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CORPORATE RESTRUCTURING AND LENDING
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CORPORATE RESTRUCTURING AND LENDING RELATIONSHIPS:
EVIDENCE FROM FIRMS EXPERIENCING LARGE PERFORMANCE SHOCKS

By

Hyun-Seung Na

A DISSERTATION

Submitted to
Michigan State University
In partial fulfillment of the requirements
For the degree of

DOCTOR OF PHILOSOPHY
Department of Finance

2007

ABSTRACT

CORPORATE RESTRUCTURING AND LENDING RELATIONSHIPS:
EVIDENCE FROM FIRMS EXPERIENCING LARGE PERFORMANCE SHOCKS

By
Hyun-Seung Na
This dissertation explores the monitoring roles of private lenders as a corporate control
mechanism. Using firms that experience large performance declines between 1997 and
200], we investigate how close lending relationships influence restructuring actions of
borrowing firms in response to their performance shocks. We find that firms with more
loans from their durable lending relationships are more likely to engage in downsizing
actions. whether they generate immediate cash flows or not. The existence of long-term
relationship lenders decreases the likelihood of expansionary actions. The announcement
returns of downsizing are also positively related to lending relationship measures such as
duration of lending relation and borrowing from lead credit facilities. While firms with
more borrowings reduce their loans following downsizing actions, those with long-run
lending relationships maintain their loan amount. In addition, firms with close ties to
their lenders have better post-restructuring performance and lower probability of being
acquired. These findings are consistent with the view that close lending relations create
value by facilitating firms to engage in value-increasing restructuring during performance

declines.

Copyright by
Hyun-Seung Na
2007

To my wife and parents

ACKNOWLEDGEMENTS

I am indebted a great deal to so many people in my long path of completing my
dissertation. I first thank my committee chair, Professor Jun-Koo Kang for his never
ending support and guidance. I chose Finance as my major when I took his
undergraduate class ten years ago and he has been my best teacher and mentor from
then on. He is the advisor that every student dreams of. I also would like to express
my gratitude to my committee members, Dr. Charlie Hadlock, Dr. Ted Fee, and Dr.
Wei-Lin Liu. I will not forget their continuous encouragement and support in my
dissertation process as well as throughout my doctoral studies.

I am extremely fortunate to have good student colleagues past and present. They are
my good friends who continuously helped me and shared personal concerns together
throughout my studies. I cannot imagine completing my path to PhD. without them.
Especially I thank Josh Pierce, who is my any-time proof-reader. My special thanks
go to my pastors Jong-Soo Kim and Bo Rin Cho, who always encouraged me and
prayed for me. I also thank my church friends for their prayers for me.

Now I would like to share my pleasure with my parents and sister who are my
supporters and firmly stand behind me always. I am also grateful to my parents-in-
law who always trusted and supported me. My future baby, who is unnamed yet,
comes with this great present to her father. Finally and most deeply, I am grateful to
my wife, Young-Ju Cho, for her unlimited love and support. I know that without her

understanding and patience, I could have never done this.

 

TABLE OF CONTENTS

LIST OF TABLES ................................................................................................ vii
1 Introduction .................................................................................................. 1
2 Hypotheses and Testable Implications ......................................................... 8
2.1 Monitoring Hypothesis ............................................ 8
2.2 Conflict-of-lnterest Hypothesis ............................. l 1
3 Sample Selection and Data ........................................................................ 15
3.1 Sample Selection .................................................... 15
3.2 Summary Statistics ................................................. l6
4 Empirical Results ....................................................................................... 19
4.1 Restructuring Activities ......................................... 19
4.2 Determinants of Restructuring Decisions .............. 22
4.3 Valuation Effects of Restructuring
Announcements ...................................................... 26
4.4 Loan changes following restructuring actions ....... 29
4.5 Industry-adjusted Long-term Performance
Following restructuring actions ............................. 3O
5 Summary and Conclusion .......................................................................... 32
APPENDIX ............................................................................................................. 35
REFERENCES ...................................................................................................... 50

vi

Table 1

Table 2

Table 3

Table 4

Table 5

Table 6

Table 7

Table 8

Table 9

LIST OF TABLES

Predicted Effects ofCIose Lending Relationships ......................... 36
Descriptive Statistics of F irm Characteristics ................................ 37

Frequency of Restructuring Actions during Performance
Declines .......................................................................................... 38

Logit Estimates of the Likelihood of Restructuring Actions
During Performance Declines ........................................................ 39

Mean and Median Cumulative Abnormal Returns surrounding
the Announcement of Restructuring Activities .............................. 41

OLS Regressions of Cumulative Abnormal Returns
[CAR (-1, 1)] surrounding the Announcements of

Restructuring Activities ................................................................. 42

OLS Regression of Loan Changes of Lead Credit Facilities
Following Downsizing Actions ..................................................... 44

OLS regressions of Industry-adjusted Performance Following
Downsizing Actions ....................................................................... 46

Logit Estimates of the Likelihood of being acquired ..................... 48

vii

1. Introduction

The monitoring role of private lenders such as banks and institutional lenders has been
widely investigated. Few studies, however, provide evidence on the role of private lenders
in firms’ investment decisions. In particular, there is little evidence that private lenders
have the ability or power to exert control over firm’s investment decisions based on their
information advantage especially when firms experience severe financial difficulties. This
study explores the monitoring roles of private lenders as a corporate control mechanism in
corporate restructuring during performance declines of their borrowing firms.

In the banking literature, lending relationships with private lenders increase firm value,
reduce information asymmetries. and decrease costs of financial distress. For example,
studies such as James (1987). Lummer and McConnell (1989), Billet, Flannery. and
Garfinkel (1995) show that banks’ decisions to lend enhance the value of borrowing firms
and abnormal borrower returns are larger when lenders have higher credit ratings or
previous relationships with their borrowers. Also, firms with close ties to their private
lenders have increased availability of financing, lower interest rates, and less collateral
requirements (Petersen and Rajan, 1994; Berger and Udell, 1995). In addition, Gilson,
John, and Lang ( 1990) find that financial distress is more likely to be resolved through
private renegotiation rather than formal bankruptcy when relatively more debt is owed to
banks. These studies generally attribute the benefits of lending relationships to the
monitoring roles of private lenders, especially those that maintain close ties with firms,
while evidence on private lenders controlling firms“ investment decisions is limited.

The lack of evidence on the monitoring role of lenders influencing firms’ investment

decisions may be due to several facts. First, the bankruptcy doctrines of equitable

subordination and lender liability in the US. may discourage private lenders from being
involved in the firm’s decision making. If senior creditors overreach their roles as creditors
by actively intervening in management and acting inequitably prior to a bankruptcy of the
firm, they can lose seniority in their debt claims and face liability from other claimants'.
Thus, especially when firms are at risk of financial distress, major lenders may be reluctant
to play an active role due to concerns about legal repercussions. Second, although private
lenders do control management, their actions might be implicit by the nature of their
relationships. Typically private lenders do not reveal their inside information captured
from their lending process and thus their monitoring activities may be conducted within
their relationships as well. Therefore, different from explicit governance activities of
shareholders such as hostile takeovers. board dismissals, and shareholder activism, the
monitoring actions of private lenders are more likely to be undetectable from outside, even
if they do exist.

To examine the monitoring roles of private lenders in firms’ investment decisions, we
investigate the effect of lending relationships on corporate restructuring actions during
performance declines. It has been well documented by previous studies that firms likely
respond to their performance declines by operational restructuring actions such as asset
contractions and layoffs and show improved post-restructuring performance (John, Lang,
and Netter, 1992; Ofek, 1993; Kang and Shivdasani, I997; Denis and Kruse, 2000).
Particularly these studies find a strong link between corporate governance mechanisms and

corporate restructuring actions during performance declines3. When firms experience a

 

' See Kroszner and Strahan (200l) for further discussion on equitable subordination and legal liability.

3 The corporate governance mechanisms facilitating restructuring actions include both external and internal
controls. Specifically they include higher leverage (Ofek, I993), outside block ownership (Kang and
Shivdasani, I997), and managerial disciplinary events (Denis and Kruse, 2000).

Id

large drop in their operating performance, the uncertain future perspectives of the firms
may increase the lenders’ incentives to monitor firms’ managerial decisions such as
restructuring decisions which affect their existing debt claims. Therefore if private lenders
have the ability to exert control over their borrowers” investment decisions, there would be
a detectable link between lending relationships and corporate restructuring actions.
Therefore investigation of a link between corporate restructuring and lending relationships
during performance declines provides an ideal opportunity to document the monitoring
roles of private lenders as a corporate control mechanism.

