Creditor Governance through Loan-to-Loan and Loan-to-Own *

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1 Creditor Governance through Loan-to-Loan and Loan-to-Own * Kai Li University of British Columbia Wei Wang Queen s University First version: November, 2012 This version: December, 2013 Abstract This paper provides new evidence on the different strategies adopted by activist creditors in distressed firms. Loan-to-loan occurs when existing bank lenders continue to provide loans after a borrower files for Chapter 11, while loan-to-own occurs when unsecured creditors including hedge funds and private equity funds become equity holders after reorganization. We show that creditors of Chapter 11 firms strategically target firms to enforce power and exert influence. We find that maintaining lending relationships is a major consideration for loan-to-loan lenders. Loan-to-loan also enables these lenders to gain power over junior claimants. In contrast, loan-to-own creditors seek board representation and improve corporate governance and the operating performance of emerged firms. Our paper highlights the importance of differentiating activist creditors by their motives and strategies when studying creditor governance. Keywords: Activist creditors; Chapter 11; creditor governance; boards; corporate governance; loan-toloan; loan-to-own JEL classification: G23; G30; G33 * We would like to thank David Barr, David Emerson, Ron Giammarino, Victoria Ivashina, Wei Jiang, Tracey McVicar, Lynnette Purda, Jiang Wang, Xiaoyun Yu, Yuejuan Yu, Gong Zhan, Ning Zhu, seminar participants at City University of Hong Kong, Fudan University, Queen s University, Renmin University, Shanghai Advanced Institute of Finance, Shanghai University of Finance and Economics, University of Hong Kong, and Tsinghua University, and conference participants at the Frontiers in Finance Conference (Banff), China International Conference in Finance (Shanghai), UBC Summer Finance Conference (Vancouver), and the European Finance Association Meetings (Cambridge) for their helpful comments. We also thank our team of dedicated research assistants Judy Gong, Shasha Guo, Mengting Li, Sammy Singh, Christian Sisak, Kathleen Wei, Ting Xu, and Hank Yang, and Lynn LoPucki at UCLA and Ben Schlafman at New Generation Research for their help with data collection, and Jerry Kaye of BMO for some institutional details. We acknowledge financial support of the Social Sciences and Humanities Research Council of Canada (SSHRC). Li further acknowledges financial support from the UBC-Sauder Research Award in the Economics of Pension Plans and the Sauder Exploratory Research Grant. Wang further acknowledges financial support from the Queen s School of Business research program. All remaining errors are our own. Sauder School of Business, University of British Columbia, 2053 Main Mall, Vancouver, BC V6T 1Z2, tel: (604) , kai.li@sauder.ubc.ca. Queen s School of Business, Queen s University, Kingston, Ontario, K7L 3N6, tel: (613) , wwang@business.queensu.ca.

2 Creditor Governance through Loan-to-Loan and Loan-to-Own Abstract This paper provides new evidence on the different strategies adopted by activist creditors in distressed firms. Loan-to-loan occurs when existing bank lenders continue to provide loans after a borrower files for Chapter 11, while loan-to-own occurs when unsecured creditors including hedge funds and private equity funds become equity holders after reorganization. We show that creditors of Chapter 11 firms strategically target firms to enforce power and exert influence. We find that maintaining lending relationships is a major consideration for loan-to-loan lenders. Loan-to-loan also enables these lenders to gain power over junior claimants. In contrast, loan-to-own creditors seek board representation and improve corporate governance and the operating performance of emerged firms. Our paper highlights the importance of differentiating activist creditors by their motives and strategies when studying creditor governance. Keywords: Activist creditors; Chapter 11; creditor governance; boards; corporate governance; loan-toloan; loan-to-own JEL classification: G23; G30; G33

3 This study examines whether and how creditors adopt a governance role and exert power over other claimants in bankrupt companies. Our central hypothesis is that different types of creditors play important but distinct governance roles that go beyond simply seeking higher recovery rates. We focus on two such strategies: loan-to-loan, whereby a prepetition bank lender participates in the (super senior) debtor-inpossession (DIP) financing and/or provides exit financing at emergence; and loan-to-own, whereby an existing unsecured creditor retains its claims and/or an activist investor provides DIP financing with the intention of converting debt into equity upon the borrower s emergence from Chapter 11. Using detailed information on the changing composition of creditors, particularly those with an a priori propensity to intervene, such as hedge funds and private equity (PE) funds, we are able to clearly delineate the mechanisms through which activist creditors exert governance influence, and thus achieve superior returns. Using a sample of 324 Chapter 11 cases between , we show that bank lenders strategically target distressed firms that allow them to strengthen their lending relationships while loan-toown creditors tend to focus on improving the distressed firm s corporate governance practices to generate superior returns. Our results show that prepetition bank lenders continue to provide loans to distressed firms with good operating performance and strong presence of institutional equity investors. The loan-toloan strategy enables these secured creditors to participate in management selection. In contrast, we show that the loan-to-own strategy allows unsecured creditors to actively intervene in the restructuring process. Through their equity ownership, loan-to-own lenders seek direct board representation and play a prominent role in improving corporate governance practices and the operating performance at emerged firms. Finally, we show that both loan-to-loan and loan-to-own strategies enable activist creditors to gain power over other classes of claimants, resulting in deviations from the Absolute Priority Rule (APR) that are in their favor. We conclude that activist creditors have distinct effects on the governance and performance outcomes of Chapter 11 firms. Our research makes important contributions to two strands of the literature: (1) creditor control rights and governance, and (2) relationship banking and bank monitoring. With regards to the former, Gilson (1989, 1990) shows that distressed companies tend to experience both CEO and director turnover, and that banks, as new shareholders, play an active governance role. Subsequent studies examine the governance role of different types of institutional investors in distressed firms, including Hotchkiss and 1

