020) -0.260** (0.116) 0.007* (0.004) Yes 85423

-14.938 (36.153) 0.218 (0.676) 0.926 (5.055) -0.038 (0.110) Yes 14921

-0.922 (0.839) 0.003 (0.017) -0.157* (0.092) -0.001 (0.003) Yes 100675

-0.756 (0.991) -0.001 (0.021) -0.161 (0.112) 0.000 (0.004) Yes 78790

-1.541 (3.531) 0.034 (0.042) 0.114 (0.259) -0.006 (0.007) Yes 21805

-0.958 (0.741) 0.000 (0.015) -0.197** (0.084) 0.000 (0.003) Yes 85431

-8.961 (29.415) 0.163 (0.486) 1.273 (3.348) -0.026 (0.079) Yes 15241

51  

    Table 11 Reductions in debt and volatility shocks The dependent variable the reduction in total debt, measured as ln

∑



1



The independent variables are equity issues (Equity) and operating cash flows (OCF=operating income before depreciation minus taxes and interest) normalized by total assets, and the logarithm of total assets (Ln(total assets)). All regressions include industry fixed effects. Volatility is net of the industry median, and volatility is instrumented with tariffs and the trade weighted FX index also net of the industry median. Volatility is interacted with Equity and OCF, as are the two instruments. Standard errors are heteroskedasticity robust. The sample consists of years in which Equity or OCF is greater or equal to 1% of total assets in the prior fiscal quarter.

Panel A: Dividend payers

Equity OCF EquityXVolatility OCFXVolatility Ln(Total assets) Constant

N

1Q (1)

2Q (2)

3Q (3)

4Q (4)

8Q (5)

0.061*** (0.017) -0.023 (0.014) -0.257 (0.250) -1.057** (0.451) -0.000** (0.000) 0.012*** (0.001)

0.092*** (0.020) -0.02 (0.024) -0.21 (0.286) -2.090*** (0.803) -0.000** (0.000) 0.023*** (0.002)

0.107*** (0.022) -0.096*** (0.031) -0.183 (0.309) -2.430** (0.986) -0.001** (0.000) 0.035*** (0.003)

0.113*** (0.024) -0.178*** (0.036) -0.062 (0.334) -2.749** (1.196) -0.001** (0.000) 0.047*** (0.003)

0.140*** (0.045) -0.419*** (0.088) 0.437 (0.897) -8.051*** (1.979) -0.002*** (0.001) 0.097*** (0.006)

20461

19326

18245

17242

13800

52  

    Panel C: Non-dividend payers

Equity OCF EquityXVolatility OCFXVolatility Ln(Total assets) Constant

N

1Q (1)

2Q (2)

3Q (3)

4Q (4)

8Q (5)

0.009*** (0.001) 0.042*** (0.006) 0.028 (0.028) -0.381*** (0.147) -0.001*** (0.000) 0.023*** (0.002)

0.011*** (0.001) 0.121*** (0.010) 0.054 (0.043) -0.721*** (0.240) -0.002*** (0.000) 0.046*** (0.003)

0.008*** (0.002) 0.146*** (0.013) 0.061 (0.056) -1.010*** (0.317) -0.002*** (0.000) 0.067*** (0.005)

0.006*** (0.002) 0.152*** (0.016) 0.104 (0.071) -1.214*** (0.377) -0.002*** (0.000) 0.086*** (0.005)

0.001 (0.004) 0.236*** (0.030) 0.166 (0.132) -1.713*** (0.593) -0.004*** (0.000) 0.153*** (0.009)

52493

48388

44812

41586

30644

Panel D: Rated firms

Equity OCF EquityXVolatility OCFXVolatility Ln(Total assets) Constant

N

1Q (1) 0.057*** (0.021) 0.013 (0.031) -0.257 (0.199) -0.059 (0.619) -0.001*** (0.000) 0.023*** (0.002)

2Q (2) 0.088*** (0.034) 0.038 (0.055) -0.374 (0.302) 0.635 (0.984) -0.002*** (0.000) 0.044*** (0.003)

3Q (3) 0.104*** (0.038) -0.08 (0.079) -0.443 (0.370) 2.264 (1.417) -0.003*** (0.000) 0.064*** (0.004)

4Q (4) 0.089** (0.039) -0.195* (0.103) -0.327 (0.353) 3.800** (1.873) -0.004*** (0.001) 0.087*** (0.005)

8Q (5) 0.093** (0.046) -0.075 (0.091) 0.064 (0.480) 2.653 (2.239) -0.011*** (0.001) 0.196*** (0.008)