Banking theories present two competing views on lenders" incentives regarding
corporate restructuring. First, when firms experience a significant drop in their operating
performance, the lenders that maintain a close relationship with their client firms may have
monitoring incentives to facilitate firms’ value-increasing restructuring to help them
survive and improve long-term performance (the monitoring hypothesis). The theoretical
implications in studies such as Diamond (1984), F ama (1985), and Admati and Pfleiderer
(1994) support this view. In contrast, the performance drops can create conflicts of interest
between the client firms and their private lenders. To the extent that firms experiencing a
performance shock have uncertain future perspectives, their lenders may have incentives to
induce these firms to engage in restructuring actions that provide mainly short-term
proceeds or short-term safety to secure their existing loan repayment (the conflicts of
interest hypothesis). In Diamond (1993), lenders inefficiently liquidate their borrowing
firms that suffer from liquidity problems since the lenders ignore the projects returning
only to their borrowers. Sharpe (1990) and Rajan (1992) also present theoretical models

that banks have bargaining power over their client firms and extract rents from them.

b.)

To identify close lending relationships we depend on two dimensions of relations
between borrowing firms and their lenders. The first measure is the duration of the private
lending relationships, which is one of the most common indicators of the close lending
relationships in the banking literature (Petersen and Rajan, 1994; Berger and Udell, 1995).
Private lenders are information producers that resolve information asymmetry between
management and outside investors, and the soft information about borrowers is
cumulatively acquired in their relationships over time. If lenders with a long-term
relationship with their borrowing firms discontinue their lending, it could be a signal that
the borrower’s future performance is in question to other outside investors, indicating more
ability to control firms for long-term relationship lenders. Since a long-term lending
relationship is also valuable to private lenders, they are likely to have more incentive to
monitor or control their borrowers when they have maintained a long-term relationship3.
Thus, lenders with a durable relationship with their borrowing firms are more likely to have
ability as well as incentives to influence restructuring decisions of their borrowers. The
second measure of closeness of lending relationships is the actual amount borrowed from
private lenders. If firms are more dependent on their private lending relationships for
financing. it would be more difficult for them to access other financing resources such as
public markets and other private lenders, especially when they experience financial
difficulties. Thus. the more firms borrow from their private lenders, the more likely the
private lenders have an ability to affect their restructuring decisions during performance

declines.

 

3 Regarding this. Dahiya, Saunders, and Srinivasan (2003) report that financial distress of borrowing firms
results in larger wealth declines for their banks when they have a past lending relationship before distress.
The result is interpreted as evidence that long-term banking relationships are valuable to lenders.

Using the above two measures of close lending relationships, we show that private
lenders with close lending relationships with their borrowers affect their restructuring
actions during performance declines. Our results support the monitoring hypothesis.
When downsizing actions are divided into actions that generate immediate cash flows,
such as asset sales, and those that do not generate immediate cash flows, for example
layoffs, firms with greater borrowing from their long—term lending relationships are more
likely to engage in both types of downsizing actions. To the extent that
non-cash-generating downsizing actions are not beneficial to private lenders who just want
to secure their short-term loan claims, the results can be interpreted as favorable to the
monitoring hypothesis rather than the conflict-of—interest hypothesis.

The investigation of the announcement effects of downsizing actions is also
consistent with the monitoring hypothesis. Firms have significantly positive stock price
reactions to downsizing actions particularly when they have more amount of borrowing
from their lead credit facility. The effect is more pronounced for cash-generating
downsizing actions. For non—cash-generating downsizing actions, the stock price reaction
is higher when firms maintain long-term lending relationships with their lead lenders. In
that lending relationship variables are positively related to the valuation effects of
downsizing announcements, these findings favor the monitoring hypothesis.

In addition. we examine the change in the amount borrowed from the lead credit
facility after downsizing actions to see whether the actions are induced to repay the
borrowing firms’ existing loans. Our results show that when firms have more debt from
their lead credit facilities, the amount borrowed reduces following downsizing actions.

However. there is a sharp difference in the loan changes between firms with and without

’Ji

long-term lending relationships. We show that firms that have long-term lending
relationships do not reduce their amount borrowed even following downsizing actions,
indicating that the downsizing actions are not for loan repayments. These findings are
similar to Hoshi, Kashyap, and Scharfstein (1990, 1991) showing that Japanese firms are
less liquidity constrained even during financial distress when they have close ties to their
main banks.

Finally, we examine the industry-adj usted long-term operating performance following
firms’ downsizing actions and show that it is higher when firms have a durable relationship
with their lead lenders and have more borrowings from their lead credit facilities. In
addition, our findings show that the firms that have a long-term lending relationship are
less likely to be acquired by other firms following their performance shocks. Thus, our
long-term performance results suggest that private lenders help firms survive performance
declines and engage in restructuring actions focused on long-term performance
improvement when they maintain close lending relationships.

In summary, our results present evidence that private lenders play a monitoring role in
corporate restructuring during performance declines. We find that lenders with close
relationships with their borrowers in terms of the duration of their relationships and the
amount of their lending encourage firms to conduct downsizing actions resulting in greater
firm value and higher long-term performance. However, we do not find strong evidence
for the monitoring roles of lenders when they do not have a long-term relationship with
their borrowers.

Previous research exploring the effects of banking relationships on corporate

restructuring is limited. There are two related papers using data from the countries with

relatively strong creditor rights. First, using a set of Japanese firms that experience
performance declines, Kang and Shivdasani (1997) find that firms with close ties to their
main banks are more likely to undertake asset contracting actions and layoffs. Their results
are interpreted as evidence that the main bank system functions as an alternative
disciplinary mechanism in the absence of external corporate control pressures such as
takeover threats in Japan. However, in their study the ties to their main banks are mainly
measured by the ownership of the main banks, and therefore the results are derived from
the disciplinary roles of banks as shareholders rather than those as creditors. Second,
Bertrand, Schoar, and Thesmar (2007) investigate how the deregulation of the French
banking industry in the 19803 affects firms’ behavior and industrial structure. Their results
show that banks are less willing to bail out poorly performing firms and firms in more
bank-dependent sectors are more likely to undertake restructuring after the deregulation.
Bertrand, Schoar, and Thesmar (2007) is different from. our study in that they examine the
effects of a shock on the banking sector rather than a shock on the corporate sector and do
not consider the different characteristics of lending relationships. Therefore, the evidence
in this study broadens our knowledge about the relationship between corporate
restructuring and firms” lending relationships with their private lenders.

This paper proceeds as follows. Section 2 discusses the two competing hypotheses and
testable implications to distinguish between them. Section 3 describes the data and sample
characteristics. In Section 4, we present the results from our empirical analysis. Section 5

summarizes and concludes the paper.

2. Hypotheses and testable implications

We develop two competing hypotheses in this section and both hypotheses assume that
private lenders have the ability to exert control over the borrowing firms’ investment
policies. Therefore, the first null hypothesis we are testing is that private lenders do not
have incentives or ability to control firm’s restructuring decisions. There are at least two
arguments for expecting no influence of private lenders on corporate restructuring during
performance declines. First, private lenders such as banks and financial institutions are
considered inside investors able to access private information of their client firms. This
indicates a possibility that private lenders are already informed about the performance
declines of their borrowers and thus reflect expected financial status in the terms of their
loan contracts so as not to be exposed to the financial risk. Second, as discussed above, the
US. bankruptcy doctrines of equitable subordination and lender liability may discourage
major private lenders from disciplining management. Thus, even if private lenders have
disciplinary incentives, they may be hindered by the regulations especially when firms are
at a high risk of bankruptcy. Therefore, by testing the two competing hypotheses described
in detail below, we are simultaneously testing the hypothesis that private lenders do not or

cannot exert control over corporate restructuring during performance declines.

2.1. illonitoring Input/taxis

According to the monitoring hypothesis, as inside investors informed lenders such as
banks and financial institutions with close lending relationships help borrowing firms
overcome their perfomiance shocks and improve their long-term perfomiance. Under this

hypothesis, informed lenders encourage their client firms to make value-maximizing

restructuring decisions for the firms‘ survival and long-run performance improvements
based on their private information. Consistent with the hypothesis, Diamond (1984)
provides a model in which a financial intermediary bears the cost of monitoring client
firms and assures right decisions in their investment. Previous studies also provide
evidence that bank loans are the only financing method associated with positive
announcement returns. which are attributed to monitoring services by informed lenders
(Smith, 1986; James. 1987; Lummer and McConnell, 1989).

Further, the monitoring hypothesis is supported by Admati and Pfleiderer (1994) who
present a model where conflicts of interest and information asymmetries can be resolved
by inside investors such as venture capitalists and informed banks. Specifically, they
assume an entrepreneur who always has an incentive to continue projects even when the
projects are not profitable. The overinvestment problem can be solved if capital is obtained
from an inside investor with close ties to the firm. While their model is more applicable to
the lenders in the countries with relatively strong creditor rights such as Japan and
Germany, under the monitoring hypothesis we expect informed private lenders to play a
role of inside investors especially when their borrowers struggle with unexpected
performance shocks and make restructuring decisions, which will critically affect their
debt claims.

