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: October, 2014 EL Classification: G32 Keywords: Capital structure, volatility, leverage, product-market competition
*: Corresponding author. We thank Dominique Badoer, Sean Cleary, Cem Demiroglu, and Jongsub Lee for valuable comments.
Cash Flow Volatility and Capital Structure Choice
October 2014
Abstract Economic theory implies that volatility plays an important role in determining firms’ capital structure, yet prior research does not find a significant relation between cash-flow volatility and leverage. We argue that the lack of significance can be explained in part by measurement error in empirical estimates of the volatility process. In this paper we identify innovations in future cash flow volatility by estimating a GARCH model of industry level cash flow volatility. To address endogeneity concerns and to separate innovations in volatility from changes in profitability we use tariff changes and changes in trade weighted exchange rates as instruments for changes in earnings and volatility. Overall we find a negative and significant relationship between leverage changes and volatility. We also find that while firms increase their use of debt and reduce their cash holdings following decreases in cash flow volatility, they do not respond to increases in volatility by retiring debt or issuing equity. This asymmetric response to changes in volatility is concentrated among firms with greater financing frictions. Overall our findings suggest that corporate debt issuances undertaken during periods of low volatility may exacerbate the effect of future negative shocks to corporate profits.
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I. Introduction Recently there has been considerable interest and debate regarding how fluctuations in volatility affect firm investment and financing decisions.1 This interest stems in part from concerns 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.2 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.3 Overall these studies find that capital raising and target leverage for financially constrained firms are highly pro-cyclical while investment-grade firms’ leverage appears to be counter-cyclical. While the impact of macro condition on financing choice suggests that fluctuations in volatility are an important determinant of target leverage, inference concerning the importance of volatility from these studies is complicated by the fact that at both the macro and micro level volatility and earnings are negatively correlated. For example in a recent review article Bloom (2014) concludes: “We can drop down a level of aggregation from looking at macro data to looking at micro data on individual industries, firms and plants. At every level uncertainty appears to rise during recessions. The result is in some senses “fractal” – that is uncertainty rises during recession in every level of disaggregation.” (Page 158). Estimating the direct effect of volatility and earnings is further complicated by the fact that both volatility and earnings are likely to be endogenous and thus identifying causal links requires finding valid instruments for both. These empirical challenges may explain why evidence of a strong link between leverage and volatility is limited (and thus the debate). Indeed, cash flow volatility typically does not make the list of the top “core” determinants of leverage. For example Frank and Goyal (2009) and Rajan and Zingales (1995)’s list of reliably important firm level factors include profitability, asset tangibility, firm size and market-to-book, but earnings volatility is notably absent from the list.
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See BLOOM, N. 2014. Fluctuations in uncertainty. Journal of Economic Perspectives, 28, 153-176. for a review of the literature on the effects of fluctuations in uncertainty on investment, product pricing and earnings. 2 See for example STEIN, J. 2013. Overheating in credit markets: origins, measurement, and policy responses. Research Symposium sponsored by the Federal Reserve Bank of St- Louis. and GREENWOOD, R. & HANSON, S. G. 2013. Issuer quality and corporate bond returns. Review of Financial Studies, 26, 1484-1524. 3 See for example KORAJCZYK, R. & LEVY, A. 2003. Capital structure choice: macroeconomic conditions and financial constraints. Journal of Financial Economics, 68, 75-109. and more recently EREL, I., JULIO, B., KIM, W. & WEISBACH, M. J. 2012. Macroeconomic conditions and capital raising. The Review of Financial Studies, 25, 342-376.
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In this paper we examine the relationship between changes in leverage and earnings volatility. We argue that the poor showing of volatility in previous empirical studies is in part due to how volatility is typically measured and in part by the inability of previous researchers to separate the effects of innovations in profitability from changes in volatility. Consider first the problem of measurement error. The two most commonly used proxies for expected future volatility are a rolling standard deviation of past earnings (typically calculated using quarterly data over the prior 2 to 10 years) and leverage adjusted equity volatility. The rolling standard deviation measure assumes earnings volatility changes very slowly over time and weighs all past variations from average earnings equally during the look-back period. Using equal weights leads to the unattractive assumption that recent changes in earnings are no more relevant (and thus are not given greater weight) than changes in earnings 2 or even 10 years in the past. While equity volatility is presumably forward looking, its use requires separating the effects of innovations in asset volatility from leverage changes and requires detailed information on the value of the firm’s debt claims.4 We examine the potential importance of measurement error by estimating a GARCH model of conditional volatility. Specifically, we specify a GARCH process in which it is assumed that the best predictor of the variance in earnings in the next quarter is a weighted average of the long-run average variance, the conditional variance for the current quarter, and new information concerning earnings volatility that is reflected in the current squared earnings residual. While a GARCH model is potentially better able to identify innovations in volatility, estimates of GARCH processes require a relatively long time series --which explains their widespread use in assessing the value at risk (VAR) with high frequency data but their infrequent use in empirical corporate finance. One solution to this problem is to limit the sample to firms with long operating histories. However that approach is likely to lead to a sample of older more stable and less financially constrained firms. To avoid this problem we estimate GARCH processes at the industry level and focus on the impact of innovations in industry-level cash-flow volatility on changes in firm leverage. Overall, consistent with the predictions of traditional trade-off models we find a negative and significant relationship between changes in firm leverage and changes in the conditional volatility of earnings. In contrast, using a rolling standard deviation of residual earnings (estimated over a number of different windows) we find, as previous studies have, no significant relationship between leverage and earnings volatility.
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See for example CHOI, J. & RICHARDSON, M. 2012. The volatility of the firm's assets and the leverage effect. Unpublished manuscript, 1-48.
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Since volatility and profitability are likely to be negatively correlated 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 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 earnings volatility. The idea that the degree of product market competition and earnings 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 earnings volatility. We attempt to isolate the impact on leverage of volatility and profitability by using changes in tariffs and industry-level trade weighted foreign exchange rates as instruments for both profitability and volatility. As discussed later we also use as a third instrument, industry-level profitability in nearby industries (based on SIC codes) that do not undergo tariff changes. Use of a third instrument allows us to test the exclusion restriction. Overall we find that changes in the conditional variance of industry earnings are negative and significantly related to tariffs and the trade weighted exchange rates. Consistent with prior research we also find that tariffs and exchange rates are positively related to industry profits. Thus both instruments pass the relevance test for volatility and profitability. We next use the instrumented values for both profitability and volatility to examine changes in market leverage. Overall, we find a negative and significant relationship between changes in market leverage and changes in instrumented volatility. We also find a negative relation between changes in market leverage and instrumented changes in profitability. Our findings, together with Xu (2012)’s findings of a negative relationship between import competition and leverage suggest that the primary channel through which increased competition affects leverage is through an increase in volatility and not through lower industry earnings. One concern with examining the relationship between changes in market leverage and volatility is that we may simply be picking up a mechanical relationship between market leverage and volatility 4
through the so-called leverage effect. The leverage effect refers the fact that for a levered firm, an increase in asset volatility increases the value of equity, and since leverage is measured using book values of debt, an increase in volatility mechanically leads to decline in market leverage. Thus, finding a negative relation between market leverage and volatility tells us very little about whether firms adjust their leverage in response to innovations in volatility. To address this concern we follow an approach similar to the one taken by Leary and Roberts (2005) Korajczyk and Levy (2003) and Hovakimian et al. (2001) and use financing spikes to identify capital structure adjustments. Specifically, we examine relations between changes in volatility and four types of financing spikes: equity issuances, debt issuance, equity repurchases and debt retirements. Each spike is defined by an increase or decrease greater than a given threshold value. Overall we find that increases in volatility are associated with a decrease in the likelihood of leverage increasing adjustments. Specifically, we find that spikes in both debt issuance and share repurchases are negatively related to changes in volatility. Consistent with an asymmetric response to changes in volatility, we find no significant relationship between changes in volatility and either reduction in debt or equity issuances. The asymmetric effect of changes on volatility on debt issuances and retirements lends empirical support for the concern that during periods of low volatility firms taken on leverage that is difficult to reduce if volatility subsequently increases. We also find cross-sectional differences in the effect of volatility on security issuance. Firms’ responses to innovations in volatility are dictated by the extent to which they are subject to financial frictions during the capital raising process. Consistent with previous studies of the effects of macro conditions on issuance decisions, we find that non-dividend paying and unrated firms react more to declines in volatility. Indeed, while we find a negative relationship between net debt issuances and volatility for financially constrained firms, we find an insignificant relation between debt issuances and volatility for financially unconstrained firms. This later result is consistent with the previous studies that find target leverage is counter-cyclical for unconstrained firms. We conduct a number of robustness checks. For example, instead of estimating a GARCH model of conditional volatility we identify changes in volatility using alternative measures of volatility such as the regime detection method described in Inclan and Tiao (1994). We also examine the relationship between our volatility measures and corporate cash holdings as well as corporate loan spreads and find results consistent with prior studies. We contribute to the literature on the capital structure choice in several ways. First we provide new evidence on the importance of volatility as a determinant of capital structure. Our results suggest 5
that the previous studies obscure the importance of changes in volatility by measuring volatility in a way that assumes industry volatility is stable and innovations in volatility are very persistent. GARCH estimates of conditional volatility indicate there are significant transitory spikes in volatility that are likely to be obscured using the standard deviation of past earnings. Second despite being transitory, decreases in volatility have a significant impact on debt issuances, particularly for financially constrained firms. This finding provides empirical support for the concern that during periods of relative calm firms may increase leverage and that the increases in leverage are subsequently difficult to unwind. Third, we show that changes in industry-level product market competition are significantly related to both earnings volatility and profitability. Our results suggest than increased product market competition has a significant impact on leverage, primarily through increasing earnings volatility that in turn reduces the debt capacity of firms within the industry. The remainder of the paper is organized as follows. In section 2 we provide a brief review of the literature examining the relationship between leverage and earnings volatility. Section 3 describes our data and methodology. In section 4, we present our empirical findings concerning the relationship between changes in leverage and changes in volatility. We also provide evidence that our measures of volatility are related to corporate cash holdings and loan pricing. In section 5 we examine the relation between volatility and security issuance and repurchase activity. Section 6 provides a summary of our findings. II. Background on the effect of volatility on leverage There are strong theoretical arguments for why 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 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.5 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 5
In BLACK, F. & COX, J. C. 1976. Valuing corporate securities: some effects of bond indenture provisions. Journal of Finance, 31, 351-367., 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.