4 Mooradian (1997) on vulture investors, Jiang, Li, and Wang (2012) on hedge funds, Strömberg, Hotchkiss, and Smith (2012) on private equity funds, and Ivashina, Iverson, and Smith (2013) on asset management firms and hedge funds. Outside Chapter 11 bankruptcy, Roberts and Sufi (2009a, 2009b) and Nini, Smith, and Sufi (2009, 2012) show that creditors exert influence on corporate decisions and CEO turnover when financial covenants are violated. We expand on these studies by examining each and every major creditor s involvement throughout the bankruptcy restructuring process, and by differentiating creditors according to their motives and the strategy they adopt. We are the first to examine the two different tools loan-to-loan and loan-to-own that are used by creditors to improve corporate governance practices and the operating performance. Further, using instrumental variable techniques, we are able to separate selection and treatment effects from the total effects, and show that activist creditors strategically target distressed borrowers to exert their influence. We find that loan-to-loan creditors choose distressed firms with strong ex ante shareholder governance, which allows them to benefit from the upside potential of emergence maintaining lending relationships without incurring potential legal liabilities. In contrast, loan-to-own creditors actively implement governance changes in distressed firms, improving their operating performance post-emergence from Chapter 11. Our analyses help identify the mechanisms that underlie creditor activism in corporate restructuring. The second area of research we contribute to is relationship banking and bank monitoring. Previous studies such as Slovin, Sushka, and Polonchek (1993), Berger and Udell (1995), Berlin and Mester (1999), Boot (2000), Dahiya, Saunders, Srinivasan (2003), and Bharath, Dahiya, Saunders, and Srinivasan (2007, 2011) have shown that relationship banking is mutually beneficial to both the lender and the borrower. Ivashina, Nair, Saunders, Massoud, and Stover (2008) further show that relationship banks help clients become takeover targets. In bankruptcy settings, Dahiya, John, Puri, and Ramirez (2003) find that the presence of DIP lenders with a prior lending relationship significantly reduces a borrower s time in Chapter 11. However, relationship lenders rarely seek representation on their clients boards due to concerns over conflicts of interest and legal liabilities (Fischel (1989) and Kroszner and Strahan (2001)). 2

5 Building on these studies, we construct our instrumental variable for loan-to-loan by exploring banks incentives to retain relationship borrowers, especially when there are a large number of local lenders. We show that maintaining lending relationships with a borrower is a major consideration for loan-to-loan creditors. We further show that the loan-to-loan strategy allows existing lenders to participate in management selection of their targeted firms playing a governance role, and to enforce power against other classes of claimants. The paper is organized as follows. Section I develops our main hypotheses. Section II describes the data and presents an overview of creditor involvement in Chapter 11 firms. Section III presents the model specification. Section IV examines the determinants of loan-to-loan and loan-to-own strategies and their impact on creditor power enforcement, governance practices, and operating performance. Section V presents our conclusions. I. Our Hypotheses Our central hypothesis is that different types of creditors play important but distinct governance roles in corporate bankruptcy to achieve superior returns. We explore two channels through which creditors can exert influence. Previous research has shown that banks value relationship borrowers, even when a borrower is in financial distress (see, for example, Dahiya et al. (2003) and Li, Lu, and Srinivasan (2013)). Furthermore, borrowers that suffer from greater information asymmetry, such as those in distress, are more likely to use their relationship lenders for future loans (Bharath et al. (2007)). Therefore, we expect that, given their informational advantage, existing bank lenders will continue to provide loans to distressed borrowers that have an ex ante high likelihood of emerging from Chapter 11. Meanwhile, intervention only increases profit if it enhances a lender s information relative to others (Kahn and Winton (1998)). Loan-to-loan (LTL) creditors are less likely to actively intervene due to their possession of superior information derived from past interactions with the borrower. Furthermore, strong shareholder monitoring reduces creditors own costs/incentives to monitor. We thus expect LTL creditors to strategically target distressed firms that have strong shareholder governance and to extend loans with strict covenants and collateral with the aim of influencing firms financial and operating policies (Ayotte and Morrison (2009)). 3