There are four testable implications for the monitoring hypothesis. as summarized in
Table 1. First, we investigate the effects of lending relationships on the likelihood of
corporate restructuring actions in response to performance declines. In particular, similar
to Ofek (1993) we divide downsizing actions between cash-generating downsizing actions

such as asset sales and non-cash-generating downsizing actions such as layoffs. Some

types of restructuring actions such as layoffs ofemployees. suspensions of operation, and
withdrawals from line of business are not associated with generating immediate cash flows
but frequently involve large charges to firms. Although such actions do not immediately
solve liquidity problems of the firms, they are likely to improve performance of the firms in
the long run. Thus, if private lenders are interested in long-term performance of their
borrowers, they may increase the likelihood of downsizing actions, whether the actions
generate immediate cash flow or not, as long as they increase the firm’s long-term
performance. Therefore, under the monitoring hypothesis, we predict positive effects of
close lending relationships on both types of downsizing actions.

Second, we examine the stock price reactions around restructuring announcements to
investigate the effects of close lending relationships on the announcement returns. The
monitoring hypothesis expects that private lenders influence firms to undertake
value-increasing restructuring actions responding to performance declines. In particular,
when firms maintain close lending relationships. their lenders may have more incentives
and ability to closely monitor their actions to ensure the right decisions. Thus, we predict a
positive relation between announcement returns of restructuring and lending relationship
measures under the monitoring hypothesis.

Third. we examine whether the actual amount of borrowing from banks or institutional
lenders decreaces after downsizing actions. The previous studies such as Petersen and
Rajan (1994) and. Hoshi, Kashyap, and Scharfstein (1990, 1991) present evidence that
firms have more availability of financing with close ties to their creditors. Consistent with
these studies, under the monitoring hypothesis the restructuring actions by firms with close

lending relationships are more focused on their performance improvement than loan

payment since they have credit availability with favorable conditions from their lenders.
Therefore, the hypothesis predicts that firms maintain their loans and lending relationships
after downsizing actions when they have close ties to their lenders.

Fourth, industry-adjusted long-term performance following corporate restructuring
activities is investigated. Since the monitoring hypothesis predicts that private lenders
monitor firms to improve their long-term performance, long-term operating performance
following restructuring actions would be positively related to the lending relationship
measures. Thus. to the extent that private lenders have the ability to exert control over the
firrns’ investment decisions, we predict that improvement of long-term performance
following restructuring activities are more pronounced when firms have close relationships
with their lenders.

Finally. as another measure of long-term performance, we examine the probability that
firms become acquisition targets and taken over by other firms following performance
declines. The exit of the firms suggests that they are more financially constrained and
suffer more from liquidity problems during the shock period. Since firms are less credit
constrained and encouraged to engage in restructuring actions for their survival by their
private lenders under the monitoring hypothesis. we predict lower probability of
acquisitions by others for firms with close lending relationships during the performance

shock period.

2. 2. Conflict-0finterest hypothesis
The conflict-of—interest hypothesis posits that during performance declines lenders’

best interests are in the short-term security of their existing loans and thus they facilitate

their client firms to choose restructuring activities that help repay their loans, even when
liquidating assets is costly to the firms. For instance, if firms bankruptcy risk increases
during performance declines, lenders may refuse to renew their existing loans, influencing
these firms to sell marketable assets or force clients to sell their profitable assets to generate
cash flows using their bargaining power. In this context, a close firm-creditor relationship
causes firms to make suboptimal decisions because of conflicts of interest between the
borrowing firms and their lenders.

This hypothesis is closely related to the theoretical work by Rajan (1992), who
develops a model where an entrepreneur always has the incentive to continue her project
and informed banks are able to control its client firms’ investment decisions using their
bargaining power over the firm. Here, banks often demand repayment of their existing
loans even when the firms have profitable projects in order to share the surplus and induce
the entrepreneur to exert lower effort than optimal, decreasing the firm’s performance.
Diamond (1993) also presents a model where lenders force borrowing firms that
experience liquidity problems to choose inefficient liquidation. Since lenders ignore
projects that return only to borrowers, profitable assets are liquidated in his framework.
These theories imply that suboptimal restructuring decisions can be caused by lenders’
self-interests and excessive bargaining power. Consistently, Houston and James (1994)
find that firms with more growth options are less likely to depend on barfl< loans when they
have an exclusive lending relationship such as a single lender relation.

We provide the following testable implications for the conflict of interest hypothesis,

also summarized in Table l.

First, for the effects of lending relationships on the likelihood of corporate
restructuring, we expect a positive relation between the likelihood of cash-generating
downsizing actions and close lending relationships under the conflict-of—interest
hypothesis. Since private lenders have a strong incentive to force firms to generate cash
flows to secure their existing loan claims, the probability of cash-generating actions
increases when lenders have bargaining power over firms. However, since non-cash
generating downsizing does not generate immediate cash flows but often incurs large
restructuring charges, these actions are not in the interests of private lenders focusing on
securing their loan claims. Therefore, close firm-creditor ties do not increase the
probability of downsizing actions without generating immediate cash flows under this
hypothesis.

Second, the conflict-of—interest hypothesis suggests that at least some of the
restructuring actions are triggered by lenders’ self-interests. If firms inefficiently liquidate
profitable assets since their lenders refuse to extend or expand their credit lines, there
would be negative implications on stock returns on their downsizing announcements.
Especially when firms are more dependent on private lending rather than other financing
resources. liquidation of profitable assets is more likely to negatively impact their finances.
Thus, under the conflict-of-interest hypothesis. we predict negative effects of lending
relationship measures on the announcement returns of restructuring.

Third, if restructuring actions are conducted to repay existing loans rather than to
improve firm value. we expect to see the actual amount borrowed from private lenders

reduce following corporate restructuring. Therefore, under the conflict-of-interest

hypothesis. the changes in loan amount are negatively associated with the existence of
close lending relationships.

Fourth, corporate restructuring actions are not focused on improving long-term
performance of the borrowing firms under the conflict-of-interest hypothesis. If lenders
control their borrowers and force them to take restructuring actions favorable to lenders
rather than borrowers. we predict that the post-restructuring performance will be less
improved for firms with close lending relationships.

Finally, if firms mainly depend on their private lending relationships for funding their
business, whether they can extend the relationships become very critical for their survival
when they experience large performance declines. Since private lenders likely demand
loan payment to secure their loan claims under the conflict-of—interest hypothesis, firms
more dependent on their private lenders are more likely to be a target of acquisitions during
the shock period. Thus, we predict positive effects of lending relationships on the
probability of acquisitions by other firms under this hypothesis.

As discussed above, we employ two measures of close lending relationships: the length
of the lending relationship and the actual amount borrowed from lead lenders. In our
analysis. if firms have a longer than three year relationship with their lenders before their
performance drops, they are classified as firms with durable lending relationships“. If
firms have multiple credit facilities, we focus on the largest credit facility in terms of the

size oferedit lines. defined as a lead credit facility. Additionally, ifa lead credit facility is

 

4 Sufi (2007) documents that the average maturity of syndicated loans in Dcu/scun database is l,lO3 days.
Therefore ifa firm maintains a lending relationship with the same lender for longer than three years, it is
likely that they renewed their credit contracts at least once in the past.

a syndicated loan with multiple lenders, we focus on the lead lender of the lead credit

facility to identify the duration of their lending relationships‘.

3. Sample selection and data
3.1. Sample selection

To identify a set of firms that are initially in a good shape and subsequently experience
a large drop in performance, we first require firms to be listed in NYSE, AMEX, or
NASDAQ during 1997 to 2001 period. We exclude financial and utility firms from the

sample. We do not impose any size restrictions on sample firms since small firms are

usually more dependent on private lending relationships,6 and thus the role of creditors is
more important for these firms. Firms are included in our sample if they initially maintain
at least two consecutive years of above-industry-adjusted-median operating performance

and subsequently drop into the bottom quarter. The two-year above-median performance

restriction is imposed to prevent firms in continuous trouble from entering the sample.7
These sample selection criteria result in 245 observations. We further delete l2
observations for which lending relationship data is not available in lOKs. Thus, our final
sample consists of233 observations in which 4 firms enter the sample twice.

Operating performance is measured as the ratio of operating income before

depreciation to total assets (Compusiat item #13 divided by Compustat item #6). We

 

5 According to Sufi (2007), lead lender is the agent who monitors the firm, governs the terms of the loan,
administers the drawdown of funds. calculates interest, and enforces financial covenants.

" In Denis and Kruse (2000), their sample firms are required to be over S 100 million oftotal assets to increase
coverage in newspaper articles.

