Does dynamic tradeoff theory explain high-frequency

Does dynamic tradeoff theory explain high-frequency debt issuers?∗

B. Espen Eckbo†

Michael Kisser‡

April, 2016

Abstract Under modern dynamic tradeoff theory of capital structure, firms trade off not only debt benefits against expected distress costs as in classical models, but also the costs of internal versus external investment finance. As a result, classical tradeoff behavior may be masked by ongoing investment activity in the data. We address this difficult econometric issue by using high-frequency net-debt issuers (HFIs, net of debt retirements and rollovers) as test assets. By revealed preference, these firms, who raised the bulk of all public and private debts over the past three decades, likely enjoy high benefits from debt financing and low debt issue costs relative to low-frequency issuers (LFIs). Under dynamic tradeoff theory with exogenous investment, HFIs should have smaller issue size, lower leverage volatility, and higher speed-of-adjustment to deviations from target leverage ratios than LFIs, all of which the data reject. However, consistent with recent dynamic tradeoff theory with endogenous investment, conditionally overlevered firms tend to issue “transitory” debt to finance investment spikes, followed by leverage ratio reductions as the investment shock abates.

∗

An earlier version of this paper was circulated with the title “Corporate debt issues and leverage dynamics”. We have benefitted from the comments and suggestions of Andras Danis, Harry DeAngelo, Pierre Chaigneau, Michael Hertzel, Michael Roberts, Karin Thorburn, Toni Whited, and seminar participants at Concordia University, the Norwegian School of Economics, the Norwegian School of Management, Tuck School of Business at Dartmouth, Tulane University, University of Adelaide, University of Bristol, University of St. Gallen, and the Vienna Graduate School of Finance. This research has also been presented at the University of Stavanger Corporate Finance Conference, and at the annual meetings of the Financial Management Association, the European Finance Association, the Society for Financial Studies (SFS) Cavalcade, and the Southern Finance Association. Financial support from Tuck’s Lindenauer Center for Corporate Governance is gratefully acknowledged. † Tuck School of Business at Dartmouth, the Norwegian School of Economics and ECGI ([email protected]) ‡ Norwegian School of Economics ([email protected])

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Introduction

Classical tradeoff theory of capital structure holds that firms choose debt financing so as to optimally balance debt benefits (tax savings, mitigation of agency costs, etc.) with expected costs of financial distress. Theoretical models range from early static formulations (Robicheck and Myers, 1966; Kraus and Litzenberger, 1973; Brennan and Schwartz, 1984) to dynamic versions treating corporate investments as exogenous but adding capital structure rebalancing costs (Fischer, Heinkel, and Zechner, 1989; Goldstein, Ju, and Leland, 2001). Debt issuance costs deter the firm from continuously rebalancing capital structure towards an optimum, suggesting that firms are at their optimal capital structure points only when they actively rebalance. Recently, dynamic tradeoff models have also been extended to include endogenous investment (Hennessy and Whited, 2005; Gamba and Triantis, 2008a; DeAngelo, DeAngelo, and Whited, 2011). Adding endogenous investment gives rise to a financing hierarchy not unlike the pecking order suggested by Myers (1984) and where financial slack and future debt capacity have positive option value. The dual existence of rebalancing costs and ongoing investment activity makes it exceptionally difficult to isolate true tradeoff behavior in the data. In theory, either factor may endogenously override a firm’s objective of returning to an optimal capital structure, exacerbating the need for careful sample selection for empirical testing. This concern may help explain why much of the existing empirical evidence on capital structure yields ambiguous results with respect to tradeoff theory (Frank and Goyal, 2008; Parsons and Titman, 2008; Graham and Leary, 2011). The concern about sample selection is also echoed by Myers (2001) in his statement that “because the [capital structure] theories are not general, testing them on a broad, heterogeneous sample of firms can be uninformative” (p. 99). Graham and Leary (2011) conclude their review with a similar recommendation: “we may gain more clarity on the drivers of financing decisions by focusing on appropriate subsets of firms” (p. 340). The main objective of this paper is to perform novel tests of dynamic tradeoff theory by stratifying the population of firm-years into such “appropriate subsets” of firms. While our sample and tests are very different, our motivation is similar in spirit to Danis, Rettl, and Whited (2014), who separate points in time at which firms are likely to have an optimal capital structure from points at which they have not. They make this separation by selecting pure capital structure rebalancings: largely offsetting debt-equity swaps in periods with insignificant investment activity. This strong conditioning allows the authors to identify the elusive positive correlation between the level of profitability and leverage predicted

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by dynamic tradeoff theory. Of course, their sampling strategy rules out a study of the joint nature of finance and investment, which is a main focus here. We sample firms who issue positive net debt (debt issues in excess of debt retirements and “rollovers”) persistently following public listing. Integrating issue persistence in the sample selection ensures that the sample firms themselves view debt financing as both valuable and with low issuance costs relative to other funding options. This self-selection is key since the value and issue costs associated with net-debt financing are latent variables driving dynamic tradeoff behavior. In fact, these sample firms, referred to as “high-frequency net-debt issuers” or HFIs, received close to two-thirds of all public and private net-debt issue proceeds over the past three decades. Moreover, after only five years of public listing, their cumulative net-debt issue frequency is on average twelve times greater than our control sample of “low-frequency net-debt issuers” (LFIs), who raised less than five percent of total net-debt issue proceeds. If a persistent high-frequency net-debt issuer does not dynamically rebalance capital structure towards a leverage target, then who does? Our sampling strategy is new to both the security issuance- and capital structure literatures (Eckbo, Masulis, and Norli, 2007; Graham and Leary, 2011). We provide empirical evidence indicating that the selection of HFIs and LFIs likely correlates with the unobservable debt benefits and issue costs. We also show that HFIs invest intensively and tend to finance large investment shocks (“spikes”) with debt. This joint financing and investment activity renders HFIs particularly interesting for the purpose of identifying the type of “transitory” debt issues implied by DeAngelo, DeAngelo, and Whited (2011). In their model, transitory debt issues optimally finance investment shocks experienced by overlevered firms and are followed by debt repurchases when the investment shock subsides. As described in Section 5 below, our empirical analysis provides some intriguing evidence consistent with the existence of transitory net-debt issues. Using Compustat cash flow statements, we construct samples of HFIs and LFIs based on the annual cross-sectional distribution of cumulative quarterly net-debt issues (both private and public debts).1 HFIs are firms in the top quartile and LFIs in the bottom quartile of this annual cumulative distribution (see Eq. (1) in Section 2 for details). Not surprisingly, market leverage ratios average 30% among HFIs and 1 We follow much of the literature and maximize contiguous sample size using annual Compustat data for firm characteristics and leverage ratio dynamics (Fama and French, 2002; Leary and Roberts, 2005; Lemmon, Roberts, and Zender, 2008; Faulkender, Flannery, Hankins, and Smith, 2012; Strebulaev and Yang, 2013; DeAngelo and Roll, 2015). Moreover, we capture multiple debt issues within a given year from quarterly Compustat data, as do Leary and Roberts (2005, 2010) and Danis, Rettl, and Whited (2014). Unlike earlier studies, we also condition the issue frequency on public listing age.

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only 7% among LFIs. Nevertheless, our sort on cumulative debt issue frequency is not the same as a sort on leverage ratios. A sort on the top annual quarter of leverage ratios produces an average leverage ratio that is nearly twice the average for the HFIs (51%). Moreover, within the bottom quarter leverage ratios, the ratio averages nearly zero, reflecting all-equity financed firms (Strebulaev and Yang, 2013). There are three reasons in particular why a sort on cumulative net-debt issue frequency both differs from a sort on leverage and is superior to a leverage-based sort in our context of testing tradeoff predictions. First, leverage ratio changes are driven by a combination of active debt issuances and passive changes in asset value. The frequency-based sort uses debt-issues only, which more directly identifies points in time at which firms perceive the financing decision to be optimal. Second, the leverage ratio may be high due to severe financial distress forcing extreme financing decisions. While included in the high end of a leverage-based sort, such firms are unlikely to also end up as HFIs due to the associated high issuance costs facing distressed firms. Third, at the low end of the leverage spectrum, relative to a leverage-based sort, our LFIs give less weight to all-equity financed firms (who are puzzling from a tradeoff point of view) and more weight to companies that occasionally finance themselves with debt. We demonstrate that the sorts on cumulative annual net-debt issue frequency into HFIs and LFIs create persistent firm memberships in these two groups. To illustrate, 86% of the firms that are classified as HFI in one year are also classified as HFIs in the next, and more than two-thirds remain HFI three years later. This persistence in HFI group membership reflects persistence in the underlying debt issuance activity. Being classified as a HFI doubles the (out-of-sample) probability of a positive net-debt issue in the next period relative to the probability of a medium-frequency issuer. Similarly, being classified as a LFI reduces the same probability by a factor of two. This out-of-sample predictive power remains strong also with two- and three-year forecast periods, and whether the issue-frequency classification is performed early or late in the firm’s public lifecycle. It is almost as if the net-debt issue frequency classification itself constitutes a firm-specific characteristic over much of the lifecycle after public listing. Our issue-frequency classification creates a substantial spread between HFIs and LFIs in terms of leverage stability, debt-issue dynamics, and other key firm characteristics. High-frequency issuers stand out as relatively large investment-intensive firms with high leverage and low Tobin’s Q. In contrast, low-frequency issuers are relatively small R&D-intensive firms with low leverage and high Q. HFIs are more profitable than LFIs and have lower cash flow volatility and higher asset tangibility—factors that extant empirical research shows more generally are associated with lower issuance costs and higher 3

leverage benefits. In fact, LFIs rely much more on internal finance than do the HFIs. Also interesting, that several of these differences in firm characteristics are apparent already soon after public listing. For HFIs, leverage ratios quickly rise and remain high after public listing, while they remain low throughout the public lifecycle for LFIs. Overall, HFIs tend to finance a relatively intensive capital expenditures (Capex) program externally through debt issues, while LFIs tend to finance a relatively intensive research and development ((R&D) program internally. Turning to our empirical tests, we begin by examining three hitherto untested implications of the dynamic tradeoff model (without investment) of Fischer, Heinkel, and Zechner (1989). As explained below (Proposition 1, Section 4), firms with greater net benefits from debt financing (net of debt issue costs) have tighter rebalancing boundaries. A tighter boundary implies greater debt issue frequency, smaller debt issue size, lower leverage ratio volatility and higher speed of adjustment to deviations from the (hypothetical) leverage target. We examine all of these predictions assuming HFIs have tighter rebalancing boundaries than LFIs. This key assumption is strongly supported the high issue frequency of HFIs, by their firm characteristics, and by our estimates of dynamic issue hazards, all relative to LFIs. Thus, we argue that the differential debt issue behavior and leverage policies of LFIs and HFIs have unique power to test these tradeoff predictions. Notwithstanding test power, the empirical evidence fails to support the above tradeoff predictions. First, net-debt issue sizes are roughly equal across HFIs and LFIs (not greater for LFIs as predicted). Second, leverage ratio volatilities are substantially higher for HFIs than for LFIs (not lower as predicted), with LFIs having relatively stable leverage ratios.2 Third and perhaps most surprising, notwithstanding that the debt-issue frequency is ten times higher for HFIs than for LFIs, the two groups of firms receive statistically indistinguishable speed-of-adjustment (SOA) coefficient estimates. Moving on to the potentially confounding influence of investment finance on the above tradeoff tests, we first show that net-debt issue size and dollars spent on Capex are highly correlated for HFIs—and in particular for large investment “spikes”. This important finding emerges from a modified version of the financing-deficit regressions developed in extant studies of the financing pecking order.3 As indicated above, when debt issues are used to finance ongoing investments, it is difficult to identify true tradeoff behavior in the data. This may be an important reason why our test results reject tradeoff predictions 2

This suggests that HFIs also drive the leverage instability of highly levered firms in DeAngelo and Roll (2015). The financing deficit captures the shortfall in internally available funds for investment (Shyam-Sunder and Myers, 1999; Frank and Goyal, 2003; Leary and Roberts, 2010; Lemmon and Zender, 2010). 3

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that ignore endogenous investment. To account for investment activity, we use the sample of HFIs to test a central empirical prediction of the structural model in DeAngelo, DeAngelo, and Whited (2011). In that model, unused debt capacity is a valuable asset as it lowers expected cost of future investment financing. Thus, debt financing today carries an opportunity cost as it lowers future debt capacity. While firms have long-horizon leverage targets as in classical dynamic tradeoff models, this opportunity cost generally lowers the leverage target (it may even be zero or negative net of cash balances). Most important for our purpose is the prediction— summarized in Proposition 2 of Section 5 below—that large investment shocks may drive debt issues also in periods when the leverage ratio exceeds the target. Such debt issues are transitory as the firm reverts back to the target leverage when the investment shock subsides. Thus, as also pointed out by DeAngelo, DeAngelo, and Whited (2011), the existence of transitory debt implies that the SOA coefficient estimate should be greater in periods without investment spikes, a prediction which we also test. As it turns out, about one-fifth (2,575 of 12,616 annual issues over the period 1984-2014) of the positive net-debt issues by HFIs occur when the firm is overlevered (relative to our empirical target ratio). Moreover, we show that roughly half of the net-debt issues by overlevered firms occur in connection with large investment spikes. A modified financing-deficit type of regression confirms that the importance of debt financing increases exponentially with investment size. Perhaps most important, we show that the SOA coefficient estimate for the HFIs is significantly (on-third) greater when estimated over periods without investment spikes, suggesting tradeoff behavior in the data. We also confirm that this apparent adjustment back to a target leverage ratio is driven by a combination of net-debt retirements and equity issues, as expected. The rest of the paper is organized as follows. Section 2 explains our procedure for identifying HFIs and LFIs and demonstrates the persistence of these classifications. Section 3 describes key firm characteristics and funding policies across the two groups of firms. Section 4 explores predictions of the classical tradeoff model of Fischer, Heinkel, and Zechner (1989), which is followed by our empirical examination of the dynamic financing and investment theory of DeAngelo, DeAngelo, and Whited (2011) in Section 5. Section 6 concludes the paper.

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2

Identifying high-frequency net-debt issuers

As indicated above, and supported by evidence later in this paper, persistent net debt issuers are likely characterized by a combination of relatively high benefits from debt financing and low debt issue costs. Such firms provide an interesting perspective on dynamic capital structure theories, which are based fundamentally on the existence of debt financing benefits and issue costs. In this section, we describe how persistent net-debt issuers (debt issues in excess of net of debt retirements and rollovers) are identified.

2.1

Sample selection

To maximize contiguous sample size, we follow much of the extant capital structure literature and use annual Compustat data for firm characteristics and leverage ratio dynamics. However, as firms tend to issue debt more than once a year, we construct our annual issue frequency count as the sum of all quarterly Compustat issues. Since quarterly Compustat cash flow statements are consistently available beginning in 1984, our sample period is 1984-2014. Table 1 details the sample sizes as we impose several commonly used sample restrictions on the annual (Panel A) and quarterly (Panel B) data. Thus, we exclude foreign firms, financial companies and regulated utilities, as well as firms with missing entries of key Compustat balance sheet and cash flow characteristics (defined in Appendix Tables 1 and 2). In Panel C, we merge the quarterly and annual financial statement information and impose two additional key sample restrictions, the first of which is to eliminate non-contiguous annual observations.4 The second restriction in Panel C requires that the firm is going public during the sample period. This restriction excludes a total of 4,307 firms that went public prior to 1984. We condition the analysis on public listing age because the debt issue frequency is related to investment and asset growth, which themselves are functions of a firm’s age and product market maturity. As shown in Panel C, the final sample consists of 8,719 firms and an unbalanced panel of 53,351 firm-years and 157,547 firm quarters.5 Since we use Compustat cash flow statements to identify security issues, our analysis includes all forms of debt issues and retirements, and public as well as private debts. In the following, we sort the sample firms on their issue activity in order to identify a subset that repeatedly issues debt in excess of 4 For example, if a firm has eleven annual observations on Compustat, but only the first eight years are contiguous, we use the first eight years only. 5 Reflecting the high degree of missing quarterly data on Compustat, if we were to rely on contiguous quarterly (not just annual) data, the sample would have been reduced from the 157,547 firm-quarters in Panel C to only 36,291.

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debt retirements and rollovers, henceforth referred to as positive net debt (N DI + ).

2.2

The issue-frequency sorting mechanism

Let Nit denote the cumulative number of quarterly positive net-debt issues by firm i as of event year t relative to the year of public listing (where t = 0). Moreover, let Iiqτ denote a binary variable that takes + on a value of one if N DIiqτ ≥ 2.5%, i.e. if firm i issues positive net debt of at least 2.5% of total assets

in quarter q of event year τ ≤ t, and zero otherwise.6 We have that

Nit =

t X 4 X

Iiqτ ,

(1)

τ =0 q=1

Firm i is classified as a high-frequency issuer (HFI) in event year t if Nit is in the upper quartile of the frequency distribution of Nt . Symmetrically, firm i is classified as a low-frequency issuer (LFI) in year t if Nit is in the lower quartile of the issue frequency distribution. We refer to a firm that is neither classified as HFI or LFI, as a medium-frequency issuer or MFI. We report results with shorter cumulation periods for robustness, including a three-year cumulation, and no cumulation (within-year issue count only). Moreover, also for robustness, we report test results when using a binary issue classification, where firms with Nit above (below) the sample median in year t are classified as HFI (LFI). Finally, we report test results holding the composition of HFIs and LFIs constant over the entire sample period. That is, after sorting firms into the HFI and LFI groups in event year t using Nit , we perform the tests using two fixed sets of firms and their available firm-years. In the remainder of this section, we first show that sorting firms based on Eq. (1) achieves a substantial spread in the average issue frequencies of HFIs and LFIs. We then demonstrate a significant tendency for firms to retain their original issue classification from early in their lifecycle as a public company, and for HFIs to maintain the high issue frequency over their lifecycle as public firms. This persistence in both the composition and issue activity renders the HFIs an interesting test sample for dynamic tradeoff theories. 6

Our main conclusions hold when using the higher net-debt issue-size threshold N DI + ≥ 5% (Appendix Table 6).

