how industry rivals respond to control threats

How Industry Rivals Respond to Control Threats

Current Draft: January 2000

Carl AckermannUniversity of Notre Dame

and

Henri ServaesLondon Business School

________________________Servaes is on leave from the University of North Carolina at Chapel Hill. Parts of this work was completedwhen both authors were at the University of North Carolina at Chapel Hill. We have benefitted fromdiscussions with Jan Mahrt-Smith, Gordon Phillips and David Scharfstein, from comments received fromAnup Agrawal, Michael Bradley, David Denis, Diane Denis, John McConnell, Todd Milbourne, AnilShivdasani, René Stulz, and Marc Zenner, and during presentations at Duke University, IMD, the KatholiekeUniversiteit Leuven, and the University of Maryland. Karl Lins provided excellent research assistance. Servaes acknowledges the support of the McColl Faculty Fellowship at the University of North Carolina atChapel Hill.

How Industry Rivals Respond to Control Threats

Abstract

This paper studies how rival firms respond when a firm in the industry encounters a control threat because itsuffers from agency problems. We find that rival firms increase leverage, cut capital expenditures, and reducetheir cash balances and free cash flows. The competitors with the largest increases in debt and the highestindustry-adjusted level of investment before the control threat have the largest cuts in capital spending. Rivalfirms are also more likely to adopt takeover defenses than other firms in the economy. Rivals gain 0.56%, onaverage, when the control threat is announced, and an additional 0.62% when the targeted firm announces adefensive recapitalization. The stock price reaction is larger for rivals with little debt, high capitalexpenditures, and low insider ownership. These results are consistent with the argument that the control threatleads to a reduction in the agency costs of the rival firms in the industry.

1. Introduction

This paper studies how competitors react when there is a threat to the independence of one of the firms

in an industry. This research is motivated by Jensen’s (1986, 1993) observation that agency costs often

manifest themselves at the industry level, in particular when future growth opportunities are sparse. Under

these conditions, if the independence of one firm is threatened because of its agency problems, we expect both

the firm and its competitors to respond. Responses can come in two forms. Firms can take actions to diminish

the control threat or they can adopt antitakeover measures to insulate themselves. We investigate both types

of responses in this paper.

Our main sample consists of the rivals of 35 firms that propose leveraged recapitalizations (recaps)

subsequent to control threats. We focus on recaps because Denis and Denis (1993) document substantial

agency costs of free cash flow before the recap, followed by a reduction in undistributed cash flows, and cuts

in capital spending after the recap. This indicates that agency problems are likely to be the primary motive

for the control threat. Other firms may have been takeover targets for synergistic reasons unrelated to agency

problems, and their rivals may not respond. Studying rivals of those firms would not reveal the full impact

of the agency costs arguments. Firms that proposed a recap without a control threat are therefore not included

in the main analysis.

In support of the industry-wide agency cost arguments discussed by Jensen (1986, 1993), we find that

the rivals of recap firms also increase leverage after the control threat, although the magnitude of the increase

is smaller than for the targeted firms. The rivals also cut capital expenditures, and both cash levels and free

cash flows decline. Consistent with the notion that increased debt leads to reduced investment, we find that

the firms with the largest increases in debt experience the largest investment cuts. These results indicate that

the agency costs decline in the entire industry. The stock price reaction of the rival firms is also consistent

with the agency cost argument. Rivals gain 0.56%, on average when the control threat is announced, and an

additional 0.62% when the recap is announced in an industry. Moreover, the stock price reaction is larger for

firms with more capital expenditures and less debt relative to the other firms in the industry, and with low

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insider ownership. These are likely to be the firms that have the largest agency costs.

An alternative response to the increased threat of a takeover is to increase takeover defenses. We

provide evidence in support of this conjecture as well: rival firms are more likely to adopt takeover defenses

than other firms in the economy. Rival profitability declines after the control threat, which appears

inconsistent with the agency cost argument. However, we document that analyst forecasts are biased upward,

on average, and after taking this bias into account there is no evidence of a profitability decline.

An alternative interpretation of our findings is that the industry is simply evolving and that capital

structure and all the other financial characteristics change because of other changes in industry structure. In

other words, the relation between our findings and the control threat and subsequent recap is spurious; all the

changes would have taken place without the control threat. We provide four pieces of evidence against this

interpretation.

First, we examine whether the increase in leverage and the changes in the other financial

characteristics reflect the continuation of a trend. If the firms are responding to changes in industry conditions,

we would expect industry indebtedness to increase slowly as more firms decide to lever up, since transactions

costs prevent firms from adjusting their capital structures instantaneously. This is not the case.

Second, we examine Value Line forecasts for debt and capital expenditures for the firms in our

sample. We find no evidence that analysts expected an increase in leverage or a decrease in capital

expenditures.

Third, we examine whether the firms in our sample had an optimal level of debt before the recap was

announced. To do this, we estimate cross-sectional regression models of capital structure for all firms on

Compustat, except for the firms in our sample. We then compute the residual of this model for the firms in

our sample and find that the recap firms have a median ratio of total debt to total assets approximately four

percentage points lower than predicted. The rival firms also have a significant leverage deficit of about three

percentage points. These results imply that the firms in our sample had ‘too little debt’, on average, and they

support Jensen’s conjecture that many firms in an industry may suffer from agency problems. They are also

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consistent with the evidence provided by Berger, Ofek, and Yermack (1997) that many firms have insufficient

debt and that firms lever up after entrenchment-reducing shocks.1 Our evidence indicates that such shocks

to one firm in an industry have repercussions for the other firms as well.

Fourth, we examine the S&P Industry Surveys for the industries covered in our sample. We find

evidence in all but three cases that the industries are going through tremendous changes over the five-year

period before the control threat (excess capacity, increased import competition, deregulation, efficiency

improving technology). However, until the control threat occurs, firms in these industries do not adjust to

these changes.

One drawback of studying leveraged recaps is that the rival firms may be responding to the increase

in leverage, and not to the control threat per se. We therefore also examine the implications of two other

classes of models that have been developed that address the competitive response to leverage increasing

transactions: (i) the deep purse hypothesis; and (ii) the limited liability hypothesis. Table 1 lists the predicted

effects of all three models on the characteristics of the rival firms.

Telser’s (1963) ‘deep pockets’ (or predatory pricing) argument, which was further developed by

Brander and Lewis (1988) and Bolton and Scharfstein (1990), suggests that rival firms will increase output

to drive the highly leveraged firm out of the market. This implies that firms will use up spare capacity, and

possibly invest in new capacity. Thus, we should observe increases in capital expenditures, and temporary

decreases in profitability, free cash flow, and cash balances. In addition, the opportunity to drive a competitor

out of the market should be good news for rival firms, and the news should be better for firms that are better

able to handle a price war. These are firms with little debt, and high levels of cash and free cash flow.

Note that many of the implications of the deep purse argument are similar to those of the agency cost

argument. In addition, the deep purse argument is consistent with the observed decline in profitability.

