Kicking Maturity Down the Road: Early Refinancing and Maturity Management in the Corporate Bond Market

Kicking Maturity Down the Road: Early Refinancing and Maturity Management in the Corporate Bond... Abstract This paper examines debt maturity management through early refinancing, where firms retire their outstanding bonds before the due date and simultaneously issue new ones as replacements. Speculative-grade firms frequently refinance their corporate bonds early to extend maturity, particularly under accommodating credit supply conditions, leading to a procyclical maturity structure. In contrast, investment-grade firms do not manage their maturity in the same manner. I exploit the protection period of callable bonds to show that the maturity extension is not driven by unobservable confounding factors. The evidence is consistent with speculative-grade firms dynamically managing maturity to mitigate refinancing risk. Received June 6, 2016; editorial decision September 21, 2017 by Editor Philip Strahan. In a frictionless (Modigliani and Miller 1958) setting, the maturity structure of debt does not affect firms’ value. Firms can simply refinance at the due date and roll over the debt. In reality, the timing of the due date can be crucial. Firms that refinance during credit market downturns might have to pay significantly higher rates, sell assets in a fire sale, reduce investment, etc.1 While Chief Financial Officers (CFOs) claim they manage debt maturity to “minimize the risk of having to borrow in bad times” (Graham and Harvey 2001), there is very little evidence about how they do this or about which firms consider this a first-order concern. The majority of the literature, both theoretical or empirical, treats maturity of a debt as a one-time decision in which firms choose maturity at issuance and then commit to it until the scheduled due date.2 The only exception is the one in which firms call their outstanding bonds and issue new ones at cheaper rates in order to reduce interest expenses. In this paper, I identify a number of empirical facts showing that a major portion of early refinancing activities are not conducted through calls, and thus they cannot be explained by interest savings. Instead, firms refinance bonds early to continuously adjust maturity after issuance, leading to a significant discrepancy between maturity at issuance and the effective maturity at retirement. First, a major portion of early refinancing activities in the corporate bond market that involves billions of dollars each year does not fit the classic interest saving explanation. About half of the early refinancing of corporate bonds are conducted through repurchases, where firms need to pay the market prices, and tender offers (typically plus some premium), to induce bond holders to comply. Purchasing outstanding bonds at market prices or higher cannot reduce interest payments for firms. Moreover, some firms refinance early through make-whole calls, in which they have to prepay all future coupons discounted near the Treasury rate. Refinancing by make-whole calls is effectively prepayment with penalty, a motivation at odds with the interest saving motive for early refinancing. Second, the vast majority of speculative-grade firms’ bonds are refinanced long before their due date. When refinancing early, speculative-grade firms issue new bonds with a longer maturity to extend the maturity structure. In contrast, only a small fraction of investment-grade firms’ bonds are refinanced early. When investment-grade firms do refinance early, they replace expensive bonds with cheaper ones with a similar maturity. Early refinancing does not change the maturity structure of investment-grade firms. Third, aggregate credit supply conditions dictate the early refinancing of bonds and hence the maturity structure of debt. Speculative-grade firms take advantage of favorable credit market conditions to refinance their bonds early, resulting in a procyclical debt maturity structure. They refinance early over 10% of total outstanding bonds during good credit periods, such as 2004–2005 and 2010-2011, but do so for only about 2% of their outstanding bonds during credit market downturns. The overall debt maturity structure of speculative-grade firms closely moves with their early refinancing of corporate bonds; it significantly extends when firms substantially refinance early, and shortens when early refinancing dries up. Investment-grade firms, in contrast, refinance early only 1%–2% of total outstanding bonds even during good credit periods. Their maturity structure is insensitive to early refinancing activities. In this paper, I interpret the empirical facts as primarily reflecting the desire of speculative-grade firms to reduce refinancing risk through two mechanisms. First, speculative-grade firms rarely issue bonds longer than 10 years, which suggests a credit supply constraint on the maturity at which they can issue. This constraint leads to a significant maturity mismatch between the assets and liabilities of speculative-grade firms, forcing them to frequently tap the capital markets for refinancing. Effectively they synthesize long-term bonds by issuing intermediate-term bond first and then refinancing early to extend maturity, in order to reduces maturity mismatch and mitigate refinancing risk. Second, changing credit supply conditions disproportionately affect the financing costs of speculative-grade firms. While financing costs remain relatively stable for investment-grade firms over a credit cycle, they sharply increase for speculative-grade firms during credit market downturns. Refinancing risk motivates dynamic maturity management, where forward-looking speculative-grade firms “kick maturity down the road” during favorable credit periods. The longer maturity structure reduces the possibility of being forced to refinance during credit market downturns, which creates a hedge against credit supply fluctuations. To identify the link between maturity management and refinancing risk, I examine whether speculative-grade firms that receive an exogenous opportunity to refinance early and therefore a chance to reshuffle the bond contract terms would choose to extend maturity. An exogenous opportunity is crucial as the subsequent maturity adjustment is not driven by unobservable confounding factors. This task is empirically challenging because of firms’ endogenous choices of early refinancing and maturity, and omitted variables can bias the ordinary least-squares (OLS) estimates of the impact of early refinancing on maturity to either direction. To identify the link, I first exploit the variation in the callable structure of corporate bonds to generate an exogenous shock to refinancing opportunities. Firms commonly embed call provisions when issuing bonds, entitling them to call bonds during a predefined period at prespecified prices. Call provisions carry two important features in this setting. First, bonds that become effectively callable promote early refinancing activities. For example, when lower interest rates are available, the value of outstanding bonds exceeds call prices and firms can transfer the value from bond holders to themselves by exercising the calls. In addition, a call price sets the upper limit for early refinancing costs and can promote early refinancing via other redemption methods. These different channels are all incentives for early refinancing activities, generating opportunities to adjust maturity. Second, a call provision has an associated protection period, defined as the period during which a firm cannot call the bond. The standard practice is to set the protection period to last 50% of maturity at issuance: for example, a 10-year bond normally has a 5-year protection period. That protection periods are standard in length and determined well in advance essentially creates exogenous variation in early refinancing opportunities, which are not plagued by endogeneity concerns. Specifically, I instrument a firm’s early refinancing activities with a dummy variable indicating some bonds are scheduled to pass the protection period and become callable in a given firm-year. Identification requires that the timing of when bonds become callable is uncorrelated with current unobservable factors that affects firms’ maturity demand. The lengthy and standard protection period plays a key role in this identification strategy, as it is unlikely that future movements of the unobservables would precisely coincide with when bonds become callable. Analysis reveals that for speculative-grade firms, a one–standard–deviation (32%) increase in the probability of early refinancing leads to a 10% increase in the fraction of book debt with maturity $$\geq$$ 5 years and to more than a 1-year extension in the average maturity for outstanding bonds. In addition to the exogenous shock, I reinforce the link between maturity management and refinancing risk through cross-sectional analyses: firms more subject to refinancing risk are more inclined to manage maturity strategically. I first demonstrate the heterogeneous behavior across credit rating segments. Unlike their speculative-grade counterpart, investment-grade firms do not extend maturity when encountering an exogenous opportunity to refinance early. In addition, speculative-grade firms tend to retire bonds with a shorter maturity, whereas investment-grade firms do not take maturity into account. Second, I group speculative-grade firms based on the degree of maturity mismatch between their assets and liabilities. Firms in industries with greater maturity mismatch significantly extend the maturity using early refinancing, whereas industries with less maturity mismatch exhibit no such behavior. These heterogeneous behaviors across different groups fit their refinancing risk exposures. 1. Literature Review This paper contributes to the literature in a number of ways. First, it adds a new dimension to the early refinancing of corporate bonds. Previous researchers mainly attribute early refinancing to firms’ desire to reduce interest payments: when yields drop, firms call their outstanding bonds and issue new ones at a lower rate (Merton 1974; Brennan and Schwartz 1977; Vu 1986; Mauer 1993; Longstaff and Tuckman 1994; Acharya and Carpenter 2002; Jarrow et al. 2010. My paper shows that early refinancing also can be conducted through tender offers, repurchases, and, even, make-whole calls to manage debt maturity even if doing so does not reduce firms’ interest payments. Instead, early refinancing of corporate bonds, together with the choice of maturity at issuance (Brunnermeier and Yogo 2009; Choi, Hackbarth, and Zechner 2016; He and Milbradt 2016) and other liquidity holdings, such as cash and credit lines (Harford, Klasa, and Maxwell (2014)), serves to reduce refinancing risk. Second, this paper adds a dynamic perspective to the debt maturity literature. Previous literature treats the maturity of a debt as a one-time decision in which firms choose a maturity at issuance and then commit to it until the due date (Diamond 1991; Guedes and Opler 1996; Leland and Toft 1996; Berger et al. 2005; He and Xiong 2012, among others). In this paper, I emphasize that maturity is a continuous and state-contingent decision, and highlight that the discrepancy between maturity at issuance and maturity at retirement can be significant. In addition, a few papers study how interest rate conditions and business cycles affect the maturity choice at issuance (Baker, Greenwood, and Wurgler 2003; Graham and Harvey 2001; Chen, Xu, and Yang 2012), whereas this paper focuses on how credit supply conditions affect the post-issuance dynamics of maturity. This view is consistent with that of He and Milbradt (2016), who theoretically show that firms tend to replace short-term debt with long-term debt when their fundamentals are improving. A closely related empirical paper is Mian and Santos (2011), who show that creditworthy firms try to actively manage the maturity of syndicated loans in normal times. Liquidity demand then becomes countercyclical for these firms because they choose not to refinance when liquidity costs rise. In contrast, my results demonstrate that in the corporate bond market, weaker firms are those that display a procyclical pattern in early refinancing and maturity extension. My results remain valid when I measure the overall debt maturity structure, which includes both outstanding loans and bonds. Moreover, the callable structure of corporate bonds allows me to identify the link between maturity extension and refinancing risk more cleanly and clearly than has been done before because omitted variables affecting firms’ maturity choice, such as unobservable new investment opportunities or credit market conditions, are unlikely to precisely coincide with when bonds turn callable. In addition, there is ample evidence about firms’ renegotiations of their bank loans (Roberts and Sufi 2009; Mian and Santos 2011). However, renegotiations for corporate bonds always have been considered difficult, if not impossible, because of the large number of bond holders outstanding (Bolton and Scharfstein 1996). My paper shows that although private debt can be augmented by renegotiation, public debt can be implicitly augmented through early refinancing. Third, this paper is also related to several studies demonstrating that capital market segmentation and credit supply conditions significantly influence financial structure and corporate behavior (Faulkender and Petersen 2006; Leary 2009; Sufi 2009; Tang 2009; Lemmon and Roberts 2010; Chernenko and Sunderam 2012; Erel et al. 2012). These studies differ from much of the capital structure literature, where capital supply is assumed to be perfectly elastic and capital structures are solely determined by corporate demand for debt. I add to this line of research by showing that credit supply conditions disproportionally affects speculative-grade firms and that maturity is an important channel. 2. Data and Summary Statistics The Mergent Fixed Income Securities Database (FISD) is a comprehensive database of publicly offered U.S. bonds. The FISD includes the majority of corporate bonds and provides details on bond issuance and issuers. Beginning in April 1995, the FISD started to track changes in the outstanding amount of publicly traded corporate bonds. Thus, in addition to the characteristics of bonds at issuance, the FISD provides a detailed history of changes in the amount of bonds outstanding. It details the actions,3 the effective dates of the changes in amount of bonds outstanding, the exact amount changed, and the remaining principal balance afterward. I supplement the FISD data with information from Bloomberg. Then I link the bond data to firm-level data from Compustat and Thompson Reuters.4 The final data set contains information on bonds outstanding for a firm in a given fiscal year including bond characteristics and contract terms at issuance, actions taken for the outstanding bonds, and the principal amount remaining after these actions. To be included in the sample, a firm has to have at least three consecutive annual observations with public bonds outstanding. The final sample includes 1,553 nonfinancial U.S. firms and 15,575 firm-year observations during the period of 1997-2012. The sample covers 29,838 bonds. I also obtain effective yields for different rating indexes and maturity groups from Bank of America, and constant maturity Treasury rates from the Federal Reserve to measure credit market conditions in general. In panel A of Table 1, I compare sample firms with all nonfinancial firms in the Compustat database during the same time period (1997–2012).5 Relative to the average Compustat firm, sample firms tend to be larger, more profitable, have higher leverage, and are more likely to have an S&P long-term issuer credit rating. These differences are not surprising given that sample firms have access to the corporate bond market. Panel B reports summary statistics for sample bonds.6 The median offering amount is $\$$ 100 million, with an average of $\$$224 million. Maturity at issuance has a mean of 10.56 years, and the average coupon rate is 6.60%. Thirty percent of the bonds are rated as speculative-grade at issuance. Almost all bonds have covenants associated with them, and the average covenant count is 4.15.7 Table 1 Summary statistics A. Firm characteristics summary Sample firms Compustat firms Variable Mean SD Median Mean SD Median Assets (mils) 11,290.26 34,366.57 2,886.00 2,699.51 15,312.83 116.40 Book leverage 0.41 0.22 0.37 0.25 0.27 0.18 Market/Book 1.57 0.75 1.33 3.38 6.31 1.54 EBITDA/Assets 0.13 0.08 0.12 $$-$$0.15 0.74 0.08 Cash/Assets 0.07 0.08 0.04 0.21 0.25 0.10 Tangible/Assets 0.39 0.25 0.35 0.29 0.27 0.20 Equity return 0.13 0.66 0.05 0.14 0.88 $$-$$0.02 S&P credit rating 0.91 0.29 1.00 0.19 0.39 0.00 Firm-year Obs 15,575 15,575 15,575 133,102 133,102 133,102 Firms 1,553 1,553 1,553 17,327 17,327 17,327 A. Firm characteristics summary Sample firms Compustat firms Variable Mean SD Median Mean SD Median Assets (mils) 11,290.26 34,366.57 2,886.00 2,699.51 15,312.83 116.40 Book leverage 0.41 0.22 0.37 0.25 0.27 0.18 Market/Book 1.57 0.75 1.33 3.38 6.31 1.54 EBITDA/Assets 0.13 0.08 0.12 $$-$$0.15 0.74 0.08 Cash/Assets 0.07 0.08 0.04 0.21 0.25 0.10 Tangible/Assets 0.39 0.25 0.35 0.29 0.27 0.20 Equity return 0.13 0.66 0.05 0.14 0.88 $$-$$0.02 S&P credit rating 0.91 0.29 1.00 0.19 0.39 0.00 Firm-year Obs 15,575 15,575 15,575 133,102 133,102 133,102 Firms 1,553 1,553 1,553 17,327 17,327 17,327 B. Bond characteristics summary Variable N Mean SD P10 P50 P90 Offering amount (mils) 29,838 223.56 353.76 2.31 100.00 541.83 Maturity at issuance 29,833 10.56 8.83 3.00 9.50 24.00 Coupon 29,822 6.62 2.48 3.50 6.70 9.63 Speculative grade (D) 26,835 0.30 0.46 0.00 0.00 1.00 Covenant (D) 17,857 0.99 0.12 1.00 1.00 1.00 Covenant count 17,825 4.04 2.35 1.00 4.00 7.00 B. Bond characteristics summary Variable N Mean SD P10 P50 P90 Offering amount (mils) 29,838 223.56 353.76 2.31 100.00 541.83 Maturity at issuance 29,833 10.56 8.83 3.00 9.50 24.00 Coupon 29,822 6.62 2.48 3.50 6.70 9.63 Speculative grade (D) 26,835 0.30 0.46 0.00 0.00 1.00 Covenant (D) 17,857 0.99 0.12 1.00 1.00 1.00 Covenant count 17,825 4.04 2.35 1.00 4.00 7.00 This table presents summary statistics for sample U.S. firms and bonds during the period 1997–2012. Panel A presents the means, standard deviations, and median of firms’ characteristics. The unit of observation is at the firm-year level. Panel B presents summary statistics of bond characteristics at issuance. All firm variables and bond variables are defined in Appendixes B and C, respectively. Table 1 Summary statistics A. Firm characteristics summary Sample firms Compustat firms Variable Mean SD Median Mean SD Median Assets (mils) 11,290.26 34,366.57 2,886.00 2,699.51 15,312.83 116.40 Book leverage 0.41 0.22 0.37 0.25 0.27 0.18 Market/Book 1.57 0.75 1.33 3.38 6.31 1.54 EBITDA/Assets 0.13 0.08 0.12 $$-$$0.15 0.74 0.08 Cash/Assets 0.07 0.08 0.04 0.21 0.25 0.10 Tangible/Assets 0.39 0.25 0.35 0.29 0.27 0.20 Equity return 0.13 0.66 0.05 0.14 0.88 $$-$$0.02 S&P credit rating 0.91 0.29 1.00 0.19 0.39 0.00 Firm-year Obs 15,575 15,575 15,575 133,102 133,102 133,102 Firms 1,553 1,553 1,553 17,327 17,327 17,327 A. Firm characteristics summary Sample firms Compustat firms Variable Mean SD Median Mean SD Median Assets (mils) 11,290.26 34,366.57 2,886.00 2,699.51 15,312.83 116.40 Book leverage 0.41 0.22 0.37 0.25 0.27 0.18 Market/Book 1.57 0.75 1.33 3.38 6.31 1.54 EBITDA/Assets 0.13 0.08 0.12 $$-$$0.15 0.74 0.08 Cash/Assets 0.07 0.08 0.04 0.21 0.25 0.10 Tangible/Assets 0.39 0.25 0.35 0.29 0.27 0.20 Equity return 0.13 0.66 0.05 0.14 0.88 $$-$$0.02 S&P credit rating 0.91 0.29 1.00 0.19 0.39 0.00 Firm-year Obs 15,575 15,575 15,575 133,102 133,102 133,102 Firms 1,553 1,553 1,553 17,327 17,327 17,327 B. Bond characteristics summary Variable N Mean SD P10 P50 P90 Offering amount (mils) 29,838 223.56 353.76 2.31 100.00 541.83 Maturity at issuance 29,833 10.56 8.83 3.00 9.50 24.00 Coupon 29,822 6.62 2.48 3.50 6.70 9.63 Speculative grade (D) 26,835 0.30 0.46 0.00 0.00 1.00 Covenant (D) 17,857 0.99 0.12 1.00 1.00 1.00 Covenant count 17,825 4.04 2.35 1.00 4.00 7.00 B. Bond characteristics summary Variable N Mean SD P10 P50 P90 Offering amount (mils) 29,838 223.56 353.76 2.31 100.00 541.83 Maturity at issuance 29,833 10.56 8.83 3.00 9.50 24.00 Coupon 29,822 6.62 2.48 3.50 6.70 9.63 Speculative grade (D) 26,835 0.30 0.46 0.00 0.00 1.00 Covenant (D) 17,857 0.99 0.12 1.00 1.00 1.00 Covenant count 17,825 4.04 2.35 1.00 4.00 7.00 This table presents summary statistics for sample U.S. firms and bonds during the period 1997–2012. Panel A presents the means, standard deviations, and median of firms’ characteristics. The unit of observation is at the firm-year level. Panel B presents summary statistics of bond characteristics at issuance. All firm variables and bond variables are defined in Appendixes B and C, respectively. 3. Early Refinancing and Maturity Management In this section, I describe the basic empirical facts with respect to the differences across credit rating segments along the following dimensions: maturity at issuance, variability of credit conditions, frequency, timing and redemption methods of early refinancing, and contract term changes through refinancing. 3.1 Maturity at issuance I first present evidence on maturity at issuance choice across investment- and speculative-grade segments. In Figure 1, I plot the distribution of maturity at issuance, with the summary statistics given at the top. The maturity at issuance of speculative-grade firms’bonds is highly clustered: the average maturity at issuance is 8.7 years, with a standard deviation of 2.4 years. Speculative-grade firms rarely issue bonds longer than 10 years, and the maximum maturity is 30 years. They only issue a few bonds shorter than 7 years. In contrast, the maturity distribution for investment-grade firms is more spread out: the average maturity at issuance is 11.6 years, with a standard deviation of 10.3 years. Investment-grade firms commonly issue bonds longer than 10 years, and the maximum maturity can reach 100 years.8 They also often issue short-term bonds with a maturity less than or equal to 5 years. Figure 1 View largeDownload slide Maturity at issuance This figure shows the histogram of maturity at issuance for sample corporate bonds. Bonds with maturity at issuance longer than 15 years are included in the 15-year category. Summary statistics are reported on top. Figure 1 View largeDownload slide Maturity at issuance This figure shows the histogram of maturity at issuance for sample corporate bonds. Bonds with maturity at issuance longer than 15 years are included in the 15-year category. Summary statistics are reported on top. My results are consistent with the literature documenting that speculative-grade firms are screened out of the long-term bond market. For example, short-term debt provides creditors with additional flexibility to monitor managers frequently and aligns managerial incentives with that of creditors (Calomiris and Kahn 1991; Diamond and Rajan 2001). Short-term debt also enables the transfer of control rights (Hart and Moore 1994), including the right to liquidate when entrenched managers have no incentives to pull the trigger. Additionally, credit rationing (Stiglitz and Weiss 1981) leads to rationing on asset maturity and hence debt maturity (Milbradt and Oehmke 2014), where lending breaks down beyond a certain maturity due to asymmetric information. 3.2 Variability of credit conditions Changing credit supply conditions also affect the financing costs that speculative-grade firms face and their ability to raise new funding in the market. In panel A of Figure 2, I plot the Bank of America Merrill Lynch US Corporate Index for different ratings from 1997 to 2013. This plot highlights the large time-series variation of yields for speculative-grade firms. While the yields for AAA, AA, and A ratings remain relatively stable throughout the period, the yields for speculative-grades are highly volatile. Take the C-rated firms as an example: the yield was lower than 15% during normal credit periods and increased to more than 25% around 2001 and 40% during the recent financial crisis. Figure 2 View largeDownload slide Bond yields and new issuance across rating segments Panel A presents monthly effective yields of the Bank of America Merrill Lynch US Corporate index for AAA, AA, A, BBB, BB, B, and C rating groups between 1997 and 2013. This index tracks the performance of US dollar denominated corporate debt publicly issued in the US domestic market. The data sequence is obtained from the FRED database from the Federal Reserve Bank of St. Louis. Panel B presents the annual aggregate new corporate bond issuance (billions) across speculative-grade and investment-grade segments. The data sequence is obtained from SIFMA. Figure 2 View largeDownload slide Bond yields and new issuance across rating segments Panel A presents monthly effective yields of the Bank of America Merrill Lynch US Corporate index for AAA, AA, A, BBB, BB, B, and C rating groups between 1997 and 2013. This index tracks the performance of US dollar denominated corporate debt publicly issued in the US domestic market. The data sequence is obtained from the FRED database from the Federal Reserve Bank of St. Louis. Panel B presents the annual aggregate new corporate bond issuance (billions) across speculative-grade and investment-grade segments. The data sequence is obtained from SIFMA. Extremely high yield plus the “flight-to-quality” phenomenon during market downturns effectively freezes new lending in the lower rating segments, as shown in panel B of Figure 2. While the new corporate bond issuance decreased overall during market downturns, the drop in the speculative-grade firms is much more significant. For example, new issuance by speculative-grade firms decreased from $\$$ 135 billion in 2007 to $\$$41.8 billion in 2008, a 70% drop. Meanwhile for investment-grade firms, the new corporate bond issuance decreased from $\$$1,002 billion in 2007 to $\$$668 billion in 2008, a 33% drop. The magnitude of the decrease for speculative-grade firms is twice as large as for investment-grade firms. Volatile borrowing conditions and supply constraint prohibiting long-term issuance expose speculative-grade firms to severe refinancing risk, and thus they tend to avoid short-term bonds because issuing at short maturity could only amplify refinancing risk due to more frequent rollover (Diamond 1991). The ex post refinancing risk is factored into the ex ante choice of maturity at issuance, leading to an equilibrium at which speculative-grade firms issue at intermediate-term (the longest term they are offered by the creditors) (Diamond 1991; Guedes and Opler 1996). I present this in Table 3 by regressing maturity at issuance on a few credit market condition measures. Corporate term spread (the difference between the 10- and 1-year corporate yield), the BAA-AAA spread, and the 3-month T-bill rate are included to measure the credit market conditions. For speculative-grade firms, I also use the difference between speculative- and investment-grade yields (HY-IG) instead of BAA-AAA in some specifications as a robustness check given speculative-grade firms might be more influenced by HY-IG. All credit market condition measures are standardized for easy comparison across groups. In addition, in this regression I exclude bonds with a maturity at issuance longer than 30 years to ensure comparability across credit rating segments. Table 3 Choice of maturity at issuance (1) (2) (3) (4) (5) (6) Term spread –0.289*** –0.271*** –0.202*** –0.199** –0.959*** –0.941*** (0.074) (0.082) (0.077) (0.087) (0.182) (0.201) T-bill rate 0.058 0.114 0.162 0.196 –0.133 –0.158 (0.128) (0.138) (0.137) (0.149) (0.235) (0.272) BAA-AAA –0.172*** –0.146** –0.698*** –0.685*** (0.055) (0.060) (0.197) (0.166) HY-IG –0.152*** –0.122** (0.056) (0.061) Bond rating –0.302*** –0.336*** –0.308*** –0.343*** –0.374* –0.311 (0.091) (0.114) (0.090) (0.112) (0.195) (0.204) Covenant count –0.037 –0.041 –0.036 –0.040 0.152 0.186* (0.032) (0.040) (0.032) (0.040) (0.108) (0.113) Seniority level –1.070*** –1.012*** –1.082*** –1.018*** 0.692 0.660 (0.161) (0.175) (0.164) (0.180) (1.519) (1.500) Offering amount 0.103 0.135 0.097 0.131 –0.352 –0.330 (0.248) (0.315) (0.252) (0.319) (0.238) (0.231) Observations 2,507 2,000 2,507 2,000 7,383 7,300 FEs Firm Firm Firm Firm Firm Firm Sample Speculative Speculative Speculative Speculative Investment Investment Firm control No Yes No Yes No Yes Adj R-squared 0.219 0.206 0.218 0.205 0.132 0.132 (1) (2) (3) (4) (5) (6) Term spread –0.289*** –0.271*** –0.202*** –0.199** –0.959*** –0.941*** (0.074) (0.082) (0.077) (0.087) (0.182) (0.201) T-bill rate 0.058 0.114 0.162 0.196 –0.133 –0.158 (0.128) (0.138) (0.137) (0.149) (0.235) (0.272) BAA-AAA –0.172*** –0.146** –0.698*** –0.685*** (0.055) (0.060) (0.197) (0.166) HY-IG –0.152*** –0.122** (0.056) (0.061) Bond rating –0.302*** –0.336*** –0.308*** –0.343*** –0.374* –0.311 (0.091) (0.114) (0.090) (0.112) (0.195) (0.204) Covenant count –0.037 –0.041 –0.036 –0.040 0.152 0.186* (0.032) (0.040) (0.032) (0.040) (0.108) (0.113) Seniority level –1.070*** –1.012*** –1.082*** –1.018*** 0.692 0.660 (0.161) (0.175) (0.164) (0.180) (1.519) (1.500) Offering amount 0.103 0.135 0.097 0.131 –0.352 –0.330 (0.248) (0.315) (0.252) (0.319) (0.238) (0.231) Observations 2,507 2,000 2,507 2,000 7,383 7,300 FEs Firm Firm Firm Firm Firm Firm Sample Speculative Speculative Speculative Speculative Investment Investment Firm control No Yes No Yes No Yes Adj R-squared 0.219 0.206 0.218 0.205 0.132 0.132 This table presents the sensitivity of maturity at issuance to various credit market conditions. Corporate term spread (the difference between the 10-year and 1-year corporate yield), the BAA-AAA spread or the difference between high-yield and investment-grade yield (HY-IG), and the 3-month T-bill rate are included to measure the credit market conditions. All credit market condition measures are standardized for easy comparison across groups. Bond characteristics include covenant count, seniority level, bond rating, and offering amount. Firm fixed effects are included in each regression. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and equity return. Bonds with maturity at issuance longer than 30 years are excluded from the regression to ensure comparability across credit rating segments. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table 3 Choice of maturity at issuance (1) (2) (3) (4) (5) (6) Term spread –0.289*** –0.271*** –0.202*** –0.199** –0.959*** –0.941*** (0.074) (0.082) (0.077) (0.087) (0.182) (0.201) T-bill rate 0.058 0.114 0.162 0.196 –0.133 –0.158 (0.128) (0.138) (0.137) (0.149) (0.235) (0.272) BAA-AAA –0.172*** –0.146** –0.698*** –0.685*** (0.055) (0.060) (0.197) (0.166) HY-IG –0.152*** –0.122** (0.056) (0.061) Bond rating –0.302*** –0.336*** –0.308*** –0.343*** –0.374* –0.311 (0.091) (0.114) (0.090) (0.112) (0.195) (0.204) Covenant count –0.037 –0.041 –0.036 –0.040 0.152 0.186* (0.032) (0.040) (0.032) (0.040) (0.108) (0.113) Seniority level –1.070*** –1.012*** –1.082*** –1.018*** 0.692 0.660 (0.161) (0.175) (0.164) (0.180) (1.519) (1.500) Offering amount 0.103 0.135 0.097 0.131 –0.352 –0.330 (0.248) (0.315) (0.252) (0.319) (0.238) (0.231) Observations 2,507 2,000 2,507 2,000 7,383 7,300 FEs Firm Firm Firm Firm Firm Firm Sample Speculative Speculative Speculative Speculative Investment Investment Firm control No Yes No Yes No Yes Adj R-squared 0.219 0.206 0.218 0.205 0.132 0.132 (1) (2) (3) (4) (5) (6) Term spread –0.289*** –0.271*** –0.202*** –0.199** –0.959*** –0.941*** (0.074) (0.082) (0.077) (0.087) (0.182) (0.201) T-bill rate 0.058 0.114 0.162 0.196 –0.133 –0.158 (0.128) (0.138) (0.137) (0.149) (0.235) (0.272) BAA-AAA –0.172*** –0.146** –0.698*** –0.685*** (0.055) (0.060) (0.197) (0.166) HY-IG –0.152*** –0.122** (0.056) (0.061) Bond rating –0.302*** –0.336*** –0.308*** –0.343*** –0.374* –0.311 (0.091) (0.114) (0.090) (0.112) (0.195) (0.204) Covenant count –0.037 –0.041 –0.036 –0.040 0.152 0.186* (0.032) (0.040) (0.032) (0.040) (0.108) (0.113) Seniority level –1.070*** –1.012*** –1.082*** –1.018*** 0.692 0.660 (0.161) (0.175) (0.164) (0.180) (1.519) (1.500) Offering amount 0.103 0.135 0.097 0.131 –0.352 –0.330 (0.248) (0.315) (0.252) (0.319) (0.238) (0.231) Observations 2,507 2,000 2,507 2,000 7,383 7,300 FEs Firm Firm Firm Firm Firm Firm Sample Speculative Speculative Speculative Speculative Investment Investment Firm control No Yes No Yes No Yes Adj R-squared 0.219 0.206 0.218 0.205 0.132 0.132 This table presents the sensitivity of maturity at issuance to various credit market conditions. Corporate term spread (the difference between the 10-year and 1-year corporate yield), the BAA-AAA spread or the difference between high-yield and investment-grade yield (HY-IG), and the 3-month T-bill rate are included to measure the credit market conditions. All credit market condition measures are standardized for easy comparison across groups. Bond characteristics include covenant count, seniority level, bond rating, and offering amount. Firm fixed effects are included in each regression. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and equity return. Bonds with maturity at issuance longer than 30 years are excluded from the regression to ensure comparability across credit rating segments. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table 3 shows that both groups react to changes in the credit market conditions in their maturity choices, particularly the term spread and the BAA-AAA spread (or HY-IG). When term spread is larger, meaning long-term bonds are relatively more expensive than the short-term ones, firms reduce the maturity at issuance. However, the results for investment-grade firms in Columns (5) and (6) display significantly higher sensitivity to term spread and the BAA-AAA spread than speculative-grade firms in Columns (1) and (4). The sensitivity of term spread is at least three times as large, and the sensitivity on the BAA-AAA spread is more than twice as large. The regression estimates fit the maturity at issuance description where speculative-grade firms cluster their maturity around the intermediated-term. 3.3 Early refinancing activities I define refinancing to have occurred if within a 3-month time window centered on the month a bond is retired, firms issue other bonds with a dollar amount comparable to the retired amount. Early refinancing refers to the cases in which refinancing happens at least 6 months before the scheduled due date. In panel A of Table 2, I summarize the dollar amount of early refinancing, maturing, and total outstanding bonds for sample firms. The total dollar amount of outstanding sample bonds grew from approximately $\$$ 680 billion in 1997 to $\$$2,553 billion in 2012. The maturing amount displays a monotone increasing trend, similar to the total outstanding amount over the sample period. Table 2 Early refinancing activities and contract term changes A. Early refinancing activities (billions) Year Early refinancing Maturing Total 1997 10.76 34.70 679.80 1998 22.34 35.17 846.10 1999 11.67 48.64 1,055.29 2000 10.28 76.02 1,195.02 2001 27.36 85.80 1,425.60 2002 25.77 96.91 1,585.69 2003 52.42 120.98 1,661.34 2004 52.54 111.90 1,671.57 2005 35.47 116.92 1,680.10 2006 35.92 127.58 1,734.73 2007 43.33 135.08 1,837.71 2008 25.92 116.91 1,993.65 2009 63.01 153.83 2,198.53 2010 77.96 118.83 2,279.62 2011 55.92 144.28 2,345.79 2012 92.39 161.35 2,552.65 A. Early refinancing activities (billions) Year Early refinancing Maturing Total 1997 10.76 34.70 679.80 1998 22.34 35.17 846.10 1999 11.67 48.64 1,055.29 2000 10.28 76.02 1,195.02 2001 27.36 85.80 1,425.60 2002 25.77 96.91 1,585.69 2003 52.42 120.98 1,661.34 2004 52.54 111.90 1,671.57 2005 35.47 116.92 1,680.10 2006 35.92 127.58 1,734.73 2007 43.33 135.08 1,837.71 2008 25.92 116.91 1,993.65 2009 63.01 153.83 2,198.53 2010 77.96 118.83 2,279.62 2011 55.92 144.28 2,345.79 2012 92.39 161.35 2,552.65 B. Contract term changes through early refinancing Speculative grade Investment grade Retired New New-retired Retired New New-retired Maturity 3.96 8.72 4.76 9.89 9.47 $$-$$0.42 Maturity at issuance 8.52 8.72 0.21 15.13 9.47 $$-$$5.66 Coupon 9.18 8.25 $$-$$0.93 6.38 4.36 $$-$$2.02 Offering yield 9.80 8.29 $$-$$1.52 6.53 4.87 $$-$$1.66 Covenant count 6.85 6.38 $$-$$0.47 3.27 3.27 0.00 Covenant (D) 1.00 1.00 0.00 1.00 0.93 $$-$$0.07 B. Contract term changes through early refinancing Speculative grade Investment grade Retired New New-retired Retired New New-retired Maturity 3.96 8.72 4.76 9.89 9.47 $$-$$0.42 Maturity at issuance 8.52 8.72 0.21 15.13 9.47 $$-$$5.66 Coupon 9.18 8.25 $$-$$0.93 6.38 4.36 $$-$$2.02 Offering yield 9.80 8.29 $$-$$1.52 6.53 4.87 $$-$$1.66 Covenant count 6.85 6.38 $$-$$0.47 3.27 3.27 0.00 Covenant (D) 1.00 1.00 0.00 1.00 0.93 $$-$$0.07 Panel A presents the aggregate dollar amount of early refinancing, maturing, and total amount outstanding for sample U.S. firms between 1984 and 2012. I define refinancing to have occurred if within a 3-month time window centered on the month a bond is retired, firms issue other bonds with a dollar amount comparable to the retired amount. Early refinancing refers to cases in which refinancing happens at least 6 months before the scheduled due date. Panel B presents bond characteristics upon early refinancing. For every early refinancing case, I match the retired bond and the newly issued bond and compare their contract terms. The results are presented for speculative-grade and investment-grade separately. Table 2 Early refinancing activities and contract term changes A. Early refinancing activities (billions) Year Early refinancing Maturing Total 1997 10.76 34.70 679.80 1998 22.34 35.17 846.10 1999 11.67 48.64 1,055.29 2000 10.28 76.02 1,195.02 2001 27.36 85.80 1,425.60 2002 25.77 96.91 1,585.69 2003 52.42 120.98 1,661.34 2004 52.54 111.90 1,671.57 2005 35.47 116.92 1,680.10 2006 35.92 127.58 1,734.73 2007 43.33 135.08 1,837.71 2008 25.92 116.91 1,993.65 2009 63.01 153.83 2,198.53 2010 77.96 118.83 2,279.62 2011 55.92 144.28 2,345.79 2012 92.39 161.35 2,552.65 A. Early refinancing activities (billions) Year Early refinancing Maturing Total 1997 10.76 34.70 679.80 1998 22.34 35.17 846.10 1999 11.67 48.64 1,055.29 2000 10.28 76.02 1,195.02 2001 27.36 85.80 1,425.60 2002 25.77 96.91 1,585.69 2003 52.42 120.98 1,661.34 2004 52.54 111.90 1,671.57 2005 35.47 116.92 1,680.10 2006 35.92 127.58 1,734.73 2007 43.33 135.08 1,837.71 2008 25.92 116.91 1,993.65 2009 63.01 153.83 2,198.53 2010 77.96 118.83 2,279.62 2011 55.92 144.28 2,345.79 2012 92.39 161.35 2,552.65 B. Contract term changes through early refinancing Speculative grade Investment grade Retired New New-retired Retired New New-retired Maturity 3.96 8.72 4.76 9.89 9.47 $$-$$0.42 Maturity at issuance 8.52 8.72 0.21 15.13 9.47 $$-$$5.66 Coupon 9.18 8.25 $$-$$0.93 6.38 4.36 $$-$$2.02 Offering yield 9.80 8.29 $$-$$1.52 6.53 4.87 $$-$$1.66 Covenant count 6.85 6.38 $$-$$0.47 3.27 3.27 0.00 Covenant (D) 1.00 1.00 0.00 1.00 0.93 $$-$$0.07 B. Contract term changes through early refinancing Speculative grade Investment grade Retired New New-retired Retired New New-retired Maturity 3.96 8.72 4.76 9.89 9.47 $$-$$0.42 Maturity at issuance 8.52 8.72 0.21 15.13 9.47 $$-$$5.66 Coupon 9.18 8.25 $$-$$0.93 6.38 4.36 $$-$$2.02 Offering yield 9.80 8.29 $$-$$1.52 6.53 4.87 $$-$$1.66 Covenant count 6.85 6.38 $$-$$0.47 3.27 3.27 0.00 Covenant (D) 1.00 1.00 0.00 1.00 0.93 $$-$$0.07 Panel A presents the aggregate dollar amount of early refinancing, maturing, and total amount outstanding for sample U.S. firms between 1984 and 2012. I define refinancing to have occurred if within a 3-month time window centered on the month a bond is retired, firms issue other bonds with a dollar amount comparable to the retired amount. Early refinancing refers to cases in which refinancing happens at least 6 months before the scheduled due date. Panel B presents bond characteristics upon early refinancing. For every early refinancing case, I match the retired bond and the newly issued bond and compare their contract terms. The results are presented for speculative-grade and investment-grade separately. The early refinancing amount, in comparison, is much more volatile and shows sharp decreases followed by large rebounds. Early refinancing declines sharply in 2000 and 2008, coinciding with the two financial market crashes of the last decade, and peaks in 1998, 2004–2005, and after 2010. When I separate firms by credit ratings, this procyclical pattern in early refinancing is particularly strong for speculative-grade firms. Panel A in Figure 3 presents the average early refinancing fraction, which is the ratio of the early refinancing dollar amount to the total dollar amount of outstanding bonds at previous year-end. As shown in panel A, speculative-grade firms refinance more than 10% of total outstanding bonds early during good credit periods, such as 2004–2005 and 2010-2011, but less than 2% during credit market downturns. In contrast, investment-grade firms consistently refinance early about 1%–2% of the total outstanding bonds throughout the period. Figure 3 View largeDownload slide Early refinancing and changes in maturity Panel A shows early refinancing of corporate bonds for U.S. firms in Compustat between 1997 and 2012. I plot the average ratio of early refinancing amount to the total amount of outstanding bonds at the previous year-end. Panel B shows the correlation between firms’ early refinancing activities (left scale) and changes in average bond maturity (right scale) proportional to previous year-end average maturity. Figure 3 View largeDownload slide Early refinancing and changes in maturity Panel A shows early refinancing of corporate bonds for U.S. firms in Compustat between 1997 and 2012. I plot the average ratio of early refinancing amount to the total amount of outstanding bonds at the previous year-end. Panel B shows the correlation between firms’ early refinancing activities (left scale) and changes in average bond maturity (right scale) proportional to previous year-end average maturity. To observe the contract term changes upon early refinancing, for each early refinancing case I match the early-retired bond to the newly-issued bond and examine the differences. The results are presented separately for the investment-grade and speculative-grade firms in panel B of Table 2. the results show that speculative-grade firms get a significant extension in maturity, whereas investment-grade firms simply issue new bonds with a similar maturity. Speculative-grade firms extend maturity from 3.96 years to 8.72 years, a more than 100% extension. Investment-grade firms’ maturity moves from 9.89 years to about 9.47 years, which is roughly the same. Speculative-grade firms do not appear to adjust the maturity they issue at, because the retired bonds and the new bonds have maturity at issuance of 8.52 and 8.72 years, respectively. Investment-grade firms, on the other hand, shorten the maturity at issuance from 15.13 years to 9.47 years. Panel B of Table 2 also shows that both types of early refinancers save on interest payments, which is consistent with the literature on early refinancing. Upon refinancing, the coupon rate decreases 0.93% for speculative-grade firms and 2.02% for investment-grade firms, while offering yield decreases 1.52% for speculative-grade firms and 1.66% for investment grade firms, respectively. For covenant strictness, speculative-grade firms experience a drop of 0.47 in their covenant count through early refinancing: on average, their covenant count decreases from 6.85 to 6.38. Investment-grade firms experience no covenant count decrease, but a 7% drop in the covenant dummy. Maturity extension through early refinancing, along with the procyclical early refinancing activities of speculative-grade firms, explains the time–series correlation between early refinancing and firms’ maturity structures presented in panel B of Figure 3. For speculative-grade firms in the left figure, maturity extends significantly when firms refinance early on a large scale. When speculative-grade firms’ early refinancing activities drop sharply, their maturity shortens correspondingly, leading to a procyclical maturity structure. For investment-grade firms in the right figure, we do not observe a similar correlation between early refinancing and firms’ maturity structure. Both early refinancing activities and maturity structure remain fairly stable and they do not display any comovement patterns. 3.4 Early refinancing: Redemption methods and timing I next turn to examine the redemption methods and timing of early refinancing. I decompose early refinancing based on methods of redemption and present the results in Figure 4. Calls, whereby issuers exercise call provisions to buy back outstanding bonds, are a common method of early refinancing. For instance, in 2003–2004, firms called about $\$$ 30 billion of outstanding corporate bonds, whereas the total amount of early refinancing was around $\$$60 billion. Tender offers account for the majority of the rest, together with some make-whole call and repurchase activities. Also, the plot shows a similar procyclical fluctuation: call and tender offer amounts sharply increase in 1998, 2003–2004, and 2010–2011, and decrease during the two market downturns in 2000 and 2008. Figure 4 View largeDownload slide Early refinancing decomposition This figure shows early refinancing activities according to different redemption methods. Appendix A provides the definitions. Figure 4 View largeDownload slide Early refinancing decomposition This figure shows early refinancing activities according to different redemption methods. Appendix A provides the definitions. Notice that early refinancing conducted through tender offers, repurchases, and make-whole calls accounts for half of the total dollar amount. Even if all the transaction costs are ignored, firms need to pay at least the market price in repurchases, and typically some premium in tender offers, to induce bond holders to comply. Given that tender offers mainly happen during good credit periods when yields are relatively low, refinancing through tender offers can be expensive and cannot help firms to save on their interest payments. Make-whole calls are even more expensive because all of the future coupons have to be paid at a discount rate close to the Treasury rate, making them effectively prepayment with penalty and the opposite of interest savings. Explanations other than interest savings are required to fit the data patterns. Figure 5 shows the ratio of time passed when refinanced over maturity at issuance. If maturity at issuance is 10 years and the bond is refinanced at the end of its sixth year, the fraction of elapsed maturity at refinancing is 60%. I denote this as an instance of early refinancing. If a firm refinances a bond at the scheduled due date, the fraction of elapsed maturity at refinancing is one, and I denote this case as an instance of refinancing at maturity. I plot the distribution for both speculative-grade firms and investment-grade firms to explore the heterogeneity across these two segments. I restrict the sample to bonds with maturity at issuance between 7 to 10 years in order to make them comparable across credit rating segments. Figure 5 shows that the majority of speculative-grade firms’ bonds are refinanced before the due date; less than 10% of refinancing cases occur at the due date. The largest chunk of refinancing happens after a bond reaches the middle of its maturity at issuance. In contrast, investment-grade firms refinance over 70% of their bonds right at maturity. Figure 5 View largeDownload slide Percentage of maturity elapsed at refinancing This figure shows the percentage of maturity elapsed at refinancing for sample bonds. The sample is restricted to bonds being refinanced and excludes bonds that are retired, but not refinanced. If a bond with maturity at issuance of 10 years is refinanced 6 years after issuance, then I denote 60% of maturity elapsed at refinancing. For a bond refinanced at scheduled maturity, maturity elapsed at refinancing is 100%. The sample is restricted to bonds with maturity at issuance between 7 and 10 years to ensure comparability across credit rating segments. Figure 5 View largeDownload slide Percentage of maturity elapsed at refinancing This figure shows the percentage of maturity elapsed at refinancing for sample bonds. The sample is restricted to bonds being refinanced and excludes bonds that are retired, but not refinanced. If a bond with maturity at issuance of 10 years is refinanced 6 years after issuance, then I denote 60% of maturity elapsed at refinancing. For a bond refinanced at scheduled maturity, maturity elapsed at refinancing is 100%. The sample is restricted to bonds with maturity at issuance between 7 and 10 years to ensure comparability across credit rating segments. There might be concerns that speculative-grade firms conduct more early refinancing to reduce interest rate costs— firms call back outstanding bonds when the call options are in the money. In order to exclude early refinancing activities that are potentially driven by interest rate reductions, I also test noncallables bonds. For noncallable bonds, firms can only refinance early through tender offers, repurchases, and make–whole calls, which do not reduce interest costs. An untabulated figure for noncallable bonds delivers a similar message as the full bond sample, if not stronger. Investment-grade firms almost always refinance right at the due date, while speculative-grade firms tend to refinance long before the due date. The timing of refinancing across speculative-grade and investment-grade firms fits the different exposures to refinancing risk. The financing costs for investment-grade firms are relatively stable throughout good or bad credit supply conditions; hence firms can simply wait until the bond matures and then roll it over. For speculative-grade firms, financing costs are volatile. Unable to foresee what might happen at the due date, speculative-grade firms are concerned about refinancing risks and prefer to refinance long before the due date. 4. Dynamic Maturity Management and Refinancing Risk In this section, I hypothesize that speculative-grade firms manage maturity to mitigate refinancing risks and describe the identification challenges. To establish the link between maturity management and refinancing risk, I first show that speculative-grade firms would extend maturity upon receiving an exogenous shock to early refinancing cost. I then examine how cross-sectional exposure to refinancing risk, indexed by credit rating segments, affects firms’ maturity extension and what kind of bonds to refinance early. Lastly, I use maturity mismatch as another index of refinancing risk exposure and show how it affects firms’ maturity management. 4.1 Hypothesis and identification challenges In this paper, I interpret the observed dynamic debt maturity management as primarily reflecting speculative-grade firms’ desire to reduce refinancing risk. Two mechanisms subject speculative-grade firms to severe refinancing risk, leading them to conduct more maturity management than their investment-grade peers. First, creditors prefer to keep speculative-grade firms on a short leash, as shown in Figure 1. The constraint imposed by the creditors caps the maturity at which speculative-grade firms can issue and excludes them from the long-term market. This constraint leads to a significant maturity mismatch between the assets and liabilities of speculative-grade firms, and the mismatch only widens as the bond’s effective maturity shrinks. Maturity mismatch forces firms to frequently tap the capital markets to refinance toward longer maturity in order to mitigate refinancing risk. Second, changing credit supply conditions disproportionately affect the financing costs of speculative-grade firms. While financing costs remain relatively stable for investment-grade firms over a credit cycle, they increase sharply for speculative-grade firms during credit market downturns, as shown in Figure 2. Extremely high yield plus the “flight-to-quality” phenomenon during market downturns effectively freezes new lending in the lower rating segments, making firms more susceptible to rollover risk (He and Xiong 2012). Extending maturity during good credit periods reduces the possibility of being forced to refinance during future market downturns, therefore hedges against credit supply fluctuations. The descriptive statistics in Section 3 show the positive correlation between speculative-grade firms’ early refinancing activities and corresponding maturity extension. However, to establish the link between the observed patterns and refinancing risk, ideally I want to show that upon receiving an exogenous opportunity to refinance early and therefore a chance to reshuffle the bond contract terms, speculative-grade firms may choose to extend maturity. An exogenous opportunity is crucial here as the subsequent maturity adjustment is not driven by contemporaneous confounding factors. If speculative-grade firms want to proactively extend maturity to reduce refinancing risk, firms with a randomly assigned opportunity are expected to extend maturity by a larger magnitude than firms without it. Given that an ideal experiment setting is not available, endogeneity remains as a big concern. Firms are choosing when to refinance their bonds early and which maturity to issue at simultaneously. For example, when a firm chooses to refinance early, the slope of the yield curve and general credit market conditions might be changing; thus, the firm would prefer to issue at longer or shorter maturity accordingly. Also, there might be new investment opportunities on the horizon and firms may want to issue new bonds according to the length of the new projects. Omitted variables can bias the estimates to either direction. To establish the link described, I need an exogenous shock to an early refinancing opportunity that is not plagued by the unobservables driving a firm’s demand for a maturity. I exploit the protection-period setting of the call provision to generate this exogenous shock, which is uncorrelated with the contemporaneous confounding factors. In additional to the exogenous shock, cross-sectional tests in line with different exposures to refinancing risk can also help to identify the link. Firms more subject to refinancing risk are expected to extend maturity more through early refinancing. Measures based on the two mechanisms described above – credit rating segments as well as maturity mismatch between assets and liabilities – can be used to index cross-sectional exposures to refinancing risk and test this prediction. 4.2 Identification: Exogenous shock 4.2.1 Institutional background: Call provision and protection period Firms commonly pay a higher yield to embed call provisions when issuing bonds, and the ratio of call provision is as high as 87.1% in the sample of speculative-grade firms. If a call provision is included at issuance, the call schedule, call prices, and protection period are contracted. The protection period is defined as the period during which the company cannot call the bond, starting from the issuing date. It provides the bond holders with a guaranteed period during which they will be able to hold the bond and receive coupon payments. For example, Kroger issued a callable 10-year senior debenture on June 15, 1993, with the scheduled due date on June 15, 2003. The embedded call provision states that Kroger would be able to call the debenture starting June 15, 1998, 5 years after the issuance day, with a price of $\$$ 104.25. The call price decreased to $\$$102.834 on June 15, 1999, to $\$$101.417 on June 15, 2000, and finally to $\$$100 on June 15, 2001. The 5-year protection period is precisely 50% of maturity at issuance in this case. Bonds turning callable promote early refinancing through different channels. First, when a lower interest rate is available, either because of a drop in the prevailing market rate or because of better firm performance, the value of an outstanding bond increases correspondingly. If the discounted value exceeds contemporaneous scheduled call prices, firms transfer values from bond holders to themselves by calling outstanding bonds at scheduled prices. The value transferred essentially make calls a subsidized way to refinance early. Second, bonds turning callable set an upper limit on the early refinancing price and can facilitate other methods of redemption. For example, firms no longer need to employ a make-whole call to retire outstanding bonds, which reduces the costs of early refinancing significantly. In tender offers, bondholders’ alternative value of not tendering decreases as the protection period ends, because in some future state(s) of the world firms can excise call provisions, which provides more incentive for bond holders to comply. In addition, call provisions grant firms the right to call back bonds at their discretion. This facilitates early refinancing because bond holders have to return the bonds upon calling. In tender offers and repurchases, bond holders retain the right to not respond if the offered prices are not attractive, or they have other nonprice reasons for holding the bonds. 4.2.2 Instrumental variable strategy: Timing of the protection period My instrumental variable (IV) strategy exploits the precise timing of the protection period. I instrument early refinancing activities with a dummy variable indicating that some bonds are scheduled to become callable for firm i in year t. The intuition is that bonds turning callable facilitate early refinancing and generate a positive shock to early refinancing opportunities. However, because protection periods are fairly standard in length and decided years in advance, the shock is disconnected from other unobservable contemporaneous determinants of maturity at refinancing. The IV strategy operates through timing: when a firm issues a new bond, it is unlikely that the firm can foresee the future changes in confounding factors and how these changes would affect its maturity structure. It is even more unlikely that the firm can precisely foresee the changes at a particular point in the future when the protection period ends, especially given that the protection period is set by an industry standard, not the firm. This lengthy yet standard protection period plays a key role. When the IV is activated, for example, 5 years in the future, confounding factors should change in all directions and have no systematic impact on maturity toward any particular direction. The following is the IV regression specification: First stage: $$D({\it Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{i,t}+\beta_{i}{\it controls}_{i,t}+e_{\it i,t}$$ Second stage: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early}_{-}{\it refi}})_{i,t}+\delta_{i}{\it controls}_{i,t}+\epsilon_{\it i,t}$$ In the first stage, $${\it D(turn_{-}callable)}$$ equals one if some outstanding bonds are scheduled to pass the protection period and become callable for firm i at year t. Take the Kroger 10-year senior debenture as an example. The turn-callable indicator for this debenture switches to one in 1998, and remains zero for all the other years, leading to $$D({\it turn}_{-}{\it callable})=1$$ for Kroger in 1998. Early refinancing activity is indexed by $${\it D(Early_{-}refi)}$$, a dummy variable equals one if firm i refinances early at year t. Contemporaneous firm characteristics are controlled in the regressions, including ln(Assets), Leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and S&P rating. ln(Assets) measures a firm’s ability to collateralize the debt and also captures the liquidation value in a distressed state. Leverage captures a firm’s financial health. Market/Book is used to measure future investment prospects. EBITDA/Assets and Cash/Assets measure a firm’s profitability and short-term liquidity, respectively. Tangible/Assets measures the pledgeability of assets. I include the S&P rating as a general control for a firm’s default risk. To control for time-unvarying unobservables that might also affect a given firm’s maturity choice, I include firm fixed effects. Year fixed effects are included to control for the interest rate and observable credit market conditions affecting firms’ maturity choices. 