7 Denis and Kruse (2000) require one year of above-industry-adjusted median performance before declines.
We require two years to make sure that the above-industry-adj usted-median performance before the declines
is not unusual for the firms.

compute industry-adjusted operating performance by subtracting the median operating
perfomiance of the firms having the same three-digit SIC from each firm’s operating
performance. As discussed in Kang and Shivdasani (1997) and Denis and Kruse (2000), I
employ accounting measures instead of stock returns to gauge firm’s performance since the
stock price is already likely to reflect its ability to overcome the performance drop or
market’s expectation of its restructuring actions responding to the shock.

For the sample firms. detailed debt ownership and institutional holding data are
collected from firms’ financial statements and Thompson Financial Ownership Database,
respectively. In particular, we obtain detailed data on debt ownership from firms’ lOKs
using a Loan Pricing Corporation is Dealscan database as an additional information source.
We obtain data on restructuring activities from Factiva. We examine Factiva during the
period associated with a performance shock, for up to three years after the start of the shock
year (fiscal year 0). Firms’ financial and managerial ownership data are obtained from

C ompustat and Compact Disclosure. respectively.

3. 2. Summary statistics

Table 2 presents summary statistics of the sample firms for the year of performance
decline (Year 0) and the year prior to performance decline (Year —l). The firms in our
sample are relatively small, having the mean (median) total assets of $ 467 ($82) million
and the mean (median) market value equity of $985 ($135) million. In comparison, the
mean total assets in Denis and Kruse (2000) who impose the $100 million asset size
requirement is $805 million. Although both book value of assets and market value of

equity decrease during the shock year, the decrease is statistically significant only for the

median market value of equity.

The comparison between ROA in the year prior to performance declines and that in the
decline year shows that the sample firms suffer from a large drop in operating performance.
The mean (median) of ROA drops from 0.17 (0.18) in Year -1 to —0.10(-0.06) in Year 0 and
the difference is statistically significant. The drops in operating performance are also
confimied when we calculate industry-adjusted ROAs by subtracting industrial medians
from each firm’s ROAs. Although the mean (median) current ratio. which is measured as
current assets divided by current liability, also declines. the difference in the current ratio
between the shock year and the year before the shock is not statistically significant. F inally,
the mean debt ratio is 0.12 in Year -1 and 0.17 in Year 0, and the difference is significant,
suggesting that the sample firms become more dependent on debt financing during
performance declines. Overall, the results in Panel A indicate that the sample firms
experience a large drop in operating performance and a significant increase in leverage
during the year of the performance drop.

Panel B of Table 2 presents the summary statistics of the ownership structure. There
are no significant changes detected during the shock year in terms of managerial ownership,
institutional ownership, and institutional block holdings, which are aggregate ownership of
financial institutions holding a 5% or more stake in the firm. Although the mean total
institutional ownership decreases by around two percent, this decrease is statistically
insignificant.

The summary of lending relationships is reported in Panel C of Table 2. Out of 233
firms in our sample, 189 (81.1%) have at least one credit facility such as a line of credit

loan and a term loan. with their private lenders such as banks and financial institutions as of

the end of the year before the performance decline. The number (percentage) of the firms
that have credit facilities decreases to a 173 (75.6%) during the year of the performance
drop.8 We also find that only 11.6 percent of the sample firms have more than one credit
facility. The average number of credit facilities that firms hold immediately before their
performance drop is 1.17 and insignificantly increases to 1.21 as of the end of the shock
yeah

The mean (median) value of the total line amount of credit facilities is as much as
21.5% (18.4%) of the firm’s total assets before performance decline and it significantly
increases to 25.7% (21.4%) in the year of performance decline. The amount of the lead
credit facility. defined as the largest credit facility of each firm, is very close to the total
amount of the credit facilities of each firm, showing concentrated lending relationships of
the sample firms. The lead credit facility accounts for more than 96% of total amount of
the credit facilities in both the shock year and the year before the shock. In addition, we
find a substantial difference between the total amount of credit facilities and the actual
borrowing amount drawn from the credit facilities. The sample firms use about 30% of
their total credit facility lines before experiencing their performance drop and the actual
amount borrowed increases to about 38% of their total credit line as of the end of the shock
year. We also find that borrowing from firms’ credit facilities (lead credit facilities) is an
important financing source for the sample firms, accounting for 52% (50%) of their total
debt before the performance decline and 60% (57%) of their total debt in the year of

performance decline.

 

3 We exclude 5 firms that are acquired during the shock year when we calculate the percentage ofthe firms
that have credit facilities in the shock year.

4. Empirical results
4.1. Restructuring activities

Prior studies find that firms experiencing performance declines are likely to respond by
implementing value-increasing restructuring actions such as asset contraction actions and
employment policy changes. and document improved post-restructuring performance
(Denis and Kruse, 2000; Kang and Shivdasani, 1997; Ofek, 1993). In this section, to
investigate the restructuring actions in response to performance declines, we present the
type and the frequency of the particular restructuring actions conducted by the sample
firms. We find that 50 firms are acquired within three years following the performance
shocks. For these firms, the information on restructuring actions is collected up to the point
that they are acquired.

Table 3 shows that 58% of the sample firms undertake at least one type of downsizing
actions in the year of the performance decline and the two years following the decline. In
the shock year, 33% of the firms undertake downsizing actions and the frequency of the
firms downsizing gradually decreases over the subsequent two years. These findings are
generally consistent with those in Denis and Kruse (2000) who report the highest
frequency of restructuring actions in the years immediately following performance
declines. The proportions of the firms that downsize are also similar to those in the
previous studies. For instance, in Denis and Kruse (2000), 53% (69%) of their sample
firms undertake at least a type of downsizing actions within two (four) years following
performance decline.

Following previous studies, we classify downsizing actions into asset contraction and

employment change actions. Asset contraction actions include asset sales, operation

suspensions. withdrawals from line of business, and spin-offs of units. Employment
change actions include layoffs, pay cuts, and early retirement incentives. Between these
two types, asset contractions occur more frequently, with 38% of the firms contracting
their assets. Specifically, out of 233 firms 75 announce asset sales. 31 suspend a part of
their operations or withdraw from a line of their business, and 5 undertake spin-offs of their
units in response to their poor performance. The percentage of the firms conducting asset
restructuring is slightly lower than those of previous papers. For example, Denis and
Kruse (2000) report that 44% of firms restructure their assets within two years of
performance decline, while Kang and Shivdasani (1997) document that 49% of U .8. firms
undertake asset contraction actions in the year of and after performance declines.

We also find that changes in employment are a typical response to performance
declines. The change is made mostly by layoffs, with 72 (31%) firms engaging in layoffs
of their employees. The proportion is generally similar to those in previous work. For two
years from the start of the shock year, 20% of the sample firms perform layoffs in Denis
and Kruse (2000) and 32 % of firms undertake employment change actions in Kang and
Shivdasani (1997).

In order to put the frequency of downsizing actions in a different perspective, we divide
downsizing actions into two different categories: cash-generating and non-cash-generating
downsizing actions. The cash-generating downsizing actions are defined as downsizing
actions that generate immediate cash flows such as asset sales, while non-cash-generating
actions include those that do not generate immediate cash flows such as layoffs and plant
closures. most of which incur restructuring charges. Ofek (1993) argues that

non-cash-generating downsizing actions are likely to focus on improving firm’s long-term

performance, while some of the cash-generating actions are conducted just to relax firms’
liquidity constraints. We find that 32% (41%) of the firms engage in cash
(non-cash)-generating downsizing actions in the year of performance decline and the
subsequent two years.

We also collect data on expansionary actions conducted during performance declines.
These expansionary actions include full or partial acquisitions, joint ventures, new plant
construction, etc. As long as the investment opportunities are economically profitable,
expansionary actions can be an optimal choice to overcome performance declines. The
results show that 61% of the firms expand their operations during their performance
decline. The percentage of firms undertaking expansionary actions is high during the first
two years and decreases in the third year, suggesting that at least some of the firms choose
expansionary actions as a response to their performance shock. Full or partial acquisitions
are the most frequent type of expansionary action firms undertake, with 53% of the sample
firms fully or partially acquiring other firms. Similarly, Kang and Shivdasani (1997) show
that 53% of sample firms expand their business in the year of and the year after
performance decline.

In summary, the findings from the restructuring actions confirm those documented in
prior studies. Firms respond to their performance decline frequently by undertaking
corporate restructuring. We also find that the types of restructuring vary across firms, such
as downsizing actions including cash-generating and non-cash-generating actions and

expansionary actions.

4. 2. Determinants of restructuring decisions

This section analyzes how the lending relationship variables are related to the
likelihood of restructuring actions reported in Table 3. We use logit regression models
where the dependent variable equals one if a particular restructuring action occurs in the
year of the performance decline and the following two years, and zero otherwise. All
explanatory variables are measured at the fiscal year-end that immediately precedes a
performance decline.