13983

13038

12183

11386

8737

53  

    Panel E: Unrated firms

Equity OCF EquityXVolatility OCFXVolatility Ln(Total assets) Constant

N

1Q (1)

2Q (2)

3Q (3)

4Q (4)

8Q (5)

0.009*** (0.001) 0.030*** (0.006) 0.033 (0.029) -0.551*** (0.145) -0.001*** (0.000) 0.018*** (0.001)

0.010*** (0.001) 0.088*** (0.011) 0.051 (0.043) -1.207*** (0.247) -0.002*** (0.000) 0.034*** (0.002)

0.007*** (0.002) 0.094*** (0.014) 0.059 (0.056) -1.722*** (0.321) -0.003*** (0.000) 0.049*** (0.003)

0.004** (0.002) 0.079*** (0.017) 0.103 (0.072) -2.236*** (0.399) -0.004*** (0.000) 0.064*** (0.003)

-0.002 (0.004) 0.082** (0.032) 0.158 (0.133) -3.935*** (0.616) -0.007*** (0.000) 0.114*** (0.005)

58971

54676

50874

47442

35707

54  

    Table 12 Dollar change in total debt and cash holdings in response to volatility shocks This table reports the dollar change implied when sources of capital are increased by one dollar for a median sized firm. Panel A reports the effect of a dollar increase in equity or operating cash flows on total cumulative total debt reduction in the quarter of the equity issue or operating cash flow realization. Panel A is based on Table 11 estimates. Panel B reports the change in cash holdings in the quarter of an increase in total debt or equity. Panel B estimates are based on Table 13. Low volatility sets volatility one standard deviation below the industry time-series mean High volatility sets volatility to one standard deviation above the industry time-series mean. Normal volatility indicates that volatility equals the industry time-series mean.

Panel A: Dollar changes in cumulative debt reductions in Q1 (based on Table 11) Equity

OCF

Dividend payers

Low volatility Normal volatility High volatility

0.139 0.061 -0.015

0.287 -0.023 -0.328

Non dividend payers

Low volatility Normal volatility High volatility

0.001 0.009 0.148

0.154 0.042 -0.070

Rated firms

Low volatility Normal volatility High volatility

0.135 0.058 -0.019

0.030 0.013 -0.004

Unrated firms

Low volatility Normal volatility High volatility

-0.001 0.009 0.053

0.191 0.030 -0.131

Panel B: Dollar changes in cash in Q1 (based on Table 13) Debt

Dividend payers

Low volatility Normal volatility High volatility

0.169 0.085 0.001

-0.316 0.054 0.438

Non dividend payers

Low volatility Normal volatility High volatility

0.639 0.352 0.073

0.894 0.276 -0.307

Rated firms

Low volatility Normal volatility High volatility

0.399 0.273 0.149

0.205 0.420 0.639

Unrated firms

Low volatility Normal volatility High volatility

0.637 0.353 0.078

0.768 0.208 -0.322

55  

Equity

    Table 13: Use of proceeds from capital raising activity for different volatility regimes – cash balances The dependent variable is the change in cash holdings, measured as ln



1

The independent variables are capital raising proceeds (Capital), and the logarithm of total assets (Ln(total assets)). Capital consists of either both equity and debt issue proceeds, debt issue proceeds or equity issue proceeds. All regressions include industry fixed effects. Volatility is net of the industry median, and volatility is instrumented with tariffs and the trade weighted FX index also net of the industry median. Volatility is interacted with Capital, as are the two instruments. Standard errors are heteroskedasticity robust. The sample consists of years in which Capital, Equtiy or Debt issue is greater or equal to 1% of total assets in the prior fiscal quarter.

Panel A: Dividend payers All sources of capital

Capital CapitalXVolatility Ln(Total assets) Constant

N

Debt issues

4Q (2)

8Q (3)

1Q (4)

4Q (5)

8Q (6)

1Q (7)

4Q (8)

8Q (9)

0.049** (0.021) -0.231 (0.543) 0.001 (0.000) -0.007* (0.004)

-0.003 (0.032) -0.503 (0.863) 0.001 (0.001) -0.003 (0.006)

-0.062 (0.112) 0.99 (2.693) -0.002 (0.002) 0.016 (0.014)

0.057** (0.028) 1.247 (0.876) 0.002*** (0.000) -0.014*** (0.004)

-0.064** (0.030) 1.072 (1.072) 0.001 (0.001) -0.002 (0.005)