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2.3

Cumulative issue frequencies

Table 2 reports characteristics of the cumulative issue frequency distribution using Eq. (1) beginning in the year of public listing (year 0). Panel A shows the net-debt issue frequency, first with a 2.5% issue size threshold for N DI + (Panel A.1) and then using a 5% issue threshold (Panel A.2). Panels B and C report the frequencies of net-debt retirements (N DI − ) and of common stock issues (EI) while maintaining the HFI and LFI classifications from Panel A.7 In the overall sample, the annual number of HFIs and LFIs averages 1,832 and 3,078, respectively, with the HFIs receiving 64% of the total dollar value of all positive net-debt issues and the LFIs receiving only 5%. As shown in Panel A.1 of Table 2, of the 3,451 firms that have been listed for five years, 1,392 or 40% fall in the LFI category and 1,195 or 35% are HFIs. Over these five years, HFIs undertake on average 6.03 net-debt issues above the 2.5% size threshold, while LFIs undertake on average only 0.40 such issues. Moreover, in year five, HFIs raised 71% and LFIs 7% of aggregate issue proceeds. After ten years of listing, HFIs have on average made 11.19 issues, while LFIs have made only 0.88 issues. Raising the issue-size threshold to 5% (Panel A.2), after five years of public listing, HFIs have issued on average 4.48 net-debt issues and LFIs close to zero issues with a 5% threshold. After ten years of listing, the cumulative number of issues with the 5% threshold averages 6.60 for HFIs and 0.45 by LFIs. Panel B shows the cumulative frequency and volume of net-debt retirements (N DI − ) while maintaining the HFI/LFI classifications from Panel A. At the 2.5% size threshold (Panel B.1) and after five years of listing, there is only a slight difference between the number of net-debt retirements of HFIs and LFIs. The former retire 3.09 times and the latter 1.17 times, respectively. Moreover, in year five, the percentage of total retirement volume is 47% for HFIs and 17% for LFIs, again much closer than for N DI + in panel A.1.8 A similar relative tendency for HFIs and LFIs to retire debt emerges when we increase the issue threshold to 5% in Panel B.2. Turning to the seasoned equity issues in Panel C (again maintaining the HFI/LFI classifications from Panel A), using the 2.5% threshold (Panel C.1), the average cumulative number of quarterly equity issues is 3.59 after ten years, with a median of 2. With an equity-issue size threshold of 5% (panel C.2), the median number of equity issues remains at two after ten years, and it stays at two also after twenty years, 7

The empirical analysis uses all contiguous years on record, also if longer than the twenty years shown in the table. The difference between HFIs and LFIs is accentuated after ten years, with HFIs making 6.06 and LFIs 1.57 net-debt retirements on average. 8

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which is similar to that reported elsewhere (Eckbo and Masulis, 1995; Fama and French, 2005; Eckbo, Masulis, and Norli, 2007; Leary and Roberts, 2010). Interestingly, the equity issue frequencies are almost indistinguishable across LFIs and HFIs. As we move further from the public listing year, LFIs issue equity more frequently than HFIs when using the 2.5% issue-size threshold. For example, after ten years of listing, LFIs have on average issued seasoned equity 4.2 times versus 2.9 times for HFIs. With a 5% threshold, the average number of issues after ten years is 2.9 for both LFIs and HFIs. In sum, while HFIs issue net debt substantially more often than LFIs, HFIs and LFIs tend to retire debt and issue equity at similar frequencies.

2.4

Persistence of the issue frequency sorts

Table 2 shows that the spread in average issue frequencies between LFIs and HFIs is high and persistent across the twenty-year event period. In this section, we also demonstrate firm-level persistence in the HFI and LFI classifications. We do so first by showing that the firms sorted into HFIs and LFIs using Eq. (1) overlap greatly with the firms sorted using different periods of cumulation. Moreover, we show that our HFI and LFI classifications have the power to predict future (out-of-sample) net-debt issue activity, as expected when firm-level issue activity is persistent.

2.4.1

Effect of shortening the period of cumulation

Table 3 examines whether a classification based on a three-year cumulative issue activity (columns 1-4), or a within-year classification (no cumulation, columns 5-8)), produces a similar set of firms in the HFI and LFI sorts as those based on Eq. (1). Alternatively, with a high degree of instability, where firms migrate from the HFI and LFI groups in event time, the degree of overlap will be small. Formally, the shorter time horizon modifies the cumulation period in Eq. (1) by adding the lag parameter 0 < s ≤ t:

Nits =

t X 4 X

Iiqτ .

(2)

τ =s q=1

In the three-year cumulation, s = t − 2 (with s = 0 for the first two years after going public), while s = t restricts the issue count to within-year (no cumulation). First, as shown in Column (1), on average 88% of the firms originally classified as HFI in Table 2 are also classified as HFI with the three-year cumulation and a 2.5% issue size threshold (Panel A). With a 9

5% issue size threshold (Panel B), the overlap is 85%, and again with little variation across years since public listing. Moreover, as shown in Column (2), there is almost no migration from the HFI to the LFI categories: 1% of the LFIs would be classified as HFIs with the 2.5% threshold and the shorter period of cumulation (on average 2% with the 5% threshold). Similarly persistent, Column (4) shows that on average 95% of the LFIs remain LFIs also with the shorter three-year cumulation period (on average 98% when using the 5% threshold in Panel B). Second, columns (5)-(8) show a high degree of overlap with the firms classified as HFI and LFI in Table 2 also when we use the within-year frequency classification. In column (5), 72% of the HFIs would be classified as HFIs also without cumulation (59% with the 5% threshold in Panel B)). Moreover, in Column (8), on average 98% of the firms classified as LFI using a within-year classification are also originally classified as LFI. Overall, Table 3 shows that the HFI classification emerging from the algorithm in Eq. (1) is strongly influenced by the recent three-year and within-year net debt issue activity, which is reassuring from an economic standpoint. Third, to further indicate issuance persistence in event time, Table 4 computes the average issuance activity using constant-composition samples of HFIs and LFIs. For example, in Column 4 of Panel A, we report the average HFI issue frequency for each of the twenty event years using the sample of HFIs formed using Eq. (1) in year five. Five years later, in event year 10, the cumulative number of issues by these HFIs averages 9.35. This is close to the average of 11.19 in column (1) which is based on an annual rolling sort of firms into the HFI category. Overall, the table shows that, whether firms are classified as HFIs or LFI early or late following public listing, the cumulative net-debt issue frequencies in columns (2)-(9) remain very similar to that in Column (1).

2.4.2

Firm-level persistence and issue predictability

While Table 4 confirms persistence in terms of issue frequency, Table 5 also shows persistence in terms of the underlying firms classified as HFI and LFI. Panel A shows to what extent firms that are classified as HFI in a given year migrate to the medium-frequency (MFI) and LFI categories over the following year (columns 1-3) and over the next three years (columns 5-7). Panel B shows the corresponding migration for firms classified as LFI. In Panel A, over the public lifecycle, on average 85% of the firms classified as HFIs in one year are also HFIs in the subsequent year. The remaining 15% migrate to become MFI (none migrate to become 10

LFI). For the LFIs in Panel B, the corresponding lifecycle average is 81%, with the remaining 19% of the LFIs migrating to MFIs and HFIs in about equal proportions. Note also that the migration frequency of LFIs to HFIs occurs almost entirely in the year of public listing. A similar degree of firm-level stability in the sorts is also seen when using the three-year horizon in Columns (5)-(8): 71% of the HFIs and 66% of the LFIs remain classified as such three years later. With the exception of years 0-2, a LFI again only migrates to become a MFI. Turning to whether the HFI and LFI sorts predict future issue activity, columns (4) and (8) in Table 5 record the issue frequencies in the following year and in three years, respectively. In Panel A, on average 52% of all HFIs also undertake a net-debt issues in the next year (Column 4), and 46% undertake an issue in three years (Column 8). For LFIs (Panel B), the corresponding issue frequencies are on average 22% and 23%, respectively. Expanding to a multivariate analysis, Table 6 presents the coefficient estimates of the following logit model:

Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + γXi,t + i,t+v .

(3)

Here, Yi,t+v is a dummy variable with a value of one if firm i undertakes at least one (quarterly) net debt issue in year t + v and zero otherwise, HF I and LF I indicate whether firm i is classified as HFI or LFI, and X is our full set of firm-specific variables, discussed in Section 3 below.9 Panel A of Table 6 displays the estimated odds ratios when excluding the vector of controls X from the estimation. With a one-year forecast horizon, HF I increases the probability of a net-debt issue in year t+1 by 136% (the estimated coefficient increases from from 1.00 to 2.36) relative to MFIs. Moreover, LF I lowers this probability by 40% (the coefficient estimate falls from 1.00 to 0.61). The predictive power of HF I and LF I remains strong also with two- and three-year forecast periods, and for firms that have been publicly traded for nine years or more.10 As shown in Panel B, the predictive power of HFI and LFI in regression Eq. (3) is robust to including our full set of firm characteristics X, which we discuss next.11 9 Note that, since this regression uses all available firm-years, the regression baseline sample consists of the mediumfrequency issuers (MFIs). 10 For example, with a three-year prediction horizon, and firms listed for at least 10 years, HFIs are 124% are more likely to issue than MFIs, while LFIs are 50% less likely to issue. 11 Similar regression results emerge if we instead use a three-year cumulation (see Appendix Table 3) or no cumulation (Appendix Table 4) to generate HFIs and LFIs used in the above logit estimation.

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3

Distinguishing characteristics of high-frequency issuers

In this section, we contrast firm characteristics of HFIs with that of LFIs, and we show how these firms use net debt in the overall funding equation, all in event time following public listing. Again, while we use a quarterly net-debt issue count in Section 2 above to arrive at the HFI and LFI classifications, in order to maximize the contiguous sample information, all firm-specific characteristics are measured annually using annual Compustat data.

3.1

Firm characteristics in event time

The firm characteristics in Table 7—scaled by book value of total assets when appropriate—show clear differences between HFIs and LFIs. Naturally, LFIs are much less levered and have higher cash balances than HFIs. Using the overall average values at the bottom of each panel, the market leverage ratio (L) is 30% for HFIs and 7% for LFIs. This difference in average leverage ratios is also reflected in column (3) which shows the fraction of the sample firms that are all-equity financed (AE): 38% for LFIs and only 4% for HFIs. Also, recall from the introduction that the leverage of HFIs is considerably lower than if we instead were to sort firms based on the leverage ratio itself. A leverage-based sort produces an average leverage ratio of high-leverage firms of 51%, which exceeds the leverage of HFIs by nearly a factor of two. HFIs and LFIs differ considerably in terms of several of the other firm characteristics in Table 7 as well. For example, the cash ratio C in column (4) is 39% for LFIs and 12% for HFIs. This suggests that much of the recent build-up of cash balances reported elsewhere (Bates, Kahle, and Stulz, 2009) is concentrated among the LFIs, causing these firms to have negative net leverage (debt minus cash) on average. In contrast, HFIs have relatively high leverage ratios whether measured using gross debt or debt net of liquid assets such as cash balances. Moreover, there are significant differences between HFIs and LFIs in terms of asset structure and growth rates. HFIs are large on average (total assets of $662 million versus $343 million for LFIs) and they have a high degree of asset tangibility (defined as PPE/Assets): 0.32 versus 0.18 for LFIs, respectively. Moreover, HFIs tend to exhibit relatively low Q: 2.02 versus 3.01 for LFIs. The relatively low Q for HFIs is also reflected in low R&D spending (R&D in column (10)), which is 4% for HFIs and 11% for LFIs. In other words, HFIs appear to be more of a “brick and mortar” type of firm creating

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pledgeable assets that to a greater extent than for LFIs may be financed with debt.12 Notwithstanding the much higher R&D expenditures and Tobin’s Q of LFIs, HFIs actually sustain higher growth rates than LFIs. This is certainly true in terms of assets (column 13) but perhaps also in terms of sales (column 14). Thus, a key difference between LFIs and HFIs, which will become important in the empirical analysis to follow, is that HFIs grow largely through investments in physical assets, while LFIs to a much greater extent create growth through R&D expenditures. Column 11 displays the ratio of capital expenditures to assets (Capex ≡ Capext /Assetst ) and column 12 further highlights the impact of asset growth by scaling capital expenditures by the book value of assets lagged one period (Capex−1 ≡ Capext /Assetst−1 ). The capital expenditures of HFIs are almost double that of LFIs (Capex−1 is 11% versus 6%, respectively). Notice also that HFIs persistently out-spend LFIs in terms of capital expenditures from the year of going public, which helps explain why our HFIs persistently issue new debt. While not tabulated, HFIs also out-spend LFIs in terms of cash outlays for acquisitions: on average 5% versus 2%, respectively.13

3.2

External funding: debt and equity issuances

While HFIs rely more heavily than LFIs on debt financing, how do HFIs differ from LFIs in their remaining total funding mix? To answer this question, Table 8 shows the average annual contribution of each of the seven sources of funds available in the firm’s cash flow statement (defined in Appendix Tables 1 and P 2). Let Rj ≡ Sj / 7i Si denote the contribution of funding source Sj , where 7 X

Si ≡ CF + + EI + N DI + + ∆C − + I − + ∆W − + O+ .

(4)

i=1

Here, CF + is the positive portion of operating cash flow, EI is proceeds from equity issues, N DI + is positive net debt issues (debt issues exceeding debt retirements), ∆C − is draw-down of cash balances, I − is sale of investments, sale of property, plant and equipment (PPE) and cash flows from other investment activities, ∆W − is reduction in net working capital, and O+ is a small residual that maintains the cash 12

The dividend rates (relative to total assets) are similar, averaging 0.01% for both HFIs and LFIs. In terms of the Fama-French-12 industries, the sample representation of HFIs and LFIs is as follows: business equipment (HFIs 15%, LFIs 36%), shops (HFIs 21%, LFIs 9%), health care (HFIs 10%, LFIs 21%), consumer non-durables (HFIs 8%; LFIs 4%), consumer durables (HFIs 4%, LFIs 2%), manufacturing (HFIs 12%, LFIs 8%), energy (HFIs 7%, LFIs 2%), chemicals (HFIs 3%, LFIs 2%), and other (HFIs 20%, LFIs 15%). 13

13

flow identity.14 To simplify the exposition, we reduce in Figure 1 the seven funding sources to four ratios by summing P liquid and illiquid asset sales into a single Asset Sales ratio: RAS ≡ (∆C − + ∆W − + O+ + I − )/ 7i Si . P The other three ratios in in the figure are the Net-Debt Issue ratio RN DI + ≡ N DI + / 7i Si , the Equity P P Issue ratio REI ≡ EI/ 7i Si , and the positive Operating Cash Flow ratio RCF + ≡ CF + / 7i Si . By construction, these four ratios sum vertically to one in Figure 1. Figure 1, and Panel A of Table 8, confirm that HFIs exhibit substantially greater net-debt funding ratios than LFIs: the annual value of RN DI + averages 23% for HFIs and only 2% for LFIs, respectively, (median values of 12% and 0%). The low unconditional contribution of net debt for LFIs mirrors, of course, the low net-debt issue frequency of these firms documented above. Moreover, as visualized in Figure 1, the low contribution of net-debt issues in the overall funding mix for LFIs starts already in the year of public listing and persists thereafter. Turning to equity issues in the overall funding mix, the value of REI in Panel A of Table 8 averages 18% for HFIs and 32% for LFIs (median values of 2% and 9%). Thus, LFIs rely to a greater extent on equity issues in its overall funding policy than do HFIs. Again, this funding pattern begins immediately following public listing and is stable throughout the lifecycle as public company.

3.3

Internal funding: cash draw-downs and illiquid asset sales

Adding the average annual external funding ratios RN DI + and REI in Table 8 yields 41% for HFIs and 34% for LFIs. Conversely, on average, HFIs rely on internal sources for 59% of the total funding, while for LFIs 66% of total funds come from internal sources. The largest component of internal funding sources is (positive) operating cash flow, RCF + . While the contribution from RCF + is similar for HFIs and LFIs— averaging 33% and 29%, respectively (with a somewhat higher contribution for HFIs after ten years of public listing)—LFIs obtain a greater portion of their funding from the sale of liquid and illiquid assets than do HFIs. In addition to the contribution from operating cash flow, internal funding sources include sale of liquid P and illiquid assets: [(∆C − + ∆W − + O+ ) + I − ]/ 7i=1 Si+ . As shown in Table 8, total asset sales average 14

In 1988, Statement of Financial Accounting Standards (SFAS) instituted a new and uniform reporting system for working capital, including its component assets and liabilities. We work with net working capital over the entire sample period. Separate analysis on the post-1988 period shows that splitting net working capital into assets and liabilities does not affect our main conclusions below.

14

37% of total funding for LFIs and 26% for HFIs. Figure 1 shows that the contribution from all asset sales, RAS is quite stable on average over the public lifecycle. Also interesting, within total asset sales, the contribution from the sale of property plant and equipment, I − , differs substantially: 8% for HFIs versus 15% for LFIs. As explored in detail by Eckbo and Kisser (2013), the high contribution of illiquid asset sales to internal funding presents a challenge to the traditional financing pecking order of Myers (1984). In this pecking order, sources of internal equity such as CF + and ∆C − have low transaction costs and so dominate external funding obtained through sales of debt and equity securities. What Table 8 shows is that the sale of illiquid assets, which are often thought of as incurring relatively high transaction costs (Shleifer and Vishny, 1992), on average contributes as much as draw-downs of cash balances (which account for 8% for HFIs and 14% for LFIs) to the ongoing funding of the firm. This is particularly noticeable for LFIs, who hold relatively large cash balances (Table 7 above). Large cash balances are typically viewed as precautionary or they might reflect high external financing constraints (Bates, Kahle, and Stulz, 2009). The high percentage of illiquid asset sales in the overall funding equation for LFIs suggests that some firms are willing to pay even the high transaction costs of illiquid asset sales before issuing debt or equity to the public. Recall from Table 7 that LFIs are characterized by relatively high annual R&D expenses on average. The extant literature suggests that high R&D firms may be reluctant to raise external funds for fear that this may disclose valuable proprietary information produced by the R&D activity (Hall and Lerner, 2010; Brown, Martinsson, and Petersen, 2012; Bena and Li, 2014). While explaining the large cash balances of LFIs go beyond this paper, to briefly examine this potential reason for the high illiquid asset sales the funding mix of LFIs, we collect information on patents held by HFIs and LHIs, using the NBER/USPTO patent data (1976-2006). LFIs appear indeed to have somewhat greater likelihood of owning one or more patents than do HFIs. In the empirical analysis below, we occasionally use the firm characteristics in Table 7 to estimate target leverage ratios. While not tabulated here, regressions based on the same firm characteristics also suggest that firms such as LFIs and HFIs have cash balance targets. When we estimate the coefficients of the cash target model using the pooled sample of all firms, and then constructing target balances for LFIs and HFIs separately using those coefficients, the differences in excess cash holdings between LFIs and HFIs are small: 0.3 and -.02 percentage points, respectively. This suggests that our firm characteristics, 15

such as R&D and Capex, also go a long way in explaining differential target cash policies between these two groups of firms.