In the regressions, we include control variables that might affect the probability of
restructuring actions. F irst. we include firm size (logarithm of the book value of total
assets) since large firms have more resources such as plants and employees and thus more
likely downsize than small firms. Second, we control for restructuring incentives of
managers and institutional shareholders by including managerial and block institutional
ownership. Block institutional ownership is defined as holdings larger than 5 percent.
Third. we include firm’s debt ratio since Ofek (1993) shows that leverage functions as a
control mechanism to facilitate restructuring actions during performance declines. Fourth,
the proportion of tangible assets in total assets is included since a higher tangible asset base
indicates that firms have a larger base to contract from, implying a higher probability of
downsizing. Fifth, we control for the ratio of operating income before depreciation to
assets (ROA) as a measure of firms prior performance. Sixth, because the degree of the
performance shock varies across the sample firms, we include the magnitude of the
perfomiance shock, which is calculated by subtracting the ROA in the shock year from that

in the year prior to the shock. Finally, we control for industrial performance change by

IJ
IJ

including the median change in ROA between the shock year and the year before the shock
year for the industry.

We use two variables as our key lending relationship variables: a dummy variable
which equals one if the firm maintains a lending relationship with its current lender for
longer than three years before the shock year, and zero otherwise, and the actual amount
borrowed from the lead credit facility. Based on the discussion in the previous section, we
expect close lending relationships to increase the probability of downsizing actions in
response to a drop in operating performance under both hypotheses.

The results are reported in Table 4. In column (1), we examine the likelihood of
downsizing actions including asset contraction actions and employment change actions.
The results show that the dummy for durable lending relationship is positive, but
insignificant, and the lead facility borrowing is positive and significant at the 1 percent
level. Therefore, when firms borrow more from their lead credit facility, they are more
likely to respond to their performance declines with downsizing actions.

To see whether the credit facility loan effects are different depending on the duration of
the lending relationships, the dummy variable for long-term relationships and borrowing
from the lead credit facility variable are interacted in column (2). The interaction term
turns out to be significant at the 5% level and positive, suggesting that the loans form
durable lending relationships increase the likelihood of downsizing actions in response to

performance declines."

 

9 Regarding the interpretation of interaction terms, Ai and Norton (2003) point out that the interaction effect
in nonlinear models cannot be evaluated by looking at the coefficients on interaction terms. We examine the
interaction effects of our logit models following their suggestions, and find that all the interaction effects in
Table 4 are consistent with our interpretation. For example, the interaction effects in columns (2), (4), and (6)
are statistically significant with the same signs at the 5% or 10% level for most observations. See Al and
Norton (2003) for further discussion regarding interaction effects in nonlinear regression models.

23

In columns (3) to (6). downsizing actions are divided into cash-generating and
non-cash-generating downsizing actions. In columns (3) and (4). the dependent variable is
set to one if firms engage in cash-generating downsizing actions, and zero otherwise. The
results in column (3) show that the amount borrowed from the lead credit facility is
positively related to the likelihood that firms undertake cash-generating downsizing
actions. The coefficient on the amount borrowed from the lead credit facility is significant
with a p-value of 0.03. In column (4), the interaction term between the amount borrowed
from the lead credit facility and a dummy for long—term lending relationship is positive and
significant at the 5% level. Therefore, the probability of cash-generating downsizing
actions increases when firms borrow more from their lead lenders that maintain a
long-term relationship.

The probability that a firm engages in downsizing actions without cash generation is
investigated in columns (5) and ( 6). The results from these columns are particularly critical
since the predictions on the effects of close lending ties on non-cash-generating
downsizing actions are different between the monitoring and the conflict-of—interest
hypotheses. In column (5), neither the long-term lending relation dummy nor the lead
credit facility loan variable is statistically significant. When the two variables, however,
are interacted in column (6), the interaction effect is positively significant at the 10% level.
Thus, the firms with strong ties to long-term relationship lenders are more likely to
undertake non-cash—generating downsizing actions. supporting the monitoring hypothesis
rather than the conflict-of—interest hypothesis.

Columns (7) and (8) present logit regression estimates for the probability of

undertaking expansionary actions. The results show that firms holding a long-term lending

relationship are less likely to expand their operations during the shock period. The amount
borrowed from the lead credit facility variable and its interaction term with the long-term
lending relationship dummy do not have significant effects on the probability of expansion.
In unreported tests where the dependent variable is set to one if firms acquire other firms
and zero otherwise, we find similar results. These results are consistent with Kang and
Shivdasani (1997) who find that Japanese firms with larger ownership of their main banks
and block holders are less likely to engage in acquisitions of other firms. In addition.
Admati and Pfleiderer (1994) suggest monitoring roles of inside investors in controlling
overinvestment incentives of the management. Therefore, to the extent that some of the
expansionary actions of the sample firms are not optimal choices and they are determined
by overinvestment incentives, we interpret these results as additional evidence that
long-term relationship lenders play a disciplinary role in mitigating the overinvestment
problems.

Among control variables, managerial ownership is negatively associated with the
probability of downsizing actions. implying the incentive of managers to continue existing
projects. As expected, firm size increases the probability of downsizing. Interestingly,
debt ratio is negatively related to downsizing actions, but only for non-cash—generating
actions. One possible explanation is that non-cash-generating downsizing actions are
frequently involved with large charges to earnings and cash outflows, so firms with a large
burden of leverage have difficulties in taking such actions. In addition, firms with better
prior performance are more likely to take expansionary actions, while the larger magnitude

of performance shock is positively associated with the probability of downsizing actions.

I J
'JI

In summary. the logit regression results favor the monitoring hypothesis in that firms
with close ties to their long-term lead lenders are more likely to engage in not only
cash-generating downsizing actions but also non-cash-generating ones. The results also
provide evidence that long-term relationship lenders discourage expansionary actions of

firms regardless of their loan amounts, fiirther supporting the monitoring hypothesis.

4.3 Valuation effects ofrestructuring announcements

In this section we investigate the valuation effects of restructuring announcements
during performance declines and the factors deriving the announcement returns. A
standard event-study approach is employed to calculate cumulative abnormal returns
around restructuring announcements. First. we identify initial public announcement dates
of restructuring actions from Factiva. The observations are excluded if there is a
confounding announcement within five days before or after the announcement date. Next,
daily abnormal returns are computed using a market model with a 200 trading day
estimation period beginning 220 days before and ending 21 days before the
announcements. Finally. cumulative abnormal returns (C ARs) are computed by summing
up the abnormal returns using different windows reported in Table 5. The t-statistics and
the Wilcoxen sign—rank z-statistics are reported to test whether the mean CAR is equal to
zero and the C ARs are symmetrically distributed around zero, respectively.

The results in Table 5 show that the announcement stock returns for downsizing actions
are significantly positive only for CAR (-2, 2) in terms of the mean. However, when the
downsizing actions are divided into cash- and non-cash- generating actions. the results for

the two subsamples show a sharp difference. The announcement retums for

cash-generating downsizing actions are significantly positive for all three windows, while
those without cash generation are significantly negative for CAR (-1, 1) and negative but
insignificant for CAR (-1 . 0) and CAR (-2. 2). The difference between the two subsamples
is statistically significant for all the windows. The results are generally consistent with the
findings in Denis and Kruse (2000) who show that the announcement effects of asset sales
are significantly positive and those of cost cutting or layoffs are insignificantly negative.
One possible explanation for the negative returns for non-cash-generating actions is that
the announcement of these actions is a signal of financial difficulties or liquidity problems
of announcing firms. Often restructuring announcements are accompanied by the
announcements on the firms’ recent earnings, which are usually negative news for the firm.
Although these announcements are excluded in our analysis due to the confounding
effects, restructuring announcements can still be interpreted as a signal that the firm’s
bankruptcy risk increases, especially if the information regarding the firm’s performance
decline is not already publicly known and the announced restructuring actions do not
generate immediate cash flows to mitigate liquidity concerns.

Since our main interest is in the relationship between the restructuring announcement
retums and the lending relationship measures. we present multivariate regression estimates
using the CAR (-1, 1) as the dependent variables in Table 6. In addition to the control
variables used in Table 4, we add a dummy variable, which equals one for the first
restructuring announcement of the firm during the sample period, and zero otherwise. We
include this variable because the initial announcement effects are expected to be stronger if

the firm has a subsequent corporate restructuring program.

In column (I), we show that the amount borrowed from the lead credit facility is
positively associated with the announcement returns of downsizing actions. In column (2).
the results show that the downsizing announcement returns for firms that maintain lending
relationships longer than three years are 5.5% greater that those for firms that maintain
shorter lending relationships. The actual amount borrowed from the lead lenders also
increases the announcement returns, but the coefficient on an interaction term between the
amount borrowed from the lead lenders and a dummy variable for long-term relationships
is not significant.