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shields.6 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. Much of the empirical research on volatility has focused on stock returns. As shown in French et al. (1987), positive innovations in volatility are correlated with negative innovations to returns. The leverage hypothesis (Black (1976)) states when the total value of the firm declines, the value of equity becomes a smaller share of the total. Since equity holders bear the full risk of the firm, the percentage volatility of equity should rise. However Schwert (1989) finds that leverage alone cannot explain historical movements in stock volatility. The volatility feedback hypothesis describes the causality as going the other way. Positive innovations in volatility increase expected returns, and if expected dividends remain unchanged, the stock price must fall (see Campbell et al. (1997)). The capital structure literature finds a weak association between volatility and leverage, as described in Graham and Leary (2011). Measures of volatility used in these studies can be classified into three categories. The most widely used measure of volatility is the rolling window measure used in Rajan and Zingales (1995), Opler et al. (1999), Valta (2012), Faulkender and Petersen (2006) and others. The rolling widow approach measures volatility as the standard deviation of past earnings 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. It may therefore come as no surprise that innovations in these measures of volatility are not significantly 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. The variance of past stock returns is used in Frank and Goyal (2009) to measure the effect of volatility on leverage. Although they de-lever equity returns to account for the leverage effect, leverage and volatility are endogenously determined, which makes this exercise problematic. An alternative approach is employed in Leary and Roberts (2005) who model volatility as the absolute change in the prior period’s earnings. This approach puts all of the weight on the most recent innovation in corporate profits without giving any weight to volatility prior to time t-1. These considerations suggest that the optimal empirical model of volatility should be one that incorporates news about past innovations in volatility with news about current innovations in volatility. The leverage and volatility feedback 6
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, C. W. & STULZ, R. 1985. The determinants of firms' hedging policies. Journal of Financial and Quantitative Analysis, 20, 391-405. make a similar argument with respect to the benefits of corporate hedging.
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hypotheses imply that an empirical model of volatility should permit negative innovations in asset returns to have more weight on current volatility than positive innovations in returns. Finally, as pointed out in Xu (2012), one reason that empirical tests may fail to find a relation between profitability/volatility and leverage is that structural models make predictions with respect to expected profitability and volatility, yet empirical measures use past realizations of returns to measure volatility.
III. Data and methodology In our empirical analysis we use COMPUSTAT’s quarterly data set. We restrict the sample to manufacturing firms (SIC codes 2000 to 3999) over the sample period beginning in quarter 1 of 1980 and ending in quarter 4 of 2005. We limit our sample to manufacturing firms because or identification strategy relies in part on tariff changes. Tariff data for 1980 to 2005 is obtained from Peter Schott’s web site and they are calculated as the ratio of duties collected to free-on-board customs value of imports times 100 (see Bernard et al. (2006)). Our second instrument is the industry trade weighed 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). 7 Industry imports and exports from 1989 to 2005 are obtained through TradeStats Express at the U.S. Department of Commerce. 8 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 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, 7 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. 8 We use 1989 as the base year for computing import and export shares for all years in the sample.
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we restrict firms to have a minimum of four quarters of data. These procedures yield 4,412 firms across 72 manufacturing industries with valid estimates of profit volatility. 3.1 Volatility estimates We measure volatility using the estimated variance of industry operating performance. Our empirical analysis focuses on innovations in volatility at the industry level. Measuring asset volatility at the industry level has several advantages. First, using industry level volatility partially alleviates endogeneity concerns because reverse causality between industry volatility and firm capital structure is less likely than if firm-level volatility were used. Second, industry-level estimates provide a lengthy timeseries 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 2012 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 assets and
is the ratio of aggregate industry operating income to aggregate industry net
is a dummy variable for calendar quarter i. Assets are measured net of cash in order to
exclude possible effects of volatility on cash holdings (Acharya et al. (2014) find that cash-flow volatility increases cash holdings). Equations (1) and (2) are measured jointly for each industry using the time series of quarterly earnings. In order to ensure that there is variation in volatility in our sample, the estimation of the GARCH process restricts the ARCH parameter ( in equation (2)) to be strictly positive and the GARCH parameter ( ) in equation (2) to be greater than or equal to zero. If the estimation procedure fails to converge, then the industry is dropped from the sample. This procedure results in a total of 76 industries with estimates of the GARCH process out of a total of 118 manufacturing industries. Restricting the sample to 1980 to 2005 further reduces the number of industries to 72. We also consider an EGARCH model, which is specified as follows. log where
log
|
|
2
is set to one in order to identify the parameters, and
3 . The appeal of the EGARCH
model is that allows negative shocks to earnings to have a greater impact on volatility than positive 9
shocks, which is consistent with findings of previous studies of fluctuations in volatility (see Bloom (2014)). In the context of the EGARCH model, negative shocks to returns have a greater effect on 2
volatility whenever
/
1 and
is less than 1.
Summary statistics for both the GARCH and EGARCH estimates of volatility are reported in Table 1. As shown, the average quarterly GARCH volatility estimate is 0.83%. The average quarter over quarter change in GARCH industry volatility is zero, but the standard deviation of changes in volatility is high (0.30% per quarter or 35% of the average level of volatility). A large proportion of industries in our sample have stationary GARCH process (
1 ). For these industries we report the half-life of the
GARCH process, defined as the number of periods over which the distance between the current variance estimate and the long-run average is halved. The average half-life is 36 quarters, but this value is driven by large outliers; the median half-life is 1 quarter, which implies that for some industries in our sample, changes in volatility persist only up to two quarters only. As discussed later, we find that changes in volatility that are expected to be more persistent (i.e. GARCH processes with longer half–lives) are associated with significantly greater changes in leverage. Figures 1-5 plot quarterly 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. All five depicted industries experienced a tariff cut over the sample period. Visual inspection indicates that the year of the tariff cut coincides with an increase in the variance of cash flows in the year of the cut and the following years. We use market leverage in our tests of capital structure. The primary reason is that capital structure theory defines leverage and firm assets in terms of their market value, as opposed to historical book values. In order to examine whether the prior finding of a weak link between leverage and volatility arises in part from measurement error, we compare the impact of the GARCH measure of volatility to measures of volatility previously employed in the literature (see Section II above for a brief survey). The following regression model is estimated. ∆
∆
∆
∆
10
∆
(4)
where
is a measure of volatility for industry j in quarter t and return on assets (ROA) is
measured at the industry level. Time-t values of volatility are used because our preferred measure of volatility at time t (GARCH or EGARCH model) is based on time t-1 information. We use contemporaneous values of profitability in order to control for the negative correlation between contemporaneous innovations in volatility and profits. To evaluate whether the effect of volatility on leverage varies with how volatility is measured we use four measures of volatility. First, we estimate volatility as a five-year rolling window of the standard deviation of past innovations in profits (
from equation (1)) (the most commonly used measure in the
literature). We next consider a longer estimation window, allowing the standard deviation to be measured with up to 10 years (40 quarters) of past abnormal earnings. The rolling window estimates use earnings information up to time t. Volatility is also measured using the GARCH model described in equations (1) and (2) as well as an EGARCH model, which allows negative values of variance than positive values of
to have a greater impact on the
.
Table 2 reports ordinary least squares estimates of equation (4). As shown, innovations in volatility using rolling window estimates are not significantly related to changes in market leverage. This finding is perhaps not surprising, given the slow-moving nature of these volatility processes. In the rolling window framework, the arrival of new information is given less weight the longer the length of the window. Therefore innovations in this measure will be small in magnitude. For example the standard deviation of the quarterly change in volatility using a 5-year rolling window method is only 7 basis points and it declines to 4 basis points for a 10- year window. In comparison, the corresponding standard deviation for a GARCH model is 30 basis points per quarter. Both GARCH and EGARCH volatility estimates have negative and statistically significant coefficients in the differences regression. As shown in columns (3) and (4), the coefficient estimate on the EGARCH estimate of volatility is several orders of magnitude smaller than for the GARCH estimate. The reason is that the EGARCH process produces much larger and more variable estimates of the conditional volatility. Table 1 indicates that the interquartile range for changes in the EGARCH volatility is 25 basis points and the corresponding interquartile range for the GARCH process is only 9 basis points. Furthermore, the EGARCH estimates produce some very large outliers: the maximum EGARCH estimate of the volatility is 35%, compared with only 8.3% for the GARCH process. Overall, these results suggest that estimates of volatility that assume a slow moving process are unlikely to detect much of an impact of volatility on capital structure. For brevity, in the analysis that follows we focus on the simpler GARCH estimates of volatility although our results are similar if we forecast future volatility using an EGARCH model. 11
An interesting question is whether the effect of volatility on leverage is greater for industries with more persistent volatility shocks. A significant proportion of the industries in our sample have stationary GARCH processes, which allows us to address this question. For each of these industries, we compute the half-life of the GARCH process by solving the following equation for the variable k: 1 . 5 2 The value of k indicates the number of quarters it would take for the GARCH process to half the distance between the current level of variance and the long-run level of variance for that industry. As shown in Table 1, the half-life of the median firm in our industry is 1 quarter. To examine whether the relation between leverage and volatility varies with persistence of the process, we split in our sample into industries with high and low half-lives according to where they fall relative to the sample median. Table 3 reports the regression results. As shown, the effect of innovations in volatility on leverage is over twice as large for industries with a half-life higher than the sample median. The coefficient on volatility for these industries is -0.69, compared with -0.25 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 10% level of confidence, indicating that volatility has a significant effect on leverage even in industries with less persistent shocks. 3.2 Instruments for volatility and profitability Potential endogeneity between market leverage, profitability and the variance of quarterly operating earnings may produce biased regression coefficients and incorrect inferences. We address endogeneity concerns by using several instruments for volatility and profitability. In order to eliminate passive changes in leverage caused by changes in retrained earnings we examine security issuance activity (further described below). Product market competition is likely to affect both firm profitability and cash flow volatility. Increased competition may lead to predatory market pricing and a greater likelihood of financial distress (see Bolton and Sharfstein (1990) who 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 and 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
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firm’s competitive environment. We adopt a similar identification strategy and instrument changes in volatility with changes in tariffs. Figure 6 plots volatility against tariffs for our sample of manufacturing firms. Industries with the highest tariffs tend to have lower earnings volatility, although the relation is not monotonic. The effect of tariff changes on volatility is further explored in Figure 7, which plots weighted average GARCH volatility estimates in event time around tariff cuts, where the weights are proportional to the number of firms in each industry-quarter. Tariff cuts are defined as a reduction in annual tariffs that is equal to at least 3 times the median reduction in tariffs for the industry as in Valta (2012). There are a total of 80 tariff cuts of this magnitude in our sample of manufacturing industries between 1980 and 2005. As shown, the weighted average level of the variance of industry profits rises significantly around tariff reductions and the effect of the tariff reduction persists up to 5 quarters. Tariff changes may also affect profitability levels, potentially confounding the interpretation of the effect of tariff reductions on leverage. 9 Figure 8 plots industry profits against tariffs. As shown, industries with higher tariffs tend to have higher average profits. Taken together, Figures 6-8 imply that import competition may affect capital structure by changing future profitability, future volatility or both, thereby making any conclusions about the effect of competition on leverage difficult to interpret.10 Because of these concerns, we identify two additional instruments for volatility and profitability. Competition in a firm’s product market may also arise because of foreign imports. Following Bertrand (2004), we use the industry trade-weighted foreign exchange rate as a source of exogenous variation in industry import shares. The idea is that changes in exchange rates have an effect that is similar to tariff changes on import competition: increases in the value of the dollar makes U.S. exports more expensive (and thus less competitive) while making imports more attractive. 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 shares because foreign exchange rates are determined by factors that are outside the scope of the firm’s product environment. Our third instrument is based on the idea of a matched control group of firms for industries that undergo a tariff cut. For each 3-digit SIC category, we identify a matching industry in the same 2-digit SIC category that does not undergo a tariff cut or increase in the same quarter. Firms in the matching 9