4

Debt issues in dynamic tradeoff theory without investment

In this section, we examine predictions of the class of dynamic tradeoff models pioneered by Fischer, Heinkel, and Zechner (1989), where investment decisions are exogenous and independent of capital structure.15 As in Figure 2, the value of a levered firm is a concave function of the market value of the unlevered assets and the leverage ratio L. With zero transaction costs, firms continuously rebalance capital structure (by issuing debt) to maintain the optimal leverage ratio L∗ . However, as explained in more detail below, a fixed rebalancing cost (C) drives a wedge between L∗ and the point where it is optimal to move the leverage ratio back to L∗ , causing potentially long periods without rebalancing activity. An important empirical implication of the refinancing wedge is that of a positive correlation between leverage and profitability only in periods with active recapitalizations. This prediction is supported by empirical tests in Danis, Rettl, and Whited (2014) based on pure rebalancing points over the period 1984-2011. In this section, we instead focus on hitherto untested implications for the issue frequency, issue size, volatility and dynamic adjustment speed in leverage ratios for HFIs relative to LFIs. In Section 5 we expand the empirical analysis to explicitly account for investment financing as well.

4.1

Empirical predictions and test approach

As in Figure 2, let the recapitalization cost C be fixed and firm value V = f (L) an increasing and concave function of the leverage ratio L below the optimal ratio L∗ . The positive marginal benefit of debt f 0 (L) may reflect a tax shield or other sources such as favorable managerial incentive effects and reduced agency cost. The firm recapitalizes (issues debt and/or retires equity) when L reaches an endogenous recapitalization bound L < L∗ , which is a function of both C and f 0 (L). The recapitalization moves L back to L∗ .16 15 Early examinations of the impact of adjustment costs on optimal financing behavior include the cash management model of Miller and Orr (1966) and the portfolio selection theory of Constantinides (1979). Later models include Goldstein, Ju, and Leland (2001) and Strebulaev (2007). 16 As emphasized by Danis, Rettl, and Whited (2014) as well, in the class of dynamic tradeoff models with exogenous investment, represented by Fischer, Heinkel, and Zechner (1989), firms optimally adjust leverage upwards (towards L∗ ) but not downwards (if L > L∗ ) as it is never optimal to reduce leverage outside of default or strategic renegotiation. Thus, Figure 2 focuses on the value function to the left of L∗ only.

16

Proposition 1 states three empirical predictions based on the above structure: Proposition 1 (dynamic tradeoff without investment): Consider two firms i and j whose unlevered assets follow a Geometric Brownian Motion. Moreover, let Ci ≥ Cj and fi0 (L) < fj0 (L) for L < L∗ . The optimal dynamic recapitalization policies of the two firms are characterized by the following: (1) Firm j recapitalizes more frequently than firm i (shorter refinancing spells). (2) When recapitalizing by issuing debt, firm j makes a smaller debt issue than firm i. (3) Firm j exhibits lower volatility and greater speed-of-adjustment in leverage ratios than firm i. Discussion: The proposition is a straightforward extension to two firms of the comparative statics in the single-firm analysis of Fischer, Heinkel, and Zechner (1989). Predictions (1) - (3) follow because the optimal leverage range of firm j, L∗ − Lj where Lj is firm j’s endogenous recapitalization bound, is smaller than that of firm i. To see why it is smaller, Figure 2 illustrates - without loss of generality - a case where the two firms have identical optimal leverage ratio L∗ and fixed cost C. The greater marginal debt benefit of firm j reduces the optimal leverage range relative to that of firm i. While not drawn in the figure, letting Ci > Cj would further increase the optimal leverage range of firm i relative to firm j. Testing Proposition 1 requires prior identification of firm types i and j where, again, fi0 (L) < fj0 (L) and/or Ci > Cj . We offer three sets of observations for why it makes sense to use LFIs to represent firm type i and HFIs to represent firm type j in these tests. First, the fact that HFIs issue far more frequently than LFIs by itself suggests that CLF I > CHF I . Second, recall from Table 7 that HFIs are more profitable, have lower cash flow volatility and higher asset tangibility than LFIs—factors that extant empirical research shows are associated not only with lower issuance costs (Eckbo, Masulis, and Norli, 2007) but also with higher leverage benefits (Frank and Goyal, 2008; Parsons and Titman, 2008; 0 0 Graham and Leary, 2011), suggesting that fHF I (L) > fLF I (L) as well. We emphasize that that our

test does not assume that HFIs and LFIs have similar leverage targets, only that CLF I > CHF I and 0 0 fHF I (L) > fLF I (L).

Third, in Figure 3, we present estimated shapes of dynamic net-debt issue hazards for LFIs and HFIs that are also consistent with LFIs facing greater fixed issue costs and/or lower debt issue benefits than HFIs. The shapes, which account for firm characteristics and unobservable firm-specific heterogeneity, 17

are estimated by parameterizing the following hazard function h of the j’th net-debt issuance spell for firm i: hi,j (t|αi ) = αi h0 (t)exp(β1 xi,j (t)),

(5)

where t is the length of the issue spell (years from the current to the next quarterly net debt issue), h0 (t) is the baseline hazard, and αi captures unobserved heterogeneity analogous to a random effect in a panel data model (where multiple issues by firm i may be correlated). The shared frailty term αi is assumed to be independent of the firm characteristics xi,j (t) and to have a zero-mean gamma distribution. The choice of a gamma distribution is standard (Leary and Roberts, 2005; Whited, 2006) (an alternative estimation using the inverse Gaussian distribution does not impact our conclusions). The baseline hazard h0 (t) measures the conditional issue probability when all covariates xi,j (t) equal zero. We follow Leary and Roberts (2005) and parametrize h0 (t) as a cubic polynomial in the time since the last issue: h0 (t) = exp(c + γ1 t + γ2 t2 + γ3 t3 ). The firm characteristics xi,j (t) are time-varying and enter the estimation each year after subtracting the sample-wide median value. Panels A and B of Figure 3 plot the estimated hazard shapes for LFIs and HFIs, respectively. The horizontal axis is years since last issue (in year 0). For example, at year five, the dynamic hazard function gives the estimated probability of a debt issue in year six conditional on not having issued debt over the previous five years.17 The hazard function for HFIs in Panel A has a high intercept and a negative slope, while the intercept is low and the slope positive for the LFIs in Panel B. These estimated differences are in fact consistent with HFIs facing lower issue costs and/or greater issue benefits than LFIs (to the degree that the benefits are not fully captured by the time-varying set of control variables). To see why, suppose that the firm has just recapitalized (moved L back up to L∗ through a debt issue) and is therefore positioned at period 0 in Figure 3. Moving forward in time, the market value of the firm’s unlevered assets—which may follow a Brownian motion with a positive drift as in Fischer, Heinkel, and Zechner (1989)—tends to increase firm value and therefore lower L on average. The positive drift term and the endogenous leverage range L∗ − L in Figure 2 jointly determine both the intercept and the slope of the dynamic hazard functions. Starting with Panel A in Figure 3, suppose HFIs have small and variable recapitalization costs so that L∗ − L is close to zero. Due to the positive drift, these firms are expected to recapitalize frequently (almost continuously using small debt issues), so the intercept of the hazard function is high (as shown). 17

The plots of the estimated hazard shapes have steps because time has been discretized to the annual frequency.

18

Moving forward in time, the longer the issue spell (the period without seeing a debt issue), the more likely the firm value has drifted down and L up above L∗ , making it gradually less likely that the firm will issue again in the next period. Thus, the slope of the hazard function is negative, which is also what we observe in Panel A. The reverse happens for the LFIs in Panel B. Since the leverage range L∗ − L is now large due to a large C, there is now a low probability of a second recapitalization following the one at time zero. Thus, the intercept is low (as shown). Moreover, as time goes by without a new debt issue, the probability of a recapitalization event now increases as firm value drifts up and moves L down closer to the lower bound L. Thus, if our LFIs in fact face a higher range L∗ − L than HFIs, the slope of the estimated hazard function in Panel B should be positive, as our estimation indicates is in fact the case. With this motivation, we turn to tests of the three predictions in Proposition 1 by contrasting the differences in net-debt issue behavior and leverage ratio dynamics of HFIs and LFIs.

4.2

Tests of Proposition 1

Parts (1) and (2) of Proposition 1 exploit directly the hypothetical differences in the curvature of the firm value function and/or issuance costs of HFIs and LFIs. Since we have sorted on relative issue frequency, we do not test Part (1), but rather use the observed relative issue frequency as an empirical proxy for the type of differences in value function curvature and issue costs that imply parts (2) and (3) of the proposition.

4.2.1

Relative issue size and leverage volatility

Beginning with relative net-debt issue size, the two first columns of Panels B of Table 8 show that the average net-debt issue sizes of HFIs and LFIs, conditional on firm-years where N DI ≥ 2.5%, are indistinguishable: 30% and 31% of total sources of funds, respectively, (median 26% and 28%). While not tabulated, if we scale the net-debt issue by total market value of the firm lagged one period, the corresponding average net-debt issue sizes are 10% and 11%, again statistically indistinguishable. This evidence fails to support the prediction that the net-debt issue sizes of LFIs should exceed those of HFIs.18 18

This conclusion also holds if we restrict the sample to pure recapitalizations, defined as in Danis, Rettl, and Whited (2014). In our sample, there are 1,199 pure recapitalizations by HFIs and 102 by LFIs, and the average net-debt issue size is 9% of lagged firm market value for both categories of firms.

19

Turning to leverage ratio volatility, the first column of Table 9 shows the average (market) leverage ratio in the year of public listing (L0 ). For example, for the HFIs in Panel A, L0 = 21% in year 0 for the full sample, while it is L0 = 18% for the sample of HFIs that have been listed ten years or more. For the LFIs in Panel B, L0 = 9% in the full sample (in year 0) and 4% for the subsample who have been listed ten years or more. Column (2) provides the average leverage ratio in event year t (Lt ), while Column (3) computes that difference between column (1) and (2)—the change in average leverage ratios across event time. For example, in year ten after listing, the HFIs in Panel A have experienced a leverage ratio change averaging L10 − L0 = 15%. The corresponding change for the LFIs in Panel B is only 2%. Columns (4)-(6) of Table 9 summarize the cross-sectional distribution of the leverage change. First, columns (4) and (5) list the percent of the sample with leverage ratio changes of at least ±20%. DeAngelo and Roll (2015) use this leverage ratio change limit to define an “unstable” leverage ratio. In Panel A, 43% of the HFIs increase leverage by at least 20% over the first five years of public listing, while only 8% of the LFIs in Panel B do so. As for reductions in the leverage ratio (Column 5), after five years of listing, 4% of both the HFIs and the LFIs have reduced their leverage ratios by at least 20%. Clearly, this indicates a substantially greater leverage volatility among HFIs than among LFIs. Column (6) of Table 9 shows the average leverage ratio volatility σL , measured as the average of the standard deviation of the firm-level leverage ratio from year 0 up to year t, using a minimum of five annual observations. For the HFIs in Panel A, the sample-wide average standard deviation is 17%, which is stable across listing age. In contrast, for LFIs, the average standard deviation of the leverage ratio is only 6%, strongly rejecting the prediction that the leverage ratio of LFIs should exceed that of LFIs.19

4.2.2

Relative speed-of-adjustment to target leverage deviations

The third and final prediction of Proposition 1 holds that the speed-of-adjustment (SOA) to target leverage ratio deviations should be greater for HFIs than for LFIs. We address this prediction by comparing the SOA parameter φ across HFIs and LFIs, estimated using the following dynamic regression:
Li,t − Li,t−1 = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t , 19

This conclusion holds also if we measure leverage volatility based on market leverage ratios net of cash holdings.

20

(6)

where the dependent variable is the change in the market leverage ratio. The lagged firm characteristics X used to estimate the current target leverage ratio L∗ are as described in Table 6: size, profitability, Q, cash ratio, tangibility, depreciation, R&D expenses, capital expenditures, the median industry leverage ratio, and both year- and firm-fixed effects (the latter represented by η). We use system GMM estimation to account for the fact that the regressor L∗ is itself estimated, and that the lagged dependent variable L also features as a regressor.20 Panel A of Table 10 uses the original frequency sort (using Eq. (1)). The estimate of φ is 0.307 for HFIs and 0.308 for LFIs, each significant at the 1% level, suggesting that it takes both HFIs and LFIs on average about three years to recover half of the target leverage deviation (the half-life implied by φ is ln(0.5)/ln(1 + φ)). Importantly, and contrary to the prediction of Proposition 1, the SOA coefficient estimates for HFIs and LFIs in the two first columns are statistically indistinguishable from each other. A similar conclusion emerges from Panel B and C, which use different sorts to generate the sample of HFIs and LFIs. In Panel B, rather than annual rebalancing, HFIs and LFIs are defined using a single event year, and then held constant for all firm-year observations. In Panel C, firms are again sorted annually, but a firm is classified as HFI (LFI) if its issue frequency that year is above (below) the sample median for that year. Thus, in Panel C, the regression is performed using the total sample of firms. Overall, the results in Table 10 show a remarkable consistency of the SOA estimates across HFIs and LFIs, and across sorting methods used to generate these high- and low-frequency issuers. In none of the regressions are the SOA coefficient estimates for HFIs different from that of LFIs, which contradicts Proposition 1. Conditional on the empirical estimate of the leverage target L∗ , this suggests that firms do not follow the issue pattern predicted by classical dynamic tradeoff theory and/or that HFIs and LFIs do not have differential issue cost structures or benefits. The similar magnitude of the SOA coefficient estimates φ for HFIs and LFIs in Table 10 is also interesting. Recall from Panel A of Table 2 that LFIs on average undertake roughly a single net-debt issue (with the 2.5% threshold) during the first ten years of listing, and only 2.7 issues over the first twenty years. This means that, for LFIs, the dynamic behavior of the market leverage ratio in Eq. (6) must be driven by changes in the denominator of the leverage ratio, i.e. by dynamics of the asset side of 20 To see this, note that Eq. (6) is equivalent to: Li,t = α + ηi + φL∗i,t (βXi,t−1 ) + (1 − φ)Li,t−1 + i,t . See Blundell and Bond (1998), Lemmon, Roberts, and Zender (2008), and Flannery and Hankins (2013) for discussions of system GMM estimation of the SOA parameter. Our specification of the leverage target L∗ also builds on the extant literature estimating SOA coefficients (Fama and French, 2002; Flannery and Rangan, 2006; Hovakimian and Li, 2012; Faulkender, Flannery, Hankins, and Smith, 2012).

21

the balance sheet, a point also made by Welch (2004). However, as suggested by the the model of DeAngelo, DeAngelo, and Whited (2011), it may also be the case that the SOA coefficient for HFIs is somewhat underestimated due to the ongoing active investment program of these high-frequency issuers. We know from Table 7 above that HFIs tend to make substantially larger capital expenditures than do LFIs over the public lifecycle. This need to finance persistent investment shocks may mask true tradeoff behavior by HFIs and attenuate the SOA coefficient estimate for these firms relative to LFIs. We turn to this issue next.

5

Transitory debt financing and investment

As discussed above, the class of dynamic tradeoff theory underlying the predictions in Proposition 1 do not incorporate investment financing decisions. In this section, we explore more recent theories modeling the dynamic interaction between investment policy and capital structure. These models range from the leverage impact of real investment options (Sundaresan and Wang, 2007; Tserlukevich, 2008; Morellec and Sch¨ urhoff, 2010) to examining debt covenants, taxes and agency issue and cash holdings (Hennessy and Whited, 2005; Titman and Tsyplakov, 2007; Gamba and Triantis, 2008b; DeAngelo, DeAngelo, and Whited, 2011). We focus on the empirical implications of the model in DeAngelo, DeAngelo, and Whited (2011) because it provides a particularly intuitive extension of some of the predictions tested above. In that model, unused debt capacity is a valuable asset as it lowers expected cost of future investment financing. Thus, debt financing now entails an opportunity cost by lowering future debt capacity. While firms may have long-horizon leverage targets as in a classical tradeoff model, this opportunity cost generally lowers the target—it may even be zero or negative net of cash balances. Moreover, the demand for investment finance may drive debt issues also when the leverage ratio exceeds the target—so-called transitory debt issues. Not surprisingly, the generally low net-debt issue frequency of the LFIs produces a sample of transitory debt issues that is too small to provide a meaningful empirical analysis. As explained below, however, one-fifth of the net-debt issues by HFIs are classified as transitory, rendering HFIs the core sample for the empirical analysis of this section.

22

5.1

Empirical predictions

We pursue two empirical predictions of the DeAngelo, DeAngelo, and Whited (2011) model. First, firms may deliberately issue debt to finance investment “spikes” even though this leads the firm to become overlevered relative to a long-horizon target leverage ratio. Such debt issues are transitory in the sense that the firm will repurchase the debt once the investment shock abates. Second, the existence of transitory debt issues implies that the SOA estimates in Section 4 above may understate the strength of firms’ incentive to rebalance leverage. This is because the time series used to estimate these SOA estimates may include periods with transitory debt issues. Proposition 2 summarizes these implications: Proposition 2 (transitory debt financing and investment): Suppose firms jointly determine financing and investment policy in a dynamic tradeoff setting such as in DeAngelo, DeAngelo, and Whited (2011). We should then observe the following: (1) Overlevered firms finance investment spikes with transitory net debt issues. (2) The speed-of-adjustment in leverage ratios is greater in periods without investment spikes. This proposition presumes a strong link between debt financing and investment. Below, we first provide evidence confirming this link, and then proceed to identify transitory net debt issues that are used in tests of Proposition 2.