6 On the one hand, given that maintaining lending relationships is a major concern for bank lenders, we do not expect LTL creditors to play any active role in turning over incumbent CEOs. On the other hand, their target firms with strong shareholder monitoring suggests higher CEO turnover, and LTL creditors have strong incentives to participate in selecting the next CEO in order to (1) facilitate the bargaining process between the debtor and creditors such as themselves, and (2) continue the lending relationships with the newly-appointed CEO. In the end, LTL may be associated with high CEO turnover during Chapter 11 because of the type of firms they target but we do not expect LTL creditors to proactively push out CEOs. We also do not expect LTL creditors to proactively reshuffle the board given strong institutional shareholder presence and/or to directly pursue board seats because of the increased risk of legal liabilities that such representation brings (Fischel (1989) and Kroszner and Strahan (2001)). Through LTL, secured creditors gain power against junior claimants such as unsecured creditors and equity holders. As a result, we expect to observe fewer incidences of APR deviations against secured lenders. In contrast, it is not straightforward to predict the effect of LTL on the likelihood of APR deviations against unsecured creditors by shareholders. On the one hand, as discussed earlier, we expect LTL creditors to target firms with strong shareholder governance, which comes with strong shareholder bargaining power against creditors. Therefore, we may observe a positive association between LTL and the likelihood of APR deviations against unsecured creditors. On the other hand, the power gained by secured creditors through LTL may spill over to unsecured creditors against equity holders, resulting in a lower likelihood of APR deviations against unsecured creditors by equity holders. Therefore, the APR outcome is an empirical question. In addition to LTL, creditors have another powerful tool at their disposal loan-to-own (LTO), whereby unsecured creditors gain equity ownership when a borrower emerges from Chapter 11 or activist investors such as hedge funds and PE funds provide DIP financing with an intention to control the restructuring process. Through LTO, creditors holding the fulcrum security in the capital structure, whereby the enterprise value first fails to fully cover outstanding claims, become the new owners of the restructured firm and DIP financing also carries clauses which allow it to be converted into equity ownership (Jiang et al. (2012)). We expect LTO creditors to (1) intervene, as these new owners benefit from the upside at emergence, and (2) actively hire top management and board members outside the bankrupt firm whose interests are better aligned with the creditors-turned-shareholders than with the old 4

7 shareholders of the distressed firm (at the time of the Chapter 11 filing). This last observation is important, because incumbent CEOs interests are typically aligned with old residual claimants due to their large holdings of old equity, potentially resulting in risk shifting. The same argument applies to incumbent directors who become ineffective in taking actions that boost the creditors-turnedshareholders payoffs. We thus expect LTO creditors, as new owners, to be actively involved in board selection and to even seek direct representation on the board due to a lack of regulatory oversight on those investors (as compared to LTL creditors). Further, we expect to observe better operating performance at emergence in LTO target firms as a result of their improved governance practices. Given the confrontational nature of LTO, we expect these creditors to dominate other claimants in the restructuring process. First, secured creditors without LTL are more likely to make concessions to LTO unsecured creditors, resulting in a higher likelihood of APR deviations against the former. Second, as new owners, LTO creditors are less likely to make concessions to old equity holders, resulting in a lower likelihood of APR deviations by the latter. In the following section we describe the data we use to test these predictions and present an overview of creditor involvement in Chapter 11 firms. II. Sample and Variable Construction A. The Bankruptcy Sample Our bankruptcy sample includes all Chapter 11 filings by large US firms between that are recorded in Lynn M. LoPucki s Bankruptcy Research Database. All the firms in our sample have assets worth at least $100 million (measured in 1980 constant dollars using the CPI deflator), at the time of a bankruptcy filing, and must have filed 10Ks in the previous three-year period. There are 497 cases in this initial sample. After checking with the New Generation Research s Bankruptcydata.com to verify the status of our sample cases as of January 1, 2011, we drop 18 cases that are pending, dismissed, or affiliated with other cases. This process results in a sample of 479 unique cases of Chapter 11 filings. We merge our sample of Chapter 11 firms with the Compustat database to retrieve firm-level financial information for each firm as of the fiscal year-end before the Chapter 11 filing. We resort to the 5

8 10Ks (available through Edgar) to manually collect any key financial information not available on Compustat. We consider the following firm- and case-level control variables: Size (natural logarithm of book assets), which measures the complexity of the case and the going concern value of the debtor; Leverage (the ratio of total liabilities to book assets) and ROA (the ratio of EBIT to book assets), both of which measure the degree of financial distress; 1 Institution (institutional equity ownership), which proxies for shareholder governance and power; NumClass (number of claim classes from bankruptcy plans), which measures the complexity of the case and the severity of conflicts of interest among claim holders; SecuredDebt (the ratio of secured debt to book assets), which indicates the degree of undercollateralization of secured debt; Debtholding_HHI (the Herfindahl-Hirschman Index (HHI) of percentage debt holdings by the top twenty unsecured creditors), which captures unsecured creditor power: their incentive to intervene as well as the ease of coordination among the largest unsecured creditors; IndDistress (an indicator variable on whether the industry is in distress as defined in Acharya, Bharath, and Srinivasan (2007)), which measures the industry-level condition and hence the possibility of a fire sale under the liquidation scenario; Prepack (an indicator variable on whether the case is prepackaged or prenegotiated), which indicates a special form of filing, where bankruptcy petition and a (pre-negotiated) reorganization plan are both submitted at the time of a Chapter 11 filing; 2 and Delaware (an indicator variable on whether the case is filed in Delaware), which measures the potential court bias suggested by prior research. 3 We use information from bankruptcy plans and disclosure statements to define two outcome variables that characterize the power struggle among different claimants during the Chapter 11 process. The first variable, APR_Screditor, measures the APR deviations against secured creditors by unsecured creditors (Weiss (1990)). It is an indicator variable that is equal to one if unsecured creditors recovery is greater than zero while secured creditors recovery is less than 100%, and zero otherwise. The second variable, APR_Ucreditor, measures the APR deviations against unsecured creditors by equity holders (Eberhart, Moore, and Roenfeldt (1990) and Betker (1995)). It is an indicator variable that is equal to one 1 See Lemmon, Ma, and Tashjian (2009) for a detailed discussion. 2 See Tashjian, Lease, and McConnell (1996) for a comprehensive study of prepackaged bankruptcies. 3 See Eisenberg and LoPucki (1999) on judge shopping in large Chapter 11 reorganizations. 6