VALTA, P. 2012. Competition and the cost of debt. Journal of Financial Economics, 105, 22. leaves open to interpretation whether competition increases loan spreads because of greater volatility or reduced profitability. 10 XU, J. Ibid.Profitability and capital structure: Evidence from import penetration. 106, 20. 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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industry therefore have profits that are correlated with the target industry and uncorrelated with tariff shocks affecting the target industry, thereby providing a third source of variation in industry profits. We implement this instrument by choosing a nearby industry with the closest 3-digit SIC code that is larger than the SIC code of the target industry. If no such industry exists, we then select the lowest 3-digit SIC code in the next highest 2-digit SIC code. Proceeding this way provides an arguably exogenous variation in industry profits that varies at a quarterly frequency. The advantage of this instrument is that it is highly correlated with industry profits but not correlated with tariff shocks (by construction). The drawback is that this instrument may not be orthogonal to firm leverage for industries that have very similar product market strategies. However, having two endogenous variables and three instruments yields an overidentified system of equations, which permits formal tests of the exogeneity assumption behind our choice of instruments. Before proceeding with such tests, we first establish the relevance of our instruments. 3.2.1 Do the instruments pass the relevance test? Table 4 reports pairwise correlations in quarterly differences at the industry level. Industry profits and volatility are significantly negatively correlated, indicating that periods of high volatility are accompanied by episodes of low profitability. This aspect of the data lends further support to our identification strategy, which permits identification of the effects of both variables on capital structure. Consistent with Figures 6-8, tariffs are significantly negatively correlated with industry volatility and significantly positively correlated with industry profits. Table 4 also shows that the trade-weighted index, an instrument for import competition, is negatively associated with volatility and positively associated with profitability. Most quarter-over-quarter changes in volatility are small in magnitude. The average change in volatility is zero and the average absolute change in volatility is 14 basis points (bps). The likelihood of small changes in volatility having an effect capital structure decisions is quite low, especially since these changes are tend to be transitory. Furthermore, small changes in volatility are unlikely to be correlated with significant reductions or increases in tariffs. As shown in Figure 8, industries undergo large tariff cuts only infrequently, making it likely that most changes in volatility are unlikely to be correlated with changes in productt market competition. Therefore, in order to further isolate the impact of changes in product market competition on volatility, we construct a subsample of firm-quarters in which large changes (in absolute value) in volatility occur. Large changes are defined as occurring whenever the quarter-over-quarter changes in volatility is greater than the 75th percentile of the distribution of changes within an industry, or if the change in volatility is less than the 25th percentile of the distribution of changes in volatility within an industry. The average across all industries of the 75th percentile of changes 14
in volatility is 6 basis points and the average for the 25th percentile is -8 basis points. However, conditional on large changes, the average absolute change in volatility increases from 13 bps to 24 bps (or 29% of the sample average level of volatility) when the sample is restricted to large changes in volatility (see panels A and B of Table 1). Panel C of Table 4 reports the correlation coefficients for the subsample of observations that have large absolute changes in volatility. As shown, the correlation between volatility and profitability increases (in absolute value) from -0.11 to -0.15 and the correlation between changes in tariffs and changes in volatility increases (in absolute value) from -0.02 to -0.03. Because significant changes in volatility are more likely to be related to large changes in tariffs and the trade-weighted index, we base all our instrumental variables tests of this subsample of observations. 3.2.2 Multivariate tests of instrumental variable relevance We further examine the association between profitability, volatility and the instrumental variables in a multivariate regression setting based on the subsample of large changes in volatility. These regressions are estimated at the firm level and correspond to the first stage regression in a two-stage least squares regression. ∆
∆ where
∆
∆
∆
∆
6
is either volatility or profits for firm i in industry j. The variables
and
equal the tariff and quarterly trade-weighted exchange rate for industry j measured at the end of quarter t, respectively. in
is the neighboring industry profits as described above. The variables
consist of a set of firm-level controls measured at time t-1 and
consists of macrovariables,
also measured at time t-1. Because tariffs and the trade-weighted index are exogenous, we measure the effect of these variables at time t and lag the firm-specific variables and macroeconomic variables. These results are reported in Panel A of Table 5. The dependent variables (and some independent variables) are common across firms in industryquarters and we therefore also estimate equation (6) at the industry-level by averaging firm-level variables across industry-quarters and weighing each industry-quarter observation by the number of firms in that industry-quarter. These results are reported in Panel B of Table 5. Both tariffs and the trade-weighted index are negatively related to changes in volatility. As shown in Panel A, the coefficients on tariffs and TWFX in column one are significantly negative. Consistent with arguments made earlier, industry profits are positively associated with. Adding neighboring ROA to the regression specification does not change the coefficients on tariffs and TWFX. Furthermore, and 15
perhaps not surprisingly, neighboring industry profits are positively correlated with target industry profits and negatively correlated with target industry volatility, indicating that this instrument is associated with both volatility and industry profits. Industry-level estimates reported in Panel B are very similar to firmlevel estimates. We investigate the strength of the instruments with F-tests of the significance of these instruments in the first-stage regressions. Two sets of tests are employed. First, the significance of all predetermined variables is tested with a Wald test of joint significance. As shown in Panel A, the corresponding Fstatistics are all larger than 10, indicating that the coefficients are jointly difference from zero. Second, we report Wald tests on the joint significance of tariffs, the trade weighted index and neighboring industry profits. The F-test for the joint significance of tariffs and TWFX equals 50 in the volatility equation and 60 in the profitability equation (see columns (1) and (2)). Adding neighboring industry profits to the set of instruments does not affect the outcome of these tests: the corresponding F-statistics are 38 and 75 in the volatility and profitability regressions. We further investigate the relevance of our instruments using the Stock and Yogo test for weak instruments. With only one endogenous variable this test reduces to the first-stage IV F-statistic reported earlier. For a system with two or more endogenous variables, the test is based on a minimum Eigenvalue. Under the null of weak instruments, this statistic has a maximal value of 7.03 for a 5% Wald test with a size of 10% (see Table 3 of Stock and Yogo (2005)). Based on a value of 9.2 we are able to reject the null hypothesis of weak instruments when tariffs and TWFX are used. The test statistic increases to 13.1 when neighboring ROA is added to the instrument set, but the maximal value is 13.4, which indicates that we cannot reject the null hypothesis of weak instruments for neighboring ROA. However, we consider both sets of instrumental variables in the following sections because the usefulness of this third instrument lies in that it allows us to test the exogeneity of all three instruments. Overall, these “first-stage” regressions indicate that, taken together, the instrumental variables tariffs and TWFX are significantly correlated with profitability and volatility.
IV. What is the effect of volatility on leverage? How does uncertainty affect capital structure? Our analysis proceeds in three steps. We first examine the effect of volatility on market leverage ratios. We then examine the effect of volatility on corporate cash policy and loan spreads. Examining the effect of volatility on cash holdings and loan spreads provides a useful robustness test of our identification strategy.
16
In the third 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 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 The effect of volatility on leverage is estimated with the following regression model ∆ where
∆
∆
∆
∆
7
is market leverage for firm i at time t, and the other variables are defined in Section 3.
Estimation results are reported in Table 6, which is based on the subsample of large changes in volatility. Column (1) reports OLS estimates. Columns (2) and (3) report 2SLS estimates using tariffs and TWFX, and tariffs, TWFX and neighboring ROA, respectively. All three columns show that large innovations in expected volatility reduce market leverage, controlling for the effect of profitability. Comparing columns (1) and (2) indicates that the reported coefficient on volatility is larger when it is instrumented, implying that the effect of this variables on capital structure is larger than implied by the OLS estimates. 11 Instrumenting ROA also increases the magnitude of this variables’ effect on leverage. As shown, the coefficient on ROA is insignificant using OLS, but significantly negative using 2SLS. Adding neighboring industry profits to the instrument set reduces the coefficient on volatility slightly in column (3), but this coefficient and the coefficient on ROA remain statistically significant at the 1% level of confidence. Having three instruments and only two endogenous variables implies that the estimation system is over-identified, which permits a test of the exogeneity assumption that all three instruments are orthogonal to the error term in the leverage equation. Table 6 reports the J-statistic for over-identifying restrictions. The J-statistics for the overidentified system is close to zero and we cannot reject the null hypothesis that it equals zero at the 10% level of confidence (p-value of 21%). The economic significance of volatility and profitability are measured by multiplying their respective regression coefficients by the change in volatility. Because the estimation sample is based on quarters with large (in absolute value) changes in volatility, we use the mean absolute quarter-overquarter change in volatility to gauge the economic significance of our results. The quarterly average 11
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 5, the difference in the OLS and IV estimates for volatility are consistent with significant upward bias in OLS estimates.
17
absolute change in volatility changes is 24 basis points (reported in Table 1, panel B). Increasing volatility by this amount implies a 2.98% decline in leverage, based on the estimates in column (2), and a 2.01% decline in leverage based on the estimates reported in column (3). In comparison, the corresponding change implied by the OLS estimates is only 6 basis points, indicating that using OLS biases the economic significance of innovations in volatility on leverage downward. Innovations in industry profitability also have a significant impact on leverage. The mean absolute change in ROA is 97 bps, implying a 2.49% change in leverage using two instruments, and a 1.89% change in leverage using three instruments. Overall, the IV-based regressions indicate that volatility has a significant effect on capital structure choice after controlling for the contemporaneous effect of profitability on leverage. 4.2 Volatility and loan spreads As discussed in section 2, one potential way in which increases in volatility affect the choice of leverage is by increasing the likelihood of default, holding debt constant. In a recent paper, Valta (2012) finds that product market competition increases corporate loan spreads though he does not examine whether the effect of changes in competition is through changes in expected volatility, profitability or both. Specifically, an interesting question is whether an increase in competition affects loan spreads through the profitability or the volatility channel, or both. The profitability channel implies that competition increases loan spreads because future profits are expected to be lower, which diminishes pledgeable income and reduces the value of the firm’s assets as collateral. The volatility channel implies that competition increases loan spreads because competition leads to aggressive product market strategies by the firm’s competitors as described in Bolton and Sharfstein (1990), which may lead to financial distress. In this section we attempt to distinguish between these two explanations for the effect of product market competition on loan spreads, and thereby provide further justification for our identification strategy. We use data on syndicated loans obtained from Dealscan Loan Pricing Corporation for the period 1992-2005. The analysis follows Valta (2012) and examines the effect of past tariff reductions on corporate loan spreads. Firm characteristics are matched to each loan based on the most recent fiscal quarter prior to the loan facility start date. Since macro-economic factors may have an important impact on loan spreads, we add controls for GDP growth, terms structure spreads and credit risk spreads to Valta’s specification. Estimates reported in columns (1) and (2) of Table 7 are based on the full sample of observations, while IV estimates reported in columns (3) and (4) are based on the subsample of large changes in volatility.