4.2.3 Instrumental variable strategy: Results Table 4, Columns (1) and (2), present the IV regression first-stage results. I use two measures to capture early refinancing activities: the first one is a dummy D(Early-refi) indicating whether firm i conducted early refinancing activities in year t; the second one is F(Early-refi), which measures the fraction of the total amount of outstanding bonds undergoing early refinancing for firm i in year t. In panel A, the first-stage shows strong results for both measures of early refinancing activities. In terms of economic magnitudes, bonds becoming callable increase the probability of early refinancing by 10.1% and the fraction of early refinancing by 2.5%. Table 4 IV strategy results (1) (2) (3) (4) D(Early_refi) F(Early_refi) D(Early_refi) F(Early_refi) D(turn_callable) 0.101*** 0.025*** 0.173*** 0.050*** (0.016) (0.010) (0.030) (0.015) D(turn_callable)*BAA-AAA –0.061*** –0.021*** (0.017) (0.007) BAA-AAA –0.059*** –0.031*** (0.008) (0.006) Observations 4,512 4,169 4,512 4,169 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes No No Sample Speculative Speculative Speculative Speculative Adj R-squared 0.123 0.011 0.114 0.004 (1) (2) (3) (4) D(Early_refi) F(Early_refi) D(Early_refi) F(Early_refi) D(turn_callable) 0.101*** 0.025*** 0.173*** 0.050*** (0.016) (0.010) (0.030) (0.015) D(turn_callable)*BAA-AAA –0.061*** –0.021*** (0.017) (0.007) BAA-AAA –0.059*** –0.031*** (0.008) (0.006) Observations 4,512 4,169 4,512 4,169 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes No No Sample Speculative Speculative Speculative Speculative Adj R-squared 0.123 0.011 0.114 0.004 B. IV strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.328** 1.143*** 3.769*** (0.014) (0.130) (0.085) (0.966) Observations 3,843 3,843 4,512 4,512 F stat (1st stage) 38.45 40.42 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 0.121 0.597 0.116 B. IV strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.328** 1.143*** 3.769*** (0.014) (0.130) (0.085) (0.966) Observations 3,843 3,843 4,512 4,512 F stat (1st stage) 38.45 40.42 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 0.121 0.597 0.116 This table presents IV results, where $$D({\it Early}_{-}{\it refi})_{\it i,t}$$ is instrumented by $$D({\it turn}_{-}{\it callable})_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F(Debt\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $$D({\it Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early}_{-}{\it refi}})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{it}$$ Table 4 IV strategy results (1) (2) (3) (4) D(Early_refi) F(Early_refi) D(Early_refi) F(Early_refi) D(turn_callable) 0.101*** 0.025*** 0.173*** 0.050*** (0.016) (0.010) (0.030) (0.015) D(turn_callable)*BAA-AAA –0.061*** –0.021*** (0.017) (0.007) BAA-AAA –0.059*** –0.031*** (0.008) (0.006) Observations 4,512 4,169 4,512 4,169 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes No No Sample Speculative Speculative Speculative Speculative Adj R-squared 0.123 0.011 0.114 0.004 (1) (2) (3) (4) D(Early_refi) F(Early_refi) D(Early_refi) F(Early_refi) D(turn_callable) 0.101*** 0.025*** 0.173*** 0.050*** (0.016) (0.010) (0.030) (0.015) D(turn_callable)*BAA-AAA –0.061*** –0.021*** (0.017) (0.007) BAA-AAA –0.059*** –0.031*** (0.008) (0.006) Observations 4,512 4,169 4,512 4,169 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes No No Sample Speculative Speculative Speculative Speculative Adj R-squared 0.123 0.011 0.114 0.004 B. IV strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.328** 1.143*** 3.769*** (0.014) (0.130) (0.085) (0.966) Observations 3,843 3,843 4,512 4,512 F stat (1st stage) 38.45 40.42 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 0.121 0.597 0.116 B. IV strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.328** 1.143*** 3.769*** (0.014) (0.130) (0.085) (0.966) Observations 3,843 3,843 4,512 4,512 F stat (1st stage) 38.45 40.42 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 0.121 0.597 0.116 This table presents IV results, where $$D({\it Early}_{-}{\it refi})_{\it i,t}$$ is instrumented by $$D({\it turn}_{-}{\it callable})_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F(Debt\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $$D({\it Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early}_{-}{\it refi}})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{it}$$ In Table 4, panel A, Columns (3) and (4), I interact the instrument with the BAA-AAA spread to show the sensitivity of the instrument over credit cycles.9 Coefficients on the BAA-AAA spread confirm the observed procyclical pattern of early refinancing activities, and coefficients on the interaction term show that while callability leads to more refinancing activities in general, the sensitivity is significantly higher when the BAA-AAA spread is low (good credit market conditions). These results address both of the two key mechanisms driving maturity extension through early refinancing: first, maturity mismatch between assets and liabilities leads speculative-grade firms to utilize opportunities like call period endings to refinance early for maturity extension; and second, the variability of credit conditions makes speculative-grade firms more likely to take advantage of the opportunity during good credit periods. Table 4, panel B, shows both the OLS regression estimates and the second stage of the IV regression estimates with D(Early-refi) as the instrumented variable. I also use two variables to measure firms’ maturity structure: F(debt$$\geq$$5Y), which is the fraction of total book debt (including loans and bonds) with maturity $$\geq$$5 years, and Bond maturity, which measures the average maturity of outstanding corporate bonds. In IV regressions, the Kleibergen-Paap Wald F-stat for the weak instrument test is much larger than 10, which is the rule of thumb for identifying a weak instrument. The results show that early refinancing leads to a larger fraction of book debt with maturity $$\geq$$ 5 years, as well as a longer average bond maturity. In terms of economic magnitudes, the estimates indicate that a one-standard-deviation (32%) increase in the probability of early refinancing leads to a 10.6% increase in F(debt$$\geq$$5Y) and a 1.21-year extension in Bond maturity. There are a few possible explanations for the IV estimates being larger than the OLS estimates in Table 4. First, IV regressions estimate the local average treatment effect (LATE) on firms responding to the shock, whereas OLS regressions estimate the average treatment effect (ATE) for all sample firms. Firms that respond to the instrument are more likely to be eager to extend maturity, causing a stronger effect of early refinancing on maturity. Second, when a firm chooses to refinance early, it might face an interest rate environment in which the short-term rate is more desirable than the long-term rate, or its investment opportunity set includes more short-term projects. That results in a firm’s demand for short-term maturity, leading to a downward bias in the OLS coefficients. 4.3 Identification: Heterogeneous maturity management 4.3.1 Maturity extension across credit rating segments The descriptive statistics reveal the correlation between early refinancing and maturity extension only for speculative-grade firms, not their investment-grade counterpart. To examine this heterogeneity across credit rating segments within the IV form, I interact the early refinancing activities with D(speculative), which equals one if firm i receives an S&P domestic long-term issuer credit rating below or equal to BB$$+$$. I run both OLS and IV regressions and present the results in Table 5. In the IV regressions, I interact both the instrument and instrumented variables with D(speculative). The IV results show that only speculative-grade firms extend maturity through early refinancing upon receiving an exogenous shock to an early refinancing opportunity, but investment-grade firms do not. In Columns (2) and (4), the coefficients for investment-grade firms remain insignificantly different from zero. In Column (2), the coefficient for speculative-grade firms is 0.368 higher than that of investment-grade firms. In Column (4), the coefficient for speculative-grade firms is 4.006 higher than that of investment-grade firms. Table 5 Heterogeneous maturity extension across credit ratings A. IV First stages (1) (2) D(Early_refi) D(Early_refi)*D(Speculative) D(turn_callable) 0.302*** 0.008** (0.029) (0.003) D(turn_callable)*D(Speculative) 0.201*** 0.098*** (0.033) (0.016) Observations 8,916 8,916 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Full Full Adj R-squared 0.226 0.183 A. IV First stages (1) (2) D(Early_refi) D(Early_refi)*D(Speculative) D(turn_callable) 0.302*** 0.008** (0.029) (0.003) D(turn_callable)*D(Speculative) 0.201*** 0.098*** (0.033) (0.016) Observations 8,916 8,916 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Full Full Adj R-squared 0.226 0.183 B. IV Second Stage F(Debt$$\geq$$5Y) Bond Maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.047*** 0.082 0.288 0.325 (0.011) (0.050) (0.179) (1.387) D(Early_refi)*D(Speculative) 0.047*** 0.368** 1.301*** 4.006** (0.018) (0.146) (0.220) (1.651) Observations 8,027 8,027 8,916 8,916 F Stat (1st stage) 21.38 30.81 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Full Full Full Full Adj R-squared 0.358 0.176 0.804 0.788 B. IV Second Stage F(Debt$$\geq$$5Y) Bond Maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.047*** 0.082 0.288 0.325 (0.011) (0.050) (0.179) (1.387) D(Early_refi)*D(Speculative) 0.047*** 0.368** 1.301*** 4.006** (0.018) (0.146) (0.220) (1.651) Observations 8,027 8,027 8,916 8,916 F Stat (1st stage) 21.38 30.81 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Full Full Full Full Adj R-squared 0.358 0.176 0.804 0.788 This table presents the OLS and IV results, where $$D({\it Early}_{-}{\it refi})_{\it i,t}$$ is interacted with D(speculative). Two measures of maturity are used: F(Debt$$\geq$$5Y ) is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included in the regressions. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. \begin{align*} {\it Maturity}{}_{\it i,t}&=\delta_{0}+\delta_{1}D({\it Early}_{-}{\it refi})_{i,t}+\delta_{2}D({\it Early}_{-}{\it refi})_{i,t}*D({\it spec}){}_{i,t}\nonumber\\ &\quad +\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{it} \end{align*} Table 5 Heterogeneous maturity extension across credit ratings A. IV First stages (1) (2) D(Early_refi) D(Early_refi)*D(Speculative) D(turn_callable) 0.302*** 0.008** (0.029) (0.003) D(turn_callable)*D(Speculative) 0.201*** 0.098*** (0.033) (0.016) Observations 8,916 8,916 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Full Full Adj R-squared 0.226 0.183 A. IV First stages (1) (2) D(Early_refi) D(Early_refi)*D(Speculative) D(turn_callable) 0.302*** 0.008** (0.029) (0.003) D(turn_callable)*D(Speculative) 0.201*** 0.098*** (0.033) (0.016) Observations 8,916 8,916 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Full Full Adj R-squared 0.226 0.183 B. IV Second Stage F(Debt$$\geq$$5Y) Bond Maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.047*** 0.082 0.288 0.325 (0.011) (0.050) (0.179) (1.387) D(Early_refi)*D(Speculative) 0.047*** 0.368** 1.301*** 4.006** (0.018) (0.146) (0.220) (1.651) Observations 8,027 8,027 8,916 8,916 F Stat (1st stage) 21.38 30.81 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Full Full Full Full Adj R-squared 0.358 0.176 0.804 0.788 B. IV Second Stage F(Debt$$\geq$$5Y) Bond Maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.047*** 0.082 0.288 0.325 (0.011) (0.050) (0.179) (1.387) D(Early_refi)*D(Speculative) 0.047*** 0.368** 1.301*** 4.006** (0.018) (0.146) (0.220) (1.651) Observations 8,027 8,027 8,916 8,916 F Stat (1st stage) 21.38 30.81 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Full Full Full Full Adj R-squared 0.358 0.176 0.804 0.788 This table presents the OLS and IV results, where $$D({\it Early}_{-}{\it refi})_{\it i,t}$$ is interacted with D(speculative). Two measures of maturity are used: F(Debt$$\geq$$5Y ) is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included in the regressions. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. \begin{align*} {\it Maturity}{}_{\it i,t}&=\delta_{0}+\delta_{1}D({\it Early}_{-}{\it refi})_{i,t}+\delta_{2}D({\it Early}_{-}{\it refi})_{i,t}*D({\it spec}){}_{i,t}\nonumber\\ &\quad +\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{it} \end{align*} The heterogeneous behavior across credit rating segments in Table 5 fits anecdotal evidence in the industry. Bank of America Merrill Lynch makes the following recommendation to speculative-grade firms: “Don’t wait too long to refinance upcoming maturities. Give yourself at least 18 months before your current financing matures, so that if any segment of the market shuts down for a few months, you’ll still have time to get something done.” HSBC in May 2010 recommended the following: “Truly global investment-grade corporations have time to arrange their refinancing. [Less highly rated companies] should be taking action now, while yields are low and margins compressed” (Gregson 2010). 4.3.2 Bond selection across credit rating segments Maturity extension in order to reduce refinancing risk would predict that firms more exposed to the risk – indexed by lower credit rating – would tend to pick bonds with shorter maturity to retire early. I run the following regression for speculative-grade firms and investment-grade firms separately. For bond i for firm j in year t, when firm j conducts early refinancing, \begin{align*} D({\it Early}_{-}{\it refinanced})_{i,j,t}&=\alpha+\beta*{\it Maturity}_{i,j,t}+\theta*{\it Coupon}_{i,j,t}\nonumber\\ &\quad +\phi_{i}*{\it Controls}_{i,j,t}+\eta_{\it j,t}+\epsilon_{i,j,t} \end{align*} The dependent variable D(Early-refinanced) equals one if firm j refinances bond i early in year t, and zero if bond i stays untouched. The coefficient on maturity $$\beta$$ and the coefficient on coupon $$\theta$$ are the focus here. Given that I include the firm-year fixed effects, the comparison is made among all the bonds outstanding for a given firm-year. The bond characteristic controls include: a dummy variable indicating whether or not bond i is callable at year t, previous year-end amount outstanding, maturity at issuance, covenant count, seniority level,10 and bond rating at the beginning of year t. The regression is estimated using a linear probability model and Table 6 presents the results. Speculative-grade firms target bonds maturing sooner. A 1-year decrease in maturity increases the probability of being refinanced early by 0.024. Thus, if one bond is 5 years shorter in maturity than the average maturity of bonds outstanding, this bond is 12% more likely to be refinanced early compared to other outstanding bonds. On the other hand, investment-grade firms do not appear to consider maturity when they refinance early as the coefficient for maturity is statistically indistinguishable from zero. Both groups target more expensive bonds: a 1% increase in the coupon rate leads to a 2.2% higher probability of the bond being refinanced early by speculative-grade firms, and a 0.7% higher probability by investment-grade firms. Saving for interest costs alone cannot explain why speculative-grade firms choose bonds with shorter maturity to refinance early, although the results fit the motive to hedge refinancing risk. Table 6 Bond characteristics and early refinancing Dependent variable: $${\it D(Early-refinanced)}_{\it ijt}$$ Speculative grade Investment grade Maturity –0.024*** 0.001 (0.006) (0.002) Coupon 0.022*** 0.007* (0.006) (0.004) Callable 0.537*** 0.383*** (0.043) (0.103) Maturity at issuance 0.012*** –0.002 (0.003) (0.001) Covenant count 0.008** 0.010*** (0.004) (0.004) Security level –0.007 –0.001 (0.021) (0.008) Bond rating –0.011 –0.006 (0.008) (0.004) Constant 0.085 –0.007 (0.209) (0.044) Observations 3,262 24,842 FEs Firm-year Firm-year Sample Speculative Investment Adj R-squared 0.496 0.415 Dependent variable: $${\it D(Early-refinanced)}_{\it ijt}$$ Speculative grade Investment grade Maturity –0.024*** 0.001 (0.006) (0.002) Coupon 0.022*** 0.007* (0.006) (0.004) Callable 0.537*** 0.383*** (0.043) (0.103) Maturity at issuance 0.012*** –0.002 (0.003) (0.001) Covenant count 0.008** 0.010*** (0.004) (0.004) Security level –0.007 –0.001 (0.021) (0.008) Bond rating –0.011 –0.006 (0.008) (0.004) Constant 0.085 –0.007 (0.209) (0.044) Observations 3,262 24,842 FEs Firm-year Firm-year Sample Speculative Investment Adj R-squared 0.496 0.415 What kind of bonds are more likely to be early refinanced? This table presents the estimates from a linear regression of being early refinanced on various bond characteristics. The unit of observation is at the bond-firm-year level. Firm-year fixed effects are included. Regressions are separately run for speculative-grade and investment-grade firms. Only firm-year observations with early refinancing activities are included in the regression. Dependent variable $$D(Early_{-}refi)_{i,j,t}$$ equals one if firm j early refinances an outstanding bond i in year t, and zero otherwise. Bond characteristics, such as maturity, coupon, a flag indicating a bond is callable in this year, maturity at issuance, covenant count, seniority level, and bond rating, are included. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. \begin{align*} {\it D(Early}_{-}{\it refinanced})_{\it i,j,t}=\alpha+\beta*{\it Maturity}_{\it i,j,t}+\theta*{\it Coupon}_{\it i,j,t}+\sum_{k}\phi_{i,k}*{\it Controls}_{\it i,j,k,t}+\theta_{\it i,t}+\epsilon_{\it i,j,t} \end{align*} Table 6 Bond characteristics and early refinancing Dependent variable: $${\it D(Early-refinanced)}_{\it ijt}$$ Speculative grade Investment grade Maturity –0.024*** 0.001 (0.006) (0.002) Coupon 0.022*** 0.007* (0.006) (0.004) Callable 0.537*** 0.383*** (0.043) (0.103) Maturity at issuance 0.012*** –0.002 (0.003) (0.001) Covenant count 0.008** 0.010*** (0.004) (0.004) Security level –0.007 –0.001 (0.021) (0.008) Bond rating –0.011 –0.006 (0.008) (0.004) Constant 0.085 –0.007 (0.209) (0.044) Observations 3,262 24,842 FEs Firm-year Firm-year Sample Speculative Investment Adj R-squared 0.496 0.415 Dependent variable: $${\it D(Early-refinanced)}_{\it ijt}$$ Speculative grade Investment grade Maturity –0.024*** 0.001 (0.006) (0.002) Coupon 0.022*** 0.007* (0.006) (0.004) Callable 0.537*** 0.383*** (0.043) (0.103) Maturity at issuance 0.012*** –0.002 (0.003) (0.001) Covenant count 0.008** 0.010*** (0.004) (0.004) Security level –0.007 –0.001 (0.021) (0.008) Bond rating –0.011 –0.006 (0.008) (0.004) Constant 0.085 –0.007 (0.209) (0.044) Observations 3,262 24,842 FEs Firm-year Firm-year Sample Speculative Investment Adj R-squared 0.496 0.415 What kind of bonds are more likely to be early refinanced? This table presents the estimates from a linear regression of being early refinanced on various bond characteristics. The unit of observation is at the bond-firm-year level. Firm-year fixed effects are included. Regressions are separately run for speculative-grade and investment-grade firms. Only firm-year observations with early refinancing activities are included in the regression. Dependent variable $$D(Early_{-}refi)_{i,j,t}$$ equals one if firm j early refinances an outstanding bond i in year t, and zero otherwise. Bond characteristics, such as maturity, coupon, a flag indicating a bond is callable in this year, maturity at issuance, covenant count, seniority level, and bond rating, are included. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. \begin{align*} {\it D(Early}_{-}{\it refinanced})_{\it i,j,t}=\alpha+\beta*{\it Maturity}_{\it i,j,t}+\theta*{\it Coupon}_{\it i,j,t}+\sum_{k}\phi_{i,k}*{\it Controls}_{\it i,j,k,t}+\theta_{\it i,t}+\epsilon_{\it i,j,t} \end{align*} 4.3.3 Maturity mismatch between assets and liabilities Firms with a larger maturity mismatch between assets and liabilities are more exposed to refinancing risk. I next examine whether they have a greater incentive to manage maturity through early refinancing. To test the heterogeneity across this dimension, I rank firms according to the Fama-French 12 industry classification based on maturity mismatch between assets and liabilities. Given that the maturity of firms’ asset side is not observable, I use the investment-grade firms as a benchmark, assuming they are relatively unconstrained in maturity at issuance and match the maturity of their assets and liabilities better. The maturity-mismatch measure is the difference in maturity at issuance between the investment-grade and speculative-grade bonds in an industry. Panel A in Table 7 presents the industry rankings, where a lower rank number indicates a larger maturity mismatch. Industries with the largest maturity mismatch are utilities; telephone and television; oil, gas, and coal; and healthcare, medical equipment, and drugs. Those industries with the smallest mismatch are wholesale and retail; business equipment; manufacturing; and consumer durables. Rankings are aligned with the general consensus of the asset life for listed industries. Table 7 Maturity mismatch analysis A. Maturity mismatch rank Rank Speculative Investment Investment-Speculative Telephone & television 1 8.80 16.23 7.44*** Utilities 2 8.92 16.16 7.24*** Oil, gas, & coal 3 8.59 14.58 5.99*** Healthcare, medical equip., & drugs 4 8.49 13.72 5.23*** Consumer nondurables 5 8.99 12.69 3.70*** Chemicals & allied products 6 8.35 10.82 2.47*** Everything else 7 8.74 10.93 2.19*** Wholesale, retail, & some services 8 8.54 9.96 1.42*** Business equipment 9 8.26 8.75 0.49 Manufacturing 10 8.92 8.61 –0.31 Consumer durables 11 8.75 6.15 –2.60*** A. Maturity mismatch rank Rank Speculative Investment Investment-Speculative Telephone & television 1 8.80 16.23 7.44*** Utilities 2 8.92 16.16 7.24*** Oil, gas, & coal 3 8.59 14.58 5.99*** Healthcare, medical equip., & drugs 4 8.49 13.72 5.23*** Consumer nondurables 5 8.99 12.69 3.70*** Chemicals & allied products 6 8.35 10.82 2.47*** Everything else 7 8.74 10.93 2.19*** Wholesale, retail, & some services 8 8.54 9.96 1.42*** Business equipment 9 8.26 8.75 0.49 Manufacturing 10 8.92 8.61 –0.31 Consumer durables 11 8.75 6.15 –2.60*** B. Maturity mismatch rank F(Debt$$\geq$$5Y) Bond maturity Rank 9-11 Rank 1-3 Rank 9-11 Rank 1-3 IV IV IV IV (1) (2) (3) (4) D(Early_refi) 0.197 1.015* 2.885 4.144** (0.464) (0.594) (2.284) (1.924) Observations 922 1,019 1,064 1,251 F stat (1st stage) 20.1 17.77 17.46 16.30 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.338 $$-$$0.679 0.556 0.337 B. Maturity mismatch rank F(Debt$$\geq$$5Y) Bond maturity Rank 9-11 Rank 1-3 Rank 9-11 Rank 1-3 IV IV IV IV (1) (2) (3) (4) D(Early_refi) 0.197 1.015* 2.885 4.144** (0.464) (0.594) (2.284) (1.924) Observations 922 1,019 1,064 1,251 F stat (1st stage) 20.1 17.77 17.46 16.30 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.338 $$-$$0.679 0.556 0.337 Panel A presents industries based on the difference of maturity at issuance between speculative-grade and investment-grade bonds. For each industry, two sample t-tests are conducted. Panel B presents the IV results of early refinancing on maturity for firms grouped by maturity mismatch measure. Industries with mismatch rank 1–3 are grouped to represent a large maturity mismatch, and the other four industries with rank 9–11 are grouped to represent a small maturity mismatch. Two measures of maturity are used: F(Debt$$\geq$$5Y) is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table 7 Maturity mismatch analysis A. Maturity mismatch rank Rank Speculative Investment Investment-Speculative Telephone & television 1 8.80 16.23 7.44*** Utilities 2 8.92 16.16 7.24*** Oil, gas, & coal 3 8.59 14.58 5.99*** Healthcare, medical equip., & drugs 4 8.49 13.72 5.23*** Consumer nondurables 5 8.99 12.69 3.70*** Chemicals & allied products 6 8.35 10.82 2.47*** Everything else 7 8.74 10.93 2.19*** Wholesale, retail, & some services 8 8.54 9.96 1.42*** Business equipment 9 8.26 8.75 0.49 Manufacturing 10 8.92 8.61 –0.31 Consumer durables 11 8.75 6.15 –2.60*** A. Maturity mismatch rank Rank Speculative Investment Investment-Speculative Telephone & television 1 8.80 16.23 7.44*** Utilities 2 8.92 16.16 7.24*** Oil, gas, & coal 3 8.59 14.58 5.99*** Healthcare, medical equip., & drugs 4 8.49 13.72 5.23*** Consumer nondurables 5 8.99 12.69 3.70*** Chemicals & allied products 6 8.35 10.82 2.47*** Everything else 7 8.74 10.93 2.19*** Wholesale, retail, & some services 8 8.54 9.96 1.42*** Business equipment 9 8.26 8.75 0.49 Manufacturing 10 8.92 8.61 –0.31 Consumer durables 11 8.75 6.15 –2.60*** B. Maturity mismatch rank F(Debt$$\geq$$5Y) Bond maturity Rank 9-11 Rank 1-3 Rank 9-11 Rank 1-3 IV IV IV IV (1) (2) (3) (4) D(Early_refi) 0.197 1.015* 2.885 4.144** (0.464) (0.594) (2.284) (1.924) Observations 922 1,019 1,064 1,251 F stat (1st stage) 20.1 17.77 17.46 16.30 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.338 $$-$$0.679 0.556 0.337 B. Maturity mismatch rank F(Debt$$\geq$$5Y) Bond maturity Rank 9-11 Rank 1-3 Rank 9-11 Rank 1-3 IV IV IV IV (1) (2) (3) (4) D(Early_refi) 0.197 1.015* 2.885 4.144** (0.464) (0.594) (2.284) (1.924) Observations 922 1,019 1,064 1,251 F stat (1st stage) 20.1 17.77 17.46 16.30 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.338 $$-$$0.679 0.556 0.337 Panel A presents industries based on the difference of maturity at issuance between speculative-grade and investment-grade bonds. For each industry, two sample t-tests are conducted. Panel B presents the IV results of early refinancing on maturity for firms grouped by maturity mismatch measure. Industries with mismatch rank 1–3 are grouped to represent a large maturity mismatch, and the other four industries with rank 9–11 are grouped to represent a small maturity mismatch. Two measures of maturity are used: F(Debt$$\geq$$5Y) is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Panel B of Table 7 presents the results of IV regressions. The IV regressions are run separately for industries with the smallest maturity-mismatch measure (rank 9–11) and largest maturity-mismatch measure (rank 1–3). Firms with smaller maturity mismatch (Columns (1) and (3)) are less exposed to refinancing risk and demonstrate a smaller response in maturity extension via early refinancing. For the outcome variable F(Debt$$\geq$$5Y), the coefficient for smaller maturity-mismatch firms (rank 9–11) is 0.197, which is not statistically significant, whereas the coefficient for larger maturity-mismatch firms (rank 1–4) is 1.015, which is statistically significant at the 10% level. For Bond maturity, the coefficient for less mismatched firms is 2.885 and not statistically significant, and the coefficient is 4.144 for more mismatched firms (rank 1–3), which is statistically significant at the 5% level. The IV results support the concept that firms with larger maturity mismatch between assets and liabilities, that is, firms that are more exposed to refinancing risk, employ early refinancing more frequently to extend maturity. There might be concerns that not issuing long-term bonds does not indicate that there is a maturity mismatch between assets and liabilities. Speculative-grade firms might only invest in relatively short-term projects, which matches the maturity structure of their bonds. Documenting the maturity of investment projects is empirically challenging due to not having access to the underlying characteristics of firms’ assets. To provide supportive evidence of maturity mismatch, I plot the distribution of maturity at issuance for speculative-grade and investment-grade firms’ bonds for the oil, gas, and coal industry and the telephone and television industry in AppendixFigure E.1, with the summary statistics shown at the top. Firms in these two industries normally have long-term assets, and asset life across rating segments should not be significantly different. If there is still a difference in maturity at issuance for their bonds, the differences are more likely to come from the maturity mismatch between assets and liabilities, instead of the length of the underlying investment projects. For these two industries in AppendixFigure E.1, maturity at issuance for the investment-grade firms becomes longer compared to the full sample investment-grade firms. The 25th percentile, median, and 75th percentile for the full sample investment-grade firms are 5, 10, and 15 years, respectively. For the oil, gas, and coal industry, the 25th percentile, median, and 75th percentile become 7, 10, and 20 years, respectively. For the telephone and television industry, they are 5, 10, and 30 years, respectively. Both of these industries issue larger fractions of bonds longer than 30 years. In contrast, speculative-grade firms in these industries still have a maturity-at-issuance distribution similar to the full sample of speculative-grade firms. The 25th percentile, median, and 75th percentile remain at 7, 9, and 10 years, respectively. They rarely issue bonds longer than 10 years, and the maximum maturity is 20 years. This evidence favors the maturity mismatch between assets and liabilities for speculative-grade firms. 5. Robustness The IV strategy requires that the timing of some bonds becoming callable is uncorrelated with current unobservables affecting firms’ maturity demand. I conduct a few robustness tests to support the validity of this identification assumption. First, there might be concerns regarding the endogenous choice to embed call provisions at issuance. Firms with certain characteristics might be more likely to choose call provisions, refinance early, and adjust maturity. However, for sample speculative-grade firms, the vast majority (87.1%) of bonds contain call provisions at issuance. The variations in the decision to embed call provisions at issuance are indeed small. In addition, I conduct the intention-to-treat (ITT) test for the IV regressions, assuming all speculative-grade firms’ bonds have protection periods that last exactly 50% of maturity at issuance. Table E.1 presents the ITT results. The first-stage regression results show strong correlation of the instrumental variable for both measures of early refinancing activities, while the second-stage IV regressions show early refinancing leads to higher F(debt$$\geq$$5Y) and Bond maturity. In terms of economics magnitudes, the results are similar to those of the IV regressions without an ITT assumption presented in Table 4. Table E.1 IV strategy with ITT A. IV ITT strategy first stage (1) (2) D(Early_refi) F(Early_refi) D(turn_callable_predicted) 0.092*** 0.038*** (0.014) (0.010) Observations 4,439 4,097 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.120 0.0148 A. IV ITT strategy first stage (1) (2) D(Early_refi) F(Early_refi) D(turn_callable_predicted) 0.092*** 0.038*** (0.014) (0.010) Observations 4,439 4,097 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.120 0.0148 B. IV ITT strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.625*** 1.143*** 4.820*** (0.014) (0.166) (0.085) (1.127) Observations 3,843 3,791 4,512 4,437 F stat (1st stage) 42.96 41.62 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 $$-$$0.251 0.597 $$-$$0.138 B. IV ITT strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.625*** 1.143*** 4.820*** (0.014) (0.166) (0.085) (1.127) Observations 3,843 3,791 4,512 4,437 F stat (1st stage) 42.96 41.62 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 $$-$$0.251 0.597 $$-$$0.138 This table presents the intention-to-treat (ITT) IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable_{-}predicted)}_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. For all bonds in the sample, protection periods are set to be precisely 50% of maturity at issuance. Two measures of maturity are used: $$F({\it Debt}\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**,and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{i,t}+\sum_{j}\delta_{j}{\it controls}_{j,i,t}+\epsilon_{\it it}$$ Table E.1 IV strategy with ITT A. IV ITT strategy first stage (1) (2) D(Early_refi) F(Early_refi) D(turn_callable_predicted) 0.092*** 0.038*** (0.014) (0.010) Observations 4,439 4,097 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.120 0.0148 A. IV ITT strategy first stage (1) (2) D(Early_refi) F(Early_refi) D(turn_callable_predicted) 0.092*** 0.038*** (0.014) (0.010) Observations 4,439 4,097 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.120 0.0148 B. IV ITT strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.625*** 1.143*** 4.820*** (0.014) (0.166) (0.085) (1.127) Observations 3,843 3,791 4,512 4,437 F stat (1st stage) 42.96 41.62 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 $$-$$0.251 0.597 $$-$$0.138 B. IV ITT strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.625*** 1.143*** 4.820*** (0.014) (0.166) (0.085) (1.127) Observations 3,843 3,791 4,512 4,437 F stat (1st stage) 42.96 41.62 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 $$-$$0.251 0.597 $$-$$0.138 This table presents the intention-to-treat (ITT) IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable_{-}predicted)}_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. For all bonds in the sample, protection periods are set to be precisely 50% of maturity at issuance. Two measures of maturity are used: $$F({\it Debt}\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**,and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{i,t}+\sum_{j}\delta_{j}{\it controls}_{j,i,t}+\epsilon_{\it it}$$ Second, instead of including firm fixed effects and year fixed effects, I run the IV regressions with various industry-year fixed effects to control for time-varying industry-wide factors affecting firms’ choices of maturity. The results are presented in AppendixTable E.2. I use four different industry specifications to ensure the robustness of the results: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. In addition, AppendixTable E.3 presents the IV regression results with standard errors clustered at various industry levels. The estimates remain robust across all the different industry specifications and the magnitudes are similar to the main regression results in Table 4. Table E.2 IV strategy: Industry-year FE F(Debt$$\geq$$5Y) Bond maturity (1) (2) 3) (4) (5) (6) (7) (8) D(Early_refi) 0.199** 0.204** 0.241** 0.337*** 3.796*** 4.246*** 4.287*** 4.835*** (0.093) (0.096) (0.100) (0.128) (0.728) (0.808) (0.864) (1.054) Observations 3,841 3,841 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 83.38 80.66 94.51 74.84 62.99 77.04 89.78 36.61 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry-year FEs FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.023 0.028 0.021 $$-$$0.043 $$-$$0.251 $$-$$0.290 $$-$$0.284 $$-$$0.410 F(Debt$$\geq$$5Y) Bond maturity (1) (2) 3) (4) (5) (6) (7) (8) D(Early_refi) 0.199** 0.204** 0.241** 0.337*** 3.796*** 4.246*** 4.287*** 4.835*** (0.093) (0.096) (0.100) (0.128) (0.728) (0.808) (0.864) (1.054) Observations 3,841 3,841 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 83.38 80.66 94.51 74.84 62.99 77.04 89.78 36.61 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry-year FEs FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.023 0.028 0.021 $$-$$0.043 $$-$$0.251 $$-$$0.290 $$-$$0.284 $$-$$0.410 This table presents IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable)}_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F(Debt\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond Maturity is the average bond maturity. Observations are at the firm-year level. Industry-year fixed effects are included according to various industry definitions: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}D{\it (turn_{-}callable)}_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Table E.2 IV strategy: Industry-year FE F(Debt$$\geq$$5Y) Bond maturity (1) (2) 3) (4) (5) (6) (7) (8) D(Early_refi) 0.199** 0.204** 0.241** 0.337*** 3.796*** 4.246*** 4.287*** 4.835*** (0.093) (0.096) (0.100) (0.128) (0.728) (0.808) (0.864) (1.054) Observations 3,841 3,841 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 83.38 80.66 94.51 74.84 62.99 77.04 89.78 36.61 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry-year FEs FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.023 0.028 0.021 $$-$$0.043 $$-$$0.251 $$-$$0.290 $$-$$0.284 $$-$$0.410 F(Debt$$\geq$$5Y) Bond maturity (1) (2) 3) (4) (5) (6) (7) (8) D(Early_refi) 0.199** 0.204** 0.241** 0.337*** 3.796*** 4.246*** 4.287*** 4.835*** (0.093) (0.096) (0.100) (0.128) (0.728) (0.808) (0.864) (1.054) Observations 3,841 3,841 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 83.38 80.66 94.51 74.84 62.99 77.04 89.78 36.61 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry-year FEs FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.023 0.028 0.021 $$-$$0.043 $$-$$0.251 $$-$$0.290 $$-$$0.284 $$-$$0.410 This table presents IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable)}_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F(Debt\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond Maturity is the average bond maturity. Observations are at the firm-year level. Industry-year fixed effects are included according to various industry definitions: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}D{\it (turn_{-}callable)}_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Table E.3 IV strategy: Standard error clustering F(Debt$$\geq$$5Y) Bond maturity (1) (2) (3) (4) (5) (6) (7) (8) D(Early_refi) 0.328*** 0.328*** 0.323*** 0.323*** 3.769*** 3.769*** 3.825*** 3.825*** (0.112) (0.114) (0.114) (0.115) (0.822) (0.816) (0.874) (0.856) Observations 3,843 3,843 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 34.40 46.85 40.80 42.40 34.95 50.72 43.66 46.50 Controls Yes Yes Yes Yes Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.121 0.121 0.124 0.124 0.116 0.116 0.107 0.107 Industry FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 F(Debt$$\geq$$5Y) Bond maturity (1) (2) (3) (4) (5) (6) (7) (8) D(Early_refi) 0.328*** 0.328*** 0.323*** 0.323*** 3.769*** 3.769*** 3.825*** 3.825*** (0.112) (0.114) (0.114) (0.115) (0.822) (0.816) (0.874) (0.856) Observations 3,843 3,843 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 34.40 46.85 40.80 42.40 34.95 50.72 43.66 46.50 Controls Yes Yes Yes Yes Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.121 0.121 0.124 0.124 0.116 0.116 0.107 0.107 Industry FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 This table presents IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable)}_{i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F({\it Debt}\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the industry-year level according to four industry definitions: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}{\it D(turn_{-}callable)}_{i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Table E.3 IV strategy: Standard error clustering F(Debt$$\geq$$5Y) Bond maturity (1) (2) (3) (4) (5) (6) (7) (8) D(Early_refi) 0.328*** 0.328*** 0.323*** 0.323*** 3.769*** 3.769*** 3.825*** 3.825*** (0.112) (0.114) (0.114) (0.115) (0.822) (0.816) (0.874) (0.856) Observations 3,843 3,843 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 34.40 46.85 40.80 42.40 34.95 50.72 43.66 46.50 Controls Yes Yes Yes Yes Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.121 0.121 0.124 0.124 0.116 0.116 0.107 0.107 Industry FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 F(Debt$$\geq$$5Y) Bond maturity (1) (2) (3) (4) (5) (6) (7) (8) D(Early_refi) 0.328*** 0.328*** 0.323*** 0.323*** 3.769*** 3.769*** 3.825*** 3.825*** (0.112) (0.114) (0.114) (0.115) (0.822) (0.816) (0.874) (0.856) Observations 3,843 3,843 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 34.40 46.85 40.80 42.40 34.95 50.72 43.66 46.50 Controls Yes Yes Yes Yes Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.121 0.121 0.124 0.124 0.116 0.116 0.107 0.107 Industry FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 This table presents IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable)}_{i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F({\it Debt}\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the industry-year level according to four industry definitions: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}{\it D(turn_{-}callable)}_{i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Third, there might be concerns regarding the endogenous choice of protection period: firms might foresee future movements in firm fundamentals or market interest rates and set the protection period to coincide with their projections. A quick review of the summary statistics shows that protection-period setting is indeed fairly standard. In the speculative-grade sample, the average protection period is 49.8 months, with a standard deviation of 13 months. The protection-period ratio on average is 47.4% of maturity at issuance, with a standard deviation of 8%. Also, I regress the protection–period setting on various bond characteristics, firm characteristics at issuance, and interest rate controls at issuance. The dependent variable is the ratio of the protection period to maturity at issuance. For example, if maturity at issuance is 10 years and the protection period lasts 5 years, the protection-period ratio is 50%. AppendixTable E.4 shows that protection-period ratio is uncorrelated with either contemporaneous firm characteristics or interest rate controls, consistent with the protection period being set in a fairly standard way. Moreover, I conduct a within-firm characteristic comparison conditional on $$D({\it turn}_{-}{\it callable})$$ switching from 0 to 1. The results in AppendixTable E.5 show that all the observable firm characteristics are statistically indistinguishable when the IV $$D({\it turn}_{-}{\it callable})$$ switches on. There are no significant firm characteristic changes when protection periods end and bonds turn callable. Table E.4 The determinants of the protection period setting Dep variable: Protection period ratio (1) (2) 3-month T-bill rate –0.007 (0.004) BAA-AAA –0.014 (0.009) Term spread –0.005 (0.007) ln(Assets) –0.016** –0.006 (0.008) (0.008) Leverage –0.026 –0.023 (0.029) (0.029) Ebitda/Assets –0.006 0.018 (0.036) (0.039) M/B –0.006 0.000 (0.009) (0.010) Cash/Assets 0.026 0.033 (0.034) (0.036) Tangible –0.018 –0.007 (0.042) (0.042) Equity_return 0.007 0.004 (0.006) (0.006) Observations 1695 1695 Firm FEs Yes Yes Year FEs No Yes Bond characteristics controls Yes Yes Sample Speculative Speculative Adj R-squared 0.209 0.220 Dep variable: Protection period ratio (1) (2) 3-month T-bill rate –0.007 (0.004) BAA-AAA –0.014 (0.009) Term spread –0.005 (0.007) ln(Assets) –0.016** –0.006 (0.008) (0.008) Leverage –0.026 –0.023 (0.029) (0.029) Ebitda/Assets –0.006 0.018 (0.036) (0.039) M/B –0.006 0.000 (0.009) (0.010) Cash/Assets 0.026 0.033 (0.034) (0.036) Tangible –0.018 –0.007 (0.042) (0.042) Equity_return 0.007 0.004 (0.006) (0.006) Observations 1695 1695 Firm FEs Yes Yes Year FEs No Yes Bond characteristics controls Yes Yes Sample Speculative Speculative Adj R-squared 0.209 0.220 This table examines a bond’s protection-period ratio on various firm characteristics and interest rate conditions at issuance. Protection-period ratio is the ratio of protection period to maturity at issuance. For example, if maturity at issuance is 10 years and protection period lasts 5 years, the protection period ratio is 50%. Bond characteristic controls include offering size, coupon, seniority level, covenant counts, bond rating, and maturity at issuance. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and equity return. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table E.4 The determinants of the protection period setting Dep variable: Protection period ratio (1) (2) 3-month T-bill rate –0.007 (0.004) BAA-AAA –0.014 (0.009) Term spread –0.005 (0.007) ln(Assets) –0.016** –0.006 (0.008) (0.008) Leverage –0.026 –0.023 (0.029) (0.029) Ebitda/Assets –0.006 0.018 (0.036) (0.039) M/B –0.006 0.000 (0.009) (0.010) Cash/Assets 0.026 0.033 (0.034) (0.036) Tangible –0.018 –0.007 (0.042) (0.042) Equity_return 0.007 0.004 (0.006) (0.006) Observations 1695 1695 Firm FEs Yes Yes Year FEs No Yes Bond characteristics controls Yes Yes Sample Speculative Speculative Adj R-squared 0.209 0.220 Dep variable: Protection period ratio (1) (2) 3-month T-bill rate –0.007 (0.004) BAA-AAA –0.014 (0.009) Term spread –0.005 (0.007) ln(Assets) –0.016** –0.006 (0.008) (0.008) Leverage –0.026 –0.023 (0.029) (0.029) Ebitda/Assets –0.006 0.018 (0.036) (0.039) M/B –0.006 0.000 (0.009) (0.010) Cash/Assets 0.026 0.033 (0.034) (0.036) Tangible –0.018 –0.007 (0.042) (0.042) Equity_return 0.007 0.004 (0.006) (0.006) Observations 1695 1695 Firm FEs Yes Yes Year FEs No Yes Bond characteristics controls Yes Yes Sample Speculative Speculative Adj R-squared 0.209 0.220 This table examines a bond’s protection-period ratio on various firm characteristics and interest rate conditions at issuance. Protection-period ratio is the ratio of protection period to maturity at issuance. For example, if maturity at issuance is 10 years and protection period lasts 5 years, the protection period ratio is 50%. Bond characteristic controls include offering size, coupon, seniority level, covenant counts, bond rating, and maturity at issuance. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and equity return. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table E.5 Conditional firm characteristics by $${\it D(turn_{-}callable)}$$ Variable D(turn_callable)=0 D(turn_callable)=1 0-1 P-value ln(Assets) ($Mils) 7.05 7.04 0.01 0.83 Leverage 0.54 0.54 0.00 0.78 Market/Book 1.37 1.35 0.02 0.44 Ebitda/Assets 0.12 0.12 0.00 0.44 Cash/Assets 0.07 0.07 0.00 0.33 ROA $$-$$0.01 $$-$$0.01 0.00 0.76 Debt/Ebitda 4.59 5.10 $$-$$0.51 0.11 Net debt issue/PPE 0.09 0.08 0.00 0.98 Tangile/Assets 0.41 0.41 0.00 0.91 Equity return 0.21 0.21 0.00 0.97 Z score 1.11 1.11 0.00 0.94 S&P rating 14.21 14.13 0.08 0.51 Variable D(turn_callable)=0 D(turn_callable)=1 0-1 P-value ln(Assets) ($Mils) 7.05 7.04 0.01 0.83 Leverage 0.54 0.54 0.00 0.78 Market/Book 1.37 1.35 0.02 0.44 Ebitda/Assets 0.12 0.12 0.00 0.44 Cash/Assets 0.07 0.07 0.00 0.33 ROA $$-$$0.01 $$-$$0.01 0.00 0.76 Debt/Ebitda 4.59 5.10 $$-$$0.51 0.11 Net debt issue/PPE 0.09 0.08 0.00 0.98 Tangile/Assets 0.41 0.41 0.00 0.91 Equity return 0.21 0.21 0.00 0.97 Z score 1.11 1.11 0.00 0.94 S&P rating 14.21 14.13 0.08 0.51 This table presents firm characteristic when $${\it D(turn_{-}callable)}$$ switches from 0 to 1, meaning some bond(s) are scheduled to become callable in that firm-year. Within each firm, only the paired observations with $${\it D(turn_{-}callable)}$$ switching from 0 to 1 are included. $${\it D(turn_{-}callable)} =1$$ indicates some. Two sample t-tests are conducted by grouping observations across years, together with P-values presented. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table E.5 Conditional firm characteristics by $${\it D(turn_{-}callable)}$$ Variable D(turn_callable)=0 D(turn_callable)=1 0-1 P-value ln(Assets) ($Mils) 7.05 7.04 0.01 0.83 Leverage 0.54 0.54 0.00 0.78 Market/Book 1.37 1.35 0.02 0.44 Ebitda/Assets 0.12 0.12 0.00 0.44 Cash/Assets 0.07 0.07 0.00 0.33 ROA $$-$$0.01 $$-$$0.01 0.00 0.76 Debt/Ebitda 4.59 5.10 $$-$$0.51 0.11 Net debt issue/PPE 0.09 0.08 0.00 0.98 Tangile/Assets 0.41 0.41 0.00 0.91 Equity return 0.21 0.21 0.00 0.97 Z score 1.11 1.11 0.00 0.94 S&P rating 14.21 14.13 0.08 0.51 Variable D(turn_callable)=0 D(turn_callable)=1 0-1 P-value ln(Assets) ($Mils) 7.05 7.04 0.01 0.83 Leverage 0.54 0.54 0.00 0.78 Market/Book 1.37 1.35 0.02 0.44 Ebitda/Assets 0.12 0.12 0.00 0.44 Cash/Assets 0.07 0.07 0.00 0.33 ROA $$-$$0.01 $$-$$0.01 0.00 0.76 Debt/Ebitda 4.59 5.10 $$-$$0.51 0.11 Net debt issue/PPE 0.09 0.08 0.00 0.98 Tangile/Assets 0.41 0.41 0.00 0.91 Equity return 0.21 0.21 0.00 0.97 Z score 1.11 1.11 0.00 0.94 S&P rating 14.21 14.13 0.08 0.51 This table presents firm characteristic when $${\it D(turn_{-}callable)}$$ switches from 0 to 1, meaning some bond(s) are scheduled to become callable in that firm-year. Within each firm, only the paired observations with $${\it D(turn_{-}callable)}$$ switching from 0 to 1 are included. $${\it D(turn_{-}callable)} =1$$ indicates some. Two sample t-tests are conducted by grouping observations across years, together with P-values presented. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Lastly, I conduct a second IV regression exploring the fact that credit supply conditions greatly influence firms’ ability to refinance early. I instrument firms’ early refinancing activities with the interaction between the callable fraction and credit supply conditions. Given that bonds past their protection period make early refinancing easier, firms with more of these bonds receive a larger shock to their early refinancing opportunities when the credit market improves, making it easier to extend maturity. AppendixTable E.6 presents the regression results. In terms of economic magnitudes, the estimates show that a one standard deviation (32%) increase in the probability of early refinancing leads to a 21.2% increase in F(debt$$\geq$$5Y) and a 0.8 year extension in Bond maturity. The magnitude is qualitatively similar to the results in Table 4. Table E.6 IV strategy II A. IV strategy II first stage (1) (2) D(Early_refi) F(Early_refi) F(callable)*BAA-AAA $$-$$0.077*** $$-$$0.062*** (0.016) (0.011) Observations 4,175 4,161 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.168 0.112 A. IV strategy II first stage (1) (2) D(Early_refi) F(Early_refi) F(callable)*BAA-AAA $$-$$0.077*** $$-$$0.062*** (0.016) (0.011) Observations 4,175 4,161 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.168 0.112 B. IV strategy II second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.177*** 0.669** 1.587*** 2.435** (0.017) (0.281) (0.107) (1.033) Observations 3,572 3,572 4,175 4,175 F stat (1st stage) 22.91 16.76 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.371 0.128 0.645 0.629 B. IV strategy II second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.177*** 0.669** 1.587*** 2.435** (0.017) (0.281) (0.107) (1.033) Observations 3,572 3,572 4,175 4,175 F stat (1st stage) 22.91 16.76 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.371 0.128 0.645 0.629 This table presents the IV results where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by the interaction between the callable fraction and BAA-AAA. Two measures of maturity are used: F(Debt$$\geq$$5Y) is the fraction of book debt with maturity of 5 years or more, and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}F({\it callable})_{i,t}*{\it BAA-AAA}{}_{t}+\beta_{2}*F({\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi})}_{\it i,t}+\beta_{2}*F({\it callable})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Table E.6 IV strategy II A. IV strategy II first stage (1) (2) D(Early_refi) F(Early_refi) F(callable)*BAA-AAA $$-$$0.077*** $$-$$0.062*** (0.016) (0.011) Observations 4,175 4,161 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.168 0.112 A. IV strategy II first stage (1) (2) D(Early_refi) F(Early_refi) F(callable)*BAA-AAA $$-$$0.077*** $$-$$0.062*** (0.016) (0.011) Observations 4,175 4,161 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.168 0.112 B. IV strategy II second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.177*** 0.669** 1.587*** 2.435** (0.017) (0.281) (0.107) (1.033) Observations 3,572 3,572 4,175 4,175 F stat (1st stage) 22.91 16.76 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.371 0.128 0.645 0.629 B. IV strategy II second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.177*** 0.669** 1.587*** 2.435** (0.017) (0.281) (0.107) (1.033) Observations 3,572 3,572 4,175 4,175 F stat (1st stage) 22.91 16.76 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.371 0.128 0.645 0.629 This table presents the IV results where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by the interaction between the callable fraction and BAA-AAA. Two measures of maturity are used: F(Debt$$\geq$$5Y) is the fraction of book debt with maturity of 5 years or more, and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}F({\it callable})_{i,t}*{\it BAA-AAA}{}_{t}+\beta_{2}*F({\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi})}_{\it i,t}+\beta_{2}*F({\it callable})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ In this paper, I am agnostic about what drives the variations in credit supply conditions. The drivers could be the countercyclical variation in the economy-wide prices of risk, mispricing due to investor biases in evaluating credit risk, or investor sentiment. Instead of trying to disentangle or quantify those theories, I take the variation in aggregate credit supply conditions as given and study firms’ reactions. As a robustness check, I use two other measures of credit supply conditions. The first is the excess bond premium (EBP) from Gilchrist et al. (2012), which is a credit spread measure purged of default risk. An increase in the excess bond premium reflects a reduction in the effective risk-bearing capacity of the financial sector and, as a result, a contraction in the credit supply. The second is the high-yield fraction, which is the fraction of new corporate issuance that is rated as speculative-grade. Greenwood et al. (2013) show that a decline in issuer quality is a reliable signal of credit market overheating. Using different measures of credit supply conditions does not affect the results in Table 4. 6. Conclusion In this paper, I illustrate how speculative-grade firms actively manage their debt maturity via early refinancing of corporate bonds. This process involves retiring outstanding bonds long before the due date and issuing new bonds with longer maturity as replacements. In particular, they refinance early when credit supply conditions are good, leading to a pro-cyclical debt maturity structure. The evidence is consistent with speculative-grade firms dynamically managing their maturity to hedge against refinancing risks. Investment-grade firms, in contrast, do not manage their maturity in the same way, as they are less exposed to refinancing risk. This paper presents a fresh perspective on the early refinancing of corporate bonds. Early refinancing serves to adjust firms’ maturity structure in addition to reducing interest payments. The findings also provide new information on the relation between debt maturity structure and refinancing risk, the heterogeneous maturity management across credit segments, and the impact of ex post refinancing risk on the ex ante choice of maturity at issuance. In addition, the findings highlight how credit supply conditions affect observed financial structure and corporate behavior, and present early refinancing of corporate bonds as an important strategic tool. While shedding light on a number of issues, my findings also raise additional questions. For example, whether or not, and how do firms trade-off interest rate reduction with maturity extension in their early refinancing decisions? I look forward to future research that addresses these and other related questions. I thank my advisors Douglas Diamond, Zhiguo He, Anil Kashyap, Gregor Matvos, and Amir Sufi for their invaluable input and the editor, Philip Strahan, for insightful comments. I would also like to thank Taylor Begley, Andras Danis, Stephen Kaplan, Kelly Shue, Michael Weisbach, Eric Zwick, and two anonymous referees, as well as seminar participants at the University of Chicago, the Fama Miller corporate finance reading group, the London Business School Trans-Atlantic Doctoral Conference, Cheung Kong Graduate School of Business, Hong Kong University of Science and Technology, University of Hong Kong, Georgia Institute of Technology, University of Notre Dame, University of Colorado at Boulder, 2015 EFA conference, and 2016 AFA conference. Research support from the Deutsche Bank, Bradley Foundation, and the John and Serena Liew Fellowship Fund at the Fama-Miller Center for Research in Finance, University of Chicago Booth School of Business is gratefully acknowledged. Appendix A. Action Types Calls: Issuers pay the principal amount prior to scheduled maturity date, in accordance with the embedded call provision of the security. Make-whole calls: Issuers buy back bonds at par plus a premium. This premium is derived by discounting all the future interest payment by the yield of a comparable Treasury security plus additional basis points. Repurchases: Issuers purchase the bond in the open market. Tender offers: Bond holders are invited to tender their bonds for cash. B. Variable Definitions Leverage=(Debt in current liabilities $$+$$ Long-term Debt)/Total assets M/B = (Total asset $$-$$ Common equity $$+$$ Common shares outstanding $$\times$$ closing price (fiscal year))/Total assets EBITDA/Assets =Earnings before interest, tax, depreciation and amortization/L.Total assets Cash/Assets=Cash and short-term investment/L.Total assets S&P credit rating=S&P domestic long-term issuer credit rating Tangible= Property, plant and equipment /L.Total assets Equity return=$$\triangle$$Closing price (fiscal year)/L.Closing price (fiscal year), adjusted for cumulative adjustment factor if applicable C. Bond Contract Terms Call provision: A flag denoting that a bond has a call provision associated with it. Coupon: The applicable annual interest rate that the bond’s issuer is obligated to pay the bond holders. Covenant: A flag denoting that a bond has covenants associated with it. Covenant count: The exact number of covenants associated with a bond. Maturity at issuance: Year to maturity of the bond at issuance. Offering amount: The total principle value of bond initially issued. Protection period: Number of months from issuance day a firm has to wait to be able to call an outstanding bond. Protection period ratio: The ratio of protection period to maturity at issuance. Speculative grade: A dummy equals 1 if first rating is below or equal to BB+ for a bond. Seniority level: Indicates if the security is a secured, senior, or subordinated issue of the issuer. D. Covenant Terms Negative pledge clause: Indicates a covenant whereby the company is prohibited from pledging or placing liens on certain assets. Change of control: Indicates the existence of a provision that allows for the redemption of the bonds or loans in the event of a corporate takeover, merger, or anti-takeover restructuring that would dissolve significant corporate assets. Limit of indebtedness covenant: Indicates a negative or restrictive covenant that places limitations on the amount of debt that the issuer can incur. This can be expressed as a percentage of assets or in monetary terms. Cross default covenant: Indicates a stipulation stating that if an issuer is in default on other borrowings, such non-payment is also considered default in respect to the issue with the cross-default covenant. Sales of assets restriction covenant: Indicates a negative or restrictive covenant that limits the ability of the issuer to sell any or all of its assets. Debt service coverage ratio covenant: Indicates cash available for total debt service or senior debt service. In corporate finance, it is the amount of cash flow available to meet annual interest and principal payments on debt, including sinking fund payments. Rating trigger provision: Indicates a clause that gives a put option to the bond holders if the bond falls below a designated credit rating, usually investment grade. Merger restrictions covenant indicator: Indicates a negative or restrictive covenant placed on the issuer, stating that the issuer may not merge or consolidate with any other entity without satisfying certain conditions. Limitation on sales and leaseback covenant: Indicates a restrictive or negative covenant that prevents the issuer from selling assets (or removing them from the balance sheet for accounting purposes) and then leasing them back from the company to which they were sold. Limitation on subsidiary debt covenant: Indicates a negative or restrictive covenant that places limitations on the amount of debt that the issuer’s subsidiaries can incur. This can be expressed as a percentage of assets or in monetary terms. Restricted payments covenant: Indicates a negative or restrictive covenant that limits an issuer’s ability to make distributions, whether in the form of cash, assets, or securities to shareholders, to redeem subordinated debt, repurchase equity, or provide dividends. E. Additional Robustness Tests Figure E.1 View largeDownload slide Maturity at issuance for industries with long-term sssets This figure shows the histogram of maturity at issuance for sample bonds for the oil, gas, and coal industry and the telephone and television industry. Bonds with maturity at issuance longer than 30 years are included in the 30-year category. Summary statistics are reported on top. Figure E.1 View largeDownload slide Maturity at issuance for industries with long-term sssets This figure shows the histogram of maturity at issuance for sample bonds for the oil, gas, and coal industry and the telephone and television industry. Bonds with maturity at issuance longer than 30 years are included in the 30-year category. Summary statistics are reported on top. Footnotes 1 See Froot, Scharfstein, and Stein (1993), Leland and Toft (1996), Brunnermeier and Yogo (2009), Acharya, Gale, and Yorulmazer (2011), He and Xiong (2012), Almeida et al. (2012), Choi, Hackbarth, and Zechner (2016) 2 See Diamond (1991), Barclay and Smith (1995), Leland and Toft (1996), Guedes and Opler (1996), Johnson (2003), Brunnermeier and Yogo (2009), He and Xiong (2012). 3 Appendix A defines actions, such as calls and tender offers. 4 Given that the ownership of a bond changes following mergers or acquisitions, I use information provided in the issuer notes from FISD, as well as the Thomson M&A database, to identify the precise effective dates of ownership changes. 5 To mitigate the impact of outliers and the possible coding errors, I winsorize all ratios at the upper and lower one percentiles, and apply the winsorization to all analyses. Appendix B defines all variables. 6 Appendix C defines contract terms. 7 Appendix D defines covenant terms. 8 For example, the Walt Disney Company issued senior debentures the so-called “sleeping beauty” bond-in 1993 that are due in 2093. 9 I remove year fixed effects in Columns (3) and (4) but include the 3-month T-bill rate, term premiums, and the BAA-AAA spread as additional controls for general credit market conditions. 10 Seniority level is coded into numeric value, with larger values representing higher seniority. References Acharya, V. V., and Carpenter. J. N. 2002 . Corporate bond valuation and hedging with stochastic interest rates and endogenous bankruptcy. Review of Financial Studies 15 : 1355 – 83 . Google Scholar CrossRef Search ADS Acharya, V. V., Gale, D. and Yorulmazer. T. 2011 . Rollover risk and market freezes. Journal of Finance 66 : 1177 – 209 . Google Scholar CrossRef Search ADS Almeida, H., Campello, M. Laranjeira, B. and Weisbenner. S. 2012 . Corporate debt maturity and the real effects of the 2007 credit crisis. Critical Finance Review 3 – 58 . Baker, M., Greenwood, R. and Wurgler. J. 2003 . The maturity of debt issues and predictable variation in bond returns. Journal of Financial Economics 70 : 261 – 91 . Google Scholar CrossRef Search ADS Barclay, M. J., and Smith. C. W. 1995 . The maturity structure of corporate debt. Journal of Finance 50 : 609 – 31 . Google Scholar CrossRef Search ADS Berger, A. N., Espinosa-Vega, M. A. Frame, W. S. and Miller. N. H. 2005 . Debt maturity, risk, and asymmetric information. Journal of Finance 60 : 2895 – 923 . Google Scholar CrossRef Search ADS Bolton, P., and Scharfstein. D. S. 1996 . Optimal debt structure and the number of creditors. Journal of Political Economy 104 : 1 – 25 . Google Scholar CrossRef Search ADS Brennan, M. J., and Schwartz. E. S. 1977 . Savings bonds, retractable bonds and callable bonds. Journal of Financial Economics 5 : 67 – 88 . Google Scholar CrossRef Search ADS Brunnermeier, M. K., and Yogo. M. 2009 . A note on liquidity risk management. American Economic Review 578 – 83 . Calomiris, C. W., and Kahn. C. M. 1991 . The role of demandable debt in structuring optimal banking arrangements. American Economic Review 497 – 513 . Chen, H., Xu, Y. and Yang. J. 2012 . Systematic risk, debt maturity, and the term structure of credit spreads. Working Paper, National Bureau of Economic Research . Chernenko, S., and Sunderam. A. 2012 . The real consequences of market segmentation. Review of Financial Studies 25 : 2041 – 69 . Google Scholar CrossRef Search ADS Choi, J., Hackbarth, D. and Zechner. J. 2016 . Corporate debt maturity profiles. Working Paper . Diamond, D. W. 1991 . Debt maturity structure and liquidity risk. The Quarterly Journal of Economics 106 : 709 – 37 . Google Scholar CrossRef Search ADS Diamond, D. W., and Rajan. R. G. 2001 . Liquidity risk, liquidity creation, and financial fragility: A theory of banking. Journal of political Economy 109 : 287 – 327 . Google Scholar CrossRef Search ADS Erel, I., Julio, B. Kim, W. and Weisbach. M. S. 2012 . Macroeconomic conditions and capital raising. Review of Financial Studies 25 : 341 – 76 . Google Scholar CrossRef Search ADS Faulkender, M., and Petersen. M. A. 2006 . Does the source of capital affect capital structure? Review of Financial Studies 19 : 45 – 79 . Google Scholar CrossRef Search ADS Froot, K. A., Scharfstein, D. S. and Stein. J. C. 1993 . Risk management: Coordinating corporate investment and financing policies. Journal of Finance 48 : 1629 – 58 . Google Scholar CrossRef Search ADS Graham, J. R., and Harvey. C. R. 2001 . The theory and practice of corporate finance: evidence from the field. Journal of Financial Economics 60 : 187 – 243 . Google Scholar CrossRef Search ADS Gregson, J. 2010 . Hitting a wall of debt. Global Finance Magazine, May 1 . Guedes, J., and Opler. T. 1996 . The determinants of the maturity of corporate debt issues. Journal of Finance 51 : 1809 – 33 . Google Scholar CrossRef Search ADS Harford, J., Klasa, S. and Maxwell. W. F. 2014 . Refinancing risk and cash holdings. Journal of Finance 69 : 975 – 1012 . Google Scholar CrossRef Search ADS Hart, O., and Moore. J. 1994 . A theory of debt based on the inalienability of human capital. Quarterly Journal of Economics 109 : 841 – 79 . Google Scholar CrossRef Search ADS He, Z., and Milbradt. K. 2016 . Dynamic debt maturity. Review of Financial Studies 29 : 2677 – 736 . Google Scholar CrossRef Search ADS He, Z., and Xiong. W. 2012 . Rollover risk and credit risk. Journal of Finance 67 : 391 – 430 . Google Scholar CrossRef Search ADS Jarrow, R., Li, H. Liu, S. and Wu. C. 2010 . Reduced-form valuation of callable corporate bonds: Theory and evidence. Journal of Financial Economics 95 : 227 – 48 . Google Scholar CrossRef Search ADS Johnson, S. A. 2003 . Debt maturity and the effects of growth opportunities and liquidity risk on leverage. Review of Financial Studies 209 – 36 . Leary, M. T. 2009 . Bank loan supply, lender choice, and corporate capital structure. Journal of Finance 64 : 1143 – 85 . Google Scholar CrossRef Search ADS Leland, H. E., and Toft. K. B. 1996 . Optimal capital structure, endogenous bankruptcy, and the term structure of credit spreads. Journal of Finance 51 : 987 – 1019 . Google Scholar CrossRef Search ADS Lemmon, M., and Roberts. M. R. 2010 . The response of corporate financing and investment to changes in the supply of credit. Journal of Financial Quantitative Analysis 45 : 555 – 87 . Google Scholar CrossRef Search ADS Longstaff, F. A., and Tuckman. B. A. 1994 . Calling nonconvertible debt and the problem of related wealth transfer effects. Financial Management 21 – 7 . Mauer, D. C. 1993 . Optimal bond call policies under transactions costs. Journal of Financial Research 16 : 23 – 37 . Google Scholar CrossRef Search ADS Merton, R. C. 1974 . On the pricing of corporate debt: The risk structure of interest rates. Journal of Finance 29 : 449 – 70 . Mian, A. R., and Santos. J. A. 2011 . Liquidity risk, and maturity management over the credit cycle. Available at SSRN 2023516 . Milbradt, K., and Oehmke. M. 2014 . Maturity rationing and collective short-termism. Journal of Financial Economics . Modigliani, F., and Miller. M. H. 1958 . The cost of capital, corporation finance and the theory of investment. American Economic Review 261 – 97 . Roberts, M. R., and Sufi. A. 2009 . Renegotiation of financial contracts: Evidence from private credit agreements. Journal of Financial Economics 93 : 159 – 84 . Google Scholar CrossRef Search ADS Stiglitz, J. E., and Weiss. A. 1981 . Credit rationing in markets with imperfect information. American Economic Review 393 – 410 . Sufi, A. 2009 . Bank lines of credit in corporate finance: An empirical analysis. Review of Financial Studies 22 : 1057 – 88 . Google Scholar CrossRef Search ADS Tang, T. T. 2009 . Information asymmetry and firms’ credit market access: Evidence from moody’s credit rating format refinement. Journal of Financial Economics 93 : 325 – 51 . Google Scholar CrossRef Search ADS Vu, J. D. 1986 . An empirical investigation of calls of non-convertible bonds. Journal of Financial Economics 16 : 235 – 65 . Google Scholar CrossRef Search ADS © The Author 2017. Published by Oxford University Press on behalf of The Society for Financial Studies. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/about_us/legal/notices) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png The Review of Financial Studies Oxford University Press