5.2

Linking net-debt issues and investment

Panel B of Table 6 (above) predicts future (out-of-sample) net debt issues using the current issue frequency classification as well as several control variables, including Capex. With a one-year forecast horizon, a ten percentage point increase in Capex raises the probability of a net-debt issue by a factor of four to seven. Even when the forecast period is increased to three years, the impact of capital expenditures continues to be statistically and economically significant. In Table 11, we further develop this empirical link between net debt issues and investment. The regression specification is motivated by the dynamic model simulations in DeAngelo, DeAngelo, and Whited (2011), which produce financing decisions that resemble a dynamic version of the static pecking order of Myers (1984). That is, while the issue cost structure embedded in the dynamic model implies

23

that firms’ financing deficits are covered primarily by net debt issues, firms may also issue some equity to preserve debt capacity for future use. As in Shyam-Sunder and Myers (1999) and Frank and Goyal (2003), we define the financing deficit as the sum of dividends and investment outlays, net of internally generated funds. However, unlike earlier studies, we net out Capex from the financing deficit, creating the variable N etDef icit. This allows us to describe how net debt issues track capital expenditures more specifically while controlling for the remaining funding needs.21 Furthermore, since firms in the DeAngelo, DeAngelo, and Whited (2011) model are more likely to issue transitory net debt when facing large spikes in investment, we follow Lemmon and Zender (2010) and include squared terms in the financing deficit regression. Lemmon and Zender (2010) find that the squared deficit term receives a negative coefficient estimate, which suggests that firms may increase the share of equity financing when deficits are large—perhaps to preserve future debt capacity. We are interested in whether such financing behavior also holds for Capex itself, leading to the following regression specification: N DIi,t = α + β1 Capexi,t + β2 N etDef iciti,t + β3 Capex2i,t + β4 N etDef icit2i,t + i,t , Ai,t

(7)

where N DI/A is net-debt issues (retirements if negative) scaled by total assets. Table 11 reports coefficient estimates as we gradually increase the explanatory variables to include the full model. In column (4) we also add industry and year fixed effects, while column (5) replaces industry with firm fixed effects. The coefficient on Capex is statistically significant at the 1% level in all five regressions, increasing steadily from 0.34 in column (1), to 0.46 after adding N etDef icit in column (2), and to 0.54 in the full model in column (5) with firm fixed effects. This indicates that capital expenditures are associated with net-debt issues also after controlling for financing of the remaining financing deficit. Moreover, the coefficient estimate and N etDef icit is significant, increasing from 0.24 in column (2) to 0.61 in column (5). Thus, in the full model in column (5), net-debt issues cover about sixty percent of a dollar financing deficit net of capital expenditures. Since the coefficient estimate on N etDef icit2 is negative and significant (at about -0.45) equity financing is more important when the financing deficit is large, as also found by Lemmon and Zender (2010). 21 Using the Compustat variable definitions in Appendix Table 2, N etDef ecit ≡ (dv + aqc + ivch − siv − ivstch − sppe − ivaco − oancf + chech)/at.

24

Interestingly, the coefficient on Capex2 is positive and significant in regressions (3) and (5). This suggests that the importance of net-debt issues does not diminish when funding relatively large capital expenditures. It also suggest that the negative impact of the squared deficit reported by Lemmon and Zender (2010) is driven by elements in the financing deficit other than Capex. In sum, Table 11 shows a strong empirical association between net-debt issues and investment, in particular after controlling for the remaining financing deficit. Moreover, the association is even stronger when capital expenditures are relatively large, which raises the possibility that some of the net debt issues may be transitory. We turn to this possibility next.

5.3

Linking transitory net debt issues and investment spikes

Let Devi,t−1 ≡ Li,t−1 − L∗i,t−1 (Xi,t−2 ) denote firm i’s deviation from its target leverage ratio lagged one period. This lagged target leverage ratio is estimated each year on a rolling basis, using information on the control variables lagged two periods, where the explanatory variables in X are as in Table 10. Conditional on the estimate of the lagged leverage target, we label a current-period positive net debt issue as “transitory” if Devi,t−1 > 0, i.e. if the firm is estimated to be overlevered going into the period. In our sample, HFIs make a total of 12,615 annual positive net debt issues, of which 2,575 or 21% are transitory according to this empirical definition. Below, we examine whether these are truly transitory in the sense of DeAngelo, DeAngelo, and Whited (2011), which also requires that the positive value of Dev be subsequently reduced as the demand for investment finance tampers off. To gain test power, we single out transitory debt issues that take place in periods with a spike in capital expenditures, denoted Ecapex. We use two definitions of Ecapex. In the first, it is the difference between the firm’s Capex−1 and the median Capex−1 in the firm’s 3-digit SIC industry (where, as in Table 7 above, Capex−1 t ≡ Capext /Assetst−1 ). This definition is in the spirit of DeAngelo, DeAngelo, and Whited (2011), who define an investment spike somewhat more broadly as an annual capital expenditure outlay (standardized by lagged total assets) that is two or more standard deviations above the mean for the 2-digit SIC industry. Our second definition, Ecapex is the difference between the firm’s Capex−1 and the current period accounting depreciation allowance (standardized by lagged total assets) which, to some extent, reflects real asset growth. Part (1) of Proposition 2 implies that overlevered firms experiencing an investment spike are more likely to issue (transitory) net debt and less likely to retire net debt in that period. To examine this 25

hypothesis, we estimate the following binomial (logit) choice model: ∗ ∗ Yi,t = α + β1 Ii,t−1 + β2 Ecapexi,t + β3 Ii,t−1 Ecapexi,t + i,t ,

(8)

where the binary choice variable Yi,t takes on a value of one if firm i undertakes at least one quarterly ∗ net-debt issue (or a net-debt retirement) in year t with a 2.5% size threshold. It−1 is a dummy variable

with a value of one if the firm is overlevered going into period t (i.e., if Devi,t−1 > 0) and zero otherwise, thus indicating that the debt issue is transitory. In our total sample, there are 2,575 such transitory net-debt issues by HFIs. Moreover, of these, 47% occur during an investment spike defined using Ecapex relative to the industry median capital expenditure rate (53% occur when defined using depreciation allowance). As shown in Table 12, we estimate Eq. (8) separately for net debt issues and net debt retirements, and we also include industry fixed effects (FF12 industries). Moreover, the table uses Ecapex measured relative to the industry median capital expenditure.22 For the net debt issues in columns (1)-(4), the first part of Proposition 2 predicts a negative coefficient β1 on the transitory-debt indicator I ∗ as the firm is less likely to issue net debt if overlevered last period, and a positive coefficient β2 on the investment spike Ecapex as the firm relies in part on net debt to finance large investment shocks. The coefficient of most interest is β3 on the interaction term I ∗ Ecapex, which is predicted to be positive: an investment spike exacerbates the demand for a transitory net-debt issue when the firm is overlevered. Conversely, we expect net-debt retirements (columns 5-8), to increase in I ∗ and to decrease in both Ecapex and in the interaction term I ∗ Ecapex. As in Table 10 above, we repeat the regression using alternative methods for classifying HFIs. The above predictions receive substantial empirical support. Focusing on net-debt issues, the coefficient β3 on the interaction term in Column (4) is positive and statistically significant at the 1% level across all regression samples. Thus, the probability of a positive net-debt issue increases significantly in periods when the firm is both overlevered and experiences an investment spike. This increase in the issue probability comes in addition to the positive effect of Ecapex on debt issue activity indicated by the estimate of β2 , which is also highly significant across the board. Being overlevered per se, however, does not appear to systematically affect the net-debt issue probability as the estimate of β1 on I ∗ is 22 As shown in Appendix Table 5, the results are very similar when Eq. (8) is estimated with Ecapex measured in excess of depreciation allowance.

26

statistically insignificant in all but the regression in Panel A.

5.4 5.4.1

Are “transitory debt issues” transitory? Investment and the probability of net-debt retirement

In DeAngelo, DeAngelo, and Whited (2011), transitory debt issues are temporary as they are optimally followed by debt repurchases when the investment shocks recede. This is reflected in Part (2) of Proposition 2, which predicts that the firm will move leverage back to the optimum more rapidly in periods without investment shocks. We address this prediction first by estimating the impact of I ∗ when using the net-debt retirements in columns (5)-(8) of Table 12, while controlling for Ecapex. As shown in Column (6), the impact of I ∗ is positive and statistically significant at the 1% level in all regression specifications. In other words, controlling for current-year Ecapex, overlevered firms are more likely to retire net debt in the subsequent year, as predicted.23 While the direct effect of an investment spike is to increase the probability of a net-debt issue (columns 3 and 4), it also has the effect of decreasing the probability of a net-debt retirement (columns 7 and 8). That is, the significantly negative coefficient estimates in Columns (7) of Table 12 shows that firms increase net debt repurchases in periods with low investment (Ecapex ≤ 0). Moreover, the interaction term I ∗ Ecapex in Column (8) also receives a negative coefficient estimate, indicating that the probability of a net-debt repurchase is exacerbated when the firm is overlevered going into the period and does not experience an investment spike. Figure 4 further illustrates these effects for a subsample of firms experiencing both an investment spike (Ecapex > 0) and a transitory net debt issue in the same year (event year 0). For these spikes (N=1,198), the figure plots the evolution over the next three years in (i) the degree of overleverage (Dev, as defined above and labelled Deviation in the graph), (ii) excess investment (Ecapex, defined relative to the industry median), (iii) cumulative net-debt issues (Cumulative N DI), and (iv) cumulative net equity issues (Cumulative N EI, equity issues net of stock repurchases and dividends). In Panel A of the figure, both Ecapex and Deviation decline over the three years following the event year. The degree of overleverage falls from 7% in year 0 to 1.5% in year 3. This decline is seen to be driven by a sharp increase in equity issues as the firm continues to issue net debt over the event period. 23

This conclusion is also supported by the regressions in Appendix Table 5, which use the alternative definition of Ecapex as capital expenditure in excess of depreciation allowance.

27

A consistent explanation is that, following the investment spike financed with a transitory net debt issue, firms on average proceed to issue some net debt in order to help finance the declining level of Ecapex, while at the same time using equity issues to drive the leverage ratio back towards the (hypothetical) target L∗ . In Panel B of Figure 4 we further restrict the sample in Panel A to include only the firms with Ecapex < 0 in years 1 through 3. Thus, in Panel B, the sample firms experienced an investment spike in year 0, undertook a transitory net debt issue to finance that spike, and then went by construction through a three-year period of relative investment inactivity. Under the DeAngelo, DeAngelo, and Whited (2011) model, given the relative investment inactivity, these firms are expected to repurchase the transitory net debt and reduce overleverage over the three-year post-event period. While the sample of such investment spikes in Panel B is small (N=149), the evidence is remarkably consistent with the prediction in Proposition 2. Given the restriction on Ecapex, these firms most likely are in a position to internally finance whatever is left of capital expenditures. In contrast to Panel A, we now see a decline also in cumulative net-debt issues, caused by net-debt retirements. As in Panel A, the firms aggressively issues equity. The net effect is for the degree of overleverage in year 0 to decline sharply over the next three years, as predicted.

5.4.2

Speed-of-adjustment conditional on negative investment spikes

Part (2) of Proposition 2 predicts greater speed-of-adjustment to target leverage ratio deviations in periods with low investment activity. We test this prediction in Table 13. The table first repeats, in Column (1), the estimates of φ for the total sample of HFIs in Table 10. Recall that the statistically significant SOA coefficient estimate of 0.307 in Panel A (and the similar coefficient estimates across panels B and C) suggests that it takes the average HFI about three years to recover half of the target leverage deviation. This coefficient estimate is obtained without differentiating periods with investment spikes from periods with low investment. Columns (2) and (4) of Table 13 show the SOA estimates for the subsamples of firm-years with negative investment spikes (Ecapex < 0). In Column (2), Ecapex is defined relative to the industry median capex, while in Column (4) it is defined relative to firm depreciation allowance. The estimates are again positive and significant at the 1% level across all regressions. More important, the SOA estimates are also significantly larger than in Column (1). For example, in Panel A, the SOA coefficient 28

in Column (2) is 0.399 and 0.436 in Column (4). Both coefficient estimates are significantly greater than the unconditional value of 0.307 in Column (1). The remaining regressions yield the identical inference. As predicted, the SOA coefficient is statistically higher in periods with negative investment spikes (low investment). Finally, we examine whether the greater SOA in periods with Ecapex < 0 reflects net-debt issues and retirements per se or other sources of leverage reductions. This is done by replacing the leverage ratio change in Table 13 with net-debt issues, N DIi,t /M Vi,t , as the dependent variable in Table 14 (where M V denotes the market value of the firm):
N DIi,t = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t . M Vi,t

(9)

As shown in Column (1), which again is based on the total sample of HFIs, the SOA coefficient estimate is positive and statistically significant across all three panels, ranging from 0.197 to 0.226. These estimates imply that net-debt issue and retirements on average close about 20% of the target leverage ratio deviation in a given year. Moreover, after conditioning on Ecapex ≤ 0, the SOA estimates in columns (2) and (4) increase significantly, to about 0.30. This evidence is broadly consistent with Part (2) of Proposition 2, and it suggests that trade-off behavior become more apparent in the data as one isolates firm-years with relatively low investment funding needs, much as predicted by DeAngelo, DeAngelo, and Whited (2011).

6

Conclusion

Under modern dynamic tradeoff theory of capital structure, firms trade off not only debt benefits against expected distress costs as in classical models, but also the costs of internal versus external investment finance. The dual existence of rebalancing costs and ongoing investment activity makes it exceptionally difficult to isolate true tradeoff behavior in the data. In theory, either factor may endogenously override a firm’s objective of returning to an optimal capital structure, exacerbating the need for careful sample selection for empirical testing. To address this issue, we perform new tests of dynamic tradeoff theory using a sample of publicly traded firms who, by their own actions, reveal a combination of relatively high debt financing benefits and low debt issue costs: high-frequency net-debt issuers or HFIs.

29

We show that HFIs, who raised the bulk of all public and private debts over the past three decades, tend to issue net-debt persistently over their lifecycle as public companies. Moreover, relative to lowfrequency net-debt issuers (LFIs), they stand out as relatively large investment-intensive firms with high leverage and low Tobin’s Q. In contrast, LFIs are relatively small R&D-intensive firms with low leverage and high Q. HFIs are also more profitable than LFIs and have lower cash flow volatility and higher asset tangibility—factors that extant empirical research shows more generally are associated with lower issuance costs and higher leverage benefits. Overall, HFIs tend to finance a relatively intensive Capex program externally through debt issues, while LFIs tend to finance a relatively intensive R&D program internally. Not surprisingly, our sort on net-debt issue frequency produces a substantial spread in leverage ratios between HFIs and LFIs, with market leverage averaging 30% and 7%, respectively. However, ours is far from a direct sort on leverage, which would have produced average market leverage ratios of 51% in the corresponding upper (“high”) quartile and close to zero in the lower (“low”) quartile of the leverage distribution. Moreover, we argue that, from the point of view of testing dynamic tradeoff theory, our frequency-based sort is superior to a direct leverage-based sort. The frequency-based sort more directly identifies points in time at which firms perceive the financing decision to be optimal. Moreover, relative to a leverage-based sort, it places less weight both on firms in financial distress (who do not issue due to large issue costs) and on all-equity financed firms (who do not issue for reasons that are poorly understood). A straightforward application of the dynamic tradeoff theory with exogenous investment, such as that of Fischer, Heinkel, and Zechner (1989), suggests that our HFIs should have (i) smaller issue size, (ii) lower leverage volatility, and (iii) higher speed-of-adjustment to deviations from target leverage ratios than LFIs. These three predictions follow directly from the tighter rebalancing boundaries caused by the greater assumed debt benefits and lower debt issue costs of HFIs relative to LFIs. This relative costbenefit assumption is supported by the high issue frequency of HFIs per se, by their firm characteristics, and by our estimates of dynamic issue hazards. These differences suggest that the debt issue behavior and leverage policies of LFIs relative to that of the HFIs have unique power to test these classical tradeoff predictions. Notwithstanding the test power, the empirical evidence fails to support the above three (hitherto untested) tradeoff predictions. Net-debt issue sizes are roughly equal across HFIs and LFIs (not greater for LFIs as predicted). Second, leverage ratio volatilities are substantially higher for HFIs than for 30

LFIs (not lower as predicted), with LFIs having relatively stable leverage ratios. Third, HFIs and LFIs receive statistically indistinguishable speed-of-adjustment (SOA) coefficient estimates. Since the debtissue frequency is typically ten times higher for HFIs than for LFIs, the latter finding raises the possibility that traditional SOA estimates are driven as much by passive changes in equity (the denominator of the leverage ratio) as by debt issue activity per se. Moving on to the potentially confounding influence of investment finance on the above tradeoff tests, we first show that net-debt issue size and dollars spent on Capex are highly correlated for HFIs— and particularly so for large investment “spikes”. This finding emerges from a modified version of the financing-deficit regressions developed in extant studies of the financing pecking order. The evidence of joint financing and investment activity by HFIs may thus be an important reason why our test results reject tradeoff predictions that ignore endogenous investment. This helps motivate our use of the HFIs to examine the recent discrete-time, structural financing and investment model of DeAngelo, DeAngelo, and Whited (2011). We focus on two intuitive predictions of the DeAngelo, DeAngelo, and Whited (2011) model. First, firms may deliberately issue debt to finance investment spikes even if it means that the firm will become overlevered relative to a long-horizon target. Such debt issues are transitory in the sense that the firm will optimally repurchase the debt once the investment shock abates. Second, the existence of transitory debt issues implies that SOA estimates that do not condition on investment spikes may understate the strength of firms’ true incentive to rebalance leverage: the SOA coefficient estimate is predicted to be greater in periods without investment spikes. In our sample, HFIs make a total of 12,615 annual positive net debt issues, of which 2,575 or 20% are transitory. Investment spikes significantly increase the likelihood of a transitory net debt issue—roughly every second transitory issue occurs in the presence of an investment spike. We also find that the absence of investment spikes significantly increases the SOA estimate (by one third). Moreover, the downward adjustment in the leverage ratio in periods without investment spikes is to some extent driven by net-debt retirements, also as predicted. In sum, we have identified a set of firms who, over their life-cycles as public companies, persistently finance new investment with debt. Dynamic tradeoff theory with exogenous investment fails to explain the net-debt issue behavior of these firms relative to that of firms who only rarely issues debt. However, we also show that the net-debt issues of the high-frequency issuers are highly correlated with large 31

investment spikes. Conditioning on periods without spikes yields significantly greater SOA coefficient estimates—driven to some extent by debt repurchases—suggesting tradeoff behavior in the data.

32

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35

Figure 1: Lifecycle financing ratios for high- and low-frequency net-debt issuers The classification of firms into high- and low-frequency net-debt issuers P is as detailed in Table 2 with the 2.5% P (HFIs and LFIs) issue size threshold. The figure plots four financing ratios Rj ≡ Sj+ / 7i Si+ , where 7i Si+ is the firm’s total cash contribution P from each of its seven (non-negative) sources of funds: 7i Si+ = EI +N DI + +CF + +∆C − +I − +∆W − +O+ . EI is proceeds + from equity issues, N DI is positive net debt issues (net of debt retirements), CF + is positive operating cash flow, ∆C − is cash drawdowns, I − is sale of illiquid assets (sale of investments, PPE and other investments), ∆W − is reduction in net working capital, and O+ is “other” sources of funds (a small residual By construction, P the folP P closing the cash flow identity). lowing four ratios in the graph sum vertically to one: REI = EI/ 7i Si+ , RN DI + = N DI + / 7i Si+ , RCF + = CF + / 7i Si+ , P and RAS = (∆C − + I − + ∆W − + O+ )/ 7i Si+ . Year 0 is the year of public listing. Variable definitions are in Appendix Tables 1 and 2. Total sample of 8,719 U.S. public firms (53,351 firm-years), with an annual average of 1,832 HFIs (total of 17,422 firm-years) and 3,078 LFIs (total of 22,110 firm-years), 1984-2014.

