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The Role of Activist Hedge Funds in Distressed Firms

Jongha Lim1, 2

Fisher College of Business

The Ohio State University

This Version: November 19th

, 2010

Abstract

Frictions to efficient bargaining provide an opportunity for a hedge fund to invest in distressed firms, facilitate

reorganizations, and capture some of the rents from doing so. This paper considers a sample of 184 financially

distressed firms for the period from 1998 to 2009, and finds that distressed investing has become an important

avenue for activism by hedge funds. Distress-focused hedge funds utilize a „fulcrum‟ investment strategy so as to

maximize their influence on the reorganization process. Empirical evidence is consistent with the view that hedge

funds capture some of the rents they create by reorganizing the distressed firms. First, distress-oriented hedge funds

tend to target economically healthy firms in which contracting difficulties are likely to prevent efficient

reorganization. Second, hedge funds‟ presence as creditors leads to effective restructuring through both debt-equity

swaps and pre-packaged filings. In addition, when hedge funds inject new equity capital into targeted firms, the

duration of distress is significantly reduced. Both debt and equity investments by hedge funds are associated with

significantly higher probabilities of successful restructuring. Lastly, distress-focused funds in my sample have

produced an annual compounded return of 8.6% over the 1998-2009 period, which is economically significant,

compared to 3.2% generated by the stock market over the same period.

1 Contacts: Jongha Lim, Doctoral student, Fisher College of Business, The Ohio State University, 2100 Neil Ave,

Columbus, Ohio, 43210; E-mail: lim_806@fisher.osu.edu. 2 I greatly appreciate the helpful comments and advice from Anil Makhija, Bernadette Minton, René Stulz, and

Michael Weisbach. I am also grateful for valuable discussions with Jack Bao, Itzhak Ben-David, Ji-woong Chung,

Isil Erel, Kewei Hou, Berk Sensoy, and Yingdi Wang. I thank seminar participants at the Ohio State University. I

thank Wonik Choi for his great support. All errors are mine.

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I. Introduction

According to the Coase Theorem, when a firm goes into financial distress, parties can costlessly

recontract and reemerge from distress without any real consequences. In the Coasian world, bargaining

among parties will always lead to an efficient outcome. However, it is likely that in practice, the

assumptions underlying the Coase Theorem do not hold. In practice, restructuring the liabilities of a

distressed firm involves substantial costs, if it is even possible at all. Information asymmetries, different

incentives, coordination problems between debt holders, illiquidity in both the markets for real and

financial assets, together with a reluctance on the part of banks and public debt holders to accept equity

for debt, all contribute to the difficulties that firms have reorganizing when in financial distress.3 In that

sense, financial distress is a situation in which contracting problems cause deviations from first-best

allocation of resources, creating real costs for the parties involved.

These contracting difficulties present a market opportunity for an active investor, who can

strategically choose to take positions in distressed companies for whom a restructuring would create

efficiencies, facilitate these firms‟ reorganization, and capture some of the rents arising from these

efficiencies. Hedge funds, whose managers have strong performance-related incentives and a great

degree of flexibility as to the securities they can hold, provide such a possible active investor. An

individual with financial resources and a specialty in distress resolution can form a hedge fund, purchase

securities that enable the investor to finesse the contractual problems preventing a profitable restructuring,

and share in the efficiency rents from a successful restructuring. This paper argues that distress-oriented

hedge funds are an institution that has evolved around these contracting difficulties, and that the returns to

these hedge funds come from rents associated with the reorganization of economically sound but

financially distressed firms.

3 See, for example, Gertner and Scharfstein (1991), Shleifer and Vishny (1992), Giammarino (1989), James (1995),

Bulow and Shoven (1978) for a theoretical discussion of the way in which these factors contribute to the costs of

resolving distress, and Jensen (1991) for the opposite Coasian view that distress is relatively costless. Among

empirical studies, Gilson (1997), James (1996), Asquith, Gertner, and Scharfstein (1994), Brown, James, and

Mooradian (1994), Pulvino (1998) provide evidence that these obstacles to bargaining generate real costs, while

Andrade and Kaplan (1998b, 1994a, and 1998) provide evidence that financial distress is relatively costless.

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This view of distress-oriented hedge funds contains a number of empirically-verifiable predictions.

First, it predicts that the strategy executed by hedge funds will be the acquisition of positions that allow

them to have the largest influence on the restructuring and to maximize the rents from successful

reorganization. Institutionally, these positions are known as the fulcrum point, which is defined as the

point in the capital structure where the enterprise value no longer fully covers the claim, and therefore is

most likely to receive equity in the reorganized company. Second, firms targeted by hedge funds are those

for which contracting problems are likely to prevent efficient reorganization. These firms are likely to be

those with relatively high transaction costs and complicated capital structures made up of securities held

by a number of different institutions with different incentives, but also those firms whose fundamental

value is such that the firm will likely be profitable conditional on restructuring. Third, this view predicts

that the presence of a hedge fund will affect the restructuring process and outcome positively. Hedge

funds can facilitate the restructuring efforts by enhancing the use of flexible means of restructuring such

as debt-equity swaps and pre-packaged filings. Also, reduced frictions in bargaining will lead to a faster

restructuring as well as to a higher probability of a successful restructuring.

I examine these hypotheses for a sample of 184 distressed firms which either restructured their debt

privately out-of-court or formally under Chapter 11 for the period from 1998 to 2009. I find evidence that

hedge funds actively participated in distressed-firm restructuring, suggesting that distressed investing has

become an important avenue for activism by hedge funds. Based on information available in news

reports, various documents filed to the U.S. Securities and Exchange Commission (SEC), and bankruptcy

documents, I identify hedge funds‟ involvements in the troubled firms‟ restructuring in 119 firms (64.7%

of the sample). In extreme cases, hedge funds were involved in all distressed firms in my sample in year

2006.

Empirical findings are consistent with the predictions described above. First, hedge funds acquire the

so-called fulcrum position to strategically obtain a measure of control over the course of a company‟s

turnaround, and at the same time to seek upside potential in the reorganized firm via post-restructuring

ownership. Hedge funds achieve this fulcrum position in various ways; they purchase fulcrum securities

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in 69 cases (37.5% of the sample), inject new equity capital in 35 cases (19% of the sample), and pursue

loan-to-own strategy in 32 cases (17.4% of the sample). Hedge funds often increase leverage to influence

the restructuring process by taking a seat on the creditors‟ committee (54 cases, 45.4% of the sample)

and/or by leading negotiations of a pre-packaged deal (28 cases, 66.7% of all pre-packs). These positions

vest hedge funds with 34.5% ownership on average in the reorganized firms.

Second, I find that hedge funds target firms in which contracting problems are likely to be more

severe but potential profitability is higher, if the firm is successfully restructured. Firms targeted by hedge

funds have more debt classes than other similar firms and more often have both public and bank debt

outstanding, both of which increase the difficulties of restructuring. Furthermore, target firms are more

likely to suffer from an imminent liquidity crunch because they tend to have relatively high current debt

due and low cash holdings. Target firms, however, do not seem to suffer from severe economic distress

compared to non-targeted distressed firms and by industry standards. Target firms have had better

operating performance than non-target firms and similar performance to the industry median firm in the

years prior to distress. Such characteristics of target firms are consistent with the hypothesis that hedge

funds capture some of the rents arising from the reorganization of economically sound but financially

distressed firms.

Third, evidence in this paper suggests that hedge funds can help facilitate reorganization. As

creditors, hedge funds enhance the use of debt-equity swaps and pre-packaged deals. Meanwhile, by

bringing fresh equity capital into a distressed firm, hedge funds facilitate the reorganization process. All

types of hedge funds‟ investments, with the exception of the purchase of old equity securities, lead to a

significantly higher probability of emergence from distress.

Interpretation of the results about the hedge funds‟ influence on the restructuring process and

outcome is complicated because of selection bias. It is possible that hedge funds invest in firms that, even

without their investment, would be more likely to swap debt into equity in the restructuring process, more

likely to achieve faster resolution, and therefore more likely to emerge successfully from distress. I

address this selection issue in three ways. First, I use an instrumental variable approach using one-year

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lagged weighted average recovery rates on all defaulted debt as an instrument. Second, I estimate a

selection model in which I use the residuals from regressions of the magnitude of hedge fund involvement

on the determinants of involvement as the selection correction term. I also provide analyses of the impact

of an unobservable confounding variable to present the severity of the potential omitted variable problem.

The main results hold even after taking selection effects into account, and therefore it appears that results

about the impact of hedge funds on the restructuring process and outcome are not purely driven by

selection.

Stock prices of the target firm move upward upon the announcement of hedge funds‟ involvement,

especially when hedge funds buy common stocks or bring new capital into the distressed firms. Average

buy-and-hold abnormal stock returns (BHAR) for seven trading days around the announcement date are

3.3%, while stock prices go up by 23.7% and by 12.7% when hedge funds inject new equity and purchase

existing common stock, respectively.

Hedge funds seem to make attractive profits by investing in distressed firms. Based on Lipper TASS

data, distressed hedge funds in my sample produced a cumulative return of 151.2% from 1998 to 2009,

while the overall hedge fund industry and the stock market produced 132.4% and 41.5%, respectively.

Altogether, empirical evidence in this paper is consistent with the hypothesis that hedge funds capture

rents they help create by helping distressed firms reorganize.

This paper is closely related to the work of Hochkiss and Mooradian (1997) and Li, Jiang, and Wang

(2010). Using a sample of 288 firms that defaulted on their public debt between 1980 and 1993, Hochkiss

and Mooradian examine the role of vulture investors, who are predecessors of distress-oriented hedge

funds, in the market for control of distressed firms. They primarily focus on the vultures‟ governance-

related roles, and find evidence suggesting that vulture investors can add value by reducing agency

problems and disciplining managers of distressed firms. Li et al. (2010) is, to the best of my knowledge,

the only study that sheds light on hedge funds‟ presence in financially distressed firms using a recent

sample. They primarily focus on the hedge funds‟ role as an emerging force underlying the changing

nature of Chapter 11 over the past decade. Using a sample of 474 Chapter 11 filings from 1996 to 2007,

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they find that hedge funds play an active role in shaping bankruptcy outcomes, in terms of a higher

probability of emergence, more frequent and larger deviations from the absolute priority rule (APR), more

CEO turnovers, and more frequent adoptions of keep employee retention plans (KERP). This paper adds

to the understanding of the nature of firms that are targeted by hedge funds and the nature of hedge funds‟

strategy in distressed firms. Moreover, this paper provides new insight about the role that distress-

oriented hedge funds can play in resolving contracting problems and facilitating reorganization of

distressed firms. This paper also contributes to the growing literature on hedge fund activism by

examining a different aspect of hedge fund activism executed in a different type of target firm. Evidence

in this paper suggests that hedge fund activism is no longer confined to a company‟s shareholders but

distressed-investing is an additional avenue for activism.

The remainder of the paper proceeds in the following manner: Section II provides institutional

background on the issues involved in resolving financial distress, the manner in which hedge funds are set

up and operate, and the associated academic literature on each. Section III describes the manner in which

I constructed my sample and collected data as well as the overview of the sample and hedge fund

involvement. Section IV presents the empirical tests of my hypotheses. Section V presents an event study

by examining buy-and-hold abnormal stock returns of target firms around the announcement date of

hedge fund involvement. Section VI provides returns to distress-focused hedge funds. Section VII

concludes.

II. Institutional background and empirical predictions

A. Contracting problems in resolving financial distress

Coasian bargaining can fail because of a number of frictions. Major impediments to efficient

bargaining documented in the previous literature include coordination problems between lenders,

information asymmetries, conflicts of interest among parties involved, a reluctance on the part of lenders

to accept equity for debt, and costs of selling assets.

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Bulow and Shoven (1978), and Giammarino (1989) demonstrate how information asymmetries and

conflicts of interest among various claimholders can lead to inefficiencies when a firm is in financial

distress. The impediment to efficient renegotiation in these models is the assumption that the firm cannot

renegotiate with public debt holders, and banks or equity holders (or both) have the bankruptcy

(liquidation) decision power and act in their own interest. In the Bulow and Shoven (1978) model, a firm

can be forced into liquidation because information problems prevent it from raising necessary funds for

continuation. Giammarino (1989) models the resolution of financial distress as a non-cooperative game of

incomplete information played by a firm and its creditor. The bargaining problems occur because of

information asymmetries between a firm and creditor, and such asymmetric information can lead creditors

to choose to incur significant dead weight costs in the resolution of financial distress.

Gertner and Scharfstein (1991) explain why investment inefficiencies still occur even when firms

can renegotiate with public debt holders because of coordination problems among public debt holders.

The inefficiency in the bargaining process arises because individual debt holders fail to take into account

their effect on the firm‟s investment decisions. In the Gertner and Scharfstein (1991) model, a firm has to

offer more senior debt or cash to mitigate coordination problems; an offer of more junior debt or equity

will not be accepted by creditors. Empirically, Asquith, Gertner, and Scharfstein (1994), Gilson (1997),

and Gilson, John, and Lang (1990) use the number of debt classes as a proxy for the creditor‟s

coordination problem and document its importance in the outcome of debt restructuring.

Given the difficulties of renegotiation with public debt holders due to the Trust Indenture Act and

coordination problems, financially distressed firms can restructure their debt by persuading private

(mostly institutional) lenders to swap their debt for stock or to forgive debt principal. However, several

papers have documented institutional lenders‟ reluctance to make either type of concession. James (1995)

presents a model illustrating factors that influence a bank lender‟s incentive to scale down its debt. The

model implies that, with public debt outstanding, a bank lender never takes equity in debt restructuring

unless public debt holders also restructure their claims. In doing so, bank lenders sometimes forgo safe

investment projects in favor of liquidation. Diamond (1993) and Gertner and Scharfstein (1991) argue

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that because bank lenders are generally secured, they have little incentive to make concessions when a

firm also has public debt outstanding. Gilson (1997) argues that regulatory restrictions on banks holding

common stock limit the ability of banks to scale down their debt in distressed firms.4 In support of this

view, it has been empirically documented that banks scarcely make concessions (Asquith et al (1994),

James (1995)), but pressure a firm to sell assets to pay down their debt (Gilson (1990), Brown, James, and

Mooradian (1994)).

Selling assets under financial distress, however, can be quite costly if assets are sold at fire-sale

prices. Shleifer and Vishny (1992) argue that when a firm in financial distress needs to sell assets, its

industry peers are likely to be experiencing problems themselves, leading to asset sales at prices below

value in best use. Pulvino (1998), using commercial aircraft transactions, provides empirical evidence that

asset fire sales exist especially when a firm is capital constrained.

B. Hedge funds investing in distressed firms

Theoretically, the presence of active investors who are willing as well as able to finesse the above-

mentioned contracting problems can enhance efficient bargaining in the resolution of financial distress.

Hedge funds can be qualified to be such possible investors due to their nature and organizational

structure.

First, hedge funds are more motivated to make profits than any other institutions, which provide

them with the willingness to get actively involved in a distressed situation in pursuit of upside potential.

A hedge fund manager usually receives both a management fee and performance fee (also known as an

incentive fee) from the fund. A typical manager charges fees of "2 and 20", which refers to a management

fee of 2% of the fund's net asset value each year and a performance fee of 20% of the fund's profit. The

range of performance fee is wide and can be well above 20%. For example, among hedge funds in my

4 For example, the Glass-Steagall Act of 1933 and the Bank Holding Company Act of 1956 restrict financial

institutions from holding equity stakes in non-financial corporations. Also, risk-based regulatory capital guidelines

require banks to set aside more capital for riskier assets like stocks.

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sample, Steven Cohen‟s SAC Capital Partners charges a performance fee as high as 50%.5 Such monetary

compensation structure provides strong incentives to seek profit-making opportunities so hedge funds will

actively and strategically choose to get involved. In contrast, many other institutions just do not have the

proper incentives to deal with the procedural complexity and “hassle” associated with distressed debt

restructuring. None of the traditional institutions, such as banks, insurance companies, and mutual funds,

see gains generated from clearing inefficiencies in distressed situations as an integral part of their

business success, and none of them pay their professionals incentive fees similar to the ones paid by

hedge funds ((Kahan and Rock (2009)). When these institutions find their previous investments perform

poorly, they have an incentive to cut their exposure to a risky firm rather than to stick with a firm hoping

for uncertain upside potential. Even when they remain active in the restructuring process, their activism is

more incidental and designed to recoup some of the losses rather than to make gains.