18
As shown, we confirm Valta’s findings that past reductions in tariffs are associated with higher loan spreads. The coefficient on Postreduction, a dummy variable for whether an industry experienced at tariff cut in the past, is positive and significant. Including macroeconomic variables in Valta’s specification does not affect the explanatory power of this variable. We also replace Postreduc with industry volatility, and replace firm-level profitability with industry profitability at the end of the quarter preceding the loan initiation. Volatility is not significant, but profitability has a negative effect on loan spreads in this specification. When we estimate the model using instrumented values of volatility and profitability, we find a positive and significant effect of volatility on loan spreads and a larger and positive effect of profitability. The economic effect of volatility is quite large. Based on column (3), multiplying the coefficient on this variable by the average change in volatility increases loan spreads by 55%. Given that the average loan spread is 192 bps, this increase in volatility corresponds to a 108 bp increase in the loan spread. A similar calculation for ROA, implies a 61 bps increase in the loan spread. These results suggest that the effect of competition on loan spreads operates through both channels (profitability and volatility), and that the effect of expected volatility is economically more important than the effect of profitability. 4.3 Volatility and cash holdings A study by Bates et al. (2009) provides evidence that firm cash holdings are positively related to industry level cash-flow volatility. As in most capital structure studies, Bates et al. (2009) estimate industry-level cash-flow volatility using the standard deviation of past earnings over a window of up to 10 years. They argue that precautionary savings motives for holding cash imply a positive relation between cash holdings and expected earnings volatility. Transaction costs generate a similar prediction: An increase in volatility increases the frequency at which the firm accesses capital markets and pays transaction costs to raise capital when it cannot fund all of its investments with internally generated resources. We further test the empirical validity of our identification strategy by measuring the effect of exogenous variations in asset volatility on corporate cash holdings. Our analysis is related to Fresard (2010) who finds that firms with greater cash holdings gain market share at the expense of their rivals following an exogenous increase in import competition. Our estimation model follows Bates et al. (2009) and Opler et al. (1999) and we regress the natural logarithm of cash to book assets net of cash on volatility and a set of control variables included in their study. Independent variables with assets in the denominator use book assets net of cash. For some specifications we also include controls for macro conditions. As before we instrument time-t volatility and profitability with tariffs, the trade-weighted 19
index and neighboring industry profits. Columns (1) of Table 8 reports OLS regressions and columns (2) and (3) report instrumental variable regressions. All three columns show that both volatility and profitability have a positive effect on cash holdings. The sign of these coefficients is consistent with Opler et al. (1999) and Gao et al. (2013) who study the determinants of cash holdings for private and public firms respectively. The magnitude of the coefficients in the 2SLS specifications is substantially larger than in the OLS regressions, which suggests that proper identification is required for gauging the economic significance of volatility on cash holdings. Based on column (2) coefficients, an increase in volatility equal to the mean absolute change in volatility increases the ratio of cash holdings to book assets by almost 190%. Given that the median level of cash holdings in the sample is 9%, a large change in asset volatility implies a 17% increase in the ratio of cash to net assets for the median firm. In comparison, an increase in ROA equal to the mean absolute change leads to only a 40% increase in the cash ratio, or an increase of 3.6% in the ratio of net cash to assets for the median firm. Summarizing, the cash holdings regressions provide further support for our identification strategy. V Issuance activity A potential concern with the results of the previous section is that even though volatility and profitability are instrumented, the regression estimates may be driven by mechanical effects between volatility and leverage because increases in volatility are associated with decreases in profitability. One way around this problem is to consider security issuance and repurchase activity instead of leverage changes. We follow Leary and Roberts (2005) and Korajczyk and Levy (2003) and define four basic financing events. An issuance 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 the 5% cutoff. Equity repurchases use a 1.25% cutoff due to their more frequent nature and limitation in 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.12 12 An alternative approach would be to follow DENIS, D. & MCKEON, S. B. 2012. Debt financing and financial flexibility: Evidence from proactive leverage increases. The Review of Financial Studies, 25, 33. and define
20
Table 9 reports some descriptive evidence on the number of basic financing spikes in the subsample based on quarters with large changes in volatility. Issuance events occur about 30% of the time in the sample, which implies that adjustments occur once every four quarters. As shown, the average number of debt issues is 1.45 per firm and this is the most frequent type of security issue. Leverage increases are less frequent than leverage decreases: the average firm has between 1 and 2 leverage increasing events and between 2 and 3 leverage decreasing events. 5.1 What is the effect of volatility on issuance activity? This section reports regressions of issuance activity using a binary choice model of security issue or repurchases. The dependent variable is a binary variable for whether a basic financing spike or leverage increase/decrease occurs at time t. We estimate the following PROBIT model of issuance.
Pr
8
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 volatility with time t-1 information, innovations in volatility and ROA are measured at time t. We also include industry fixed effects
in order to control for unobserved
characteristics that explain differences in the average likelihood of security issuance across industries. Equation (8) 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 ROA are instrumented with tariffs, and the trade weighted FX index, all measured at time t. IVPROBIT estimates are reported in Table 10. 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 debt issuance and share repurchase regressions, which justifies the use of instrumental variables in this setting. The IVPROBIT estimates of the coefficient on volatility in this specification are significantly negative and economically significant for leverage increases, debt issues, and share repurchases. 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 ibid. 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 have lower issuance costs than equity issues.
21
Furthermore, the sign on these variable indicates that firms increase their leverage in response to a decrease in volatility. As shown, volatility is inversely related to the likelihood of issuing debt. The Average Partial Effect (APE) for volatility is -0.20 in column (3), which implies that an increase in volatility equal to the sample mean of absolute changes leads to 5% decline in the probability of issuing debt. 13 A similar calculation implies a 6% reduction in the probability of a leverage increasing transaction in response to an increase in volatility. The last column shows that innovations in volatility have a significant economic effect on the likelihood of repurchasing shares. Increasing volatility by 24 bps reduces the probability of repurchasing shares by over 9%. The effect of volatility on leverage decreases is significantly positive. However, when equity issues and debt repurchases are analyzed separately, we find no significant effect of volatility on these forms of issuance behavior. 5.2 Which firms proactively respond to changes in asset volatility? This section investigates whether firms that face financing frictions related to the process of raising capital react differently to large changes in volatility. Financing frictions affect corporate investment policy (Hennessy and Whited (2007)), access to capital (Faulkender and Petersen (2006)) as well as dynamic capital structure choice (Faulkender et al. (2012)). Since various frictions in the process of raising capital may hinder access to capital markets, a natural question to ask is whether access to the capital markets, and to debt markets in particular, varies with the level of volatility. As before, all estimation results in this section are based on the subsample of large changes in volatility. Firms are classified according to commonly used measures of financing constraints. For simplicity we restrict these measures to whether the firm paid a dividend in the prior quarter and whether the firm has a credit rating. IVPROBIT regression results are reported in Table 11, which reports separate regression estimates for each group of firms. Volatility and profitability are instrumented with tariffs and the trade-weighted FX index. Panel A reports debt issuance estimates. As shown, the magnitude of the coefficient on volatility for constrained firms is different in sign and larger in magnitude than for unconstrained firms, regardless of the classification scheme. Constrained firms are less likely to issue debt when volatility is high, while unconstrained firms’ debt issuance activity does not appear to be a function of cash-flow volatility. These results are consistent with Korajczyk and Levy (2003) who find that leverage is pro-cyclical for constrained firms and counter-cyclical for unconstrained firms.
13
The APE for the IVPROBIT is estimated using a linear probability model and the 2SLS estimator. The APE for volatility equals the coefficient estimate on that variable.
22
Further evidence that constrained firms respond differently to innovations in volatility is reported in panels C, which estimates the likelihood of leverage increases. Constrained firms are much more likely to undertake leverage increasing transactions when volatility is low. As shown, the coefficients on volatility are significantly negative in Panel C for unrated firms and firms that do not pay dividends. The corresponding coefficients for rated and dividend-paying firms are not significantly related to the level of volatility. The effect of volatility on leverage decreases is significantly positive for all four groups of firms. However, when equity and debt repurchases are analyzed separately, we fail to find any significant effect of volatility on the likelihood of undertaking these types of transactions. A shown, the effect of volatility on equity issuance is positive but not significant (Panel E), nor is the effect of volatility on debt repurchases significantly different from zero (Panel B). Volatility has a much larger effect on share repurchases for constrained firms than for unconstrained firms. Panel F shows that unrated firms and firms that do not pay a dividend are less likely to repurchase their own shares when volatility is high, and this effect is statistically significant. The effect of volatility on share repurchases by rated and dividend-paying firms is not significant. Overall, these results indicate that large changes in volatility significantly affect the likelihood of leverage increasing transactions by firms that face financing frictions when raising capital. Positive innovations in expected volatility reduce the likelihood that these types of firms will raise debt or repurchase their own shares. In contrast, leverage increases by firms that enjoy less restricted access to capital markets do not seem affected by innovations in expected volatility. The asymmetrical effect of volatility on leverage increasing transactions (debt issues and share repurchases) compared with leverage decreasing transactions (debt repurchases and equity issues) indicates that financing frictions may prevent firms from reducing leverage in response to large increases in volatility. 5.3 Regimes changes in volatility In this section, we explore the robustness of our main findings to alternative models of volatility. Instead of a GARCH process, we estimate a model of volatility that detects regime changes in the sequence of squared innovations in industry profitability from equation (1). Further details on the regimeshift model are provided in Appendix A. The regime change model uses the full sample of observations to detect regime changes. Regression estimates are based on industries for which a change in volatility regime is detected.