9 if equity holders recovery is greater than zero while unsecured creditors recovery is less than 100%, and zero otherwise. 4 B. Identifying Key Creditors in Bankruptcy Restructuring B.1. Prepetition Lenders To determine the type and amount of secured debt owned by each firm, we first examine the 10Ks as of the last fiscal year-end before the Chapter 11 filing (available through Capital IQ), the bankruptcy plans confirmed by the court (available through Bankruptcydata.com), and the 8Ks (available through Edgar). Capital IQ often provides the names of the financial institutions that provide loan facilities. For consistency and completeness, we obtain information about the loan facilities provided to our sample firms from DealScan. We drop loan facilities that matured more than six months before the Chapter 11 filing and loans whose primary purpose is DIP financing. We use DealScan data to identify major lenders in each loan facility, 5 and verify their names with data from the SDC Syndicated Loan database and Capital IQ by amount and maturity. Out of the 479 cases in our initial sample, 64 cases do not have bank loans outstanding at the time of their Chapter 11 filing. We are able to identify the names of major lenders in 390 of the remaining 415 cases. We construct our measure of a prior lending relationship following previous studies (e.g., Bharath et al. (2011)). A relationship lender is a financial institution that is a major lender in any of the loans provided to the borrower in the five-year period prior to the Chapter 11 filing. We match the names of major lenders identified from company filings and Capital IQ at the time of the Chapter 11 filing to the names of major lenders identified through DealScan in the five-year period prior to the filing. In the case of bank mergers, we assume that the lending relationships of the merging banks carry over to the merged bank. We define an indicator variable for relationship lending at the case level, REL(Dummy), which 4 To determine the occurrence and magnitude of APR deviations, we use market values of equity and warrants at emergence, obtained from various sources including CRSP, Bloomberg, Datastream, and BankruptcyData.com, to calculate debt recovery. 5 See Jiang, Li, and Shao (2010) for a discussion of identifying major lenders in syndicated loans when using the DealScan database. Lenders with the following roles are considered major lenders: administrative agent, agent, arranger, book runner, co-agent, co-arranger, co-lead arranger, co-lead manager, co-manager, co-syndications agent, coordinating arranger, documentation agent, joint arranger, joint lead manager, lead arranger, lead bank, lead manager, manager, managing agent, mandated lead arranger, mandated arranger, senior arranger, senior co-arranger, senior co-lead arranger, senior co-manager, senior lead manager, senior lender, senior manager, senior managing agent, sole lender, and syndications agent. 7

10 takes a value of one if at least one of the lenders at the time of the bankruptcy filing is a relationship lender, and zero otherwise. REL(Dummy) captures lender incentives to continue with lending. 6 B.2. The Largest Unsecured Creditors and the Unsecured Creditors Committee The twenty largest unsecured creditors in each Chapter 11 firm are identified from the list of the largest such creditors given in the bankruptcy petition form (available through BankruptcyData.com and the Public Access to Court Electronic Records (PACER)). We are able to obtain this list for 375 cases. In 407 cases, there are unsecured creditors committees formed during reorganization; we are able to obtain the names of the committee members from BankruptcyData.com, LexisNexis, and Factiva in 314 cases. In 72 other cases, there are no unsecured creditors committees. Ultimately, we have information about the unsecured creditors committee for 386 cases. We drop cases for which we do not have information on the secured lenders, the largest unsecured creditors, or the members of the unsecured creditors committee when such a committee is formed. This step results in a final sample of 324 Chapter 11 cases between B.3. Debtor-in-Possession (DIP) Lenders To determine whether a Chapter 11 firm receives DIP financing, we first search LexisNexis, Factiva, bankruptcy plans, and the 8Ks using the following key phrases: debtor-in-possession financing, DIP financing, post-petition financing, and secured financing. We cross-check the DIP lender 6 For our empirical test, we also construct alternative measures of relationship lending based on the strength and length of the relationship(s) following Petersen and Rajan (1994), Berger and Udell (1995), and Bharath et al. (2011). REL(Amount) is the ratio of the amount of loans by a lender to a borrower in the five-year period prior to a Chapter 11 filing to the total amount of loans by the borrower over the same period. When there are multiple lenders involved, we take the maximum value of REL(Amount) of all of the lenders as the case-level measure. REL(Number) is the ratio of the number of loans by a lender to a borrower in the five-year period prior to a Chapter 11 filing to the total number of loans by the borrower over the same period. We take the maximum value of REL(Number) of all of the lenders as the case-level measure. Proportion of relationship lenders is the proportion of relationship lenders among all of the lenders at the time of a Chapter 11 filing. Length of relationship is the number of years since a lender provided loans to a borrower for the first time up to the time of the Chapter 11 filing. We take the maximum value of Length of relationship of all of the relationship lenders as the case-level measure. 7 We compare the industry and year distributions and the firm characteristics of our final sample to the initial sample of 479 cases to ensure against sample bias. We find that the industry distribution of our final sample, based on the Fama-French 12 industries, is similar to that of the initial sample. The share of our sample firms in each industry is about 60-70% relative to the initial sample. The annual distribution is similar in the two samples; however, our final sample tends to have a larger proportion of cases from the post-2001 period, possibly due to relatively poor coverage of earlier years by our main data provider (BankruptcyData.com). There is no significant difference in firm and case characteristics including emergence, Chapter 11 duration, prepack, firm size, leverage, ROA, secured debt to assets, and institutional ownership between our final sample and the initial sample. 8