0

.1

Financing Ratio .2 .3 .4

.5

.6

A: Average financing ratios since public listing for high-frequency net-debt issuers (HFIs)

0

5

10 Years since public listing R(NDI+) R(CF+)

15

20

R(EI) R(AS)

0

.1

.2

Financing Ratio .3 .4 .5

.6

B: Average financing ratios since public listing for low-frequency net-debt issuers (LFIs)

0

5

10 Years since public listing R(NDI+) R(CF+)

36

15 R(EI) R(AS)

20

Figure 2: Proposition 1 illustrated: optimal dynamic recapitalization with fixed recapitalization cost Under classical tradeoff theory, firm value V = f (L) is a concave function of the leverage ratio L, yielding an optimal (target) leverage ratio L∗ . Under dynamic tradeoff theory with exogenous investment and fixed rebalancing costs C (Fischer, Heinkel, and Zechner, 1989), firms optimally recapitalize (issue debt) when L reaches a lower boundary L. In this figure, for illustrative simplicity, two firms i and j have similar exogenous stochastic functions driving equity values, as well as identical target L∗ and recapitalization cost C. However, firm j has a steeper firm value function than firm i everywhere to the left of L∗ , which results in the recapitalization boundaries Li < Lj . As stated in Part (1) of Proposition 1, since the “no recapitalization” range (L − L∗ |L < L∗ ) is greater for firm i than for firm j, firm j recapitalizes more frequently than firm i. Second, when recapitalizing, firm j makes a smaller debt issue than firm j (Part 2). Third, firm j exhibit lower leverage ratio volatility than firm i, and greater speed-of-adjustment back to L∗ (part 3).

Firm value V(L)

Firm j

Firm i Fixed recapitalization cost C

Recap boundary for Firm j

Recap boundary for Firm i

Li

Lj

Leverage ratio L

L*

37

Figure 3: Estimated dynamic hazard curves for high- and low-frequency net-debt issuers The classification of firms into high- and low-frequency net-debt issuers (HFIs and LFIs) is as detailed in Table 2 with the 2.5% issue size threshold. The figure plots the baseline hazard h0 (t) that is obtained from estimating the following exponential shared frailty hazard model (indexed as the j’th issue by firm i): hi,j (t|αi ) = αi h0 (t)exp(βxi,j (t)), where t is the length of the issue spell (measured in years until the next quarterly net debt issue), h0 (t) is the baseline hazard (parameterized as h0 (t) = exp(Constant + γ1 t + γ2 t2 + γ3 t3 )) and αi captures unobserved heterogeneity analogous to a random effect in panel data model - it is assumed to be independent of the firm characteristics xi,j (t) and to have a zero-mean gamma distribution. The covariates in x include investment (Capex), market leverage ratio (L), cash ratio (C), the logarithm of assets (Size), operating cash flow (P rof ), tangibility (T an), Tobin’s Q (Q), and research and development expenditures (R&D). These variables are entered after subtracting their median values, thus the hazards are relative to the median sample firm. Total sample of 8,719 U.S. public firms (53,351 firm-years), with an annual average of 1,832 HFIs (17,422 firm-years) and 3,078 LFIs (22,110 firm-years), 1984-2014.

0

.2

Baseline Hazard .4

.6

.8

A: Estimated baseline hazard for high-frequency net-debt issuers (HFIs)

1

2

3 4 Years since last quarterly net-debt issue

5

6

0

Baseline Hazard .005 .01

.015

B: Estimated baseline hazard for low-frequency net-debt issuers (LFIs)

0

2

4 6 Years since last quarterly net-debt issue

38

8

10

Figure 4: Evolution of financing and investment after transitory net-debt issues by HFIs The classification of firms into high-frequency net-debt issuers (HFIs) is as detailed in Table 2 with the 2.5% issue size threshold. The event in year 0 is a transitory debt issue by a firm that is also experiencing an investment spike (Ecapex > 0 where Ecapex is the difference between Capex−1 and the median Capex−1 in the firm’s 3-digit SIC industry). A transitory net-debt issue is one where N DI exceeds 2.5% of total assets and the firm is overlevered in the year prior to the debt issue (Devt−1 = Lt−1 − L∗t−1 (βXt−2 ) > 0, where L∗ is estimated on a rolling basis using the firm characteristics X in Table 10). The figure plots subsequent over-leverage (“Deviation” or Dev at the beginning of the period), Ecapex, cumulative net-debt issues (“Cumulative N DI”) and cumulative net equity issues (“Cumulative N EI”, net of stock repurchases and dividends). Panel B further restricts the sample in Panel A to firms with a short-lived investment spike in year 0, so that Ecapex < 0 in years 1 through 3. The latter subsample mimics in particular the firms predicted by DeAngelo, DeAngelo, and Whited (2011) to aggressively repurchase its transitory debt. The number (N) of firm-year observations in year 0 below are drawn from the total sample of 8,719 U.S. public firms (53,351 firm-years), which contains an annual average of 1,832 HFIs (17,422 firm-years), 1984-2014.

.02

Percent of firm market value .04 .06 .08 .1

.12

A: Year 0 event: a transitory net debt issue in a year with an investment spike (N=1,198)

0

1 2 Year since transitory net-debt issue Deviation Cumulative NEI

3

Cumulative NDI Ecapex

-.05

Percent of firm market value 0 .05

.1

B: Year 0 event: as in Panel A but with Ecapex < 0 in years 1 through year 3 (N=149)

0

1 2 Year since transitory debt issue Deviation Cumulative NEI 39

Cumulative NDI Ecapex

3

Table 1: Sample selection

Sample restriction

Observations

Firms

A: Annual CRSP/Compustat sample, 1984-2014 Full annual sample U.S. domiciled firms only Nongovernmental, industrial firms onlya No multiple annual observations No missing information on book value of assets Firm age positiveb Consistent cash-flow statement datac Consistent balance sheet datad = Intermediate sample

206671 -20547 -59139 -1754 -266 -170 -3247 -2217 119331

21386 -2245 -5543 0 -23 -15 -89 -75 13396

B: Quarterly CRSP/Compustat sample, 1984-2014 Full quarterly sample U.S. domiciled firms only Nongovernmental, industrial firms onlya No multiple annual observations No missing information on book value of assets Consistent cash-flow statement datac Consistent balance sheet datad = Intermediate Sample

833,782 -153,800 -167,046 -6,682 1,629 -126,949 -19,511 358,165

21,924 -3,374 -4,524 0 -9 -365 -134 13,518

C: Merged CRSP/Compustat sample, 1984-2014 Merged Sample Went public during sample period Contiguous annual observationse Final quarterly CRSP/Compustat sample Final annual CRSP/Compustat sample

332,416 -138,555 -36,314 157,547 53,351

13,026 -4,307 0 8,719 8,719

a

Eliminates utilities (SIC codes 4899-5000), financial firms (SIC codes 5999-7000), and government entities (SIC codes greater than 8999).

b

Firm age is the difference between the reporting date of the annual financial statement and the date of the first month a company is reported in the CCM monthly stock price database, rounded to the next smaller integer.

c

For cash-flow data consistency, we first drop observations with negative values for the following Compustat variable names (see Appendix Table 1 and 2 for variable definitions): dltis, dltr, sstk, prstkc, dv, capx, aqc, ivch, sppe and siv. Moreover, we set missing entries for items in the cash flow statement to zero and drop observations in case total sources or uses of funds equal zero or deviate by more than 1% from each other.

d

For balance sheet data consistency, we set missing entries for deferred taxes and investment tax credit (txditc) and preferred stock liquidation value (pstkl) equal to zero and subsequently require non-missing data for the market value of the firm’s equity (prcc f × csho), Tobin’s Q (lt + pstkl - txditc + prcc f × csho)/at), total debt (dltt + dlc), cash holdings (che), property plant and equipment (ppent). We further drop observations in case the book leverage ratio is outside the unit interval or cash holdings are negative. The last criterium is not applied to the quarterly dataset (given that consistency is ensured at the annual level).

e

We eliminate observations once the underlying annual data become non-contiguous.

40

Table 2: Cumulative security issues and retirements by high- and low-frequency net-debt issuers Starting in the year of public listing (t = 0), firm i is classified as a high- or low frequency net-debt issuer (HFI or LFI) in year t as follows: First, calculate firm i’s cumulative number of quarterly positive net-debt issues in year Pt P4 + t, Nit , as follows: Nit = τ =0 q=1 Iiqτ , where Iiqτ takes a value of one if N DIiqτ ≥ k in quarter q of event + year τ ≤ t and zero otherwise. N DI is the positive portion of total debt issue minus debt retirement as given by quarterly Compustat cash flow statements. The issue size threshold k is either 2.5% or 5%. Then, firm i is classified as a HFI (LFI) in year t if Nit is in the upper (lower) quartile of the frequency distribution of Nt . The reported cumulative quarterly number of issues for the HFIs and LFIs are within-group averages, while the reported mean and median are for the total sample of firms. The issue count for debt retirements and equity issues in panels B and C are for the firms classified as HFI or LFI in Panel A. Variable definitions are found in Appendix Tables 1 and 2. Total sample of 8,719 public US firms and 53,351 firm-years, 1984-2014.

Total sample of firms Event year

All

LFI

HFI

Fraction received of aggregate issue proceeds LFI

HFI

Cumulative quarterly number of issues Average Sample Sample Average LFI Mean Median HFI

A.1 Positive net-debt issues with a 2.5% threshold (N DI + ≥ 2.5%) 0 8719 6378 2341 0.02 0.98 0.00 0.31 1 7347 3657 3690 0.02 0.98 0.00 0.88 2 5996 2305 2269 0.01 0.81 0.00 1.41 3 4929 1558 1574 0.01 0.65 0.00 1.92 4 4119 1116 1150 0.01 0.58 0.00 2.42 5 3451 1392 1195 0.07 0.71 0.40 2.86 6 2931 1045 866 0.06 0.50 0.40 3.31 7 2503 795 646 0.11 0.47 0.41 3.73 8 2128 612 653 0.05 0.57 0.41 4.17 9 1791 458 499 0.02 0.52 0.41 4.63 10 1503 507 377 0.11 0.47 0.88 5.09 15 718 217 204 0.07 0.73 1.50 7.14 20 285 90 83 0.05 0.28 2.66 9.65

0 1 1 1 2 2 3 3 3 4 4 6 8

1.15 1.74 3.11 4.38 5.61 6.03 7.31 8.51 8.79 9.91 11.19 14.38 18.53

A.2 Positive net-debt issues with a 5% threshold (N DI + 0 8719 6865 1854 0.07 0.93 1 7347 4354 2993 0.09 0.91 2 5996 2905 1517 0.06 0.68 3 4929 2036 1664 0.05 0.70 4 4119 1518 1664 0.05 0.77 5 3451 1167 1003 0.04 0.68 6 2931 887 969 0.02 0.56 7 2503 659 929 0.03 0.66 8 2128 917 590 0.16 0.55 9 1791 703 560 0.08 0.54 10 1503 551 506 0.11 0.53 15 718 180 199 0.05 0.65 20 285 80 72 0.10 0.40

0 0 1 1 1 1 1 2 2 2 2 3 4

1.11 1.52 2.75 3.04 3.31 4.48 4.71 4.93 6.08 6.31 6.60 9.17 12.43

Continued on next page

41

≥ 5%) 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.45 0.45 0.45 0.51 1.13

0.24 0.62 0.96 1.27 1.56 1.83 2.09 2.36 2.58 2.87 3.13 4.21 5.67

Table 2 – Cumulative security issues, cont’d

Total sample of firms Event year

All

LFI

HFI

Fraction received of aggregate issue proceeds LFI

HFI

B.1 Net-debt retirements with a 2.5% threshold (N DI − 0 8719 6378 2341 0.86 0.14 1 7347 3657 3690 0.39 0.61 2 5996 2305 2269 0.16 0.50 3 4929 1558 1574 0.10 0.42 4 4119 1116 1150 0.08 0.39 5 3451 1392 1195 0.17 0.47 6 2931 1045 866 0.13 0.46 7 2503 795 646 0.09 0.40 8 2128 612 653 0.12 0.43 9 1791 458 499 0.11 0.44 10 1503 507 377 0.14 0.41 15 718 217 204 0.11 0.54 20 285 90 83 0.07 0.26

Cumulative quarterly number of issues Average Sample Sample Average LFI Mean Median HFI ≥ 2.5%) 0.53 0.64 0.71 0.75 0.82 1.17 1.17 1.17 1.19 1.15 1.57 2.31 3.39

B.2 Net-debt retirements with a 5% threshold (N DI + ≥ 5%) 0 8719 6865 1854 0.89 0.11 0.40 1 7347 4354 2993 0.51 0.49 0.44 2 5996 2905 1517 0.33 0.32 0.47 3 4929 2036 1664 0.24 0.48 0.51 4 4119 1518 1664 0.21 0.59 0.55 5 3451 1167 1003 0.14 0.40 0.58 6 2931 887 969 0.10 0.50 0.58 7 2503 659 929 0.09 0.51 0.57 8 2128 917 590 0.26 0.40 0.88 9 1791 703 560 0.20 0.49 0.89 10 1503 551 506 0.18 0.48 0.88 15 718 180 199 0.10 0.51 0.97 20 285 80 72 0.03 0.48 1.41 Continued on next page

42

0.47 0.76 1.09 1.43 1.78 2.13 2.44 2.79 3.12 3.48 3.81 5.75 7.69

0 1 1 1 1 2 2 2 2 3 3 5 7

0.30 0.88 1.40 1.98 2.55 3.09 3.67 4.36 4.82 5.37 6.06 9.12 11.88

0.36 0.52 0.70 0.89 1.06 1.24 1.38 1.55 1.72 1.88 2.04 2.94 3.82

0 0 0 1 1 1 1 1 1 1 1 2 3

0.23 0.65 1.02 1.31 1.54 2.03 2.24 2.46 3.00 3.12 3.35 5.16 6.65

Table 2 – Cumulative security issues, cont’d

Total sample of firms Event year

All

LFI

HFI

Fraction received of aggregate issue proceeds LFI

HFI

Cumulative quarterly number of issues Average Sample Sample Average LFI Mean Median HFI

C.1 Equity issues with a 2.5% threshold 0 8719 6378 2341 0.75 1 7347 3657 3690 0.42 2 5996 2305 2269 0.30 3 4929 1558 1574 0.21 4 4119 1116 1150 0.21 5 3451 1392 1195 0.35 6 2931 1045 866 0.32 7 2503 795 646 0.24 8 2128 612 653 0.29 9 1791 458 499 0.20 10 1503 507 377 0.38 15 718 217 204 0.32 20 285 90 83 0.21

0.25 0.58 0.50 0.46 0.38 0.44 0.37 0.31 0.43 0.47 0.25 0.37 0.13

0.97 1.35 1.69 2.06 2.37 2.72 3.13 3.54 3.83 4.15 4.20 5.43 6.12

0.94 1.30 1.63 1.95 2.24 2.52 2.81 3.06 3.27 3.46 3.59 4.31 4.37

1 1 1 1 2 2 2 2 2 2 2 3 3

0.87 1.25 1.56 1.90 2.13 2.31 2.51 2.69 2.82 2.90 2.90 3.03 3.01

C.2 Equity issues with a 5% 0 8719 6865 1854 1 7347 4354 2993 2 5996 2905 1517 3 4929 2036 1664 4 4119 1518 1664 5 3451 1167 1003 6 2931 887 969 7 2503 659 929 8 2128 917 590 9 1791 703 560 10 1503 551 506 15 718 180 199 20 285 80 72

0.21 0.52 0.40 0.50 0.50 0.39 0.45 0.54 0.36 0.46 0.38 0.39 0.21

0.92 1.22 1.48 1.73 1.96 2.23 2.49 2.71 2.68 2.80 2.87 3.86 3.73

0.90 1.19 1.46 1.72 1.93 2.15 2.36 2.53 2.66 2.79 2.87 3.37 3.41

1 1 1 1 1 1 2 2 2 2 2 2 2

0.80 1.16 1.48 1.74 1.96 2.19 2.39 2.56 2.65 2.79 2.87 2.86 2.81

threshold 0.79 0.48 0.38 0.27 0.28 0.28 0.23 0.21 0.37 0.31 0.35 0.23 0.40

43

Table 3: Overlap between net-debt-issue-frequency sorts with different periods of cumulation In the original sort (Table 2), firms are classified as HFI or LFI in event year t based on cross-sectional distribution of Pthe P quarterly net-debt issues cumulated from the year of public listing (year t = 0): Nit = tτ =0 4q=1 Iiqτ , where Iiqτ takes a + value of one if N DIiqτ ≥ k in quarter q of event year τ ≤ t and zero otherwise. This table shows the overlap between the firms in the original HFI and LFI sort and two alternative sorts: one based on a three-year trailing cumulation of quarterly net-debt issues, and the other based on zero cumulation (within-year quarterly issue count only). The table displays the fraction of the original HFIs and LFIs that would also be classified as HFI or LFI under the two alternative sorts. Total sample of 8,719 public US firms and 53,351 firm-years, 1984-2014.

Overlap with 3-year trailing cumulation P P Nit3 = tτ =t−2 4q=1 Iiqτ Overlap between HFIs with 3-year cumulation and the original HFI LFI (1) (2)

Overlap with zero cumulation P Nit0 = 4q=1 Iiqτ Overlap between HFIs with zero cumulation and the original HFI LFI (5) (6)

Overlap between LFIs with zero cumulation and the original HFI LFI (7) (8)

A Net-debt issue size threshold of k = 2.5% 0 1.00 0.00 0.00 1.00 1 1.00 0.00 0.00 1.00 2 1.00 0.00 0.00 1.00 3 0.84 0.00 0.00 1.00 4 0.78 0.00 0.01 1.00 5 0.84 0.00 0.05 0.83 6 0.81 0.00 0.05 0.84 7 0.80 0.00 0.07 0.85 8 0.74 0.00 0.10 0.85 9 0.74 0.00 0.11 0.87 10 0.74 0.04 0.12 0.81 15 0.59 0.04 0.15 0.77 20 0.51 0.09 0.19 0.70

1.00 0.76 0.76 0.73 0.71 0.65 0.64 0.61 0.57 0.58 0.59 0.50 0.41

0.00 0.00 0.00 0.00 0.00 0.06 0.06 0.07 0.05 0.05 0.08 0.09 0.10

0.00 0.24 0.24 0.27 0.29 0.35 0.36 0.39 0.43 0.42 0.41 0.50 0.59

1.00 1.00 1.00 1.00 1.00 0.94 0.94 0.93 0.95 0.95 0.92 0.91 0.90

Avg.