Second, hedge funds have great amount of flexibility as to the securities they can hold and

investments they can make. Such flexibility comes from several reasons. First of all, unlike conventional

financial institutions, hedge funds are not burdened by regulatory schemes, oversight, or reporting

requirements due to the fact that they open to only a limited range of “accredited” or “qualified”

investors. For example, unlike other investment advisers, hedge funds do not have diversification

requirements. This freedom from diversification requirements provides a great advantage to hedge funds

since using an activist strategy in distressed firms requires taking a position meaningful enough to

influence the restructuring process. Moreover, unlike other financial institutions, especially banks that are

required to keep their balance sheet high quality and set aside additional capital for risky assets, hedge

funds have no restriction on the “riskiness” of their portfolios. Second, hedge funds have higher risk

tolerance, partly due to their inherently less risk-averse nature, and partly due to the freedom to use

various hedging strategies, such as shorting securities or investing in derivatives. Third, distress-focused

hedge funds are better equipped with a specialized skill set. Investing in distressed securities, especially

as an activist, requires a highly specialized skill set; the sophistication to properly assess the value of a

5 “The Most Powerful Trader on Wall Street You‟ve Never Heard Of,” Business Week, July 21, 2003.

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distressed company or its likely evolution through the restructuring process, strong negotiating skills,

industry expertise, legal background, rich experience in the bankruptcy process, a large network, financial

resources to acquire substantial amounts of claims, and so forth. Not many investors, even large

institutional investors, meet such qualifications.

Such flexibility of hedge funds gives them the ability, and the incentive schemes give them the

willingness, to participate actively in debt renegotiation and to lessen contracting difficulties. The

existence of such investors can mitigate coordination problems prevailing in distressed firms. In the

extreme, in the Gertner and Scharfstein (1991) model, all inefficiencies are eliminated if a firm can

directly bargain with a small number of public debt holders who take into account their effect on the

firm‟s investment decision. Hedge funds‟ flexibility can also help a more flexible resolution take place.

Many practitioners testify that hedge funds are more willing and able to take junior debt or even equity in

exchange for debt claims than are other investors.6 Such flexibility regarding the distribution method may

counterbalance traditional lenders‟ strong preferences for debt or cash. Moreover, hedge funds can bring

new capital that otherwise would not have been available. In an effort to obtain some measure of control

or influence over a company‟s turnaround, hedge funds often make second-lien loans and even equity

infusions. Such fresh capital can provide other resources beyond costly asset sales to be used to repay

debt.

C. Empirical predictions

This view of distress-focused hedge funds contains a number of empirical predictions. First, it

predicts that the investment strategy executed by hedge funds will be acquiring positions that allow them

to have the largest influence on the restructuring and also to maximize the upside potential in the firm.

Institutionally, these positions are known as fulcrum points. The fulcrum is the point in a company‟s

capital structure at which its liabilities exceed its assets. Investors at the fulcrum point can have a bigger

6 See, for example, “When Hedge Funds Invest in Distressed Debt”, New York Law Journal, October 15, 2007,

“Riding Fulcrum Seesaw: How Hedge Funds Will Change the Dynamics of Future Bankruptcies”, The Bankruptcy

Strategist, October 2007.

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say over the negotiation and the formation of restructuring plans compared to more senior creditors who

are deep in-the-money (they will be fully satisfied anyway) or more junior creditors and/or equity holders

who are deep out-of-the money (they will have no significant power anyway). By acquiring the fulcrum

position in a corporate capital structure, hedge funds can seek an opportunity to have a measure of control

over the company‟s turnaround, and an opportunity to increase the upside potential in the firm. Therefore,

empirically I expect to observe a widespread use of fulcrum investment strategies by hedge funds.

Second, it predicts that hedge funds will target firms that are likely to suffer more severe contracting

problems but whose fundamental value is such that the firm will be profitable conditional on

restructuring. A firm is expected to suffer more contracting difficulties when it has a relatively

complicated capital structure with many different classes of debt outstanding. In addition, the

combination of secured bank debt and numerous public debt issues will impede successful restructurings

even further (Asquith et al. (1994), James (1995), etc.) since the presence of public debt exacerbates the

debt-overhang problem and therefore cuts banks‟ incentive to engage in restructuring efforts. For

example, Gertner and Scharfstein (1991) and Blow and Shoven (1978) theoretically show that banks will

be less willing to extend the maturity of their debt or provide a new loan with public debt outstanding,

since by doing so, the banks becomes effectively junior to public debt holders. Furthermore, banks have

to share the rents arising from restructuring with public debt holders who contribute no efforts to facilitate

the restructuring. Efficient bargaining is even harder when a firm faces a more imminent liquidity crisis,

such as when a large portion of long-term debt liabilities are due but cash holdings are low. While hedge

funds are expected to target firms with more contracting problems, they are also expected to target

relatively economically sound firms to make their investment profitable. If this is so, we will observe that

firms targeted by hedge funds exhibit sound operating performance prior to distress compared to non-

targeted distressed firms and other industry peers.

Third, it predicts that hedge funds‟ presence will affect the restructuring process and outcome

positively, at least to some extent. I consider the impact of hedge funds on the means, duration, and the

likelihood of restructuring.

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Hedge funds‟ flexibility can enhance the prospects of reorganization by enhancing the use of flexible

means of restructuring. Such flexible means of restructuring can be efficient but are often thwarted by

frictions to bargaining. This paper considers two such means of restructuring: debt-equity swaps and pre-

packaged filings.

Bryan (1984) describes debt-equity swaps as “an innovative financing transaction” and “a solution to

falling profits and debt-laden balance sheet”. However, in practice debt-equity swaps face considerable

friction, although they are not non-existent. First, debt-to-equity exchanges are hard to execute because of

coordination problems among public debt holders. In extreme cases, as in the Gertner and Scharfstein

(1991) model, an offer of equity for debt is not even feasible at all due to coordination and holdout

problems among public debt holders. Negotiating with private lenders to swap their debt for stock is also

not without frictions, given institutional lenders‟ reluctance to receive equity instead of making

concessions.7 Compared with traditional bank lenders, hedge funds are known to be more willing and able

to accept equity or subordinated securities under a plan of reorganization. The presence of such flexible

investors can enhance the use of debt-equity swap as a mean of debt restructuring.

The second means of restructuring is pre-packaged filings. Several studies document that pre-

packaged filings can be a quicker and more efficient alternative to formal Chapter 11 or to private

workouts. For example, Tashjian, Lease, and McConnell (1996) claim that pre-packs can be “a cheap and

fast substitute for traditional Chapter 11 filings,” as well as “an inexpensive solution to free-rider/holdout

problems in an out-of-court restructuring”. Although pre-packs can significantly reduce the time that

firms spend in court and obviate the need for costly creditors‟ committees, in practice successful pre-

packaged filings were extremely rare until the 1980s.8 The major reason why pre-packs are hard to

execute is difficulties in negotiation and bargaining. By nature, pre-packs require the presence of

7 See, for example, Gilson (1997), Brown et al (1994), Asquith et al.(1994), Frank and Torous (1994), and James

(1996). 8 Gilson et al. (1990) find only one pre-packaged filing in their sample, and quote professional bankruptcy

consultants saying that only 5% to 10% of the largest bankruptcies begin as pre-packaged filings and fewer than half

of these are successful. The frequency of pre-packs has been growing since the late 1980s. For example, Tashijian et

al (1996) find 49 pre-packs over the period of 1980-1993.

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investors who hold a significant amount of claims and are willing to voluntarily and actively participate in

negotiations and formation of the plan. Hedge funds are such investors, and the presence of hedge funds

can enable investors to overcome contracting difficulties and negotiate a pre-pack.

The influence of hedge funds is likely to be different depending on the type of hedge fund

involvement. For example, their presence as creditors will increase debt-equity swaps, while their

presence as new equity investors will decrease such exchanges, assuming that they seek larger control in

the reorganized firm. Their presence as old equity holders will decrease debt-equity swaps to the extent

that hedge funds effectively stave off dilution of their shares, or will have no effect to the extent that the

power and voice of old equity holders are limited at the bargaining table.

Regarding the duration of restructuring, if hedge funds help facilitate the restructuring process, we

will observe a shorter duration with hedge funds‟ presence, all else equal. However, if hedge funds tend to

get involved in more complicated cases that take longer to resolve, it is possible that the active role of

hedge funds could be offset by this selection process.

Regarding the likelihood of restructuring, if hedge funds help distressed firms reorganize, we will

observe more emergences from distress with the presence of hedge funds.

Later in section IV, I provide empirical results on these predictions.

III. Data and Overview of the Sample

A. Sample construction and data collection procedure

A1. Distressed firm sample

This study analyzes 184 publicly traded US companies that either went bankrupt or had to restructure

their debt privately due to financial distress during the period from 1998 to 2009. To create a sample of

such firms, I follow Gilson (1990) and Gilson et al. (1990). First, I start from a list of publicly traded

companies whose common stock price dropped steeply. I rank all US firms in CRSP by their three-year

unadjusted stock returns for each year in the sample period and consider the bottom 5% in each year. This

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process leads to an initial sample of 2,190 cases for 1,875 firms.9 This list is likely to contain a relatively

large number of financially troubled firms. Second, I search Factiva news for reference to each firm, for a

five-year period centered on the year(s) in which the firm was sampled to determine whether there was a

default, bankruptcy filing, or attempt at debt restructuring to avoid such events. I supplemented Factiva

with the Mergent Corporate Manual, S&P Market Insight, and Bankruptcy DataSource. This process

leads to a sample of 815 cases of financial distress. Third, I restrict the sample to firms that are not

missing 10-K filing at the fiscal year-end prior to distress, which reduces the sample to 658 cases. Fourth,

I restrict the sample to large firms with total assets over $500 million to ensure that the search process can

identify hedge fund involvement to a meaningful degree, resulting in a final sample of 220 cases, of

which, 184 cases are complete by the end of 2009.

The largest difficulty in selecting a sample of financially distressed firms following the above

procedure is that, unlike Chapter 11 bankruptcy, there is no formal definition of debt restructuring and

therefore one must determine the beginning and the end of such events. Following Gilson (1990) and

Gilson et al. (1990) again, I define a debt restructuring as a transaction in which an existing debt contract

is replaced by a new contract, with one of the following consequences: 1) required interest or principal

payments on the debt are reduced; 2) the maturity of the debt is extended; or 3) creditors are given equity

securities (common stock or securities convertible into common stock). Additionally, I also include new

capital infusions (loans, equity investment, placement of new securities, etc.), into a distressed firm.

Moreover, I require that the restructuring be undertaken in response to an anticipated or actual default, or

to avoid bankruptcy. To ensure that, I require that restructuring transactions be accompanied with phrases

such as “going concern qualification,” “possibility of bankruptcy,” “looming bankruptcy,” “possible

default,” “cash crunch,” “credit crunch,” “financially distressed,” “financial troubles,” “insufficient

liquidity to repay debt,” etc. What I do not include in my sample is mere covenant violations or actions

taken to remedy such violations (e.g. giving waivers, amending credit agreement), although the

restructuring process could have started from such events. This is to ensure that the sample includes only

9 If a firm comes in in the bottom 5% stratum for two or more consecutive years, I count it as one observation.

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restructuring that is undertaken by a financially-distressed firm, since non-financially distressed firms also

frequently violate covenants and restructure their debt quite often (Roberts and Sufi (2009), Nini, Smith,

and Sufi (2009)).

The following timeline illustrates the dating convention that I use:

D represents the beginning of restructuring, which is the date on which a firm either filed for

bankruptcy or starts to restructure its debt privately. A private debt restructuring is assumed to begin on

the date it is first mentioned in Factiva news, unless an earlier date can be determined from other source

documents. Beginning events include a bankruptcy filing or default concerns mentioned, going-concern

qualifications, the downgrade of a credit rating that leads to a severe cash crunch, the hiring of an

investment banker as a financial advisor to review financial restructuring, the initial announcement of a

debt restructuring plan, and technical/payment default. If a firm restructures its debt through several

transactions, I take them as a single restructuring if the next transaction occurs within one year of the

previous one.

R represents the resolution date. For Chapter 11, it is the date on which the firm‟s reorganization

plan becomes effective. For private restructuring, it is either the date on which a restructuring agreement

is formally consummated or the date of the last reference in Factiva news, unless a more accurate date can

be found from other sources.

I determine the restructuring outcomes from Factiva searches, Lopucki‟s Bankruptcy Database, New

Generation Research‟s Bankruptcy DataSource, Mergent Corporate Manual, 10-K filings, and 8-K filings.

If a troubled firm emerges from the distressed situation and continues to operate as a stand-alone

company or as a subsidiary of an acquirer without losing its identity, it is considered successfully

reorganized and “Emerged.” I consider a firm “Acquired” if it is acquired as a result of a bankruptcy plan

or a private workout and assimilated into the acquirer. If a firm sold substantially all of its assets through

Year

DD-1 R R+1…

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363 sales, I also classify that as an acquisition. If an investor took a majority stake in a reorganized firm

(like in buyouts) but the firm continues its business operations without losing its identity, then I don‟t

classify that as an acquisition but as an emergence. If a distressed company ceased business and its assets

were sold in a piecemeal fashion, or if a firm filed for Chapter 7, or if a case is converted to Chapter 7 or

settled under the plan of liquidation within Chapter 11, I classify that as “Liquidation.”

A2. Identifying hedge fund involvement

To identify hedge fund involvement in distressed firms, I start by compiling a list of hedge fund

managing firms that are known to be specialized in distressed investing.10

I obtain 292 hedge fund

managers from Altman‟s personal compilation (Altman (2007, 2008)), after screening out mutual funds

and traditional investment banks. I add 70 from the constituents of the distressed hedge funds index

constructed by HedgeFund.net. I additionally obtain 57 managers from DealScan and Preqin, by adding

lenders (investors) whose identities are specified as distress-focused hedge funds or private equity firms.

After eliminating overlapping managers, I have a list of 301 distress-oriented hedge fund managers.

Second, I examined the sample of 184 financially distressed firms and identify hedge fund

involvement by searching various sources including Factiva news, SEC filings, Bankruptcy DataSource

from New Generation Research Inc., and bankruptcy documents such as plans of reorganization and

disclosure statements over the period [D-1, R+1]. A hedge fund is considered actively involved in the

restructuring process if one of the following criteria are met: 1) news stories describe the hedge fund‟s

involvement; 2) the hedge fund provides fresh capital, either equity or debt, to the target firm; 3) the

hedge fund buys securities of the distressed firms during the period [D-1, R]. In the case of security

purchases, I consider hedge funds‟ activities to be involvements in the target firm only when hedge funds

are believed to have stayed in the firm, at least partially, over the entire restructuring process. Therefore, I

do not include cases in which hedge funds had built up positions earlier but liquidated later before the

10

In this study, I do not distinguish distress-focused private equity firms from hedge funds.

17

restructuring took place. Also, mere trading or transfers of claims during distressed periods are not

included since hedge funds can be in and out without any influence.

I search for news stories about hedge funds‟ involvement in the target firm from Factiva and Lexis-

Nexis. Information on the new financing is obtained from Factiva news, DealScan, Loan Agreement

filings or Material Contract filings to the SEC, and Mergent Corporate Manual. Information on the claim

purchases is obtained from a variety of sources. Information on the claims holdings is from the news

stories, SC-13Ds, 13Fs, N-30Ds (N-CSRs), 10-Ks, proxy statements, and the list of the largest unsecured

creditors available from the Bankruptcy DataSource. I keep the dollar amount they paid to acquire such

claims (or the market value of their holdings) whenever it is available from the news and the filings.

Information on equity holdings is relatively straightforward to obtain, since hedge funds should file

13Ds (for active investors) or 13Gs (for passive investors) if they acquire more than 5% of the firm‟s

equity, and 13Fs if it exercises investment discretion over $100 million or more in 13F securities.11

I can

also get information on equity interest from the firm‟s 10-Ks or proxy statements.

Information on debt holdings is more complicated to obtain, since hedge funds are barely required to

file any documents when they acquire debt securities. Occasionally, news stories contain information

about hedge funds‟ debt holdings, especially if they are significantly large. If the debt securities acquired

are on the list of 13(f) securities, they will show up in 13F filings. If a firm restructures its debt through

the Chapter 11 process, more information is available since the firm makes the list of the largest

unsecured creditors and their holdings, and reveals important investors‟ holdings in court documents and

disclosure statements. If the hedge fund received more than 5% equity interest in the reorganized firm

over the course of restructuring, then the fund should file a 13D filing and I can obtain information on the

original debt positions that vest the fund with such equity ownership from “Item 3: Source and Amount of

Funds or Other Consideration”. Finally, if the hedge fund is a registered investor,12

it has to file N-30D

11

13F securities are mostly equity securities, but also include some convertible bonds and notes. The official list of

13(f) securities is published quarterly and is available for free on the SEC's website. 12

Usually hedge funds are exempt from the registration requirements, but some register voluntarily. Also, in

December 2004, the SEC adopted the new registration requirements that required most hedge fund advisers to

18

(N-CSR), a semi-annual shareholder report containing information about the fund‟s portfolio and

performance.

