Debt retirement at IPO and firm growth

Accepted Manuscript Title: Debt retirement at IPO and firm growth Author: Pengda Fan PII: DOI: Reference:

S0148-6195(18)30068-7 https://doi.org/10.1016/j.jeconbus.2018.08.004 JEB 5822

To appear in:

Journal of Economics and Business

Received date: Revised date: Accepted date:

22-3-2018 24-8-2018 31-8-2018

Please cite this article as: Fan P, Debt retirement at IPO and firm growth, Journal of Economics and Business (2018), https://doi.org/10.1016/j.jeconbus.2018.08.004 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Debt retirement at IPO and firm growth Pengda Fan† Graduate School of Economics, Kyushu University Highlights

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Highly leveraged firms tend to use proceeds of IPO to repay more existing debt Increased debt capacity and reduced interest burden enable firms to expand their business Firms retiring more debt also present better long-term operating performance

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Abstract

This paper examines whether debt retirement at the time of initial public offering (IPO) can stimulate firm growth. Our findings demonstrate that highly leveraged firms tend to

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use the proceeds of IPOs to repay more existing debt. Then, increased debt capacity and

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reduced interest burden enable firms to expand their businesses. Firms that retire more

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debt also present better performance in the long-term. These results indicate that firms

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that have previously been labeled low-growth firms can achieve high growth in the

An early version of this paper was presented at IFABS Asia 2017 Ningbo China Conference, the

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post-IPO period by conducting debt retirement.

annual meeting of the Japan Finance Association. I gratefully acknowledge the helpful comments

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from anonymous referees and Wilson Tong (the editor). The author thanks Konari Uchida, Yusuke Kinari, Minoru Otsubo, Nobuaki Hori, Kazuo Yamada, Katsuhiko Okada for their helpful comments. This research has received "Mizuho Securities Endowment, Kyoto University

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Excellence in Research Award, 2017". I am supported by Research Fellowships for Young Scientists from JSPS and JSPS KAHENHI, Grant Number JP18J10277. All remaining errors are my own. †

Corresponding author. Graduate School of Economics, Kyushu University 6-19-1, Hakozaki,

Higashiku, Fukuoka 812-8581 JAPAN. Tel.: +81-90-8669-8841 E-mail: [email protected] 1

JEL classification code: G21; G30; G31; G32 Key words: Debt overhang; IPOs; Debt retirement; Firm growth 1. Introduction

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High leverage constrains investment and firm growth because it creates potential underinvestment incentives (Hart and Moore, 1995; Myers, 1977). Given that private

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firms cannot tap the public equity market, which makes it difficult for them to

deleverage, this negative aspect of high leverage is supposed to be more evident for them. Meanwhile, initial public offerings (IPOs) provide private firms a vital opportunity

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to rebalance capital structure (Pagano et al., 1998). In particular, firms can alleviate

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debt-overhang problems through debt retirement by using IPO proceeds. Indeed, a

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nontrivial number of firms have indicated that they primarily use IPO proceeds for debt

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retirement (e.g., 42% of sample firms in Busaba et al. (2001) and 31% of those in

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Dunbar and Foerster (2008)).1 However, recent studies have posited that firms conduct debt retirement because they are not currently engaged in any profitable projects (Amor

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and Kooli, 2017; Andriansyah and Messinis, 2016; Wyatt, 2014), which indicates that debt-retiring IPOs may exhibit a slower growth rate compared with other IPOs. Given

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that there are two potential explanations for debt-retiring IPOs, the objective of this research is to determine whether IPO debt retirement can stimulate firm growth.

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We address this issue using Japanese IPO data collected for the period between 2001

and 2014. Japanese data are advantageous for addressing our hypothesis for the following two reasons: First, the traditional Japanese system is bank-oriented; second,

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When a firm files to go public, it must disclose the intended use of IPO proceeds. 2

the venture capital industry is less developed in Japan than in the US.2 These facts suggest that private Japanese companies rely predominantly on bank debt and are, thus, likely to suffer from underinvestment problems prior to the IPO. Our empirical analyses find that both leverage and interest burden are positively associated with the level of debt repayment during the year of the IPO, which is

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consistent with the premise of our hypothesis that potential debt overhang motivates debt retirement at the IPO. As for the effects of debt retirement, our results indicate that

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firms that retire more debt substantially expand their firm size, compared with firms

that retire less bank debt. These findings support our proposal that debt retirement can

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contribute to firm growth during the post-IPO period.

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Regarding the mechanism through which debt retirement stimulates firm growth, we

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find that, compared with firms that retire less debt, those retiring more debt actually

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increase debt and experience significantly greater reductions in interest spread during the post-IPO period. With regard to post-IPO operating performance, our findings reveal

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that debt-retiring IPOs successfully achieve higher growth without scarifying the profitability.

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This paper contributes to the extant literature in three ways: First, we extend the

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literature on the short- and long-term stock returns of debt-retiring IPOs (Amor and Kooli, 2017; Andriansyah and Messinis, 2016; Wyatt, 2014) by directly examining the link between debt retirement and firm growth around the IPO. Importantly, we highlight

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the benefits of restructuring bank debt at the IPO by providing evidence that debt retirement can boost firm growth during the post-IPO period. Second, our paper is also related to the literature on the negative impacts of debt financing on firm growth (Ahn According to the annual report on Japanese startup businesses 2016 published by venture enterprise center, the total amount of venture capital invested in 2015 is approximately 130 billion Japanese yen, which is significantly smaller than that in the US, 7148 billion Japanese yen. 3 2

et al., 2006; Aivazian et al., 2005; Denis and Denis, 1993; Pawlina, 2010); it extends this strand of literature by examining this phenomenon within the context of IPO firms. The last strand of literature examines how listing status affects firms’ growth and performance (Asker et al., 2015; Bernstein, 2015; Takahashi and Yamada, 2015). While IPOs allow firms to expand their business, it is well documented that they tend to exhibit

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long-term underperformance. Our study finds that debt-retiring IPOs successfully exhibit high growth while maintaining profitability, which emphasizes the importance of

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considering the heterogeneity in the motivation to introduce an IPO (Brau et al., 2012; Levis, 2011).

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The remainder of this paper is organized as follows: Section 2 presents a literature

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review and introduces the hypothesis. Section 3 describes the sample selection and

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empirical methods used. Section 4 presents the empirical results. Section 5 verifies the

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robustness of the results. Section 6 concludes the research with a brief summary. 2. Literature review and hypothesis

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Initial public offerings are conventionally viewed as an opportunity for companies to raise equity capital for growth opportunities (Lowry, 2003). Consistent with this

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investment-financing motivation for IPOs, Kim and Weisbach (2008) have found that

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firms spend a substantial amount of the proceeds of an IPO on research and development (R&D) and capital expenditure in the post-IPO period.3 However, firms may go public for a variety of reasons other than equity financing. For instance, a

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nontrivial number of firms have indicated that they primarily used their IPO proceeds for debt retirement (Busaba et al., 2001; Dunbar and Foerster, 2008). With respect to the motivation to retire debt, one strand of literature posits that firms

In contrast to our results, they find that capital raised at the IPO is less likely to be used for debt retirement. 4 3

that lack investment opportunities and tend to issue overvalued stock are more likely to use IPO proceeds to retire debt (Amor and Kooli, 2017; Andriansyah and Messinis, 2016; Wyatt, 2014). Specifically, Wyatt (2014) has argued that the use of IPO proceeds for debt retirement is a negative indication of future investment opportunities, and he has also found that the offering price of debt-retiring IPOs tends to be discounted more

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than those that use the proceeds for capital expenditure. In a similar vein, Amor and Kooli (2017) have demonstrated that debt-retiring IPOs exhibit poor long-term stock

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performance and interpret it as evidence of market timing. Overall, this idea is consistent with Modigliani and Miller's (1958) capital structure irrelevance theorem,

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which suggests that the firm growth of debt-retiring IPOs is inferior to that of other IPOs.

