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Performance shocks, turnaround strategies and corporate recovery: Evidence from Australia
Alfred Yawson
School of Banking and Finance, the University of New South Wales, Sydney, Australia
Abstract
Using a sample of Australian firms, we document improvements in operating
performance following performance shocks. These improvements result in part from turnaround
strategies adopted by management. Evidence suggests that changes made to leverage and
operating expenses result in a negative contemporaneous effect on performance improvement.
The adjustments made to working capital (revenue growth) result in a lagged negative (positive)
impact on performance. Furthermore, whilst asset sales have a negative contemporaneous effect,
layoffs, divestitures and new CEOs have a lagged positive impact on performance improvement.
The interaction of financial and restructuring strategies is found to result in an incremental impact
on firm performance. Overall, there is evidence to suggest that financial and corporate
restructuring strategies are efficient responses to performance shocks.
JEL Classification: G32; G34
Keywords: Operating performance; Financial strategies; Corporate restructuring
1. Introduction
A corporate turnaround may be defined as the recovery of a firm’s financial performance
following a performance decline. Turnaround strategies have been described in the business
strategy literature as a master plan of actions necessary to reverse a declining business situation
(Barker and Duhaime, 1997). Although a host of business conditions may reflect such an
exigency, a substantial decline in financial performance is often considered a prime motivation
for turnaround actions (eg., John et al., 1992; Ofek, 1993; Kang and Shivdasani, 1997; Denis and
Kruse, 2000). A successful turnaround strategy results in a firm achieving a considerable
improvement in performance. This paper focuses on two turnaround strategies (financial and
corporate restructuring) that are utilised by firms when they experience performance shocks.
The paper proceeds as follows. First, we analyse financial strategies adopted by
management in response to performance shocks. Specifically, we examine changes to financial
leverage, dividend payout ratio, operating expenses, revenue growth and working capital, and
relate these variables to changes in operating performance following a performance shock.
Second, previous studies suggest a relationship between corporate restructuring activities and
firm performance (Kang and Shivdasani, 1997; Denis and Kruse, 2000). This paper complements
and extends prior studies by investigating the impact of restructuring activities on the corporate
recovery process. In this regard, the current paper examines the use of asset sales, employee
layoffs, new CEO appointments and sale of subsidiaries (divestitures) as restructuring efforts
aimed at achieving a turnaround. Third, there are suggestions in the turnaround literature to
indicate that turnaround actions could have interactive effects on firm performance
(Arogyaswamy et al., 1995). For example, the appointment of a new CEO coupled with an
aggressive revenue growth strategy by a firm can jointly result in a stronger performance
improvement than what could be achieved by either of these events implemented alone. As a
result, this paper investigates the impact of the interaction between financial and corporate
restructuring actions on firm performance. Fourth, the implementation of turnaround actions can
take a long time, but management will usually concentrate activity at the onset of the problem.
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The timely implementation of turnaround actions is therefore necessary to prevent further losses.
What is less emphasised in the literature is the time limit by which a turnaround action will be
expected to impact firm performance. Consequently, this paper investigates both the
contemporaneous and lagged impact of turnaround actions on firm performance. This
investigation will undoubtedly help firms in the planning and implementation of turnaround
activities.
Our motivation for pursuing these investigations is threefold. First, although anecdotal
evidence suggests that management are concerned about the financial strategies that affect their
performance, the impact of a financial strategy change on firm performance has not received the
attention it deserves in the academic literature. A thorough understanding of the effect of a
financial strategy change on firm performance will enable firms adjust their financial strategies
accordingly with the view to performance improvement. Second, a good exploration of corporate
restructuring strategies available to firms and their impact on future performance can serve as a
guide to firms designing workable strategies to counteract performance shocks. Third, an
understanding of the impact of the interaction between financial and corporate restructuring
strategies on performance improvement can go a long way in assisting firms to design a
combination of strategies to achieve a turnaround.