23
Table 12 reports binary choice model regressions of the effect of volatility regimes on the likelihood of a proactive change in leverage for the subsample of industries in which a regime shift is detected. The dependent variable is the likelihood of a basic financing spike or leverage change, and the independent variables are measured at time t-1. The variable of interest is the low volatility regime dummy LowVol, which replaces the GARCH estimates of volatility in the regression specification. This variable equals one when a firm is in a low volatility regime (as opposed to a high volatility regime). The regime dummy and ROA are instrumented with past quarterly changes in tariffs, and levels of the tradeweighted FX rate and neighboring profits all measured at time t-1. The IVPROBIT estimator assumes a normal distribution for the endogenous variables and it is therefore not appropriate in this case because the regime change variables are binary. We use instead the 2SLS linear probability model, which makes no distributional assumptions about the endogenous variables. As shown in Table 11, low volatility regimes increase the likelihood of issuing debt and increasing leverage, but do not have a significant effect on the likelihood of issuing or repurchasing equity. The effect of low volatility regimes is economically significant: transitioning from a high to a low volatility regime increases the likelihood of a leverage increase by 21.8%. Likewise, a transition to a low volatility environment increases the probability of a debt issue by 16.9%. Overall, using a regime change model produces results that are consistent with the use of GARCH estimates. VI Conclusion Economic theory implies that volatility plays an important role in determining firms’ capital structure, yet prior research does not find a significant relation between cash-flow volatility and leverage. We argue that the lack of significance can be explained by measurement error in empirical estimates of the volatility process. Specifically, the use of rolling windows to estimate 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 three contributions. First, we identify innovations in the process for volatility at the industry level with a GARCH model of volatility. The advantage of our approach is that industry-level estimates provide a longer time-series for estimating these types of models, which are typically employed using high-frequency data. Unlike rolling-window estimates of volatility, GARCH estimates put no constraint on the weights on past volatility and current innovations in the squared residuals. We allow the time-series of data in each industry to dictate the relative importance of these two components of the volatility process. This produces volatility estimates that differ across industries in the persistence of 24
innovations in volatility, and in the effect of past profitability shocks on current volatility. Our results show a significant negative association between leverage and volatility that controls for the joint effect of profitability on leverage. Moreover, the effect of volatility on leverage is significantly stronger in industries with more persistent shocks to volatility. Second, we address the potential endogeneity of leverage and volatility using an identification strategy based on industry-level changes in product market competition. Contrary to earlier results on tariffs and leverage (see for example Xu (2012)), we do not find evidence that more profitability leads to higher leverage. Third, we show that innovations in volatility primarily affect issuance behavior of firms that face financing frictions when they access capital markets. We find that unrated firms and nondividend paying firms are less likely to issue debt and repurchase their own shares in response to increases in expected volatility. Debt issues by rated and dividend-paying firms are not significantly related to innovations in volatility. We also find that volatility has an asymmetric effect on capital structure choice. Neither debt reductions nor equity issues are significantly related to large changes in volatility, which indicates that firms may have difficulty undoing leverage increasing transactions undertaken during periods of low volatility. Overall our results show that innovations in volatility are an economically significant determinant of capital structure choice.
25
Figure 1: GARCH estimates of industry variance for SIC code 371 (Motor Vehicles and Motor Equipment) This figure plots GARCH(1,1) estimates of the variance of industry profits (percent) for SIC code 371. The 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.
26
Figure 2: GARCH estimates of industry variance for SIC code 331 (Primary Metals and Basic Steel Products) This figure plots GARCH(1,1) estimates of the variance of industry profits (percent) for SIC code 331. The 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.
27
Figure 3: GARCH estimates of industry variance for SIC code 203 (Canned, Frozen, and Preserved Fruits, Vegetables, and Food Specialties) This figure plots GARCH(1,1) estimates of the variance of industry profits (percent) for SIC code 203. The 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 1995.
28
Figure 4: GARCH estimates of industry variance for SIC code 286 (Inorganic chemicals) This figure plots GARCH(1,1) estimates of the variance of industry profits (percent) for SIC code 286. The 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 1995.
29
Figure 5: GARCH estimates of industry variance for SIC code 355 (Special industry machinery) This figure plots GARCH(1,1) estimates of the variance of industry profits (percent) for SIC code 355. The 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 1995.
30
0
2
GARCH volatility(%) 4 6
8
0
5
10
15
20
25
Tariff(%)
Figure 6: This figure plots quarterly estimates of the variance of industry (at the 3-digit SIC code) profitability against tariffs from 1980 to 2005. Tariffs equal the ratio of duties to free-on-board customers’ value of imports in percentage points. Industry volatility is based on GARCH(1,1) estimates.
31
0
5
Industry ROA(%) 10
15
0
5
10
15
20
25
Tariff(%)
Figure 7: This figure plots quarterly industry profitability (measured as aggregate industry return on net assets) against tariffs from 1980 to 2005. Tariffs equal the ratio of duties to free-on-board customers value of imports in percentage.
32
0
Nb. tariff rate reductions- Valta method 5 10 15 20
25
1975
1980
1985
1990 Year
1995
2000
2005
Figure 8: This figure reports the number of tariff reductions per calendar year across industries defined at the 3-digit SIC code. Tariff cuts are defined as in Valta (2012).
33
.85
GARCH estimate of volatility(%) .9 .95 1 1.05
1.1
-10
-5
0 Event time (quarters)
5
10
Figure 9: 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. Industry-year observations are weighted by the number of firms in each industry and tariff cuts are defined as in Valta (2012).
34
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 4,412 firms in 72 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. ROA equals 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 ( . GARCH is the Generalized Autoregressive Conditional Heteroskedasticity estimate of volatility using a GARCH(1,1) process. EGARCH is the Exponential GARCH(1,1) estimate of volatility. Neigh. ROA indicates ROA for a neighboring 3-digit SIC code that does not experience a tariff shock. Panel A reports summary statistics for all observations. Panel B reports summary statistics for the subsample of large changes in volatility, where large changes are defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry.
Panel A: Summary statistics for all observations Variable
Unit
Description
mean
sd
p25
p50
p75
min
max
N
ML ROA D.ROA Abs(D.ROA) Neigh. ROA 5YWindow D.5YWindow 10YWindow D.10YWindow Volatility D.Volatility Abs(D.Volatility) Halflife VolatilityE D.VolatilityE
Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Quarters Percent Percent
Market leverage Return on net assets industry Change in return on net assets Abs. value of D.ROA Return on net assets neigh. Ind. 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 Abs. value of D.Volatility GARCH halflife EGARCH volatility estimate Quarterly change in VolatilityE
21.22 4.41 0.00 0.88 3.75 0.81 0.00 0.86 0.00 0.83 0.00 0.14 36.3 0.85 0.00
22.10 1.69 1.32 0.99 1.71 0.39 0.07 0.37 0.04 0.43 0.30 0.26 257.3 0.72 0.83
1.81 3.38 -0.58 0.24 2.65 0.56 -0.02 0.60 -0.01 0.59 -0.05 0.01 0.5 0.55 -0.13
14.18 4.24 0.01 0.59 3.70 0.72 0.00 0.81 0.00 0.77 -0.01 0.05 1.0 0.75 -0.01
34.51 5.32 0.59 1.11 4.59 1.03 0.01 1.05 0.00 0.99 0.04 0.15 5.1 0.95 0.12
0.00 0.07 -10.56 0.00 -4.89 0.14 -1.28 0.23 -0.84 0.13 -5.56 0.00 0.2 0.22 -23.34
91.26 15.98 8.64 10.56 18.79 3.91 0.74 3.91 0.68 8.32 6.15 6.15 2664.1 35.19 34.43
125071 125071 125071 125071 117389 125071 125071 125071 125071 125071 125071 125071 108753 117969 117969
35
LowVol Tariff TWFX TANG MB ATDEF LATDEF RD RDD GRGDP Credit Term
Binary Percent Fraction Ratio $M 2009 Fraction Binary Decimal Percent Percent
Low volatility regime 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
0.47 2.36 2.06 0.26 1.88 1577 4.76 0.01 0.56 0.03 0.94 1.54
0.50 2.58 0.44 0.18 3.03 8045 2.20 0.03 0.50 0.02 0.33 1.08
0.00 0.57 1.75 0.12 0.72 25 3.22 0.00 0.00 0.02 0.69 0.58
0.00 1.56 2.14 0.22 1.10 97 4.57 0.00 1.00 0.04 0.86 1.41
1.00 3.55 2.33 0.36 1.90 474 6.16 0.02 1.00 0.04 1.14 2.41
0.00 0.00 0.37 0.00 0.27 0 -6.78 0.00 0.00 -0.03 0.55 -0.68
1.00 25.89 3.83 0.78 37.77 284360 12.56 0.34 1.00 0.08 2.69 3.51
125071 120314 125071 125071 125071 125071 125071 125071 125071 125071 125071 125071
Panel B: Summary statistics for subsample of large changes in volatility Variable
Unit
Description
mean
sd
p25
p50
p75
min
max
N
ML ROA D.ROA Abs(D.ROA) Neigh. ROA 5YWindow D.5YWindow 10YWindow D.10YWindow Volatility D.Volatility Abs(D.Volatility) Halflife
Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Percent Quarters
Market leverage Return on net assets industry Change in return on net assets Abs. value of D.ROA Return on net assets neigh. Ind. 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 Abs. value of D.Volatility GARCH halflife
21.08 4.47 -0.07 0.97 3.70 0.87 0.00 0.89 0.00 0.94 0.00 0.24 33.6
22.07 1.85 1.49 1.13 1.75 0.40 0.07 0.38 0.04 0.51 0.42 0.34 241.7
1.82 3.29 -0.64 0.26 2.59 0.59 -0.01 0.61 -0.01 0.63 -0.13 0.06 0.5
13.98 4.28 -0.01 0.62 3.61 0.79 0.00 0.84 0.00 0.86 0.00 0.13 1.1
34.19 5.55 0.59 1.22 4.58 1.08 0.01 1.08 0.00 1.11 0.13 0.29 5.1
0.00 0.07 -10.56 0.00 -2.92 0.14 -1.02 0.23 -0.67 0.22 -5.56 0.00 0.2
91.24 15.74 8.64 10.56 15.37 3.67 0.74 3.67 0.68 8.32 6.15 6.15 2664.1
61261 61261 61261 61261 57064 61261 61261 61261 61261 61261 61261 61261 53534
36
VolatilityE D.VolatilityE LowVol Tariff TWFX TANG MB ATDEF LATDEF RD RDD GRGDP Credit Term
Percent Percent Binary Percent Fraction Ratio $M 2009 Fraction Binary Decimal Percent Percent
EGARCH volatility estimate Quarterly change in VolatilityE Low volatility regime 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
0.98 0.00 0.43 2.39 2.08 0.26 1.88 1526 4.72 0.01 0.58 0.03 0.96 1.55
37
0.95 1.17 0.49 2.66 0.45 0.18 2.98 8333 2.20 0.03 0.49 0.02 0.35 1.06
0.59 -0.22 0.00 0.55 1.75 0.12 0.73 24 3.18 0.00 0.00 0.02 0.68 0.60
0.80 -0.01 0.00 1.55 2.15 0.22 1.12 93 4.54 0.00 1.00 0.04 0.87 1.41
1.11 0.24 1.00 3.66 2.34 0.36 1.90 461 6.13 0.02 1.00 0.04 1.18 2.39
0.24 -23.34 0.00 0.00 0.37 0.00 0.27 0 -6.73 0.00 0.00 -0.03 0.55 -0.68
35.19 34.43 1.00 25.89 3.81 0.78 37.77 273352 12.52 0.34 1.00 0.08 2.69 3.51
57762 57762 61261 58624 61261 61261 61261 61261 61261 61261 61261 61261 61261 61261
Table 2 Leverage and 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 ROA whose differences are measured at time t. Industry volatility is based on the following model of quarterly aggregate industry profits.