Nevertheless, many pieces of evidence point against the deep purse argument. First, we find a stock price

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1 See also Safieddine and Titman (1999) for evidence that firms increase leverage after unsuccessful

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reaction of rival firms at the announcement of the control threat as well as at the announcement of the

recapitalization. Unless the recap is anticipated when the control threat is announced, the deep purse

hypothesis does not predict a stock price reaction. Second, capital expenditures are reduced after the control

threat, while the deep purse argument suggests an increase or no change. Third, the firms that increase

leverage the most are the ones that have the largest cuts in capital expenditures. One may argue that firms

need to borrow when they price aggressively. But if that is the case, we would not expect an inverse relation

between the change in borrowing and the change in investment. Fourth, we find a negative relation between

insider ownership and the stock price reaction at the announcement of the recap. The deep purse argument

has no explicit prediction for this relation, but, if anything we would expect a higher stock price reaction for

firms with more insider ownership. These firms are more likely to take the right actions for shareholders, and

one such action may be to engage in predatory pricing. Fifth, the ability of competitors to take advantage of

the reduced flexibility of the recap firm should be stronger in less competitive industries. We do not find

support for this conjecture. Sixth, all the predictions of predatory pricing should be stronger when the recap

firm issues more debt. We find no evidence of this. Seventh, rival firms do not respond to recaps that are not

preceded by events in the market for corporate control, which is inconsistent with the predatory pricing

arguments. Eighth, predatory pricing arguments do not predict the increase in the adoption of takeover

defenses, which we document.

The second alternative explanation, the limited liability hypothesis, has been proposed by Brander and

Lewis (1986) and Maksimovic (1988). According to this hypothesis all firms in an industry have an incentive

to increase output when one firm increases leverage. If profits are affected by a random shock that has a

positive effect on profits with good realizations, and a negative effect with bad realizations, then the leverage

increasing firm has an incentive to increase output, because this will increase the value of the option-like

component of equity. It then becomes in the best interest of the rival firms to follow this trend. Thus, capital

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takeover attempts.

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expenditures will increase,2 but since all firms produce more than they would without debt financing,

aggregate industry profits are reduced. The other financial characteristics are expected to remain unchanged.3

4 One difference between the deep purse explanation and this explanation is that in the former only the rival

firms increase output, while in the latter the recap firm also increases output. Several pieces of evidence

presented in this article and in other research are inconsistent with this hypothesis. First, both the recap firm

and its rivals decrease output, while the limited liability hypothesis predicts an increase. Second, the recap

produces a positive stock price reaction for the rival firms. The limited liability hypothesis predicts a decline.

In addition, the limited liability hypothesis does not predict the increase in the adoption of takeover defenses

or the stock price reaction to the announcement of the control threat.

Overall, our results are supportive of the agency cost arguments. They also indicate that the benefits

of the control threat accrue to the industry rivals as well as to the shareholders of the target firm.

Our paper is primarily concerned with the response of rival firms to control threats. Several other

articles such as Phillips (1995), Chevalier (1995a, 1995b) and Kovenock and Phillips (1997), have studied the

effect of increases in leverage on product market competition. Phillips (1995) reports a negative relation

between leverage and output in three of the four industries he studies and Kovenock and Phillips (1997) find

that debt leads to decreased investment in 10 industries where one of the four largest firms recapitalized in the

1979-1990 period. These findings are consistent with our finding that increases in debt lead to reduced capital

spending. As we demonstrate in this paper, however, a control threat is sufficient for the rival firms to reduce

investment. Chevalier (1995a, 1995b) analyzes pricing and entry and exit decisions in the supermarket

industry subsequent to leveraged buyouts. She finds evidence of predatory pricing: prices decrease following

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2 As in the deep purse hypothesis, firms will first use up spare capacity before they incur expenditures toexpand capacity.

3 It is possible that free cash flow and cash balances decrease as profitability decreases.

4 Showalter (1995) shows that these implications do not necessarily hold under price competition. However, our findings are not consistent with Showalter’s implications either. For the sake of brevity, wedo not discuss these implications in the paper.

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LBOs when the rival firms have little debt, and one of the rivals has a large market share in the LBO firm’s

market. She also reports that supermarkets are more likely to enter and expand into markets where LBO firms

have a large market share. These articles do not examine the changes in leverage of the rival firms, nor do

they explore the relation between these changes and the changes in the other financial characteristics. Only

Chevalier (1995a) reports the stock price reaction of the rivals, but she does not examine the cross-sectional

determinants of the abnormal returns.

The remainder of this paper is organized as follows. Section 2 describes our data collection

procedure. Section 3 describes the results, and section 4 concludes.

2. Data collection

We employ the sample of 39 proposed recapitalizations assembled by Denis and Denis (1993, 1995).

Their sample is constructed by searching the Dow Jones News Retrieval Service over the 1984-1988 period

for any form of the word ‘recapitalize’. Most of our analysis is concerned with the 35 firms for which the

recap occurs subsequent to a control threat. MBO proposals, takeover attempts, purchases of blocks of shares,

and announcements of proxy fights are all classified as control threats. We also gather data for the four

transactions not preceded by control threats because some of our tests will focus on the difference between

these transactions and the main sample. We employ a sample of firms that propose recaps following a control

threat because Denis and Denis (1993) have documented agency costs of free cash flow for these companies.5

As mentioned previously, other firms may have been takeover targets for synergistic reasons unrelated to

agency problems. We do not expect their rivals to respond.

We do not rely on CRSP or Compustat to construct a sample of industry rivals. As indicated by Guenther

and Rosman (1994) and Kahle and Walkling (1996) the CRSP SIC codes are not very representative of the

industries in which the firms actually operate, which leads to less precise inferences. Compustat SIC codes

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5 See also Hendershott (1996), who argues that agency costs are more pronounced in firms thatrecapitalize in response to control threats.

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appear to be more reliable. Unfortunately, however, firms change industries during their lives and Compustat

does not keep a record of a firm's SIC code history. As such, industry adjustments can change when more

recent versions of the Compustat database are employed. Obviously, this problem becomes more severe as

more time elapses between the events being examined and the database version employed. We therefore rely

on the Earnings Supplement of the Standard and Poor’s Industry Surveys. This guide is published monthly

and categorizes firms into industries using criteria similar to the ones used by Compustat.6 We believe that

our selection method leads to a sample of rivals of the same quality as Compustat, but one which does not

suffer from problems due to changes in industry classification over time.

We search the Earnings Supplement in the month before the recap is announced to identify industry

rivals. Four recap firms are not listed in the guide; two of these four are listed in Value Line and we employ

the Value Line industry classification for these firms. The remaining two firms are discarded from the

sample.7 The number of rivals for the remaining 33 firms ranges from 1 (for INCO, Ltd) to 46 (for Union

Carbide and FMC Corp). Recap firms have 12.1 rivals, on average, with a median of 9.8

Balance sheet data are collected from Compustat and from Moody’s Manuals and annual reports in

those cases where Compustat data are unavailable or incomplete. Stock returns data are obtained from CRSP,

analyst forecasts and ownership data from Value Line.

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6 Conversations with Standard and Poor’s indicate that the industry rivals are the same as the ones listedon the Compustat database at the time, except that some smaller rivals are not included in the EarningsSupplement.