A3. Other variables

Other firm-level accounting data are from Compustat. Detailed information about a firm‟s debt

structure, including bank debt portion, the number of debt classes, and the existence of public debt is from

the Mergent Corporate Manual. To determine if a bankruptcy is pre-packaged, I refer to Factiva,

Bankruptcy DataSource, Lopucki‟s Bankruptcy Research Database, plans of reorganization, and

disclosure statements. The weighted average recovery rate on defaulted debt is from Altman (2008).

B. Overview of the sample firms and hedge fund involvement

The sample studied in this paper consists of 184 large,13

publicly traded US firms that either

restructured their debt privately out-of-court or formally under Chapter 11 protection over 1998-2009.

Panel A of Table 1 shows the distribution of distressed firms by year. The sample consists of 146 Chapter

11 cases and 38 private workout cases. The annual distribution of distressed firms is consistent with the

historical patterns of corporate defaults and bankruptcies. Observations are especially concentrated in the

period from 2000 to 2002, when corporate accounting scandals and the dotcom bubbles yielded a record

high corporate failure rate. By contrast, the number of observations is very small over the 2006-2007

period, when the credit market was characterized by historically low default rates. The number of

observations for year 2008 and 2009 is low, since many cases that began in 2008 and 2009 were still

pending as of the end of 2009. Twenty six cases that occurred out in 2008 and 2009 are dropped from the

final sample due to that reason. Panel B of Table 1 presents the summary statistics for the sample. The

size of firms in my sample is much larger than those in other studies, due to the $500 million size cut-off

register with the SEC by February 1, 2006 as investment advisers under the Investment Advisers Act, although in

June 2006, the U.S. Court of Appeals for the District of Columbia overturned it. 13

Those firms contain total assets over $500 million prior to distress.

19

restriction imposed on the sample construction. Other than the size, however, other firm characteristics

are comparable to the sample characteristics of previous studies that examine financially distressed firms.

The right three columns in Panel A of Table 1 show the annual distribution of hedge fund presence in

the sample firms. It is evident in this panel that hedge funds have actively participated in the restructuring

of large, publicly traded US firms during the past decade. Hedge funds have been actively involved in 119

of the total 184 financially distressed firms (64.7% of the sample), suggesting that distressed investing has

become an important avenue for activism by hedge funds. Hedge funds‟ presence is observed more often

in Chapter 11 cases than in private workouts; hedge funds were involved in 100 out of 146 Chapter 11

cases (68.6% of all Chapter 11 cases), and in 19 out of 38 private workout cases (50% of all private

workout cases). Table 2 presents a list of Top 20 players. Reorganization activities are highly

concentrated among a small number of hedge funds; the ten most active hedge funds account for over

30%, and the top 20 accounts for 45% of total involvement. It is consistent with the practitioners‟

observation that, even among the universe of hedge funds, only very specialized and dedicated funds can

and actually do use activist strategies in distressed firms, likely because of the high quantity of risk

involved and specialized skills required.

IV. Empirical results

A. Characteristics of hedge funds’ investment strategy

This section describes the characteristics of hedge funds‟ investment strategies. Table 3 provides a

detailed picture of hedge funds‟ activities in distressed firms, and suggests the large evidence of a so-

called fulcrum investment strategy executed by hedge funds. Hedge funds employ a fulcrum strategy in

various ways.

The most popular way hedge funds become involved in a distressed firm is to purchase existing

securities that are most likely to be converted into interests in new equity - in practitioner‟s words, the

fulcrum securities. Hedge funds seem to be very good at determining which layer of a company‟s capital

structure is the fulcrum security; hedge funds in my sample bought claims of a distressed firm in 95 cases

20

(51.6%), and more than 70% of the time ultimately received equity distributions. Hedge funds are not

only good at picking a fulcrum security but also sometimes seem to take actions to make their holdings a

fulcrum security. Even when hedge funds purchase secured debt such as bank debt, which is less likely to

be swapped for equity due to its secured status, hedge funds received equity distribution for 69.6% of the

purchases they made. Also, anecdotal evidence suggests that hedge funds often choose to receive equity

in lieu of cash or new debt that other investors in the same class were to receive.14

The next popular way to achieve a fulcrum position is to inject new equity into the distressed firm. In

36 cases (19% of the sample), hedge funds made an equity infusion in various forms, such as right

offering back-stops or plan sponsorships, which rendered them the reorganized debtor‟s controlling

stockholder with an average of 64.4% ownership.

Third, hedge funds frequently use loan-to-own strategies, in which they make loans, typically senior

secured ones, with the intention of acquiring an equity interest. A loan-to-own strategy is advantageous to

an investor who wants to exert influence over the course of restructuring, because through the secured

creditor position the investor can have significant leverage with respect to the negotiation and the ultimate

formation of the restructuring plan.

I classify a hedge fund as having executed the loan-to-own strategy if one of the following conditions

is met: 1) the hedge fund made loans attached with some equity provisions, such as warrants or

convertible provisions, 2) the hedge fund made loans accompanied by new equity investments, 3) the

hedge fund made loans and at the same time held equity securities, such as common stocks or preferred

stocks, 4) the hedge fund made loans and held debt securities that ended up receiving interests in new

equity, 5) the hedge fund made loans and led pre-packaged filings to receive controlling shares.

According to this definition of loan-to-own strategy, hedge funds executed a loan-to-own strategy in 31

14

For example, ESL Investment acquired significant stakes (as high as 51% of the class) in various claims of

Kmart‟s debt, including pre-petition loans, unsecured bonds, and trade claims. Besides the $200 million new equity

investment, ELS also agreed to swap its holdings of Kmart debt for new Kmart stocks in lieu of the cash that the

other members in that class were to receive. In the other extreme example, Elliott Management and Silver Point

Capital purchased a large share of Delphi's debtor-in-possession financing in the secondary market, and forgave

their loans (more than $3.4bill) in exchange for control of the company.

21

cases (16.8% of the sample) in my sample, and ended up getting 58.1% equity ownership via such a

strategy.

Finally, hedge funds actively participate in pre-negotiations of the restructuring plan to achieve their

goal. In my sample, hedge funds are actively involved in 31 out of 42 pre-packaged deals. Among those

31 cases, hedge funds took a leading role in sculpting the restructuring plan or strongly supported the plan

in all cases except for three (Finova, Conseco, Guilford Mills). In those three cases, hedge funds actually

balked at the restructuring plan, which would have given controlling shares to other investor, had it gone

through.

Hedge funds often strengthen their position by serving on or forming by themselves a committee

representing their interests. Moreover, unlike other money managers that are required to maintain

diversified portfolios, hedge funds often take a large portion in a single firm. In my sample, when hedge

funds purchase existing securities, the average holding is 27.7% of the class. Such highly concentrated

positions give leverage to hedge funds in alchemizing their investments into a significant, even

controlling, stake in a reorganized firm.

In summary, the evidence in this section is consistent with the argument that hedge funds actively seek

the fulcrum positions, which render them the largest influence on the restructuring and at the same time

maximize their shares in efficiency rents arising from the successful restructuring.

B. Characteristics of targeted firms

This section examines the characteristics of firms that are targeted by hedge funds to see if hedge

funds tend to invest in a firm that is expected to have more contracting difficulties but is economically

healthier. To measure economic soundness, I consider a firm‟s EBITDA prior to distress, which has been

used in various forms by different studies.15

I employ positive (or negative) EBITDA as a categorical

15

For example, Hotchkiss (1995) uses negative EBITDA prior to Chapter 11 filing as evidence of economic distress,

while Andrade and Kaplan (1998) consider highly leveraged transactions (HLTs) not to be economically distressed

based on the above industry median EBITDA/Sales ratio, and Lemmon, Ma, and Tashjian (2009) employ the firm‟s

pre-bankruptcy industry-adjusted EBITDA/Total assets ratio as a proxy for viability as a going concern.

22

variable, and industry-adjusted EBITDA/Total assets as a continuous variable. EBITDA normalized by

total assets is preferred to EBITDA normalized by sales, since distressed firms often report a tiny sales

volume, which leads to an extreme value of the EBITDA/Sales ratio.

The first proxy I use for contracting difficulties is the number of long-term debt contracts, measured

by the number of different headings in the long-term debt section of the Mergent Corporate Manual

report. This measure is employed as a proxy for the degree of coordination problems and conflicts of

differing incentives among debt holders by many previous studies including Gilson (1997), Gilson et al.

(1990), and Asquith et al. (1994). The second measure of contracting difficulties is an indicator variable

that takes a value of one if a firm has both bank and public debt outstanding and zero otherwise. This

measure stems from the previous theoretical and empirical studies documenting that the combination of

secured bank debt and numerous public debt issues makes restructuring bargaining harder, due to secured

bank lenders‟ reluctance to make concessions with public debt outstanding together with the generic

difficulties of public debt restructuring (Asquith et al. (1994), James (1995), etc.). Finally, I construct a

debt complexity by combining these two measures, which takes a value of one if the number of debt

classes is above the median number in the sample and a firm has both bank and public debt outstanding.

Table 4 provides a comparison of firm characteristics between targeted and non-targeted firms in

univariate analyses. In terms of the size, two groups have no statistically significant difference. However,

operating cash flow characteristics show a significant difference. Consistent with the prediction that

hedge funds will choose economically healthier firms, the target firms show much better EBITDA

profiles prior to distress; 26.3% more of the target companies generated positive operating cash flow, and

the industry-adjusted operating cash flow ratio is 5.5% higher.

Meanwhile, target firms seem to suffer from more contracting difficulties than other firms. Target

firms have 2.5 more debt contracts on average and three more by median than the non-targeted firms.

Also, 31.1% more of the target firms have both bank debt and public debt outstanding than non-targeted

firms. Finally, targeted firms‟ debt complexity is 21.9% higher than those of non-targeted firms.

Moreover, target firms have much lower levels of cash holdings and higher (although statistically not

23

significant) levels of current debt due now, which implies a more imminent liquidity crunch. A more

imminent liquidity crunch, together with more tangible assets and more bank debt, can lead to more costly

asset fire sales. Such evidence is consistent with the prediction that hedge funds will target those firms in

which contracting problems are larger.

Table 5 examines the determinants of each type of hedge fund involvement to see if the predictions

hold in multivariate tests. Overall, the results from multivariate analysis support the results from

univariate analysis. Most hedge fund involvements are significantly positively associated with the

measure of economic health.16

Also, debt complexity is significantly positively related to hedge funds‟

presence on the debt side. Another noteworthy pattern is that hedge funds are more likely to use a pre-

packaged filing but less likely to buy unsecured debt or equity securities when a firm has bigger current

debt due, which suggests a more imminent liquidity crisis. This finding is plausible, since hedge funds

would need quicker and more effective means of resolution when the problems are more impending.

Coefficients on debt complexity, under-collateralized bank debt, and lagged recovery rate in Table 5

suggest that hedge funds carefully choose their entry point to maximize the control over restructuring

bargaining while paying as little as possible to obtain such control.

Debt complexity is positively related to hedge funds‟ involvement as creditors while negatively related

to equity infusions by hedge funds. When debt structure is relatively complicated, a firm is likely to suffer

from more contracting problems and the role of creditors becomes more essential to efficient bargaining.

In such a case, it makes more sense for hedge funds to choose to be creditors than equity holders. When

debt structure is relatively simple and differing incentives among debt holders is a not large problem,

hedge funds can play a more important role by bringing in fresh equity capital.

When bank debt is under-collateralized, measured by whether bank debt exceeds the value of fixed

assets, hedge funds are more likely to choose secured claims. Under-collateralized bank debt implies that

bank debt (which accounts for most of secured debt) is more likely to be impaired and therefore more

16

The results using the indicator variable of positive EBITDA are reported in the table. When the industry-adjusted

EBITDA/Total assets ratio is used, the results are the same.

24

likely to be the fulcrum point. When secured bank debt is at risk of impairment, it provides an opportunity

for hedge funds not only to acquire secured claims at discount prices but also to achieve a fulcrum

position. Accordingly, we observe that under-collateralized bank debt increases the likelihood of hedge

funds‟ involvement as secured creditors. In contrast, hedge funds‟ involvement as new equity providers is

significantly negatively related to the under-collateralized bank debt. To the extent that banks‟ rights are

impaired and therefore banks have a bigger say at the bargaining table, the degree of control that hedge

funds can enjoy as new equity providers will be reduced. Therefore hedge funds are less likely to choose

to be equity investors in such cases. These findings correspond to Li et al. (2010), who find that hedge

funds‟ strategic choices of the entry point allow them to have a big impact on reorganization.

Lagged recovery rate on defaulted debt is positively related to hedge funds‟ presence on the debt side,

while negatively related to their presence on the equity side. The recovery rate on defaulted debt in the

previous year would affect the price of defaulted debt in the following year. A high recovery rate on

defaulted debt in the previous year is likely to boost overall demand for defaulted debt, which in turn will

drive the price level up. A higher price of defaulted debt means less of an opportunity to purchase

distressed debt at an attractive bargaining price. Then the equity side might provide a more profitable

investment opportunity to distressed investors. Accordingly, we observe more investment on the equity

side when the previous years‟ recovery rate was high, or vice versa.

In sum, the results in this section support the hypothesis that hedge funds strategically choose firms

in which contracting problems are likely to prevent efficient reorganization but economic potential is

substantial if successful restructuring could take place, thereby maximizing their influence on the

restructuring process and the rents from reorganization.

C. Impact on restructuring process and outcome

C1. Means of restructuring

This section investigates the relationship between hedge funds‟ presence and the use of flexible

means of restructuring. The first four columns of Table 6 show the results of probit estimation of the

25

likelihood of debt-equity-swap as a function of hedge fund presence, controlling for asset tangibility, cash

holdings, leverage, having bank debt outstanding, under-collateralized bank debt, and pre-packaged

filings. Tangibility is expected to decrease the information asymmetries regarding the firms‟ true value

and therefore increase the probability of using debt-equity swaps. Cash holdings are expected to be

negatively associated with the likelihood of using debt-equity swaps, if cash provides an alternative

distribution method to equity. Leverage is expected to be positively related with the likelihood of using

debt-equity swaps, since high leverage signals the need to cut debt level. Having bank debt outstanding is

expected to decrease the probability of using debt-equity swaps, if bank lenders are reluctant to receive

equity. Having under-collateralized bank debt is expected to increase the use of debt-equity swaps if it

implies the severity of financial distress to the degree that secured bank debts are at risk of impairment.

Pre-packaged filings are expected to increase the likelihood of using debt-equity swap, since parties can

customize distribution methods more flexibly through private negotiations before filing.

Consistent with predictions, hedge funds involvement as creditors increases the probability of using

a debt-equity swap by 28.2%, while new equity infusion by hedge funds decreases such probability by

29.3%, all else equal. The associations are strongly significant at the 1% significance level. Hedge funds

as old equity holders, however, are not significantly related to the probability of using debt-equity swaps.

The fact that the likelihood of using debt-equity swaps varies significantly depending on the type of hedge

fund involvement suggests that hedge funds play a considerable role in determining the means of

restructuring.

Table 7 presents probit estimates of the likelihood of pre-packaged filings. Following Chatterjee,

Dhillon, Ramirez (1996), I control for the amount of current debt due, the amount of bank debt, debt

complexity, and pre-distress operating performance. As predicted, hedge funds‟ involvement is

significantly positively related to the likelihood of pre-packs; the presence of a hedge fund increases the

probability of pre-packs by 13.4%, all else equal. Such effects mostly come from hedge funds‟ presence

as creditors, supporting the hypothesis that hedge funds can have a role in resolving contracting problems

among debt holders.

26

C2. Duration of restructuring

This section investigates if hedge funds‟ presence is associated with a shorter or longer duration of

distress. The first four columns of Table 8 report the base-case results from Poisson regressions of the

duration of distress on hedge funds‟ presence and other firm characteristics. The dependent variable is the

number of months that elapsed from the onset of distress until resolution. I control for size, operating

performance prior to distress, leverage, the number of debt classes, portion of bank debt, pre-packaged

filings, and Chapter 11 filings. As expected, variables measuring the complexity of the bankruptcy case

show significant effects. The number of debt contracts significantly extends a firm‟s stay in the

restructuring process. Restructuring under the formal Chapter 11 process takes longer than the private

workouts due to procedural complexity and legal requirements. A pre-packaged filing has a significantly

negative effect on the duration, again supporting that pre-packs can be fast and efficient means of

restructuring.