11 names with those in the DealScan database whenever it carries such information. We then determine whether a DIP lender has any prepetition lending relationships with the debtor, and if so, we collect information on the total amount of the prepetition loans. B.4. Providers of Exit Financing If a firm is not liquidated, an exit facility is often provided by prepetition lenders, DIP lenders, or new lenders. We check DealScan and the 10Ks that are filed immediately after emergence, and news search through Factiva and LexisNexis to determine whether such exit financing is available to the emerged or acquired firm. B.5. Creditor Type Using information provided by the Thomson Reuters Ownership database and online manual searches, we classify creditors into the following types: 1) commercial banks and trust companies; 2) insurance companies; 3) mutual funds; 4) hedge funds and PE funds; 5) investment advisors; 6) investment banks; 7) pension funds; 8) endowment funds; 9) corporations and their financing arms; and 10) all other types of creditors including the government, law firms, individuals, investment trusts, family trusts etc. 8 Following previous studies (e.g., Jiang et al. (2012), Strömberg et al. (2012), and Ivashina et al. (2013)), we treat hedge funds and PE funds as activist investors. We view (commercial and investment) banks and trust companies as traditional lenders following studies by Gilson (1990) and Ivashina et al. (2008) (thereafter we refer to them as bank lenders for simplicity). 9 Insurance companies, asset managers, mutual funds, pension funds, endowment funds, corporations, government entities, individuals, and family investment trusts etc. are grouped together as other investors. 10 B.6. Loan-to-Loan (LTL) There are two natural routes leading to LTL by bank lenders. The first occurs when prepetition bank lenders provide DIP financing. The second route occurs when either prepetition bank lenders or DIP 8 We thank Wei Jiang for providing detailed classifications of institutions in the Thomson Reuters Ownership database. In fact, only 389 of the 3,828 unique creditors identified in our sample have valid institutional IDs in the Thomson Reuters Ownership database. We classify the majority of our creditors through web searches. 9 Investment banks are also considered as lenders because they often participate in syndicated loans. 10 Some banks could be custodian banks rather than direct investors, a distinction that we are not able to make. 9

12 bank lenders provide exit financing. Our key variable of interest, LTL, is defined as an indicator variable that takes a value of one if either route is taken, and zero otherwise. B.7. Loan-to-Own (LTO) We identify LTO through bankruptcy plans confirmed by the court. We define LTO as an indicator variable that takes a value of one if unsecured creditors receive equity upon emergence or at least one hedge fund or PE fund provides DIP financing, and zero otherwise. 11 Activist investors often enter into the distressed firm with an intention to own the restructuring process and ultimately, the firm at the end of the process. Unsecured debt is the natural entry point for these investors as it is the fulcrum security in the capital structure. Our inclusion of DIP financing by activist investors as LTO is motivated by the observations that DIP lenders (especially of the activist type) are able to control the bankrupt firm by bargaining for board seats and receiving shares of the newly reorganized company (Skeel (2003)) and that DIP loans can be easily turned into equity ownership because they carry trigger clauses that replace DIP loan with equity to avoid default (Jiang et al. (2012)). 12 C. Corporate Governance Outcomes at Emergence For a subsample of 109 emerged firms, we have information obtained from the 10Ks and proxy statements (available through Edgar) on the CEO and board members in the fiscal year before the Chapter 11 filing and in the fiscal year after emergence. For these firms, we are able to determine whether there is CEO turnover during reorganization, if there is a new CEO whether she is hired externally, and whether board members are independent. Further, from the published biographies we are able to determine whether any directors at the emerged firm are affiliated with creditors associated with the Chapter 11 process. 11 Note that LTO only occurs in firms emerged from Chapter 11. In cases where unsecured creditors do not receive any new equity in the emerged firm, there are a number of possibilities, including only secured creditors receiving new equity, old equity being retained at emergence (i.e., no new equity to creditors), issuing new equity to new investors at emergence, and new investors injecting equity capital to gain full control during restructuring. 12 We find that in about 70% of the LTO cases where unsecured debt is converted into new equity at emergence, there is at least one hedge fund or PE fund among the largest unsecured creditors or siting on the unsecured creditors committee. In the rest of the cases, investment banks, asset managers, law firms, family trust, and etc. are listed as the major institutional types of unsecured creditors. We do not rule out the possibility of indirect involvement by activist investors given that investment banks (with asset management units), asset managers, and law firms could act as an agent for activist investors, or the possibility of these investors behaving like activist investors once them becoming equity holders. 10