Our sample consists of Australian firms that achieved a positive industry adjusted
operating performance in one year followed by a substantial decline the following year over the
period 1991 to 2003. Similar to the findings by Denis and Kruse (2000), our sample firms
experience significant improvements in operating performance in each of the first three years
following the performance shock. Consistent with predictions from the turnaround literature,
there is evidence that adjustments made to financial leverage and operating expenses have a
negative contemporaneous effect on performance improvement. Revenue growth has a one year
lagged positive impact on firm performance. We attribute these findings to the efficiency gained
through appropriate adjustments to financial policies. In addition, over 19% of the sample firms
responded to performance shocks by employing corporate restructuring activities. These
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restructuring activities, which are similar to those documented by Ofek (1993), Kang and
Shivdasani (1997) and Denis and Kruse (2000), include employee layoffs, asset sales, divestitures,
new CEO appointments and friendly takeovers. Further analysis revealed that asset sales have a
negative contemporaneous effect, whilst employee layoffs, divestitures and CEO appointments
have lagged positive effect on firm performance. Furthermore, the results suggest an interactive
impact on firm performance when both financial and corporate restructuring strategies are used to
deal with performance shocks.
A number of studies have examined restructuring activities pursued by poorly performing
firms (eg., John et al., 1992; Ofek, 1993; John and Ofek, 1995; Kang and Shivdasani, 1997; Denis
and Kruse, 2000). This study is closely related to Ofek (1993), Kang and Shivdasani (1997), and
Denis and Kruse (2000). Ofek (1993) was primarily concerned with the effect of leverage on
restructuring decisions by poorly performing firms. However, Ofek (1993) did not extend his
investigation to the performance of firms following performance declines. Kang and Shivdasani
(1997) examine corporate restructuring activities following performance declines for a sample of
Japanese and US firms. They find that both the Japanese and the US firms engage in various
restructuring activities following performance declines. The study by Denis and Kruse (2000)
focuses on restructuring activities by poorly performing firms during the active takeover period
(1985-1988) and less active period (1989-1992). Both Kang and Shivdasani (1997) and Denis and
Kruse (2000) conclude, from univariate results, that a firm’s performance improves following a
restructuring activity. However, these studies do not provide any information on the functional
relationship between a restructuring activity and firm performance.
The current paper differs in several important respects. First, whilst previous studies are
primarily concerned with corporate restructuring activities, this paper highlights the relative
importance of both financial and corporate restructuring strategies in dealing with performance
shocks. By modelling financial and corporate restructuring activities together, the paper attempts
to address the issue of which, if any, better results in performance improvement. Furthermore,
previous studies do not emphasise the important issue of timescale required for turnaround
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actions to impact firm performance. This information is important for managers in selecting
appropriate turnaround actions that could result in a quick performance improvement whilst
keeping the long term corporate strategy in mind. The current paper fills this gap. Another
important distinction is that, this paper evaluates the interaction between financial and corporate
restructuring strategies in dealing with performance shocks.
The rest of the paper is organized as follows. Section 2 sets out the theoretical
background of the paper. Section 3 describes the data and reports some descriptive statistics.
Section 4 focuses on the impact of a financial strategy change on future firm performance.
Section 5 examines corporate restructuring activities and future firm performance. Section 6
examines the effectiveness of both financial and corporate restructuring activities in dealing with
performance shocks. Section 7 concludes the paper with a summary and discussions of the main
results.
2. Theoretical background
The literature has identified several generic turnaround strategies that could be employed
to deal with performance declines.1 This paper summarises these turnaround strategies in two
parts; financial and corporate restructuring strategies.
2.1. Financial strategies
As a first step to improving efficiency with the view of achieving a turnaround,
management should review the financial strategies that affect the operations of the firm (Pound,
1992; Ofek, 1993; Chowdhury and Lang, 1996). Financial strategies tend to provide a short term
1 These turnaround strategies include change of management, strong financial control, organizational
change, product-market orientation, improved marketing, and growth via acquisitions, asset and cost
reductions, investment, debt restructuring and other financial strategies. For detailed discussions of these
turnaround strategies, see Slatter (1984).