Overall, hedge funds‟ presence neither lengthens nor shortens the duration of restructuring,

controlling for the complexity of the case. Hedge funds‟ presence as creditors is positively associated with

the duration, but is not statistically significant. However, a new equity infusion by hedge funds is

associated with a significantly shorter duration of restructuring. When hedge funds inject new equity,

duration is shortened by 27.8% (1–exp (-0.325)), which is significant at the 5% significance level. This

value is also economically significant given that the average duration of firms without new equity

infusion from hedge funds is 15 months. New financings, regardless of whether they come from hedge

funds, will facilitate the restructuring process since a financially distressed firm desperately needs fresh

capital. However, financially distressed firms often face serious challenges in obtaining financing, and

hedge funds are likely to be the only kind of investors who are willing to make new equity investment in

a troubled firm. Therefore, findings in this section suggest that hedge funds can help facilitate a distressed

firm‟s restructuring by supplying a firm with critically needed capital.

27

C3. Likelihood of restructuring

This section investigates the effect of hedge funds on the likelihood of restructuring. Table 9 shows

in univariate tests that firms in which hedge funds are involved are significantly more likely to be

successfully reorganized and emerge as a stand-alone company while significantly less likely to cease to

exist as a business and be liquidated.

The first four columns of Table 10 report the results from base-case probit regressions of the

likelihood of restructuring on hedge funds‟ presence and other firm characteristics. The dependent

variable is an indicator variable that takes a value of one if a firm restructured its debt successfully and

emerged from distress as a stand-alone company, and takes a value of zero if liquidated or acquired. The

results in Table 10 show that all kinds of hedge fund presence, except holdings in old equity, lead to a

significantly higher probability of emergence from distress. Overall, the probability of successful

restructuring is 34.8% higher with hedge funds‟ presence. Hedge funds‟ presence as creditor and new

equity investor increases the probability of emergence by 30.1% and 15%, respectively.

One noteworthy fact from Table 10 is the significantly negative coefficients on the over-

collateralized bank debt variable, an indicator variable that takes a value of one if the ratio of bank debt to

fixed assets is less than one. A firm is more likely to be sold in a piece-meal fashion or to be sold to

another company when bank debt (mostly secured) is over-collateralized. This result is consistent with

previous studies that find Chapter 11 cases are more likely to result in a sale if secured lenders are over-

secured (Ayotte and Morrison (2009)), and asset fire sales are often driven by over-secured lenders

(Gilson (1990), Brown et al. (1994)). It makes an interesting contrast to hedge funds, since hedge funds‟

presence is significantly positively related to more emergences, even when they are secured creditors

(unreported).

C4. Selection vs. Treatment

A concern when interpreting the above estimates is that some potential unobservable confounding

variables (omitted variables) affect both hedge fund involvement and the outcome variables. Because of

28

such a possibility, the observed significant relationship between hedge fund involvement and the outcome

variables does not necessarily arise from hedge funds‟ treatment effects on the outcome, but may arise

from hedge funds‟ selection of firms. Econometrically, selection bias can arise because the treatment was

correlated with the error term in the outcome equation through the error terms of the selection equation. In

other words, selection bias would occur if εi and ηi are correlated in the following system of equations:

)3(00

)2(01

)1(

*

*

iiii

iiii

iiii

ZWW

ZWW

uWXY

Yi denotes outcome variable, Xi denotes variables explaining outcomes, Wi is an indicator variable

showing hedge fund involvement in firmi, and Zi denotes observable variables influencing hedge funds‟

participation in firmi. Yi is observed only when hedge funds choose either to participate or not to

participate, but not both. In this case, simple probit (or Poisson, in case of duration) estimates of δ will

overestimate the treatment effect of hedge funds‟ involvement.

In this section, I try to address selection bias concerns in three ways. First, I use the instrumental

variable (IV) approach to estimate average treatment effects of hedge fund involvement. Second, I use the

control function approach using the magnitude of involvement as the dependent variable in the first stage.

Third, I provide analyses of the impact of unobservable confounding variables to present the severity of a

potential omitted variable problem.

First, I estimate IV regressions using lagged recovery rate on defaulted debt as an instrument for

hedge funds‟ involvement.17

More precisely, I use the fitted probability of selection, iˆ , as an IV, not a

regressor.18

To get an IV estimator of average treatment effect (ATE), I do the following: First, I estimate

17

The market‟s weighted average recovery rate on defaulted debt in the previous year is expected to affect hedge

funds‟ choice of participation (or choice of entry point) this year but is unlikely to affect specific firm‟s restructuring

outcome. Consistent with this prediction, we can observe coefficients on the lagged recovery in Table 5 are

significant and have different signs for different type of hedge fund involvement. 18

This is not the same as using iˆ as a regressor like in the typical two-step IV estimation. The first stage, when

iˆ is used as an IV is 210 ˆˆˆˆˆ

iii xW . Using iˆ as an IV, not a regressor, is recommended because (a) it is

robust to having the model for ),1( zxWP wrong, (b) no need to account for generated instruments in standard

29

)1( ZWP for each type of hedge fund involvement using a probit regression and obtain the first stage

fitted probability, )ˆ(ˆii Z . Next, I estimate

)4(00 iiii uXWY

by IV using instruments (1, iˆ , xi), where the coefficient α estimates ATE. To test the appropriateness of

the instrument, I also provide selected diagnostic tests. For the under-identification test, I provide

Kleibergen-Paap LM statistics, which provide a robust version of a test for the rank of matrix E(z'x). The

null hypothesis is that the system of equations is under-identified. Kleibergen-Paap Wald F-statistics are

provided for a weak instrument test. The null is weak instruments. In conjunction, Stock-Yogo critical

values for the weak instrument test based on 10% maximal IV size are provided (at 5% significance

level). Anderson-Rubin Wald χ2-statistics are provided for a weak-instrument-robust test of the

significance of endogenous regressor. The null is that endogenous regressor is insignificant in the main

outcome equation. Lastly, I provide Anderson-Rubin Wald F-statistics for the test of endogeneity of the

„allegedly‟ endogenous regressor. The null is exogeneity of the regressor being tested. IV estimation

results are reported in the Column 5-8 of Table 6, 8, and 10.

In the second way to address selection bias, I estimate a selection model. The remedy I use here is

similar to the traditional Heckman estimation in the sense that I use residuals from the first stage selection

equation as the selection control function term in the second stage estimation of the main outcome

equation. However, unlike in the traditional Heckman model in which the first stage selection function is

usually a binary response model, I utilize the fact that I can observe the magnitude of the hedge fund

involvement. As a proxy for the magnitude of involvement, I use the dollar amount of investment when

hedge funds inject new capital, and the amount of purchase as a percentage of the corresponding class

when hedge funds buy existing securities. The availability of the magnitude of involvement is beneficial

since it allows me to introduce an independent source of variation in the selection correction term, and

errors, (c) estimator is efficient IV estimator if )(),( oo uVarzxuVar and probit model for W is correct. For more

explanation, see Wooldridge (2002, Chapter 6, 18)

30

thereby to work around the thorny near-multicollinearity issue of the Heckman estimation19

. The

procedural steps are as follows: In the first stage, I run regressions of the magnitude of hedge fund

involvement on the observable determinants of involvements reported in Table 4 and obtain the residuals.

Then in the second-stage outcome regressions, I use the residuals from the first stage as the selection

correction term in lieu of the inverse Mills ratio. The results from the two-step selection model estimation

are reported in the last three columns of Tables 6, 8, and 10.

In the third way to address the concern about selection bias, instead of trying to estimate a selection-

corrected estimator, I alternatively examine how large the endogeneity problem has to be in order to

change a statistical inference. To do so, I analyze the potential impact of unobserved confounding

variables using the approach in Frank (2000). This method is based on the notion that for an unobserved

variable to affect the results it needs to be correlated with both the treatment variable and the outcome

variable (controlling for the other variables). Frank (2000) derives the Impact Threshold for a

Confounding Variable (ITCV), indicating the minimum impact of a confounding variable that would be

needed to render the coefficient statistically insignificant. The ITCV is defined as the product of the

correlation between the endogenous variable and the confounding variable (rx·cv) and the correlation

between the outcome variable and the confounding variable (ry·cv). High ITCV means the OLS results are

robust to omitted variable concerns. Table 11 presents the analysis of the impact of unobservable

confounding variables. Columns 1 and 2 of each type of hedge fund involvement show the coefficients

and t-statistics from OLS regression of the outcome variable on the corresponding type of involvement

and other control variables.20

Column 3 of each type of hedge fund involvement provides the ITCV for

the corresponding type of hedge fund involvement. To develop a benchmark for the size of likely

correlations involving the unobserved confounding variable, in Column 4 of each type of hedge fund

19

Strictly speaking, the model can be estimated even when there are no exclusion restrictions since Inverse Mills

Ratio (IMR) is non-linear function Z. However, if IMR has very little variation relative to the remaining variables in

the main outcome regression, it is very possible that it becomes roughly linear in parts of its domain. The

identification issue arises mainly because of the binary nature of the selection variable Wi, which implies that we do

not observe the error term ηi and we must take its expectation, which is the IMR. For more explanation, see Li and

Prabhala (2007). 20 I use OLS regressions as base-case regressions here, since ITCV can be calculated only for linear models.

31

involvement I calculate the impact of the inclusion of each independent variable on the coefficient on the

variable of interest. The impact is defined as the product of the partial correlation between the variable of

interest (the potentially endogenous variable) and the control variable and the correlation between the

outcome variable and the control variable (partialling out the effect of the other control variables).

Column 5 of each type of involvement shows a more conservative measure of impact, the product of the

simple correlation between the potential endogenous variable and the control variable and the simple

correlation between the outcome variable and the control variable.

Overall, the inferences from the base-case results hold even after trying different remedies for

potential endogeneity concerns. Column 5-8 in Table 6 show that the same inference about the impact of

hedge funds on the likelihood of debt-equity swap holds in the IV estimation; as in the base-case probit

estimation, the coefficient is significantly positive for HF Creditor and significantly negative for HF

Equity infusion. The diagnostics provided at the bottom of Column 5-8 suggest the validity of IV

estimation. The last three columns of Table 6 provide the results from the selection model estimation, and

restate the previous results. Regarding the severity of the potential endogeneity problem, the endogeneity

test at the bottom of IV estimation and the coefficients on the residuals (selection control function) from

the selection model estimation suggest that the potential endogeneity problem is not big. To be more

conservative, I examine the impact of unobservable confounding variables presented in Panel A of Table

11. ITCVs are fairly high for all types of hedge fund involvement. For example, ITCV is 0.213 for HF

Creditor, implying that the correlation between HF Creditor and DEswap with the unobservable

confounding variable each needs to be about 0.462(= 213.0 ) for the base-case result to be overturned.

Columns 4-5 providesbenchmarks to determine how likely it is to have such a confounding variable. The

variable with the largest impact on the coefficient for HF Creditor is Tangibility, with a value of 0.034.

This suggests that we would need a confounding variable with a stronger impact than Tangibility to

overturn the results on HF Creditor. Specifically, the unobserved confounding variable must be more

highly correlated with HF Creditor and DEswap than Tangibility. Under the assumption that I have a

32

good set of control variables, this provides some confidence in the estimates of the effect of hedge fund

involvement on the likelihood of debt-equity swap.

Results in Table 8 and Panel B of Table 11 suggest that hedge funds‟ presence as creditors is

associated with a longer duration of distress once endogeneity concerns are taken into account. The

coefficient on the residuals from the selection model and ITCV suggest that HF Creditor is endogenous,

and the selection-corrected estimate of the effects of HF Creditor on duration becomes significantly

positive. However, the impact of HF Equity infusion on duration is robust to potential endogeneity

concerns. Consequently I can conclude that new equity capital injected by hedge funds help a firm

emerge from distress more quickly.

With regard to the impact of hedge funds on the likelihood of restructuring, results in Table 8 and

Panel C of Table 11 show that the base-case results are robust to potential endogeneity concerns.

Endogeneity tests from IV estimation, coefficients on the residuals from the selection model, and ITCV of

HF Equity infusion in Table 11 suggest that the potential endogeneity of hedge fund involvement

variables are not ignorable. However, the coefficients on HF Creditor are strongly significant even after

corrections for selection bias. The coefficient on HF Equity infusion loses significance in IV estimation.

However, the diagnostic implies that instruments are weak in the case of HF Equity infusion, so we

cannot rely on the IV estimation results much. In selection model estimation, HF Equity infusion remains

strongly significant.

Based on the results in this section, our inference about the impact of hedge funds on the process and

outcome of restructuring seems to be robust to endogeneity concerns, and it is hard to conclude that the

relationships between hedge funds‟ presence and the restructuring process and outcome are purely driven

by selection effects.

V. Event study

This section presents an event study that analyzes the target firm‟s buy-and-hold abnormal stock

returns around the announcement of hedge fund involvement. Stock price data are from CRSP. I lose

33

some observations due to firms whose stock is delisted prior to hedge funds involvement. Also, I restrict

the sample to „clean‟ events for which there is no concurrent news about bankruptcy filing or default

during the event window, since such news is normally accompanied by a big drop in stock prices and

therefore can mask the impact of an announcement of hedge fund involvement. This process produces a

final sample of 75 firms for the event study. I calculate buy-and-hold abnormal stock returns (BHAR)

following Barber and Lyon (1997);

)5(])(1[]1[11 t

it

t

it RERitBHAR

, using a value-weighted market index as the expected return for each stock.

Table 12 provides means and t-values of BHAR for [-3, +3] and [-1, +1] window around the

announcement date. Overall, the announcements of hedge fund involvement in distress firms are

accompanied by positive BHAR, although it‟s significant only when hedge funds buy common stocks or

bring new capital into firms. Figure 2 shows such patterns graphically. Around the announcement date,

stock prices jump, especially when hedge funds inject new equity capital or buy common stocks. These

results on the stock price reaction to the equity side investment are consistent with Hotchkiss and

Mooradian (1997). However, unlike Hotchkiss and Mooradian (1997) and Jiang et al (2010) who find

significantly positive stock price reaction to vultures‟ (hedge funds‟) purchases of public debt, I do not

find significant effect on hedge funds‟ public debt purchases. This may be due to the small number of

observations. However, actual stock prices do not need to react positively upon hedge funds‟ debt

purchase even though hedge funds add value to the restructuring process, given that debt securities held

by hedge funds are often – especially in Chapter 11 - swapped for new equity and old common stocks are

cancelled. If the market expects that hedge funds‟ presence on the debt side would facilitate debt-equity

34

swap and that existing common stocks are likely to be cancelled in the course of restructuring, prices of

the current stocks would rather move negatively on the announcement of debt purchases by hedge funds.

VI. Returns to hedge funds

In this section, I examine the returns to hedge funds on distressed investing. Figures 3 and 4 show

the performances of the CSFB Distressed Index (solid black line), CSFB Hedge Fund Index (solid gray

line), and S&P 500 Index (dotted gray line) over the period 1998-2009. Data are from Lipper TASS.

Figure 3 tells us that while distressed hedge funds showed steady growth over the period 1998-2009, the

significant growth of this hedge fund strategy occurred during the period from 2002 to 2007. Distressed

hedge funds performed much better than the overall hedge fund industry and stock market during this

period. Figure 4 shows that distressed hedge funds tend to outperform traditional investment vehicles,

especially in bear markets such as the 2001-2002 and 2008-2009 periods.

Table 13 shows the statistics underlying Figure 3 and 4. Column 1 shows reported returns of distress-

oriented funds managed by hedge fund managers in my sample. “Distress-oriented” is defined as funds

whose reported investment focus in TASS is either “Bankruptcy”, or “Distressed bond”, or “Distressed

market”. Column 2 of Table 13 shows returns to CSFB Distressed Index, which represents the overall

performance of hedge funds investing in distressed firms. Columns 3 and 4 provide performance of the

overall hedge fund industry (represented by CSFB Hedge Fund Index) and of the stock market

(represented by S&P 500 Index), respectively. Panel A provides overall risk and return characteristics,

and Panel B provides annual returns from 1998 to 2009. According to Table 13, distress-focused hedge

funds tend to deliver higher returns while maintaining better risk-return profiles: Both distressed funds in

my sample and CSFB Distressed Index generated higher returns than aggregate hedge funds and the stock

market while having lower volatility. As a result, distressed hedge funds exhibit much higher Sharpe

ratios.