is the ratio of aggregate industry operating income to aggregate industry net assets and is a The variable dummy variable for calendar quarter i. Volatility measures are based on the residual term . 5Y Window is the 5year rolling window standard deviation of past . 10Y Window is the 10-year rolling window standard deviation of . GARCH is the Generalized Autoregressive Conditional past . Abs change is the absolute value of Heteroskedasticity estimate of volatility using a GARCH(1,1) process. EGARCH is the Exponential GARCH(1,1) estimate of volatility. Standard are errors clustered by firm are in parentheses. Volatility estimate:
5Y window (1)
10Y window (2)
GARCH (4)
EGARCH (5)
Volatility(t)
0.348 (0.263) -0.024 (0.017) 6.452*** (0.848) -0.037*** (0.008) -1.050*** (0.151) -2.346*** (0.760) -0.118* (0.068) -0.175 (0.114) 19.001*** (1.053) 0.028 (0.035) -0.331*** (0.038)
0.508 (0.469) -0.024 (0.017) 6.449*** (0.848) -0.037*** (0.008) -1.049*** (0.151) -2.341*** (0.760) -0.118* (0.068) -0.172 (0.114) 19.060*** (1.050) 0.028 (0.035) -0.332*** (0.038)
-0.288*** (0.061) -0.032* (0.018) 6.469*** (0.847) -0.037*** (0.008) -1.050*** (0.151) -2.353*** (0.760) -0.117* (0.068) -0.18 (0.114) 19.086*** (1.050) 0.033 (0.035) -0.336*** (0.037)
-0.073*** (0.020) -0.027 (0.018) 7.082*** (0.845) -0.034*** (0.008) -0.926*** (0.151) -2.289*** (0.779) -0.138* (0.073) -0.195* (0.117) 19.231*** (1.078) 0.034 (0.036) -0.334*** (0.039)
0.005 125071 4412
0.005 125071 4412
0.005 125071 4412
0.005 117969 4176
ROA(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
38
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 ROA whose differences are measured at time t. Industry volatility is based on the following model of quarterly aggregate industry profits.
is the ratio of aggregate industry operating income to aggregate industry net assets and is a The variable 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 1 quarter (the sample median). 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 1 quarter. 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. Half-life: Volatility(t) ROA(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
-0.690*** (0.106) -0.025 (0.023) 6.236*** (1.380) -0.045*** (0.014) -1.387*** (0.284) -3.694*** (1.275) -0.316*** (0.097) -0.248 (0.168) 19.326*** (1.574) 0.020 (0.051) -0.334*** (0.056)
-0.253* (0.134) -0.079*** (0.029) 6.454*** (1.245) -0.025** (0.010) -0.695*** (0.176) -0.365 (1.013) -0.01 (0.104) -0.116 (0.175) 18.027*** (1.623) 0.070 (0.053) -0.296*** (0.058)
0.011
0.006 55572 1930
0.005 53181 1884
39
0.150 0.907 0.232 0.039 0.041 0.031 0.587 0.565 0.494 0.638
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. Panel A reports correlations for the whole sample. Panel B reports correlations for the subset of observations that have large changes in volatility, where large is defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry.
Panel A: All observations Volatility Volatility ROA Tariff TWFX GRGDP Credit Term
ROA
Tariff
TWFX
GRGDP
Credit
Term
1.000 -0.1199 (0.000) -0.0224 (0.000) -0.0132 (0.000) -0.0172 (0.000) -0.0179 (0.000) 0.0142 (0.000)
1.000 0.0608 (0.000) 0.0625 (0.000) 0.0464 (0.000) 0.0168 (0.000) -0.0722 (0.000)
1 0.0516 (0.000) -0.035 (0.000) 0.0573 (0.000) 0.0003 (0.912)
1 -0.0242 (0.000) 0.0229 (0.000) -0.0954 (0.000)
1 -0.1334 (0.000) -0.2726 (0.000)
1 0.0537 (0.000)
1
Panel B: Observations with large changes in volatility Volatility ROA Tariff TWFX GRGDP Credit Term
Volatility 1.000
ROA
-0.1515 (0.000) -0.0303 (0.000) -0.0222 (0.000) -0.0243 (0.000) -0.0219 (0.000) 0.0174 (0.000)
1.000 0.0299 (0.000) 0.0469 (0.000) 0.0414 (0.000) 0.0005 (0.894) -0.0682 (0.000)
Tariff
GRGDP
Credit
Term
1 0.0882 (0.000) -0.0423 (0.000) 0.0636 (0.000) 0.0294 (0.000)
40
TWFX
1 -0.0446 (0.000) 0.0245 (0.000) -0.0761 (0.000)
1 -0.1559 (0.000) -0.3205 (0.000)
1 0.0914 (0.000)
1
Table 5 Determinants of industry volatility and profitability This table is based on the subset of observations that have large changes in volatility, where large is defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry. The dependent variables are industry volatility (Volatility) and industry profitability (ROA). 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 i. Volatility measures are based on the residual term . Volatility denotes a GARCH estimate of the volatility of the residual term. Panel A reports firm-level regressions where each observation corresponds to a firm-quarter. Panel B 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 industry-quarter. Standard errors (in parentheses) are clustered by firm in Panel A. Panel B reports heteroskedasticity robust standard errors.
Panel A: Firm-level regressions Model 1
Tariff TWFX
Model 2
Volatility (1) -0.054*** (0.007) -0.126*** (0.021)
ROA (2) 0.114*** (0.040) 0.942*** (0.089)
0.128*** (0.045) -0.002** (0.001) 0.005 (0.011) -0.036
-0.824*** (0.218) 0.008* (0.004) -0.045 (0.043) -0.176
Neigh. ROA TANG MB Ln(ATDEF) RD
41
Volatility (3) -0.051*** (0.007) -0.089*** (0.020) -0.007*** (0.001) 0.126*** (0.048) -0.002** (0.001) 0.007 (0.012) -0.077
ROA (4) 0.042 (0.040) 0.998*** (0.091) 0.050*** (0.005) -0.779*** (0.226) 0.007 (0.004) -0.057 (0.047) -0.021
RDD Credit GRGDP Term Constant
AdjRSq N Nfirms Fstat(all) Fstat(IV only) Min. Eigenvalue 2SLS Wald test (10% size)
(0.065) -0.002 (0.009) -0.02 (0.015) -0.066 (0.071) 0.021*** (0.003) 0.001 (0.003)
(0.265) 0.158*** (0.035) -0.157*** (0.044) 4.647*** (0.351) 0.177*** (0.015) -0.213*** (0.012)
(0.068) -0.007 (0.010) -0.019 (0.016) -0.081 (0.067) 0.024*** (0.004) 0.002 (0.002)
(0.252) 0.176*** (0.036) -0.099** (0.044) 4.885*** (0.363) 0.143*** (0.015) -0.231*** (0.013)
0.002 61261 4293 18.6 50.8
0.009 61261 4293 41.3 60.4
0.003 55242 4249 18.9 38.3
0.012 55242 4249 45.5 75.0
9.2 7.0
Continued on next page.
42
13.1 13.4
Panel B: Industry-level regressions
Model 1
Tariff TWFX
Model 2
Volatility (1) -0.052*** (0.007) -0.131*** (0.022)
ROA (2) 0.052 (0.034) 0.959*** (0.106)
1.937*** (0.330) -0.041*** (0.004) 0.131 (0.095) -0.093 (0.147) -1.522*** (0.505) -0.032 (0.023) -0.042*** (0.015) 0.024*** (0.004) -0.002 (0.005)
-12.014*** (1.596) 0.125*** (0.018) -0.939** (0.464) 5.348*** (0.463) -14.544*** (1.581) 0.687*** (0.065) 0.064 (0.050) 0.180*** (0.014) -0.214*** (0.014)
Neigh. ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term Constant
43
Volatility (3) -0.050*** (0.007) -0.089*** (0.022) -0.007*** (0.001) 1.888*** (0.369) -0.034*** (0.004) 0.167 (0.108) -0.112 (0.151) -3.076*** (0.540) -0.093*** (0.026) -0.037** (0.015) 0.027*** (0.004) -0.001 (0.005)
ROA (4) -0.015 (0.033) 1.032*** (0.107) 0.042*** (0.004) -11.063*** (1.764) 0.124*** (0.018) -1.144** (0.520) 5.674*** (0.495) -8.411*** (1.625) 0.985*** (0.072) 0.105** (0.052) 0.140*** (0.015) -0.231*** (0.015)
AdjRSq N Fstat(all) Fstat(IV only)
0.005 2551 31.8 43.41
0.024 2551 134.6 43.36
44
0.007 2220 31.1 37.41
0.026 2220 112.4 66.42
Table 6 The effect of industry volatility and profitability on leverage This table is based on the subset of observations that have large changes in volatility, where large is defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry. 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 ROA whose differences are measured at time t. Industry volatility is based on the following model of quarterly aggregate industry profits.
is the ratio of aggregate industry operating income to aggregate industry net assets and is a The variable dummy variable for calendar quarter i. Volatility measures are based on the residual term and volatility denotes a GARCH estimate of the volatility of the residual term. IV1 indicates that the instruments are time-t differences in tariffs and TWFX; IV2 indicates that the instruments are time-t differences in tariffs, neighboring ROA and tradeweighted FX rate. Jstat p-value indicates the p-value for Hansen’s test of overidentifying restrictions.