13 Using this data, we define four governance outcome variables: (1) CEOTurnover, which measures whether there is CEO turnover during the restructuring process; (2) ExernalCEO, which measures whether the new CEO is hired externally; (3) BoardTurnover, which measures board turnover, i.e., the number of directors leaving a board between the pre-filing year and the emergence year, scaled by the board size in the pre-filing year; and (4) IndependentDirectors, which measures the fraction of independent directors on a board at emergence. 13 D. Sample Overview and Summary Statistics Most of the variables used in this study and their data sources are provided in Table 1 (except for the governance outcome variables). Firm-level variables are for the fiscal year-end prior to the bankruptcy filing date, except for ROA_Emg, which is measured as the ratio of EBIT to assets at the first fiscal yearend after emergence. To mitigate the influence of outliers, we winsorize all continuous variables at the 1 st and 99 th percentiles. The median size of our sample firms, measured by total assets (Assets), is $784 million in 2008 constant dollars. Both the mean and median ratios of book leverage to total assets (Leverage) are close to one, much higher than the mean (median) leverage ratio of 68% (59%) of the Compustat universe a direct sign of financial distress. Our sample firms also tend to have a lower return on assets and lower institutional ownership relative to the Compustat universe. In a typical firm, secured debt accounts for 31% of a firm s assets. The median HHI for the top twenty unsecured creditors is 33%, suggesting relatively high concentration of unsecured creditors holdings. 14 There is a large variation of concentration in our sample firms. About 17% of our sample firms filed for Chapter 11 in a distressed period for their industry. The 109 firms that emerged as public firms have better operating performance after their emergence than before their Chapter 11 filing (untabulated). In terms of bankruptcy case characteristics, about a third of our sample cases have a prepackaged Chapter 11 filing, and close to half of the filings take place in the state of Delaware. Over 80% of the 13 We do not view the CEO being Chairman of the Board (i.e., CEO duality) as a governance outcome variable in our study because it is not clear to us whether CEO duality indicates good or bad governance in a bankruptcy setting. On the one hand, separating the two positions may indicate good governance as it limits CEO power and entrenchment. On the other hand, given that the new CEO is likely to be selected by creditors in our bankruptcy setting, it is not clear why creditors would rather not provide the CEO with more power in challenging times. 14 To assess economic significance of the median HHI for the top twenty unsecured creditors being 33%, had the top twenty creditors each hold 5% of the total unsecured debt, then HHI would have been 5% ( = ). 11

14 Chapter 11 firms form the unsecured creditors committee (untabulated). About two thirds of the sample firms emerge from Chapter 11, about a quarter are liquidated, and the rest are acquired. The mean (median) number of months spent under Chapter 11 is 17 months (12 months). APR deviations are not common in our sample; they occur in about 30% of the cases (untabulated), including in about 15% of the cases with deviations against secured creditors (APR_Screditor) and in 18% of the cases against unsecured creditors (APR_Ucreditor). Overall, the frequency of deviations is much lower than in the 1980s and early 1990s when APR deviations were the norm rather than the exception. In terms of creditors involvement in our sample firms, about 70% of the firms receive DIP financing (much higher than the 31% reported in Dahiya et al. (2003) covering the period), with 47% of the cases obtaining DIP financing from prepetition lenders (untabulated). The DIP loans are not small; the average loan accounts for 16% of prepetition total assets. About 36% of our sample firms receive exit financing; 24% of them receive it from either DIP lenders (which could be new lenders), or prepetition lenders (which may or may not provide DIP financing) (untabulated). Overall, LTL occurs in 49% of our sample firms. LTO occurs in 51% of our sample firms. 15 Unsecured creditors achieve LTO through receiving equity of emerged firms in 43% of the cases, representing an 84% share (i.e., = 43%/51%) of the LTO cases, whereas activist investors achieve LTO through DIP financing in 11% of the cases, representing a 22% share (i.e., = 11%/51%). This evidence for the prevalence of unsecured creditors pursuing LTO is not surprising, as unsecured debt claims are more likely to be the fulcrum securities in the capital structure which are more likely to be impaired and/or to be swapped for equity at bankruptcy resolution. Table 2 presents the summary statistics of the governance characteristics for a subsample of firms that emerge as publicly listed firms. The statistics show that corporate governance practices are greatly improved after emergence from Chapter 11. More than two thirds of the emerged firms experience CEO turnover during the Chapter 11 restructuring with a majority of the new hires coming from the outside. On average, close to 80% of the directors on a board before the Chapter 11 filing are replaced as of the first 15 Note that LTO only occurs in cases where the distressed firm emerges from the Chapter 11 process. As a result, conditional on emergence, the probability of LTO for our sample firms is 79% (i.e., =.506/0.642, where the denominator is the likelihood of emergence for sample firms). 12

15 fiscal year-end after emergence. Of the 861 directors who initially sit on the boards of our sample firms before the Chapter 11 filing, only 173 directors retain their seats after emergence. The emerged firms also have a much higher fraction of independent directors on their boards. The difference in the fraction of independent directors on a board before filing and after emergence is statistically significant at the 1% level. The average board size decreases after emergence, which is consistent with Gilson s findings (1990) for a sample of 61 Chapter 11 cases between However, our board turnover rate of 79% is much higher than the 54% rate reported in his study. Below, we establish that creditor involvement helps improve governance practices at emerged firms. Our sample consists of 7,203 case-creditor observations involving 3,828 unique creditors of all types, identified from various sources. Table 3 reports the roles taken by different types of creditors in our sample. The percentages indicate the fraction of total cases in which at least one creditor of a given type is observed in a given role. We show that commercial banks, investment banks, and trust companies are the most common LTL creditors in 48% of our sample cases; they appear in a majority of the cases as either secured or unsecured creditors, members of the unsecured creditors committee, or providers of DIP and exit financing. These lenders also frequently appear as LTO creditors (in 46% of the cases). However, one must note that in none of our LTO cases do banks and trust companies appear as the only unsecured creditors; their presence is always accompanied by other types of investors. Activist investors, particularly hedge funds and PE funds, are far more likely to sit on the unsecured creditors committee during the Chapter 11 process (in 48% of the cases) than to simply appear as the largest unsecured creditors at the time of the Chapter filing (in 30% of the cases). We also note that many unsecured creditors committee members, who are typically among the seven largest unsecured creditors, are often not found on the Chapter 11 petition form, suggesting that these activist investors may enter distressed companies well after the Chapter 11 filing. Activist investors rarely show up as secured lenders, but are far more likely to participate through LTO (in 37% of the cases) than through LTL (for which they appear in only 2% of the cases). This observation is consistent with the findings of Jiang et al. (2012), who show that fulcrum securities tend to be the most popular entry point for activist investors. Mutual funds have limited capacity to invest in highly illiquid securities such as defaulted bonds because of their open-ended structure. Pension funds avoid investing in Chapter 11 firms because they are 13