35

The numbers in Figure 3, Figure 4, and Table 13 are based on returns to the entire fund, not deal-

specific returns. They also contain returns to passive investing in distressed firms‟ securities, whereas

hedge funds in my sample are executing activist strategies. Returns to investment in a specific firm are

not available from any commercial database. To get a sense of the deal-specific returns to hedge funds in

my sample, I calculate hypothetical returns on investments made by hedge funds. I present returns on

securities purchases, equity infusion, and debt financing separately. For securities purchases, returns on

investment are calculated as the recovery rate for the corresponding class divided by purchase price

(measured in cents on the dollar). Recovery rates are obtained from plans of reorganization. Purchase

prices are collected from 13D, 13F, and N-30D (N-CSR) filings whenever available. When exact

purchase price is not available, I assume that hedge funds bought securities at the market prices around

the announcement date of hedge fund involvement. Market prices of securities are collected from

TRACE, High Yield/Distressed Bank Loan Pricing, Trends and Prices of Leveraged Syndicated Loans,

and Factiva news. Annualized returns are provided, assuming a two-year investment horizon. For new

equity infusion, returns on investment are calculated as the reorganization equity value times equity

shares received due to such equity infusion divided by the dollar amount of equity infusion. Again,

annualized returns are provided, assuming a two-year investment horizon. For debt financing, spreads

over LIBOR are provided as proxies for returns on investment. The hypothetical returns confirm that

returns to distressed hedge funds are considerable. Among security purchases, public debt is the most

popular as well as the most profitable choice. Equity infusions are not as frequently used as security

purchases, but they provide the highest returns in my sample.

Results in this section show that hedge funds make attractive profits from active involvement in

distressed firms, which is consistent with the hypothesis that hedge funds capture rents they help create by

helping distressed firms reorganize

VII. Summary and Conclusion

36

This paper examines the roles of activist hedge funds in distressed firms and finds evidence of hedge

funds‟ influence on the resolution of financial distress. Distress-oriented hedge funds actively participate

in economically sound firms, in which contracting difficulties are likely to prevent efficient

reorganization, in order to capture some efficiency rents arising from the reorganization. Hedge funds

largely advance the so-called fulcrum investment strategy to achieve their goal. Through the fulcrum

position that they obtain, hedge funds significantly affect the restructuring process and outcome. Hedge

funds‟ presence as creditors can facilitate timely and effective restructuring by enhancing debt-equity

swaps and pre-packaged filings. New equity investment brought by hedge funds can significantly shorten

the time spent until resolution. Both types of involvement are associated with a significantly higher

probability of successful restructuring, which cannot be explained solely by a selection story. Hedge

funds also make attractive profits by executing activist strategy in distressed firms and helping firms

reorganize. In sum, evidence in this paper suggests that distressed investing has become an important

avenue for activism by hedge funds, and hedge funds capture rents they help create by finessing

contracting problems and enhancing efficient bargaining in the resolution of financial distress.

37

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40

Figure 1. Growth of U.S. Distressed Debt Market and Distress-focused Hedge Funds

Figure 1 shows the size of U.S distressed debt market and assets under management by distress-focused hedge funds

over the period from 1990 to 2007. Numbers are from Altman (2008), Hedge Fund Research, Inc, and

HedgeFund.Net.

Figure 2. Event study around Announcements of Hedge Fund Involvement

Figure 2 shows buy-and-hold abnormal stock returns (BHAR) from the three trading days prior to the three trading

days after an announcement of hedge fund involvement. The sample includes only „clean‟ events for which there is

no concurrent news about the onset of financial distress, such as a bankruptcy filing or a default. The „clean‟ sample

consists of 75 firms for which full sets of stock data are available from CRSP for the event window. BHAR is

calculated following Barber and Lyon (1997), using a value-weighted market index (Rmt) as the expected return for

each stock.

0

100

200

300

400

500

600

700

800

1990 1992 1993 1995 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

$B

illio

n

Distressed-focused Hedge Fund AUM Distressed Debt Market

-5.0%

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

-3 -2 -1 0 1 2 3

Bu

y an

d H

old

Ab

no

rmal

Re

turn

Trading Days around the Announcement of Hedge Fund Involvement

HF Overall HFEquity Infusion HF Buy Common stock

41

Figure 3. Performance Comparison – Changes in the Index Value

Figure 3 shows monthly changes in the index value for the CSFB Distressed index, the CSFB Hedge Fund index,

and the S&P 500 index from January 1998 to December 2009. Initial values are normalized at 1000 as of January

1998. Data are from Lipper TASS.

Figure 4. Performance Comparison – 12-Month Rolling Monthly Returns.

Figure 4 shows rolling 12-month returns for the CSFB Distressed Index, the CSFB Hedge Fund Index, and the S&P

500 Index from January 1998 to December 2009. Data are from Lipper TASS.

0

500

1000

1500

2000

2500

3000

3500

'98 '99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09

Ind

ex

Val

ue

CSFB Distressed Index CSFB Hedge Fund Index S&P500

-50.0

-40.0

-30.0

-20.0

-10.0

0.0

10.0

20.0

30.0

40.0

50.0

'99 '00 '01 '02 '03 '04 '05 '06 '07 '08 '09

(%)

CSFB Distressed IndexI CSFB Hedge Fund Index S&P500

42

Table 1. Overview of the Sample

Table 1 presents an overview of the distressed firm sample and hedge funds‟ presence in the sample. The sample

consists of 184 publicly traded US firms with total assets over $500 million that restructured their debt either

privately or formally over the 1998-2009 period. Panel A presents calendar year distribution of financially distressed

firms and hedge funds‟ presence. Panel B presents descriptive statistics for the firms in my sample. All figures in

Panel B are as of the fiscal year-end preceding the start of distress. Variables are winsorized at 1% and 99% level.

Panel A: Number of distressed firms and hedge fund involvement by year

Hedge fund involvement

Year Total Chapter 11Private

Workout

1998 3 1 2 3 (100.0%) 1 (100.0%) 2 (100.0%)

1999 13 12 1 6 (46.2%) 6 (50.0%) 0 (0.0%)

2000 22 19 3 17 (77.3%) 15 (78.9%) 2 (66.7%)

2001 38 35 3 20 (52.6%) 20 (57.1%) 0 (0.0%)

2002 37 29 8 26 (70.3%) 21 (72.4%) 5 (62.5%)

2003 17 15 2 14 (82.4%) 12 (80.0%) 2 (100.0%)

2004 14 11 3 11 (78.6%) 9 (81.8%) 2 (66.7%)

2005 14 11 3 8 (57.1%) 7 (63.6%) 1 (33.3%)

2006 3 1 2 3 (100.0%) 1 (100.0%) 2 (100.0%)

2007 5 1 4 2 (40.0%) 1 (100.0%) 1 (25.0%)

2008 14 8 6 7 (50.0%) 5 (62.5%) 2 (33.3%)

2009 3 3 0 2 (66.7%) 2 (66.7%) 0 (0.0%)

Total 184 146 38 119 (64.7%) 100 (68.5% ) 19 (50.0% )

Distressed firm sample

Total

(% )

Chapter 11

(% )

Private

Workout (% )

Panel B: Summary statistics

N Mean Min Median Max Std. dev

Total Asset ($MM) 183 5,640 513 1,655 103,914 14,990

Sales ($MM) 183 2,813 46 863 37,028 6,239

Market Cap ($MM) 181 631 4 146 11,818 1,744

Positive EBITDA 175 0.686 0.000 1.000 1.000 0.466

Ind adj. EBITDA/Total asset 175 -0.061 -0.432 -0.033 0.173 0.107

Ind adj. EBITDA/Sale 174 -0.228 -3.794 -0.034 0.498 0.713

Tangibility 175 0.360 0.001 0.346 0.864 0.247

Leverage 183 0.635 0.028 0.637 1.670 0.328

Cash/Total assets 180 0.045 0.000 0.026 0.253 0.053

Current debt/Total asset 183 0.237 0.000 0.052 1.428 0.350

Number of debt classes 182 6.593 1.000 5.000 30.000 5.464

Bank debt/Total long-term debt 177 0.276 0.000 0.217 0.994 0.273

Has bank debt outstanding 177 0.723 0.000 1.000 1.000 0.449

Has publicdebt outstanding 184 0.728 0.000 1.000 1.000 0.446

Has both bank and public debt 184 0.571 0.000 1.000 1.000 0.496

Debt complexity 184 0.326 0.000 0.000 1.000 0.470

Bank debt is undercollateralized 176 0.250 0.000 0.000 1.000 0.434

Prepackaged 184 0.228 0.000 0.000 1.000 0.421

43

Table 2. The Most Active Distress-oriented Hedge Funds

Table 2 presents a list of the top 20 players in the sample. The number of total involvements exceeds the number of

firms that have hedge fund presence, since multiple hedge funds can be involved in one firm.

Hedge Fund ManagerFreq. of

Involvement

Percentage

(%)

Cerberus Capital Management 23 (5.9%)

Oaktree Capital Management 18 (4.6%)

Angelo, Gordon & Company 13 (3.3%)

Highland Capital Management 12 (3.1%)

Appaloosa Management 11 (2.8%)

Silver Point Capital 9 (2.3%)

D.E.Shaw Group 8 (2.1%)

Third Point Management 8 (2.1%)

R2 Investment 8 (2.1%)

Avenue Capital Group 7 (1.8%)

Fortress Investment Group 7 (1.8%)

Greenwich Street Capital Partners 7 (1.8%)

Citadel Investment Group 6 (1.5%)

Farallon Capital Management 6 (1.5%)

Stanfield Capital Management 6 (1.5%)

Apollo Management 5 (1.3%)

Ares Management 5 (1.3%)

Black Diamond Capital Management 5 (1.3%)

Contrarian Capital Management 5 (1.3%)

Elliott Associates 5 (1.3%)

Total involvement by Top 10 players 117 (30.0%)

Total invovement by Top 20 players 174 (44.6%)

Total of All hedge funds involvement 391

44

Table 3. Characteristics of Investment Strategies by Hedge Funds

Definitions of variables are described in Appendix A. Table 3 presents descriptions of investment strategies executed by hedge funds. Numbers are at the firm

level. Column 1 contains the number of firms with each type of hedge fund involvement. Column 2 provides the amount of securities purchases as a percentage

of the corresponding class for sub-strategies of claim purchases and pre-packaged deal, and provides the dollar amount of financing for sub-strategies of new

equity infusion and loan-to-own. Column 3 provides price paid (shown in cents on the dollar) to purchase debt securities, and interest rate spreads over LIBOR

for loans. Column 4 shows the frequency that hedge funds served on a committee under each sub-strategy. Column 5 shows the percentage that each sub-strategy

received equity interest in reorganized firms. Column 6 shows hedge fund ownership in reorganized firms. Columns 7, 8, and 9, respectively, show the frequency

that each sub-strategy led to a seat on the board, block holder ownership, and controlling ownership of the reorganized firm.

Ownership in

Reorganized

Firm (% )a

Purchase of existing claims 97/184 (52.7% ) 27.7% (n=87) 52 (54.7% ) 69 (72.6% ) 32.7% 54 (56.8% ) 67 (70.5% ) 19 (20.0% )

Secured debt 23/184 (12.5%) 32.7% (n=19) 5 (21.7%) 16 (69.6%) 43.0% 10 (43.5%) 15 (65.2%) 7 (30.4%)

Unsecured debt 64/184 (34.8%) 29.8% (n=54) 44 (68.8%) 50 (78.1%) 30.5% 35 (54.7%) 43 (67.2%) 9 (14.1%)

Preferred stock 6/184 (3.3%) 42.5% (n=5) 2 (33.3%) 4 (66.7%) 37.0% 2 (33.3%) 4 (66.7%) 0 (0.0%)

Common stock 19/184 (10.3%) 3.8% (n=18) 7 (36.8%) 3 (16.7%) 29.6% 8 (42.1%) 9 (47.4%) 3 (15.8%)

New equity infusion 35/184 (19.0% ) $225.0 (n=35) 2 (5.7% ) 35 (100.0% ) 64.4% 27 (77.1% ) 31 (88.6% ) 23 (65.7% )

Plan sponsor 31/184 (16.8%) 216.1 (n=31) 0 (0.0%) 31 (100.0%) 64.7% 23 (74.2%) 27 (87.1%) 21 (67.7%)

Right offering 6/184 (3.3%) 70.8 (n=6) 2 (33.3%) 6 (100.0%) 50.3% 6 (100.0%) 6 (100.0%) 3 (50.0%)

Loan-to-own 32/184 (17.4% ) $103.5 (n=32) (n=10) 7 (21.9% ) 24 (75.0% ) 58.1% 16 (50.0% ) 22 (68.8% ) 12 (37.5% )

Rescue financing 11/184 (6.0%) 118.7 (n=11) (n=4) 1 (9.1%) 6 (54.5%) 48.2% 4 (36.4%) 6 (54.5%) 2 (18.2%)

DIP/exit financing 23/184 (12.5%) 120.2 (n=23) (n=7) 6 (26.1%) 19 (82.6%) 58.2% 12 (52.2%) 17 (73.9%) 10 (43.5%)

Pre-packaged deals 31/42 (73.8% ) 35.6% (n=29) 19 (61.3% ) 28 (84.8% ) 53.4% 19 (61.3% ) 23 (74.2% ) 10 (32.3% )

Supported 28/42 (66.7%) 36.8% (n=26) 17 (60.7%) 26 (92.9%) 56.9% 19 (67.9%) 21 (75.0%) 10 (35.7%)

Opposed 3/42 (7.1%) 23.2% (n=3) 2 (66.7%) 2 (66.7%) 18.3% 0 (0.0%) 2 (66.7%) 0 (0.0%)

All hedge fund involvement 119/184 (64.7%) 54 (45.4%) 95 (79.8%) 34.5% 70 (58.8%) 83 (69.7%) 34 (28.6%)

a. Sum of the total hedge fund ownership

b. Percentage based on the number of each sub-position.

Board

member (% )b

Blockholder

(% )b

Control

(% )b

Committee

(% )b

Resulted in positions in reorganized companyPositions held by hedge funds before and during restructuring

# Obs. (% )

Converted to

Equity Position

(% )b

Price Paid (c/$) /

Interest Charged (bp)

Amount

(% of Class / $MM)

n.a

n.a

n.a

n.a

0.583 (n=13)

0.418 (n=37)

LIBOR+450~525

LIBOR+631

LIBOR+369~471

n.a

n.a

45

Table 4. Target Firm Characteristics

Definitions of variables are described in Appendix A. Table 4 presents a comparison of firm characteristics between hedge fund targeted firms and non-targeted

firms. Variables are winsorized at 1% and 99% level. ***, **, * correspond to statistical significance at 1%, 5%, and 10% significance level, respectively.

N Mean Median Std. N Mean Median Std. Diff(1)-(2) Diff(1)-(2)

Size

Total Asset ($MM) 118 6,077 1,529 15,852 65 4,846 1,748 13,366 1,231 0.557 -220 -0.093

Sales ($MM) 118 3,070 898 6,776 65 2,347 776 5,140 723 0.810 122 1.187

Market Cap ($MM) 116 597 108 1,672 65 693 165 1,878 -95 -0.341 -57 -0.926

Operating cash flows

Positive EBITDA 113 0.779 1.000 0.417 62 0.516 1.000 0.504 0.263 3.697 *** 0.000 3.569 ***

Ind-adj. EBITDA/Total asset 115 -0.013 -0.017 0.084 63 -0.068 -0.035 0.150 0.055 2.703 *** 0.019 2.469 **

Asset characteristics

Tangibility 112 0.385 0.358 0.237 63 0.316 0.267 0.259 0.069 1.756 * 0.091 1.989 **

Current Asset/Total asset 101 0.289 0.270 0.163 46 0.350 0.325 0.200 -0.061 -1.814 * -0.054 -1.663 *

Cash/Total asset 118 0.066 0.031 0.081 63 0.091 0.056 0.088 -0.024 -1.854 * -0.025 -2.470 **

Capital Structure

Leverage 118 0.658 0.646 0.316 65 0.594 0.590 0.347 0.064 1.239 0.056 1.248

Current debt/Total asset 118 0.259 0.052 0.370 65 0.196 0.053 0.309 0.063 1.224 -0.001 0.548

Number of debt classes 118 7.559 5.000 6.712 64 5.063 4.000 3.523 2.497 3.291 *** 1.000 3.134 ***

Has both bank and public debt 119 0.681 1.000 0.468 65 0.369 0.000 0.486 0.311 4.207 *** 1.000 4.068 ***

Debt complexity 119 0.403 0.000 0.493 65 0.185 0.000 0.391 0.219 3.301 *** 0.000 3.017 ***

t-value

Difference in Median

(Wilcoxon rank-sum test)Hedge Fund Involvement (1) No Involvement (2)

Difference in Mean

(t-test)

z-value

46

Table 5. Determinants of Hedge Fund Involvement

Definitions of variables are described in Appendix A. Table 5 reports the marginal effects from probit regressions estimating the likelihood of each type of hedge

fund involvement. The dependent variable is an indicator variable that takes a value of one for each type of hedge fund presence, and zero otherwise. P-values are

in parentheses. ***, **, * correspond to statistical significance at 1%, 5%, 10% significance level, respectively.