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However, there is an alternative explanation, which has been overlooked by the

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aforementioned literature: That is, in a market with imperfect information, debt

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overhang may constrain firm growth prior to the IPO, which motivates debt retirement. In general, bank debt is an important financing source for growth companies (Robb

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and Robinson, 2014). However, firms’ reliance on debt financing also incurs significant costs. Myers' (1977) debt-overhang theory demonstrates that, with sufficiently high

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leverage, firms (shareholders) are more likely to forgo positive net present value

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projects in the future because the majority of the payoff from these projects will eventually go to creditors. The existing empirical literature largely supports this idea. Denis and Denis (1993) have found that firms that conduct leveraged recapitalization

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tend to experience substantial decreases in undistributed cash flow, capital expenditures, and total assets during the post-recapitalization period. Complementing the finding based on highly leveraged transactions, Aivazian et al. (2005) have documented that the investment to capital ratio substantially declines by 4% when leverage increases by 10% for publicly traded Canadian companies. These findings 5

indicate that increased leverage reduces managerial discretion over cash flow because internally generated cash is generally used to repay existing debt, rather than to invest in profitable projects. If high leverage leads to underinvestment, it is natural to predict a negative association between leverage and firm value. Consistent with this prediction, McConnell

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and Servaes (1995) have demonstrated that higher leverage is related to lower Tobin’s Q among high-growth firms. Furthermore, Cai and Zhang (2011), who divided firms into

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10 portfolios based on monthly leverage change, have found that the alpha in Fama and French's (1993) 3-factor model decreases as the change in leverage increases. This

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negative effect of leverage change on firm value also suggests that high leverage will lead

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to suboptimal investment policies.

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Therefore, firms with large outstanding debt should have a strong incentive to

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rebalance capital structure to avoid forgoing growth opportunities. This is particularly the case for highly leveraged private firms because they cannot tap the public equity

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market, making it difficult for them to deleverage. Meanwhile, IPOs provide them with a vital opportunity to rebalance capital structure (Pagano et al., 1998). Thus, increased

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debt capacity and improved debt financing conditions are expected to contribute to

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firms’ growth during the post-IPO period. Based on these facts, the following hypothesis is constructed:

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Hypothesis: Highly leveraged firms tend to retire more debt at the time of the IPO, which contributes to firm growth during the post-IPO period, due to the alleviation of potential debt-overhang problems

3. Sample and empirical methods 6

3.1 Sample and data We collected information regarding firms that went public in the Japanese stock market between 2001 and 2014 from the Japanese IPO White Papers. After financial institutions and utilities were removed, the resulting sample comprised 1,474 IPOs. For the remaining companies, we manually collected stated IPO objectives from

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prospectuses that have been available since 2001. Firms’ short- and long-term average

interest spreads were also collected, all of which have been available on FinancialQuest

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since 2000. We used a 10-year Japan Government Bond yield as a proxy for the risk-free

rate, which we collected from the Bank of Japan website. Financial data (such as bank

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debt and bank debt repayment) were also taken from the Nikkei NEEDS FinancialQuest

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database. We limited our attention to firms for which the stated objectives of the IPO

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and weighted average interest spread for the year before the IPO (Year –1) were

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available. It is also worth noting that, after the second filtering, the sample was

comprises 633 firms.

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restricted to firms with outstanding bank debt prior to the IPO. Thus, our final sample

[Insert Table 1 about here]

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Table 1 presents the sample distribution by year and stated objectives of IPO. We

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define debt-retiring IPOs as firms that stated bank debt retirement as the full or partial intended use of IPO proceeds. Table 1 suggests that approximately 5% (27%) of the sample companies stated bank debt retirement as their full (or partial) intended usage

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of IPO proceeds in their prospectuses. Although approximately 27% of the sample firms cited debt retirement as one of the

primary usages of IPO proceeds, they also mentioned using it for capital (or R&D) expenditures. This additional use makes it difficult to assess the effect of debt retirement. As argued by Amor and Kooli (2017), it is important to control what IPO 7

firms really do with the proceeds after the offering, rather than simply relying on information released in the prospectuses. To examine this assumption, we present summary statistics for debt-retiring IPOs that stated bank debt retirement as the full use of IPO proceeds in Appendix B. Panel A of Appendix B indicates that firms in the service, wholesale, and retail industries are more likely to use all of their IPO proceeds

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to retire bank debt. Importantly, Panel B clearly demonstrates that the amount of debt

retirement as a percentage of pre-IPO total assets for debt-retiring IPOs is significantly

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lower than those who do not state bank debt retirement as the primary use of proceeds

in their prospectus (14.9% vs 26.7%). Motivated by potential contamination, this

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research identifies debt-retiring IPOs upon the actual level of debt retirement in the

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year following the IPO.4 Specifically, we equally divide the sample companies (633

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companies) into four groups based on REPAY (the two-year average of the total amount

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of bank debt retired in Year 0 and 1, scaled by total assets in the year before the IPO, where Year 0 indicates the first post-IPO fiscal year). Qualitatively similar results are

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obtained when we use only the amount of debt retired in Year 0. Those in the top quantile (bottom two quantiles) of REPAY are defined as High-Retiring IPOs

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(Low-Retiring IPOs). Similarly, we also define SREPAY and LREPAY as the two-year

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average for the short- and long-term bank debt retired in Year 0 and 1, respectively, scaled by the firm’s total assets in the year before the IPO (see Appendix A for the definition of the variables).

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To test our hypothesis, the key explanatory variables used are leverage and interest

burden. LEVERAGE is defined as the total amount of interest-bearing debt over the total number of assets. SSPREAD is the short-term interest spread (average short-term

Kim and Weisbach (2008) also examine the ex-post use of IPO proceeds by relying on accounting measures. 8 4

interest rate minus the 10-year Japanese government bond yield). LSPREAD is the long-term interest spread (average long-term interest rate minus the 10-year Japanese government bond yield). ASPREAD is the weighted average of SSPREAD and LSPREAD. As with previous studies, we further took the natural logarithm transformation of interest spread (SSPREAD, LSPREAD, and ASPREAD) because the distribution of the

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interest spread is heavily positively skewed (Campello et al., 2011; Goss and Roberts,

2011; Graham et al., 2008). All continuous variables in this research are winsorized at

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the top and bottom 1% values. [Insert Table 2 about here]

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Table 2 presents the variables for High- and Low-Retiring IPOs separately. The data

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indicate that High-Retiring IPOs retire 63% of bank debt in the year following the IPO,

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which is substantially larger than for Low-Retiring IPOs (7%). In addition, High-Retiring

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IPOs (Low-Retiring IPOs) retire 28% (5%) of long-term bank debt, which is evidence of early redemption. Consistent with our hypothesis, firms with large outstanding debt are

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more likely to use IPO proceeds to retire debt, compared with those with little outstanding debt. Furthermore, High-Retiring IPOs bear significantly higher interest

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spread than Low-Retiring IPOs, which also supports our conjecture that firms suffering

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from debt-overhang problems tend to retire more debt using IPO proceeds. With regard to other variables, we find that High-Retiring IPOs tend to be small,

young, high sales growth firms with few tangible assets. We find no significant

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difference in ROA and CASH (Cash and its equivalents over assets), Industry MTB Ratio (the mean of industry’s market-to-book ratio at the year before IPO), and PROCEEDS (primary proceeds deflated by lagged total assets). 3.2 Empirical methods To examine the determinants of debt retirement at IPO, an ordinary least squares 9

(OLS) analysis was conducted. The dependent variable of the analysis is REPAY and the main explanatory variables are LEVERAGE, SSPREAD, LSPREAD, and ASPREAD. To investigate the effect of debt retirement on firm growth, we compared the firm growth of High-Retiring IPOs with that of Low-Retiring IPOs. Specifically, for each High-Retiring IPO, a matching Low-Retiring IPO was selected using a matching

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propensity score from those in the lowest two quantiles for the variable REPAY. LEVERAGE, ASPREAD, Ln(Assets), AGE, TANGIBLE, Sales Growth Ratio, ROA, CASH,

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Industry MTB Ratio, PROCEEDS, industry, and year dummies were used to estimate the

propensity score.5 In accordance with Takahashi and Yamada (2015), we used two

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measurements as proxies for firm growth: industry-median adjusted sales and

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by the primary proceeds raised by the IPO.

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employment growth. These measurements were chosen because they are less affected

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4. Empirical results

4.1 Determinants of debt retirement in IPO year

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This section examines our hypothesis by analyzing an OLS regression of debt repayment at the IPO, in which the dependent variable is REPAY. Model (1) in Table 3

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depicts a positive and significant coefficient for LEVERAGE, which suggests that highly

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leveraged firms are more likely to conduct debt retirement by using IPO proceeds. As an alternative measure of cost due to high leverage, we replaced LEVERAGE with the interest spread variables. Models (2) through (4) also exhibit positive and significant

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coefficients for SSPREAD, LSPREAD, and ASPREAD, respectively, indicating that high-interest burdens motivate IPO firms to retire debt. When we included LEVERAGE and the interest spread variables simultaneously, the interest spread variables become

We also employ the same matching mechanism to examine the effect of debt retirement on interest burden and post-IPO operating performance. 10 5

insignificant. These results are consistent with the premise of our hypothesis that debt overhang constrains firm growth prior to IPO, which in turn motivates firms to retire debt using IPO proceeds.6 With respect to other control variables, small firms are more likely to conduct debt retirement. Indeed, firms with less tangible assets are more likely to retire debt.

tend to go public when their industry-wide market valuation is high.