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solution to performance problems. The literature suggests that a firm that has suffered a
performance shock could recover by properly evaluating its cash generation policies to ensure the
availability of liquid resources to sustain operations. Firms can increase their cash flows by
increasing sales revenue, reducing dividend payments and controlling operating costs.2 Sales
revenue could be improved by raising selling prices, increasing cash discounts to customers and
relaxing customer credit criteria. Pant (1987) shows that revenue generation strategies account for
most profit turnarounds. Also, a reduction in dividend payments will allow firms to conserve cash
to sustain operations (Grullon et al., 2002; Lie, 2004) whilst a decrease in operating costs can
conserve cash, can also lead to an improved operating margin at the same time.
Firms may also restructure their debt obligations in response to performance shocks
(Ofek, 1993). Debt restructuring could result in either an increase or a decrease in the proportion
of debt in the capital structure. An increase in debt obligation can improve liquidity, and also,
provide incentives for management to improve performance (Jensen, 1989). In theory, it should
be relatively difficult for a firm experiencing performance problems to raise additional loans.
Empirical evidence suggests that such firms will attempt to retire some of their existing loans and
also convert part of it into equity to reduce interest payments and the likelihood of bankruptcy
(Slatter, 1984). Furthermore, as a high level of debt can cause financial distress, a firm is likely to
reduce its debt levels when it experiences a performance shock (Ofek 1993; Kahl, 2002).
Prudent working capital management is also important in turnaround situations. Firms are
expected to reduce excess investments in working capital as part of the recovery process. A
reduction in working capital could be achieved by reducing debtors by instituting efficient debt
collection mechanisms, and also, reducing the amount tied up in inventories. Firms can also,
though rarely, extend payments to creditors (Slatter, 1984). It is expected that prudent
2 Although theoretical models suggests that firms are reluctant to decrease dividends due to the negative
signal it may send to the market (eg., Bhattacharya, 1979), firms may be compelled to do so in the presence
of severe performance declines to improve liquidity (eg., Ofek, 1993; Gullon et al., 2002; Lie, 2004).
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adjustments to financial policies following performance shocks should result in a performance
improvement.
2.2. Corporate restructuring strategies
Firms can also implement corporate restructuring strategies following performance
shocks. This paper evaluates change of management, asset reduction (sale of assets and
divestitures) and employee layoffs as restructuring strategies necessary to mitigate a profit
shortfall.
2.2.1. Change of management
Turnaround situations often require new CEOs (Slatter, 1984). A new CEO is required to
provide a new sense of direction, develop new financial and operating strategies and revitalise the
firm. A change in CEO may occur even if the performance decline was brought about by
conditions beyond the control of the incumbent management. For example, if the entire industry
is not performing well due to an industry specific shock, management should not be held
responsible for poor performance (Morck et al., 1989). Even though CEOs may become
scapegoats in those instances, their removal signal to the stakeholders that something positive is
being done to improve performance.
There is overwhelming empirical evidence to support this theory. Prior evidence suggests
that board of directors demonstrate their responsiveness to poor performance by replacing poorly
performing CEOs. For example, Warner et al., (1988), Weisbach (1988), Ofek (1993) and Denis
and Kruse (2000) all show that the likelihood of top management changes is negatively related to
firm performance. These empirical findings suggest that a replacement of a CEO should result in
a performance improvement. In support of this view, Denis and Denis (1995) document an
improvement in operating performance following dismissals of CEOs. Thus, new CEOs are
expected to play important roles in the corporate recovery process.
2.2.2. Asset reduction
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Empirical evidence suggests that managers who value firm size are more reluctant to reduce
the assets under their command (Stulz, 1990). However, asset reduction becomes necessary when
a firm suffers a performance shock (Lang et al., 1995; Kang and Shivdasani, 1997; Berger and
Ofek, 1997; Denis and Kruse, 2000; Denis and Shome, 2004). The logic behind an asset
reduction strategy is that, by disposing of redundant assets, a firm can operate the more useful
assets. Furthermore, asset reduction could be used to improve cash flow. For a firm that has a
huge debt overhang, cash realised from asset reductions could be used to reduce financial
leverage (Kahl, 2002).