(1) (2) (3) (4) (5) (6) (3)

HF

Overall

HF

Creditor

HF

Secured

Debtholder

HF

Unsecured

Debtholder

HF

New Equity

Infusion

HF

Old Equity

Holder

HF

Prepack

Log total assets -0.028 -0.048 -0.126*** 0.015 -0.026* 0.022 -0.031

(0.190) (0.149) (0.000) (0.705) (0.057) (0.118) (0.226)

Positive EBITDA 0.272*** 0.316*** 0.213*** 0.135 0.164*** 0.073 0.070*

(0.000) (0.000) (0.005) (0.169) (0.000) (0.121) (0.070)

Tangibility 0.139 0.379*** 0.159 0.337*** 0.095 -0.023 0.153

(0.325) (0.000) (0.325) (0.001) (0.393) (0.780) (0.358)

Cash/Total asset -0.026 -0.120 0.365 -0.113 0.089 0.502*** 0.197

(0.958) (0.816) (0.467) (0.822) (0.859) (0.010) (0.605)

Current debt due/total asset 0.048 0.049 0.076 -0.176* 0.040 -0.207*** 0.163**

(0.710) (0.758) (0.529) (0.094) (0.604) (0.003) (0.025)

Debt complexity 0.150** 0.202** 0.303** 0.136** -0.144*** 0.054 0.014

(0.035) (0.027) (0.016) (0.047) (0.000) (0.307) (0.816)

Bank debt is undercollateralized 0.069 0.122 0.206*** 0.049 -0.118** 0.108 -0.027

(0.401) (0.112) (0.009) (0.470) (0.043) (0.226) (0.622)

Has public debt 0.320*** 0.324*** 0.124 0.289*** 0.104** -0.080 0.110**

(0.002) (0.001) (0.188) (0.006) (0.038) (0.288) (0.032)

Lagged recovery -0.262 -0.686*** -0.269 -0.603** 0.591** 0.184* -0.061

(0.205) (0.005) (0.351) (0.021) (0.020) (0.071) (0.783)

Number obs. 165 165 165 165 165 165 165

Pseudo R2 0.167 0.219 0.186 0.148 0.134 0.111 0.104

# of obs. with Dependent=1 108 89 60 62 32 21 29

47

Table 6. Hedge Fund Presence and the Likelihood of Using Debt-equity-swaps

Definitions of variables are described in Appendix A. The first 4 columns of Table 6 report the results from probit regressions of the likelihood of using debt-

equity swaps on hedge fund presence and firm characteristics. The dependent variable is an indicator variable that takes a value of one if a firm used debt-equity

swaps in restructuring its debt and zero otherwise. Marginal effects and p-values (in parentheses) are reported. Columns 5 to 8 report the results from a two-step

IV estimation of the likelihood of using debt-equity swaps. More specifically, in the first step, I estimate the binary response model for each type of hedge fund

involvement using a probit regression, and obtain the fitted probabilities, iˆ . In the second step, I estimate the following equation by IV using instruments 1,

iˆ , and xi, to estimate average treatment effects (ATE) of each type of hedge fund involvement;

000 iiii uxwy ,

where y is an indicator variable that takes a value of one if a firm used debt-equity swaps in restructuring its debt and zero otherwise, w is a binary treatment

indicator (i.e. takes a value of one for each type of hedge fund involvement), and x is a vector of other control variables (observed covariates). Coefficients and p-

values (in parentheses) are reported. The first stage binary response model estimation results are the same as those reported in Table 5. I also provide selected

diagnostics to test the validity of IV estimation at the bottom of the table: Kleibergen-Paap LM statistics for an under-identification test, Kleibergen-Paap Wald

F-statistics for a weak instrument test, Anderson-Rubin Wald χ2-statistics for a weak-instrument-robust test of the significance of endogenous regressor, and

Anderson-Rubin Wald F-statistics for an endogeneity test. Columns 9 to 11 report the results of selection model estimation using the control function approach.

Unlike in the traditional Heckman estimation in which the first stage self-selection function is usually the binary response model, the first stage is the regressions

of the magnitude of hedge fund involvement on the determinants of hedge funds‟ involvements reported in Table 5, and in the second stage of outcome

regressions I use the residuals from the first stage as the selection correction term in lieu of the inverse Mills ratio. The magnitude of involvement is log (dollar

amount of investment) when hedge funds injected new equity capital, and the amount of purchase as a percentage of the corresponding class when hedge funds

bought existing securities. Marginal effects and p-values are reported. ***, **, * correspond to statistical significance at 1%, 5%, 10% significance level,

respectively.

48

(1) (2) (3) (4) (1) (2) (3) (4) (2) (3) (4)

HF Overall 0.191** 0.460***

(0.014) (0.005)

HF Creditor 0.282*** 0.400*** 0.381***

(0.000) (0.005) (0.001)

HF Equity infusion -0.293*** -0.478* -0.301*

(0.003) (0.084) (0.051)

HF Old equity holder -0.128 -0.166 -0.380

(0.386) (0.553) (0.129)

Tangibility 0.708*** 0.650*** 0.758*** 0.723*** 0.520*** 0.449*** 0.517*** 0.530*** 0.595*** 0.717*** 0.697***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Cash/total assets -0.181 -0.048 -0.426 -0.213 -0.165 -0.077 -0.622* -0.454* -0.227 -0.718 -0.347

(0.624) (0.908) (0.259) (0.537) (0.711) (0.851) (0.057) (0.058) (0.624) (0.231) (0.530)

Leverage 0.017 0.006 0.039 0.007 0.054 0.039 0.082 0.049 0.111 0.102 0.007

(0.842) (0.948) (0.546) (0.929) (0.602) (0.705) (0.106) (0.435) (0.174) (0.465) (0.961)

Has bank debt 0.053 0.037 0.074 0.096 -0.055 -0.025 0.019 0.029 0.017 0.024 0.066

(0.566) (0.702) (0.451) (0.323) (0.408) (0.728) (0.750) (0.667) (0.881) (0.829) (0.554)

Bank debt is undercollateralized 0.278*** 0.280*** 0.244*** 0.285*** 0.224*** 0.206*** 0.177*** 0.246*** 0.338*** 0.267*** 0.327***

(0.000) (0.000) (0.001) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000) (0.006) (0.000)

Prepackaged 0.268*** 0.252*** 0.330*** 0.302*** 0.156* 0.157** 0.277*** 0.239*** 0.286*** 0.347*** 0.329***

(0.003) (0.005) (0.000) (0.000) (0.052) (0.031) (0.000) (0.001) (0.006) (0.000) (0.000)

Residuals -0.112 -0.000 2.948

(0.833) (0.818) (0.140)

Number obs. 169 169 169 169 165 165 165 165 146 163 164

Pseudo R2 (Centered R2) 0.198 0.224 0.210 0.183 0.161 0.248 0.279 0.273 0.242 0.275 0.250

Validity of IV estimation

Under identification test (Kleibergen-Paap rk LM stat) 7.76*** 7.88*** 8.44*** 3.51*

(p-value) (0.005) (0.005) (0.004) (0.061)

Weak instrument test (Kleibergen-Paap rk Wald F-stat) 34.72** 34.02** 18.71** 6.04

Stock-Yogo critical value (10% maximal IV size) (16.38) (16.38) (16.38) (16.38)

Significance of endogenous regressor (Anderson-Rubin Wald Chi-sqr. stat) 5.90** 8.00*** 4.30** 0.33

(p-value) (0.015) (0.005) (0.038) (0.567)

Endogeneity test of endogenous regressor (Anderson-Rubin F-stat) 1.19 1.14 0.42 0.13

(p-value) (0.274) (0.286) (0.516) (0.721)

Probit Estimation IV Estimation Selection Model Estimation

49

Table 7. Hedge Fund Presence and the Likelihood of Pre-packaged Deals

Definitions of variables are described in Appendix A. Table 7 reports results from probit regressions of the

likelihood of pre-packaged filings on hedge funds presence and firm characteristics. The dependent variable is an

indicator variable that takes a value of one if a firm restructured its debt through pre-packaged filing and zero

otherwise. Marginal effects and p-values (in parenthesis) are reported. ***, **, * correspond to statistical

significance at 1%, 5%, 10% significance level, respectively.

(1) (2) (3) (4)

HF Overall 0.134**

(0.034)

HF Creditor 0.152**

(0.022)

HF Equity infusion 0.090

(0.322)

HF Old equity holder 0.033

(0.752)

Current debt due/total asset 0.189** 0.181** 0.205** 0.212**

(0.029) (0.037) (0.022) (0.019)

Bank debt/total longe-term debt -0.014 -0.016 -0.004 -0.020

(0.917) (0.903) (0.977) (0.874)

Debt complexity -0.067 -0.069 -0.024 -0.033

(0.304) (0.306) (0.730) (0.629)

Ind-adj. EBITDA/Total assets -0.223 -0.223 -0.227 -0.190

(0.433) (0.428) (0.433) (0.512)

Number of observations 169 169 169 169

Pseudo R2 0.053 0.060 0.038 0.033

50

Table 8. Hedge Fund Presence and Duration

Definitions of variables are described in Appendix A. The first 4 columns of Table 8 report the results from Poisson regressions of the duration of distress on

hedge fund presence and firm characteristics. The dependent variable is the number of months that have elapsed from the onset of distress until resolution.

Coefficients and p-values (in parentheses) are reported. Columns 5 to 8 report the results from two-step IV estimation of the effect of hedge fund presence on

duration. More specifically, in the first step, I estimate the binary response model for each type of hedge fund involvement by probit regressions, and obtain the

fitted probabilities, iˆ . In the second step, I estimate the following equation by IV using instruments 1,

iˆ , and xi, to estimate ATE of each type of hedge fund

involvement;

000 iiii uxwy ,

where y is the number of months spent in distress (measured in log), w is a binary treatment indicator (i.e. takes value of one for each type of hedge fund

involvement and zero otherwise), and x is a vector of other control variables (observed covariates). Coefficients and p-values (in parentheses) are reported. First

stage binary response model estimation results are the same as those reported in Table 5. I also provide selected diagnostics to test the validity of IV estimation at

the bottom of the table: Kleibergen-Paap LM statistics for an under-identification test, Kleibergen-Paap Wald F-statistics for a weak instrument test, Anderson-

Rubin Wald χ2-statistics for a weak-instrument-robust test of the significance of endogenous regressor, and Anderson-Rubin Wald F-statistics for an endogeneity

test. Columns 9 to 11 report the results of selection model estimation using the control function approach. Unlike in the traditional Heckman estimation in which

the first stage self-selection function is usually a binary response model, the first stage is the regression of the magnitude of hedge fund involvement on the

determinants of hedge funds‟ involvements reported in Table 5, and in the second stage outcome regressions I use the residuals from the first stage as the

selection correction term in lieu of the inverse Mills ratio. The magnitude of involvement is log (dollar amount of investment) when hedge funds injected new

equity capital, and the amount of purchase as a percentage of the corresponding class when hedge funds bought existing securities. Marginal effects and p-values

are reported. ***, **, * correspond to statistical significance at 1%, 5%, 10% significance level, respectively.

51

(1) (2) (3) (4) (1) (2) (3) (4) (2) (3) (4)

HF Overall -0.026 0.904*

(0.868) (0.061)

HF Creditor 0.165 0.625* 0.455**

(0.319) (0.073) (0.025)

HF Equity infusion -0.325** -0.460** -0.551**

(0.049) (0.041) (0.039)

HF Old equity holder 0.024 0.534 -0.135

(0.906) (0.565) (0.746)

Log (Total assets) -0.031 -0.021 -0.036 -0.031 -0.003 -0.001 -0.047 -0.053 -0.022 -0.017 -0.008

(0.669) (0.776) (0.599) (0.660) (0.976) (0.993) (0.710) (0.689) (0.777) (0.802) (0.917)

Ind-adj. EBITDA/Total assets 0.095 0.021 0.223 0.076 -0.468 -0.435 -0.064 -0.309 -0.081 0.443 0.113

(0.850) (0.968) (0.660) (0.879) (0.533) (0.560) (0.921) (0.600) (0.881) (0.437) (0.826)

Leverage -0.893*** -0.881*** -0.906*** -0.887*** -0.636*** -0.668*** -0.722*** -0.692*** -0.920*** -1.012*** -0.987***

(0.001) (0.001) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.001) (0.000) (0.000)

Log (number of debt claims) 0.406*** 0.377*** 0.400*** 0.403*** 0.169 0.209** 0.305*** 0.298*** 0.275** 0.357*** 0.364***

(0.001) (0.002) (0.000) (0.000) (0.173) (0.047) (0.003) (0.006) (0.029) (0.001) (0.001)

Bank debt/total long-term debt 0.196 0.183 0.141 0.191 0.227 0.291 0.297 0.307 0.168 0.129 0.225

(0.402) (0.457) (0.549) (0.425) (0.235) (0.110) (0.180) (0.182) (0.495) (0.568) (0.352)

Prepackaged -1.087*** -1.025*** -1.127*** -1.074*** -0.568* -0.755*** -1.098*** -1.003*** -1.129*** -1.180*** -1.126***

(0.000) (0.000) (0.000) (0.000) (0.065) (0.003) (0.000) (0.000) (0.000) (0.000) (0.000)

Chapter 11 0.476*** 0.393** 0.469*** 0.468*** 0.144 0.089 0.335* 0.385** 0.439*** 0.472*** 0.478***

(0.007) (0.036) (0.006) (0.004) (0.484) (0.696) (0.071) (0.026) (0.006) (0.006) (0.004)

Residuals -0.948* 0.002 1.563

(0.063) (0.362) (0.566)

Number obs. 169 169 169 169 165 165 165 165 146 163 164

Pseudo R2 (Centered R2) 0.177 0.182 0.191 0.177 0.017 0.142 0.168 0.155 0.206 0.209 0.189

Validity of IV estimation

Under identification test (Kleibergen-Paap rk LM stat) 6.20* 5.85* 5.18* 3.25*

(p-value) (0.01) (0.02) (0.02) (0.07)

Weak instrument test (Kleibergen-Paap rk Wald F-stat) 28.62** 22.63** 53.15** 5.34

Stock-Yogo critical value (10% maximal IV size) (16.38) (16.38) (16.38) (16.38)

Significance of endogenous regressor (Anderson-Rubin Wald Chi-sqr. stat) 2.95* 2.55 3.96** 0.28

(p-value) (0.09) (0.11) (0.05) (0.60)

Endogeneity test of endogenous regressor (Anderson-Rubin F-stat) 3.97** 2.127 1.075 0.162

(p-value) (0.05) (0.14) (0.30) (0.69)

Poisson Estimation IV Estimation Selection Model Estimation

52

Table 9. Outcomes of Restructuring

Definitions of variables are described in Appendix A. Table 9 shows the restructuring outcome for hedge-fund-

involved group and non-involvement group. ***, **, * mean that the differences between two groups are

statistically significant at 1%, 5%, 10% significance level, respectively.