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[Insert Table 3 about here]

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Inconsistent with Amor and Kooli (2017), we find no evidence that High-Retiring IPOs

Some argue that High-Retiring IPOs differ from Low-Retiring IPOs with regard to

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various firm-specific characteristics associated with the propensity to use debt and the

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incentive to reduce leverage. To address this endogeneity concern, we reran our OLS

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regression based on a matched sample. Specifically, for each High-Retiring IPO, a

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matching Low-Retiring IPO was selected using a propensity score that matched those in the lowest two quantiles of REPAY.7 The control variables in Table 3 were used to

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estimate the propensity score.8 The results are presented in Table 4, while Panel A presents the summary statistics of the control variables used to estimate a firm’s

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propensity to be a High-Retiring IPO. Compared with the unmatched sample in Table 2,

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we find that the majority of firm characteristics—(Ln(assets), TANGIBLE, ROA, CASH, Industry MTB Ratio, PROCEEDS)—become insignificant in this matched subsample. Importantly, in Panel B, the coefficients of LEVERAGE and the interest spread variables

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remain highly significant, which is consistent with our proposal. [Insert Table 4 about here] Qualitatively similar results are obtained if we use LREPAY (early redemption of long-term debt) as our dependent variable. 7 Qualitatively similar results are obtained if matching Low-Retiring IPO is selected from those in the lowest three quantiles of REPAY. 8 In the first-stage logit regression, we drop leverage and interest spread variables because our motivation here is to control other firm-specific characteristics. 11 6

4.2 Post-IPO capital expenditures Our hypothesis states that debt retirement from the IPO can stimulate firm growth. As a preliminary investigation, we compared capital expenditures during the post-IPO period between High- and Low-Retiring IPOs. CAPEXP/TAYear−1 represents capital expenditures deflated by the firm’s total assets in the year preceding the IPO. The

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results are displayed in Table 5. While the median level of capital expenditure was not significantly different between the two subsamples during the post-IPO period, the

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mean High-Retiring IPOs exhibit a significantly higher level of capital expenditure than

do the Low-Retiring IPOs. In addition, when we focus on change in capital expenditure,

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we find stronger evidence to support the claim that High-Retiring IPOs increase capital

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expenditure more than do Low-Retiring IPOs. By the end of Year 2, the High-Retiring

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IPOs had increased capital expenditure by 14.3%, which is significantly higher than that

[Insert Table 5 about here]

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4.3 Post-IPO firm growth

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of Low-Retiring IPOs (4.1%).

One may argue that capital expenditure is likely to be affected by the proceeds raised

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from the IPO. To address this concern, we used sales and employment growth to

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measure firm growth. We computed the industry-median adjusted percentage change of growth from Year –1 to Years 0, 1, or 2 to test this idea. Panel A of Table 6 reports the full sample results. Consistent with our hypothesis, we find that High-Retiring IPOs exhibit

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higher sales growth than do Low-Retiring IPOs. By the end of Year 2, High-Retiring IPOs had increased sales by 61%, which is significantly higher than that of Low-Retiring IPOs (19%). Given that High-Retiring IPOs tend to raise more proceeds than do Low-Retiring IPO, one possible explanation is that excess firm growth is not necessarily driven by debt retirement but may be due to large amounts of IPO proceeds. To address this 12

concern further, in Panel B, we drop those High-Retiring IPOs the amount of debt retirement of which is smaller than half of the primary proceeds raised at the IPO. In Panel C, we formally examine the effect of debt retirement on firm growth by comparing the excess firm growth between the matched subsamples (See subsection 3.2 Empirical methods). Even after controlling for the alternative proposal and the endogeneity

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concerns, we still find that High-Retiring IPOs successfully expand their business more during the post-IPO period.

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[Insert Table 6 about here]

Subsequently, we compared employment growth between High- and Low-Retiring

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IPOs (Matching IPOs). Table 7 clearly demonstrates that High-Retiring IPOs

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substantially create more jobs throughout Years 0 to 2 than do Low-Retiring IPOs. By

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the end of Year 2, the High-Retiring IPOs increased employment by 93%, which is

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significantly higher than that of Low-Retiring IPOs (45%). These findings are consistent with previous literature (Takahashi and Yamada, 2015), which has found that an IPO

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enables small and high-growth firms to access external financing, which contributes to job creation and business expansion.

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[Insert Table 7 about here]

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4.4 Post-IPO financing behavior Because we have found evidence to support the claim that using IPO proceeds to

retire debt can stimulate firm growth, we need to examine the premise of our

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hypothesis, which states that increased debt capacity and improved debt financing conditions enable firms to expand their business. For this purpose, we first tracked post-IPO financing patterns. Table 8 compares the capital structure variables (bank debt and public debt) deflated by the total assets of the firms in the year preceding IPO, between High- and 13

Low-Retiring IPOs. The results suggest that High-Retiring IPOs borrow significantly more bank debt than do Low-Retiring IPOs. For instance, while High-Retiring IPOs significantly increased their bank debt during the post-IPO period, at least half of the Low-Retiring IPOs actually reduced their dependence on bank debt. By the end of Year 2, High-Retiring IPOs increased bank debt by 18% (median) of pre-IPO total assets, which

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is significantly higher than for Low-Retiring IPOs (median: –3%). Similarly, we also find

that High-Retiring IPOs significantly increased public debt. While the median value

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remains zero, the mean value of the change in public debt from Year 1 to Year 2 is 12% (3%) for High-Retiring IPOs (Low-Retiring IPOs). A similar pattern is observed for the

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firms’ financing behaviors (new borrowings and bond issuances), and these findings are

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consistent with our hypothesis that debt retirement increases debt capacity.

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[Insert Table 8 about here]

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4.5 Interest spread change from Year –1 to Year 0 We further investigated whether firms that retire more bank debt at the time of IPO

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decrease more interest burden. Table 9 illustrates the results of the OLS regression for interest spreads around IPO. The dependent (independent) variables are the one-year

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changes to SSPREAD, LSPREAD, and ASPREAD (firm-specific variables) from Year 1 to

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Year 0 (Year 0 is the IPO year). HIGH-REPAY is valued at one (zero) for firms in the top (bottom two quantiles) quantile of REPAY. To address any endogeneity concerns, our analyses are based on matched samples (See subsection 3.2 Empirical methods).9 We

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expect that High-Retiring IPOs can decrease more interest burden by retiring a large amount of existing bank debt. Consistent with our premise, it can be observed that the HIGH-REPAY in Models 1–3 engender negative and significant coefficients for SSPREAD, LSPREAD, and ASPREAD. The estimated coefficients in Model (3) suggest that, for 9

Qualitatively similar results are obtained for the full sample. 14

High-Retiring IPOs, they improve ASPREAD by 0.075, which amounts to 15.74 million Japanese Yen for the average sample firm.10 [Insert Table 9 about here] 4.6 Post-IPO operating performance Finally, we examined post-IPO operating performance. It is extremely difficult to

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predict average profitability accurately. On the one hand, improved debt financing

conditions enable firms to invest in more profitable projects, which would have been

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forgone if they had not gone public. On the other hand, it can also lead to excess

expanding, which is detrimental for profitability. In accordance with Amor and Kooli

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(2017), we examined the industry-median adjusted operating performance around IPO.

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In this regard, OCF_TA represents operating cash flow to total assets, adjusted by the

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median value of all non-IPO firms in the corresponding industry and year. Table 10

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displays the fixed effects regression results based on a matched sample. POSTIPO is valued at one for the post-IPO period (Years 0–2) and zero for Year 1. POST-IPO 

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HIGH-REPAY is the interaction term between POSTIPO and HIGH-REPAY. The coefficient of POSTIPO is negative, indicating that Low-Retiring IPOs underperform by 4%

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compared with the year prior to the IPO. Importantly, POST-IPO  HIGH-REPAY has a

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positive and significant coefficient. On average, High-Retiring IPOs outperform Low-Retiring IPOs by approximately 3.5% in terms of OCF_TA. Moreover, the sum of POST-IPO and POST-IPO  HIGH-REPAY is –0.4% (F-tests cannot reject the null

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hypothesis that the sum of the coefficients is not significantly different from zero), which suggests that High-Retiring IPOs do not underperform in the long-term. Overall, our findings suggest that debt-retiring IPOs successfully obtain high growth while

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[exp(0.075) – 1]  1.726%  (11712 million) = 15.74 million Japanese Yen. 15

maintaining profitability during the post-IPO period. [Insert Table 10 about here] 5. Robustness checks We conducted several robustness checks to verify the robustness of our main results. Firstly, we conducted fixed effect regressions to examine the effects of debt retirement

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on firm growth. We used the following three measurements as proxies for firm growth:

the natural logarithm of total assets, sales, and number of employees. POST-IPO 

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HIGH-REPAY in Appendix C (both full and matched samples) engenders positive and

significant coefficients in all of the models, which emphasizes the benefits of

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rebalancing bank debt for post-IPO firm growth.