In columns (3) through (6). we divide downsizing actions depending on whether they
immediately generate cash flows or not. The results in column (3) find that the stock
returns of cash-generating downsizing actions are positively related to the loans drawn
from the lead credit facility before the actions. However, the insignificant effects of the
long-term lending relationship dummy and the interaction between this dummy and the
lead credit facility loans in column (4) suggest that the presence of long-term lenders does
not make a significant difference in announcement returns of cash-generating downsizing
actions.

When downsizing actions are not associated with the generation of immediate cash
flows, a dummy variable for long-term lending relationship is positively and significantly
related to the stock retums in column (5). Column (6) shows that the announcement
returns increase by 11% with the existence ofa long-term lending relationship with a lead
lender. The size of lead credit facility loans has no significant effect on the announcement

returns.

Overall, the results from the multiple regressions provide evidence of positive relations
between downsizing announcement returns and lending relationship measures, supporting
the monitoring hypothesis. Larger loans may provide lead lenders with stronger incentives
to monitor the firm’s decisions particularly when they sell their assets, some of which serve
as their collateral. In addition, we find that the stock market applauds non-cash-generating
downsizing actions especially when firms have long-term relationship lenders. who are
more likely to be interested in the long-term performance of their borrowers.

Among control variables, managerial ownership is positively related to stock retums,
especially for cash-generating downsizing actions. Thus, the downsizing actions
conducted by firms with larger managerial ownership are valued more favorably by the
market. We also find that total leverage effects are sharply different from the effects of
facility loans. The total debt ratios are significantly and negatively associated with
downsizing announcement returns, especially those of the cash-generating downsizing

actions.

4.4 Loan changes following restructuring actions

This section examines how the changes in loans from lead credit facilities following
downsizing actions are related to the lending relationship measures. As discussed in the
previous section, the monitoring hypothesis predicts that firms maintain their borrowing
following downsizing actions when they are closely tied to their lenders. In contrast, the
conflict-of—interest hypothesis predicts a reduction of loans after downsizing actions for the
firms with close lending relations. The loan changes are computed for the firms

downsizing within two years after the start of the performance decline by subtracting the

ratio of the actual loan amount to assets in the year prior to downsizing actions (Year -1 )
from that in the year after downsizing actions (Year 1). If firms undertake multiple
downsizing actions, we choose the first downsizing action of each firm. We then run
multiple regression models using the loan changes as dependent variables and the same
variables as those in the previous regressions as explanatory variables. The results are
presented in Table 7.

The estimates in column (I) show that there is a negative relation between the loans
from the lead credit facility and the loan changes following downsizing actions. Therefore,
firms with larger loans from their lead credit facilities are more likely to repay their loans
from the credit facility after their downsizing actions. However, we also show that the
interaction term between the long-term relationship dummy and the lead credit facility
loans is significantly positive. In terms of magnitudes, the coefficient on the interaction
term is slightly larger than that on the loan amount before downsizing variable, indicating
that firms with long-term relationship lenders maintain their loans even following
downsizing actions. These results reduce the possibility that our results in the previous
sections are derived by the lenders’ pressure to force the borrowing firms to repay their

existing loans. further supporting the monitoring hypothesis.

4. 5 Ind:tstry-adjusted long-term performance following restructuring actions

In this section, we examine the three-year industry-adjusted operating performance
following downsizing actions. If private lenders monitor firms with a long-term
perspective and facilitate firms to engage in restructuring actions focused on improvement

in long-term performance. we expect a positive relation between long-term performance

and lending relationship measures. To explore this issue. we compute the changes in
industry-adjusted ratio of operating income before depreciation to assets for three years
from the year that downsizing actions occur. The changes are measured for those firms
that downsize within two years from the start of the decline year. We run multiple
regressions where dependent variable is the change in operating performance.

The results in column (1) of table 8 show no significant effects of lending relationship
measures on the long-term performance following downsizing actions. However, when we
include the interaction term between long-term lending relationship dummy and loans
from lead credit facility. the interaction variable is significantly positive at the 1% level.
Thus, post-restructuring perfomiance improves when firms borrow more from their lead
long-term lenders, again supporting the monitoring hypothesis.

Because some firms are acquired following performance declines, our long-term
performance results might be affected by the disappearance of these firms.‘0 Moreover,
the acquisitions suggest that the firms experience difficulties in financing possibly because
their lenders decide to break the lending relationships with the firms. To address this issue,
we conduct logit regression analyses where the dependent variable equals one if firms are
acquired within the three years from the start of the performance shock year, and zero
otherwise.

The results in Table 9 help alleviate this concern. In column (1), the probability of
acquisitions is negatively related to the existence of long-term lending relationships. Thus,
if firms have a relationship with their lead lender for longer than three years, they are less

likely to be acquired following performance declines. In column (2), the long-term lending

 

'0 Out of 233 sample firms, 50 firms are acquired within three years form the start of the shock year. No
firms go bankrupt during this same period.

relation dummy is again significantly negative, while its interaction term with loans from
the lead credit facility is not significant. These results suggest that durable lending
relationships help firms survive during their performance shock.

In summary, the results from the analysis on post-restructuring performance again
support the monitoring hypothesis rather than the conflict-of—interest hypothesis. When
firms have a durable relationship with their lead lenders, not only the post-restructuring
performance improves as loans from the credit facility increase, but the probability of

being taken over by other fimis is significantly lower.

5. Summary and conclusion

We investigate the influence of lending relationships on corporate restructuring using
233 firms that had large performance declines between 1997 and 2001. We consider two
competing hypotheses regarding the effects of lending relationships. According to the
monitoring hypothesis, private lenders such as banks and financial institutions help their
borrowing firms survive and improve long-run performance by facilitating
value-increasing restructuring actions. In contrast, the conflict-of—interest hypothesis
argues that private lenders focus on the short-term security of their existing loans and thus
force their borrowers to undertake restructuring actions to ensure repayment of current
loans. The assumption in both hypotheses is that private lenders have incentives and
ability to control investment decisions of their borrowing firms.

We focus on two dimensions of lending relations to identify close lending
relationships. First, we consider the duration of the private relationships since long-term

lenders are expected to have more soft information about their borrowers and more

incentives to monitor the firms because the long-run relation may be more valuable to
lenders. Second, we employ the firm’s actual amount borrowed from its largest credit
facility as our second measure. If firms are more dependent on their private lenders for
financing, they are more likely to be under the influence of their lenders.

Our empirical findings are consistent with the monitoring hypothesis. We first find
that the likelihood of downsizing actions increase when firms have more borrowings from
their long-term lenders. Firms with close lending relationships in terms of our two
measures are more likely to downsize, whether the actions immediately generate cash
flows or not. Since non-cash-generating downsizing actions are often involved with large
charges, the close lending relationships increasing the probability of such actions support
the monitoring hypothesis. In addition, we show that the likelihood of expansionary
actions decreases with the presence of long-term lending relations.

Our results on announcement returns of downsizing also support the monitoring
hypothesis. We find that larger loans from lead credit facilities are positively related to the
stock price reactions to downsizing announcements. especially for
cash-generating-downsizing actions. The stock returns are also greater when firms with
long-term relation lenders announce non-cash-generating downsizing actions. The
positive association between lending relationship measures and the announcement returns
is consistent with the monitoring hypothesis.

We show that firms also maintain their loans from their lead credit facilities following
downsizing actions when they have loans from durable lending relationships, while other

firms reduce their amount borrowed. The results are against the view that firms with close

b.)
b)

ties to their lenders downsize more because of the lenders’ pressure on loan repayment,
again favoring the monitoring hypothesis.

In addition, we find that firms have better post-restructuring performance when they
have more borrowing from their long-term relationship lenders, indicating the monitoring
roles of private lenders with long-run perspective. Moreover, firms maintaining durable
lending ties are less likely to be acquired during their performance decline, suggesting that
they are less financially constrained in the period.

Overall our findings suggest that private lenders have the incentives and ability to
influence firm’s investment decisions particularly when firms have financial difficulties.
Our results are consistent with the view that close lending relationships create value by
monitoring the borrowers to make right decisions in their investments. Additional future
evidence regarding the influence of lenders on firm’s investment may be able to broaden

our knowledge about the roles of private lenders as active monitors.

34

APPENDIX

Table 1
Predicted effects of close lending relationships

These predictions are provided to distinguish between the monitoring hypothesis and the
conflict-of—interest hypothesis. The monitoring hypothesis predicts that when firms
experience performance declines, lenders that maintain a close relationship with their
client firms have monitoring incentives to facilitate firms’ value-increasing restructuring
to help them survive and improve long-term performance. In contrast, the conflict-of-
interest hypothesis argues that the performance drops can create conflicts of interest
between the client firms and their private lenders and thatlenders have incentives to
induce borrowing firms to engage in restructuring actions that provide mainly short-term
proceeds or short-term safety to secure repayment of their existing loans.