16 required by law to maintain diversified and prudent portfolios and are discouraged from taking risk at the individual security level. Insurance companies are required to hold fixed income instruments with high credit quality and usually shun firms in distress. In fact, holding a large position in a portfolio firm and/or being involved in the management of the firm bring(s) legal uncertainties and obligations to these investors, and also impose(s) restrictions on their trading due to insider trading considerations. Therefore, these types of investors tend to remain passive (Black (1990)). Despite the above caveat, we show that mutual funds and pension funds, along with asset managers, sit on the unsecured creditors committee in 33% of the cases, and achieve LTO in 25% of the cases. Our evidence suggests that the mutual funds and pension funds in our sample may be unusually active in the universe of those funds. 16 Corporations holding large unsecured trade claims often sit on the unsecured creditors committee. In 14% of the cases in our sample, these corporate creditors provide DIP financing due to their customersupplier relationship, but they rarely actively pursue LTL. 17 Nonetheless, in 35% of the cases, these corporate creditors end up with equity ownership. As suggested in earlier studies (e.g., Gilson and Vetsuypens (1994)), trade creditors are rarely identified as having played any role in replacing management or board members. In contrast to other types of creditors that actively pursue LTO, corporate creditors are more likely to passively end up in LTO. The other types of creditors including government, law firms, advisors, etc. achieve LTO, either actively or passively, in 31% of the cases. III. Model Specification A relationship between the presence of activist creditors and governance and performance outcomes could be the result of two effects: 1) a selection effect, whereby informed creditors pick LTL or LTO targets that offer the most favorable outcome, but the actual change in outcome is exogenous to the creditors actions, and 2) a treatment effect, whereby creditors actions would change the outcome even if they were randomly assigned to distressed firms Note that professional asset managers could act as agents for other activist investors. 17 In fact, GE Commercial Finance, GMAC, and Leucadia National are the only three corporate lenders that achieve LTL. 18 See Li and Prabhala (2007) for an overview of self-selection in corporate finance. 14

17 A priori, a combination of these two effects is likely at work. Activist creditors could potentially benefit from their company-picking skills even if they remained passive, but they are more likely to choose cases where they can more effectively influence the outcome in their favor. It is worth noting that our measures for activist creditors involvement embed their activist roles. For example, LTO is defined as cases where creditors end up becoming shareholders, and therefore have a keen interest in emerged companies upside potential. If creditors can achieve the desired outcome just by picking the right company without exerting influence during the Chapter 11 process, they can remain passive and avoid assuming additional roles, such as sitting on the unsecured creditors committee and/or becoming shareholders. To capture both the selection and the treatment effects, we use the following model: ActivistCreditor, Creditor 1 if ActivistCreditor 0;and 0 if otherwise, Outcome i ActivistCreditor (1) In the above system, ActivistCreditor is an indicator variable for creditor involvement through either LTL or LTO, and Outcome can be 1) an outcome of creditor power enforcement against other claimants (APR_Screditor and APR_Ucreditor); 2) an outcome of corporate governance in the emerged firms (such as CEOTurnover and BoardTurnover); or 3) a performance outcome (ROA_Emg). Econometrically, a selection problem amounts to a nonzero correlation between the error disturbances of the two equations in (1), that is corr(, ) 0. Consequently, the estimated ˆ is upward (downward) i i biased if corr(, ) is positive (negative). For identification, we need instrumental variables that i i effectively predict activist creditors roles, but do not affect the outcome other than through their involvement. Sussman and Zeira (1995) present a model that describes how banks monitoring costs increase with distance from their borrowers. Further, geographic proximity lowers banks costs of access to soft information about their borrowers. Consistent with these arguments, Peterson and Rajan (2002) and Degryse and Ongena (2005) show that transportation and monitoring costs cause price discrimination in 15

18 bank lending against distant borrowers, especially for small borrowers, which tend to rely on their local credit market due to their greater information asymmetry and local lenders relative informational advantage. The relationship banking literature further suggests that relationship lenders have an informational advantage over their borrowers, and value relationships when borrower transparency is low (see, for example, Berger and Udell (1995) and Bharath et al.(2007, 2011)). When a firm is in distress, it faces greater information asymmetry, and thus a bankrupt firm will have difficulty approaching a new lender that is not local, due to the distant lender s severe adverse selection problem. A bankrupt firm is, therefore, likely to turn to local lenders or to a relationship lender (Dahiya et al. (2003) and Li et al. (2013)). As a result, distressed firms located in regions with a large number of banks are more likely to choose local banks as a substitute for relationship lending, whereas distressed firms in remote areas with limited commercial banking presence have no choice but to turn to either relationship lenders, or hedge funds and other institutions as lenders of last resort (Brophy, Ouimet, and Sialm (2009)). Therefore, relationship lenders are more likely to achieve LTL with borrowers located in regions with a limited commercial banking presence. 19 Given these observations, our instrumental variable for LTL is designed to capture the availability of local lending facilities for distressed borrowers. We collect sample firms headquarter state and county codes (as per the Federal Information Processing Standards (FIPS)) and merge them with the 2000 US Census Bureau location data to identify the Metropolitan Statistical Area or Micropolitan Statistical Area (both referred to as MSA in this study), in which our sample firms are located. Next, we retrieve the population of each MSA and its population rank as of April 1, 2000, from the US Census Bureau. 20 Our instrument for LTL is the population rank of the MSA in which a Chapter 11 firm is located. A higher population rank (i.e., a lower value of the rank number) indicates the availability of a large number of local lending facilities, and therefore a lower likelihood of LTL by relationship lenders. 19 Our rationale for the LTL instrument is primarily based on the business model of commercial banks, while our bank lenders as discussed earlier include investment banks and trust companies as well. It is worth noting that in a majority of the LTL cases, investment banks and commercial banks tend to show up together. If we remove investment bank LTL and consider only commercial bank LTL, the frequency of LTL occurrence changes from 49% to 44%, implying that in about 5% of the cases only investment banks are involved in LTL. Our main findings remain unchanged if we remove those 5% of the LTL cases. 20 There are a total of 922 MSAs, including 362 Metropolitan Statistical Areas and 560 Micropolitan Statistical Areas, according to the 2000 Census. 16