7 Our results are unchanged when all four firms are discarded from the analysis.

8 Six industries have two recaps in our sample, and no industry has more than two. Our results are notaffected by the fact that the second recap in an industry shows up as a rival firm for the first recap. We obtainvery similar results if we discard the recap firms from the rival sample or discard all industries with more thanone recap. These results are not reported in the tables. See also Mitchell and Mulherin (1996) for adiscussion of industry clustering in takeovers.

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3. Results

3.1. Changes in the financial ratios

In this section we examine the changes in the financial ratios of the firms that are subject to control

threats (called target firms from now on) and their industry rivals. Ratios are averaged for the two years prior

to the control threat (or recap announcement if no explicit control threat date can be identified) and the two

years after the threat and subsequent recap. We present the level of the variable over the two year period

before the control threat, and the change over the subsequent period. Since our analysis focuses on the

response of companies to the control threat, we include firms even if the recap was not completed.

Table 2 contains the results for the target firms. The results are consistent with Denis and Denis

(1993): leverage increases, profitability increases, the firms cut capital expenditures, and the level of free cash

flow declines. Free cash flow is computed as operating income minus interest payments, dividend payments,

and tax payments.

More interesting, for our purposes, are the results in Table 3, where we study the characteristics of

industry rivals. In Panel A we treat each industry rival as an individual observation, such that industries with

more rivals receive more weight. Also, if the rivals respond to the same event, then their actions are not

independent; the p-values, which are based on regular t-tests (for means) and signed rank tests (for medians)

should therefore be interpreted cautiously. Also note that the number of observations is not consistent for all

ratios, because we lack information for some variables for some years. On average, rival firms have a ratio

of long term debt to total assets of 20.30% (median = 18.20%) in the two years prior to the control threat. In

the two years after the recapitalization this ratio increases by 3.34 percentage points, on average (median =

1.04 percentage points). Total debt also increases substantially, from 45%, on average, to 49.16%. Our results

remain unchanged when we examine the change in the ratio of debt to the market value of the firm (not

reported in a table). Thus, the increase in leverage cannot be explained by the positive stock price reaction

of rival firms to the control threat (documented in section 3.6).

Target firms and their rivals differ in terms of profitability. As Table 2 illustrates, target firms

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increase profitability by 1.29 percentage points, on average (median = 0.96 percentage points). On the other

hand, the profitability of the industry rivals declines by 1.78 percentage points (median decline is 0.87

percentage points).

The mean capital expenditures of industry rivals decline by 0.72 percentage points, from a pre-control

threat level of 8.15%, while the median decline is 0.36 percentage points. This decline is in the same direction

as the decline for the target firms, albeit smaller in magnitude. It is also interesting to note that the absolute

dollar level of capital expenditures does not increase over this period (not reported in the table). The rivals

spend $222.41 million on capital expenditures, on average, in the two years prior to the control threat and

$219.95 million in the two years after the recapitalization.

The average free cash flow and cash to assets ratios of the rival firms also decline, by 1.59 and 1.20

percentage points, respectively.

In Panel B of Table 3, we first compute the average ratios of the rival firms for each target firm, and

then compute the average for all targets. In general, these results are similar to those of Panel A, although the

significance of some of the findings is reduced. For example, while there is still a decline in profitability from

the pre- to the post-control threat period, the median decline is no longer significant at conventional levels.

The results on leverage, capital expenditures, and free cash flow remain significant, however. The mean ratio

of long term debt to total assets increases by 2.82 percentage points, which represents an increase of over 10%.

Capital expenditures decline by 0.96 percentage points, on average, which represents a decline of over 10%,

and the average free cash flow ratio declines by one percentage point, which is a decline of over 15%. The

averages also do not reveal interesting cross-sectional variability. As we document subsequently, the changes

are much stronger for firms with more severe agency conflicts.

When we compare the changes in the financial ratios to the predictions listed in Table 1, we find that

none of the hypotheses receive complete support. The cut in capital expenditures is inconsistent with the deep

pockets hypothesis and the limited liability hypothesis, while the decline in profits is not consistent with the

agency cost explanation. However, our predictions are relative to expectations, which do not necessarily

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correspond to the pre-control threat levels. We analyze the expected levels of some of these variables in

Section 3.3.

We also conduct three additional tests in this section to contrast the agency cost hypothesis with the

alternative hypotheses. The results of these tests are not reported in a table for the sake of brevity. First, we

examine the recapitalizations that are not preceded by control threats. The agency cost hypothesis does not

predict any changes for rival firms in these cases (unless the control threat was never made public), while the

two alternative hypotheses treat all recaps in a similar fashion. Consistent with the agency cost hypothesis,

we do not find any significant changes in the characteristics of industry rivals for recaps that are not preceded

by control threats. Second, we separately examine recapitalizations that are announced, but not completed.

Rivals should not respond to these transactions according to the alternative hypotheses, since leverage is not

actually increased. In contrast, the agency cost hypothesis suggests that the control threat is what is leading

to the response, not the recap per se. We would therefore expect a similar response. Consistent with the

agency cost hypothesis, we find that the changes in the financial characteristics of the rivals do not depend on

whether the recap is completed or not. Finally, the alternative hypotheses predict that the reaction of the rivals

depends on the amount of debt issued by the target firm. All the effects should be stronger when the target

firm adds more debt. Again, we find no evidence to support this prediction.

In the next subsection, we examine the relation between the changes in leverage and the changes in

the other ratios to further analyze the implications of the agency cost hypothesis and contrast it with the

alternative explanations.

3.2. Relation between changes in financial ratios

Denis and Denis (1993) show that the increase in debt taken on by the recap firms reduces managerial

discretion over cash flows and consequently limits investment. According to the agency cost hypothesis, this

should also be the case for the industry rivals. Thus, we expect the firms that increase debt the most to have

the largest cuts in capital expenditures. The limited liability hypothesis has the opposite prediction.

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According to the limited liability hypothesis, the rival firms have an incentive to lever up and increase output.

Firms that increase leverage the most have the most powerful incentive to increase output, because profits will

be higher in good states of the world. The deep pockets hypothesis does not necessarily imply that firms will

increase leverage; it is possible, however, that firms may need to borrow to engage in price competition. But

if that is the case, we would not expect a negative relation between borrowing and investment.

To determine whether the cuts in capital expenditures are related to the increases in leverage, we

estimate the following cross-sectional regression model for the industry rivals:

∆Capital Expenditures / Assets = a + b ∆Interest Expenses / Assets + e

where ∆Capital Expenditures / Assets is computed as the average ratio of capital expenditures to assets in the

two post-recap years, minus the same ratio averaged over the two years before the control threat and ∆Interest

Expenses / Assets is the change in the ratio of interest expenses to assets from the two pre-control threat years

to the two post-recap years. We obtain similar results when we replace the change in interest expenses by the

change in leverage, but the change in interest expenses provides a more direct measure of the impact of the

increase in debt on investment. We estimate the model using the robust regression method discussed by Berk

(1990). This method uses weighted least squares where the weight is inversely related to the absolute value

of the residual of that observation; as a result, outliers receive less weight in this method, but they are not

discarded. We also include a dummy variable for each transaction in the sample (coefficients are not

reported). The model yields the following results (p-values are in parentheses):9

∆Capital Expenditures / Assets = −0.0183 − 0.3421 ∆Interest Expenses / Assets N=319 (0.37) (0.00) Adj. R2 = 0.33

This result indicates that the change in interest expenses resulting from the increase in leverage leads to a

substantial cut in investment spending. For every dollar increase in interest expenses, firms reduce capital

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9 Using OLS we obtain the following result (p-values in parentheses): ∆Capital Expenditures/Assets= −0.0184 (0.61) − 0.4022 (0.00) ∆Interest Expenses/Assets. The similarity between the OLS results androbust regression results suggests that outliers do not pose a major problem in the estimation of this model.