0.95

0.72

0.02

0.28

0.98

B Net-debt issue size threshold of k = 5% 0 1.00 0.00 0.00 1 1.00 0.00 0.00 2 1.00 0.00 0.00 3 0.83 0.00 0.01 4 0.63 0.00 0.09 5 0.90 0.00 0.10 6 0.84 0.00 0.16 7 0.79 0.00 0.21 8 0.78 0.17 0.22 9 0.74 0.14 0.26 10 0.71 0.11 0.29 15 0.68 0.08 0.32 20 0.67 0.14 0.33

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.83 0.86 0.89 0.92 0.86

1.00 0.72 0.72 0.57 0.49 0.54 0.44 0.41 0.44 0.42 0.38 0.35 0.29

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.06 0.04 0.03 0.03 0.04

0.00 0.28 0.28 0.43 0.51 0.46 0.56 0.59 0.56 0.58 0.62 0.65 0.71

1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 0.94 0.96 0.97 0.97 0.96

Avg.

0.98

0.59

0.01

0.41

0.99

0.88

0.85

0.01

0.02

Overlap between LFIs with 3-year cumulation and the original HFI LFI (3) (4)

0.04

0.11

44

Table 4: Average annual number of net-debt issues with alternative constant-composition sorts In the original sort (Table 2), firms are classified as HFI or LFI in event year t based on thePcross-sectional distribution P4 t of quarterly net-debt issues cumulated from the year of public listing (year t = 0): Nit = q=1 Iiqτ , where Iiqτ τ =0 + takes a value of one if N DIiqτ ≥ k in quarter q of event year τ ≤ t and zero otherwise. With this annual rebalancing, firms may enter and leave the HFI and LFI classifications through time. This table shows the average annual number of net-debt issues if the composition of the HFI and LFI sorts are held constant over the entire sample period. In Column (2), the constant-composition sample of HFI and LFI is formed based on the distribution of Nit in event year t = 3. In Column (4), it is based on the distribution in year t = 4, etc., up to and including year t = 10 in Column (9). All sorts are based on the 2.5% net-debt issue size threshold. Total sample of 8,719 U.S. public firms and 53,351 firm-years, 1984-2014.

Event year

Original sort (1)

A: High 0 1 2 3 4 5 6 7 8 9 10 15 20

Constant composition sorts with Nit fixed in event-year t: t=3 t=4 t=5 t=6 t=7 t=8 t=9 t=10 (2) (3) (4) (5) (6) (7) (8) (9)

frequency net-debt issuers (HFIs) 1.15 0.56 0.55 0.51 0.52 1.74 1.90 1.91 1.71 1.77 3.11 3.22 3.24 2.88 3.01 4.38 4.38 4.47 3.98 4.15 5.61 5.22 5.61 5.04 5.25 6.03 5.99 6.49 6.03 6.31 7.31 6.70 7.28 6.81 7.31 8.51 7.34 7.95 7.46 8.04 8.79 7.95 8.58 8.09 8.72 9.91 8.51 9.23 8.70 9.38 11.19 9.16 9.96 9.35 10.06 14.38 11.80 12.80 12.01 12.77 18.53 15.04 16.49 15.02 15.78

B: Low frequency net-debt issuers (LFIs) 0 0.00 0.00 0.00 0.08 0.08 1 0.00 0.00 0.00 0.17 0.14 2 0.00 0.00 0.00 0.23 0.20 3 0.00 0.00 0.00 0.28 0.24 4 0.00 0.00 0.00 0.34 0.29 5 0.40 0.15 0.15 0.40 0.33 6 0.40 0.33 0.33 0.62 0.40 7 0.41 0.55 0.55 0.88 0.62 8 0.41 0.76 0.76 1.13 0.84 9 0.41 0.96 0.96 1.41 1.08 10 0.88 1.16 1.16 1.64 1.26 15 1.50 2.34 2.34 3.04 2.54 20 2.66 3.15 3.15 4.05 3.36

45

0.53 1.84 3.13 4.30 5.39 6.54 7.61 8.51 9.22 9.88 10.60 13.41 16.30

0.50 1.70 2.90 3.98 4.96 6.01 6.99 7.89 8.79 9.48 10.15 12.85 15.56

0.48 1.65 2.84 3.94 4.97 6.09 7.08 8.06 9.03 9.91 10.60 13.51 16.32

0.51 1.72 2.94 4.11 5.19 6.34 7.35 8.37 9.36 10.29 11.19 14.29 16.98

0.06 0.12 0.17 0.21 0.26 0.29 0.34 0.41 0.59 0.80 0.95 2.16 2.87

0.06 0.10 0.15 0.19 0.23 0.26 0.30 0.36 0.41 0.57 0.73 1.72 2.33

0.05 0.10 0.15 0.18 0.22 0.24 0.28 0.32 0.36 0.41 0.51 1.37 1.88

0.09 0.23 0.32 0.39 0.47 0.53 0.60 0.65 0.71 0.79 0.88 1.80 2.43

Table 5: Classification persistence and future issue frequency The classification of firms into HFIs (LFIs) is based on the the cumulative quarterly net debt issue frequency classification as detailed in Table 2, using the 2.5% issue size threshold. Columns (1) to (3) display next period’s issue frequency classification of the currently defined HFIs (Panel A) or LFIs (Panel B). Column (4) shows the fraction of next period’s net debt issues for the two groups. Columns (5) to (8) display the corresponding characteristics three years into the future. Total sample of 8,719 U.S. public firms (53,351 firm-years), with an annual average of 1,832 HFIs (total of 17,422 firm-years) and 3,078 LFIs (total of 22,110 firm-years), 1984-2014.

Age

Next year Classified as HFI MFI LFI (1) (2) (3)

A: High frequency 0 1.00 0.00 1 0.70 0.30 2 0.78 0.22 3 0.81 0.19 4 1.00 0.00 5 0.82 0.18 6 0.85 0.15 7 1.00 0.00 8 0.86 0.14 9 0.84 0.16 10 1.00 0.00 15 0.88 0.12 20 0.85 0.15 Avg. 0.85 0.15 B: Low 0 1 2 3 4 5 6 7 8 9 10 15 20 Avg.

Issue Freq. (4)

In three years Classified as Issue HFI MFI LFI Freq. (5) (6) (7) (8)

net-debt issuers (HFIs) 0.00 0.54 0.59 0.41 0.00 0.52 0.50 0.50 0.00 0.55 0.75 0.25 0.00 0.55 0.76 0.24 0.00 0.56 0.79 0.21 0.00 0.52 0.78 0.22 0.00 0.51 0.80 0.20 0.00 0.50 0.80 0.20 0.00 0.48 0.79 0.21 0.00 0.49 0.78 0.22 0.00 0.52 0.91 0.09 0.00 0.47 0.86 0.14 0.00 0.42 0.91 0.09 0.00 0.52 0.71 0.29

0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00

0.47 0.45 0.48 0.49 0.48 0.45 0.45 0.47 0.45 0.47 0.45 0.44 0.38 0.46

frequency net-debt issuers (LFIs) 0.33 0.00 0.67 0.33 0.23 0.35 0.06 0.18 0.76 0.24 0.06 0.40 0.01 0.18 0.81 0.19 0.04 0.17 0.00 0.15 0.85 0.15 0.01 0.15 0.00 0.03 0.97 0.12 0.00 0.14 0.00 0.12 0.88 0.18 0.01 0.28 0.00 0.11 0.89 0.17 0.01 0.26 0.00 0.10 0.90 0.15 0.00 0.11 0.00 0.09 0.91 0.13 0.00 0.09 0.00 0.01 0.99 0.12 0.00 0.09 0.00 0.06 0.94 0.13 0.00 0.20 0.00 0.09 0.91 0.16 0.00 0.08 0.00 0.00 1.00 0.07 0.00 0.13 0.11 0.09 0.81 0.22 0.08 0.26

0.42 0.54 0.80 0.85 0.86 0.71 0.74 0.89 0.91 0.91 0.80 0.92 0.87 0.66

0.33 0.27 0.19 0.17 0.18 0.20 0.18 0.16 0.15 0.18 0.16 0.18 0.21 0.23

46

Table 6: Out-of-sample predictions of net-debt issue activity using high- and low-frequency sorts The table presents odds ratios of a logit model determining the probability of a net debt issue in year t + v, conditional on the current issue frequency classification and a vector X of covariates: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + γXi,t + i,t+v where Yi,t+v is a dummy variable equal to one in case at least one quarterly net debt issue occurred in year t + v. In this regression, HF Ii,t (LF Ii,t ) is a dummy variables that takes on a value of one if firm i is classified as a high-frequency (low-frequency) net-debt issuer in period t, and zero otherwise. Thus, the baseline sample is medium-frequency issuers (MFIs, all firms that are neither HFI or LFI). The classification of firms into HFIs and LFIs is as in Table 2, using the 2.5% issue size threshold. The covariates in Xi,t are: investment (Capex), R&D expenditures (R&D), market leverage ratio (L), cash ratio (C), logarithm of assets (Size), operating cash flow (P rof ), tangibility (T an), Tobin’s Q (Q) and depreciation expenditures (Depr). All covariates are winsorized at the 1(99) percent level or must lie between zero and one (cash ratio and leverage). Variable definitions are in Appendix Tables 1 and 2. *, ** indicate significance at the 5% and 1% level, respectively. Total sample of 8,719 public US firms, with an annual average of 1,832 HFIs and 3,078 LFIs, 1984-2014. Firm-specific explanatory variables (X) N

HFI

LFI

Capex

R&D

L

C

Size

P rof

T an

Q

Depr

A: Simple logit model: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + i,t+v Net debt issue in All 44632 Age > 4 18790 Age > 9 7934

year t+1: 2.36** 0.61** 2.28** 0.42** 2.37** 0.43**

Net debt issue in All 37285 Age > 4 15859 Age > 9 6661

year t+2: 2.08** 0.68** 2.12** 0.49** 2.30** 0.49**

Net debt issue in All 31289 Age > 4 13356 Age > 9 5547

year t+3: 2.00** 0.71** 2.07** 0.52** 2.24** 0.51**

B: Expanded logit model: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + γXi,t + i,t+v Net debt issue in All 44632 Age > 4 18790 Age > 9 7934

year t+1: 1.84** 0.87** 1.83** 0.67** 1.85** 0.68**

65.40** 66.06** 44.67**

0.69** 1.34 1.69

0.70** 0.62** 0.69*

0.04** 0.02** 0.01**

0.95** 0.99 1.00

0.43** 0.35** 0.37**

0.67** 0.58** 0.49**

1.01 1.04** 1.05*

0.01** 0.06** 0.15

Net debt issue in All 37285 Age > 4 15859 Age > 9 6661

year t+2: 1.67** 1 1.73** 0.75** 1.84** 0.75**

7.22** 6.23** 3.12

0.33** 1 0.60

0.84* 0.77* 0.91

0.09** 0.05** 0.05**

0.95** 0.98* 0.99

0.46** 0.40** 0.33**

1 1 0.76

1 1.04** 1.04*

0.04** 0.25* 0.48

Net debt issue in All 31289 Age > 4 13356 Age > 9 5547

year t+3: 1.63** 1 1.68** 0.79** 1.76** 0.74**

3.18** 4.13** 2.54

0.23** 0.39** 0.47

1 1 1.44*

0.14** 0.09** 0.11**

0.94** 0.97** 0.99

0.47** 0.45** 0.46**

1 1 0.79

1 1.03* 1.02

0.12** 0 0.19

47

Table 7: Average firm characteristics of high- and low-frequency issuers following public listing The sort of firms into high- and low-frequency issuers (HFIs and LFIs) is as in Table 2 (using Eq. (1) and a 2.5% issue size threshold). The table lists, starting with the year of public listing (event year 0), average annual values of key firm characteristics, several of which are scaled by current book value of assets. The characteristics are market leverage ratio (L), book leverage ratio (BL), fraction of the sample that are all-equity financed (AE), cash ratio (C), book asset value (Assets), operating profitability (P rof ), asset tangibility (T an, defined as P P E/Assets), Tobin’s Q (Q), dividend payout (Div), R&D expenditures, capital expenditures (Capex), Capex−1 ≡ Capext /Assetst−1 , asset growth rate (gA ) and sales growth rate (gS ). Variable definitions are in Appendix Tables 1 and 2. Total sample of 8,719 public US firms, with an annual average of 1,832 HFIs and 3,078 LFIs, 1984-2014.

P rof (6)

T an (7)

Q (8)

Div (9)

R&D (10)

Capex (11)

Capex−1 (12)

gA (13)

gS (14)

A: High frequency net-debt issuers (HFIs) 0 0.21 0.30 0.04 0.23 418 -0.05 1 0.25 0.31 0.04 0.16 432 -0.06 2 0.32 0.35 0.02 0.10 475 -0.01 3 0.34 0.35 0.02 0.09 436 0.01 4 0.37 0.35 0.03 0.08 525 0.03 5 0.36 0.34 0.03 0.08 551 0.03 6 0.37 0.34 0.03 0.08 605 0.04 7 0.36 0.34 0.04 0.08 702 0.05 8 0.33 0.32 0.06 0.09 789 0.06 9 0.34 0.33 0.05 0.08 956 0.05 10 0.33 0.32 0.04 0.08 891 0.06 15 0.32 0.29 0.06 0.08 1540 0.08 20 0.24 0.22 0.11 0.07 2329 0.10

0.29 0.31 0.33 0.33 0.33 0.33 0.34 0.33 0.33 0.33 0.33 0.30 0.30

2.83 2.38 1.98 1.88 1.75 1.69 1.67 1.64 1.62 1.63 1.63 1.50 1.57

0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.00 0.01 0.01

0.05 0.06 0.04 0.03 0.02 0.03 0.02 0.02 0.02 0.02 0.02 0.01 0.01

0.11 0.10 0.09 0.08 0.08 0.07 0.07 0.07 0.07 0.07 0.07 0.06 0.06

0.12 0.14 0.11 0.10 0.10 0.09 0.09 0.08 0.09 0.09 0.08 0.07 0.07

n.A. 0.44 0.34 0.28 0.24 0.17 0.17 1.20 0.17 0.14 0.15 0.10 0.08

n.A. 2.12 0.51 2.71 0.25 0.20 0.15 0.15 0.46 0.15 0.16 0.07 0.08

0.00

0.32

2.02

0.01

0.04

0.09

0.11

0.31

0.97

net-debt issuers (LFIs) 0.25 0.39 238 -0.03 0.32 0.38 245 -0.08 0.39 0.39 244 -0.07 0.46 0.40 250 -0.06 0.52 0.42 264 -0.05 0.40 0.37 378 -0.04 0.45 0.38 379 -0.04 0.49 0.39 452 -0.02 0.51 0.40 521 -0.02 0.57 0.42 653 -0.03 0.46 0.38 757 0.00 0.46 0.35 812 0.01 0.43 0.35 1151 0.02

0.20 0.19 0.18 0.17 0.16 0.18 0.18 0.16 0.16 0.15 0.16 0.16 0.18

3.50 3.05 2.87 2.93 2.93 2.71 2.82 2.81 2.61 2.60 2.51 2.31 2.37

0.01 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.01 0.01 0.01 0.01

0.07 0.11 0.13 0.13 0.14 0.13 0.13 0.13 0.13 0.14 0.11 0.11 0.10

0.07 0.07 0.06 0.05 0.05 0.05 0.05 0.04 0.04 0.04 0.04 0.03 0.04

0.07 0.08 0.07 0.06 0.05 0.06 0.06 0.05 0.05 0.04 0.05 0.04 0.04

n.A. 0.28 0.18 0.17 0.14 0.19 0.19 0.20 0.26 0.07 0.13 0.12 0.13

n.A. 1.12 1.12 1.06 0.35 0.34 0.74 0.35 0.20 0.18 1.51 0.09 0.24

0.38

0.18

3.01

0.01

0.11

0.06

0.06

0.19

0.84

Event year

Avg.

L (1)

0.30

BL (2)

0.32

B: Low frequency 0 0.09 0.13 1 0.08 0.10 2 0.07 0.07 3 0.05 0.06 4 0.04 0.05 5 0.08 0.09 6 0.07 0.08 7 0.06 0.07 8 0.06 0.07 9 0.04 0.05 10 0.06 0.07 15 0.07 0.07 20 0.09 0.10 Avg.

0.07

0.09

AE (3)

0.04

C (4)

0.12

0.39

Assets (5)

662

343

-0.04

48

Table 8: Corporate funding sources of high-and low-frequency net-debt issuers following public listing The sort of firms into high- and low-frequency issuers (HFIs and LFIs) is as in Table 2 (using Eq. (1) and a 2.5% issue P size threshold). In Panel A, the annual (non-negative) cash contribution of the i’th funding source is the ratio Rj ≡ Sj / 7i Si , where the denominator is the sum of the seven individual funding sources in the firm’s total cash flow statement: 7 X

Si = N DI + + EI + CF + + ∆C − + ∆W − + I − + O+

i

The four columns are: RN DI + is the net debt issue ratio (N DI + in the numerator), REI is the equity issue ratio, RCF + is the operating cash flow contribution, R∆C − is the contribution from cash draw-downs, R∆W − is contribution of reductions in net working capital and RI − is the fraction of funds provided by illiquid asset sales. Panel B displays Rj only for periods with positive net debt issues (N DI + > 0, using the 2.5% issue size threshold). Variable definitions are in Appendix Tables 1 and 2. Total sample of 8,719 public US firms, with an annual average of 1,832 HFIs and 3,078 LFIs, 1984-2014.