Volatility ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term Constant
AdjRSq N Nfirms Jstat pvalue
OLS (1) -0.24*** (0.060) -0.01 (0.020) 5.35*** (1.090) -0.03*** (0.010) -1.31*** (0.210) -1.53 (1.190) (0.090) (0.090) -0.020 (0.160) 23.01*** (1.460) 0.09* (0.050) -0.51*** (0.050)
IV1 (2) -12.31*** (4.550) -2.56** (1.000) 4.81*** (1.360) -0.04** (0.020) -1.36*** (0.250) -2.42 (1.560) (0.280) (0.220) -0.70** (0.350) 33.95*** (4.780) 0.77*** (0.270) -1.03*** (0.220)
IV2 (3) -8.29*** (3.120) -1.94*** (0.520) 4.86*** (1.250) -0.04*** (0.010) -1.41*** (0.240) -0.96 (1.360) (0.190) (0.150) (0.310) (0.240) 30.65*** (2.940) 0.51*** (0.150) -0.89*** (0.130)
0.006 61261 4293
61261 4293
55242 4249 0.214
45
Table 7: Loan spread regressions This table is based on the subset of observations that have large changes in volatility, where large is defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry. The dependent variable is the log of the all-in drawn spread on loan facilities obtained from Dealscan for the period 1992 to 2005. Independent variables are lagged one quarter and standard errors are clustered by firm. All variables are in levels and all regressions include industry and loan fixed effects. Columns (1) and (2) use OLS. In columns (3), volatility and ROA are instrumented with tariffs, TWFX. Column (4) adds neighboring ROA to the set of instruments. Postreduc = equals 1 if industry has experienced a tariff rate reduction by time t that is larger than 3 times the median tariff cut and zero otherwise. ROA(firm) equals firm-level return on assets. Cash flow vol. = ratio of standard deviation of last 8 quarterly earnings changes over average of book assets over past 8 quarters. Default probability is the Bharath Shumway (2008) estimate of the probability of default. Standard errors are clustered by firm.
Postreduc
OLS (1) 0.110*** (0.036)
Volatility ROA Cash flow vol. ROA(firm) Ln(ATDEF) MB BL TANG Default probability Loan size Loan maturity GRGDP TERM CREDIT
-0.442 (0.538) -0.024*** (0.003) -0.273*** (0.008) -0.047*** (0.013) 0.737*** (0.061) -0.461*** (0.088) 0.692*** (0.062) -0.141*** (0.054) -0.035* (0.021) -0.75 (0.722) 0.018** (0.008) 0.382*** (0.050)
OLS (2)
IV1 (3)
IV 2 (4)
0.011 (0.032) -0.021** (0.009)
2.333** (1.121) 0.325* (0.190)
1.295** (0.661) 0.073 (0.091)
-0.272*** (0.011) -0.047*** (0.016) 0.767*** (0.086) -0.396*** (0.107) 0.783*** (0.107) -0.248*** (0.069) -0.047** (0.024) -0.02 (1.024) -0.004 (0.011) 0.473*** (0.064)
-0.281*** (0.018) -0.049** (0.021) 0.990*** (0.163) -0.532*** (0.188) 0.950*** (0.250) -0.382*** (0.131) -0.025 (0.050) 4.196 (3.087) 0.029 (0.031) 1.036*** (0.313)
-0.291*** (0.013) -0.039** (0.017) 0.939*** (0.121) -0.428*** (0.144) 0.756*** (0.154) -0.382*** (0.087) -0.041 (0.035) 3.172 (2.390) 0.006 (0.020) 0.688*** (0.167)
46
AdjRSq N Nfirms
0.654 7540 1722
0.615 4297 1207
47
4049 1159
0.381 3757 1132
Table 8: Cash holdings regressions This table is based on the subset of observations that have large changes in volatility, where large is defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry. The dependent variable is the natural logarithm of cash to assets net of cash. All right-hand-side variables are lagged one quarter, except for volatility and ROA, which are measured at time t. In columns (2), volatility and ROA are instrumented with tariffs and TWFX. Column (3) adds neighboring ROA to the set of instruments. Book assets are net of cash whenever they occur in the denominator of a ratio. All ratios are winsorized at the 1st and 99th percentiles. NWC equals working capital net of cash over book assets. BL is the ratio of total debt over book assets. CAPX is the quarterly change in net property, plant and equipment over book assets. Standard errors are clustered by firm.
Volatility ROA MB Log(ATDEF) NWC CAPX BL R&D/Sales Dividend dummy Credit GRGDP Term
N Nfirms AdjRsq
OLS (1) 0.046** (0.022) 0.01 (0.007) 0.080*** (0.005) 0.018* (0.010) -0.704*** (0.078) -0.919*** (0.308) -2.225*** (0.125) 0.032*** (0.012) -0.206*** (0.048) -0.04 (0.028) -1.417* (0.739) 0.062*** (0.012)
IV1 (2) 7.927*** (2.480) 0.411** (0.190) 0.080*** (0.006) 0.032** (0.016) -0.917*** (0.112) -2.443*** (0.731) -2.526*** (0.170) 0.050*** (0.016) -0.200*** (0.072) 1.026*** (0.327) 4.956 (4.043) -0.128 (0.079)
IV2 (3) 6.838*** (1.729) 0.434*** (0.162) 0.080*** (0.006) 0.033** (0.015) -0.902*** (0.106) -2.644*** (0.684) -2.507*** (0.172) 0.049*** (0.016) -0.190*** (0.070) 0.750*** (0.212) 5.401* (3.275) -0.072 (0.053)
65856 4712 0.331
51199 4020
47722 3997
48
Table 9: Summary counts on basic financing spikes This table counts the number and measures the frequency of basic financing spikes for the subset of observations that have large changes in volatility, where large is defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry. 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 43,074 6,218 4,012 5,334 4,411 5,867 8,318
Percent of obs. 70.31 10.15 6.55 8.71 7.2 9.58 13.58
49
Mean
Min
Median
Max
1.45 0.93 1.24 1.03 1.37 1.94
0 0 0 0 0 0
1 0 0 0 1 1
17 12 20 21 17 20
Table 10: Issuance activity and basic financing spikes This table is based on the subset of observations that have large changes in volatility, where large is defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry. 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 ROA 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 ROA 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. J-stat pvalue indicates the p-value from Hansen’s test of overidentifying restrictions estimated with a linear probability model using 2SLS. Panel B reports measures of economic significance derived from the coefficients from estimating a 2SLS linear probability model. Economic significance equals the product of the 2SLS LPM coefficient estimate (the Average Partial Effect (APE)) and the standard deviation of the quarterly change in the variable of interest. 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
-1.728*** (0.643) -0.139*** (0.043) 0.252*** (0.055) -0.003 (0.003) -0.055*** (0.004) (0.078) (0.326) 0.083*** (0.023) -0.013 (0.090) 0.979 (1.543) 0.042 (0.029) -0.034 (0.041)
2.333*** (0.655) 0.025 (0.044) -0.347*** (0.056) 0.082*** (0.003) -0.064*** (0.004) 1.738*** (0.287) -0.045** (0.023) 0.322*** (0.090) 4.881*** (1.517) -0.034 (0.029) 0.821*** (0.040)
-1.402** (0.620) -0.141*** (0.042) 0.399*** (0.053) 0.008*** (0.003) -0.078*** (0.004) 1.034*** (0.286) 0.125*** (0.022) -0.032 (0.087) 1.861 (1.485) 0.031 (0.028) 0.098** (0.039)
0.716 (0.708) -0.007 (0.046) -0.479*** (0.061) 0.012*** (0.003) -0.054*** (0.005) (0.078) (0.358) 0.062** (0.025) 0.095 (0.097) 1.011 (1.671) 0.013 (0.032) 1.460*** (0.040)
1.13 (0.722) 0.04 (0.052) 0.085 (0.059) 0.086*** (0.003) -0.086*** (0.005) 1.791*** (0.267) -0.114*** (0.024) 0.201** (0.099) 3.843** (1.618) 0.032 (0.031) -0.455*** (0.050)
-2.570*** (0.754) -0.185*** (0.055) 0.170** (0.067) 0.014*** (0.004) 0.099*** (0.005) -2.102*** (0.435) 0.067** (0.027) -0.376*** (0.110) -0.811 (1.801) 0.063* (0.033) -0.828*** (0.052)
ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term ML
50
N Chi2 Chi2df Chi2 pvalue
61202 16.09 2 0.00
61197 17.10 2 0.00
61224 13.82 2 0.00
61132 1.52 2 0.47
61062 2.46 2 0.29
61118 20.72 2 0.00
Panel B: Economic significance of IVPROBIT estimates Lev. Incr.
Lev. Decr.
Debt iss.
Debt reduc.
Equ. Iss.
Equ. Rep.