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spending by 34 cents. This is consistent with the agency cost hypothesis, but it does not support limited

liability or deep pockets. In addition, if firms simply increase leverage because their stock price has increased

(as documented in section 3.6) we would not expect the increase in interest expenses to lead to a reduction in

capital spending.

Because the change in debt is really a function of free cash flow and excess investment, we also

estimate a model of the change in investment from the period before the control threat to the post-recap period

as a function of industry-adjusted investment in the pre-control threat period. The model yields the following

result (transaction dummies are included, but not reported and p-values are listed in parentheses):10

∆Capital Expenditures / Assets = −0.02 (0.34) − 0.377 (0.00) Industry-adjusted capitalexpenditures pre-control threat

N=324 Adj. R2=0.45

Thus, rival firms that invest the most before the control threat cut investment the most after the recap. This

implication does not follow from the deep pockets hypothesis or the limited liability hypothesis. Interestingly,

this result is caused entirely by firms that invest more than their industry (regression not reported), which

indicates that it is not caused by mean reversion of capital spending.

In sum, the regression models presented in this section are consistent with the agency cost argument,

but they are not consistent with the alternative hypotheses: deep pockets and limited liability.

3.3. Are the firms in the industry responding to changes in industry conditions?

In this section we examine whether the control threat for one of the rival firms leads to the changes

reported in Table 3 or whether the industry is simply moving toward a new debt and output level, which

implies that some firms will recapitalize, while other firms may increase leverage by other means. Zingales

(1998) discusses this endogeneity criticism in more detail. We perform three sets of tests to investigate this

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10 Using OLS, we obtain the following result (p-values in parentheses): ∆Capital Expenditures/Assets= −0.0180 (0.52) − 0.742 (0.00) Industry-adjusted capital expenditures pre-control threat.

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possibility.11

First, we examine whether the findings presented in Table 3 simply reflect a trend in the ratios of the

companies that operate in these industries. If the industry is moving toward a new equilibrium, we should find

that some of the financial ratios change gradually. This is because firms incur transaction costs when they

issue new debt and not every firm will adjust at the same time. If the changes simply reflect a pattern, there

should also be a change in the ratios over the two year period prior to the control threat. Five of the six ratios

examined do not change significantly, however, over the two year period before the control threat takes place.

Only the mean ratio of total debt to assets increases by a little over one percentage point (p-value = 0.03) over

this period. Of the five ratios that do not change significantly, only one changes in the same direction as

reported in Table 3, and this is the ratio of capital expenditures to total assets. The other ratios change in the

opposite direction: the long term debt ratio declines, profitability increases, free cash flow increases and the

cash balance increases. These findings suggest that the results reported in Table 3 are not the reflection of a

time trend.

Second, we examine whether the changes we observe are predicted by financial analysts. It is

possible that expectations change, possibly because of an industry shock, such that the relation between the

events surrounding the control threat and the post-recap rival changes is spurious. A simple analysis of the

time trend would not reveal this. We gather projected debt levels, capital expenditure levels, and operating

margins from the latest issue of the Value Line Investment Survey prior to the control threat announcement.

Value Line generally provides expected averages for a three year period, so we interpolate when necessary.

Also, because Value Line does not contain forecasts of total assets, we analyze and report the figures on debt

and capital expenditures, without scaling by firm size.

To determine whether the Value Line forecasts are generally unbiased, we gather data on two random

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11 In section 3.6, we provide event study evidence which also suggests that the rival firms in oursample respond to the control threat and subsequent recap, and that the response depends on some of their characteristics.

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firms for every recap, and study the accuracy of the forecasts. We find that the forecasts of debt and capital

spending are unbiased, in that there is no significant difference between the predicted and actual levels. The

actual operating margins at the end of the second fiscal year are significantly lower than predicted, however.

The mean difference is 1.4 percentage points (median = 0.87 percentage points). We will take this

overoptimism bias into account in the interpretation of our findings.

Table 4 reports the results. The results on leverage are unambiguous: the rival firms take on more

debt than predicted. The difference is large and always significant. For example, Value Line predicts that the

rivals will have median long term debt of $158.5 million two years after the recap. The actual median debt

level is $232.1 million, which is more than 40% higher than predicted. Mean comparisons lead to similar

results. The results on capital expenditures are less clearcut. The averages are consistent with the findings

of Table 3: firms spend less on new investment than predicted. But the median firm actually spends more than

predicted, and this effect is significant in the second year after the recap. This suggests that the investment

cuts are much more pronounced in the larger firms. Since insider ownership is negatively related to firm size,

these companies are more likely to suffer from agency problems.

The mean operating margins of the firms are below expectations and the difference is significant in

the second year after the control threat; the medians are above expectations, however, and the difference is

significant in the first year after the control threat. As we discussed previously, analysts tend to be

overoptimistic. Taking this bias into account, the mean operating margin is never significantly below

expectations, and the median operating margin is significantly above expectations in both years. Thus, while

we find a decline in profitability for the rival firms, their profitability actually increases relative to expectations

after taking into account the analyst bias.

In the third and final set of tests, we consider the debt level of the firms in our sample before the

control threat. If the increase in leverage after the control threat and recap announcement simply reflects firm

adjustments to new industry conditions, then we would not expect the capital structure of the firms in our

sample to be out of line before the control threat. On the other hand, if all firms in the industry suffer from

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free cash flow agency problems, we expect the firms in our sample to have less debt than predicted.

To examine this conjecture, we estimate cross-sectional regression models of leverage for each year

during the 1982-1987 period using all firms on Compustat, except the firms in our sample. Two sets of

regressions are estimated, one using the ratio of long term debt to total assets as the dependent variable, and

one using the ratio of total debt to total assets as the dependent variable. The independent variables we employ

are based on Titman and Wessels (1988), Opler and Titman (1996), and Berger, Ofek, and Yermack (1997):

(i) return on assets, measured as operating income divided by total assets; (ii) non-interest tax shields,

measured as investment tax credits divided by total assets; (iii) asset collateral value, measured as net property,

plant and equipment divided by total assets; (iv) company size, measured as the natural logarithm of total

assets; and (v) asset uniqueness, measured as R&D divided by total assets, and also as selling, general and

administrative expenses divided by total assets. The ratio of R&D to total assets also captures growth

opportunities. We set all variables larger than the 99th percentile of their distribution equal to the 99th

percentile and all variables smaller than the first percentile equal to the first percentile. We then use the

estimated regression models to predict the optimal level of debt for each firm, and compare the optimal to the

actual level for the firms in our sample in the period prior to the control threat.