RN DI + Year

HFI

LFI

RI −

RO+

LFI

HFI

LFI

HFI

LFI

A: Funding sources averaged across 0 0.24 0.01 0.49 0.68 1 0.27 0.02 0.16 0.19 2 0.28 0.02 0.15 0.18 3 0.25 0.02 0.15 0.17 4 0.25 0.01 0.13 0.17 5 0.22 0.03 0.12 0.16 6 0.21 0.03 0.10 0.18 7 0.20 0.04 0.09 0.16 8 0.19 0.03 0.10 0.16 9 0.20 0.02 0.10 0.14 10 0.18 0.03 0.10 0.15 15 0.15 0.04 0.08 0.13 20 0.15 0.06 0.04 0.13

all firm-years 0.16 0.18 0.03 0.26 0.30 0.16 0.30 0.31 0.08 0.33 0.31 0.07 0.35 0.32 0.06 0.38 0.34 0.07 0.40 0.33 0.07 0.41 0.34 0.07 0.43 0.33 0.06 0.43 0.35 0.07 0.47 0.38 0.06 0.49 0.36 0.07 0.57 0.41 0.07

0.04 0.26 0.19 0.17 0.17 0.16 0.15 0.15 0.14 0.14 0.15 0.14 0.13

0.03 0.06 0.09 0.10 0.11 0.11 0.11 0.11 0.10 0.10 0.09 0.13 0.07

0.03 0.07 0.09 0.10 0.09 0.09 0.08 0.09 0.07 0.09 0.06 0.09 0.09

0.03 0.08 0.08 0.08 0.08 0.08 0.10 0.09 0.10 0.09 0.09 0.07 0.09

0.03 0.15 0.19 0.21 0.21 0.20 0.22 0.22 0.25 0.25 0.22 0.22 0.17

0.02 0.01 0.02 0.02 0.02 0.02 0.02 0.01 0.01 0.01 0.01 0.01 0.01

0.02 0.01 0.01 0.01 0.02 0.01 0.01 0.01 0.01 0.01 0.01 0.01 0.01

Average Median

0.33 0.29

0.14 0.00

0.08 0.00

0.07 0.00

0.08 0.00

0.15 0.00

0.01 0.00

0.02 0.00

B: Funding sources averaged across firm-years with positive net-debt issues Average 0.30 0.31 0.19 0.12 0.29 0.27 0.07 0.10 0.06 0.05 0.06 Median 0.26 0.28 0.02 0.03 0.24 0.22 0.00 0.00 0.00 0.00 0.00

0.14 0.03

0.02 0.00

0.01 0.00

0.32 0.09

HFI

R∆W − HFI

0.18 0.02

LFI

R∆C − LFI

0.02 0.00

HFI

RCF +

HFI

0.23 0.12

LFI

REI

0.29 0.18

0.08 0.00

49

Table 9: Leverage ratio dynamics following public listing The sort of firms into high- and low-frequency issuers (HFIs and LFIs) is as in Table 2 (using Eq. (1) and a 2.5% issue size threshold). Column (1) lists the average (market) leverage ratio in the year of public listing, L0 , for successively smaller samples of firms surviving in event time following the listing year. Column (2) shows the average leverage ratio in event year t, while Column (3) computes the leverage change (the difference between columns 1 and 2). Column (4) and (5) list the fraction of firms experiencing a leverage ratio change of at least ±20%, while Column (6) shows leverage volatility σL computed as the average standard deviation of the firm-level leverage ratio up to event year t, using a minimum of five yearly observations. Total sample of 8,719 U.S. public firms, with an annual average of 1,832 HFIs and 3,078 LFIs, 1984-2014.

Leverage ratio at public listing L0

Leverage ratio in event year t Lt

(1)

(2)

Change in leverage ratio from public listing Lt − L0 Mean (3)

% > 0.2 (4)

% ≤ −0.2 (5)

σL (6)

A: High-frequency issuers (HFIs) 0 0.21 0.21 1 0.17 0.25 2 0.17 0.32 3 0.17 0.34 4 0.16 0.37 5 0.17 0.36 6 0.18 0.37 7 0.18 0.36 8 0.18 0.33 9 0.17 0.34 10 0.18 0.33 15 0.18 0.32 20 0.17 0.24

n.A. 0.09 0.15 0.18 0.20 0.19 0.19 0.18 0.16 0.17 0.15 0.13 0.07

n.A. 0.19 0.35 0.40 0.46 0.43 0.44 0.41 0.40 0.38 0.36 0.31 0.27

n.A. 0.02 0.03 0.03 0.04 0.04 0.04 0.05 0.05 0.05 0.07 0.09 0.10

n.A. n.A. n.A. n.A. n.A. 0.17 0.17 0.17 0.16 0.16 0.16 0.16 0.16

Avg.

0.13

0.29

0.03

0.17

B: Low-frequency issuers (LFIs) 0 0.09 0.09 1 0.07 0.08 2 0.06 0.07 3 0.05 0.05 4 0.04 0.04 5 0.06 0.08 6 0.05 0.07 7 0.05 0.06 8 0.04 0.06 9 0.03 0.04 10 0.04 0.06 15 0.04 0.07 20 0.05 0.09

n.A. 0.01 0.01 0.01 0.00 0.02 0.02 0.02 0.02 0.01 0.02 0.03 0.04

n.A. 0.04 0.05 0.04 0.03 0.08 0.07 0.06 0.07 0.05 0.07 0.08 0.12

n.A. 0.01 0.02 0.02 0.03 0.04 0.04 0.03 0.03 0.02 0.03 0.03 0.06

n.A. n.A. n.A. n.A. n.A. 0.06 0.05 0.05 0.05 0.04 0.06 0.06 0.07

Avg.

0.01

0.04

0.02

0.05

Event year

0.18

0.06

0.30

0.07

50

Table 10: Speed of adjustment to target leverage deviations The table reports estimates of the speed-of-adjustment coefficient φ in the regression:
Li,t − Li,t−1 = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t . where the dependent variable is firm i’s change in market leverage ratio L from time t − 1 to t, i,t is the regression error term, α is the constant, ηi is a firm fixed effect, L∗i,t (βXi,t−1 ) is the (estimated) target leverage ratio where the determinants Xi,t−1 are the lagged values of size, profitability, Q, cash ratio, tangibility, depreciation, R&D expenses, capital expenditures, the median industry leverage ratio and year-fixed effects. Coefficients are estimated using system GMM (implemented using the stata command xtabond2), assuming that all regressors are predetermined. We use a maximum number of lags of 3 (5) for leverage (target leverage ratio regressors). In Panel A, the classification of firms into HFIs and LFIs is based on the the original cumulative quarterly net debt issue frequency sort in Table 2 (with the 2.5% issue size threshold). Panel B uses constant composition samples of HFIs and LFIs, as in Table 4 (holding the HFIs and LFIs from a given event year t fixed for all available firm-years). In Panel C, a firm is defined as HFI (LFI) in a given year if the firm’s issue frequency is above (below) the median frequency (with annual rebalancing). Variable definitions are in Appendix Tables 1 and 2. Standard errors in parentheses, with * and ** indicating significance at the 5% and 1% level, respectively. Total sample of 8,719 U.S. public firms (53,351 firm-years), 1984-2014.

GMM estimates of SOA-coefficient φ HFI

LFI

HFI - LFI

0.308** (0.031)

-0.002 (-0.049)

A: Original HFI/LFI sort 0.307** (0.020)

B: HFI/LFI with constant composition sorts (CCS) CCS from t = 3

0.302** (0.020)

0.306** (0.032)

-0.004 (-0.116)

CCS from t = 5

0.303** (0.019)

0.265** (0.025)

0.038 (1.218)

CCS from t = 7

0.329** (0.024)

0.312** (0.034)

0.017 (0.410)

CCS from t = 9

0.314** (0.022)

0.333** (0.041)

-0.019 (-0.413)

D: HFI/LFI if above/below median issue frequency 0.284** (0.015)

0.298** (0.026)

51

-0.014 (-0.482)

Table 11: Link between debt issues and investment for high-frequency issuers The table reports coefficient estimates using the following regression: N DIi,t = α + β1 Capexi,t + β2 N etDef iciti,t + β3 Capex2i,t + β4 N etDef icit2i,t + i,t . Ai,t where N DI/A is net-debt issues (or retirements) scaled by total assets, and Capex and N etDef icit are capital expenditures and the financing deficit net of Capex, respectively, also scaled by total assets. N etDef icit ≡ dv + aqc + ivch − siv − ivstch − sppe − ivaco − oancf + chech, where all Compustat mnemonics are scaled by total assets. The classification of firms into HFIs is based on the the cumulative quarterly net debt issue frequency classification as detailed in Table 2, using the 2.5% issue size threshold. Variable definitions are in Appendix Tables 1 and 2. Standard errors are in parentheses, *, ** indicate significance at the 5% and 1% level, respectively. Total sample of 8,719 U.S. public firms (53,351 firm-years), with an annual average of 1,832 HFIs (total of 17,422 firm-years), 1984-2014.

Regression coefficient estimate (std. err.) (1) (2) (3) (4) (5) Capex

0.34** (0.01)

0.46** (0.01) 0.24** (0.01)

no no no 17422 0.05

no no no 17422 0.25

N etDef icit Capex2 N etDef icit2 Industry Firm Age N R2

52

0.47** (0.03) 0.54** (0.01) 0.33** (0.09) -0.43** (0.01) no no no 17422 0.41

0.44** (0.03) 0.54** (0.01) 0.35** (0.09) -0.42** (0.01) yes no yes 17422 0.42

0.54** (0.05) 0.61** (0.01) 0.27* (0.12) -0.45** (0.01) no yes yes 17422 0.45

Table 12: Link between transitory debt issues and investment for high-frequency issuers The table presents coefficient estimates from the following logit regression: ∗ ∗ Yi,t = α + β1 Ii,t−1 + β2 Ecapexi,t + β3 Ii,t−1 Ecapexi,t + i,t ,

In columns (1)-(4), Yi,t = 1 if firm i undertakes at least one quarterly net debt issue in year t that exceeds 2.5% of total assets (N DIi,t ≥ 2, 5%) and zero otherwise. In columns (5)-(8), Yi,t = 1 if the firm undertakes at least one quarterly net ∗ debt retirement (N DIi,t ≤ −2.5%). Ii,t−1 is a dummy indicating that the firm is over-levered at the end of year t − 1, i.e., ∗ Li,t−1 − Li,t−1 (Xi,t−2 ) > 0, where the control variables X are as in Table 10. Ecapexi,t is the size of the investment spike, −1 computed as the difference between Capex−1 in the firm’s 3-digit SIC industry. The estimation i,t and the median Capex also includes industry dummies for eight of the 12 Fama-French (FF12) industries (excluding financial firms and regulated utilities). In Panel A, the classification of a firm as HFI is based on the the original cumulative quarterly net debt issue frequency sort in Table 2 (with the 2.5% issue size threshold). Panel B uses constant composition samples of HFIs, as in Table 4 (holding the HFIs from a given event year t are held fixed for all available firm-years). In Panel C, a firm is defined as HFI in a given year if the firm’s issue frequency exceeds the median frequency (with annual rebalancing). Variable definitions are in Appendix Tables 1 and 2. *, ** indicate significance at the 5% and 1% level, respectively. Total sample of 8,719 U.S. public firms (53,351 firm-years), 1984-2014. Debt issues (Yi,t = 1 if N DIi,t ≥ 2.5%) N (1)

Debt retirements (Yi,t = 1 if N DIi,t ≤ −2.5%)

∗ It−1 (2)

Ecapext (3)

∗ Ecapex It−1 t (4)

Industry FE

N (5)

∗ It−1 (6)

Ecapext (7)

∗ Ecapex It−1 t (8)

Industry FE

-0.16**

4.10**

3.73**

Yes

9122

0.30**

-1.68**

-1.06*

Yes

A: Original HFI sort 9122

B: HFI with constant composition sorts (CCS) CCS from t = 3

9981

0.08

4.65**

2.58**

Yes

9981

0.44**

-1.05**

-1.72**

Yes

CCS from t = 5

10321

0.04

4.48**

2.71**

Yes

10321

0.49**

-1.02**

-1.74**

Yes

CCS from t − 7

6946

0.02

4.85**

4.36**

Yes

6946

0.46**

-0.97**

-1.11

Yes

CCS from t = 9

6346

-0.07

5.40**

2.77**

Yes

6346

0.47**

-1.66**

-0.78

Yes

2.83**

Yes

17892

0.43**

-1.61**

-1.23**

Yes

C: HFI if above median issue frequency 17892

-0.03

4.23**

53

Table 13: Investment spikes and speed-of-adjustment to target leverage deviations The table reports estimates of the speed-of-adjustment coefficient φ in the regression:
Li,t − Li,t−1 = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t . where the dependent variable is firm i’s change in market leverage ratio L from time t − 1 to t, i,t is the regression error term, α is the constant, ηi is a firm fixed effect, L∗i,t (βXi,t−1 ) is the (estimated) target leverage ratio where the determinants Xi,t−1 are the lagged values of size, profitability, Q, cash ratio, tangibility, depreciation, R&D expenses, capital expenditures, the median industry leverage ratio and year-fixed effects. Coefficients are estimated using system GMM (implemented using the stata command xtabond2), assuming that all regressors are predetermined. We use a maximum number of lags of 3 (5) for leverage (target leverage ratio regressors). In Panel A, the classification of firms into HFIs is based on the the original cumulative quarterly net debt issue frequency sort in Table 2 (with the 2.5% issue size threshold). Panel B uses constant composition samples of HFIs, as in Table 4 (holding the HFIs from a given event year t fixed for all available firm-years). In Panel C, a firm is defined as HFI in a given year if the firm’s issue frequency is above the median frequency (with annual rebalancing). Ecapex is the size of the investment spike. Under the −1 industry definition, Ecapexi,t is computed as the difference between Capex−1 in the firm’s 3-digit i,t and the median Capex SIC industry. Under the depreciation definition, Ecapexi,t is computed as the difference (using Compustat mnemonics) between capx and dp, scaled by the lagged value of assets (at). Variable definitions are in Appendix Tables 1 and 2. Standard errors in parentheses, with * and ** indicating significance at the 5% and 1% level, respectively. Total sample of 8,719 U.S. public firms (53,351 firm-years), with an annual average of 1,832 HFIs (total of 17,422 firm-years), 1984-2014.

GMM estimates of SOA coefficient φ

All HFIs (1)

Industry definition Ecapex ≤ 0 (2) (2)-(1)

Depreciation definition Ecapex ≤ 0 (4) (4)-(1)

0.399** (0.026)

0.436** (0.027)

A: Original HFI sort 0.307** (0.020)

0.092** (2.82)

0.129** (3.85)

B: HFI with constant composition sorts (CCS) CCS from t = 3

0.302** (0.020)

0.401** (0.025)

0.099** (3.12)

0.423** (0.028)

0.122** (3.57)

CCS from t = 5

0.303** (0.019)

0.401** (0.025)

0.098** (3.18)

0.409** (0.027)

0.105** (3.22)

CCS from t = 7

0.329** (0.024)

0.419** (0.031)

0.090* (2.33)

0.446** (0.033)

0.117** (2.91)

CCS from t = 9

0.314** (0.022)

0.389** (0.029)

0.075* (2.06)

0.420** (0.034)

0.106** (2.63)

0.370** (0.021)

0.086** (3.38 )

C: HFI if above median issue frequency 0.284** (0.015)

0.354** (0.020)

54

0.070** (2.84)

Table 14: Investment spikes and speed-of-adjustment based on net-debt issues and retirements The table reports estimates of the speed-of-adjustment coefficient φ in the regression:
N DIi,t = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t . M Vi,t where the dependent variable is firm i’s change in market leverage ratio L from time t − 1 to t, i,t is the regression error term, α is the constant, ηi is a firm fixed effect, L∗i,t (βXi,t−1 ) is the (estimated) target leverage ratio where the determinants Xi,t−1 are the lagged values of size, profitability, Q, cash ratio, tangibility, depreciation, R&D expenses, capital expenditures, the median industry leverage ratio and year-fixed effects. Coefficients are estimated using system GMM (implemented using the stata command xtabond2), assuming that all regressors are predetermined. We use a maximum number of lags of 3 (5) for leverage (target leverage ratio regressors). In Panel A, the classification of firms into HFIs is based on the the original cumulative quarterly net debt issue frequency sort in Table 2 (with the 2.5% issue size threshold). Panel B uses constant composition samples of HFIs, as in Table 4 (holding the HFIs from a given event year t fixed for all available firm-years). In Panel C, a firm is defined as HFI in a given year if the firm’s issue frequency is above the median frequency (with annual rebalancing). Ecapex is the size of the investment spike. Under the −1 industry definition, Ecapexi,t is computed as the difference between Capex−1 in the firm’s 3-digit i,t and the median Capex SIC industry. Under the depreciation definition, Ecapexi,t is computed as the difference (using Compustat mnemonics) between capx and dp, scaled by the lagged value of assets (at). Variable definitions are in Appendix Tables 1 and 2. Standard errors in parentheses, with * and ** indicating significance at the 5% and 1% level, respectively. Total sample of 8,719 U.S. public firms (53,351 firm-years), with an annual average of 1,832 HFIs (total of 17,422 firm-years), 1984-2014.

GMM estimates of SOA coefficient φ

All HFIs (1)

Industry definition Ecapex ≤ 0 (2) (2)-(1)

Depreciation definition Ecapex ≤ 0 (4) (4)-(1)

0.293** (0.019)

0.313** (0.020)

A: Original HFI sort 0.218** (0.015)

0.075** (3.08)

0.095** (3.80)

B: B: HFI with constant composition sorts (CCS) CCS from t = 3

0.197** (0.014)

0.278** (0.018)

0.082** (3.62)

0.275** (0.019)

0.079** (3.34)

CCS from t = 5

0.211** (0.014)

0.283** (0.019)

0.072** (3.09)

0.285** (0.019)

0.074** (3.13)

CCS from t = 7

0.216** (0.017)

0.279** (0.024)

0.063* (2.17)

0.292** (0.025)

0.076** (2.51)

CCS from t = 9

0.226** (0.018)

0.276** (0.024)

0.050 (1.71)

0.297** (0.023)

0.072* (2.48)

0.255** (0.015)

0.068** (3.73)

C: HFI if above median issue frequency 0.187** (0.011)

0.251** (0.014)

55

0.064** (3.57)

Appendix Table 1: Variable construction using Compustat mnemonics Variable I: Selected L BL C Size Prof Tan Q R&D Div Capex Capex−1 Depr

Description (Compustat mnemonics) firm characteristics (All Financial Statements) Market leverage: (dlcc + dlt)/(prcc f*csho + dlcc + dlt) Book leverage: (dlcc + dlt)/at Cash ratio: che/at log(at) Profitability: (oancf + nwc inv)/at Tangibility: ppent/at Tobin’s Q: (lt + pstkl - txditc + prcc f*csho)/at xrd/at dv/at capx/at capx/at(lagged) dp/at

Ecapex

Industry definition: Capex−1 - median Capex−1 (of 3-digit SIC industry) Depreciation definition: (capx - dp)/at(lagged)

Mean

Median

St.Dev.