Asset reduction could be accomplished in different ways, including closure of plants, the
sale of assets and divestitures (Ofek, 1993; Denis and Shome, 2004). In a smaller firm with no
subsidiaries, asset reduction could be accomplished through disposing of nonperforming assets.
Divestitures are the preserve of larger firms that have substantial investment in different business
segments and subsidiaries. In support of this view, John et al., (1992) and John and Ofek (1995)
find divestitures to be a dominant strategy for large firms coping with performance declines. It
follows that firms that are able to dispose of their unwanted assets following performance shocks
should achieve improvement.
2.2.3. Employee layoffs
Employee layoffs have become a widespread turnaround strategy in recent years (eg.,
Inverson and Pullman, 2000; Chen et al., 2001). Layoffs can occur when a firm experiences a
declining product demand, and also, when a new technology changes the production process in a
way that reduces the demand for labour. Although firms can provide several reasons for reducing
their workforce, layoff decisions are usually made after a period of remarkable underperformance
measured by a firm’s accounting earnings and stock returns (Kang and Shivdasani, 1997; Chen et
al., 2001). The successful implementation of a layoff strategy will enable firms to cut down on
labour costs and also increase labour productivity, especially when the layoff decision stems from
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getting rid of unproductive workers. The variables used to proxy the above theories and their
expected signs are briefly summarised in Table 1.
Insert Table 1 about here
3. Data and descriptive statistics
The objective of our sampling methodology is to identify firms that have experienced a
substantial drop in operating performance. Operating performance is defined as the ratio of
earnings before interest, taxes, depreciation and amortisation (EBITDA) to total assets. Operating
performance is preferred because share price incorporates the market expectation of the value of
any turnaround strategy that may be employed by firms following performance shocks (Morck et
al., 1989; Denis and Kruse, 2000). This measure has consistently been applied in recent studies
that examine firm performance in different situations (eg., Kang and Shivdasani, 1997; Denis and
Kruse, 2000; Lie, 2004; Kim et al., 2004).
As a starting point, we identify the population of firms listed on the Australian Stock
Exchange (ASX) for the period 1991 to 1999. Financial data for these firms are obtained from the
financial information stored on the Fin Analysis database (Aspect Financial). To ensure the
integrity of the data, we cross checked the data with the annual financial statements stored on
Connect 4 database. Similar to Kang and Shivdasani (1997), Denis and Kruse (2000) and Kim et
al. (2004), each firm’s ratio of EBITDA to total assets is adjusted by subtracting the median ratio
of EBITDA to total assets for all firms that fall into the same ASX industry sector description.
This results in a firm specific measure of performance, which is within the control of
management. The industry adjusted performance should also alleviate concerns about industry
effect in operating performance that is likely to bias the analysis. Firms are included in the sample
when their industry adjusted operating income is positive in one year but the ratio becomes
negative the following year. In other words, the sample includes firms that perform above the
industry median in one year but performs below their industry median the following year. Thus,
the sample consists of firms that underperformed their industry peers from 1992 to 2000. This
99
process results in an initial sample of 415 observations. Since we are interested in turnaround
strategies following performance shocks, we attempt to avoid including firms that may be
financially distressed prior to the year of performance shock (Ofek, 1993; Kang and Shivdasani,
1997). Such firms might have already put some turnaround actions in place, making it difficult to
determine their immediate and subsequent impact on firm performance. Consequently, we require
that the ratio of interest expense to operating income in the year prior to the performance decline
be less than one (Kang and Shivdasani, 1997). This criterion resulted in the elimination of 16
firms, leaving a final sample of 399 firms. Even though not a prior condition, none of the firms
entered the sample twice.