Table 5 contains the results of this analysis. We report the leverage deficit for the target firms in

Panel A and for the industry rivals in Panel B. Means are reported first, followed by medians. The target

firms have much less long term debt than predicted. The average long term debt deficit in the two year period

before the control threat is almost five percentage points (median = seven percentage points). The shortage

in total debt is somewhat smaller and not statistically significant. These findings indicate that the target firms

did not have sufficient debt before the control threat was announced. What is interesting in light of the

analyses in this paper is that the results for the rival firms are similar. Before the control threat, these firms

also have less debt than predicted. The leverage deficit is smaller than for the target firms, but it is both

statistically and economically significant. The average long term debt ratio, for example, is 2.5 percentage

points below the predicted level, which is a shortage of about 10% compared to the predicted level. It is also

18

interesting to note that the increases in debt for the rival firms reported in Table 3 are similar in magnitude to

the deficit documented in Table 5.12

These findings indicate that the majority of the firms in these industries have too little debt and

provide further support for the agency cost hypothesis. They are also consistent with the findings of Berger,

Ofek, and Yermack (1997) and Safieddine and Titman (1999). Berger et al. (1997) provide evidence that

entrenched managers have lower leverage. They also find that leverage increases after entrenchment-reducing

shocks to managerial security, such as unsuccessful tender offers or CEO replacements. Safieddine and

Titman (1999) find that target firms who terminate takeover attempts substantially increase leverage and cut

capital spending in subsequent years. Our evidence suggests that entrenchment-reducing shocks also affect

the other firms in the industry.

In addition to the above tests, we also read all the S&P Industry Surveys for the five year period

before the announcement of the control threat to determine whether the description of the industries in our

sample fits Jensen’s (1993) framework. In particular, Jensen argues that excess capacity may be caused by

deregulation, changes in technology, organizational innovation, and the globalization of trade. We find

detailed industry descriptions for 31 transactions. In all but three cases, we find that at least one of these issues

is a major industry theme in the years leading up to the control threat. Excess capacity is discussed in 22

cases, price competition from imports is discussed in seven cases, technology leading to changes in efficiency

is discussed for eight cases, and deregulation in five cases. These reports suggest that most of the industries

in our sample needed to cut investment. As we mentioned previously, there is little evidence of a decline in

��

12 We have completed two robustness tests of this analysis. First, we use two alternative growthmeasures: (i) Tobin’s q (approximated by the market value of assets, plus the book value of debt, divided bythe book value of assets); (ii) the ratio of capital expenditures to total assets. Our results are similar to theones reported in the table. Second, we estimate two specifications that include the cash balances of the firms. In the first specification, we simply include the ratio of cash to total assets as an explanatory variable. Theargument is that firms with higher cash balances can support more debt. We find a positive coefficient onthe cash ratio, but our results remain unchanged. In the second specification, we employ the net debt ratioas the dependent variable. The net debt ratio is computed as debt (total or long term) minus cash, divided bytotal assets. Again, our results remain unchanged.

19

investment, however, before the control threat takes place. This is striking given that many of the industry

accounts we describe mentioned excess capacity for several years before the control threat. The lack of an

industry response is consistent with Jensen’s conjecture that few firms voluntarily reduce investment. Thus,

this evidence suggests that the industry conditions are changing but that most of the firms operating in the

industries do not voluntarily adapt to the changes in the industry.

3.4. Robustness of the results over time

It is possible that the changes in the financial ratios reported in Table 3 are caused by changes in

macroeconomic conditions. The recession in 1990 may have caused many of the observed changes: increases

in leverage, and decreases in capital expenditures and profitability [see also Denis and Denis (1995)]. To

analyze this possibility, we divide the sample into control threats that took place in 1986 and before and

control threats that took place in 1987 and 1988. The data for the 1986 and before sample are not affected by

the recession. Interestingly, we find that the patterns documented in Panels A and B of Table 3 persist for the

subsamples (not reported in a table), albeit that a few of the subsample changes are not significant. The

changes are similar in magnitude in both periods, but the significance of the results declines because of the

reduction in the number of observations. Only in one case is the change not in the same direction as in Table

3: there is an insignificant increase in the cash level of the rival firms in the pre-1986 period. The consistency

of our results across time suggests that they are not caused by changes in macroeconomic conditions.

3.5. Adoption of takeover defenses

Up to this point, we have examined one possible response from the rival firms, which is to reduce

investment and to commit to such policy by increasing leverage. Alternatively, some managers who value

control may decide to adopt takeover defenses instead. Of course, the response of some firms may consist of

a combination of both options. In this subsection, we examine the adoption rate of takeover defenses by the

��

20

firms in our sample. During the time period we study many firms throughout the economy increased the

adoption of takeover defenses, however, and it is therefore important to control for this secular increase. We

gather data on all takeover defenses adopted by U.S. companies over the period 1983-1989 from a book

compiled by the Investor Responsibility Research Center (IRRC). This book contains data on 18 different

types of takeover defenses adopted by over 1500 U.S. corporations. The list of defenses ranges from poison

pills to cumulative voting. We gather data on all defenses, but we focus on three types that are adopted

frequently over our sample period: poison pills, classified boards, and fair price amendments. We compare

the rate of adoption by 1325 companies who are not in our sample with the 256 companies in our sample who

are also covered by IRRC. Our prediction is that the rate of adoption by our sample companies is higher than

the general rate of adoption in the economy. Since the control threats we study occur over time, we weight

the yearly adoption rates in the economy by the number of control threats in our sample to compute the

equivalent economy-wide adoption rate.

Our results are contained in Table 6. In the year prior to the control threat, 16.4% of the firms in our

sample adopted a takeover defense. This rate is not significantly different from the general adoption rate in

the economy of 13.7%. In the year of the control threat, there is a dramatic increase in the adoption of

takeover defenses for our sample firms to 25.4%. Other firms in the economy also increased their rate of

adoption, but only to 15.0%. The difference between the two adoption rates is highly significant. In addition,

the increase in the adoption rate for the firms in our sample is significantly larger than the increase in the

economy (p-value = 0.05) (not reported in the table). In the year after the control threat the adoption rate in

our sample declines again to the pre-control threat level. These findings are consistent with the conjecture that

some of the rivals attempt to insulate themselves against potential takeover threats.

3.6. Event-study evidence

As discussed previously, the agency cost hypothesis also has implications for the stock price reaction

of the rival firms at the announcement of the control threat, as well as for the cross-sectional variation in the

21

stock price reaction. To examine these implications, we study the abnormal returns of the industry rivals

around the announcement of the control threat and the announcement of the recapitalization. We focus on the

abnormal returns for the two day window starting on the announcement date. It is important to use a short

window for the industry rivals to maximize the signal-to-noise ratio. The disadvantage is that not all relevant

information with respect to the transaction may be released over this period.

Abnormal returns are computed using standard event-study methodology. The market model

parameters are estimated using continuously compounded returns over the 200 day period starting 250 days

before the announcement date. We also compute the dollar abnormal return to measure the total impact of the

control threat and subsequent recap announcement on the combined wealth of all firms in the industry.