 

 

Testable implications Monitoring hypothesis Conflict of interests hypothesis
The likelihood of restructuring Positive on both cash-generating Positive only on cash-generating
downsizing and non-cash- downsizing actions

generating downsizing actions

Valuation effects of restructuring Positive Negative
announcements
Changes in borrowing following Not negative Negative

restructuring

Long-term performance Positive Negative
following restructuring

Probability of being acquired Negative Positive

 

Table 2
Descriptive statistics of firm characteristics

The sample consists of 233 non-financial firms listed on the NYSE, NASDAQ, or AMEX
that experience a large one year drop in their operating performance (ROA) following at
least two years of above-industry-adjusted-median performance during the 1997-2001
period. Firms are included in our sample if they initially maintain at least two
consecutive years of above-industry-adjusted-median operating performance and
subsequently drop into the bottom quartile. Year —1 denotes the year prior to the decline
in performance and Year 0 denotes the year of performance decline. The difference in
each variable between Year —1 and Year 0 is tested using a t-test and a Wilcoxon z-test,
and statistics for the tests are reported in the last two columns.

 

 

 

 

Year -—1 Year 0 Test of difference
Mean Median Mean Median t-test Wllcoxen
z-test

Panel A: Financial cliart'tcteristics
Assets (millions) 466.54 81.94 409.24 77.18 -0.22 -0.45
Milk.“ va'ue Ofequ'ly 985.04 l35.06 456.17 73.73 -1.10 -3.78
(millions)
Raw ROA 0.171 0.178 -0.099 -0.060 -20.49 -16.74
Industry-adjusted ROA 0.132 0.123 -0.109 -0.084 -26.17 -18.39
Current ratio 4.045 3.068 3.689 2.512 --1 .08 -2.40
Total debt / assets 0.123 0.039 0.165 0.062 2.19 l 36

Panel B: Ownership structure c/iaracteristics

Managerial ownership 0.252 0.194 0.245 0.199 -0.42 0.27
Institutional ownership 0.325 0.299 0.307 0.272 -0.86 -1.07
'"St‘m‘mnal “0°" ownersmp 0.1 10 0.090 0. l 22 0.078 1.07 -0.67

larger than 5 percent

Panel C .' Lending relationship characteristics

Number (percentage) of firms , ,,
with credit facilities '89 (8 ' 49°) '7’ (75-69“
Number (percentage) of firms 7 . ,
with multiple credit facilities ~7 (I "6%) ’3 (”-09")
Number of credit facilities 1.169 1.000 1.214 1.000 0.90 1.06
/Total amount oferedit faCllltles 0.215 0.184 0.257 0.214 2.08 2'17
assets
. . . , ,
Amour" 0f 'ead cred" facm” ' 0.208 0. l 70 0.249 0.199 2.06 2.23
assets
To‘a' bormwmg from cred" 0.064 0.00 0.099 0.036 2.70 2.78

facilities / assets

BOT'F’WW-l from 'ead cred" 0.061 0.00 0.094 0.031 2.56 2.68
faClllty ./ assets

 

Table 3
Frequency of restructuring actions during performance declines

The sample consists of 233 non-financial firms listed on the NYSE, NASDAQ, or
AMEX that experience a large one year drop in their operating performance (ROA)
following at least two years of above-industry-adjusted-median performance during the
1997-2001 period. Finns are included in our sample if they initially maintain at least two
consecutive years of above-industry-adjusted-median operating performance and
subsequently drop into the bottom quartile. The information on restructuring activities by
sample firms is collected from newspaper articles using the FACTIVA system. We
examine news articles on FA C T] VA for up to three years after the start of the shock year
(fiscal year 0). For each restructuring event, the first number in each cell represents the
actual number of firms announcing a particular restructuring action and the number in
parentheses represents the percentage of firms that announce the restructuring action.

 

 

Year 0 Year 1 Year 2 Years 0 to 2
(233 fimis) (228 firms) (205 firms)
._. __ . , 78 63 51 136
All clounsz-tng atlIOHS (A + B) (33.4) (27.6) (24.9) (58.4)
A ")t ")ntr 'tion 'tims (4) 47 39 38 89
5‘“ “ a" "‘ ‘ ‘ " (20.2) (17.1) (18.5) (38.2)
Asset sales 33 3| 32 75
(14.2) (13.6) (15.6) (32.2)
Operation suspensions or 17 10 6 31
withdrawals from line ofbusiness (7.3) (4.4) (2.9) (13.3
. _- . . 5 0 1 5
Spin-offs of units (2.1) (0.0) (0.5) (2")
,_ ‘ 41 31 14 72
Emploimtnt changes (8) (17.6) (13.6) (6.8) (30.9)
Lavoffs 41 31 14 72
“ (17.6) (13.6) (6.8) (30.9)
Pay cuts or early retirement I 0 0 l
incentives (0.4) (0.0) (0.0) (0.4)
Downsizing actions by cash
generation
. g ‘ . 34 31 32 74
With cash generation (14.6) (13.6) (15.6) (31.8)
.. . 56 39 20 95
Without cash generation (240) (17.)) (98) (40.8)
Er) ns'iontr' ' 'tims 8| 7| 5| I43
7" ‘ ‘ l a" ‘ ‘ (34.8) (31.1) (24.9) (61.4)
Full or artial ac uisitions 68 60 44 '24
p ‘1 (29.2) (26.3) (2l.5) (53.2)
Other ex ansiom ’actions 2] '8 H 44
‘p‘ ”l (9.0) (7.9) (5.4) (18.9)

 

Table 4
Logit estimates of the likelihood of restructuring actions during performance
declines

The sample consists of 233 non-financial firms listed on the NYSE, NASDAQ, or
AMEX that experience a large one year drop in their operating performance (ROA)
following at least two years of above-industry-adjusted-median performance during the
1997-2001 period. Firms are included in our sample if they initially maintain at least two
consecutive years of above-industry-adjusted-median operating performance and
subsequently drop into the bottom quartile. The information on restructuring activities by
sample firms is collected from newspaper articles using the FACTIVA system. We
examine news articles on FA C T I VA for up to three years after the start of the shock year
(fiscal year 0). The dependent variable equals one if firms undertake the particular
restructuring actions described in Table 2. The dummy for lead lending relationship
longer than 3 years takes a value of one if firms maintain lending relationships with their
lead lenders for longer than three years before performance declines. Borrowing from
lead credit facility is the actual amount borrowed from the largest credit facility of the
firms immediately before performance declines. All accounting variables are measured
at the end of the fiscal year immediately before the performance declines of the sample
firms. Numbers in parentheses are p-values. The symbols *, **, and *** denote
significance at the 10%, 5%, and 1% levels, respectively.

39

 

 

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41

Table 6
OLS regressions of cumulative abnormal returns [CAR (-1, 1)] surrounding the
announcements of restructuring activities

The sample consists of 233 non-financial firms listed on the NYSE, NASDAQ, or AMEX
that experience a large one year drop in their operating performance (ROA) following at
least two years of above-industry-adjusted-median performance during the 1997-2001
period. Firms are included in our sample if they initially maintain at least two
consecutive years of above-industry-adjusted-median operating performance and
subsequently drop into the bottom quartile. Initial public announcement dates of
restructuring are identified from a Factiva search. If there is a confounding
announcement within five days before or after the announcement date, the observations
are deleted. Daily abnormal returns are computed using a market model with a 200
trading day estimation period beginning 220 days before and ending 21 days before the
restructuring announcement. Cumulative abnormal returns [CAR (-1, 1)] are computed
by summing up the abnormal returns using a three-day window. The dummy for lead
lending relationship longer than 3 years takes a value of one if firms maintain lending
relationships with their lead lenders for longer than three years before performance
declines. Borrowing from lead credit facility is the actual amount borrowed from the
largest credit facility immediately before performance decline. The dummy variable for
the first downsizing action is set to one if the downsizing action is the first downsizing
action for the firm, and zero otherwise. All the independent variables are measured as of
the end of the fiscal year immediately before the downsizing announcements. Numbers
in parentheses are p—values. The symbols *, **, and *** denote significance at the 10%,
5%, and 1% levels, respectively.