3.1. Annual distribution of sample firms
Table 2 presents the annual distribution of the sample firms in the year of the
performance shock. The total number of firms identified for each year ranges from 13 (3.26% of
sample) in 1992 to 68 (17.04% of sample) in 2000. The mean and median number of firms across
the sample period is 44, indicating that performance shocks do not cluster in any particular year.
The mean annual performance, however, differs across years. The mean performance over the
sample period is -0.18, but individual years exhibit deviations from this value. For example, the
average performance for 1992, 1997 and 1999 is lower than the sample average, whilst the
remaining period recorded higher annual averages. The median firm performance is fairly
distributed across the sample period with the exception of 1992 value, which seems to be a
function of the sample size.
Insert Table 2 about here
3.2. Analysis of firm performance
The operating performance of the sample firms in the base year (year -1) and the year of
the performance shock (year 0) are reported in Table 3. The table provides industry adjusted
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operating performance (panel A) and changes in performance (panel B) from year -1 to year +3.
Barber and Lyon (1996) demonstrate that nonparametric tests are more powerful than parametric
test in studies of operating performance. Moreover, mean operating performance values are more
likely to be influenced by outliers. Hence while both mean and median values are reported, the
subsequent discussions focus mainly on median values. As reported in Table 3, the median
industry adjusted EBITDA as a proportion of total assets in year -1 is 0.04. The median industry
adjusted operating performance declined to -0.05 in year 0. The median change in performance
from year -1 to year 0 is -0.08. Stated differently, the sample firms suffered a median
performance decline of 231%. This change in performance is statistically significant at the 1%
level.
Pourciau (1993) provides evidence that newly appointed CEOs manage earnings in order
to report lower income in the early years of their tenure. We investigate whether the appointment
of new CEOs can explain the reported performance declines of the sample firms. Out of the 399
firms in the sample, 4 firms appointed new CEOs in year -1. However the performance declines
of these firms are not statistically different from those of the remaining firms. Thus, the sample
firms were performing well but suffered a huge industry adjusted performance shock necessary to
motivate turnaround actions.
The statistics in panel B of Table 3 show that, consistent with Denis and Kruse (2000),
the sample firms achieved significant improvements in operating performance from year 0
through to year +3. The median change in performance from year 0 to year +1 was 0.02. The
median firm consistently achieved performance improvement in years +2 and +3, with median
industry adjusted values of 0.014 and 0.026, respectively. The improvement in the median firm's
performance is statistically significant at the 1% level in each of the three horizons.3
3 It is possible that the improvement in operating performance reflects times series properties of accounting
earnings (Penman, 1991; Fama and French, 1995). We follow the procedure in recent research in dealing
1111
Insert Table 3 about here
3.3. Survivorship bias
Naturally, we are able to report operating performance for only those firms that survived
the sample period. Thus, we are unable to trace the future performance of firms that exit ASX by
way of acquisition or bankruptcy. This is likely to introduce survivorship bias in the analysis. As
indicated in Table 4, the sample firms decreased from year 0 to year +3. By the end of year +3,
the sample firms had reduced from 399 to 367, a reduction of 8.02%. Of the 32 firms that could
not survive the sample period, 30 (93.75%) of them were acquired in friendly deals whilst 2
(6.25%) of them could not meet the data requirements. It is possible that firms that survived as
independent firms performed better in the year of the performance shock than those that could not
survive, making the performance improvement reported in section 3.2 a suspect. To assess the
extent of survivorship bias in these results, we compare the performance of survivors and non
survivors in year 0. Although not a complete measure, poorer performance of non survivors
relative to the survivors in year 0 would indicate a potential upward bias in the reported changes
in performance. The results presented in Table 4 indicate no significant difference in the median
performance of the survivors and non survivors. Hence, there is no evidence to conclude that
survivorship bias gives rise to the improvement in performance reported in section 3.2.