If the rivals have confounding announcements during the two day event window (verified in the Wall

Street Journal Index), or if market model parameters cannot be computed, they are excluded from this analysis.

We also report abnormal returns around the announcement of the recap because in a number of cases an

explicit announcement date of a control threat cannot be identified. The recap announcement may therefore

be the first clear indication to the market that the independence of one firm in the industry has been threatened.

Table 7 contains the results. In Panel A, we summarize the abnormal returns for the firms that are

subject to a control threat. Panel B contains the findings for the industry rivals. We can obtain exact control

threat announcement days for only 23 firms. Not surprisingly, we find that the target firms gain more than

15% over the two-day window when the control threat is announced, while the median announcement effect

is over 12%. When the recap is announced, target firms gain another 2.69%, on average. It would be

incorrect, however, to assume that the effect of the transaction is simply the sum of both returns, since other

announcements regarding the control threat take place during the intermediate period.

More interesting, for our purposes, is the reaction of the rival firms, documented in Panel B of Table

7. The 288 rivals gain 0.56%, on average, when the control threat is announced (median = 0.13%) and 0.62%

at the announcement of the recap (median = 0.27%). Both the means and medians are significantly different

from zero. The total dollar gain of the rivals at the announcement of the control threat is slightly over $1

22

billion. In addition, rivals gain $3.47 billion when the recap is announced. These findings suggest that both

announcements have a substantial impact on the valuation of all the firms in the industry, beyond just the

target firm. Admittedly, the median stock price reaction is small, but as we demonstrate later the firms with

the lowest stock price reaction are the ones with the lowest agency costs. For a large number of rivals, the

impact is substantial: 38% (33%) of the rival firms have a stock price reaction above 1% at the announcement

of the recap (takeover threat) and 18% (17%) of the rival firms have a stock price reaction above 3% at the

announcement of the recap (takeover threat). Since firms with contaminating announcements have been

removed from the analysis, our results indicate that the events have a substantial valuation impact for a large

number of companies.

The event study evidence is not consistent with the alternative explanations for the change in

characteristics of the rival firms after the control threat and subsequent recap. The limited liability hypothesis

does not predict rival stock price effects at the announcement of the control threat, unless an increase in

leverage is already anticipated at that time. But in that case, the stock price reaction is in the wrong direction,

since the limited liability hypothesis predicts a decline in industry profits caused by the increase in output by

all firms. The positive stock price reaction around the recap announcement is therefore also inconsistent with

limited liability.

In the same vein, the predatory pricing hypothesis does not predict a stock price reaction for the rivals

at the control threat announcement, unless the leverage change is already anticipated. However, the positive

reaction around the recap announcement is consistent with competitors preying on the recap firm. To further

investigate the merits of this alternative interpretation of our findings, we also examine data on the rivals of

the four recaps that were not associated with control threats. If the recap announcement provides further

information about the control threat, then we would not expect the stock price of rival firms to change around

recap announcements that are not preceded by control threats. On the other hand, if the stock price reaction

is caused by the anticipated change in leverage, it should not matter whether the recap announcement is related

to a control threat or not. The average stock price reaction of the forty rivals to recaps not associated with

23

control threats is -0.10% (median = -0.02%), compared to the stock price reaction of 0.62% reported in Panel

B of Table 7 when there is a control threat. The difference in returns is marginally significant (p-value = 0.10).

This result is not consistent with the predatory pricing hypothesis. In addition, if the predatory pricing

argument is valid, we should find a more positive stock price reaction for rival firms when the targets increase

leverage the most. We find no evidence to support this (not reported in a table).

To further analyze the merits of the agency cost hypothesis, we estimate cross-sectional regression

models of the abnormal returns. According to the agency cost hypothesis, firms that have little debt and a lot

of free cash flow should benefit the most from the recapitalization because these firms have the highest agency

costs. Unfortunately, the implications of the predatory pricing hypothesis are similar. Firms that are in the

best financial condition are most able to endure a price war. These are firms with a lot of free cash flow and

little debt. However, according to the predatory pricing hypothesis, we would not expect these effects at the

announcement of the control threat, only at the recap announcement. In addition, we include data on insider

ownership and capital spending in the regression models. The agency cost hypothesis predicts a negative

relation between insider ownership and the abnormal returns, because firms with little ownership are more

likely to have agency problems and therefore the most to gain from a reduction in agency costs. The predatory

pricing hypothesis has no specific prediction regarding the relation between insider ownership and the

abnormal returns. If anything, however, we would expect a positive relation. If predatory pricing is in the

best interest of shareholders, we would expect a stronger stock price reaction for firms whose managers have

the best interests of shareholders in mind. The agency cost hypothesis also predicts that firms with the highest

levels of capital spending should have the strongest stock price reaction because these firms are likely to be

overinvesting. The predatory pricing hypothesis does not predict a relationship between capital spending and

the announcement effect. The other alternative hypothesis, the limited liability hypothesis, has no implications

for the relation between any of these factors and rival firm abnormal returns.

Table 8 contains the results of cross-sectional regression models of the abnormal returns of the rival

firms. We report models for the announcement of the control threat (column i) and for the announcement of

24

the recapitalization (column ii). Since we cannot identify a specific announcement date for the control threat

in a number of instances, the regressions in column (i) are based on fewer observations. We include the

natural logarithm of insider ownership because we expect the marginal effect of ownership on abnormal

returns to decline as ownership increases. We lose 59 firms because they are not listed on Value Line or

because Value Line does not report ownership data for these companies. We include a separate dummy for

each transaction, but do not report the coefficients on these dummies. Again, to reduce the importance of

outliers, we employ Berk’s (1990) robust regression method.

The regressions results are consistent with the agency cost interpretation. When the control threat is

announced (column i), firms with the lowest level of industry-adjusted debt and highest level of industry-

adjusted capital expenditures have the highest stock price reaction. These are the firms with the most intense

agency problems. Consistent with this interpretation, we also find that the stock price reaction is lower for

firms with higher insider ownership. Only the negative coefficient on the industry-adjusted free cash flow

level is inconsistent with the agency cost interpretation; it is not significantly different from zero, however.

Our results are also economically significant: an increase in industry-adjusted debt by one standard deviation

reduces the announcement effect by 0.38%, which is two thirds of the mean value. Decreasing industry-

adjusted capital spending by one standard deviation has approximately the same effect. At the announcement

of the recapitalization, we also find a negative relation between the stock price reaction of rival firms and both

their leverage and their level of insider ownership.13 This is again supportive of the agency cost arguments.14

In unreported models we also include the Herfindahl index and interactions between the Herfindahl

��

13 Controlling for firm size in the cross-sectional regressions does not affect the results, and thecoefficients on size (measured by the natural logarithm of total assets) are never significant at conventionallevels.

14 A related argument for the positive stock price reaction for rival firms around the control threatannouncement is that the rivals firms are ‘in play’ as well. We do not disagree with this interpretation. Inour view, the industry rivals can either take actions to reduce agency costs, or face an increased takeoverthreat. Both interpretations are consistent with the positive stock price reaction and the cross-sectionalvariability in the reaction.