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We also changed the definition of REPAY; it is redefined as the two-year average of

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total bank debts retired in Years 0 and 1, scaled either by IPO proceeds or by total

definitions of debt retirement.

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liabilities during the IPO year. Thus, our main conclusion is robust to different

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Lastly, our sample size was substantially reduced due to the lack of interest spread variables. To address any concerns associated with sample size, we relaxed the

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requirement that firms should have available information regarding interest spreads.

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Qualitatively, similar results were obtained for this subsample (Appendix D). 6. Conclusion

A nontrivial number of firms have indicated that their primary motivation for IPOs

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was debt retirement. This paper investigates whether debt retirement from IPO proceeds can stimulate firm growth. Our findings indicate that firms suffering from potential debt-overhang problems tend to use IPO proceeds to repay existing debt. The increased debt capacity and reduced interest burden enable firms to expand their businesses. In addition, firms that retire more debt perform relatively better in the 16

long-term than do other IPOs. These results highlight the costs of debt financing for

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private firms, as well as the benefits of retiring debt using IPO proceeds.

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IP T

Ahn, S., Denis, D.J., Denis, D.K., 2006. Leverage and investment in diversified firms. Journal of Financial Economics 79, 317-337. Aivazian, V.A., Ge, Y., Qiu, J., 2005. The impact of leverage on firm investment: Canadian evidence. Journal of Corporate Finance 11, 277-291. Amor, S.B., Kooli, M., 2017. Intended use of proceeds and post-IPO performance. Quarterly Review of Economics and Finance 65, 168-181. Andriansyah, A., Messinis, G., 2016. Intended use of IPO proceeds and firm performance: A quantile regression approach. Pacific-Basin Finance Journal 36, 14-30. Asker, J., Farre-Mensa, J., Ljungqvist, A., 2015. Corporate investment and stock market listing: A puzzle? Review of Financial Studies 28, 342-390. Bernstein, S., 2015. Does going public affect innovation? Journal of Finance 70, 1365-1403. Brau, J.C., Couch, R.B., Sutton, N.K., 2012. The desire to acquire and IPO long-run underperformance. Journal of Financial and Quantitative Analysis 47, 493-510. Busaba, W.Y., Benveniste, L.M., Guo, R.-J., 2001. The option to withdraw IPOs during the premarket: empirical analysis. Journal of Financial Economics 60, 73-102. Cai, J., Zhang, Z., 2011. Leverage change, debt overhang, and stock prices. Journal of Corporate Finance 17, 391-402. Campello, M., Lin, C., Ma, Y., Zou, H., 2011. The real and financial implications of corporate hedging. Journal of Finance 66, 1615-1647. Denis, D.J., Denis, D.K., 1993. Managerial discretion, organizational structure, and corporate performance: A study of leveraged recapitalizations. Journal of Accounting and Economics 16, 209-236. Dunbar, C.G., Foerster, S.R., 2008. Second time lucky? Withdrawn IPOs that return to the market. Journal of Financial Economics 87, 610-635. Fama, E.F., French, K.R., 1993. Common risk factors in the returns on stocks and bonds. Journal of Financial Economics 33, 3-56. Goss, A., Roberts, G.S., 2011. The impact of corporate social responsibility on the cost of bank loans. Journal of Banking & Finance 35, 1794-1810. Graham, J.R., Li, S., Qiu, J., 2008. Corporate misreporting and bank loan contracting. Journal of Financial Economics 89, 44-61. Hart, O., Moore, J., 1995. Debt and Seniority: An Analysis of the Role of Hard Claims in Constraining Management. American Economic Review, 567-585. Kim, W., Weisbach, M.S., 2008. Motivations for public equity offers: An international perspective. Journal of Financial Economics 87, 281-307. Levis, M., 2011. The performance of private equity‐backed IPOs. Financial Management 40, 253-277. Lowry, M., 2003. Why does IPO volume fluctuate so much? Journal of Financial Economics 67, 3-40. McConnell, J.J., Servaes, H., 1995. Equity ownership and the two faces of debt. Journal of Financial Economics 39, 131-157. Modigliani, F., Miller, M.H., 1958. The cost of capital, corporation finance and the theory of investment. American Economic Review, 261-297. Myers, S.C., 1977. Determinants of corporate borrowing. Journal of Financial Economics 5, 147-175. Pagano, M., Panetta, F., Zingales, L., 1998. Why do companies go public? An empirical analysis. Journal of Finance 53, 27-64. 18

Table 1 Sample distribution Non-debt-retiring IPOs

Total IPOs

63 38 51 61 49 37 31 8 3 5 13 15 24 14 412

97 67 69 88 72 84 46 11 6 6 17 18 31 21 633

SC R

[65%] [57%] [74%] [69%] [68%] [44%] [67%] [73%] [50%] [83%] [76%] [83%] [77%] [67%] [68%]

U

N

Debt-retiring IPOs (Partial use of proceeds) 11 [11%] 23 [34%] 14 [20%] 23 [26%] 16 [22%] 42 [50%] 12 [26%] 3 [27%] 3 [50%] 1 [17%] 4 [24%] 2 [11%] 6 [19%] 7 [33%] 167 [27%]

M

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 Total

Debt-retiring IPOs (Full use of proceeds) 23 [24%] 6 [9%] 4 [6%] 4 [5%] 7 [10%] 5 [6%] 3 [7%] 0 [0%] 0 [0%] 0 [0%] 0 [0%] 1 [6%] 1 [3%] 0 [0%] 54 [5%]

A

Year

IP T

Pawlina, G., 2010. Underinvestment, capital structure and strategic debt restructuring. Journal of Corporate Finance 16, 679-702. Robb, A.M., Robinson, D.T., 2014. The capital structure decisions of new firms. Review of Financial Studies 27, 153-179. Takahashi, H., Yamada, K., 2015. IPOs, growth, and the impact of relaxing listing requirements. Journal of Banking & Finance 59, 505-519. Wyatt, A., 2014. Is there useful information in the ‘use of proceeds’ disclosures in IPO prospectuses? Accounting & Finance 54, 625-667.

A

CC E

PT

ED

This table indicates yearly distribution of our sample IPOs based on stated objectives of IPO. Debt-retiring IPOs are those that state bank debt retirement in the prospectus as a primary (full or partial) use of IPO proceeds. All other IPOs are defined as non-debt-retiring IPOs.

19

Table 2 Summary statistics

ASPREAD Ln (Assets) AGE TANGIBLE Sale Growth Ratio ROA CASH Industry MTB Ratio PROCEEDS

0.068 [0.068] N=316 0.018 [0.009] N=316 0.050 [0.047] N=316 0.270 [0.257] N=316 4.773 [4.863] N=316 5.135 [5.170] N=316 4.961 [4.994] N=316 9.143 [8.971] N=316 27 [24] N=316 0.242 [0.213] N=316 0.237 [0.139] N=313 0.098 [0.084] N=316 0.209 [0.169] N=316 1.359 [1.285] N=316 0.185 [0.091] N=316

0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.001***] 0.000*** [0.000***] 0.086* [0.009***] 0.000*** [0.000***] 0.915 [0.449] 0.416 [0.182] 0.103 [0.214] 0.234 [0.311]

IP T

LSPREAD

0.632 [0.434] N=158 0.355 [0.187] N=158 0.282 [0.236] N=158 0.495 [0.502] N=158 5.185 [5.246] N=158 5.353 [5.350] N=158 5.266 [5.305] N=158 8.596 [8.618] N=158 17 [13] N=157 0.210 [0.157] N=157 0.441 [0.282] N=157 0.099 [0.092] N=158 0.220 [0.194] N=158 1.449 [1.371] N=158 0.221 [0.108] N=157

SC R

SSPREAD

0.256 [0.096] N=474 0.131 [0.020] N=474 0.127 [0.064] N=474 0.345 [0.321] N=474 4.911 [4.941] N=474 5.208 [5.230] N=474 5.063 [5.086] N=474 8.960 [8.834] N=474 24 [20] N=473 0.231 [0.194] N=473 0.305 [0.164] N=470 0.098 [0.088] N=474 0.213 [0.171] N=474 1.389 [1.327] N=474 0.197 [0.096] N=473