Insert Table 4 about here
4. Financial strategies following performance shocks
The theory on corporate turnaround suggests that the performance improvements
achieved by the sample firms following the performance shock may result in part from the
with this problem by including a change in performance in the previous year in the estimated models (eg.,
Aboody et al., 1999; Lie, 2004).
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financial strategies adopted by management. We investigate this view by first analysing the
changes made to financial strategies in response to performance shocks. It is expected that there
will be significant differences in financial strategies before and after the performance shock.
Table 5 reports the mean and median changes in financial strategies from year -1 to year 0 and
from year 0 to year +1. The change in dividend payout ratio from year -1 to year 0 is 0.036 whilst
the change from year 0 to year +1 is -0.069. The mean difference between these changes is
statistically significant at the 5% level. Thus consistent with the findings by Grullon et al., (2002)
and Lie (2004), an average firm reduces its dividend payout ratio when it experiences a
performance shock. This seems to suggest that firms become financially constrained when they
encounter performance difficulties and they attempt to conserve liquid resources by cutting down
on dividend payments. Moreover, even though the average change in revenue growth from year 0
to year +1 is negative, it represents a significant improvement over the growth achieved from year
-1 to year 0. A median firm, however, achieves a positive growth in revenue from year 0 to year
+1. This is significantly different from the median growth achieved from year -1 to year 0,
suggesting that firms pursue aggressive growth strategies when they encounter performance
problems. Furthermore, a median firm is able to reduce its operating expenses from year 0 to year
+1 and the median change compared with the change from year -1 to year 0 is statistically
significant at the 1% level. The reduction in operating expenses is necessary to improve operating
margins. As hypothesised, sample firms reduce their investments in working capital from year 0
to year +1 as a way of reducing costs associated higher investment in current assets. Finally,
firms reduce their financial leverage after a performance shock even though the difference is not
statistically significant. The descriptive statistics provide evidence that firms experience
significant changes in their financial strategies following performance shocks.
Insert Table 5 about here
4.1. Impact of financial strategies on firm performance
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Indeed, if a change in a financial strategy reflects an appropriate managerial response to a
performance shock, we shall expect such change to be significantly related to performance
improvement. We conduct this investigation by estimating the following cross sectional equation.
titiittiti
tititiit
SIZEEBITDAWCGROEXPLEVDIVEBITDA
εβββββββατ
++∆+∆+∆+
∆+∆+∆+=∆
−
+
7,1654
321,
(1)
We estimate equation 1 separately for changes in industry adjusted operating performance from
year t to year t + τ, where τ = years +1, +2 and +3 and ti measures the change from year 0 to year
+1 (see table 1 for definition of variables). The log of total assets (SIZE) in the prior year controls
for a possible size effect in the corporate recovery process. The change in the ratio of EBITDA to
total asset in prior year controls for the time series properties in accounting earnings that can
affect future operating performance (Penman, 1991; Fama and French, 1995). Although in theory,
past earnings should impact current earnings, we are unsure of the functional form of this
relationship. Following Aboody et al., (1999) and Lie (2004), we assume that future performance
is linearly related to past performance.
To determine the parameter estimates of the models, we first attempt to identify the
correlation between the independent variables (financial and corporate restructuring) used in the
models. The correlation coefficients are reported in Table 6. The highest correlation coefficient of
0.36 is between divestitures and firm size. This is to be expected because large firms are more
likely to divest when they encounter performance shocks (John et al., 1992; John and Ofek, 1995).
Although there are correlations between a few other explanatory variables, their coefficients are
quite low so multicollinearity should not pose a problem.
Insert Table 6 about here
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Table 7 presents OLS regression results from equation 1.4 The results provide evidence that
improvements in operating performance in year +1 are significantly and negatively associated
with changes to financial leverage and operating expense (t=2.45 and 2.19). These results
demonstrate that the ability of management to reduce leverage and operating expense results in an
immediate improvement in firm performance. There is weak evidence that financial leverage has
a one year lagged impact on performance improvement. One explanation to this finding is that a
reduction of leverage following performance shocks potentially releases funds from interest
payments into other productive areas, which potentially improve operating performance. Growth
in revenue, dividend payout ratio and working capital do not have any contemporaneous effect on
firm performance.