0.18 0.20 0.25 4.49 -0.01 0.25 2.48 0.07 0.01 0.07 0.08 0.05

0.08 0.13 0.14 4.46 0.07 0.17 1.68 0.00 0.00 0.04 0.04 0.04

0.23 0.21 0.27 1.90 0.28 0.23 2.34 0.14 0.02 0.08 0.10 0.04

0.03 0.03

0.00 0.00

0.09 0.09

II: Sources EI DI CF+ ∆C − I− ∆W − O+

of funds (Cash Flow Statement) Equity Issues: sstk Debt Issues: dltis + max[dlcch,0] Positive operating Cash Flow: max[oancf + nwc inv,0] Draw-down of Cash balance: max[chech*(-1),0] Asset sales: siv + min[ivstch,0] + min[ivaco,0] + sppe Decrease in net Working capital: max[nwc inv*(-1),0] Other sources: max[fincf oth,0]

21.7 80.9 63.3 8.7 29.9 6.1 2.8

1.2 0.2 4.8 0.0 0.1 0.0 0.0

121.3 494.4 353.9 62.0 365.6 43.0 35.7

III: Uses of ER DR CF− ∆C + I+ ∆W + O−

funds (Cash Flow Statement) Distributions to equity-holders: dv + prstkc Debt Retirements: dltr + min[dlcch,0]*(-1) Negative operating Cash Flow: max[(oancf + nwc inv)*(-1),0] Build-up of Cash balance: max[chech,0] Investments: ivch + aqc + min[ivstch*(-1),0] + min[ivaco*(-1),0] + capx Increase in net Working capital: max[nwc inv,0] Other uses: max[fincf oth*(-1),0]

22.4 64.7 4.3 16.7 90.7 10.4 4.3

0.0 1.0 0.0 0.2 9.0 0.5 0.0

211.0 425.9 23.4 108.4 569.0 58.3 88.8

IV: Composite Variables (Cash Flow Statement) N DI Debt issue minus debt retirement: DI - DR N DI + Positive portion of debt issue minus debt retirement: max[DI - DR,0] N DI − Negative portion of debt issue minus debt retirement: max[DR - DI,0]

16.2 26.8 10.7

0.0 0.0 0.0

214.2 196.1 82.7

N etDef icit

0.09

-0.02

0.36

Dv + aqc + ivch - siv - ivstch - sppe - ivaco - oancf + chech

56

Appendix Table 2: Compustat mnemonics used for variable construction Variable

Description (Compustat mnemonics)

Mean

Median

St.Dev.

I: Compustat balance sheet items che Cash and cash equivalents ppent Property, plant and equipment (net of depreciation) at Total assets dlc Debt in current liabilities dltt Long-term debt lt Tota liabilities pstkl Preferred stock liquidation value txditc Deferred taxes and investment tax credit prcc f Stock price cshoq Common shares outstanding

81.1 181.1 581.0 17.5 147.2 325.4 4.1 22.4 14.1 19.3

10.7 12.2 86.3 0.6 2.0 28.2 0.0 0.0 8.3 8.5

680.0 891.9 2610.2 125.6 741.7 1484.4 78.9 226.1 20.2 75.4

II: Compustat income statement items sale Revenues xrd Research and development expenditures dp Depreciation expenses (Income statement)

587.7 13.4 25.9

75.8 0.0 3.1

2812.9 110.8 117.3

18.8 27.4 12.9 4.2 54.7

0.7 3.3 0.8 0.5 2.5

260.8 123.8 140.1 73.3 351.2

Capital Expenditures Acquisitions Increase in Investments Sale of Investments Sale of Property, Plant and Equipment Short-term Investments - Change Investing Activities ? Other capx + aqc + ivch ? siv ? sppe - ivstch - ivaco

38.0 20.2 22.4 20.4 1.7 -2.7 0.4 60.9

3.4 0.0 0.0 0.0 0.0 0.0 0.0 5.6

204.5 180.3 386.1 354.8 24.7 97.1 119.2 346.7

Sale of Common and Preferred Stock Purchase of Common and Preferred Stock Cash Dividends Long-Term Debt - Issuance Long-Term Debt - Reduction Changes in Current Debt Other Financing Cash Flow [ = (txbcof + fiao) ] sstk + prstkc + dv + dltis + dltr + dlcch + fincf oth

21.7 14.1 8.2 78.7 62.7 0.1 -1.4 14.1

1.2 0.0 0.0 0.0 0.7 0.0 0.0 1.4

121.3 146.3 102.3 488.0 422.5 58.4 94.9 255.2

8.0

0.2

126.0

III: Compustat cash flow statement statement items ibc Income Before Extraordinary Items dpc Depreciation and Amortization ocf otha Other Operating Cash Flow ( = xidoc + txdc +esubc + sppiv + fopo + fsrco + exre) nwc invb Investment into Net Working Capital [ = (recch + invch + apalch + txach + aoloch)*(-1)] oancf ibc + dpc + ocf oth + nwc inv capx aqc ivch siv sppe ivstch ivacoc inv total sstk prstkc dv dltis dltr dlcch fincf othd fin total chech

Change in cash and cash equivalents

a

ocfoth is the sum of extraordinary items and discontinued operations (xidoc), deferred taxes (txdc), equity in net loss (esubc), loss from sale of PPE and investments (sppiv), funds from operations–other (fopo), other sources of funds (fsrco) and exchange rate effects (exre). The item fsrco is 0 if the company reports according to format code 7 (scf=7), exre is zero in case of format codes scf=1, 2 or 3.

b

nwcinv is constructed as follows: For format code 7, it is the sum of (multiplied by minus 1) accounts receivable-decrease (recch), inventory-decrease (invch), accounts payable and accrued liabilities-increase (apalch), income taxes-accrued-increase (txach), assets and liabilities-other (aoloch). For format code 1, it is the variable wcapc. In case of format codes 2 and 3, it is wcapc ∗ (−1).

c

ivaco is replaced by fuseo*(-1) in case of format codes 1, 2 or 3.

d

fincfoth is the sum of excess tax benefits of stock options (txbcof) and other financing activities (fiao).

57

Appendix Table 3: Out-of-sample predictions of net-debt issue activity using three-year trailing highand low-frequency sorts The table presents odds ratios of a logit model determining the probability of a net debt issue in year t + v, conditional on the current issue frequency classification and a vector X of covariates: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + γXi,t + i,t+v where Yi,t+v is a dummy variable equal to one in case at least one quarterly net debt issue occurred in year t + v. In this regression, HF Ii,t (LF Ii,t ) is a dummy variables that takes on a value of one if firm i is classified as a three-year trailing high-frequency (low-frequency) net-debt issuer in period t, and zero otherwise. The baseline sample is medium-frequency issuers (MFIs, all firms that are neither HFI or LFI). The classification of firms into HFIs and LFIs is as in columns (1) to (4) of Table 3, using the 2.5% issue size threshold. The covariates in Xi,t are: investment (Capex), R&D expenditures (R&D), market leverage ratio (L), cash ratio (C), logarithm of assets (Size), operating cash flow (P rof ), tangibility (T an), Tobin’s Q (Q) and depreciation expenditures (Depr). All covariates are winsorized at the 1(99) percent level or must lie between zero and one (cash ratio and leverage). Variable definitions are in Appendix Tables 1 and 2. *, ** indicate significance at the 5% and 1% level, respectively. Total sample of 8,719 public US firms, with an annual average of 1,832 HFIs and 3,078 LFIs, 1984-2014. Firm-specific explanatory variables (X) N

HFI

LFI

Capex

R&D

L

C

Size

P rof

T an

Q

Depr

A: Probability of issuing net debt: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + i,t+v Net debt issue in All 44632 Age > 4 18790 Age > 9 7934

year t+1: 2.16** 0.54** 2.23** 0.44** 2.28** 0.44**

Net debt issue in All 37285 Age > 4 15859 Age > 9 6661

year t+2: 1.80** 0.60** 1.85** 0.50** 1.79** 0.46**

Net debt issue in All 31289 Age > 4 13356 Age > 9 5547

year t+3: 1.73** 0.65** 1.71** 0.52** 1.72** 0.49**

B: Probability of issuing net debt: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + γXi,t + i,t+v Net debt issue in All 44632 Age > 4 18790 Age > 9 7934

year t+1: 1.75** 0.76** 1.80** 0.61** 1.85** 0.61**

14.73** 14.67** 11.06**

0.65** 1 1.00

0.65** 0.57** 0.61**

0.04** 0.02** 0.02**

0.95** 1 1.01

0.42** 0.37** 0.35**

0.81** 0.70** 0.59**

1 1.03* 1.04

0.02** 0.11** 0.25

Net debt issue in All 37285 Age > 4 15859 Age > 9 6661

year t+2: 1.50** 0.81** 1.51** 0.68** 1.50** 0.62**

3.40** 3.13** 1.76

0.31** 0.59* 0.35*

0.82** 0.78* 0.90

0.10** 0.05** 0.06**

0.94** 1 0.99

0.47** 0.42** 0.31**

1 1 0.84

1 1.03** 1.04

0.05** 0.28* 0.57

Net debt issue in All 31289 Age > 4 13356 Age > 9 5547

year t+3: 1.46** 0.88** 1.41** 0.72** 1.44** 0.66**

1.93** 2.23** 1.41

0.21** 0.33** 0.28*

1 1 1.44*

0.14** 0.10** 0.13**

0.94** 0.97* 1.00

0.48** 0.47** 0.44**

1 1 0.86

1 1 1.01

0.14** 0 0.27

58

Appendix Table 4: Out-of-sample predictions of net-debt issue activity using within-year high- and low-frequency sorts The table presents odds ratios of a logit model determining the probability of a net debt issue in year t + v, conditional on the current issue frequency classification and a vector X of covariates: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + γXi,t + i,t+v where Yi,t+v is a dummy variable equal to one in case at least one quarterly net debt issue occurred in year t + v. In this regression, HF Ii,t (LF Ii,t ) is a dummy variables that takes on a value of one if firm i is classified as a within-year trailing high-frequency (low-frequency) net-debt issuer in period t, and zero otherwise. The baseline sample is medium-frequency issuers (MFIs, all firms that are neither HFI or LFI). The classification of firms into HFIs and LFIs is as in columns (5) to (8) of Table 3, using the 2.5% issue size threshold. The covariates in Xi,t are: investment (Capex), R&D expenditures (R&D), market leverage ratio (L), cash ratio (C), logarithm of assets (Size), operating cash flow (P rof ), tangibility (T an), Tobin’s Q (Q) and depreciation expenditures (Depr). All covariates are winsorized at the 1(99) percent level or must lie between zero and one (cash ratio and leverage). Variable definitions are in Appendix Tables 1 and 2. *, ** indicate significance at the 5% and 1% level, respectively. Total sample of 8,719 public US firms, with an annual average of 1,832 HFIs and 3,078 LFIs, 1984-2014. Firm-specific explanatory variables (X) N

HFI

LFI

Capex

R&D

L

C

Size

P rof

T an

Q

Depr

A: Probability of issuing net debt: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + i,t+v Net debt issue in All 44632 Age > 4 18790 Age > 9 7934

year t+1: 3.58** n.A. 3.84** n.A. 3.66** n.A.

Net debt issue in All 37285 Age > 4 15859 Age > 9 6661

year t+2: 2.62** n.A. 2.86** n.A. 2.79** n.A.

Net debt issue in All 31289 Age > 4 13356 Age > 9 5547

year t+3: 2.30** n.A. 2.50** n.A. 2.47** n.A.

B: Probability of issuing net debt: Yi,t+v = α + β1 HF Ii,t + β2 LF Ii,t + γXi,t + i,t+v Net debt issue in All 44632 Age > 4 18790 Age > 9 7934

year t+1: 2.23** n.A. 2.43** n.A. 2.34** n.A.

11.04** 10.21** 7.36**

0.61** 1 1.10

0.71** 0.68** 0.73*

0.04** 0.02** 0.02**

0.95** 1 1.01

0.42** 0.37** 0.38**

1 0.76* 0.63*

1 1.03** 1.04*

0.02** 0.16** 0.45

Net debt issue in All 37285 Age > 4 15859 Age > 9 6661

year t+2: 1.73** n.A. 1.89** n.A. 1.88** n.A.

2.87** 2.45** 1.35

0.30** 0.54* 0.37*

0.9 0.89 1.06

0.09** 0.04** 0.04**

0.94** 0.98 0.99

0.46** 0.41** 0.33**

1.07 0.95 0.85

1 1.04** 1.05*

0.06** 0.37 0.88

Net debt issue in All 31289 Age > 4 13356 Age > 9 5547

year t+3: 1.57** n.A. 1.67** n.A. 1.68** n.A.

1.69** 1.85* 1.19

0.20** 0.30** 0.29*

1.15 1.28* 1.67**

0.14** 0.08** 0.10**

0.94** 0.97* 1.00

0.48** 0.45** 0.44**

1.12 1 0.87

1 1.03* 1.02

0.15** 0.52 0.39

59

Appendix Table 5: Link between transitory debt issues and investment for high-frequency issuers-II The table presents coefficient estimates from the following logit regression: ∗ ∗ Yi,t = α + β1 Ii,t−1 + β2 Ecapexi,t + β3 Ii,t−1 Ecapexi,t + i,t ,

In columns (1)-(4), Yi,t = 1 if firm i undertakes at least one quarterly net debt issue in year t that exceeds 2.5% of total assets (N DIi,t ≥ 2, 5%) and zero otherwise. In columns (5)-(8), Yi,t = 1 if the firm undertakes at least one quarterly net ∗ debt retirement (N DIi,t ≤ −2.5%). Ii,t−1 is a dummy indicating that the firm is over-levered at the end of year t − 1, i.e., ∗ Li,t−1 − Li,t−1 (Xi,t−2 ) > 0, where the control variables X are as in Table 10. Ecapexi,t is the size of the investment spike, computed as the difference (using Compustat mnemonics) between capx and dp, scaled by the lagged value of assets (at). The estimation also includes industry dummies for eight of the 12 Fama-French (FF12) industries (excluding financial firms and regulated utilities). In Panel A, the classification of a firm as HFI is based on the the original cumulative quarterly net debt issue frequency sort in Table 2 (with the 2.5% issue size threshold). Panel B uses constant composition samples of HFIs, as in Table 4 (holding the HFIs from a given event year t are held fixed for all available firm-years). In Panel C, a firm is defined as HFI in a given year if the firm’s issue frequency exceeds the median frequency (with annual rebalancing). Variable definitions are in Appendix Tables 1 and 2. *, ** indicate significance at the 5% and 1% level, respectively. Total sample of 8,719 U.S. public firms (53,351 firm-years), 1984-2014. Debt issues (Yi,t = 1 if N DIi,t ≥ 2.5%) N (1)

Debt retirements (Yi,t = 1 if N DIi,t ≤ −2.5%)

∗ It−1 (2)

Ecapext (3)

∗ Ecapex It−1 t (4)

Industry FE

N (5)

∗ It−1 (6)

Ecapext (7)

∗ Ecapex It−1 t (8)

Industry FE

-0.20**

4.09**

3.34**

Yes

9122

0.31**

-1.89**

-0.83

Yes

A: Original HFI sort 9122

B: HFI with constant composition sorts (CCS) CCS from t = 3

9981

0.05

4.61**

2.94**

Yes

9981

0.44**

-1.51**

-1.48**

Yes

CCS from t = 5

10321

0

4.35**

2.34**

Yes

10321

0.49**

-1.52**

-1.52**

Yes

CCS from t = 7

6946

-0.02

5.08**

3.63**

Yes

6946

0.46**

-1.60**

-0.79

Yes

CCS from t = 9

6346

-0.1

5.75**

2.07*

Yes

6346

0.46**

-2.38**

-0.41

Yes

2.27**

Yes

17892

0.44**

-1.77**

-1.73**

Yes

C: HFI if above median issue frequency 17892

-0.05

4.05**

60

Appendix Table 6: Main results under different issue size thresholds In the original sort (Table 2), firms are classified as HFI or LFI in event year t based on the cross-sectional Pt P4 distribution of quarterly net-debt issues cumulated from the year of public listing (year t = 0): Nit = q=1 Iiqτ , where τ =0 + Iiqτ takes a value of one if N DIiqτ ≥ k in quarter q of event year τ ≤ t and zero otherwise. This table employs alternative values for the thresholds k and thereby produces robustness checks of the main results of this paper. Panel A displays tests of classical dynamic trade-off theory (Proposition 1). Panel B shows tests of the transitory debt model of DDW (Proposition 2). Variable definitions are in Appendix Tables 1 and 2. *, ** indicate significance at the 5% and 1% level, respectively. Total sample of 8,719 public US firms, with an annual average of 1,832 HFIs and 3,078 LFIs, 1984-2014. Threshold k = 0%

Threshold k = 1.25%

Threshold k = 3.75%

Threshold k = 5%

A: Test of classical dynamic trade-off theory (HFIs versus LFIs) HFI

LFI

H-L

HFI

LFI

H-L

HFI

LFI

H-L

HFI

LFI

H-L

0.09 0.09

0.09 0.08

0.00 0.01

0.11 0.10

0.14 0.13

-0.02* -0.02

0.13 0.11

0.14 0.12

-0.01 -0.01

0.17 0.20

0.05 0.20

0.12** 0.00

0.16 0.21

0.05 0.20

0.11** 0.01

0.16 0.21

0.06 0.19

0.11** 0.02**

Proposition 1: HFIs make smaller net debt issues than LFIs Size of N DI (in % of MV), conditional on Issue 0.08 0.07 Recapitalization 0.08 0.08

0.01 0.00

Proposition 1: HFIs have lower leverage volatility than LFIs Volatility estimate, based on Leverage 0.16 Net leverage 0.20

0.05 0.20

0.11** 0.00

Proposition 1: HFIs have higher SOA estimates than LFIs   SOA coefficient φ based on Li,t − Li,t−1 = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t 0.281**

0.322**

-0.04

0.294**

0.344**

-0.05

0.310**

0.298**

0.01

0.317**

0.273**

0.04

Diff.

HFI

Ecapex ≤ 0

Diff.

HFI

Ecapex ≤ 0

Diff.

HFI

Ecapex ≤ 0

Diff.

0.11** 0.14

0.310** 0.310**

0.402** 0.436**

0.09** 0.13**

0.317** 0.317**

0.412** 0.434**

0.10** 0.12**

0.08** 0.10**

0.216** 0.216**

0.287** 0.306**

0.07** 0.09**

0.207** 0.207**

0.286** 0.301**

0.08** 0.09**

B: Test of the transitory debt model HFI

Ecapex ≤ 0

Proposition 2: SOA coefficient increases when Ecapex ≤ 0   SOA coefficient φ based on Li,t − Li,t−1 = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t Ecapex: Industry Ecapex: Depreciation

0.281** 0.281**

0.374** 0.410**

0.09** 0.13**

0.294** 0.294**

0.407** 0.44

Proposition 2: SOA is driven by net debt repurchases when Ecapex ≤ 0 SOA coefficient φ based on Industry Depreciation

N DIi,t M Vi,t

0.208** 0.208**

  = α + ηi + φ L∗i,t (βXi,t−1 ) − Li,t−1 + i,t 0.29 0.298**

0.08 0.09**

0.218** 0.218**

0.300** 0.314**

61