# Obs. (%) # Obs. # Obs. (%)

Reorganized 133 (72.3% ) 99 (83.2% ) ***34 (52.3% )

In Chapter 11 97 (52.7%) 81 (68.1%) ***16 (24.6%)

In private workout 36 (19.6%) 18 (15.1%) *18 (27.7%)

Acquired 15 (8.2% ) 9 (7.6% ) 6 (9.2% )

In Chapter 11 14 (7.6%) 8 (6.7%) 6 (9.2%)

In private workout 1 (0.5%) 1 (0.8%) 0 (0.0%)

Liquidated 36 (19.6% ) 11 (9.2% )***

25 (38.5% )

In Chapter 11 34 (18.5%) 11 (9.2%)***

23 (35.4%)

In private workout 2 (1.1%) 0 (0.0%) 2 (3.1%)

All No InvolvementHF Involvement

(%)

53

Table 10. Hedge Fund Presence and the Likelihood of Restructuring

Definitions of variables are described in Appendix A. The first 4 columns of Table 10 report the results from probit regressions of the likelihood of restructuring

on hedge fund presence and firm characteristics. The dependent variable is an indicator variable thst takes a value of one if a firm restructured its debt

successfully and emerged from distress as a stand-alone company, and takes a value of zero if liquidated or acquired. Marginal effects and p-values (in

parentheses) are reported. Columns 5 to 8 report the results from two-step IV estimation of the likelihood of restructuring. More specifically, in the first step, I

estimate the binary response model for each type of hedge fund involvement by probit regressions, and obtain the fitted probabilities, iˆ . In the second step, I

estimate the following equation by IV using instruments 1, iˆ ., and xi, to estimate ATE of each type of hedge fund involvement;

000 iiii uxwy ,

where y is an indicator variable which takes a value of 1 if a firm successfully restructured its debt and emerged, w is a binary treatment indicator (i.e. takes value

of 1 for each type of hedge fund involvement), and x is a vector of other control variables (observed covariates). Coefficients and p-values (in parentheses) are

reported. First stage binary response model estimation results are the same as reported in Table 5. I also provide selected diagnostics to test the validity of IV

estimation at the bottom of the table: Kleibergen-Paap LM statistics for an under-identification test, Kleibergen-Paap Wald F-statistics for a weak instrument test,

Anderson-Rubin Wald χ2-statistics for a weak-instrument-robust test of the significance of endogenous regressor, and Anderson-Rubin Wald F-statistics for an

endogeneity test. Columns 9 to 11 report the results of selection model estimation using control function approach. Unlike in the traditional Heckman estimation

in which the first stage self-selection function is usually binary response model, the first stage is the regressions of the magnitude of hedge fund involvement on

the determinants of hedge funds‟ involvements reported in Table 5, and in the second stage outcome regressions I use the residuals from the first stage as the

selection correction term in lieu of the inverse Mills ratio. The magnitude of involvement is log (dollar amount of investment) when hedge funds injected new

equity capital, and the amount of purchase as a percentage of the corresponding class when hedge funds bought existing securities. Marginal effects and p-values

are reported. ***, **, * correspond to statistical significance at 1%, 5%, 10% significance level, respectively.

54

(1) (2) (3) (4) (1) (2) (3) (4) (2) (3) (4)

HF Overall 0.348*** 0.754***

(0.000) (0.000)

HF Creditor 0.301*** 0.680*** 0.353***

(0.000) (0.000) (0.002)

HF Equity infusion 0.150*** 0.060 0.254***

(0.006) (0.811) (0.000)

HF Old equity holder 0.033 0.164 -0.147

(0.617) (0.672) (0.502)

Log(total assets) 0.050 0.042 0.059 0.058 0.040 0.053*** 0.048 0.042 0.046 0.046 0.053

(0.180) (0.265) (0.131) (0.117) (0.144) (0.006) (0.191) (0.198) (0.270) (0.190) (0.109)

Ind adj. EBITDA/total assets -0.024 0.254 -0.063 -0.007 -0.109 -0.073 0.063 0.037 0.145 -0.294 -0.008

(0.938) (0.328) (0.840) (0.982) (0.718) (0.802) (0.822) (0.908) (0.593) (0.304) (0.977)

Cash/total assets -0.404 -0.432 -0.529 -0.632 -0.347 -0.204 -0.436 -0.511 -0.466 -0.444 -0.481

(0.349) (0.141) (0.185) (0.140) (0.539) (0.701) (0.315) (0.253) (0.258) (0.240) (0.246)

Leverage 0.161** 0.121 0.144 0.144 0.047 0.029 0.029 0.045 0.076 0.068 0.053

(0.038) (0.133) (0.164) (0.219) (0.588) (0.722) (0.744) (0.585) (0.345) (0.450) (0.631)

Has bank debt 0.055 0.020 0.138** 0.121** -0.042 -0.006 0.147** 0.139** 0.016 0.162** 0.158**

(0.480) (0.731) (0.034) (0.039) (0.614) (0.935) (0.012) (0.019) (0.748) (0.016) (0.014)

Bank debt is overcollateralized -0.142*** -0.182*** -0.169*** -0.156*** -0.134** -0.118*** -0.142*** -0.122*** -0.203*** -0.172*** -0.144***

(0.000) (0.000) (0.000) (0.000) (0.013) (0.009) (0.004) (0.007) (0.000) (0.000) (0.000)

Prepackaged 0.196*** 0.194*** 0.216*** 0.226*** 0.178*** 0.201*** 0.269*** 0.268*** 0.207*** 0.186*** 0.211***

(0.000) (0.000) (0.000) (0.000) (0.008) (0.004) (0.001) (0.002) (0.000) (0.000) (0.000)

Chapter 11 -0.294*** -0.274*** -0.280*** -0.287*** -0.511*** -0.631*** -0.370*** -0.366*** -0.346*** -0.268*** -0.286***

(0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000)

Residuals -0.712** -0.002*** 1.628

(0.047) (0.000) (0.202)

Number of obs. 169 169 169 169 165 165 165 165 146 163 164

Pseudo R2 (Centered R2) 0.316 0.343 0.231 0.211 0.107 0.215 0.224 0.207 0.351 0.266 0.232

Validity of IV estimation

Under identification test (Kleibergen-Paap rk LM stat) 7.63*** 7.31*** 7.66*** 2.87*

(p-value) (0.006) (0.007) (0.006) (0.090)

Weak instrument test (Kleibergen-Paap rk Wald F-stat) 39.44** 32.16** 15.60 3.67

Stock-Yogo critical value (10% maximal IV size) (16.38) (16.38) (16.38) (16.38)

Significance of endogenous regressor (Anderson-Rubin Wald Chi-sqr. stat) 18.81*** 17.69*** 0.06 0.18

(p-value) (0.000) (0.000) (0.814) (0.674)

Endogeneity test of endogenous regressor (Anderson-Rubin F-stat) 4.45** 4.78** 0.087 0.083

(p-value) (0.035) (0.029) (0.768) (0.773)

Selection Model EstimationProbit Estimation IV Estimation

55

Table 11. Analysis of the Impact of Unobservable Confounding Variables

Definitions of variables are described in Appendix A. Table 11 shows the effect of each type of hedge fund involvement on various kinds of restructuring

outcomes, with an assessment of the impact of unobservable confounding variables based on Frank (2000). Panels A, B, and C show the results for the likelihood

of using debt-equity swaps, duration of distress, and likelihood of restructuring, respectively. Columns 1 and 2 of each type of hedge fund involvement show

coefficients and t-statistics from OLS regression of the outcome variable on the corresponding type of involvement and other control variables. Column 3 of each

type of hedge fund involvement provides the Impact Threshold for a Confounding Variable (ITCV) for the corresponding type of hedge fund involvement

variable, indicating the minimum impact of a confounding variable that would be needed to render the coefficient statistically insignificant. The ITCV is defined

as the product of the correlation between the potentially endogenous variable and the confounding variable and the correlation between the y-variable and the

confounding variable. If the ITCV is high (low), the OLS results are robust (not robust) to omitted variable concerns. To assess the likelihood of such a variable

existing, Column 4 of each type of hedge fund involvement shows the impact of the inclusion of each independent variable on the coefficient on hedge fund

involvement variable. The Impact is defined as the product of the partial correlation between the potentially endogenous variable and the control variable and the

correlation between the y-variable and the control variable (partialling out the effect of the other control variables). Column 5 of each type of involvement shows

a more conservative measure of impact, the product of the simple correlation between the potential endogenous variable and the control variable and the simple

correlation between the y-variable and the control variable. ***, **, * correspond to statistical significance at 1%, 5%, 10% significance level, respectively.

56

Endogenous variable

(2) (3) (4) (5) (2) (3) (4) (5) (2) (3) (4) (5) (2) (3) (4) (5)

t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw

Endog var. 0.165 ** 2.46 0.058 0.250 *** 4.83 0.213 -0.231 * -2.79 -0.346 -0.103 -0.81 -0.194

Tangibility 0.560 *** 5.57 0.029 0.038 0.498 *** 5.11 0.034 0.072 0.589 *** 8.20 -0.001 0.005 0.586 *** 7.26 -0.012 -0.024

Cash/total asssets -0.199 -0.63 0.002 0.018 -0.105 -0.30 0.009 0.024 -0.294 -1.19 0.004 0.000 -0.214 -0.78 -0.003 -0.004

Leverage 0.036 0.48 0.000 0.012 0.028 0.35 0.000 0.021 0.043 0.77 0.000 0.001 0.025 0.35 -0.002 -0.013

Has bank debt 0.031 0.40 0.013 0.034 0.021 0.26 0.028 0.032 0.068 0.90 -0.001 0.007 0.076 0.91 0.005 0.012

Bank debt is undercollaterilzed 0.235 *** 3.72 0.004 0.032 0.223 *** 3.80 0.003 0.042 0.210 *** 3.21 -0.006 0.030 0.245 *** 3.46 0.015 0.018

Prepackaged 0.214 ** 2.70 0.032 0.035 0.196 ** 2.68 0.025 0.041 0.260 *** 3.19 0.013 0.021 0.244 *** 3.09 0.006 0.002

Endogenous variable

(2) (3) (4) (5) (2) (3) (4) (5) (2) (3) (4) (5) (2) (3) (4) (5)

t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw

Endog var. 0.113 0.52 -0.087 0.232 1.20 -0.034 -0.162 -0.82 -0.195 0.128 0.64 -0.083

Log(total assets) -0.032 -0.25 0.002 0.002 -0.024 -0.20 0.004 -0.003 -0.039 -0.30 0.003 -0.008 -0.039 -0.30 -0.003 0.010

Ind adj. EBITDA/total assets -0.249 -0.39 -0.002 0.003 -0.306 -0.46 -0.002 0.003 -0.155 -0.24 0.000 0.000 -0.235 -0.39 -0.001 0.002

Leverage -0.562 ** -2.48 0.008 -0.006 -0.554 ** -2.55 0.008 -0.010 -0.570 ** -2.47 -0.024 -0.020 -0.563 ** -2.48 0.007 0.008

Log(number of debt classes) 0.319 *** 2.84 0.036 0.056 0.300 ** 2.52 0.036 0.051 0.338 *** 3.06 0.009 0.020 0.337 ** 2.92 -0.002 0.009

Bank debt portion 0.334 1.48 0.007 0.005 0.328 1.50 0.005 0.004 0.328 1.40 -0.009 -0.006 0.335 1.40 0.008 0.005

Prepackaged (non-HF) -0.879 *** -3.73 0.056 0.056 -0.847 *** -3.72 0.045 0.039 -0.955 *** -4.81 0.032 0.030 -0.919 *** -4.76 0.010 0.010

Chapter 11 0.388 ** 2.04 0.036 0.023 0.316 1.54 0.061 0.042 0.409 ** 2.05 0.036 0.028 0.420 ** 2.16 -0.008 -0.008

Endogenous variable

(2) (3) (4) (5) (2) (3) (4) (5) (2) (3) (4) (5) (2) (3) (4) (5)

t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw t-stat ITCV Impact ImpactRaw

Endog var. 0.325 *** 4.17 0.177 0.345 *** 5.30 0.236 0.151 ** 2.26 0.046 0.030 0.40 -0.099

Log(total assets) 0.045 1.49 0.005 0.002 0.051 ** 2.01 -0.001 -0.002 0.052 1.46 -0.008 -0.004 0.049 1.47 0.014 0.009

Ind adj. EBITDA/total assets -0.009 -0.03 0.001 0.014 -0.009 -0.03 0.001 0.014 0.036 0.10 0.002 0.009 0.079 0.22 0.002 0.008

Cash/total assets -0.465 -0.96 0.002 0.018 -0.391 -0.79 0.005 0.025 -0.502 -1.13 0.001 0.000 -0.526 -1.12 -0.009 -0.003

Leverage 0.095 1.27 0.000 0.004 0.092 1.31 0.001 0.006 0.093 0.94 0.002 0.001 0.101 0.95 -0.007 -0.005

Has bank debt 0.039 0.50 0.024 0.048 0.040 0.60 0.023 0.046 0.117 1.76 -0.001 -0.008 0.114 * 1.82 0.008 0.017

Bank debt is overcollaterilzed -0.145 *** -4.23 0.001 0.022 -0.138 *** -4.56 0.003 0.029 -0.165 *** -4.21 -0.017 -0.021 -0.145 *** -4.25 0.012 0.011

Prepackaged 0.235 *** 3.87 0.033 0.025 0.238 *** 3.74 0.029 0.030 0.264 *** 3.14 0.028 0.015 0.278 *** 3.37 0.009 -0.001

Chapter 11 -0.432 *** -6.38 -0.051 -0.044 -0.502 *** -7.48 -0.101 -0.079 -0.359 *** -5.16 0.027 0.014 -0.370 *** -5.72 0.015 0.016

(1)

Coeff.

(1)

Coeff.

(1)

Coeff.

(1)

Coeff.

(1)

Coeff.

(1)

Coeff.

(1)

Coeff.

(1)

Coeff.

(1)

Coeff.Coeff.

(1)

Coeff.

(1)

Coeff.

HF Overall HF Creditor HF Equity infusion HF Old equity holder

Panel A. Outcome Variable: Debt-equity swap

Panel C. Outcome Variable: Emergence

Panel B. Outcome Variable: Duration

HF Overall HF Creditor HF Equity infusion HF Old equity holder

HF Overall HF Creditor HF Equity infusion HF Old equity holder

(1)

57

Table 12. Buy-Hold Abnormal Stock Returns around Announcements of Hedge Fund Involvement

Table 12 shows average buy-and-hold abnormal stock returns (BHAR) around announcements of hedge fund

involvement, measured over the interval [-3,+3] and [-1,+1]. Day 0 is the date of the first public announcement of

hedge fund involvement. The sample includes only „clean‟ events for which there is no concurrent news concerning

the onset of financial distress such as a bankruptcy filing or a default. The „clean‟ sample consists of 75 firms for

which full set of stock data are available from CRSP for the event window. BHAR is calculated following Barber

and Lyon (1997);

])(1[]1[

11 t

it

t

it RERitBHAR

, using a value-weighted market index (Rmt) as the expected return for each stock. ***, **, * correspond to statistical

significance at the 1%, 5%, 10% significance level, respectively.

No. obs t-value t-value

All hedge fund involvement 75 3.3% 0.96 3.1% 1.40

Purchase of existing claims 55 1.8% 0.53 2.3% 0.93

Secured debt 4 7.3% 0.48 -2.0% -0.34

Unsecured debt 32 -4.7% -1.17 -0.3% -0.07

Preferred stock 3 -0.4% -0.06 0.8% 0.61

Common stock 17 12.7% ** 2.06 8.2% ** 2.04

New equity infusion 11 23.7% * 1.71 12.6% 1.62

New debt financing 22 4.3% 0.66 8.9% * 1.70

[-3,+3] [-1,+1]

Mean Ret. Mean Ret.

58

Table 13. Performance Characteristics of Distress-focused Hedge Funds from Jan.1998 to Dec. 2009

Table 13 shows performance characteristics of distress-focused hedge funds from January 1998 to December 2009

in comparison to performance of overall hedge fund industry and of the stock market. Panel A provides overall risk

and return characteristics, and Panel B provides annual returns from 1998 to 2009. The first column shows returns of

distress-focused hedge funds managed by hedge fund managers that actively get involved in my sample of distressed

firms. The second column shows returns of the CSFB Distressed Index, which represents overall performance of the

distress-focused hedge funds. As a benchmark, column 3 and 4 provide return characteristics of the overall hedge

fund industry (represented by CSFB Hedge Fund Index) and of the stock market (represented by S&P 500 Index),

respectively. Data are from Lipper TASS.

Panel A. Risk and return characteristics

Distressed- funds in my

sample

CSFB Distressed

Index

CSFB Hedge Fund

Index S&P 500

Cumulative returns 151.19 184.17 132.39 41.47

Avg. ann. compounded returns 8.63 9.89 7.83 3.20

Avg. monthly returns 0.66 0.75 0.61 0.36

Annualized standard deviation 5.79 6.90 7.25 16.53

Annualized Sharpe ratio 2.99 3.05 2.10 0.27

Largest Monthly Loss -6.58 -12.45 -7.55 -16.80

Largest Monthly Gain 4.11 4.15 8.53 9.78

Panel B. Annual Returns (%)

Year Distressed- funds in my

sample

CSFB Distressed

Index

CSFB Hedge Fund

Index S&P 500

1998 -0.70 -1.7 -0.36 28.58

1999 23.62 22.2 23.43 21.04

2000 12.04 1.9 4.85 -9.10

2001 11.80 20.0 4.42 -11.89

2002 3.88 -0.7 3.04 -22.10

2003 20.32 25.1 15.44 28.68

2004 10.94 15.6 9.64 10.88

2005 9.21 11.7 7.61 4.91

2006 11.98 15.6 13.86 15.80

2007 7.50 8.4 12.56 5.49

2008 -22.16 -20.5 -19.07 -37.00

2009 15.12 20.9 18.57 26.46

59

Table 14. Hypothetical Returns of Hedge Fund Investment in My Sample

Table 14 shows hypothetical deal-specific returns on investment made by distress-focused hedge funds in my

sample. Panels A, B, and C provide returns on security purchases, equity infusion, and debt financing, respectively.