Table 6

 

Downsizing actions
without cash
generation

Downsizing actions

Downsizing actions . .
Wlth cash generation

 

 

(l) (2) (3) (4) (5) (6)
[Meme t -0.176“ -0.163” 0415‘” -O.4l 1’“ -0013 -0009
p (0.02) (0.04) (0.00) (0.00) (0.90) (0.93)
Dummy for lead lending 0.030 0.055‘ -0037 -0030 0.102’” 0.107‘”
relationship longer than 3 years (A) (0.26) (0.08) (0.35) (0.58) (0.01) (0.01)
Borrowing from lead credit facility / 0.301‘ 0.500“ 0.530’” 0.554" -0.053 0.127
assets (B) (0.06) (0.02) (0.01) (0.02) (0.87) (0.85)
-0370 -0.063 -0.228
(A) (B) (0.14) (0.84) (0.76)
Mam , m] ownershi 0.444‘” 0.435“ 0.846'“ 0.845‘" 0.068 0.060
’5“ p (0.00) (0.00) (0.00) (0.00) (0.57) (0.62)
Institutional block ownershi -0.026 -0.046 0.140 0.137 -0. 144 -0.152
p (0.80) (0.66) (0.40) (0.42) (0.24) (0.23)
LOO (assets) 0.021 ” 0.0 I 8 0.056‘“ 0.055‘” 0.001 0.000
= ~ (0.05) (0.1 1) (0.00) (0.00) (0.93) (0.98)
Total debt / assets -0.220“ -0191" -0377‘“ -0367" -O.208 -0202
' (0.02) (0.05) (0.00) (0.01) (0.21) (0.23)
Tannibl . asms “mew -0.278 -0.302 0.433 0.416 -0.612 -0.6 12
v ° “ ’ (0.54) (0.51) (0.74) (0.75) (0.18) (0.19)
ROA —0.173‘ -0168" -0.305” -0299“ -O.l65 -O.l60
(0.06) (0.07) (0.02) (0.03) (0.22) (0.24)
Change in ROA from year -I to year -0.022 -0.023 0.079 0.077 -0.028 -0.033
0 (0.79) (0.78) (0.50) (0.51) (0.81) (0.78)
Industrial median change in ROA -0.030 -0.067 -0.240 -0.242 -0.369 -O.383
from year -I to year 0 (0.93) (0.85) (0.66) (0.66) (0.38) (0.36)
Dummy for the first downsizing -0.02l -0.025 0.023 0.02] —0.037 -0.037
action (045) (0.37) (0.56) (0.62) (0.37) (0.36)
Number of observations I69 I69 82 82 87 87
Adjusted R2 0143 0.150 0.284 0.274 0.112 0.101

 

Table 7
OLS regression of loan changes of lead credit facilities following downsizing actions

The sample consists of 233 non-financial firms listed on the NYSE, NASDAQ. or AMEX
that experience a large one year drop in their operating performance (ROA) following at
least two years of above-industry-adjusted-median performance during the 1997-2001
period. Firms are included in our sample if they initially maintain at least two
consecutive years of above-industry-adjusted-median operating performance and
subsequently drop into the bottom quartile. The dependent variables are loan changes,
which are computed for the firms downsizing within two years after the start of
performance declines by subtracting the ratio of the actual loan amount to assets in the
year prior to downsizing actions (Year -1) from that in the year after downsizing actions
(Year 1). If firms undertake multiple downsizing actions, we choose the first downsizing
actions of each firm. The dummy variable for lead lending relationship longer than 3
years takes a value of one if firms maintain lending relationships with their lead lenders
for longer than three years before performance decline. Borrowing from lead credit
facility is the actual amount borrowed from the largest credit facility immediately before
performance decline. Numbers in parentheses are p-values. The symbols *, **. and ***
denote significance at the 10%, 5%, and 1% levels, respectively.

44

Table 7

 

Changes in borrowing from lead credit facility
following downsizing actions (Year-1 to Yearl)

 

 

(I) (2)
Interce t 0.085 0.098
p (0.26) (0.17)
Dummy for lead lending relationship longer than 3 0.024 -0.013
years (A) (0.43) (0.69)
. . . . -0510“ 0723‘”
Borrowmg from lead credlt fac1|1ty /assets (B) (0.02) (0.00)
0.760‘”
(A) ”3) (0.01)
Manauerial ownershi -0'017 '0'032
v p (0.86) (0.73)
. . . 0225’ -0.221‘
Institutlonal block ow nershlp (0.10) (0.09)
, -0.007 -0.004
Log (asscIS) (0.56) (0.70)
0.081 0.039
/
Total debt , assets (0.56) (0.77)
,- 0.246 0.164
Tanglble assets / assets (05]) (0.65)
-0.045 -0.094
ROA (0.81) (0.61)
. . 0.035 0.023
Change In ROA from year -I to year 0 (0.49) (0.64)
Industrial median change in ROA from year -I to 0.057 0.1 17
year 0 (0.87) (0.73)
Number of observations 7 I 71
Adjusted R3 0.034 0.127

 

45

Table 8
OLS regressions of industry-adjusted performance following downsizing actions

The sample consists of 233 non-financial firms listed on the NYSE, NASDAQ, or AMEX
that experience a large one year drop in their operating performance (ROA) following at
least two years of above-industry-adjusted-median performance during the 1997-2001
period. Firms are included in our sample if they initially maintain at least two
consecutive years of above-industry-adjusted-median operating performance and
subsequently drop into the bottom quartile. The dependent variables are the changes in
industry-adjusted ratio of operating income before depreciation to assets for three years
from the year that the downsizing actions occur. The changes are measured for those
firms that downsize within two years from the start of the decline year. If firms
undertake multiple downsizing actions, we choose the first downsizing actions of each
firm. The dummy variable for lead lending relationship longer than 3 years takes a value
of one if firms maintain lending relationships with their lead lenders for longer than three
years before performance decline. Borrowing from lead credit facility is the actual
amount borrowed from the largest credit facility of the firms immediately before
performance declines. Numbers in parentheses are p—values. The symbols *, **, and ***
denote significance at the 10%, 5%, and 1% levels. respectively.

46

Table 8

 

Three-year post-restructuring
Industry-adjusted performance

 

 

(I) (2)

Interce t 0.074 0.1 12
P (0.49) (0.28)
Dummy for lead lending relationship longer than 3 years 0.023 -0.028
(A) (0.59) (0.54)
. . , -0.I3I -0.604

Lead fac111ty loans 7 assets (B) (0.71) (0.12)

1.207‘“

(A) (B) (0.01)
Managerial ownership ‘0'152 ‘0' 154
~ (0.29) (0.27)

. . . 0.170 0.160
Institutlonal block on nershlp (043) (0.44)
‘ 0.006 0.008

Log (assets) (0.72) (0.62)
0.075 0.025

Total debt / assets (0.74) (0.91)
. 0.079 -0.014
Fixed assets / assets (0.88) (0.98)
-0.290 -0.444
ROA (0.36) (0.15)
. , 0.271‘ 0284‘
Change 1n ROA from year -I to year 0 (0.08) (0.06)
. . . -0.748 0553
Industrial median change In ROA from year -I to year 0 (0.19) (0.32)

Number of observations 70 70

Model p-value -0.013 0.077

 

47

Table 9
Logit estimates of the likelihood of being acquired

The sample consists of 233 non-financial firms listed on the NYSE, NASDAQ, or AMEX
that experience a large one year drop in their operating performance (ROA) following at
least two years of above-industry-adjusted-median performance during the I997-2001
period. Firms are included in our sample if they initially maintain at least two
consecutive years of above-industry-adj usted-median operating performance and
subsequently drop into the bottom quartile. The dependent variable is equal to one if
firms are acquired within the three years from the start of the performance shock year,
and zero otherwise. The dummy variable for lead lending relationship longer than 3 years
takes a value of one if firms maintain lending relationships with their lead lenders for
longer than three years before performance decline. Borrowing from lead credit facility
is the actual amount borrowed from the largest credit facility immediately before
performance declines. Numbers in parentheses are p-values. The symbols *, **, and ***
denote significance at the 10%, 5%, and 1% levels, respectively.

48

Table 9

 

Likelihood of being acquired

 

 

(l) (2)
lnterce t -l.459 -1.434
p (0.14) (0.15)
Dummy for lead lending relationship longer than 3 -0.758' -I .094"
years (A) (0.07) (0.03)
. . , -0.654 -2.122
Lead fac111ty loansx assets (B) (0.80) (0.47)
5.594
(A) (B) (0.16)
Manaoerial ownershi 1008 09%
° p (0.30) (0.32)
, . . , y . 2.904' 3.013‘
Instltutlonal block ow nershlp (0.08) (0.07)
0.025 0.033
Low (3559“) (0.88) (0.85)
, g 0.935 0.797
Total debt . assets (.053) (0.60)
., 3 -I.221 -l.128
leed assets / assets (09]) (0.91)
-2.359 -2.356
ROA (0.17) (0.17)
. 0.023 -0.032
Change In ROA from year -I to year 0 (0.98) (098)
Industrial median change in ROA from year -I to year 1.630 1.570
0 (0.71) (0.73)
Number of observations I96 196
Model p-value 0.46 0.38

 

49

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