U

LEVERAGE

P- value

N

LREPAY

Low-Retiring IPOs

A

SREPAY

High- Retiring IPOs

M

REPAY

Full sample

A

CC E

PT

ED

This table reports summary statistics for High- and Low- Retiring IPOs. The entire sample consists of 633 IPOs in Japan during 2001 to 2014. We equally divide sample companies into four groups upon the average of total bank debts retired in Year 0 and 1 (Year 0 indicates the first post-IPO fiscal year) scaled by total assets at the year before IPO (REPAY). Those in the top quantile of REPAY are defined as High-Retiring IPOs, those in the bottom two quantiles are defied as Low-Retiring IPOs. SREPAY (LREPAY) is the average of short-term (long-term) bank debts retired in Year 0 and 1 scaled by total assets at the year before IPO. LEVERAGE is total interest-bearing debts over total assets. SSPREAD is short-term interest spread (average short-term interest rate minus 10-year Japanese government bond yield). LSPREAD is long-term interest spread (average long-term interest rate minus 10-year Japanese government bond yield). ASPREAD is the weighted average of SSPREAD and LSPREAD. We further take natural logarithm transformation of interest spread as with previous studies. Ln (Assets) is natural logarithm of total assets. AGE is firm age from foundation. TANGIBLE is tangible assets over total assets. Sale Growth Ratio is percentage sales growth ratio from previous year. ROA is operating income divided by total assets. CASH is cash and its equivalents over total assets. Industry MTB Ratio is the mean of industry's market-to-book ratio at the year before IPO. PROCEEDS is primary proceeds deflated by lagged total assets. Financial data (e.g., leverage, interest spread variables) preceding the IPO are presented. All continuous variables are winsorized at the top and bottom one percent values. P-values are for mean (median) difference between. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

20

Table 3 Determinants of bank debt retirement at IPO Dependent variable LEVERAGE SSPREAD

(1) REPAY

(2) REPAY

(3) REPAY

(4) REPAY

0.644***(4.48) 0.117***(2.79)

LSPREAD ASPREAD

0.133**(2.39) 0.127**(2.01) -0.028*(-1.74)

-0.029*(-1.83)

-0.037**(-2.48)

-0.034**(-2.13)

AGE TANGIBLE

0.001 (0.66) -0.254**(-2.20)

-0.001 (-0.51) -0.094 (-0.91)

-0.000 (-0.21) -0.144 (-1.33)

-0.001 (-0.44) -0.149 (-1.36)

Sale Growth Ratio ROA

0.094 (1.51) 0.357 (1.07)

0.085 (1.32) 0.036 (0.11)

0.108* (1.73) -0.054 (-0.16)

CASH Industry MTB Ratio

-0.045 (-0.25) -0.006 (-0.18)

-0.195 (-1.02) 0.028 (0.88)

-0.187 (-0.99) 0.029 (0.93)

PROCEEDS Constant

0.063 (0.78) 0.250 (1.27)

0.052 (0.64) -0.030 (-0.10)

0.025 (0.30) -0.050 (-0.15)

0.035 (0.43) -0.029 (-0.08)

Year dummy Industry dummy

YES YES

YES YES

YES YES

YES YES

N R2

468 0.249

468 0.207

468 0.199

468 0.200

IP T

Control variables Ln (Assets)

0.092 (1.42) -0.012 (-0.04)

N

U

SC R

-0.201 (-1.05) 0.025 (0.80)

A

CC E

PT

ED

M

A

This table explores determinants of bank debt retirement at IPO by conducting OLS regression. The dependent variable is REPAY (the average of total bank debts retired in Year 0 and 1 scaled by total assets at the year before IPO, where Year 0 indicates the first post-IPO fiscal year). LEVERAGE is total interest-bearing debts over total assets. SSPREAD is short-term interest spread (average short-term interest rate minus 10-year Japanese government bond yield). LSPREAD is long-term interest spread (average long-term interest rate minus 10-year Japanese government bond yield). ASPREAD is the weighted average of SSPREAD and LSPREAD. We further take natural logarithm transformation of interest spread as with previous studies. Ln (Assets) is natural logarithm of total assets. AGE is firm age from foundation. TANGIBLE is tangible assets over total assets. Sale Growth Ratio is percentage sales growth ratio from previous year. ROA is operating income divided by total assets. CASH is cash and its equivalents over total assets. Industry MTB Ratio is the mean of industry's market-to-book ratio at the year before IPO. PROCEEDS is primary proceeds deflated by lagged total assets. All continuous variables are winsorized at the top and bottom one percent values. Financial data (e.g., leverage, interest spread variables) preceding the IPO are presented. All estimations include industry and year dummies (not reported). z-statistics based on heteroskedasticity-consistent method are shown in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

21

Table 4 Matched sample results: bank debt retirement at IPO

LEVERAGE SSPREAD

0.813***(4.76)

U

0.170***(3.04)

IP T (4) REPAY

0.254***(3.03)

Industry dummy N

YES 308

R2

0.220

0.193**(2.22)

YES YES

YES YES

YES YES

YES 308

YES 308

YES 308

0.174

0.171

0.165

A

YES YES

M

Control variables Year dummy

N

LSPREAD ASPREAD

P- value 0.852 [0.960] 0.033** [0.001***] 0.357 [0.211] 0.017** [0.007***] 0.719 [0.920] 0.595 [0.932] 0.890 [0.658] 0.875 [0.449]

SC R

Panel A: Summary statistics for matched subsamples High- Retiring IPOs Low-Retiring IPOs Ln (Assets) 8.591 [8.618] 8.618 [8.552] N=154 N=154 AGE 17 [13] 21 [18] N=154 N=154 TANGIBLE 0.205 [0.157] 0.227 [0.161] N=154 N=154 Sale Growth Ratio 0.449 [0.293] 0.313 [0.180] N=154 N=154 ROA 0.099 [0.092] 0.103 [0.087] N=154 N=154 CASH 0.222 [0.199] 0.231 [0.179] N=154 N=154 Industry MTB Ratio 1.461 [1.371] 1.452 [1.333] N=154 N=154 PROCEEDS 0.223 [0.107] 0.217 [0.122] N=154 N=154 Panel B: OLS regression of bank debt retirement at IPO based on matched subsamples (1) (2) (3) Dependent variable REPAY REPAY REPAY

A

CC E

PT

ED

This table explores determinants of bank debt retirement at IPO by using matched subsamples. Specifically, for each High-Retiring IPO, a matching Low-Retiring IPO is selected by using propensity score matching from those in the lowest two quantiles of REPAY. Control variables in Table 3 are used to estimate the propensity score. Panel A presents summary statistics of the control variables used to estimate the propensity score. Panel B reruns OLS regression as with Table 3. The dependent variable is REPAY (the average of total bank debts retired in Year 0 and 1 scaled by total assets at the year before IPO, where Year 0 indicates the first post-IPO fiscal year). LEVERAGE is total interest-bearing debts over total assets. SSPREAD is short-term interest spread (average short-term interest rate minus 10-year Japanese government bond yield). LSPREAD is long-term interest spread (average long-term interest rate minus 10-year Japanese government bond yield). ASPREAD is the weighted average of SSPREAD and LSPREAD. We further take natural logarithm transformation of interest spread as with previous studies. Ln (Assets) is natural logarithm of total assets. AGE is firm age from foundation. TANGIBLE is tangible assets over total assets. Sale Growth Ratio is percentage sales growth ratio from previous year. ROA is operating income divided by total assets. CASH is cash and its equivalents over total assets. Industry MTB Ratio is the mean of industry's market-to-book ratio at the year before IPO. PROCEEDS is primary proceeds deflated by lagged total assets. Financial data (e.g., leverage, interest spread variables) preceding the IPO are presented. All continuous variables are winsorized at the top and bottom one percent values. All estimations include industry and year dummies. For the sake of brevity, we do not report the results for control variables. z-statistics based on heteroskedasticity-consistent method are shown in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

22

Table 5 Post-IPO Capital expenditures

CAPEXP Year 1 / TA Year−1 CAPEXP Year 2 / TA Year−1 CAPEXP Year 0 − Year−1 / TA Year−1 CAPEXP Year 1 − Year−1 / TA Year−1 CAPEXP Year 2 − Year−1 / TA Year−1

Low-Retiring IPOs 0.081 [0.043] N=315 0.100 [0.055] N=308 0.096 [0.058] N=293 0.026 [0.005] N=313 0.048 [0.017] N=306 0.041 [0.014] N=287

P- value 0.003*** [0.460] 0.000*** [0.275] 0.000*** [0.205] 0.033** [0.030**] 0.001*** [0.174] 0.000*** [0.062*]

IP T

High- Retiring IPOs 0.126 [0.049] N=154 0.195 [0.058] N=155 0.215 [0.058] N=144 0.049 [0.008] N=151 0.106 [0.017] N=152 0.143 [0.018] N=141