25

index and capital structure. If the positive announcement effect for rivals stems from the reduced flexibility

of firms that recapitalize, we would expect this effect to be stronger in industries with a lower degree of

competition (high Herfindahl index) and low leverage [see Lang and Stulz (1992)]. The coefficients on the

Herfindahl index and its interaction with capital structure are not significantly different from zero.

There is an additional interpretation for the positive stock price reaction for rival firms at the recap

announcement. Competition may weaken in an industry when one firm increases leverage. However, it is not

clear why this should be more beneficial to firms with little debt. In fact, one could argue that high debt firms

with little free cash flow are more likely to benefit from reduced competition. In addition, we have

documented that none of our results depend on how much debt the recap firm actually adds.

Other papers have also examined the stock price reaction of industry rivals to takeover

announcements, but only for horizontal acquisitions. The purpose of this research was to examine whether

horizontal mergers have anti-competitive effects. Eckbo (1983) and Stillman (1983), among others, do find

that the stock prices of rival firms increase when horizontal mergers are announced. Both papers reject the

collusion hypothesis, however, in favor of the argument that the merger may lead to greater efficiency in the

industry, which is beneficial for all firms. Two of the target firms in our sample received bids from industry

rivals. The abnormal return for the rivals (excluding the bidder, of course) in these cases is 0.59%, which is

very similar to the 0.56% abnormal return for the full sample. Thus, the announcement effect in our sample

does not appear to be caused by the fact that the potential bidder originates from the same industry.

Several authors have studied the industry rival stock price effect around the announcement of leverage

increasing events. Chevalier (1995b) has studied the stock price reaction of rival firms around leverage

increasing events in the supermarket industry. She also finds that rival firms benefit when a major competitor

increases leverage. When she subdivides her sample into supermarkets that compete directly with the leverage

increasing firm and those that do not, she only finds positive effects for the direct competitors. This is

consistent with the argument that product market competition following the leveraged transaction is expected

to become softer. Hertzel (1991) examines the effects of stock repurchases on rival firms, but he finds no

26

significant reaction. Slovin, Sushka, and Bendeck (1991) analyze the stock price reaction of rival firms to

going private transactions. They find a positive excess return of 1.32%, and suggest that rival firm prices rise

because shareholders gain from the costly acquisition of information associated with buyout activities.

However, they do not relate the stock price reaction to the financial characteristics of the rivals.

4. Conclusion

This paper studies the effects of control threats to one firm in an industry on rival firms. This research

is motivated by Jensen’s (1986, 1993) argument that entire industries may suffer from agency problems. If

one firm’s independence is threatened because of agency problems, then the managers of other firms who want

to remain in control realize that they need to reduce agency problems as well or be faced with similar control

threats.

Our results are consistent with Jensen’s explanation: after the control threat and subsequent recap

announcement, the rival firms increase debt, cut capital expenditures, and reduce their cash balances and free

cash flows. The rivals with the largest increases in debt and the highest level of industry-adjusted investment

before the control threat have the largest cuts in capital expenditures. In addition, we find that both the target

firms and their industry rivals have less debt than predicted by an empirical capital structure model. We also

find that industry rivals adopt more takeover defenses than other firms in the industry. Event study results

provide further evidence in support of agency costs. The industry rivals gain 0.56%, on average, when a

control threat is announced, with larger returns accruing to firms with little debt, high capital expenditures,

and low insider ownership. At the announcement of the recapitalization, industry rivals gain a further 0.62%.

Again, we find that the gains are larger for firms with little debt and low levels of insider ownership.

Alternative hypotheses have been proposed to explain how rivals respond to recapitalizations, but

these arguments have little to say about what happens when a control threat is announced, unless the defensive

recap was already anticipated by the market. Nevertheless, we carefully study the alternative explanations:

predatory pricing and limited liability. While some of our results are also consistent with these alternatives,

27

several pieces of evidence cannot be reconciled with their predictions. For example, limited liability cannot

explain the positive stock price reaction of rival firms or their decrease in capital expenditures. Predatory

pricing is inconsistent with the cross-sectional variation in the stock price reaction of the rivals and with the

relations among several of the changes in the financial characteristics of the rivals. Moreover, we find no

announcement effect for rival firms when recaps are not preceded by control threats, which is again

inconsistent with predatory pricing.

Overall, our results suggest that the benefits of control threats are larger than previously documented,

since they also affect the behavior of industry rivals.

28

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Brander, James A. and Tracy R. Lewis, 1988, Bankruptcy costs and the theory of oligopoly, Canadian Journalof Economics 21, 221-243.

Chevalier, Judith A., 1995a, Do LBO supermarkets charge more? An empirical analysis of the effects ofLBOs on supermarket pricing, Journal of Finance 50, 1095-1112.

Chevalier, Judith A., 1995b, Capital structure and product-market competition: Empirical evidence from thesupermarket industry, American Economic Review 85, 415-435.

Denis, David J. and Diane K. Denis, 1993, Managerial discretion, organizational structure, and corporateperformance: A study of leveraged recapitalizations, Journal of Accounting and Economics 16, 209-236.

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Eckbo, B. Espen, 1983, Horizontal mergers, collusion, and stockholder wealth, Journal of FinancialEconomics 11, 241-273.

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30

Table 1Predicted effects of control threats and subsequent increases in leverage on rival firms

This table lists the predicted effect of different theories on the financial characteristics of rival firms. Thepredicted effect is relative to expectations, not necessarily relative to prior levels.

Hypothesis Deep Pockets Limited Liability Agency Costs

Leverage No change Increase Increase

Profitability Decrease Decrease Increase

Capital Expenditures Increase / nochangea

Increase / nochangea

Decrease

Cash & Free Cash Flow Decrease Decrease Decrease

Stock Price Reaction at ControlThreat Announcement

No effect / positiveb No effect / negativeb Positive

Stock Price Reaction at RecapAnnouncement

Positive Negative Positive

a Capital expenditures may not increase if the company initially uses up excess capacity.b The stock price may react at the control threat announcement if the increase in leverage is anticipated at thattime.

31

Table 2Change in financial characteristics of firms announcing leveraged recapitalizations after control threats

The sample of recapitalizations is obtained from Denis and Denis (1993). Only recapitalizations precededby a control threat are included in the analysis. Means are listed in the first line, medians are listed in thesecond line. A t-test is performed to compare means. A sign rank test is performed to compare medians. TheP-values of these tests are in parentheses. Free cash flow is computed as: operating income - dividendpayments - interest payments - tax payments. The tax payments take into account deferred taxes.