19 To verify that MSAs with a large population host a large number of bank branches and unique banks, we collect data on the location (ZIP codes) of US depository bank branches as of 2000 from the Federal Deposit Insurance Corporation (FDIC) and merge it with the US Census Bureau location data for the MSA in which each branch is located. We find that, on average, there are 81 bank branches and 16 unique banks located in an MSA. However, the average value increases to 1,291 and 134 for bank branches and unique banks, respectively, if we keep only the top twenty most populated MSAs. In addition, we find that the number of unique banks in an MSA is highly correlated with the population (at 86%) or the population rank (at 48%). This suggests that the population rank of an MSA in which a Chapter 11 firm is located correlates closely with the availability of local lending facilities. 21 For LTO creditors, access to institutional investors and their capital is critical for exit at a later date. The local bias literature suggests that investors tend to hold local stocks because they are more familiar with local companies than with distant companies; as a result, local investors earn abnormal returns in local stocks (see, for example, Coval and Moskowitz (1999, 2001) and Gaspar and Massa (2007)). Further, geographic proximity may reduce the potential costs of monitoring by institutional investors. Chhaochharia, Kumar, and Niessen-Ruenzi (2012) show that firms with high local institutional ownership tend to have better internal governance. There are a number of cities that have a high concentration of institutional investors, such as mutual funds, hedge funds, and private equity funds. We expect that geographic proximity to these top financial centers provides a reasonable estimate of the ease and (low) costs of exit by LTO creditors at a later date. Given these observations, our first instrumental variable for LTO is designed to capture institutional investors local preference, which is critical for LTO creditors exit at a later date. We use the median distance between our sample firm headquarter and top financial centers where institutional investors are more likely to be concentrated. 22 The list of the top five US cities for mutual funds is taken from Nelson s Directory of Investment Managers. The list of the top five US cities for private equity firms is taken from Preqin, an alternative investment data provider. The list of the top five US cities for 21 It is worth noting that our main findings remain unchanged if we use the actual population of an MSA. 22 Hong, Kubik, and Stein (2005) and Christoffersen and Sarkissian (2009) use the Nelson s Directory of Investment Managers (now known as the Lipper MarketPlace) to identify the headquarters of mutual funds and to establish the top financial centers (MSA) based on the number of fund families. Other studies that use the Nelson s Directory of Investment Managers include Coval and Moskowitz (1999, 2001), Gasper and Massa (2007), and Chhaochharia et al. (2012). 17

20 hedge funds is based on the Top 100 Hedge Funds List compiled by the magazine Institutional Investors. Eight cities are among the top five in one of the three lists: New York, Boston, Chicago, San Francisco, Philadelphia, Dallas, Greenwich, and Menlo Park. These cities are used to construct our first instrument for LTO. Our second instrument is motivated by Jiang et al. (2012). In general, hedge funds and PE funds are more likely to increase their investment in distressed targets if the distress-investing hedge funds have been doing well. We use the monthly average return on an index of distress-investing hedge funds using data from CISDM) over the three-month period before Chapter 11 filing as the second instrument. Because our Chapter 11 cases are filed at different points in time, the distress-investing hedge fund returns, despite being a time-series variable, is able to generate cross-sectional variation to explain the LTO decisions by unsecured creditors of the activist type. Both instruments are unlikely to affect individual Chapter 11 case outcomes for the following reasons. First, there is no evidence that distance to financial centers affects the likelihood of APR deviations or board turnover in bankruptcy. Second, the distress-investing hedge fund returns observed before a Chapter 11 filing is unlikely to affect case outcomes that are observed on average 17 months later due to the lack of autocorrelation in returns. IV. Empirical Results In this section, we present the main empirical results for the determinants of LTL and LTO strategies, and their effects on creditor power enforcement and on governance and performance outcomes. 23 Given LTL s and LTO s different nature and the distinct instrumental variables used for identification, we present the main results for each separately. A. Loan-to-Loan 23 When the outcome variable is binary, we use the estimation method for a binary outcome model with a binary endogenous explanatory variable (see Wooldridge (2002) Chapter ). When the outcome variable is continuous, we use the treatment regression method (see Maddala (1983) Chapter 5.7). Both are estimated with the Maximum Likelihood Estimation (MLE) method. Nevertheless, we use the treatment regressions based on the MLE estimation for binary outcome variables as a robustness check. 18