CAPEXP Year 0 / TA Year−1

A

CC E

PT

ED

M

A

N

U

SC R

This table compares capital expenditures during the post-IPO period between High- and Low-Retiring IPOs. CAPEXP / TAYear−1 is capital expenditures deflated by total assets of the firms in the year preceding IPO. CAPEXP Year n− Year−1 / TA Year−1 is the change of capital expenditures from Year n (0, 1, 2) to Year -1, deflated by total assets of the firms in the year preceding IPO. P-values are for mean (median) difference between. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

23

Table 6 Post-IPO Industry-median adjusted sales growth Panel A: Full sample Year 0 to Year -1 Year 1 to Year -1 Year 2 to Year -1

High- Retiring IPOs

Low-Retiring IPOs

P- value

0.444 [0.295] N=158 0.790 [0.451] N=158 1.289 [0.610] N=149

0.155 [0.091] N=316 0.248 [0.103] N=313 0.397 [0.191] N=297

0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***]

Year 1 to Year -1 Year 2 to Year -1

0.446 [0.283] N=152 0.781 [0.451] N=153 1.264 [0.617] N=144

0.155 [0.091] N=316 0.248 [0.103] N=313 0.397 [0.191] N=297

High- Retiring IPOs 0.435 [0.283] N=154 0.773 [0.451] N=154 1.255 [0.605] N=145

Low-Retiring IPOs 0.215 [0.130] N=154 0.381 [0.235] N=153 0.558 [0.287] N=146

P- value 0.000*** [0.000***] 0.000*** [0.001***] 0.000*** [0.004***]

Panel C: Matched subsamples Year 0 to Year -1

U

Year 1 to Year -1

N

Year 2 to Year -1

0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***]

SC R

Year 0 to Year -1

IP T

Panel B: Drop High-Retiring IPOs with the amount of debt retirement smaller than half of the primary proceeds High- Retiring IPOs Low-Retiring IPOs P- value

A

CC E

PT

ED

M

A

This table compares firm sales growth during the post-IPO period between High- and Low-Retiring IPOs. We compute the industry-median adjusted percentage sales growth from Year −1 to Year 0, 1 and 2. Panel A reports the full sample results. In Panel B, we drop those High-Retiring IPOs the amount of debt retirement of which is smaller than half of the primary proceeds raised at the IPO. Panel C shows the results based on matched subsamples. Specifically, for each High-Retiring IPO, a matching Low-Retiring IPO is selected by using propensity score matching from those in the lowest two quantiles of REPAY. LEVERAGE, ASPREAD, Ln (Assets), AGE, TANGIBLE, Sale Growth Ratio, ROA, CASH, Industry MTB Ratio, PROCEEDS, industry and year dummies are used to estimate the propensity score. LEVERAGE is total interest-bearing debts over total assets. ASPREAD is the weighted average interest spread. Ln (Assets) is natural logarithm of total assets. AGE is firm age from foundation. TANGIBLE is tangible assets over total assets. Sale Growth Ratio is percentage sales growth ratio from previous year. ROA is operating income divided by total assets. CASH is cash and its equivalents over total assets. Industry MTB Ratio is the mean of industry's market-to-book ratio at the year before IPO. PROCEEDS is primary proceeds deflated by lagged total assets. P-values are for mean (median) difference between. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

24

Table 7 Post-IPO Industry-median adjusted employment growth Panel A: Full sample Year 0 to Year -1 Year 1 to Year -1 Year 2 to Year -1

High- Retiring IPOs

Low-Retiring IPOs

P- value

0.298 [0.198] N=158 0.620 [0.317] N=158 0.933 [0.414] N=148

0.126 [0.068] N=315 0.287 [0.135] N=312 0.447 [0.187] N=293

0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***]

Year 1 to Year -1 Year 2 to Year -1

0.294 [0.188] N=152 0.622 [0.325] N=152 0.940 [0.418] N=143

0.126 [0.068] N=315 0.287 [0.135] N=312 0.447 [0.187] N=293

High- Retiring IPOs 0.297 [0.203] N=154 0.614 [0.317] N=154 0.919 [0.414] N=144

Low-Retiring IPOs 0.180 [0.092] N=154 0.410 [0.210] N=143 0.657 [0.332] N=145

P- value 0.001*** [0.003***] 0.011** [0.009***] 0.063* [0.255]

Panel C: Matched subsamples Year 0 to Year -1

U

Year 1 to Year -1

N

Year 2 to Year -1

0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***]

SC R

Year 0 to Year -1

IP T

Panel B: Drop High-Retiring IPOs with the amount of debt retirement smaller than half of the primary proceeds High- Retiring IPOs Low-Retiring IPOs P- value

A

CC E

PT

ED

M

A

This table compares employment growth during the post-IPO period between High- and Low-Retiring IPOs. We compute the industry-median adjusted percentage employment growth from Year −1 to Year 0, 1 and 2. Panel A reports the full sample results. In Panel B, we drop those High-Retiring IPOs the amount of debt retirement of which is smaller than half of the primary proceeds raised at the IPO. Panel C shows the results based on matched subsamples. Specifically, for each High-Retiring IPO, a matching Low-Retiring IPO is selected by using propensity score matching from those in the lowest two quantiles of REPAY. LEVERAGE, ASPREAD, Ln (Assets), AGE, TANGIBLE, Sale Growth Ratio, ROA, CASH, Industry MTB Ratio, PROCEEDS, industry and year dummies are used to estimate the propensity score. LEVERAGE is total interest-bearing debts over total assets. ASPREAD is the weighted average interest spread. Ln (Assets) is natural logarithm of total assets. AGE is firm age from foundation. TANGIBLE is tangible assets over total assets. Sale Growth Ratio is percentage sales growth ratio from previous year. ROA is operating income divided by total assets. CASH is cash and its equivalents over total assets. Industry MTB Ratio is the mean of industry's market-to-book ratio at the year before IPO. PROCEEDS is primary proceeds deflated by lagged total assets. P-values are for mean (median) difference between. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

25

Table 8 Post-IPO financing behavior

BANKDEBT Year 1 / TA Year−1 BANKDEBT Year 2 / TA Year−1 BANKDEBT Year 0 − Year−1 / TA Year−1 BANKDEBT Year 1 − Year−1 / TA Year−1 BANKDEBT Year 2 − Year−1 / TA Year−1 Public debt (scaled by total assets at Year -1)

High- Retiring IPOs

Low-Retiring IPOs

P- value

0.744 [0.520] N=158 1.111 [0.674] N=158 1.588 [0.685] N=148 0.165 [0.034] N=158 0.504 [0.162] N=158 0.998 [0.177] N=148

0.227 [0.193] N=316 0.293 [0.199] N=312 0.354 [0.207] N=293 -0.022 [-0.039] N=316 0.042 [-0.036] N=312 0.110 [-0.028] N=289

0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***]

0.037 [0.000] 0.022 [0.000] N=158 N=316 PUBLICDEBT Year 1 / TA Year−1 0.101 [0.012] 0.031 [0.000] N=158 N=312 PUBLICDEBT Year 2 / TA Year−1 0.144 [0.012] 0.047 [0.000] N=148 N=293 PUBLICDEBT Year 0 − Year−1 / TA Year−1 0.012 [0.000] 0.000 [0.000] N=158 N=316 PUBLICDEBT Year 1 − Year−1 / TA Year−1 0.068 [0.000] 0.009 [0.000] N=158 N=312 PUBLICDEBT Year 2 − Year−1 / TA Year−1 0.117 [0.000] 0.025 [0.000] N=148 N=289 New short- and long-term borrowings (scaled by total assets at Year -1)

0.005*** [0.010***] 0.000*** [0.000***] 0.000*** [0.000***] 0.001*** [0.030**] 0.000*** [0.000***] 0.000*** [0.000***]

N

U

SC R

PUBLICDEBT Year 0 / TA Year−1

IP T

Bank debt (scaled by total assets at Year -1) BANKDEBT Year 0 / TA Year−1

0.721 [0.468] N=158 NEW BANKDEBT Year 1 / TA Year−1 1.090 [0.641] N=158 NEW BANKDEBT Year 2 / TA Year−1 1.145 [0.527] N=148 NEW BANKDEBT Year 0 − Year−1 / TA Year−1 0.238 [0.115] N=158 NEW BANKDEBT Year 1 − Year−1 / TA Year−1 0.606 [0.230] N=158 NEW BANKDEBT Year 2 − Year−1 / TA Year−1 0.930 [0.118] N=148 New bond issues (scaled by total assets at Year -1) NEW PUBLICDEBT Year 0 / TA Year−1 0.020 [0.000] N=158 NEW PUBLICDEBT Year 1 / TA Year−1 0.080 [0.000] N=158 NEW PUBLICDEBT Year 2 / TA Year−1 0.076 [0.000] N=148 NEW PUBLICDEBT Year 0 − Year−1 / TA Year−1 0.009 [0.000] N=158 NEW PUBLICDEBT Year 1 − Year−1 / TA Year−1 0.071 [0.000] N=158 NEW PUBLICDEBT Year 2 − Year−1 / TA Year−1 0.064 [0.000] N=148