Kicking Maturity Down the Road: Early Refinancing and Maturity Management in the Corporate Bond Market

The Review of Financial Studies , Volume Advance Article (8) – Oct 12, 2017

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Abstract

Abstract This paper examines debt maturity management through early refinancing, where firms retire their outstanding bonds before the due date and simultaneously issue new ones as replacements. Speculative-grade firms frequently refinance their corporate bonds early to extend maturity, particularly under accommodating credit supply conditions, leading to a procyclical maturity structure. In contrast, investment-grade firms do not manage their maturity in the same manner. I exploit the protection period of callable bonds to show that the maturity extension is not driven by unobservable confounding factors. The evidence is consistent with speculative-grade firms dynamically managing maturity to mitigate refinancing risk. Received June 6, 2016; editorial decision September 21, 2017 by Editor Philip Strahan. In a frictionless (Modigliani and Miller 1958) setting, the maturity structure of debt does not affect firms’ value. Firms can simply refinance at the due date and roll over the debt. In reality, the timing of the due date can be crucial. Firms that refinance during credit market downturns might have to pay significantly higher rates, sell assets in a fire sale, reduce investment, etc.1 While Chief Financial Officers (CFOs) claim they manage debt maturity to “minimize the risk of having to borrow in bad times” (Graham and Harvey 2001), there is very little evidence about how they do this or about which firms consider this a first-order concern. The majority of the literature, both theoretical or empirical, treats maturity of a debt as a one-time decision in which firms choose maturity at issuance and then commit to it until the scheduled due date.2 The only exception is the one in which firms call their outstanding bonds and issue new ones at cheaper rates in order to reduce interest expenses. In this paper, I identify a number of empirical facts showing that a major portion of early refinancing activities are not conducted through calls, and thus they cannot be explained by interest savings. Instead, firms refinance bonds early to continuously adjust maturity after issuance, leading to a significant discrepancy between maturity at issuance and the effective maturity at retirement. First, a major portion of early refinancing activities in the corporate bond market that involves billions of dollars each year does not fit the classic interest saving explanation. About half of the early refinancing of corporate bonds are conducted through repurchases, where firms need to pay the market prices, and tender offers (typically plus some premium), to induce bond holders to comply. Purchasing outstanding bonds at market prices or higher cannot reduce interest payments for firms. Moreover, some firms refinance early through make-whole calls, in which they have to prepay all future coupons discounted near the Treasury rate. Refinancing by make-whole calls is effectively prepayment with penalty, a motivation at odds with the interest saving motive for early refinancing. Second, the vast majority of speculative-grade firms’ bonds are refinanced long before their due date. When refinancing early, speculative-grade firms issue new bonds with a longer maturity to extend the maturity structure. In contrast, only a small fraction of investment-grade firms’ bonds are refinanced early. When investment-grade firms do refinance early, they replace expensive bonds with cheaper ones with a similar maturity. Early refinancing does not change the maturity structure of investment-grade firms. Third, aggregate credit supply conditions dictate the early refinancing of bonds and hence the maturity structure of debt. Speculative-grade firms take advantage of favorable credit market conditions to refinance their bonds early, resulting in a procyclical debt maturity structure. They refinance early over 10% of total outstanding bonds during good credit periods, such as 2004–2005 and 2010-2011, but do so for only about 2% of their outstanding bonds during credit market downturns. The overall debt maturity structure of speculative-grade firms closely moves with their early refinancing of corporate bonds; it significantly extends when firms substantially refinance early, and shortens when early refinancing dries up. Investment-grade firms, in contrast, refinance early only 1%–2% of total outstanding bonds even during good credit periods. Their maturity structure is insensitive to early refinancing activities. In this paper, I interpret the empirical facts as primarily reflecting the desire of speculative-grade firms to reduce refinancing risk through two mechanisms. First, speculative-grade firms rarely issue bonds longer than 10 years, which suggests a credit supply constraint on the maturity at which they can issue. This constraint leads to a significant maturity mismatch between the assets and liabilities of speculative-grade firms, forcing them to frequently tap the capital markets for refinancing. Effectively they synthesize long-term bonds by issuing intermediate-term bond first and then refinancing early to extend maturity, in order to reduces maturity mismatch and mitigate refinancing risk. Second, changing credit supply conditions disproportionately affect the financing costs of speculative-grade firms. While financing costs remain relatively stable for investment-grade firms over a credit cycle, they sharply increase for speculative-grade firms during credit market downturns. Refinancing risk motivates dynamic maturity management, where forward-looking speculative-grade firms “kick maturity down the road” during favorable credit periods. The longer maturity structure reduces the possibility of being forced to refinance during credit market downturns, which creates a hedge against credit supply fluctuations. To identify the link between maturity management and refinancing risk, I examine whether speculative-grade firms that receive an exogenous opportunity to refinance early and therefore a chance to reshuffle the bond contract terms would choose to extend maturity. An exogenous opportunity is crucial as the subsequent maturity adjustment is not driven by unobservable confounding factors. This task is empirically challenging because of firms’ endogenous choices of early refinancing and maturity, and omitted variables can bias the ordinary least-squares (OLS) estimates of the impact of early refinancing on maturity to either direction. To identify the link, I first exploit the variation in the callable structure of corporate bonds to generate an exogenous shock to refinancing opportunities. Firms commonly embed call provisions when issuing bonds, entitling them to call bonds during a predefined period at prespecified prices. Call provisions carry two important features in this setting. First, bonds that become effectively callable promote early refinancing activities. For example, when lower interest rates are available, the value of outstanding bonds exceeds call prices and firms can transfer the value from bond holders to themselves by exercising the calls. In addition, a call price sets the upper limit for early refinancing costs and can promote early refinancing via other redemption methods. These different channels are all incentives for early refinancing activities, generating opportunities to adjust maturity. Second, a call provision has an associated protection period, defined as the period during which a firm cannot call the bond. The standard practice is to set the protection period to last 50% of maturity at issuance: for example, a 10-year bond normally has a 5-year protection period. That protection periods are standard in length and determined well in advance essentially creates exogenous variation in early refinancing opportunities, which are not plagued by endogeneity concerns. Specifically, I instrument a firm’s early refinancing activities with a dummy variable indicating some bonds are scheduled to pass the protection period and become callable in a given firm-year. Identification requires that the timing of when bonds become callable is uncorrelated with current unobservable factors that affects firms’ maturity demand. The lengthy and standard protection period plays a key role in this identification strategy, as it is unlikely that future movements of the unobservables would precisely coincide with when bonds become callable. Analysis reveals that for speculative-grade firms, a one–standard–deviation (32%) increase in the probability of early refinancing leads to a 10% increase in the fraction of book debt with maturity $$\geq$$ 5 years and to more than a 1-year extension in the average maturity for outstanding bonds. In addition to the exogenous shock, I reinforce the link between maturity management and refinancing risk through cross-sectional analyses: firms more subject to refinancing risk are more inclined to manage maturity strategically. I first demonstrate the heterogeneous behavior across credit rating segments. Unlike their speculative-grade counterpart, investment-grade firms do not extend maturity when encountering an exogenous opportunity to refinance early. In addition, speculative-grade firms tend to retire bonds with a shorter maturity, whereas investment-grade firms do not take maturity into account. Second, I group speculative-grade firms based on the degree of maturity mismatch between their assets and liabilities. Firms in industries with greater maturity mismatch significantly extend the maturity using early refinancing, whereas industries with less maturity mismatch exhibit no such behavior. These heterogeneous behaviors across different groups fit their refinancing risk exposures. 1. Literature Review This paper contributes to the literature in a number of ways. First, it adds a new dimension to the early refinancing of corporate bonds. Previous researchers mainly attribute early refinancing to firms’ desire to reduce interest payments: when yields drop, firms call their outstanding bonds and issue new ones at a lower rate (Merton 1974; Brennan and Schwartz 1977; Vu 1986; Mauer 1993; Longstaff and Tuckman 1994; Acharya and Carpenter 2002; Jarrow et al. 2010. My paper shows that early refinancing also can be conducted through tender offers, repurchases, and, even, make-whole calls to manage debt maturity even if doing so does not reduce firms’ interest payments. Instead, early refinancing of corporate bonds, together with the choice of maturity at issuance (Brunnermeier and Yogo 2009; Choi, Hackbarth, and Zechner 2016; He and Milbradt 2016) and other liquidity holdings, such as cash and credit lines (Harford, Klasa, and Maxwell (2014)), serves to reduce refinancing risk. Second, this paper adds a dynamic perspective to the debt maturity literature. Previous literature treats the maturity of a debt as a one-time decision in which firms choose a maturity at issuance and then commit to it until the due date (Diamond 1991; Guedes and Opler 1996; Leland and Toft 1996; Berger et al. 2005; He and Xiong 2012, among others). In this paper, I emphasize that maturity is a continuous and state-contingent decision, and highlight that the discrepancy between maturity at issuance and maturity at retirement can be significant. In addition, a few papers study how interest rate conditions and business cycles affect the maturity choice at issuance (Baker, Greenwood, and Wurgler 2003; Graham and Harvey 2001; Chen, Xu, and Yang 2012), whereas this paper focuses on how credit supply conditions affect the post-issuance dynamics of maturity. This view is consistent with that of He and Milbradt (2016), who theoretically show that firms tend to replace short-term debt with long-term debt when their fundamentals are improving. A closely related empirical paper is Mian and Santos (2011), who show that creditworthy firms try to actively manage the maturity of syndicated loans in normal times. Liquidity demand then becomes countercyclical for these firms because they choose not to refinance when liquidity costs rise. In contrast, my results demonstrate that in the corporate bond market, weaker firms are those that display a procyclical pattern in early refinancing and maturity extension. My results remain valid when I measure the overall debt maturity structure, which includes both outstanding loans and bonds. Moreover, the callable structure of corporate bonds allows me to identify the link between maturity extension and refinancing risk more cleanly and clearly than has been done before because omitted variables affecting firms’ maturity choice, such as unobservable new investment opportunities or credit market conditions, are unlikely to precisely coincide with when bonds turn callable. In addition, there is ample evidence about firms’ renegotiations of their bank loans (Roberts and Sufi 2009; Mian and Santos 2011). However, renegotiations for corporate bonds always have been considered difficult, if not impossible, because of the large number of bond holders outstanding (Bolton and Scharfstein 1996). My paper shows that although private debt can be augmented by renegotiation, public debt can be implicitly augmented through early refinancing. Third, this paper is also related to several studies demonstrating that capital market segmentation and credit supply conditions significantly influence financial structure and corporate behavior (Faulkender and Petersen 2006; Leary 2009; Sufi 2009; Tang 2009; Lemmon and Roberts 2010; Chernenko and Sunderam 2012; Erel et al. 2012). These studies differ from much of the capital structure literature, where capital supply is assumed to be perfectly elastic and capital structures are solely determined by corporate demand for debt. I add to this line of research by showing that credit supply conditions disproportionally affects speculative-grade firms and that maturity is an important channel. 2. Data and Summary Statistics The Mergent Fixed Income Securities Database (FISD) is a comprehensive database of publicly offered U.S. bonds. The FISD includes the majority of corporate bonds and provides details on bond issuance and issuers. Beginning in April 1995, the FISD started to track changes in the outstanding amount of publicly traded corporate bonds. Thus, in addition to the characteristics of bonds at issuance, the FISD provides a detailed history of changes in the amount of bonds outstanding. It details the actions,3 the effective dates of the changes in amount of bonds outstanding, the exact amount changed, and the remaining principal balance afterward. I supplement the FISD data with information from Bloomberg. Then I link the bond data to firm-level data from Compustat and Thompson Reuters.4 The final data set contains information on bonds outstanding for a firm in a given fiscal year including bond characteristics and contract terms at issuance, actions taken for the outstanding bonds, and the principal amount remaining after these actions. To be included in the sample, a firm has to have at least three consecutive annual observations with public bonds outstanding. The final sample includes 1,553 nonfinancial U.S. firms and 15,575 firm-year observations during the period of 1997-2012. The sample covers 29,838 bonds. I also obtain effective yields for different rating indexes and maturity groups from Bank of America, and constant maturity Treasury rates from the Federal Reserve to measure credit market conditions in general. In panel A of Table 1, I compare sample firms with all nonfinancial firms in the Compustat database during the same time period (1997–2012).5 Relative to the average Compustat firm, sample firms tend to be larger, more profitable, have higher leverage, and are more likely to have an S&P long-term issuer credit rating. These differences are not surprising given that sample firms have access to the corporate bond market. Panel B reports summary statistics for sample bonds.6 The median offering amount is $\$$ 100 million, with an average of $\$$224 million. Maturity at issuance has a mean of 10.56 years, and the average coupon rate is 6.60%. Thirty percent of the bonds are rated as speculative-grade at issuance. Almost all bonds have covenants associated with them, and the average covenant count is 4.15.7 Table 1 Summary statistics A. Firm characteristics summary Sample firms Compustat firms Variable Mean SD Median Mean SD Median Assets (mils) 11,290.26 34,366.57 2,886.00 2,699.51 15,312.83 116.40 Book leverage 0.41 0.22 0.37 0.25 0.27 0.18 Market/Book 1.57 0.75 1.33 3.38 6.31 1.54 EBITDA/Assets 0.13 0.08 0.12 $$-$$0.15 0.74 0.08 Cash/Assets 0.07 0.08 0.04 0.21 0.25 0.10 Tangible/Assets 0.39 0.25 0.35 0.29 0.27 0.20 Equity return 0.13 0.66 0.05 0.14 0.88 $$-$$0.02 S&P credit rating 0.91 0.29 1.00 0.19 0.39 0.00 Firm-year Obs 15,575 15,575 15,575 133,102 133,102 133,102 Firms 1,553 1,553 1,553 17,327 17,327 17,327 A. Firm characteristics summary Sample firms Compustat firms Variable Mean SD Median Mean SD Median Assets (mils) 11,290.26 34,366.57 2,886.00 2,699.51 15,312.83 116.40 Book leverage 0.41 0.22 0.37 0.25 0.27 0.18 Market/Book 1.57 0.75 1.33 3.38 6.31 1.54 EBITDA/Assets 0.13 0.08 0.12 $$-$$0.15 0.74 0.08 Cash/Assets 0.07 0.08 0.04 0.21 0.25 0.10 Tangible/Assets 0.39 0.25 0.35 0.29 0.27 0.20 Equity return 0.13 0.66 0.05 0.14 0.88 $$-$$0.02 S&P credit rating 0.91 0.29 1.00 0.19 0.39 0.00 Firm-year Obs 15,575 15,575 15,575 133,102 133,102 133,102 Firms 1,553 1,553 1,553 17,327 17,327 17,327 B. Bond characteristics summary Variable N Mean SD P10 P50 P90 Offering amount (mils) 29,838 223.56 353.76 2.31 100.00 541.83 Maturity at issuance 29,833 10.56 8.83 3.00 9.50 24.00 Coupon 29,822 6.62 2.48 3.50 6.70 9.63 Speculative grade (D) 26,835 0.30 0.46 0.00 0.00 1.00 Covenant (D) 17,857 0.99 0.12 1.00 1.00 1.00 Covenant count 17,825 4.04 2.35 1.00 4.00 7.00 B. Bond characteristics summary Variable N Mean SD P10 P50 P90 Offering amount (mils) 29,838 223.56 353.76 2.31 100.00 541.83 Maturity at issuance 29,833 10.56 8.83 3.00 9.50 24.00 Coupon 29,822 6.62 2.48 3.50 6.70 9.63 Speculative grade (D) 26,835 0.30 0.46 0.00 0.00 1.00 Covenant (D) 17,857 0.99 0.12 1.00 1.00 1.00 Covenant count 17,825 4.04 2.35 1.00 4.00 7.00 This table presents summary statistics for sample U.S. firms and bonds during the period 1997–2012. Panel A presents the means, standard deviations, and median of firms’ characteristics. The unit of observation is at the firm-year level. Panel B presents summary statistics of bond characteristics at issuance. All firm variables and bond variables are defined in Appendixes B and C, respectively. Table 1 Summary statistics A. Firm characteristics summary Sample firms Compustat firms Variable Mean SD Median Mean SD Median Assets (mils) 11,290.26 34,366.57 2,886.00 2,699.51 15,312.83 116.40 Book leverage 0.41 0.22 0.37 0.25 0.27 0.18 Market/Book 1.57 0.75 1.33 3.38 6.31 1.54 EBITDA/Assets 0.13 0.08 0.12 $$-$$0.15 0.74 0.08 Cash/Assets 0.07 0.08 0.04 0.21 0.25 0.10 Tangible/Assets 0.39 0.25 0.35 0.29 0.27 0.20 Equity return 0.13 0.66 0.05 0.14 0.88 $$-$$0.02 S&P credit rating 0.91 0.29 1.00 0.19 0.39 0.00 Firm-year Obs 15,575 15,575 15,575 133,102 133,102 133,102 Firms 1,553 1,553 1,553 17,327 17,327 17,327 A. Firm characteristics summary Sample firms Compustat firms Variable Mean SD Median Mean SD Median Assets (mils) 11,290.26 34,366.57 2,886.00 2,699.51 15,312.83 116.40 Book leverage 0.41 0.22 0.37 0.25 0.27 0.18 Market/Book 1.57 0.75 1.33 3.38 6.31 1.54 EBITDA/Assets 0.13 0.08 0.12 $$-$$0.15 0.74 0.08 Cash/Assets 0.07 0.08 0.04 0.21 0.25 0.10 Tangible/Assets 0.39 0.25 0.35 0.29 0.27 0.20 Equity return 0.13 0.66 0.05 0.14 0.88 $$-$$0.02 S&P credit rating 0.91 0.29 1.00 0.19 0.39 0.00 Firm-year Obs 15,575 15,575 15,575 133,102 133,102 133,102 Firms 1,553 1,553 1,553 17,327 17,327 17,327 B. Bond characteristics summary Variable N Mean SD P10 P50 P90 Offering amount (mils) 29,838 223.56 353.76 2.31 100.00 541.83 Maturity at issuance 29,833 10.56 8.83 3.00 9.50 24.00 Coupon 29,822 6.62 2.48 3.50 6.70 9.63 Speculative grade (D) 26,835 0.30 0.46 0.00 0.00 1.00 Covenant (D) 17,857 0.99 0.12 1.00 1.00 1.00 Covenant count 17,825 4.04 2.35 1.00 4.00 7.00 B. Bond characteristics summary Variable N Mean SD P10 P50 P90 Offering amount (mils) 29,838 223.56 353.76 2.31 100.00 541.83 Maturity at issuance 29,833 10.56 8.83 3.00 9.50 24.00 Coupon 29,822 6.62 2.48 3.50 6.70 9.63 Speculative grade (D) 26,835 0.30 0.46 0.00 0.00 1.00 Covenant (D) 17,857 0.99 0.12 1.00 1.00 1.00 Covenant count 17,825 4.04 2.35 1.00 4.00 7.00 This table presents summary statistics for sample U.S. firms and bonds during the period 1997–2012. Panel A presents the means, standard deviations, and median of firms’ characteristics. The unit of observation is at the firm-year level. Panel B presents summary statistics of bond characteristics at issuance. All firm variables and bond variables are defined in Appendixes B and C, respectively. 3. Early Refinancing and Maturity Management In this section, I describe the basic empirical facts with respect to the differences across credit rating segments along the following dimensions: maturity at issuance, variability of credit conditions, frequency, timing and redemption methods of early refinancing, and contract term changes through refinancing. 3.1 Maturity at issuance I first present evidence on maturity at issuance choice across investment- and speculative-grade segments. In Figure 1, I plot the distribution of maturity at issuance, with the summary statistics given at the top. The maturity at issuance of speculative-grade firms’bonds is highly clustered: the average maturity at issuance is 8.7 years, with a standard deviation of 2.4 years. Speculative-grade firms rarely issue bonds longer than 10 years, and the maximum maturity is 30 years. They only issue a few bonds shorter than 7 years. In contrast, the maturity distribution for investment-grade firms is more spread out: the average maturity at issuance is 11.6 years, with a standard deviation of 10.3 years. Investment-grade firms commonly issue bonds longer than 10 years, and the maximum maturity can reach 100 years.8 They also often issue short-term bonds with a maturity less than or equal to 5 years. Figure 1 View largeDownload slide Maturity at issuance This figure shows the histogram of maturity at issuance for sample corporate bonds. Bonds with maturity at issuance longer than 15 years are included in the 15-year category. Summary statistics are reported on top. Figure 1 View largeDownload slide Maturity at issuance This figure shows the histogram of maturity at issuance for sample corporate bonds. Bonds with maturity at issuance longer than 15 years are included in the 15-year category. Summary statistics are reported on top. My results are consistent with the literature documenting that speculative-grade firms are screened out of the long-term bond market. For example, short-term debt provides creditors with additional flexibility to monitor managers frequently and aligns managerial incentives with that of creditors (Calomiris and Kahn 1991; Diamond and Rajan 2001). Short-term debt also enables the transfer of control rights (Hart and Moore 1994), including the right to liquidate when entrenched managers have no incentives to pull the trigger. Additionally, credit rationing (Stiglitz and Weiss 1981) leads to rationing on asset maturity and hence debt maturity (Milbradt and Oehmke 2014), where lending breaks down beyond a certain maturity due to asymmetric information. 3.2 Variability of credit conditions Changing credit supply conditions also affect the financing costs that speculative-grade firms face and their ability to raise new funding in the market. In panel A of Figure 2, I plot the Bank of America Merrill Lynch US Corporate Index for different ratings from 1997 to 2013. This plot highlights the large time-series variation of yields for speculative-grade firms. While the yields for AAA, AA, and A ratings remain relatively stable throughout the period, the yields for speculative-grades are highly volatile. Take the C-rated firms as an example: the yield was lower than 15% during normal credit periods and increased to more than 25% around 2001 and 40% during the recent financial crisis. Figure 2 View largeDownload slide Bond yields and new issuance across rating segments Panel A presents monthly effective yields of the Bank of America Merrill Lynch US Corporate index for AAA, AA, A, BBB, BB, B, and C rating groups between 1997 and 2013. This index tracks the performance of US dollar denominated corporate debt publicly issued in the US domestic market. The data sequence is obtained from the FRED database from the Federal Reserve Bank of St. Louis. Panel B presents the annual aggregate new corporate bond issuance (billions) across speculative-grade and investment-grade segments. The data sequence is obtained from SIFMA. Figure 2 View largeDownload slide Bond yields and new issuance across rating segments Panel A presents monthly effective yields of the Bank of America Merrill Lynch US Corporate index for AAA, AA, A, BBB, BB, B, and C rating groups between 1997 and 2013. This index tracks the performance of US dollar denominated corporate debt publicly issued in the US domestic market. The data sequence is obtained from the FRED database from the Federal Reserve Bank of St. Louis. Panel B presents the annual aggregate new corporate bond issuance (billions) across speculative-grade and investment-grade segments. The data sequence is obtained from SIFMA. Extremely high yield plus the “flight-to-quality” phenomenon during market downturns effectively freezes new lending in the lower rating segments, as shown in panel B of Figure 2. While the new corporate bond issuance decreased overall during market downturns, the drop in the speculative-grade firms is much more significant. For example, new issuance by speculative-grade firms decreased from $\$$ 135 billion in 2007 to $\$$41.8 billion in 2008, a 70% drop. Meanwhile for investment-grade firms, the new corporate bond issuance decreased from $\$$1,002 billion in 2007 to $\$$668 billion in 2008, a 33% drop. The magnitude of the decrease for speculative-grade firms is twice as large as for investment-grade firms. Volatile borrowing conditions and supply constraint prohibiting long-term issuance expose speculative-grade firms to severe refinancing risk, and thus they tend to avoid short-term bonds because issuing at short maturity could only amplify refinancing risk due to more frequent rollover (Diamond 1991). The ex post refinancing risk is factored into the ex ante choice of maturity at issuance, leading to an equilibrium at which speculative-grade firms issue at intermediate-term (the longest term they are offered by the creditors) (Diamond 1991; Guedes and Opler 1996). I present this in Table 3 by regressing maturity at issuance on a few credit market condition measures. Corporate term spread (the difference between the 10- and 1-year corporate yield), the BAA-AAA spread, and the 3-month T-bill rate are included to measure the credit market conditions. For speculative-grade firms, I also use the difference between speculative- and investment-grade yields (HY-IG) instead of BAA-AAA in some specifications as a robustness check given speculative-grade firms might be more influenced by HY-IG. All credit market condition measures are standardized for easy comparison across groups. In addition, in this regression I exclude bonds with a maturity at issuance longer than 30 years to ensure comparability across credit rating segments. Table 3 Choice of maturity at issuance (1) (2) (3) (4) (5) (6) Term spread –0.289*** –0.271*** –0.202*** –0.199** –0.959*** –0.941*** (0.074) (0.082) (0.077) (0.087) (0.182) (0.201) T-bill rate 0.058 0.114 0.162 0.196 –0.133 –0.158 (0.128) (0.138) (0.137) (0.149) (0.235) (0.272) BAA-AAA –0.172*** –0.146** –0.698*** –0.685*** (0.055) (0.060) (0.197) (0.166) HY-IG –0.152*** –0.122** (0.056) (0.061) Bond rating –0.302*** –0.336*** –0.308*** –0.343*** –0.374* –0.311 (0.091) (0.114) (0.090) (0.112) (0.195) (0.204) Covenant count –0.037 –0.041 –0.036 –0.040 0.152 0.186* (0.032) (0.040) (0.032) (0.040) (0.108) (0.113) Seniority level –1.070*** –1.012*** –1.082*** –1.018*** 0.692 0.660 (0.161) (0.175) (0.164) (0.180) (1.519) (1.500) Offering amount 0.103 0.135 0.097 0.131 –0.352 –0.330 (0.248) (0.315) (0.252) (0.319) (0.238) (0.231) Observations 2,507 2,000 2,507 2,000 7,383 7,300 FEs Firm Firm Firm Firm Firm Firm Sample Speculative Speculative Speculative Speculative Investment Investment Firm control No Yes No Yes No Yes Adj R-squared 0.219 0.206 0.218 0.205 0.132 0.132 (1) (2) (3) (4) (5) (6) Term spread –0.289*** –0.271*** –0.202*** –0.199** –0.959*** –0.941*** (0.074) (0.082) (0.077) (0.087) (0.182) (0.201) T-bill rate 0.058 0.114 0.162 0.196 –0.133 –0.158 (0.128) (0.138) (0.137) (0.149) (0.235) (0.272) BAA-AAA –0.172*** –0.146** –0.698*** –0.685*** (0.055) (0.060) (0.197) (0.166) HY-IG –0.152*** –0.122** (0.056) (0.061) Bond rating –0.302*** –0.336*** –0.308*** –0.343*** –0.374* –0.311 (0.091) (0.114) (0.090) (0.112) (0.195) (0.204) Covenant count –0.037 –0.041 –0.036 –0.040 0.152 0.186* (0.032) (0.040) (0.032) (0.040) (0.108) (0.113) Seniority level –1.070*** –1.012*** –1.082*** –1.018*** 0.692 0.660 (0.161) (0.175) (0.164) (0.180) (1.519) (1.500) Offering amount 0.103 0.135 0.097 0.131 –0.352 –0.330 (0.248) (0.315) (0.252) (0.319) (0.238) (0.231) Observations 2,507 2,000 2,507 2,000 7,383 7,300 FEs Firm Firm Firm Firm Firm Firm Sample Speculative Speculative Speculative Speculative Investment Investment Firm control No Yes No Yes No Yes Adj R-squared 0.219 0.206 0.218 0.205 0.132 0.132 This table presents the sensitivity of maturity at issuance to various credit market conditions. Corporate term spread (the difference between the 10-year and 1-year corporate yield), the BAA-AAA spread or the difference between high-yield and investment-grade yield (HY-IG), and the 3-month T-bill rate are included to measure the credit market conditions. All credit market condition measures are standardized for easy comparison across groups. Bond characteristics include covenant count, seniority level, bond rating, and offering amount. Firm fixed effects are included in each regression. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and equity return. Bonds with maturity at issuance longer than 30 years are excluded from the regression to ensure comparability across credit rating segments. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table 3 Choice of maturity at issuance (1) (2) (3) (4) (5) (6) Term spread –0.289*** –0.271*** –0.202*** –0.199** –0.959*** –0.941*** (0.074) (0.082) (0.077) (0.087) (0.182) (0.201) T-bill rate 0.058 0.114 0.162 0.196 –0.133 –0.158 (0.128) (0.138) (0.137) (0.149) (0.235) (0.272) BAA-AAA –0.172*** –0.146** –0.698*** –0.685*** (0.055) (0.060) (0.197) (0.166) HY-IG –0.152*** –0.122** (0.056) (0.061) Bond rating –0.302*** –0.336*** –0.308*** –0.343*** –0.374* –0.311 (0.091) (0.114) (0.090) (0.112) (0.195) (0.204) Covenant count –0.037 –0.041 –0.036 –0.040 0.152 0.186* (0.032) (0.040) (0.032) (0.040) (0.108) (0.113) Seniority level –1.070*** –1.012*** –1.082*** –1.018*** 0.692 0.660 (0.161) (0.175) (0.164) (0.180) (1.519) (1.500) Offering amount 0.103 0.135 0.097 0.131 –0.352 –0.330 (0.248) (0.315) (0.252) (0.319) (0.238) (0.231) Observations 2,507 2,000 2,507 2,000 7,383 7,300 FEs Firm Firm Firm Firm Firm Firm Sample Speculative Speculative Speculative Speculative Investment Investment Firm control No Yes No Yes No Yes Adj R-squared 0.219 0.206 0.218 0.205 0.132 0.132 (1) (2) (3) (4) (5) (6) Term spread –0.289*** –0.271*** –0.202*** –0.199** –0.959*** –0.941*** (0.074) (0.082) (0.077) (0.087) (0.182) (0.201) T-bill rate 0.058 0.114 0.162 0.196 –0.133 –0.158 (0.128) (0.138) (0.137) (0.149) (0.235) (0.272) BAA-AAA –0.172*** –0.146** –0.698*** –0.685*** (0.055) (0.060) (0.197) (0.166) HY-IG –0.152*** –0.122** (0.056) (0.061) Bond rating –0.302*** –0.336*** –0.308*** –0.343*** –0.374* –0.311 (0.091) (0.114) (0.090) (0.112) (0.195) (0.204) Covenant count –0.037 –0.041 –0.036 –0.040 0.152 0.186* (0.032) (0.040) (0.032) (0.040) (0.108) (0.113) Seniority level –1.070*** –1.012*** –1.082*** –1.018*** 0.692 0.660 (0.161) (0.175) (0.164) (0.180) (1.519) (1.500) Offering amount 0.103 0.135 0.097 0.131 –0.352 –0.330 (0.248) (0.315) (0.252) (0.319) (0.238) (0.231) Observations 2,507 2,000 2,507 2,000 7,383 7,300 FEs Firm Firm Firm Firm Firm Firm Sample Speculative Speculative Speculative Speculative Investment Investment Firm control No Yes No Yes No Yes Adj R-squared 0.219 0.206 0.218 0.205 0.132 0.132 This table presents the sensitivity of maturity at issuance to various credit market conditions. Corporate term spread (the difference between the 10-year and 1-year corporate yield), the BAA-AAA spread or the difference between high-yield and investment-grade yield (HY-IG), and the 3-month T-bill rate are included to measure the credit market conditions. All credit market condition measures are standardized for easy comparison across groups. Bond characteristics include covenant count, seniority level, bond rating, and offering amount. Firm fixed effects are included in each regression. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and equity return. Bonds with maturity at issuance longer than 30 years are excluded from the regression to ensure comparability across credit rating segments. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table 3 shows that both groups react to changes in the credit market conditions in their maturity choices, particularly the term spread and the BAA-AAA spread (or HY-IG). When term spread is larger, meaning long-term bonds are relatively more expensive than the short-term ones, firms reduce the maturity at issuance. However, the results for investment-grade firms in Columns (5) and (6) display significantly higher sensitivity to term spread and the BAA-AAA spread than speculative-grade firms in Columns (1) and (4). The sensitivity of term spread is at least three times as large, and the sensitivity on the BAA-AAA spread is more than twice as large. The regression estimates fit the maturity at issuance description where speculative-grade firms cluster their maturity around the intermediated-term. 3.3 Early refinancing activities I define refinancing to have occurred if within a 3-month time window centered on the month a bond is retired, firms issue other bonds with a dollar amount comparable to the retired amount. Early refinancing refers to the cases in which refinancing happens at least 6 months before the scheduled due date. In panel A of Table 2, I summarize the dollar amount of early refinancing, maturing, and total outstanding bonds for sample firms. The total dollar amount of outstanding sample bonds grew from approximately $\$$ 680 billion in 1997 to $\$$2,553 billion in 2012. The maturing amount displays a monotone increasing trend, similar to the total outstanding amount over the sample period. Table 2 Early refinancing activities and contract term changes A. Early refinancing activities (billions) Year Early refinancing Maturing Total 1997 10.76 34.70 679.80 1998 22.34 35.17 846.10 1999 11.67 48.64 1,055.29 2000 10.28 76.02 1,195.02 2001 27.36 85.80 1,425.60 2002 25.77 96.91 1,585.69 2003 52.42 120.98 1,661.34 2004 52.54 111.90 1,671.57 2005 35.47 116.92 1,680.10 2006 35.92 127.58 1,734.73 2007 43.33 135.08 1,837.71 2008 25.92 116.91 1,993.65 2009 63.01 153.83 2,198.53 2010 77.96 118.83 2,279.62 2011 55.92 144.28 2,345.79 2012 92.39 161.35 2,552.65 A. Early refinancing activities (billions) Year Early refinancing Maturing Total 1997 10.76 34.70 679.80 1998 22.34 35.17 846.10 1999 11.67 48.64 1,055.29 2000 10.28 76.02 1,195.02 2001 27.36 85.80 1,425.60 2002 25.77 96.91 1,585.69 2003 52.42 120.98 1,661.34 2004 52.54 111.90 1,671.57 2005 35.47 116.92 1,680.10 2006 35.92 127.58 1,734.73 2007 43.33 135.08 1,837.71 2008 25.92 116.91 1,993.65 2009 63.01 153.83 2,198.53 2010 77.96 118.83 2,279.62 2011 55.92 144.28 2,345.79 2012 92.39 161.35 2,552.65 B. Contract term changes through early refinancing Speculative grade Investment grade Retired New New-retired Retired New New-retired Maturity 3.96 8.72 4.76 9.89 9.47 $$-$$0.42 Maturity at issuance 8.52 8.72 0.21 15.13 9.47 $$-$$5.66 Coupon 9.18 8.25 $$-$$0.93 6.38 4.36 $$-$$2.02 Offering yield 9.80 8.29 $$-$$1.52 6.53 4.87 $$-$$1.66 Covenant count 6.85 6.38 $$-$$0.47 3.27 3.27 0.00 Covenant (D) 1.00 1.00 0.00 1.00 0.93 $$-$$0.07 B. Contract term changes through early refinancing Speculative grade Investment grade Retired New New-retired Retired New New-retired Maturity 3.96 8.72 4.76 9.89 9.47 $$-$$0.42 Maturity at issuance 8.52 8.72 0.21 15.13 9.47 $$-$$5.66 Coupon 9.18 8.25 $$-$$0.93 6.38 4.36 $$-$$2.02 Offering yield 9.80 8.29 $$-$$1.52 6.53 4.87 $$-$$1.66 Covenant count 6.85 6.38 $$-$$0.47 3.27 3.27 0.00 Covenant (D) 1.00 1.00 0.00 1.00 0.93 $$-$$0.07 Panel A presents the aggregate dollar amount of early refinancing, maturing, and total amount outstanding for sample U.S. firms between 1984 and 2012. I define refinancing to have occurred if within a 3-month time window centered on the month a bond is retired, firms issue other bonds with a dollar amount comparable to the retired amount. Early refinancing refers to cases in which refinancing happens at least 6 months before the scheduled due date. Panel B presents bond characteristics upon early refinancing. For every early refinancing case, I match the retired bond and the newly issued bond and compare their contract terms. The results are presented for speculative-grade and investment-grade separately. Table 2 Early refinancing activities and contract term changes A. Early refinancing activities (billions) Year Early refinancing Maturing Total 1997 10.76 34.70 679.80 1998 22.34 35.17 846.10 1999 11.67 48.64 1,055.29 2000 10.28 76.02 1,195.02 2001 27.36 85.80 1,425.60 2002 25.77 96.91 1,585.69 2003 52.42 120.98 1,661.34 2004 52.54 111.90 1,671.57 2005 35.47 116.92 1,680.10 2006 35.92 127.58 1,734.73 2007 43.33 135.08 1,837.71 2008 25.92 116.91 1,993.65 2009 63.01 153.83 2,198.53 2010 77.96 118.83 2,279.62 2011 55.92 144.28 2,345.79 2012 92.39 161.35 2,552.65 A. Early refinancing activities (billions) Year Early refinancing Maturing Total 1997 10.76 34.70 679.80 1998 22.34 35.17 846.10 1999 11.67 48.64 1,055.29 2000 10.28 76.02 1,195.02 2001 27.36 85.80 1,425.60 2002 25.77 96.91 1,585.69 2003 52.42 120.98 1,661.34 2004 52.54 111.90 1,671.57 2005 35.47 116.92 1,680.10 2006 35.92 127.58 1,734.73 2007 43.33 135.08 1,837.71 2008 25.92 116.91 1,993.65 2009 63.01 153.83 2,198.53 2010 77.96 118.83 2,279.62 2011 55.92 144.28 2,345.79 2012 92.39 161.35 2,552.65 B. Contract term changes through early refinancing Speculative grade Investment grade Retired New New-retired Retired New New-retired Maturity 3.96 8.72 4.76 9.89 9.47 $$-$$0.42 Maturity at issuance 8.52 8.72 0.21 15.13 9.47 $$-$$5.66 Coupon 9.18 8.25 $$-$$0.93 6.38 4.36 $$-$$2.02 Offering yield 9.80 8.29 $$-$$1.52 6.53 4.87 $$-$$1.66 Covenant count 6.85 6.38 $$-$$0.47 3.27 3.27 0.00 Covenant (D) 1.00 1.00 0.00 1.00 0.93 $$-$$0.07 B. Contract term changes through early refinancing Speculative grade Investment grade Retired New New-retired Retired New New-retired Maturity 3.96 8.72 4.76 9.89 9.47 $$-$$0.42 Maturity at issuance 8.52 8.72 0.21 15.13 9.47 $$-$$5.66 Coupon 9.18 8.25 $$-$$0.93 6.38 4.36 $$-$$2.02 Offering yield 9.80 8.29 $$-$$1.52 6.53 4.87 $$-$$1.66 Covenant count 6.85 6.38 $$-$$0.47 3.27 3.27 0.00 Covenant (D) 1.00 1.00 0.00 1.00 0.93 $$-$$0.07 Panel A presents the aggregate dollar amount of early refinancing, maturing, and total amount outstanding for sample U.S. firms between 1984 and 2012. I define refinancing to have occurred if within a 3-month time window centered on the month a bond is retired, firms issue other bonds with a dollar amount comparable to the retired amount. Early refinancing refers to cases in which refinancing happens at least 6 months before the scheduled due date. Panel B presents bond characteristics upon early refinancing. For every early refinancing case, I match the retired bond and the newly issued bond and compare their contract terms. The results are presented for speculative-grade and investment-grade separately. The early refinancing amount, in comparison, is much more volatile and shows sharp decreases followed by large rebounds. Early refinancing declines sharply in 2000 and 2008, coinciding with the two financial market crashes of the last decade, and peaks in 1998, 2004–2005, and after 2010. When I separate firms by credit ratings, this procyclical pattern in early refinancing is particularly strong for speculative-grade firms. Panel A in Figure 3 presents the average early refinancing fraction, which is the ratio of the early refinancing dollar amount to the total dollar amount of outstanding bonds at previous year-end. As shown in panel A, speculative-grade firms refinance more than 10% of total outstanding bonds early during good credit periods, such as 2004–2005 and 2010-2011, but less than 2% during credit market downturns. In contrast, investment-grade firms consistently refinance early about 1%–2% of the total outstanding bonds throughout the period. Figure 3 View largeDownload slide Early refinancing and changes in maturity Panel A shows early refinancing of corporate bonds for U.S. firms in Compustat between 1997 and 2012. I plot the average ratio of early refinancing amount to the total amount of outstanding bonds at the previous year-end. Panel B shows the correlation between firms’ early refinancing activities (left scale) and changes in average bond maturity (right scale) proportional to previous year-end average maturity. Figure 3 View largeDownload slide Early refinancing and changes in maturity Panel A shows early refinancing of corporate bonds for U.S. firms in Compustat between 1997 and 2012. I plot the average ratio of early refinancing amount to the total amount of outstanding bonds at the previous year-end. Panel B shows the correlation between firms’ early refinancing activities (left scale) and changes in average bond maturity (right scale) proportional to previous year-end average maturity. To observe the contract term changes upon early refinancing, for each early refinancing case I match the early-retired bond to the newly-issued bond and examine the differences. The results are presented separately for the investment-grade and speculative-grade firms in panel B of Table 2. the results show that speculative-grade firms get a significant extension in maturity, whereas investment-grade firms simply issue new bonds with a similar maturity. Speculative-grade firms extend maturity from 3.96 years to 8.72 years, a more than 100% extension. Investment-grade firms’ maturity moves from 9.89 years to about 9.47 years, which is roughly the same. Speculative-grade firms do not appear to adjust the maturity they issue at, because the retired bonds and the new bonds have maturity at issuance of 8.52 and 8.72 years, respectively. Investment-grade firms, on the other hand, shorten the maturity at issuance from 15.13 years to 9.47 years. Panel B of Table 2 also shows that both types of early refinancers save on interest payments, which is consistent with the literature on early refinancing. Upon refinancing, the coupon rate decreases 0.93% for speculative-grade firms and 2.02% for investment-grade firms, while offering yield decreases 1.52% for speculative-grade firms and 1.66% for investment grade firms, respectively. For covenant strictness, speculative-grade firms experience a drop of 0.47 in their covenant count through early refinancing: on average, their covenant count decreases from 6.85 to 6.38. Investment-grade firms experience no covenant count decrease, but a 7% drop in the covenant dummy. Maturity extension through early refinancing, along with the procyclical early refinancing activities of speculative-grade firms, explains the time–series correlation between early refinancing and firms’ maturity structures presented in panel B of Figure 3. For speculative-grade firms in the left figure, maturity extends significantly when firms refinance early on a large scale. When speculative-grade firms’ early refinancing activities drop sharply, their maturity shortens correspondingly, leading to a procyclical maturity structure. For investment-grade firms in the right figure, we do not observe a similar correlation between early refinancing and firms’ maturity structure. Both early refinancing activities and maturity structure remain fairly stable and they do not display any comovement patterns. 3.4 Early refinancing: Redemption methods and timing I next turn to examine the redemption methods and timing of early refinancing. I decompose early refinancing based on methods of redemption and present the results in Figure 4. Calls, whereby issuers exercise call provisions to buy back outstanding bonds, are a common method of early refinancing. For instance, in 2003–2004, firms called about $\$$ 30 billion of outstanding corporate bonds, whereas the total amount of early refinancing was around $\$$60 billion. Tender offers account for the majority of the rest, together with some make-whole call and repurchase activities. Also, the plot shows a similar procyclical fluctuation: call and tender offer amounts sharply increase in 1998, 2003–2004, and 2010–2011, and decrease during the two market downturns in 2000 and 2008. Figure 4 View largeDownload slide Early refinancing decomposition This figure shows early refinancing activities according to different redemption methods. Appendix A provides the definitions. Figure 4 View largeDownload slide Early refinancing decomposition This figure shows early refinancing activities according to different redemption methods. Appendix A provides the definitions. Notice that early refinancing conducted through tender offers, repurchases, and make-whole calls accounts for half of the total dollar amount. Even if all the transaction costs are ignored, firms need to pay at least the market price in repurchases, and typically some premium in tender offers, to induce bond holders to comply. Given that tender offers mainly happen during good credit periods when yields are relatively low, refinancing through tender offers can be expensive and cannot help firms to save on their interest payments. Make-whole calls are even more expensive because all of the future coupons have to be paid at a discount rate close to the Treasury rate, making them effectively prepayment with penalty and the opposite of interest savings. Explanations other than interest savings are required to fit the data patterns. Figure 5 shows the ratio of time passed when refinanced over maturity at issuance. If maturity at issuance is 10 years and the bond is refinanced at the end of its sixth year, the fraction of elapsed maturity at refinancing is 60%. I denote this as an instance of early refinancing. If a firm refinances a bond at the scheduled due date, the fraction of elapsed maturity at refinancing is one, and I denote this case as an instance of refinancing at maturity. I plot the distribution for both speculative-grade firms and investment-grade firms to explore the heterogeneity across these two segments. I restrict the sample to bonds with maturity at issuance between 7 to 10 years in order to make them comparable across credit rating segments. Figure 5 shows that the majority of speculative-grade firms’ bonds are refinanced before the due date; less than 10% of refinancing cases occur at the due date. The largest chunk of refinancing happens after a bond reaches the middle of its maturity at issuance. In contrast, investment-grade firms refinance over 70% of their bonds right at maturity. Figure 5 View largeDownload slide Percentage of maturity elapsed at refinancing This figure shows the percentage of maturity elapsed at refinancing for sample bonds. The sample is restricted to bonds being refinanced and excludes bonds that are retired, but not refinanced. If a bond with maturity at issuance of 10 years is refinanced 6 years after issuance, then I denote 60% of maturity elapsed at refinancing. For a bond refinanced at scheduled maturity, maturity elapsed at refinancing is 100%. The sample is restricted to bonds with maturity at issuance between 7 and 10 years to ensure comparability across credit rating segments. Figure 5 View largeDownload slide Percentage of maturity elapsed at refinancing This figure shows the percentage of maturity elapsed at refinancing for sample bonds. The sample is restricted to bonds being refinanced and excludes bonds that are retired, but not refinanced. If a bond with maturity at issuance of 10 years is refinanced 6 years after issuance, then I denote 60% of maturity elapsed at refinancing. For a bond refinanced at scheduled maturity, maturity elapsed at refinancing is 100%. The sample is restricted to bonds with maturity at issuance between 7 and 10 years to ensure comparability across credit rating segments. There might be concerns that speculative-grade firms conduct more early refinancing to reduce interest rate costs— firms call back outstanding bonds when the call options are in the money. In order to exclude early refinancing activities that are potentially driven by interest rate reductions, I also test noncallables bonds. For noncallable bonds, firms can only refinance early through tender offers, repurchases, and make–whole calls, which do not reduce interest costs. An untabulated figure for noncallable bonds delivers a similar message as the full bond sample, if not stronger. Investment-grade firms almost always refinance right at the due date, while speculative-grade firms tend to refinance long before the due date. The timing of refinancing across speculative-grade and investment-grade firms fits the different exposures to refinancing risk. The financing costs for investment-grade firms are relatively stable throughout good or bad credit supply conditions; hence firms can simply wait until the bond matures and then roll it over. For speculative-grade firms, financing costs are volatile. Unable to foresee what might happen at the due date, speculative-grade firms are concerned about refinancing risks and prefer to refinance long before the due date. 4. Dynamic Maturity Management and Refinancing Risk In this section, I hypothesize that speculative-grade firms manage maturity to mitigate refinancing risks and describe the identification challenges. To establish the link between maturity management and refinancing risk, I first show that speculative-grade firms would extend maturity upon receiving an exogenous shock to early refinancing cost. I then examine how cross-sectional exposure to refinancing risk, indexed by credit rating segments, affects firms’ maturity extension and what kind of bonds to refinance early. Lastly, I use maturity mismatch as another index of refinancing risk exposure and show how it affects firms’ maturity management. 4.1 Hypothesis and identification challenges In this paper, I interpret the observed dynamic debt maturity management as primarily reflecting speculative-grade firms’ desire to reduce refinancing risk. Two mechanisms subject speculative-grade firms to severe refinancing risk, leading them to conduct more maturity management than their investment-grade peers. First, creditors prefer to keep speculative-grade firms on a short leash, as shown in Figure 1. The constraint imposed by the creditors caps the maturity at which speculative-grade firms can issue and excludes them from the long-term market. This constraint leads to a significant maturity mismatch between the assets and liabilities of speculative-grade firms, and the mismatch only widens as the bond’s effective maturity shrinks. Maturity mismatch forces firms to frequently tap the capital markets to refinance toward longer maturity in order to mitigate refinancing risk. Second, changing credit supply conditions disproportionately affect the financing costs of speculative-grade firms. While financing costs remain relatively stable for investment-grade firms over a credit cycle, they increase sharply for speculative-grade firms during credit market downturns, as shown in Figure 2. Extremely high yield plus the “flight-to-quality” phenomenon during market downturns effectively freezes new lending in the lower rating segments, making firms more susceptible to rollover risk (He and Xiong 2012). Extending maturity during good credit periods reduces the possibility of being forced to refinance during future market downturns, therefore hedges against credit supply fluctuations. The descriptive statistics in Section 3 show the positive correlation between speculative-grade firms’ early refinancing activities and corresponding maturity extension. However, to establish the link between the observed patterns and refinancing risk, ideally I want to show that upon receiving an exogenous opportunity to refinance early and therefore a chance to reshuffle the bond contract terms, speculative-grade firms may choose to extend maturity. An exogenous opportunity is crucial here as the subsequent maturity adjustment is not driven by contemporaneous confounding factors. If speculative-grade firms want to proactively extend maturity to reduce refinancing risk, firms with a randomly assigned opportunity are expected to extend maturity by a larger magnitude than firms without it. Given that an ideal experiment setting is not available, endogeneity remains as a big concern. Firms are choosing when to refinance their bonds early and which maturity to issue at simultaneously. For example, when a firm chooses to refinance early, the slope of the yield curve and general credit market conditions might be changing; thus, the firm would prefer to issue at longer or shorter maturity accordingly. Also, there might be new investment opportunities on the horizon and firms may want to issue new bonds according to the length of the new projects. Omitted variables can bias the estimates to either direction. To establish the link described, I need an exogenous shock to an early refinancing opportunity that is not plagued by the unobservables driving a firm’s demand for a maturity. I exploit the protection-period setting of the call provision to generate this exogenous shock, which is uncorrelated with the contemporaneous confounding factors. In additional to the exogenous shock, cross-sectional tests in line with different exposures to refinancing risk can also help to identify the link. Firms more subject to refinancing risk are expected to extend maturity more through early refinancing. Measures based on the two mechanisms described above – credit rating segments as well as maturity mismatch between assets and liabilities – can be used to index cross-sectional exposures to refinancing risk and test this prediction. 4.2 Identification: Exogenous shock 4.2.1 Institutional background: Call provision and protection period Firms commonly pay a higher yield to embed call provisions when issuing bonds, and the ratio of call provision is as high as 87.1% in the sample of speculative-grade firms. If a call provision is included at issuance, the call schedule, call prices, and protection period are contracted. The protection period is defined as the period during which the company cannot call the bond, starting from the issuing date. It provides the bond holders with a guaranteed period during which they will be able to hold the bond and receive coupon payments. For example, Kroger issued a callable 10-year senior debenture on June 15, 1993, with the scheduled due date on June 15, 2003. The embedded call provision states that Kroger would be able to call the debenture starting June 15, 1998, 5 years after the issuance day, with a price of $\$$ 104.25. The call price decreased to $\$$102.834 on June 15, 1999, to $\$$101.417 on June 15, 2000, and finally to $\$$100 on June 15, 2001. The 5-year protection period is precisely 50% of maturity at issuance in this case. Bonds turning callable promote early refinancing through different channels. First, when a lower interest rate is available, either because of a drop in the prevailing market rate or because of better firm performance, the value of an outstanding bond increases correspondingly. If the discounted value exceeds contemporaneous scheduled call prices, firms transfer values from bond holders to themselves by calling outstanding bonds at scheduled prices. The value transferred essentially make calls a subsidized way to refinance early. Second, bonds turning callable set an upper limit on the early refinancing price and can facilitate other methods of redemption. For example, firms no longer need to employ a make-whole call to retire outstanding bonds, which reduces the costs of early refinancing significantly. In tender offers, bondholders’ alternative value of not tendering decreases as the protection period ends, because in some future state(s) of the world firms can excise call provisions, which provides more incentive for bond holders to comply. In addition, call provisions grant firms the right to call back bonds at their discretion. This facilitates early refinancing because bond holders have to return the bonds upon calling. In tender offers and repurchases, bond holders retain the right to not respond if the offered prices are not attractive, or they have other nonprice reasons for holding the bonds. 4.2.2 Instrumental variable strategy: Timing of the protection period My instrumental variable (IV) strategy exploits the precise timing of the protection period. I instrument early refinancing activities with a dummy variable indicating that some bonds are scheduled to become callable for firm i in year t. The intuition is that bonds turning callable facilitate early refinancing and generate a positive shock to early refinancing opportunities. However, because protection periods are fairly standard in length and decided years in advance, the shock is disconnected from other unobservable contemporaneous determinants of maturity at refinancing. The IV strategy operates through timing: when a firm issues a new bond, it is unlikely that the firm can foresee the future changes in confounding factors and how these changes would affect its maturity structure. It is even more unlikely that the firm can precisely foresee the changes at a particular point in the future when the protection period ends, especially given that the protection period is set by an industry standard, not the firm. This lengthy yet standard protection period plays a key role. When the IV is activated, for example, 5 years in the future, confounding factors should change in all directions and have no systematic impact on maturity toward any particular direction. The following is the IV regression specification: First stage: $$D({\it Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{i,t}+\beta_{i}{\it controls}_{i,t}+e_{\it i,t}$$ Second stage: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early}_{-}{\it refi}})_{i,t}+\delta_{i}{\it controls}_{i,t}+\epsilon_{\it i,t}$$ In the first stage, $${\it D(turn_{-}callable)}$$ equals one if some outstanding bonds are scheduled to pass the protection period and become callable for firm i at year t. Take the Kroger 10-year senior debenture as an example. The turn-callable indicator for this debenture switches to one in 1998, and remains zero for all the other years, leading to $$D({\it turn}_{-}{\it callable})=1$$ for Kroger in 1998. Early refinancing activity is indexed by $${\it D(Early_{-}refi)}$$, a dummy variable equals one if firm i refinances early at year t. Contemporaneous firm characteristics are controlled in the regressions, including ln(Assets), Leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and S&P rating. ln(Assets) measures a firm’s ability to collateralize the debt and also captures the liquidation value in a distressed state. Leverage captures a firm’s financial health. Market/Book is used to measure future investment prospects. EBITDA/Assets and Cash/Assets measure a firm’s profitability and short-term liquidity, respectively. Tangible/Assets measures the pledgeability of assets. I include the S&P rating as a general control for a firm’s default risk. To control for time-unvarying unobservables that might also affect a given firm’s maturity choice, I include firm fixed effects. Year fixed effects are included to control for the interest rate and observable credit market conditions affecting firms’ maturity choices. 