-0.239 (0.112) -0.027 (0.008)
0.498 (0.133) 0.011 (0.009)
-0.199 (0.112) -0.027 (0.008)
0.119 (0.091) -0.001 (0.006)
0.240 (0.096) 0.017 (0.006)
-0.380 (0.108) -0.028 (0.007)
Econ. Significance Volatility
-0.058
0.121
-0.048
0.029
0.058
-0.092
ROA
-0.026
0.011
-0.026
-0.001
0.017
-0.027
Average partial effects Volatility ROA
51
Table 11 Financial constraints and issuance activity This table is based on the subset of observations that have large changes in volatility, where large is defined as a quarterly change in volatility that is either smaller than the 25th percentile of changes in the firm’s industry, or larger than the 75th percentile of changes in the firm’s industry. This table reports a regression model of the probability of undertaking a leverage increasing or leverage decreasing transaction at time t. Firms are divided into groups according to whether they paid a dividend in at time t-1 or had a credit rating at time t-1. 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 ROA, which are measured at time t. Each regression includes industry and year fixed effects. Each column reports coefficient estimates from the IVPROBIT model of Rivers and Vuong (1988). Volatility and ROA are instrumented with tariffs and the trade-weighted FX rate. Standard errors are in parentheses. Chi2 is the Chi-squared test for exogeneity of the instrumented variables. Panel A: Net debt issuance
Volatility ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term ML
N Chi2 Chi2df Chi2 pvalue
No dividend (1) -1.272** (0.513) -0.078** (0.036) 0.447*** (0.058) 0.006** (0.003) -0.080*** (0.005) 1.212*** (0.286) 0.157*** (0.026) -0.078 (0.095) 0.055 (1.885) 0.044 (0.031) 0.093** (0.042)
Dividend (2) 0.406 (1.271) -0.082 (0.226) 0.184 (0.140) 0.041 (0.028) -0.043*** (0.012) 2.541 (1.770) 0.035 (0.057) 0.195 (0.160) 0.612 (4.063) -0.011 (0.041) 0.089 (0.244)
Unrated (3) -1.412** (0.596) -0.065* (0.038) 0.444*** (0.057) 0.005** (0.003) -0.100*** (0.005) 1.261*** (0.289) 0.125*** (0.025) -0.05 (0.090) 0.463 (1.606) 0.053* (0.032) 0.199*** (0.041)
Rated (4) 1.281 (0.821) -0.17 (0.171) -0.088 (0.159) -0.007 (0.020) -0.158*** (0.018) 2.583 (2.225) 0.083 (0.072) 0.164 (0.267) 8.450** (4.277) -0.078 (0.060) -0.914*** (0.191)
45513 13.32 2 0.00
15621 0.36 2 0.83
51441 9.14 2 0.01
9713 4.97 2 0.08
52
Panel B: Debt repurchases
Volatility ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term ML
N Chi2 Chi2df Chi2 pvalue
No dividend (1) -0.358 (0.574) 0.001 (0.040) -0.395*** (0.067) 0.010*** (0.003) -0.055*** (0.006) -0.108 (0.359) 0.03 (0.028) 0.038 (0.103) -0.304 (2.097) 0.042 (0.034) 1.432*** (0.043)
Dividend (2) 3.571* (2.029) 0.604* (0.352) -0.816*** (0.215) 0.062 (0.045) -0.047*** (0.018) -3.979 (3.884) 0.067 (0.094) 0.097 (0.239) -11.725* (6.438) 0.104 (0.064) 2.479*** (0.372)
Unrated (3) -0.221 (0.667) -0.023 (0.043) -0.461*** (0.066) 0.012*** (0.003) -0.056*** (0.006) -0.077 (0.359) 0.049* (0.028) 0.064 (0.099) 0.906 (1.791) 0.042 (0.035) 1.543*** (0.042)
Rated (4) 1.398 (1.044) 0.444** (0.209) -0.898*** (0.193) 0.040* (0.024) -0.018 (0.022) -4.23 (3.237) -0.041 (0.090) 0.094 (0.321) -2.099 (4.939) 0.149** (0.073) 1.540*** (0.235)
45460 0.50 2 0.78
15380 5.75 2 0.06
51429 0.67 2 0.72
9643 4.50 2 0.11
53
Panel C: Leverage increases
Volatility ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term ML
N Chi2 Chi2df Chi2 pvalue
No dividend (1) -1.417*** (0.529) -0.065* (0.037) 0.283*** (0.060) -0.005* (0.003) -0.057*** (0.005) 0.25 (0.322) 0.123*** (0.027) -0.045 (0.098) -0.935 (1.954) 0.056* (0.032) 0.004 (0.044)
Dividend (2) 0.122 (1.260) -0.143 (0.223) 0.069 (0.138) 0.077*** (0.027) -0.036*** (0.011) 0.655 (1.804) -0.039 (0.056) 0.18 (0.159) 1.087 (3.995) -0.026 (0.041) -0.146 (0.240)
Unrated (3) -1.572** (0.613) -0.048 (0.039) 0.300*** (0.059) -0.006* (0.003) -0.074*** (0.005) 0.138 (0.325) 0.081*** (0.025) 0.007 (0.092) 0.083 (1.655) 0.061* (0.033) 0.067 (0.043)
Rated (4) 1.34 (0.819) -0.227 (0.171) -0.218 (0.163) -0.009 (0.020) -0.137*** (0.018) 2.837 (2.272) 0.061 (0.072) 0 (0.271) 5.216 (4.316) -0.101 (0.062) -1.034*** (0.193)
45499 13.58 2 0.00
15621 0.57 2 0.75
51441 9.32 2 0.01
9655 6.66 2 0.04
54
Panel D: Leverage decreases
Volatility ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term ML
N Chi2 Chi2df Chi2 pvalue
No dividend (1)
Dividend (2)
Unrated (3)
Rated (4)
1.099** (0.479) -0.013 (0.034) -0.248*** (0.054) 0.079*** (0.002) -0.058*** (0.005) 1.563*** (0.254) -0.050** (0.024) 0.255*** (0.087) 5.553*** (1.707) -0.007 (0.028) 0.646*** (0.038)
4.877** (2.213) 0.641* (0.376) -0.590** (0.232) 0.098** (0.047) -0.026 (0.019) -3.515 (3.578) 0.017 (0.097) 0.278 (0.257) -12.539* (6.811) 0.071 (0.068) 2.312*** (0.404)
1.392** (0.573) -0.022 (0.037) -0.316*** (0.055) 0.082*** (0.003) -0.066*** (0.005) 1.572*** (0.262) -0.062*** (0.023) 0.264*** (0.085) 4.684*** (1.494) -0.011 (0.030) 0.837*** (0.039)
2.537** (1.040) 0.529** (0.209) -0.759*** (0.189) 0.121*** (0.023) -0.047** (0.021) 1.014 (2.646) -0.037 (0.086) 0.473 (0.315) 2.101 (4.967) 0.148** (0.072) 1.218*** (0.233)
45540 5.96 2 0.05
15552 10.68 2 0.00
51447 7.26 2 0.03
9672 10.72 2 0.00
55
Panel E: Equity issuance
Volatility ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term ML
N Chi2 Chi2df Chi2 pvalue
No dividend (1) 0.628 (0.558) 0.051 (0.040) 0.143** (0.061) 0.082*** (0.002) -0.072*** (0.005) 1.761*** (0.257) -0.086*** (0.027) 0.168* (0.101) 3.853** (1.914) 0.045 (0.032) -0.669*** (0.052)
Dividend (2) 10.232 (6.488) 1.34 (1.238) 0.602 (0.559) 0.058 (0.118) 0.007 (0.035) -1.862 (5.893) -0.266 (0.244) 0.58 (0.568) -22.535 (19.040) 0.076 (0.128) 2.268 (1.446)
Unrated (3) 0.757 (0.639) 0.022 (0.044) 0.127** (0.061) 0.084*** (0.003) -0.098*** (0.005) 1.702*** (0.261) -0.121*** (0.027) 0.132 (0.094) 3.316* (1.704) 0.036 (0.033) -0.540*** (0.053)
Rated (4) 1.923 (1.247) 0.261 (0.242) -0.238 (0.219) 0.137*** (0.023) -0.104*** (0.024) 1.918 (2.693) -0.087 (0.097) 0.829** (0.370) 9.155 (5.910) 0.155* (0.086) -0.044 (0.277)
45456 1.89 2 0.39
15084 8.83 2 0.01
51368 1.37 2 0.50
9438 2.68 2 0.26
56
Panel F: Equity repurchase
Volatility ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term ML
N Chi2 Chi2df Chi2 pvalue
No dividend (1) -1.674*** (0.629) -0.074 (0.047) 0.304*** (0.072) 0.014*** (0.003) 0.113*** (0.006) -1.761*** (0.427) 0.05 (0.031) -0.307** (0.121) -3.718 (2.267) 0.052 (0.036) -0.723*** (0.056)
Dividend (2) -1.527 (1.430) -0.629** (0.268) -0.407** (0.163) 0.098*** (0.031) 0.095*** (0.013) -0.982 (2.101) 0.139** (0.064) -0.343* (0.193) 9.421** (4.730) -0.029 (0.047) -1.617*** (0.284)
Unrated (3) -2.676*** (0.762) -0.116** (0.050) 0.106 (0.075) 0.016*** (0.004) 0.116*** (0.006) -1.906*** (0.445) 0.055* (0.031) -0.409*** (0.118) -1.374 (2.009) 0.089** (0.039) -0.740*** (0.058)
Rated (4) 0.421 (0.752) -0.281* (0.170) 0.465*** (0.155) 0.012 (0.018) 0.123*** (0.017) -4.043 (2.565) 0.079 (0.070) -0.177 (0.261) 1.076 (4.360) -0.072 (0.064) -0.944*** (0.186)
45410 9.54 2 0.01
15591 7.89 2 0.02
51354 21.25 2 0.00
9495 2.72 2 0.26
57
Table 12 Issuance activity, basic financing spikes and volatility regimes This table reports a regression model of the probability of undertaking a leverage increasing or leverage decreasing transaction at time t. Each column reports coefficient estimates from a 2SLS linear probability model regression. 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. LowVol indicates a low-volatility regime. The type of regime is determined using the change-point algorithm Inclan and Tiao (1994). Change points are estimated at the industry level. All independent variables are in levels and are measured at time t-1. Each regression includes industry and year fixed effects. LowVol and ROA are instrumented with quarterly changes in tariffs, and levels in neighboring ROA and the trade-weighted FX rate, all measured at time t-1. Standard errors clustered by firm are in parentheses.
LowVol ROA TANG MB Ln(ATDEF) RD RDD Credit GRGDP Term
Lev. Incr. (1)
Lev. Decr. (2)
Debt iss. (3)
Debt reduc. (4)
Equ. Iss. (5)
Equ. Rep. (6)
0.218*** (0.083) -0.023 (0.016) 0.062*** (0.012) 0 (0.000) -0.007*** (0.001) -0.158*** (0.039) 0.014*** (0.004) 0.017* (0.009) 0.109 (0.175) -0.001
0.03 (0.093) 0.001 (0.016) -0.053*** (0.014) 0.022*** (0.001) -0.015*** (0.001) 0.756*** (0.067) -0.003 (0.005) 0.004 (0.010) 0.12 (0.194) 0.004
0.169** (0.081) -0.02 (0.016) 0.095*** (0.012) 0.001** (0.000) -0.012*** (0.001) -0.034 (0.043) 0.019*** (0.004) 0.016* (0.009) 0.186 (0.179) -0.001
-0.034 (0.059) 0.004 (0.012) -0.044*** (0.008) 0.001*** (0.000) -0.006*** (0.001) -0.054* (0.028) 0.004 (0.003) -0.007 (0.008) -0.203 (0.138) 0.001
0.107 (0.081) 0.003 (0.013) 0.008 (0.013) 0.023*** (0.001) -0.013*** (0.001) 0.792*** (0.068) -0.003 (0.004) 0.006 (0.008) 0.127 (0.158) 0.005**
0.095 (0.089) -0.015 (0.014) 0.042*** (0.014) 0.002*** (0.001) 0.015*** (0.001) -0.246*** (0.046) 0.014** (0.006) -0.003 (0.007) 0.131 (0.146) -0.001
58
ML
(0.003) 0.01 (0.010)
(0.003) 0.180*** (0.010)
(0.003) 0.036*** (0.010)
(0.002) 0.228*** (0.008)
(0.002) -0.030*** (0.008)
(0.002) -0.113*** (0.011)
N Nfirms
107521 4097
107521 4097
107521 4097
107521 4097
107521 4097
107521 4097
59
Appendix A: Regimes changes in volatility This model is restrospective in nature in that it uses the entire time series of data to identify changes in regime in the series of squared innovations. Let 5
, where
1, … , 6
equals the residual profit term from equation (1). The algorithm computes the centered
cumulative sum of squares
and identify points where the centered sum reaches a pre-specified
boundary with a high probability. See Inclan and Tiao (1994) for a full description. We estimate this model for each industry and define a change point whenever
reaches a boundary. Low volatility
regimes begin when the upper boundary is reached, which indicates a transition from a high to a low volatility regime. High volatility regimes begin when the lower boundary is reached. Some industries (23 out of 76) do not experience a change point. These industries are dropped from the sample. We estimate this model for each remaining industry and plot
(the residual earnings) for select industries in Figure
A1.
60
Figure A1: This figure reports quarterly demeaned industry profits for select industries. Demeaned industry profits are based on the quarterly earnings model in equation (1) in the text. The vertical grey bars indicate a new volatility regime identified using the change point algorithm.
Panel A: Quarterly demeaned profits for SIC 204.
61
Panel B: Quarterly demeaned industry profits for SIC 286.
62
Panel C: Quarterly demeaned industry profits for SIC 344
63
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