A

CC E

PT

ED

M

A

NEW BANKDEBT Year 0 / TA Year−1

0.057 [0.021] N=316 0.109 [0.042] N=312 0.148 [0.053] N=293 -0.030 [-0.014] N=316 0.022 [-0.006] N=312 0.061 [0.000] N=289

0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.000***] 0.000*** [0.001***]

0.008 [0.000] N=316 0.017 [0.000] N=312 0.021 [0.000] N=293 0.001 [0.000] N=316 0.011 [0.000] N=312 0.015 [0.000] N=289

0.004*** [0.005***] 0.000*** [0.000***] 0.000*** [0.003***] 0.061* [0.663] 0.000*** [0.003***] 0.000*** [0.861]

This table compares capital structure variables (bank debt and public debt) and firms’ debt financing behaviors (new borrowings and bond issues) between High- and Low-Retiring IPOs. BANKDEBT / TAYear−1 (PUBLICDEBT / TAYear−1 ) is short- and long-term bank debts (public debts) deflated by the total assets in the year preceding IPO. NEW BANKDEBT / TAYear−1 (NEW PUBLICDEBT / TAYear−1 ) is new borrowings (new bond issues), which is available from cash flow statements, scaled by total assets at the year before IPO (Year -1). P-values are for mean (median) difference between. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

26

Table 9 Interest spread change from Year -1 to Year 0 Dependent variable

(1) Change of SSPREAD

(2) Change of LSPREAD

(3) Change of ASPREAD -0.075***(-2.98)

LEVERAGE Ln (Assets)

-0.348 (-1.06) 0.061 (0.71)

0.131 (0.92) -0.002 (-0.05)

-0.142 (-0.76) 0.012 (0.21)

TANGIBLE Sale Growth Ratio

0.339 (0.94) 0.024 (0.59)

0.201 (0.90) -0.034 (-1.08)

0.208 (0.90) 0.018 (0.60)

ROA CASH

0.004 (0.01) -0.015 (-0.06)

0.659***(2.91) -0.082 (-0.52)

0.214 (0.79) -0.188 (-0.91)

MATURITY Constant

0.018 (0.17)

0.208*(1.93)

0.131*(1.72) 0.047 (0.54)

Year dummy Industry dummy

YES YES

YES YES

YES YES

N R2

251 0.120

280 0.090

SC R

IP T

HIGH-REPAY -0.087**(-2.26) -0.080***(-3.08) Control variables (One-Year Change from Year -1 to Year 0)

235 0.122

A

CC E

PT

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M

A

N

U

This table reports the interest spreads change from Year -1 to Year 0, where Year 0 indicates the first post-IPO fiscal year. The dependent variables are the one-year changes (Year-1 to Year 0) of SSPREAD, LSPREAD and ASPREAD respectively. HIGH-REPAY takes on a value of one (zero) for those in the top (bottom two quantiles) quantile of REPAY. The changes of firm-specific variables from Year -1 to Year 0 are included as independent variables. Matched sample results are reported. Specifically, for each High-Retiring IPO, a matching Low-Retiring IPO is selected by using propensity score matching from those in the lowest two quantiles of REPAY. LEVERAGE, ASPREAD, Ln (Assets), AGE, TANGIBLE, Sale Growth Ratio, ROA, CASH, Industry MTB Ratio, PROCEEDS, industry and year dummies are used to estimate the propensity score. LEVERAGE is total interest-bearing debts over total assets. ASPREAD is the weighted average interest spread. Ln (Assets) is natural logarithm of total assets. AGE is firm age from foundation. TANGIBLE is tangible assets over total assets. Sale Growth Ratio is percentage sales growth ratio from previous year. ROA is operating income divided by total assets. CASH is cash and its equivalents over total assets. MATURITY is measured as the ratio of long-term bank debts to total bank debts. All continuous variables are winsorized at the top and bottom one percent values. Financial data (e.g., leverage, interest spread variables) preceding the IPO are presented. All regressions include industry and year dummies (not reported). z-statistics based on heteroskedasticity-consistent method are shown in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

27

Dependent variable POSTIPO

OCF_TA -0.039***(-3.20)

POST-IPO * HIGH-REPAY Control variables

0.035**(2.38)

LEVERAGE Ln(assets)

-0.358***(-8.33) -0.039***(-2.64)

AGE Sale Growth ratio

-0.023***(-4.40) 0.048***(3.68)

Constant Year dummy

0.845***(6.32) YES

N R2

1204 0.236

IP T

Table 10 Fixed effects regression: Post-IPO operating performance

A

CC E

PT

ED

M

A

N

U

SC R

This table reports the fixed effects regressions of industry-adjusted operating performance. OCF_TA is operating cash flow to total assets, which is adjusted by the median operating performance of all non-IPO firms in corresponding industry and year. POSTIPO takes a value one for post-IPO period (Year 0 to Year 2), zero for Year -1. HIGH-REPAY takes on a value of one (zero) for those in the top (bottom two) quantile of REPAY. POST-IPO * HIGH-REPAY is the interaction term between POSTIPO and HIGH-REPAY. Result based on the matched sample is reported. Specifically, for each High-Retiring IPO, a matching Low-Retiring IPO is selected by using propensity score matching from those in the lowest two quantiles of REPAY. LEVERAGE, ASPREAD, Ln (Assets), AGE, TANGIBLE, Sale Growth Ratio, ROA, CASH, Industry MTB Ratio, PROCEEDS, industry and year dummies are used to estimate the propensity score. Financial data (e.g., leverage, interest spread variables) preceding the IPO are used. All continuous variables are winsorized at the top and bottom one percent values. z-statistics based on heteroskedasticity-consistent method are shown in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

28

Appendix A Variable definition

A

CC E

PT

ED

M

A

N

U

SC R

IP T

This appendix presents the definition of the variables used in this study REPAY The two-year average of total bank debts retired in Year 0 and 1 scaled by total assets at the year before IPO, where Year 0 indicates the first post-IPO fiscal year SREPAY The two-year average of short-term bank debts retired in Year 0 and 1 scaled by total assets at the year before IPO, where Year 0 indicates the first post-IPO fiscal year LREPAY The two-year average of long-term bank debts retired in Year 0 and 1 scaled by total assets at the year before IPO, where Year 0 indicates the first post-IPO fiscal year LEVERAGE Total interest-bearing debts over total assets SSPREAD Log (Average short-term interest rate minus 10-year Japanese government bond yield) LSPREAD Log (Average long-term interest rate minus 10-year Japanese government bond yield) ASPREAD Log (Weighted average of SSPREAD and LSPREAD) Ln(Assets) Natural logarithm of total assets AGE Firm age from foundation TANGIBLE Tangible assets over total assets Sales Growth Ratio Percentage sales growth ratio from previous year ROA Operating income divided by total assets Cash Cash and its equivalents over assets Industry MTB Ratio The mean of industry's market-to-book ratio at the year before IPO PROCEEDS Primary proceeds deflated by lagged total assets CAPEXP / TAYear−1 Capital expenditures deflated by total assets in the year preceding IPO BANKDEBT / TAYear−1 Short-term and long-term bank debts deflated by the total assets in the year preceding IPO PUBLICDEBT / TAYear−1 Public debts deflated by the total assets in the year preceding IPO NEW BANKDEBT/ TAYear−1 New borrowings, which is available from cash flow statements, scaled by total assets at the year before IPO (Year -1) NEW PUBLICDEBT/ TAYear−1 New bond issues, which is available from cash flow statements, scaled by total assets at the year before IPO (Year -1) HIGH-REPAY A binary dummy variable that takes on a value of one (zero) for those in the top quantile (bottom two quantiles) of REPAY MATURITY Measured as the ratio of long-term bank debts to total bank debts OCF_TA Operating cash flow to total assets, adjusted by the median OCF_TA of all non-IPO firms in corresponding industry and year POSTIPO Dummy variable which takes a value one for post-IPO period (Year 0 to Year 2), zero for Year -1 POST-IPO * HIGH-REPAY The interaction term between POSTIPO and HIGH-REPAY

29

Appendix B Debt-retiring IPOs that state debt retirement in the prospectus as a full use of IPO proceeds Panel A: Industrial Distribution Industry Service