Levelyear -2 & -1

Change betweenyears -2 & -1 and

years 1 & 2

N

Long term debt over total assets 0.19570.1664

0.3612 (0.00)0.3164 (0.00)

31

Total debt over total assets 0.43310.4096

0.4371 (0.00)0.3614 (0.00)

31

Operating return on assets 0.15730.1446

0.0129 (0.02)0.0096 (0.03)

30

Capital expenditures over total assets 0.08500.0845

-0.0204 (0.03)-0.0177 (0.01)

30

Free cash flow over total assets 0.06850.0656

-0.0558 (0.01)-0.0303 (0.00)

29

Cash over total assets 0.06350.0477

-0.0033 (0.82)-0.0106 (0.78)

31

32

Table 3Change in financial characteristics of competitors of firms announcing leveraged recapitalizations after a

control threat

The sample of recapitalizations is obtained from Denis and Denis (1993). The rival firms are obtained fromthe Earnings Supplement to the Standard and Poor’s Industry Surveys. Only recapitalizations preceded bya control threat are included in the analysis. Means are listed in the first line. Medians are listed in thesecond line. A t-test is performed to compare means. A sign rank test is performed to compare medians. TheP-values of these tests are in parentheses. Free cash flow is computed as: operating income - dividendpayments - interest payments - tax payments. The tax payments take into account deferred taxes.

Panel A: Individual firm observations

Levelyear -2 & -1

Change betweenyears -2 & -1 and

years 1 & 2

N


0.0334 (0.00)0.0104 (0.00)

336


0.0418 (0.00)0.0268 (0.00)

334


-0.0178 (0.00)-0.0087 (0.00)

337


-0.0072 (0.02)-0.0036 (0.00)

324


-0.0159 (0.01) -0.0046 (0.02)

317


-0.0120 (0.01)-0.0058 (0.00)

337

33

Table 3 (continued)

Panel B: Data aggregated by control threat

Levelyear -2 & -1

Change betweenyears -2 & -1

and years 1 & 2

N


0.0282 (0.03)0.0225 (0.03)

32


0.0329 (0.01)0.0287 (0.00)

32


-0.0122 (0.05)-0.0139 (0.15)

32


-0.0096 (0.03)-0.0059 (0.12)

32


-0.0101 (0.03)-0.0088 (0.10)

32


-0.0069 (0.09)-0.0075 (0.07)

32

34

Table 4Comparison of Value Line projections with actual data for the rival firms

The rival firms are obtained from the Earnings Supplement to the Standard and Poor’s Industry Surveys. Predicted values are obtained from the Value Line Investment Survey. Means are listed in the first line. Medians are listed in the second line. A t-test is performed to compare means. A sign rank test is performedto compare medians. Only the rivals of firms that announced recaps subsequent to a control threat areincluded in the analysis.

Prediction Actual P-value N

Long term debt ($ millions) Year +1 550.7163.0

637.5218.7

0.000.00

294

Year +2 555.3158.5

670.4232.1

0.000.00

274

Capital expenditures ($ millions) Year +1 243.7 50.6

215.1 53.0

0.070.24

264

Year +2 257.6 56.0

218.3 64.1

0.160.05

247

Operating margin (in %) Year +1 0.15720.1400

0.14590.1589

0.210.06

276

Year +2 0.16300.1450

0.14690.1570

0.080.74

255

35

Table 5Actual versus predicted debt levels for the target firms and their rivals

Two regression models are estimated for all firms on Compustat, except for the firms in our sample. The firstmodel employs long term debt over total assets as the dependent variable. The second model employs totaldebt over total assets as the dependent variable. Independent variables are: (i) operating income / assets, (ii)investment tax credits / assets, (iii) property, plant and equipment / assets, (iv) log assets, (v) R&D / assets,and (vi) selling, general and administrative expenses / assets. A separate model is estimated each year. Thismodel is employed to predict the debt levels for the firms in our sample. Predicted and actual debt levels areaveraged for the two year period before the control threat. Only data for transactions announced subsequentto a control threat are included in this analysis. Means are listed in the first line, medians are listed in thesecond line. A t-test is performed to compare means. A sign rank test is performed to compare medians. Thep-values of these tests are in parentheses.

Predicted levelyears -2 & -1

Actual levelyears -2 & -1

Difference (p-value)

N

Panel A: Target firms

Long term debt 0.24430.2434

0.19530.1682

0.0490 (0.00)0.0752 (0.00)

28

Total debt 0.47130.4707

0.43870.4230

0.0327 (0.28)0.0477 (0.29)

28

Panel B: Rival firms

Long term debt 0.22460.2221

0.19930.1840

0.0254 (0.00)0.0381 (0.00)

357

Total debt 0.46950.4707

0.44990.4404

0.0195 (0.00)0.0303 (0.00)

357

36

Table 6Adoption rate of takeover defenses by rival companies

Data on takeover defenses are gathered from IRRC (1990). IRRC reports data on takeover defenses by 1581 companies, of which 256 companies arein our sample. The economy-wide adoption rate is computed as the weighted average of the adoption rate in the economy over several years, wherethe weight depends on the number of control threats in our sample. Three takeover defenses are analyzed in this table: poison pills, classified boards,and fair price amendments.

Sampleadoption rate(%)

Economy-wideadoption rate(%)

Difference(p-value)

Year before control threat 16.4 13.7 2.6 (0.29)

Year of control threat 25.4 15.0 10.4 (0.00)

Year after control threat 17.2 14.3 2.8 (0.27)

37

Table 7Abnormal returns of firms subject to control threats and their industry rivals

Abnormal returns are based on market model residuals. The market model is estimated over the 250 dayperiod starting 200 days before the announcement of the event. A standard t-test is performed to test equalityof the mean to zero. A rank sum test is performed to test equality of the median to zero. P-values of thesetests are in parentheses. The dollar abnormal return is computed by multiplying the market value of equitytwo days before the announcement with the percentage abnormal return. The rival firms are obtained fromthe Earnings Supplement to the Standard and Poor’s Industry Surveys.

Panel A: Target firms

Mean(p-value)

Median(p-value)

Sum$ millions

N

Control threat announcement 0.1562 (0.00) 0.1215 (0.00) 10088.283 23

Recap announcement 0.0269 (0.09) 0.0249 (0.04) 434.350 35

Panel B: Rival firms

Mean(p-value)

Median(p-value)

Sum$ millions

N

Control threat announcement 0.0056 (0.01) 0.0013 (0.01) 1001.055 288

Recap announcement 0.0062 (0.00) 0.0027 (0.00) 3471.402 353

38

Table 8Cross-sectional regression of rival firm abnormal returns on measures of capital structure, capital

spending, free cash flow, and insider ownership

The rival firms are obtained from the Earnings Supplement to the Standard and Poor’s Industry Surveys.Abnormal returns are based on market model residuals. The market model is estimated over the 250 dayperiod starting 200 days before the announcement of the event. The debt, capital spending and free cash flowratios are industry-adjusted, and measured over the two years prior to the control threat. Ownership data areobtained from the last issue of Value Line prior to the announcement of the control threat. The p-values ofthe t-tests of equality of the regression coefficients to zero are in parentheses. Separate dummy variables foreach event are included in the analysis, but not reported in the table.

Control threatannouncement

(i)

Recapitalizationannouncement

(ii)

Intercept 0.0276 (0.07) 0.0071 (0.01)

Long term debt to total assets -0.0313 (0.02) -0.0319 (0.02)

Capital expenditures to total assets 0.0813 (0.02) -0.0398 (0.28)

Free cash flow to total assets -0.0745 (0.11) 0.0314 (0.39)

Log insider ownership -0.0022 (0.07) -0.0038 (0.00)

N 218 299

Adj. R2 0.4029 0.2351

how industry rivals respond to control threats

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