4.2.3 Instrumental variable strategy: Results Table 4, Columns (1) and (2), present the IV regression first-stage results. I use two measures to capture early refinancing activities: the first one is a dummy D(Early-refi) indicating whether firm i conducted early refinancing activities in year t; the second one is F(Early-refi), which measures the fraction of the total amount of outstanding bonds undergoing early refinancing for firm i in year t. In panel A, the first-stage shows strong results for both measures of early refinancing activities. In terms of economic magnitudes, bonds becoming callable increase the probability of early refinancing by 10.1% and the fraction of early refinancing by 2.5%. Table 4 IV strategy results (1) (2) (3) (4) D(Early_refi) F(Early_refi) D(Early_refi) F(Early_refi) D(turn_callable) 0.101*** 0.025*** 0.173*** 0.050*** (0.016) (0.010) (0.030) (0.015) D(turn_callable)*BAA-AAA –0.061*** –0.021*** (0.017) (0.007) BAA-AAA –0.059*** –0.031*** (0.008) (0.006) Observations 4,512 4,169 4,512 4,169 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes No No Sample Speculative Speculative Speculative Speculative Adj R-squared 0.123 0.011 0.114 0.004 (1) (2) (3) (4) D(Early_refi) F(Early_refi) D(Early_refi) F(Early_refi) D(turn_callable) 0.101*** 0.025*** 0.173*** 0.050*** (0.016) (0.010) (0.030) (0.015) D(turn_callable)*BAA-AAA –0.061*** –0.021*** (0.017) (0.007) BAA-AAA –0.059*** –0.031*** (0.008) (0.006) Observations 4,512 4,169 4,512 4,169 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes No No Sample Speculative Speculative Speculative Speculative Adj R-squared 0.123 0.011 0.114 0.004 B. IV strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.328** 1.143*** 3.769*** (0.014) (0.130) (0.085) (0.966) Observations 3,843 3,843 4,512 4,512 F stat (1st stage) 38.45 40.42 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 0.121 0.597 0.116 B. IV strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.328** 1.143*** 3.769*** (0.014) (0.130) (0.085) (0.966) Observations 3,843 3,843 4,512 4,512 F stat (1st stage) 38.45 40.42 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 0.121 0.597 0.116 This table presents IV results, where $$D({\it Early}_{-}{\it refi})_{\it i,t}$$ is instrumented by $$D({\it turn}_{-}{\it callable})_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F(Debt\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $$D({\it Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early}_{-}{\it refi}})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{it}$$ Table 4 IV strategy results (1) (2) (3) (4) D(Early_refi) F(Early_refi) D(Early_refi) F(Early_refi) D(turn_callable) 0.101*** 0.025*** 0.173*** 0.050*** (0.016) (0.010) (0.030) (0.015) D(turn_callable)*BAA-AAA –0.061*** –0.021*** (0.017) (0.007) BAA-AAA –0.059*** –0.031*** (0.008) (0.006) Observations 4,512 4,169 4,512 4,169 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes No No Sample Speculative Speculative Speculative Speculative Adj R-squared 0.123 0.011 0.114 0.004 (1) (2) (3) (4) D(Early_refi) F(Early_refi) D(Early_refi) F(Early_refi) D(turn_callable) 0.101*** 0.025*** 0.173*** 0.050*** (0.016) (0.010) (0.030) (0.015) D(turn_callable)*BAA-AAA –0.061*** –0.021*** (0.017) (0.007) BAA-AAA –0.059*** –0.031*** (0.008) (0.006) Observations 4,512 4,169 4,512 4,169 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes No No Sample Speculative Speculative Speculative Speculative Adj R-squared 0.123 0.011 0.114 0.004 B. IV strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.328** 1.143*** 3.769*** (0.014) (0.130) (0.085) (0.966) Observations 3,843 3,843 4,512 4,512 F stat (1st stage) 38.45 40.42 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 0.121 0.597 0.116 B. IV strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.328** 1.143*** 3.769*** (0.014) (0.130) (0.085) (0.966) Observations 3,843 3,843 4,512 4,512 F stat (1st stage) 38.45 40.42 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 0.121 0.597 0.116 This table presents IV results, where $$D({\it Early}_{-}{\it refi})_{\it i,t}$$ is instrumented by $$D({\it turn}_{-}{\it callable})_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F(Debt\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $$D({\it Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early}_{-}{\it refi}})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{it}$$ In Table 4, panel A, Columns (3) and (4), I interact the instrument with the BAA-AAA spread to show the sensitivity of the instrument over credit cycles.9 Coefficients on the BAA-AAA spread confirm the observed procyclical pattern of early refinancing activities, and coefficients on the interaction term show that while callability leads to more refinancing activities in general, the sensitivity is significantly higher when the BAA-AAA spread is low (good credit market conditions). These results address both of the two key mechanisms driving maturity extension through early refinancing: first, maturity mismatch between assets and liabilities leads speculative-grade firms to utilize opportunities like call period endings to refinance early for maturity extension; and second, the variability of credit conditions makes speculative-grade firms more likely to take advantage of the opportunity during good credit periods. Table 4, panel B, shows both the OLS regression estimates and the second stage of the IV regression estimates with D(Early-refi) as the instrumented variable. I also use two variables to measure firms’ maturity structure: F(debt$$\geq$$5Y), which is the fraction of total book debt (including loans and bonds) with maturity $$\geq$$5 years, and Bond maturity, which measures the average maturity of outstanding corporate bonds. In IV regressions, the Kleibergen-Paap Wald F-stat for the weak instrument test is much larger than 10, which is the rule of thumb for identifying a weak instrument. The results show that early refinancing leads to a larger fraction of book debt with maturity $$\geq$$ 5 years, as well as a longer average bond maturity. In terms of economic magnitudes, the estimates indicate that a one-standard-deviation (32%) increase in the probability of early refinancing leads to a 10.6% increase in F(debt$$\geq$$5Y) and a 1.21-year extension in Bond maturity. There are a few possible explanations for the IV estimates being larger than the OLS estimates in Table 4. First, IV regressions estimate the local average treatment effect (LATE) on firms responding to the shock, whereas OLS regressions estimate the average treatment effect (ATE) for all sample firms. Firms that respond to the instrument are more likely to be eager to extend maturity, causing a stronger effect of early refinancing on maturity. Second, when a firm chooses to refinance early, it might face an interest rate environment in which the short-term rate is more desirable than the long-term rate, or its investment opportunity set includes more short-term projects. That results in a firm’s demand for short-term maturity, leading to a downward bias in the OLS coefficients. 4.3 Identification: Heterogeneous maturity management 4.3.1 Maturity extension across credit rating segments The descriptive statistics reveal the correlation between early refinancing and maturity extension only for speculative-grade firms, not their investment-grade counterpart. To examine this heterogeneity across credit rating segments within the IV form, I interact the early refinancing activities with D(speculative), which equals one if firm i receives an S&P domestic long-term issuer credit rating below or equal to BB$$+$$. I run both OLS and IV regressions and present the results in Table 5. In the IV regressions, I interact both the instrument and instrumented variables with D(speculative). The IV results show that only speculative-grade firms extend maturity through early refinancing upon receiving an exogenous shock to an early refinancing opportunity, but investment-grade firms do not. In Columns (2) and (4), the coefficients for investment-grade firms remain insignificantly different from zero. In Column (2), the coefficient for speculative-grade firms is 0.368 higher than that of investment-grade firms. In Column (4), the coefficient for speculative-grade firms is 4.006 higher than that of investment-grade firms. Table 5 Heterogeneous maturity extension across credit ratings A. IV First stages (1) (2) D(Early_refi) D(Early_refi)*D(Speculative) D(turn_callable) 0.302*** 0.008** (0.029) (0.003) D(turn_callable)*D(Speculative) 0.201*** 0.098*** (0.033) (0.016) Observations 8,916 8,916 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Full Full Adj R-squared 0.226 0.183 A. IV First stages (1) (2) D(Early_refi) D(Early_refi)*D(Speculative) D(turn_callable) 0.302*** 0.008** (0.029) (0.003) D(turn_callable)*D(Speculative) 0.201*** 0.098*** (0.033) (0.016) Observations 8,916 8,916 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Full Full Adj R-squared 0.226 0.183 B. IV Second Stage F(Debt$$\geq$$5Y) Bond Maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.047*** 0.082 0.288 0.325 (0.011) (0.050) (0.179) (1.387) D(Early_refi)*D(Speculative) 0.047*** 0.368** 1.301*** 4.006** (0.018) (0.146) (0.220) (1.651) Observations 8,027 8,027 8,916 8,916 F Stat (1st stage) 21.38 30.81 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Full Full Full Full Adj R-squared 0.358 0.176 0.804 0.788 B. IV Second Stage F(Debt$$\geq$$5Y) Bond Maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.047*** 0.082 0.288 0.325 (0.011) (0.050) (0.179) (1.387) D(Early_refi)*D(Speculative) 0.047*** 0.368** 1.301*** 4.006** (0.018) (0.146) (0.220) (1.651) Observations 8,027 8,027 8,916 8,916 F Stat (1st stage) 21.38 30.81 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Full Full Full Full Adj R-squared 0.358 0.176 0.804 0.788 This table presents the OLS and IV results, where $$D({\it Early}_{-}{\it refi})_{\it i,t}$$ is interacted with D(speculative). Two measures of maturity are used: F(Debt$$\geq$$5Y ) is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included in the regressions. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. \begin{align*} {\it Maturity}{}_{\it i,t}&=\delta_{0}+\delta_{1}D({\it Early}_{-}{\it refi})_{i,t}+\delta_{2}D({\it Early}_{-}{\it refi})_{i,t}*D({\it spec}){}_{i,t}\nonumber\\ &\quad +\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{it} \end{align*} Table 5 Heterogeneous maturity extension across credit ratings A. IV First stages (1) (2) D(Early_refi) D(Early_refi)*D(Speculative) D(turn_callable) 0.302*** 0.008** (0.029) (0.003) D(turn_callable)*D(Speculative) 0.201*** 0.098*** (0.033) (0.016) Observations 8,916 8,916 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Full Full Adj R-squared 0.226 0.183 A. IV First stages (1) (2) D(Early_refi) D(Early_refi)*D(Speculative) D(turn_callable) 0.302*** 0.008** (0.029) (0.003) D(turn_callable)*D(Speculative) 0.201*** 0.098*** (0.033) (0.016) Observations 8,916 8,916 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Full Full Adj R-squared 0.226 0.183 B. IV Second Stage F(Debt$$\geq$$5Y) Bond Maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.047*** 0.082 0.288 0.325 (0.011) (0.050) (0.179) (1.387) D(Early_refi)*D(Speculative) 0.047*** 0.368** 1.301*** 4.006** (0.018) (0.146) (0.220) (1.651) Observations 8,027 8,027 8,916 8,916 F Stat (1st stage) 21.38 30.81 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Full Full Full Full Adj R-squared 0.358 0.176 0.804 0.788 B. IV Second Stage F(Debt$$\geq$$5Y) Bond Maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.047*** 0.082 0.288 0.325 (0.011) (0.050) (0.179) (1.387) D(Early_refi)*D(Speculative) 0.047*** 0.368** 1.301*** 4.006** (0.018) (0.146) (0.220) (1.651) Observations 8,027 8,027 8,916 8,916 F Stat (1st stage) 21.38 30.81 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Full Full Full Full Adj R-squared 0.358 0.176 0.804 0.788 This table presents the OLS and IV results, where $$D({\it Early}_{-}{\it refi})_{\it i,t}$$ is interacted with D(speculative). Two measures of maturity are used: F(Debt$$\geq$$5Y ) is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included in the regressions. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. \begin{align*} {\it Maturity}{}_{\it i,t}&=\delta_{0}+\delta_{1}D({\it Early}_{-}{\it refi})_{i,t}+\delta_{2}D({\it Early}_{-}{\it refi})_{i,t}*D({\it spec}){}_{i,t}\nonumber\\ &\quad +\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{it} \end{align*} The heterogeneous behavior across credit rating segments in Table 5 fits anecdotal evidence in the industry. Bank of America Merrill Lynch makes the following recommendation to speculative-grade firms: “Don’t wait too long to refinance upcoming maturities. Give yourself at least 18 months before your current financing matures, so that if any segment of the market shuts down for a few months, you’ll still have time to get something done.” HSBC in May 2010 recommended the following: “Truly global investment-grade corporations have time to arrange their refinancing. [Less highly rated companies] should be taking action now, while yields are low and margins compressed” (Gregson 2010). 4.3.2 Bond selection across credit rating segments Maturity extension in order to reduce refinancing risk would predict that firms more exposed to the risk – indexed by lower credit rating – would tend to pick bonds with shorter maturity to retire early. I run the following regression for speculative-grade firms and investment-grade firms separately. For bond i for firm j in year t, when firm j conducts early refinancing, \begin{align*} D({\it Early}_{-}{\it refinanced})_{i,j,t}&=\alpha+\beta*{\it Maturity}_{i,j,t}+\theta*{\it Coupon}_{i,j,t}\nonumber\\ &\quad +\phi_{i}*{\it Controls}_{i,j,t}+\eta_{\it j,t}+\epsilon_{i,j,t} \end{align*} The dependent variable D(Early-refinanced) equals one if firm j refinances bond i early in year t, and zero if bond i stays untouched. The coefficient on maturity $$\beta$$ and the coefficient on coupon $$\theta$$ are the focus here. Given that I include the firm-year fixed effects, the comparison is made among all the bonds outstanding for a given firm-year. The bond characteristic controls include: a dummy variable indicating whether or not bond i is callable at year t, previous year-end amount outstanding, maturity at issuance, covenant count, seniority level,10 and bond rating at the beginning of year t. The regression is estimated using a linear probability model and Table 6 presents the results. Speculative-grade firms target bonds maturing sooner. A 1-year decrease in maturity increases the probability of being refinanced early by 0.024. Thus, if one bond is 5 years shorter in maturity than the average maturity of bonds outstanding, this bond is 12% more likely to be refinanced early compared to other outstanding bonds. On the other hand, investment-grade firms do not appear to consider maturity when they refinance early as the coefficient for maturity is statistically indistinguishable from zero. Both groups target more expensive bonds: a 1% increase in the coupon rate leads to a 2.2% higher probability of the bond being refinanced early by speculative-grade firms, and a 0.7% higher probability by investment-grade firms. Saving for interest costs alone cannot explain why speculative-grade firms choose bonds with shorter maturity to refinance early, although the results fit the motive to hedge refinancing risk. Table 6 Bond characteristics and early refinancing Dependent variable: $${\it D(Early-refinanced)}_{\it ijt}$$ Speculative grade Investment grade Maturity –0.024*** 0.001 (0.006) (0.002) Coupon 0.022*** 0.007* (0.006) (0.004) Callable 0.537*** 0.383*** (0.043) (0.103) Maturity at issuance 0.012*** –0.002 (0.003) (0.001) Covenant count 0.008** 0.010*** (0.004) (0.004) Security level –0.007 –0.001 (0.021) (0.008) Bond rating –0.011 –0.006 (0.008) (0.004) Constant 0.085 –0.007 (0.209) (0.044) Observations 3,262 24,842 FEs Firm-year Firm-year Sample Speculative Investment Adj R-squared 0.496 0.415 Dependent variable: $${\it D(Early-refinanced)}_{\it ijt}$$ Speculative grade Investment grade Maturity –0.024*** 0.001 (0.006) (0.002) Coupon 0.022*** 0.007* (0.006) (0.004) Callable 0.537*** 0.383*** (0.043) (0.103) Maturity at issuance 0.012*** –0.002 (0.003) (0.001) Covenant count 0.008** 0.010*** (0.004) (0.004) Security level –0.007 –0.001 (0.021) (0.008) Bond rating –0.011 –0.006 (0.008) (0.004) Constant 0.085 –0.007 (0.209) (0.044) Observations 3,262 24,842 FEs Firm-year Firm-year Sample Speculative Investment Adj R-squared 0.496 0.415 What kind of bonds are more likely to be early refinanced? This table presents the estimates from a linear regression of being early refinanced on various bond characteristics. The unit of observation is at the bond-firm-year level. Firm-year fixed effects are included. Regressions are separately run for speculative-grade and investment-grade firms. Only firm-year observations with early refinancing activities are included in the regression. Dependent variable $$D(Early_{-}refi)_{i,j,t}$$ equals one if firm j early refinances an outstanding bond i in year t, and zero otherwise. Bond characteristics, such as maturity, coupon, a flag indicating a bond is callable in this year, maturity at issuance, covenant count, seniority level, and bond rating, are included. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. \begin{align*} {\it D(Early}_{-}{\it refinanced})_{\it i,j,t}=\alpha+\beta*{\it Maturity}_{\it i,j,t}+\theta*{\it Coupon}_{\it i,j,t}+\sum_{k}\phi_{i,k}*{\it Controls}_{\it i,j,k,t}+\theta_{\it i,t}+\epsilon_{\it i,j,t} \end{align*} Table 6 Bond characteristics and early refinancing Dependent variable: $${\it D(Early-refinanced)}_{\it ijt}$$ Speculative grade Investment grade Maturity –0.024*** 0.001 (0.006) (0.002) Coupon 0.022*** 0.007* (0.006) (0.004) Callable 0.537*** 0.383*** (0.043) (0.103) Maturity at issuance 0.012*** –0.002 (0.003) (0.001) Covenant count 0.008** 0.010*** (0.004) (0.004) Security level –0.007 –0.001 (0.021) (0.008) Bond rating –0.011 –0.006 (0.008) (0.004) Constant 0.085 –0.007 (0.209) (0.044) Observations 3,262 24,842 FEs Firm-year Firm-year Sample Speculative Investment Adj R-squared 0.496 0.415 Dependent variable: $${\it D(Early-refinanced)}_{\it ijt}$$ Speculative grade Investment grade Maturity –0.024*** 0.001 (0.006) (0.002) Coupon 0.022*** 0.007* (0.006) (0.004) Callable 0.537*** 0.383*** (0.043) (0.103) Maturity at issuance 0.012*** –0.002 (0.003) (0.001) Covenant count 0.008** 0.010*** (0.004) (0.004) Security level –0.007 –0.001 (0.021) (0.008) Bond rating –0.011 –0.006 (0.008) (0.004) Constant 0.085 –0.007 (0.209) (0.044) Observations 3,262 24,842 FEs Firm-year Firm-year Sample Speculative Investment Adj R-squared 0.496 0.415 What kind of bonds are more likely to be early refinanced? This table presents the estimates from a linear regression of being early refinanced on various bond characteristics. The unit of observation is at the bond-firm-year level. Firm-year fixed effects are included. Regressions are separately run for speculative-grade and investment-grade firms. Only firm-year observations with early refinancing activities are included in the regression. Dependent variable $$D(Early_{-}refi)_{i,j,t}$$ equals one if firm j early refinances an outstanding bond i in year t, and zero otherwise. Bond characteristics, such as maturity, coupon, a flag indicating a bond is callable in this year, maturity at issuance, covenant count, seniority level, and bond rating, are included. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. \begin{align*} {\it D(Early}_{-}{\it refinanced})_{\it i,j,t}=\alpha+\beta*{\it Maturity}_{\it i,j,t}+\theta*{\it Coupon}_{\it i,j,t}+\sum_{k}\phi_{i,k}*{\it Controls}_{\it i,j,k,t}+\theta_{\it i,t}+\epsilon_{\it i,j,t} \end{align*} 4.3.3 Maturity mismatch between assets and liabilities Firms with a larger maturity mismatch between assets and liabilities are more exposed to refinancing risk. I next examine whether they have a greater incentive to manage maturity through early refinancing. To test the heterogeneity across this dimension, I rank firms according to the Fama-French 12 industry classification based on maturity mismatch between assets and liabilities. Given that the maturity of firms’ asset side is not observable, I use the investment-grade firms as a benchmark, assuming they are relatively unconstrained in maturity at issuance and match the maturity of their assets and liabilities better. The maturity-mismatch measure is the difference in maturity at issuance between the investment-grade and speculative-grade bonds in an industry. Panel A in Table 7 presents the industry rankings, where a lower rank number indicates a larger maturity mismatch. Industries with the largest maturity mismatch are utilities; telephone and television; oil, gas, and coal; and healthcare, medical equipment, and drugs. Those industries with the smallest mismatch are wholesale and retail; business equipment; manufacturing; and consumer durables. Rankings are aligned with the general consensus of the asset life for listed industries. Table 7 Maturity mismatch analysis A. Maturity mismatch rank Rank Speculative Investment Investment-Speculative Telephone & television 1 8.80 16.23 7.44*** Utilities 2 8.92 16.16 7.24*** Oil, gas, & coal 3 8.59 14.58 5.99*** Healthcare, medical equip., & drugs 4 8.49 13.72 5.23*** Consumer nondurables 5 8.99 12.69 3.70*** Chemicals & allied products 6 8.35 10.82 2.47*** Everything else 7 8.74 10.93 2.19*** Wholesale, retail, & some services 8 8.54 9.96 1.42*** Business equipment 9 8.26 8.75 0.49 Manufacturing 10 8.92 8.61 –0.31 Consumer durables 11 8.75 6.15 –2.60*** A. Maturity mismatch rank Rank Speculative Investment Investment-Speculative Telephone & television 1 8.80 16.23 7.44*** Utilities 2 8.92 16.16 7.24*** Oil, gas, & coal 3 8.59 14.58 5.99*** Healthcare, medical equip., & drugs 4 8.49 13.72 5.23*** Consumer nondurables 5 8.99 12.69 3.70*** Chemicals & allied products 6 8.35 10.82 2.47*** Everything else 7 8.74 10.93 2.19*** Wholesale, retail, & some services 8 8.54 9.96 1.42*** Business equipment 9 8.26 8.75 0.49 Manufacturing 10 8.92 8.61 –0.31 Consumer durables 11 8.75 6.15 –2.60*** B. Maturity mismatch rank F(Debt$$\geq$$5Y) Bond maturity Rank 9-11 Rank 1-3 Rank 9-11 Rank 1-3 IV IV IV IV (1) (2) (3) (4) D(Early_refi) 0.197 1.015* 2.885 4.144** (0.464) (0.594) (2.284) (1.924) Observations 922 1,019 1,064 1,251 F stat (1st stage) 20.1 17.77 17.46 16.30 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.338 $$-$$0.679 0.556 0.337 B. Maturity mismatch rank F(Debt$$\geq$$5Y) Bond maturity Rank 9-11 Rank 1-3 Rank 9-11 Rank 1-3 IV IV IV IV (1) (2) (3) (4) D(Early_refi) 0.197 1.015* 2.885 4.144** (0.464) (0.594) (2.284) (1.924) Observations 922 1,019 1,064 1,251 F stat (1st stage) 20.1 17.77 17.46 16.30 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.338 $$-$$0.679 0.556 0.337 Panel A presents industries based on the difference of maturity at issuance between speculative-grade and investment-grade bonds. For each industry, two sample t-tests are conducted. Panel B presents the IV results of early refinancing on maturity for firms grouped by maturity mismatch measure. Industries with mismatch rank 1–3 are grouped to represent a large maturity mismatch, and the other four industries with rank 9–11 are grouped to represent a small maturity mismatch. Two measures of maturity are used: F(Debt$$\geq$$5Y) is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table 7 Maturity mismatch analysis A. Maturity mismatch rank Rank Speculative Investment Investment-Speculative Telephone & television 1 8.80 16.23 7.44*** Utilities 2 8.92 16.16 7.24*** Oil, gas, & coal 3 8.59 14.58 5.99*** Healthcare, medical equip., & drugs 4 8.49 13.72 5.23*** Consumer nondurables 5 8.99 12.69 3.70*** Chemicals & allied products 6 8.35 10.82 2.47*** Everything else 7 8.74 10.93 2.19*** Wholesale, retail, & some services 8 8.54 9.96 1.42*** Business equipment 9 8.26 8.75 0.49 Manufacturing 10 8.92 8.61 –0.31 Consumer durables 11 8.75 6.15 –2.60*** A. Maturity mismatch rank Rank Speculative Investment Investment-Speculative Telephone & television 1 8.80 16.23 7.44*** Utilities 2 8.92 16.16 7.24*** Oil, gas, & coal 3 8.59 14.58 5.99*** Healthcare, medical equip., & drugs 4 8.49 13.72 5.23*** Consumer nondurables 5 8.99 12.69 3.70*** Chemicals & allied products 6 8.35 10.82 2.47*** Everything else 7 8.74 10.93 2.19*** Wholesale, retail, & some services 8 8.54 9.96 1.42*** Business equipment 9 8.26 8.75 0.49 Manufacturing 10 8.92 8.61 –0.31 Consumer durables 11 8.75 6.15 –2.60*** B. Maturity mismatch rank F(Debt$$\geq$$5Y) Bond maturity Rank 9-11 Rank 1-3 Rank 9-11 Rank 1-3 IV IV IV IV (1) (2) (3) (4) D(Early_refi) 0.197 1.015* 2.885 4.144** (0.464) (0.594) (2.284) (1.924) Observations 922 1,019 1,064 1,251 F stat (1st stage) 20.1 17.77 17.46 16.30 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.338 $$-$$0.679 0.556 0.337 B. Maturity mismatch rank F(Debt$$\geq$$5Y) Bond maturity Rank 9-11 Rank 1-3 Rank 9-11 Rank 1-3 IV IV IV IV (1) (2) (3) (4) D(Early_refi) 0.197 1.015* 2.885 4.144** (0.464) (0.594) (2.284) (1.924) Observations 922 1,019 1,064 1,251 F stat (1st stage) 20.1 17.77 17.46 16.30 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.338 $$-$$0.679 0.556 0.337 Panel A presents industries based on the difference of maturity at issuance between speculative-grade and investment-grade bonds. For each industry, two sample t-tests are conducted. Panel B presents the IV results of early refinancing on maturity for firms grouped by maturity mismatch measure. Industries with mismatch rank 1–3 are grouped to represent a large maturity mismatch, and the other four industries with rank 9–11 are grouped to represent a small maturity mismatch. Two measures of maturity are used: F(Debt$$\geq$$5Y) is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Panel B of Table 7 presents the results of IV regressions. The IV regressions are run separately for industries with the smallest maturity-mismatch measure (rank 9–11) and largest maturity-mismatch measure (rank 1–3). Firms with smaller maturity mismatch (Columns (1) and (3)) are less exposed to refinancing risk and demonstrate a smaller response in maturity extension via early refinancing. For the outcome variable F(Debt$$\geq$$5Y), the coefficient for smaller maturity-mismatch firms (rank 9–11) is 0.197, which is not statistically significant, whereas the coefficient for larger maturity-mismatch firms (rank 1–4) is 1.015, which is statistically significant at the 10% level. For Bond maturity, the coefficient for less mismatched firms is 2.885 and not statistically significant, and the coefficient is 4.144 for more mismatched firms (rank 1–3), which is statistically significant at the 5% level. The IV results support the concept that firms with larger maturity mismatch between assets and liabilities, that is, firms that are more exposed to refinancing risk, employ early refinancing more frequently to extend maturity. There might be concerns that not issuing long-term bonds does not indicate that there is a maturity mismatch between assets and liabilities. Speculative-grade firms might only invest in relatively short-term projects, which matches the maturity structure of their bonds. Documenting the maturity of investment projects is empirically challenging due to not having access to the underlying characteristics of firms’ assets. To provide supportive evidence of maturity mismatch, I plot the distribution of maturity at issuance for speculative-grade and investment-grade firms’ bonds for the oil, gas, and coal industry and the telephone and television industry in AppendixFigure E.1, with the summary statistics shown at the top. Firms in these two industries normally have long-term assets, and asset life across rating segments should not be significantly different. If there is still a difference in maturity at issuance for their bonds, the differences are more likely to come from the maturity mismatch between assets and liabilities, instead of the length of the underlying investment projects. For these two industries in AppendixFigure E.1, maturity at issuance for the investment-grade firms becomes longer compared to the full sample investment-grade firms. The 25th percentile, median, and 75th percentile for the full sample investment-grade firms are 5, 10, and 15 years, respectively. For the oil, gas, and coal industry, the 25th percentile, median, and 75th percentile become 7, 10, and 20 years, respectively. For the telephone and television industry, they are 5, 10, and 30 years, respectively. Both of these industries issue larger fractions of bonds longer than 30 years. In contrast, speculative-grade firms in these industries still have a maturity-at-issuance distribution similar to the full sample of speculative-grade firms. The 25th percentile, median, and 75th percentile remain at 7, 9, and 10 years, respectively. They rarely issue bonds longer than 10 years, and the maximum maturity is 20 years. This evidence favors the maturity mismatch between assets and liabilities for speculative-grade firms. 