9 7

Electric equipment Food products

6 4

Other Products Real Estate

4 3

Precision Instruments Land Transportation

2 2

Construction Glass and Ceramics Products

1 1

Iron and Steel Machinery

1 1

Transportation Equipment

1

SC R

Wholesale Retail

IP T

Number of Debt-retiring IPOs (Full use of proceeds) 12

Panel B: Summary Statistics

ASPREAD

ED

Ln (Assets) AGE

CC E

PROCEEDS

PT

Sale Growth Ratio ROA

U

N

M

LEVERAGE

Debt-retiring IPOs (Partial use of proceeds) N=167 (2) (3) Mean P- value [Median] 0.178 0.389 [0.122] [0.753] 0.349 0.289 [0.339] [0.190] 5.000 0.674 [5.019] [0.854] 8.940 0.275 [7.723] [0.069*] 25 0.242 [21] [0.140] 0.255 0.419 [0.163] [0.052*] 0.092 0.885 [0.090] [0.743] 0.250 0.022** [0.141] [0.000***]

A

REPAY

Debt-retiring IPOs (Full use of proceeds) N=54 (1) Mean [Median] 0.149 [0.115] 0.375 [0.394] 5.026 [5.032] 9.184 [8.951] 28 [24] 0.210 [0.101] 0.094 [0.091] 0.139 [0.075]

Non-debt-retiring IPOs N=412 (4) Mean [Median] 0.267 [0.134] 0.370 [0.359] 5.088 [5.112] 8.880 [7.753] 22 [17] 0.352 [0.181] 0.098 [0.086] 0.203 [0.098]

(5) P- value 0.024** [0.194] 0.864 [0.624] 0.305 [0.178] 0.139 [0.034**] 0.028** [0.003***] 0.044** [0.006***] 0.670 [0.734] 0.238 [0.111]

A

Panel A indicates industrial distribution of Debt-retiring IPOs that state bank debt retirement in the prospectus as a full use of IPO proceeds. Panel B compares firm characteristics between Debt-retiring IPOs (Full use of proceeds) with Debt-retiring IPOs (Partial use of proceeds) and Non-debt-retiring IPOs. P-value in Column (3) (Column (5)) are for mean (median) difference between (1) and (2) ( (1) and (4)). See Appendix A for variable definition. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

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Appendix C Fixed effects regression: Post-IPO firm growth Panel A: Full Sample Model (2) Ln(Sales)

Model (3) Ln(employment)

POSTIPO POST-IPO * HIGH-REPAY

0.113***(4.78) 0.279***(5.98)

0.042**(1.83) 0.287***(6.81)

0.031*(1.89) 0.174***(4.94)

LEVERAGE AGE

0.986***(4.95) 0.111***(5.39)

0.736***(3.64) 0.041 (0.87)

0.616***(3.66) 0.099***(5.16)

ROA Sale Growth ratio

0.550**(2.38) 0.068 (1.58)

1.607***(6.63)

0.106 (0.49) 0.059*(1.86)

Constant Year dummy

5.746***(12.46) YES

7.495***(7.67) YES

2.637***(6.13) YES

N R2

1839 0.473

1847 0.369

1837 0.378

Dependent variable

Model (4) Ln(Assets)

Model (5) Ln(Sales)

POSTIPO POST-IPO * HIGH-REPAY

0.155***(4.09) 0.195***(3.72)

0.070**(1.99) 0.206***(4.49)

LEVERAGE AGE

1.096***(4.75) 0.121***(5.28)

0.750***(3.19) -0.115 (-0.93)

ROA Sale Growth ratio

0.585**(2.00) 0.083*(1.79)

1.495***(5.65)

Constant Year dummy

5.478***(13.68) YES

9.568***(4.97) YES

2.388***(6.45) YES

N R2

1206 0.518

1208 0.404

1206 0.416

SC R

Panel B: Matched Sample

U

Model (6) Ln(employment)

N

A M

IP T

Dependent variable

Model (1) Ln(Assets)

0.066**(2.53) 0.104***(2.60) 0.689***(3.46) 0.105***(5.14) 0.164 (0.60) 0.079**(2.26)

A

CC E

PT

ED

This table reports the fixed effects regressions of post-IPO firm growth. We use three measurements as proxies for firm growth: the natural logarithm of total assets, sales, and the number of employees. POSTIPO takes a value one for post-IPO period (Year 0 to Year 2), zero for Year -1. HIGH-REPAY takes on a value of one (zero) for those in the top (bottom two) quantile of REPAY. POST-IPO * HIGH-REPAY is the interaction term between POSTIPO and HIGH-REPAY. Panel A shows the results based on the entire sample while Panel B presents the results based on the matched sample. Specifically, for each High-Retiring IPO, a matching Low-Retiring IPO is selected by using propensity score matching from those in the lowest two quantiles of REPAY. LEVERAGE, ASPREAD, Ln (Assets), AGE, TANGIBLE, Sale Growth Ratio, ROA, CASH, Industry MTB Ratio, PROCEEDS, industry, and year dummies are used to estimate the propensity score. All continuous variables are winsorized at the top and bottom one percent values. All regressions include year dummies (not reported). z-statistics based on heteroskedasticity-consistent method are shown in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

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Appendix D Post-IPO firm growth based on a larger sample Panel A: Full Sample Model (2) Ln(Sales)

Model (3) Ln(employment)

POSTIPO POST-IPO * HIGH-REPAY

0.280***(14.43) 0.163***(4.26)

0.101***(6.22) 0.195***(6.03)

0.055***(4.66) 0.142***(5.41)

LEVERAGE AGE

1.107***(6.88) 0.091***(2.90)

0.786***(5.75) -0.032 (-0.56)

0.685***(6.51) 0.068***(3.12)

ROA Sale Growth ratio

0.717***(4.23) -0.049 (-1.61)

1.613***(10.23)

0.122 (0.94) -0.027 (-1.25)

Constant Year dummy

6.542***(6.94) YES

10.481***(5.93) YES

3.904***(5.96) YES

N R2

3867 0.460

3892 0.366

3849 0.417

Model (5)

Ln(Assets) 0.104***(3.73)

Ln(Sales) 0.039 (1.45)

Model (6)

POST-IPO * HIGH-REPAY LEVERAGE

0.243***(5.46) 1.032***(4.79)

0.223***(5.56) 0.928***(4.37)

AGE ROA

0.098***(4.33) 0.536**(2.21)

0.034 (0.59) 1.874***(7.44)

Sale Growth ratio Constant

0.091*(1.82) 6.119***(12.71)

7.512***(6.65)

Year dummy

YES

N

1525

R2

0.453

M

A

N

U

Model (4) Dependent variable POSTIPO

SC R

Panel B: Matched Sample

IP T

Dependent variable

Model (1) Ln(Assets)

Ln(employment) 0.027 (1.36) 0.133***(3.92) 0.726***(3.83) 0.096***(4.29) 0.168 (0.72) 0.065*(1.71) 2.854***(6.13)

YES

YES

1528

1523

0.385

0.376

A

CC E

PT

ED

This table replicates the fixed effects regressions of post-IPO firm growth as with Appendix C by using a larger sample. Specifically, we relax the requirement that firms should have available information regarding interest spreads. Then, our entire sample size increases to 1343. We further equally divide 1343 companies into four groups upon the average of total bank debts retired in Year 0 and 1 (Year 0 indicates the first post-IPO fiscal year) scaled by total assets at the year before IPO (REPAY).Those in the top quantile of REPAY are defined as High-Retiring IPOs, those in the bottom two quantiles are defied as Low-Retiring IPOs. As a result, we have 335 High-retiring IPOs and 672 low retiring IPOs. We use three measurements as proxies for firm growth: the natural logarithm of total assets, sales, and the number of employees. POSTIPO takes a value one for post-IPO period (Year 0 to Year 2), zero for Year -1. HIGH-REPAY takes on a value of one (zero) for those in the top (bottom two) quantile of REPAY. POST-IPO * HIGH-REPAY is the interaction term between POSTIPO and HIGH-REPAY. Panel A shows the results based on the entire sample while Panel B presents the results based on the matched sample. Specifically, for each High-Retiring IPO, a matching Low-Retiring IPO is selected by using propensity score matching from those in the lowest two quantiles of REPAY. LEVERAGE, ASPREAD, Ln (Assets), AGE, TANGIBLE, Sale Growth Ratio, ROA, CASH, Industry MTB Ratio, PROCEEDS, industry, and year dummies are used to estimate the propensity score. All continuous variables are winsorized at the top and bottom one percent values. All regressions include year dummies (not reported). z-statistics based on heteroskedasticity-consistent method are shown in parentheses. *, **, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

32