For security purchases, returns on investment are calculated as the recovery rate for the corresponding class divided

by price paid. Purchase prices paid by hedge funds are collected from 13D, 13F, and N-30D (N-CSR) filings

whenever available. When the exact purchase price is not available, I assume that hedge funds bought securities at

the market prices around the announcement date of hedge fund involvement. Market prices of securities are

collected from TRACE, High Yield/Distressed Bank Loan Pricing, Trends and Prices of Leveraged Syndicated

Loans, and Factiva news. Recovery rates are from plans of reorganization. Annualized returns are provided,

assuming a two-year investment horizon. For new equity infusion, returns on investment are calculated as

reorganization equity value times equity shares received due to such equity infusion divided by the dollar amount of

equity infusion. Annualized returns are provided, assuming a two-year investment horizon. For debt financing,

spreads over LIBOR are provided as proxies for returns on investment. The number of observations is the number of

involvements, which is not necessarily the same as the number of firms with such involvement since multiple hedge

funds can get involved in one firm.

Panel A: Returns on Purchase of Existing Securities

No. obs

All purchases 142 0.554 (n=126) 0.183 (n=84)

Preferred stock 7 0.270 (n=7) 0.040 (n=5)

Secured private debt 26 0.583 (n=16) 0.762 (n=24) 0.090 (n=16)

Unsecured private debt 32 0.488 (n=21) 0.651 (n=21) 0.180 (n=21)

Unsecured public debt 77 0.384 (n=43) 0.470 (n=65) 0.241 (n=40)

Panel B: Returns on Equity infusion

No. obs

All equity infusion 40 208.8 (n=40) 684.8 (n=32) 0.266 (n=30)

Panel C: Returns on Debt financing

No. obs

All debt financing 79 86.8 (n=78) 445~537 (n=39)

a. Assuming 2 year investment horizon, Winsorized at 99%,1% level

LIBOR

Price paid (c/$) Recovery rate (%)

Reorg. Eq. Value ($MM) Amt. Inv't ($MM)

Amt. Fin ($MM)

n.a

Ann. Returns (%) a

Ann. Returns (%) a

Spread (bp)

n.a

Base rate

60

Appendix A: Descriptions of variables

Variable Description Data source

Total assets Book value of total assets in millions (AT) Compustat

Sales Sales volume in millions (SALE) Compustat

Market capMarket value of common stocks in millions, measured by closing price at the end of fiscal year times

number of shares outstanding (PRCC_F*CSHO)Compustat

Positive EBITDAAn indicator variable takes a value of 1 if operating cash flow (EBITDA) prior to distress is positive

and 0 otherwise.Compustat

Ind-adj. EBITDA/Total assetsThe ratio of EBITDA to total assets (EBITDA/AT), adjusted by subtracting industry median. Industry

median is calculated at two-digit SIC level.Compustat

Ind-adj. EBITDA/SalesThe ratio of EBITDA to sales (EBITDA/SALE), adjusted by subtracting industry median. Industry

median is calculated at tw-digit SIC level.Compustat

Tangibility The ratio of property, plant, and equipment to total assets (PPENT/AT) Compustat

Leverage The ratio of total long-term debt to total assets ((DLTT+DLC)/AT) Compustat

Cash/Total assets The ratio of cash and cash equivalents to total assets (CHE/AT) Compustat

Current debt/Total assets The ratio of current portion of long-term debt to total assets (DLC/AT) Compustat

Number of debt classes Number of long-term debt contracts Mergent Corporate Manual

Bank debt/Total long-term debt The amount of bank debt divided by total long-term debt Mergent Corporate Manual / Compustat

Has public debt outstanding An indicator variable takes a value of 1 if a firm has public debt outstanding, 0 othewise. Mergent Corporate Manual / Compustat

Has both bank and public debt outstandingAn indicator variable takes a value of 1 if a firm has both bank and public debt outstanding, 0

othewise.Mergent Corporate Manual / Compustat

Debt complexityAn indicator variable takes a value of 1 if number of debt classes is above the median and a firm has

both bank debt and public debtMergent Corporate Manual / Compustat

Bank debt is under-collateralizedAn indicator variabe takes a value of 1 if the ratio of bank debt amount to fixed assets exceeds 1, 0

otherwise.Mergent Corporate Manual / Compustat

Chapter 11 An indicator variable takes a value of 1 if a firm filed Chapter 11 protection, 0 otherwise.Factiva / Bankruptcy DataSource / Lopucki's

Bankruptcy Research Database

Prepackaged An indicator variable takes a value of 1 if a bankruptcy is pre-packaged or pre-negotiated, 0 otherwise.

Factiva / Bankruptcy DataSource / Lopucki's

Bankruptcy Research Database / Plan of

reorganizations / Disclosure statements

Lagged Recovery One-year lagged weighted average recovery rate on defaulted debt per face value Altman (2008)

EmergedAn indicator variable takes a value of 1 if a firm is successfully reorganized and emerged as a stand-

alone company, 0 otherwise.

Factiva / Bankruptcy DataSource / Lopucki's

Bankruptcy Research Database / Plan of

reorganizations / Disclosure statements

AcquiredAn indicator variable takes a value of 1 if a firm is acquired by other firm and assimiliated into the

acquirere, 0 otherwise.

Factiva / Bankruptcy DataSource / Lopucki's

Bankruptcy Research Database / Plan of

reorganizations / Disclosure statements

Liquidated

An indicator variable takes a value of 1 if a firm ceased business and its assets were sold in a

piecemeal fashion, or if a firm filed for Chapter 7, or if a case is converted to Chapter 7 or settled under

the plan of liquidation within Chapter 11.

Factiva / Bankruptcy DataSource / Lopucki's

Bankruptcy Research Database / Plan of

reorganizations / Disclosure statements

HF Overall An indicatore variable takes a value of 1 if hedge fund involved in a firm, 0 otherwsie.

HF CreditorAn indicatore variable takes a value of 1 if hedge fund involved in a firm as a creditor (both secured

and unsecured), 0 otherwsie.

HF SecuredAn indicatore variable takes a value of 1 if hedge fund involved in a firm as a secured debt holder, 0

otherwsie.

HF UnsecuredAn indicatore variable takes a value of 1 if hedge fund involved in a firm as an unsecured debt holder,

0 otherwsie.

HF Equity infusionAn indicatore variable takes a value of 1 if hedge fund injected new equity capital to a distressed firm,

0 otherwsie.

HF Old equityAn indicatore variable takes a value of 1 if hedge fund hold old (pre-distress) equity securities, 0

otherwsie.

HF Loan-to-own

An indicator variable takes a value of 1 if one of the following is met:

- Loan & equity provision (e.g. warrants, convertible provisions)

- Loan & equity investment

- Loan & equity position (CS or PS)

- Loan & prepackaged deal (for controlling share)

- Loan & other securities which received equity distribution

HF Prepack An indicatore variable takes a value of 1 if hedge fund led a prepackaged deal, 0 otherwsie.

HF Committee An indicatore variable takes a value of 1 if hedge fund served on a committee, 0 otherwsie.

HF BoardAn indicatore variable takes a value of 1 if hedge fund took a seat on the board of reorganized firm, 0

otherwsie.

HF BlockholderAn indicatore variable takes a value of 1 if hedge fund became a blockholder of the reorganized firm, 0

otherwsie.

HF ControlAn indicatore variable takes a value of 1 if hedge fund became a controlling shareholder of the

reorganized firm, 0 otherwsie.

Factiva / 10-Ks / Proxy statements / DealScan /

Material Contracts / Loan agreements / 13Ds /

13Gs /13Fs / Bankruptcy DataSource / Plan of

reorganization / Disclosure statement / N-CSR /

61

Appendix B: Descriptions of selected cases of hedge fund involvement

AMF Bowling Inc. (7/3/2001-2/1/2002): Farallon Asset Management LLP, Satellite Asset Management,

Angelo Gordon & Co., and Oaktree Capital Management bought AMF‟s senior secured lender claims for

cents on the dollar and held 60% the class ($620 million) collectively. They also provided DIP financing

to AMF. The investors received cash, $150 million in new notes, and equity equal to 92.5 % of the

reorganized company's common stock. They became the largest investor holding controlling shares

collectively and took the majority of the board in the new company.

Anchor Glass Container Corp (4/15/2002-8/8/2002): On March 15th, 2002, Anchor Glass Container

Corporation announced that it had entered into a definitive agreement with Cerberus Capital Management

pursuant to which Cerberus would invest $100 million of new capital in Anchor and Anchor would effect

a significant restructuring of its existing debt and equity securities. The investment from Cerberus

included $80 million of equity capital, acquiring 100% of the Company's Series C Participating Preferred

Stock for $75.0 million and 100% of the Company's Common Stock for $5.0 million. Rick Deneau, the

President and Chief Operating Officer of Anchor, stated that "the Cerberus agreement is an important

milestone for Anchor and will allow us to complete the significant financial restructuring that was

necessary”. Cerberus became a new equity owner of reorganized Anchor. After it came out of bankruptcy,

Anchor offered an IPO of 7.5 million shares priced at $16. Cerberus continued to own about 63% stake

after the offering.

Bally Total Fitness (7/31/2007-10/1/2007, 11/3/2008-8/18/2009): Harbinger Capital Partners, teaming

up with Liberation Investment Group who was Bally's second-largest shareholder, offered the pre-

packaged restructuring plan, which gave Harbinger Capital Partners 80% ownership of Bally, and gave

shareholders the ability to own the remaining 20%. The creditors paid in full. The Harbinger plan also

included $233.6 million equity infusion into the firm by Harbinger. Bally has emerged from Chapter 11 in

2007 as a private company controlled by Harbinger. But in late 2008, Bally filed for another Chapter

11.Shortly after it sought bankruptcy protection, Bally had discussions to sell the company to JPMorgan

Securities Inc. and hedge fund Anchorage Advisors, who owned more than 40% of Bally's $242 million

term loan. Anchorage Advisors also provided $39 million exit financing. Holders of Bally's pre-petition

secured debt received 94 % of the reorganized Bally's equity, giving Anchorage Advisors 84.2%

ownership.

Borders Group Inc. (3/20/2008-4/9/2008): On April 9th,

2008, Borders entered into a financing

agreement with Pershing Square Capital Management, who held about 18% ownership in Border‟s

common stock. The agreement includes a $42.5 million senior secured term loan, a "put" right of $135

million for Borders Group's international subsidiaries (subject to the satisfaction or waiver of certain

conditions), and 9.55 million warrants to purchase common stock initially issued to Pershing Square,

exercisable at $7.00 per share, subject to adjustment. The agreement provides for a future issuance, under

62

certain circumstances, of 5.15 million additional warrants exercisable at $7.00 per share, subject to

adjustment. The new agreement with Pershing Square, which is led by William Ackman, offers Borders a

lower interest rate of 9.8 percent on its $42.5 million loan, down from 12.5 percent before. Assuming the

exercise of warrants, Pershing Square‟s beneficial ownership would increase to 33.6%. Further, Richard

Mcguire from Pershing Square Capital Mgt was named as Chairman of the board.

Delphi Corp. (10/8/2005-7/30/2009): Appaloosa Management held about 9.3% of Delphi's common

shares, and a significant share of its bonds, and led the formation of the equity committee. David Tepper

of Appaloosa had been pushing a restructuring plan that could have left him in control of a far bigger

stake in Delphi. After weeks of negotiations, the group was near an agreement under which Mr. Tepper

would inject billions of dollars more into Delphi and wind up controlling up to one-third of Delphi's stock

when it emerges from bankruptcy. The plan was approved in the court on Jan.12, 2007. The deal gave the

Appaloosa-Cerberus investors exclusive rights to buy Delphi convertible preferred stock valued at $1.2

billion and $200 million in common stock. The group could end up with a stake in Delphi of between

30% and 70% after the company exits from bankruptcy protection. Later, Cerberus backed out, and the

court approved a new plan of $2.22 billion on August 3, 2007. But later, the group terminated the deal

again. After two hedge funds backed out of Delphi, Elliott Associates and Silver Point Capital purchased

a large share of Delphi's debtor-in-possession financing in the secondary market later of the bankruptcy

process. The investors then forgave their loans (more than $3.4 billion) in exchange for taking control of

the company. The lenders' group also had agreed to provide Delphi with $750 million in new financing.

Exide Technologies Inc (5/20/2005-11/30/2006): Exide Technologies restructured its capital structure

through a rights offering. The Company sold 10,928,730 shares of its common stock to existing

stockholders excluding Tontine Capital Partners. The company also sold 10,499,841 shares of common

stock to investors led by Tontine pursuant to standby commitments under the previously announced

Standby Purchase Agreement, generating aggregate gross proceeds of approximately $75 million. Later,

the company also completed the sale of 14,285,714 additional shares of common stock to Tontine and

Legg Mason for $50 million. As a result of right offering participation, Tontine obtained about 28%

ownership. As part of the agreement, Tontine received rights to nominate two board members.

Federal Mogul Corp. (10/1/2001-11/4/2007): Carl Icahn was the largest bondholder at Federal-Mogul

Corp., controlling more than $1 billion (approximately 50%) face value of Federal Mogul's unsecured

bonds. Mr. Icahn had been credited as a driving force behind the agreement between the Unsecured

Creditors Committee and the Asbestos Claimants Committee. At its core, the agreement provided that the

holders of Noteholder Claims and Asbestos Personal Injury Claims will receive nothing on account of

their respective claims except 100% of the common stock of Reorganized Federal-Mogul. Specifically,

49.9% of the Reorganized Federal-Mogul common Stock would be distributed to the Noteholders and

50.1% distributed to a Trust established for the benefit of the holders of Asbestos Personal Injury Claims.

Icahn also agreed to pay up to $375 million in cash and $400 million in a note to a trust for asbestos

claims. In return, he received the ability to control 43% of the company once it emerged from Chapter 11.

63

Later he became a chairman of the board of new reorganized firm. After the company emerged from

Chapter 11, Mr. Icahn exercised an option to buy 50.1 million shares of Federal-Mogul Corp., increasing

his stake to 75%.

Kmart Corp (1/22/2002-4/22/2003): After Kmart filed for bankruptcy, ESL Investment acquired

significant stakes (as high as 51% of the class) in various claims of Kmart‟s debt including pre-petition

loans, unsecured bonds, and trade claims. Such stakes vested ESL a seat on the official committee of

financial institutions. ESL was deeply involved in the restructuring process. ESL entered into an

agreement with Kmart to invest $200 million in cash, and also agreed to swap its holdings of Kmart debt

for new Kmart stocks in lieu of the cash that the other members in that class were to receive. As a result,

ESL emerged as the largest shareholder of the reorganized Kmart, beneficially owning about 50% of the

new Kmart‟s common stock. ESL also was actively involved in the management change, replacing the

CEO, designating four of nine directors, and serving as a chairman of the board.

Milacron Inc. (3/1/2004-6/9/20, 3/10/2009-6/29/2009): In 2004, Milacron experienced financial trouble.

Facing a cash crunch and a deadline to pay off $115 million in bonds, bankruptcy was a possibility. But

just before the deadline, Milacron managed to get 11th hour refinancing, issuing $140 million new bonds

convertible to stock, priced at the wide spread of LIBOR plus 650 bps, and staved off a bankruptcy filing.

Almost all of new bonds were placed with hedge funds, including Soros Fund Management, Black

Diamond Capital Management, and Stanfield Capital Management. Five years later, however, facing a

combination of a global recession that cut demand for its machinery and the credit crunch that dried up

alternative financing sources, the company again turned to hedge funds for help. Milacron filed Chapter

11 after a pre-packaged deal with Avenue Capital Group and DDJ Capital Management. Avenue and DDJ

collectively held about 93% of the company's $225 million senior secured notes, and agreed to a debt-

equity swap which gave them a control of the company instead of giving up their debt claims. Avenue

Capital and DDJ also agreed to provide $80 million debtor-in-possession (DIP) term loan facility and to

purchase substantially all of Milacron's assets.

Polymer Group Inc. (5/11/2002-1/3/2003): MatlinPatterson Global Opportunities Fund held about $400

million in notes (about 67% of the class), for which the fund paid, on average, 37.5 cents on the dollar.

Before bankruptcy filing, Polymer and MatlinPatterson agreed to a pre-packaged deal, whereby

MatlinPatterson would exchange most of its $400 million notes holdings for at least an 81 % share of the

reorganized Polymer Group, while pumping $50 million in cash as “standby purchaser” of the new

convertible preferred stocks and $25 million from a line of credit into the corporation. MatlinPatterson

also received rights to appoint five members of a 9-person board of directors of the reorganized company.