5. Robustness The IV strategy requires that the timing of some bonds becoming callable is uncorrelated with current unobservables affecting firms’ maturity demand. I conduct a few robustness tests to support the validity of this identification assumption. First, there might be concerns regarding the endogenous choice to embed call provisions at issuance. Firms with certain characteristics might be more likely to choose call provisions, refinance early, and adjust maturity. However, for sample speculative-grade firms, the vast majority (87.1%) of bonds contain call provisions at issuance. The variations in the decision to embed call provisions at issuance are indeed small. In addition, I conduct the intention-to-treat (ITT) test for the IV regressions, assuming all speculative-grade firms’ bonds have protection periods that last exactly 50% of maturity at issuance. Table E.1 presents the ITT results. The first-stage regression results show strong correlation of the instrumental variable for both measures of early refinancing activities, while the second-stage IV regressions show early refinancing leads to higher F(debt$$\geq$$5Y) and Bond maturity. In terms of economics magnitudes, the results are similar to those of the IV regressions without an ITT assumption presented in Table 4. Table E.1 IV strategy with ITT A. IV ITT strategy first stage (1) (2) D(Early_refi) F(Early_refi) D(turn_callable_predicted) 0.092*** 0.038*** (0.014) (0.010) Observations 4,439 4,097 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.120 0.0148 A. IV ITT strategy first stage (1) (2) D(Early_refi) F(Early_refi) D(turn_callable_predicted) 0.092*** 0.038*** (0.014) (0.010) Observations 4,439 4,097 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.120 0.0148 B. IV ITT strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.625*** 1.143*** 4.820*** (0.014) (0.166) (0.085) (1.127) Observations 3,843 3,791 4,512 4,437 F stat (1st stage) 42.96 41.62 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 $$-$$0.251 0.597 $$-$$0.138 B. IV ITT strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.625*** 1.143*** 4.820*** (0.014) (0.166) (0.085) (1.127) Observations 3,843 3,791 4,512 4,437 F stat (1st stage) 42.96 41.62 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 $$-$$0.251 0.597 $$-$$0.138 This table presents the intention-to-treat (ITT) IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable_{-}predicted)}_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. For all bonds in the sample, protection periods are set to be precisely 50% of maturity at issuance. Two measures of maturity are used: $$F({\it Debt}\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**,and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{i,t}+\sum_{j}\delta_{j}{\it controls}_{j,i,t}+\epsilon_{\it it}$$ Table E.1 IV strategy with ITT A. IV ITT strategy first stage (1) (2) D(Early_refi) F(Early_refi) D(turn_callable_predicted) 0.092*** 0.038*** (0.014) (0.010) Observations 4,439 4,097 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.120 0.0148 A. IV ITT strategy first stage (1) (2) D(Early_refi) F(Early_refi) D(turn_callable_predicted) 0.092*** 0.038*** (0.014) (0.010) Observations 4,439 4,097 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.120 0.0148 B. IV ITT strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.625*** 1.143*** 4.820*** (0.014) (0.166) (0.085) (1.127) Observations 3,843 3,791 4,512 4,437 F stat (1st stage) 42.96 41.62 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 $$-$$0.251 0.597 $$-$$0.138 B. IV ITT strategy second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.107*** 0.625*** 1.143*** 4.820*** (0.014) (0.166) (0.085) (1.127) Observations 3,843 3,791 4,512 4,437 F stat (1st stage) 42.96 41.62 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.317 $$-$$0.251 0.597 $$-$$0.138 This table presents the intention-to-treat (ITT) IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable_{-}predicted)}_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. For all bonds in the sample, protection periods are set to be precisely 50% of maturity at issuance. Two measures of maturity are used: $$F({\it Debt}\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**,and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early}_{-}{\it refi})_{\it i,t}=\beta_{0}+\beta_{1}D({\it turn}_{-}{\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{i,t}+\sum_{j}\delta_{j}{\it controls}_{j,i,t}+\epsilon_{\it it}$$ Second, instead of including firm fixed effects and year fixed effects, I run the IV regressions with various industry-year fixed effects to control for time-varying industry-wide factors affecting firms’ choices of maturity. The results are presented in AppendixTable E.2. I use four different industry specifications to ensure the robustness of the results: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. In addition, AppendixTable E.3 presents the IV regression results with standard errors clustered at various industry levels. The estimates remain robust across all the different industry specifications and the magnitudes are similar to the main regression results in Table 4. Table E.2 IV strategy: Industry-year FE F(Debt$$\geq$$5Y) Bond maturity (1) (2) 3) (4) (5) (6) (7) (8) D(Early_refi) 0.199** 0.204** 0.241** 0.337*** 3.796*** 4.246*** 4.287*** 4.835*** (0.093) (0.096) (0.100) (0.128) (0.728) (0.808) (0.864) (1.054) Observations 3,841 3,841 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 83.38 80.66 94.51 74.84 62.99 77.04 89.78 36.61 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry-year FEs FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.023 0.028 0.021 $$-$$0.043 $$-$$0.251 $$-$$0.290 $$-$$0.284 $$-$$0.410 F(Debt$$\geq$$5Y) Bond maturity (1) (2) 3) (4) (5) (6) (7) (8) D(Early_refi) 0.199** 0.204** 0.241** 0.337*** 3.796*** 4.246*** 4.287*** 4.835*** (0.093) (0.096) (0.100) (0.128) (0.728) (0.808) (0.864) (1.054) Observations 3,841 3,841 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 83.38 80.66 94.51 74.84 62.99 77.04 89.78 36.61 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry-year FEs FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.023 0.028 0.021 $$-$$0.043 $$-$$0.251 $$-$$0.290 $$-$$0.284 $$-$$0.410 This table presents IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable)}_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F(Debt\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond Maturity is the average bond maturity. Observations are at the firm-year level. Industry-year fixed effects are included according to various industry definitions: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}D{\it (turn_{-}callable)}_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Table E.2 IV strategy: Industry-year FE F(Debt$$\geq$$5Y) Bond maturity (1) (2) 3) (4) (5) (6) (7) (8) D(Early_refi) 0.199** 0.204** 0.241** 0.337*** 3.796*** 4.246*** 4.287*** 4.835*** (0.093) (0.096) (0.100) (0.128) (0.728) (0.808) (0.864) (1.054) Observations 3,841 3,841 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 83.38 80.66 94.51 74.84 62.99 77.04 89.78 36.61 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry-year FEs FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.023 0.028 0.021 $$-$$0.043 $$-$$0.251 $$-$$0.290 $$-$$0.284 $$-$$0.410 F(Debt$$\geq$$5Y) Bond maturity (1) (2) 3) (4) (5) (6) (7) (8) D(Early_refi) 0.199** 0.204** 0.241** 0.337*** 3.796*** 4.246*** 4.287*** 4.835*** (0.093) (0.096) (0.100) (0.128) (0.728) (0.808) (0.864) (1.054) Observations 3,841 3,841 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 83.38 80.66 94.51 74.84 62.99 77.04 89.78 36.61 Controls Yes Yes Yes Yes Yes Yes Yes Yes Industry-year FEs FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.023 0.028 0.021 $$-$$0.043 $$-$$0.251 $$-$$0.290 $$-$$0.284 $$-$$0.410 This table presents IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable)}_{\it i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F(Debt\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond Maturity is the average bond maturity. Observations are at the firm-year level. Industry-year fixed effects are included according to various industry definitions: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}D{\it (turn_{-}callable)}_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Table E.3 IV strategy: Standard error clustering F(Debt$$\geq$$5Y) Bond maturity (1) (2) (3) (4) (5) (6) (7) (8) D(Early_refi) 0.328*** 0.328*** 0.323*** 0.323*** 3.769*** 3.769*** 3.825*** 3.825*** (0.112) (0.114) (0.114) (0.115) (0.822) (0.816) (0.874) (0.856) Observations 3,843 3,843 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 34.40 46.85 40.80 42.40 34.95 50.72 43.66 46.50 Controls Yes Yes Yes Yes Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.121 0.121 0.124 0.124 0.116 0.116 0.107 0.107 Industry FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 F(Debt$$\geq$$5Y) Bond maturity (1) (2) (3) (4) (5) (6) (7) (8) D(Early_refi) 0.328*** 0.328*** 0.323*** 0.323*** 3.769*** 3.769*** 3.825*** 3.825*** (0.112) (0.114) (0.114) (0.115) (0.822) (0.816) (0.874) (0.856) Observations 3,843 3,843 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 34.40 46.85 40.80 42.40 34.95 50.72 43.66 46.50 Controls Yes Yes Yes Yes Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.121 0.121 0.124 0.124 0.116 0.116 0.107 0.107 Industry FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 This table presents IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable)}_{i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F({\it Debt}\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the industry-year level according to four industry definitions: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}{\it D(turn_{-}callable)}_{i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Table E.3 IV strategy: Standard error clustering F(Debt$$\geq$$5Y) Bond maturity (1) (2) (3) (4) (5) (6) (7) (8) D(Early_refi) 0.328*** 0.328*** 0.323*** 0.323*** 3.769*** 3.769*** 3.825*** 3.825*** (0.112) (0.114) (0.114) (0.115) (0.822) (0.816) (0.874) (0.856) Observations 3,843 3,843 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 34.40 46.85 40.80 42.40 34.95 50.72 43.66 46.50 Controls Yes Yes Yes Yes Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.121 0.121 0.124 0.124 0.116 0.116 0.107 0.107 Industry FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 F(Debt$$\geq$$5Y) Bond maturity (1) (2) (3) (4) (5) (6) (7) (8) D(Early_refi) 0.328*** 0.328*** 0.323*** 0.323*** 3.769*** 3.769*** 3.825*** 3.825*** (0.112) (0.114) (0.114) (0.115) (0.822) (0.816) (0.874) (0.856) Observations 3,843 3,843 3,828 3,828 4,512 4,512 4,497 4,497 F stat (1st stage) 34.40 46.85 40.80 42.40 34.95 50.72 43.66 46.50 Controls Yes Yes Yes Yes Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Speculative Speculative Speculative Speculative Adj R-squared 0.121 0.121 0.124 0.124 0.116 0.116 0.107 0.107 Industry FF17 FF38 SIC2 SIC3 FF17 FF38 SIC2 SIC3 This table presents IV results, where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by $${\it D(turn_{-}callable)}_{i,t}$$, a dummy variable indicating that some bonds are scheduled to become callable in year t for firm i. Two measures of maturity are used: $$F({\it Debt}\geq5Y)$$ is the fraction of book debt with maturity of 5 years or more and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the industry-year level according to four industry definitions: Fama-French 17 industry, Fama-French 38 industry, SIC 2, and SIC 3. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}{\it D(turn_{-}callable)}_{i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi}})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Third, there might be concerns regarding the endogenous choice of protection period: firms might foresee future movements in firm fundamentals or market interest rates and set the protection period to coincide with their projections. A quick review of the summary statistics shows that protection-period setting is indeed fairly standard. In the speculative-grade sample, the average protection period is 49.8 months, with a standard deviation of 13 months. The protection-period ratio on average is 47.4% of maturity at issuance, with a standard deviation of 8%. Also, I regress the protection–period setting on various bond characteristics, firm characteristics at issuance, and interest rate controls at issuance. The dependent variable is the ratio of the protection period to maturity at issuance. For example, if maturity at issuance is 10 years and the protection period lasts 5 years, the protection-period ratio is 50%. AppendixTable E.4 shows that protection-period ratio is uncorrelated with either contemporaneous firm characteristics or interest rate controls, consistent with the protection period being set in a fairly standard way. Moreover, I conduct a within-firm characteristic comparison conditional on $$D({\it turn}_{-}{\it callable})$$ switching from 0 to 1. The results in AppendixTable E.5 show that all the observable firm characteristics are statistically indistinguishable when the IV $$D({\it turn}_{-}{\it callable})$$ switches on. There are no significant firm characteristic changes when protection periods end and bonds turn callable. Table E.4 The determinants of the protection period setting Dep variable: Protection period ratio (1) (2) 3-month T-bill rate –0.007 (0.004) BAA-AAA –0.014 (0.009) Term spread –0.005 (0.007) ln(Assets) –0.016** –0.006 (0.008) (0.008) Leverage –0.026 –0.023 (0.029) (0.029) Ebitda/Assets –0.006 0.018 (0.036) (0.039) M/B –0.006 0.000 (0.009) (0.010) Cash/Assets 0.026 0.033 (0.034) (0.036) Tangible –0.018 –0.007 (0.042) (0.042) Equity_return 0.007 0.004 (0.006) (0.006) Observations 1695 1695 Firm FEs Yes Yes Year FEs No Yes Bond characteristics controls Yes Yes Sample Speculative Speculative Adj R-squared 0.209 0.220 Dep variable: Protection period ratio (1) (2) 3-month T-bill rate –0.007 (0.004) BAA-AAA –0.014 (0.009) Term spread –0.005 (0.007) ln(Assets) –0.016** –0.006 (0.008) (0.008) Leverage –0.026 –0.023 (0.029) (0.029) Ebitda/Assets –0.006 0.018 (0.036) (0.039) M/B –0.006 0.000 (0.009) (0.010) Cash/Assets 0.026 0.033 (0.034) (0.036) Tangible –0.018 –0.007 (0.042) (0.042) Equity_return 0.007 0.004 (0.006) (0.006) Observations 1695 1695 Firm FEs Yes Yes Year FEs No Yes Bond characteristics controls Yes Yes Sample Speculative Speculative Adj R-squared 0.209 0.220 This table examines a bond’s protection-period ratio on various firm characteristics and interest rate conditions at issuance. Protection-period ratio is the ratio of protection period to maturity at issuance. For example, if maturity at issuance is 10 years and protection period lasts 5 years, the protection period ratio is 50%. Bond characteristic controls include offering size, coupon, seniority level, covenant counts, bond rating, and maturity at issuance. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and equity return. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table E.4 The determinants of the protection period setting Dep variable: Protection period ratio (1) (2) 3-month T-bill rate –0.007 (0.004) BAA-AAA –0.014 (0.009) Term spread –0.005 (0.007) ln(Assets) –0.016** –0.006 (0.008) (0.008) Leverage –0.026 –0.023 (0.029) (0.029) Ebitda/Assets –0.006 0.018 (0.036) (0.039) M/B –0.006 0.000 (0.009) (0.010) Cash/Assets 0.026 0.033 (0.034) (0.036) Tangible –0.018 –0.007 (0.042) (0.042) Equity_return 0.007 0.004 (0.006) (0.006) Observations 1695 1695 Firm FEs Yes Yes Year FEs No Yes Bond characteristics controls Yes Yes Sample Speculative Speculative Adj R-squared 0.209 0.220 Dep variable: Protection period ratio (1) (2) 3-month T-bill rate –0.007 (0.004) BAA-AAA –0.014 (0.009) Term spread –0.005 (0.007) ln(Assets) –0.016** –0.006 (0.008) (0.008) Leverage –0.026 –0.023 (0.029) (0.029) Ebitda/Assets –0.006 0.018 (0.036) (0.039) M/B –0.006 0.000 (0.009) (0.010) Cash/Assets 0.026 0.033 (0.034) (0.036) Tangible –0.018 –0.007 (0.042) (0.042) Equity_return 0.007 0.004 (0.006) (0.006) Observations 1695 1695 Firm FEs Yes Yes Year FEs No Yes Bond characteristics controls Yes Yes Sample Speculative Speculative Adj R-squared 0.209 0.220 This table examines a bond’s protection-period ratio on various firm characteristics and interest rate conditions at issuance. Protection-period ratio is the ratio of protection period to maturity at issuance. For example, if maturity at issuance is 10 years and protection period lasts 5 years, the protection period ratio is 50%. Bond characteristic controls include offering size, coupon, seniority level, covenant counts, bond rating, and maturity at issuance. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, and equity return. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table E.5 Conditional firm characteristics by $${\it D(turn_{-}callable)}$$ Variable D(turn_callable)=0 D(turn_callable)=1 0-1 P-value ln(Assets) ($Mils) 7.05 7.04 0.01 0.83 Leverage 0.54 0.54 0.00 0.78 Market/Book 1.37 1.35 0.02 0.44 Ebitda/Assets 0.12 0.12 0.00 0.44 Cash/Assets 0.07 0.07 0.00 0.33 ROA $$-$$0.01 $$-$$0.01 0.00 0.76 Debt/Ebitda 4.59 5.10 $$-$$0.51 0.11 Net debt issue/PPE 0.09 0.08 0.00 0.98 Tangile/Assets 0.41 0.41 0.00 0.91 Equity return 0.21 0.21 0.00 0.97 Z score 1.11 1.11 0.00 0.94 S&P rating 14.21 14.13 0.08 0.51 Variable D(turn_callable)=0 D(turn_callable)=1 0-1 P-value ln(Assets) ($Mils) 7.05 7.04 0.01 0.83 Leverage 0.54 0.54 0.00 0.78 Market/Book 1.37 1.35 0.02 0.44 Ebitda/Assets 0.12 0.12 0.00 0.44 Cash/Assets 0.07 0.07 0.00 0.33 ROA $$-$$0.01 $$-$$0.01 0.00 0.76 Debt/Ebitda 4.59 5.10 $$-$$0.51 0.11 Net debt issue/PPE 0.09 0.08 0.00 0.98 Tangile/Assets 0.41 0.41 0.00 0.91 Equity return 0.21 0.21 0.00 0.97 Z score 1.11 1.11 0.00 0.94 S&P rating 14.21 14.13 0.08 0.51 This table presents firm characteristic when $${\it D(turn_{-}callable)}$$ switches from 0 to 1, meaning some bond(s) are scheduled to become callable in that firm-year. Within each firm, only the paired observations with $${\it D(turn_{-}callable)}$$ switching from 0 to 1 are included. $${\it D(turn_{-}callable)} =1$$ indicates some. Two sample t-tests are conducted by grouping observations across years, together with P-values presented. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Table E.5 Conditional firm characteristics by $${\it D(turn_{-}callable)}$$ Variable D(turn_callable)=0 D(turn_callable)=1 0-1 P-value ln(Assets) ($Mils) 7.05 7.04 0.01 0.83 Leverage 0.54 0.54 0.00 0.78 Market/Book 1.37 1.35 0.02 0.44 Ebitda/Assets 0.12 0.12 0.00 0.44 Cash/Assets 0.07 0.07 0.00 0.33 ROA $$-$$0.01 $$-$$0.01 0.00 0.76 Debt/Ebitda 4.59 5.10 $$-$$0.51 0.11 Net debt issue/PPE 0.09 0.08 0.00 0.98 Tangile/Assets 0.41 0.41 0.00 0.91 Equity return 0.21 0.21 0.00 0.97 Z score 1.11 1.11 0.00 0.94 S&P rating 14.21 14.13 0.08 0.51 Variable D(turn_callable)=0 D(turn_callable)=1 0-1 P-value ln(Assets) ($Mils) 7.05 7.04 0.01 0.83 Leverage 0.54 0.54 0.00 0.78 Market/Book 1.37 1.35 0.02 0.44 Ebitda/Assets 0.12 0.12 0.00 0.44 Cash/Assets 0.07 0.07 0.00 0.33 ROA $$-$$0.01 $$-$$0.01 0.00 0.76 Debt/Ebitda 4.59 5.10 $$-$$0.51 0.11 Net debt issue/PPE 0.09 0.08 0.00 0.98 Tangile/Assets 0.41 0.41 0.00 0.91 Equity return 0.21 0.21 0.00 0.97 Z score 1.11 1.11 0.00 0.94 S&P rating 14.21 14.13 0.08 0.51 This table presents firm characteristic when $${\it D(turn_{-}callable)}$$ switches from 0 to 1, meaning some bond(s) are scheduled to become callable in that firm-year. Within each firm, only the paired observations with $${\it D(turn_{-}callable)}$$ switching from 0 to 1 are included. $${\it D(turn_{-}callable)} =1$$ indicates some. Two sample t-tests are conducted by grouping observations across years, together with P-values presented. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. Lastly, I conduct a second IV regression exploring the fact that credit supply conditions greatly influence firms’ ability to refinance early. I instrument firms’ early refinancing activities with the interaction between the callable fraction and credit supply conditions. Given that bonds past their protection period make early refinancing easier, firms with more of these bonds receive a larger shock to their early refinancing opportunities when the credit market improves, making it easier to extend maturity. AppendixTable E.6 presents the regression results. In terms of economic magnitudes, the estimates show that a one standard deviation (32%) increase in the probability of early refinancing leads to a 21.2% increase in F(debt$$\geq$$5Y) and a 0.8 year extension in Bond maturity. The magnitude is qualitatively similar to the results in Table 4. Table E.6 IV strategy II A. IV strategy II first stage (1) (2) D(Early_refi) F(Early_refi) F(callable)*BAA-AAA $$-$$0.077*** $$-$$0.062*** (0.016) (0.011) Observations 4,175 4,161 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.168 0.112 A. IV strategy II first stage (1) (2) D(Early_refi) F(Early_refi) F(callable)*BAA-AAA $$-$$0.077*** $$-$$0.062*** (0.016) (0.011) Observations 4,175 4,161 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.168 0.112 B. IV strategy II second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.177*** 0.669** 1.587*** 2.435** (0.017) (0.281) (0.107) (1.033) Observations 3,572 3,572 4,175 4,175 F stat (1st stage) 22.91 16.76 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.371 0.128 0.645 0.629 B. IV strategy II second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.177*** 0.669** 1.587*** 2.435** (0.017) (0.281) (0.107) (1.033) Observations 3,572 3,572 4,175 4,175 F stat (1st stage) 22.91 16.76 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.371 0.128 0.645 0.629 This table presents the IV results where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by the interaction between the callable fraction and BAA-AAA. Two measures of maturity are used: F(Debt$$\geq$$5Y) is the fraction of book debt with maturity of 5 years or more, and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}F({\it callable})_{i,t}*{\it BAA-AAA}{}_{t}+\beta_{2}*F({\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi})}_{\it i,t}+\beta_{2}*F({\it callable})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ Table E.6 IV strategy II A. IV strategy II first stage (1) (2) D(Early_refi) F(Early_refi) F(callable)*BAA-AAA $$-$$0.077*** $$-$$0.062*** (0.016) (0.011) Observations 4,175 4,161 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.168 0.112 A. IV strategy II first stage (1) (2) D(Early_refi) F(Early_refi) F(callable)*BAA-AAA $$-$$0.077*** $$-$$0.062*** (0.016) (0.011) Observations 4,175 4,161 Controls Yes Yes Firm FEs Yes Yes Year FEs Yes Yes Sample Speculative Speculative Adj R-squared 0.168 0.112 B. IV strategy II second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.177*** 0.669** 1.587*** 2.435** (0.017) (0.281) (0.107) (1.033) Observations 3,572 3,572 4,175 4,175 F stat (1st stage) 22.91 16.76 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.371 0.128 0.645 0.629 B. IV strategy II second stage F(Debt$$\geq$$5Y) Bond maturity OLS IV OLS IV (1) (2) (3) (4) D(Early_refi) 0.177*** 0.669** 1.587*** 2.435** (0.017) (0.281) (0.107) (1.033) Observations 3,572 3,572 4,175 4,175 F stat (1st stage) 22.91 16.76 Controls Yes Yes Yes Yes Firm FEs Yes Yes Yes Yes Year FEs Yes Yes Yes Yes Sample Speculative Speculative Speculative Speculative Adj R-squared 0.371 0.128 0.645 0.629 This table presents the IV results where $${\it D(Early_{-}refi)}_{\it i,t}$$ is instrumented by the interaction between the callable fraction and BAA-AAA. Two measures of maturity are used: F(Debt$$\geq$$5Y) is the fraction of book debt with maturity of 5 years or more, and Bond maturity is the average bond maturity. Observations are at the firm-year level. Firm fixed effects and year fixed effects are included. Additional firm characteristic controls include ln(Assets), leverage, EBITDA/Assets, Cash/Assets, Tangible/Assets, Market/Book, equity return, and S&P long-term rating. Sample firms are restricted to speculative-grade firms. Standard errors are clustered at the firm level. *,**, and *** denote statistical significance at the 10%, 5%, and 1% level, respectively. 1st: $${\it D(Early_{-}refi)}_{\it i,t}=\beta_{0}+\beta_{1}F({\it callable})_{i,t}*{\it BAA-AAA}{}_{t}+\beta_{2}*F({\it callable})_{\it i,t}+\sum_{j}\beta_{j}{\it controls}_{\it j,i,t}+e_{\it i,t}$$ 2nd: $${\it Maturity}{}_{\it i,t}=\delta_{0}+\delta_{1}D(\widehat{{\it Early_{-}refi})}_{\it i,t}+\beta_{2}*F({\it callable})_{\it i,t}+\sum_{j}\delta_{j}{\it controls}_{\it j,i,t}+\epsilon_{\it it}$$ In this paper, I am agnostic about what drives the variations in credit supply conditions. The drivers could be the countercyclical variation in the economy-wide prices of risk, mispricing due to investor biases in evaluating credit risk, or investor sentiment. Instead of trying to disentangle or quantify those theories, I take the variation in aggregate credit supply conditions as given and study firms’ reactions. As a robustness check, I use two other measures of credit supply conditions. The first is the excess bond premium (EBP) from Gilchrist et al. (2012), which is a credit spread measure purged of default risk. An increase in the excess bond premium reflects a reduction in the effective risk-bearing capacity of the financial sector and, as a result, a contraction in the credit supply. The second is the high-yield fraction, which is the fraction of new corporate issuance that is rated as speculative-grade. Greenwood et al. (2013) show that a decline in issuer quality is a reliable signal of credit market overheating. Using different measures of credit supply conditions does not affect the results in Table 4. 6. Conclusion In this paper, I illustrate how speculative-grade firms actively manage their debt maturity via early refinancing of corporate bonds. This process involves retiring outstanding bonds long before the due date and issuing new bonds with longer maturity as replacements. In particular, they refinance early when credit supply conditions are good, leading to a pro-cyclical debt maturity structure. The evidence is consistent with speculative-grade firms dynamically managing their maturity to hedge against refinancing risks. Investment-grade firms, in contrast, do not manage their maturity in the same way, as they are less exposed to refinancing risk. This paper presents a fresh perspective on the early refinancing of corporate bonds. Early refinancing serves to adjust firms’ maturity structure in addition to reducing interest payments. The findings also provide new information on the relation between debt maturity structure and refinancing risk, the heterogeneous maturity management across credit segments, and the impact of ex post refinancing risk on the ex ante choice of maturity at issuance. In addition, the findings highlight how credit supply conditions affect observed financial structure and corporate behavior, and present early refinancing of corporate bonds as an important strategic tool. While shedding light on a number of issues, my findings also raise additional questions. For example, whether or not, and how do firms trade-off interest rate reduction with maturity extension in their early refinancing decisions? I look forward to future research that addresses these and other related questions. I thank my advisors Douglas Diamond, Zhiguo He, Anil Kashyap, Gregor Matvos, and Amir Sufi for their invaluable input and the editor, Philip Strahan, for insightful comments. I would also like to thank Taylor Begley, Andras Danis, Stephen Kaplan, Kelly Shue, Michael Weisbach, Eric Zwick, and two anonymous referees, as well as seminar participants at the University of Chicago, the Fama Miller corporate finance reading group, the London Business School Trans-Atlantic Doctoral Conference, Cheung Kong Graduate School of Business, Hong Kong University of Science and Technology, University of Hong Kong, Georgia Institute of Technology, University of Notre Dame, University of Colorado at Boulder, 2015 EFA conference, and 2016 AFA conference. Research support from the Deutsche Bank, Bradley Foundation, and the John and Serena Liew Fellowship Fund at the Fama-Miller Center for Research in Finance, University of Chicago Booth School of Business is gratefully acknowledged. Appendix A. Action Types Calls: Issuers pay the principal amount prior to scheduled maturity date, in accordance with the embedded call provision of the security. Make-whole calls: Issuers buy back bonds at par plus a premium. This premium is derived by discounting all the future interest payment by the yield of a comparable Treasury security plus additional basis points. Repurchases: Issuers purchase the bond in the open market. Tender offers: Bond holders are invited to tender their bonds for cash. B. Variable Definitions Leverage=(Debt in current liabilities $$+$$ Long-term Debt)/Total assets M/B = (Total asset $$-$$ Common equity $$+$$ Common shares outstanding $$\times$$ closing price (fiscal year))/Total assets EBITDA/Assets =Earnings before interest, tax, depreciation and amortization/L.Total assets Cash/Assets=Cash and short-term investment/L.Total assets S&P credit rating=S&P domestic long-term issuer credit rating Tangible= Property, plant and equipment /L.Total assets Equity return=$$\triangle$$Closing price (fiscal year)/L.Closing price (fiscal year), adjusted for cumulative adjustment factor if applicable C. Bond Contract Terms Call provision: A flag denoting that a bond has a call provision associated with it. Coupon: The applicable annual interest rate that the bond’s issuer is obligated to pay the bond holders. Covenant: A flag denoting that a bond has covenants associated with it. Covenant count: The exact number of covenants associated with a bond. Maturity at issuance: Year to maturity of the bond at issuance. Offering amount: The total principle value of bond initially issued. Protection period: Number of months from issuance day a firm has to wait to be able to call an outstanding bond. Protection period ratio: The ratio of protection period to maturity at issuance. Speculative grade: A dummy equals 1 if first rating is below or equal to BB+ for a bond. Seniority level: Indicates if the security is a secured, senior, or subordinated issue of the issuer. D. Covenant Terms Negative pledge clause: Indicates a covenant whereby the company is prohibited from pledging or placing liens on certain assets. Change of control: Indicates the existence of a provision that allows for the redemption of the bonds or loans in the event of a corporate takeover, merger, or anti-takeover restructuring that would dissolve significant corporate assets. Limit of indebtedness covenant: Indicates a negative or restrictive covenant that places limitations on the amount of debt that the issuer can incur. This can be expressed as a percentage of assets or in monetary terms. Cross default covenant: Indicates a stipulation stating that if an issuer is in default on other borrowings, such non-payment is also considered default in respect to the issue with the cross-default covenant. Sales of assets restriction covenant: Indicates a negative or restrictive covenant that limits the ability of the issuer to sell any or all of its assets. Debt service coverage ratio covenant: Indicates cash available for total debt service or senior debt service. In corporate finance, it is the amount of cash flow available to meet annual interest and principal payments on debt, including sinking fund payments. Rating trigger provision: Indicates a clause that gives a put option to the bond holders if the bond falls below a designated credit rating, usually investment grade. Merger restrictions covenant indicator: Indicates a negative or restrictive covenant placed on the issuer, stating that the issuer may not merge or consolidate with any other entity without satisfying certain conditions. Limitation on sales and leaseback covenant: Indicates a restrictive or negative covenant that prevents the issuer from selling assets (or removing them from the balance sheet for accounting purposes) and then leasing them back from the company to which they were sold. Limitation on subsidiary debt covenant: Indicates a negative or restrictive covenant that places limitations on the amount of debt that the issuer’s subsidiaries can incur. This can be expressed as a percentage of assets or in monetary terms. Restricted payments covenant: Indicates a negative or restrictive covenant that limits an issuer’s ability to make distributions, whether in the form of cash, assets, or securities to shareholders, to redeem subordinated debt, repurchase equity, or provide dividends. E. Additional Robustness Tests Figure E.1 View largeDownload slide Maturity at issuance for industries with long-term sssets This figure shows the histogram of maturity at issuance for sample bonds for the oil, gas, and coal industry and the telephone and television industry. Bonds with maturity at issuance longer than 30 years are included in the 30-year category. Summary statistics are reported on top. Figure E.1 View largeDownload slide Maturity at issuance for industries with long-term sssets This figure shows the histogram of maturity at issuance for sample bonds for the oil, gas, and coal industry and the telephone and television industry. Bonds with maturity at issuance longer than 30 years are included in the 30-year category. Summary statistics are reported on top. Footnotes 1 See Froot, Scharfstein, and Stein (1993), Leland and Toft (1996), Brunnermeier and Yogo (2009), Acharya, Gale, and Yorulmazer (2011), He and Xiong (2012), Almeida et al. (2012), Choi, Hackbarth, and Zechner (2016) 2 See Diamond (1991), Barclay and Smith (1995), Leland and Toft (1996), Guedes and Opler (1996), Johnson (2003), Brunnermeier and Yogo (2009), He and Xiong (2012). 3 Appendix A defines actions, such as calls and tender offers. 4 Given that the ownership of a bond changes following mergers or acquisitions, I use information provided in the issuer notes from FISD, as well as the Thomson M&A database, to identify the precise effective dates of ownership changes. 5 To mitigate the impact of outliers and the possible coding errors, I winsorize all ratios at the upper and lower one percentiles, and apply the winsorization to all analyses. 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