-0.074** (0.030) -0.853 (0.979) -0.001 (0.001) 0.008 (0.007)

0.088*** (0.032) -0.274 (0.510) -0.001 (0.001) 0.01 (0.009)

0.101 (0.080) -1.201 (1.460) -0.001 (0.002) 0.01 (0.018)

0.058 (0.100) 0.151 (1.132) -0.006** (0.003) 0.040** (0.020)

6846

5768

4855

6390

5382

4571

1947

1600

1288

56  

Equity issues

1Q (1)

    Panel B: Non-dividend payers All sources of capital

Capital CapitalXVolatility Ln(Total assets) Constant

N

Debt issues

Equity issues

1Q (1)

4Q (2)

8Q (3)

1Q (4)

4Q (5)

8Q (6)

1Q (7)

4Q (8)

8Q (9)

0.348*** (0.007) -0.811*** (0.252) 0.016*** (0.000) -0.106*** (0.005)

0.436*** (0.016) -2.096*** (0.562) 0.014*** (0.001) -0.101*** (0.009)

0.470*** (0.032) -4.840*** (1.114) 0.004** (0.002) -0.051*** (0.013)

0.292*** (0.024) -1.977*** (0.653) 0.005*** (0.001) -0.045*** (0.004)

0.335*** (0.070) -4.230*** (1.550) 0.001 (0.001) -0.023*** (0.008)

0.321*** (0.107) -5.729*** (2.161) -0.004** (0.002) 0.005 (0.012)

0.385*** (0.008) -0.958*** (0.258) 0.022*** (0.001) -0.154*** (0.014)

0.467*** (0.018) -2.324*** (0.570) 0.019*** (0.002) -0.141*** (0.032)

0.480*** (0.034) -5.009*** (1.102) 0.005* (0.003) -0.054 (0.054)

35427

27964

22276

19944

15476

12398

23458

18651

14769

Panel C: Rated firms All sources of capital

Capital CapitalXVolatility Ln(Total assets) Constant

N

Debt issues

1Q (1)

4Q (2)

8Q (3)

1Q (4)

4Q (5)

8Q (6)

1Q (7)

4Q (8)

8Q (9)

3.989 (241.915) -50.117 (3243.464) -0.035 (2.531) 0.019 (4.023)

-0.085 (3.365) 3.336 (39.973) 0.006 (0.030) -0.045 (0.048)

-0.133 (1.813) 5.062 (25.336) 0.009 (0.017) -0.04 (0.054)

0.446*** (0.059) 0.712 (0.984) 0.005*** (0.001) -0.055*** (0.016)

0.227*** (0.074) -0.227 (1.315) 0.002 (0.002) -0.032 (0.026)

0.07 (0.139) 0.56 (1.916) 0.006* (0.003) -0.050* (0.027)

0.289*** (0.065) -0.411 (0.749) 0.002 (0.002) -0.030* (0.017)

0.306*** (0.114) -0.469 (1.164) -0.001 (0.006) -0.025 (0.034)

0.476*** (0.147) -1.859 (2.499) 0.003 (0.008) -0.01 (0.069)

2537

2027

1644

1847

1450

1181

1318

1087

889

57  

Equity issues

   

Panel D: Unrated firms All sources of capital

Capital CapitalXVolatility Ln(Total assets) Constant

N

Debt issues

1Q (1)

4Q (2)

8Q (3)

1Q (4)

4Q (5)

8Q (6)

1Q (7)

4Q (8)

8Q (9)

0.344*** (0.007) -0.780*** (0.252) 0.017*** (0.001) -0.104*** (0.004)

0.428*** (0.016) -2.033*** (0.563) 0.014*** (0.001) -0.100*** (0.007)

0.455*** (0.031) -4.734*** (1.105) 0.005*** (0.002) -0.062*** (0.011)

0.220*** (0.021) -1.794*** (0.547) 0.004*** (0.001) -0.034*** (0.004)

0.241*** (0.059) -3.867*** (1.276) 0.000 (0.001) -0.016** (0.007)

0.207** (0.083) -5.058*** (1.700) -0.005*** (0.002) 0.008 (0.011)

0.385*** (0.008) -0.943*** (0.263) 0.024*** (0.001) -0.158*** (0.012)

0.464*** (0.018) -2.304*** (0.587) 0.021*** (0.002) -0.142*** (0.023)

0.472*** (0.035) -5.022*** (1.129) 0.007** (0.003) -0.070** (0.034)

36966

29359

23479

21679

17034

13747

23401

18617

14710

58  

Equity issues

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