Consistent with expectations, the financial improvement achieved in year +2 is
significantly positively related to revenue growth (t=2.32). Thus, as predicted, sample firms are
able to pursue revenue growth strategies to improve performance but their impact is felt only in
the subsequent years. Surprisingly, none of the financial strategies explains the performance
improvement in year +3, supporting the view that financial strategies are short term efficiency
measures aimed at a quick turnaround (Chowdhury and Lang, 1996). The results in Table 7 also
show that, the change in operating performance in year t is significantly and negatively associated
with performance improvement in year +1 (t=2.08). It is significantly positively associated with
changes in performance in years +2 and +3 (t=12.71 and 6.11), confirming the theory that prior
accounting earnings can influence current performance (Penman, 1991; Fama and French, 1995).
Although financial strategies play a role in the corporate recovery process, most of the
performance improvement, especially in years +2 and +3, are explained by prior operating
performance.
4The reported statistics reflect the winsorisation of the observations that lie ±3 standard deviation from the
mean. The reported t-statistics are based on White (1980) robust standard errors.
1515
Insert Table 7 about here
5. Corporate restructuring activities following performance shocks
This section describes the appointment of new CEOs, asset sales, divestitures and
employee layoff activities following performance shocks. The list of divestitures is compiled
from Thomson Financials Securities Data Collection Platinum database. A divestiture is defined
as a sale of subsidiary by the parent to a third party, which could include investor group
comprising the management of the divested subsidiary. In order to eliminate very small
divestitures that are likely to introduce noise in the models, we require the value of the transaction
to be at least $US10 million. This value is much lower than US$100 million and US50 million
cut off points used by Mulherin and Boone, (2000) for the U.S and Powell and Yawson (2004)
for the UK market, respectively. However, the smaller size of the Australian market warrants the
use of a much lower value. Comparatively, the minimum transaction value used in this paper is
much higher than the minimum value of A$0.5 million imposed by da Silva Rosa et al., (2004) in
their study of the market for takeover advisers in Australia. The information on the appointment
of new CEOs and employee layoff announcements are obtained from Signal G records through
the Securities Industry Research Centre of the Asia-Pacific (SIRCA).5 Asset sale is defined as sale
of plant, property and equipment with a value of at least 5% of the total book value of assets.
Table 8 summarises the major restructuring activities pursued by the sample firms.
Consistent with US evidence, the most common corporate restructuring action following
performance shocks is asset reduction (10.8% of the sample). Within this group, 12 firms (3.01%
of sample) divested subsidiaries whilst 31 firms (7.8% of sample) disposed of assets.6 All the
5 Signal G is the data feed provided by the ASX to communicate corporate announcements to brokers and investors. 6 In identifying divestitures, layoffs and CEO appointments, multiple events for a given firm are
consolidated. For example, if a firm divested two or more times in the same year, only one observation is
recorded. This approach reduces the number of activities, but it is unlikely to bias the results.
1616
subsidiaries and the assets were sold for cash, indicating the importance managers put on cash
inflow in the corporate turnaround process. Also, twenty two firms (5.5 % of sample) reported a
change in CEO. In comparison, Ofek (1993) reports 21% CEO replacement for US firms whilst
Kang and Shivdasani (1997) document 14% for Japanese firms. Thus, the replacement of CEOs
following performance shocks in Australia is lower than those reported for the US and Japanese
firms. It should be emphasised that the studies used for comparison are rather old. To the extent
that the speed at which CEOs are replaced following performance shocks has changed in the US
and Japan over the past decade, the comparison will not be valid. Also, in the pre-performance
shock year, none of the sample firms announced employee layoffs. However, 8 firms (2.0% of
sample) announced employee layoffs between year 0 and year +1. There were 10 firms (2.5% of
sample) that engaged in at least two different restructuring activities from year 0 to year +1.7
Insert Table 8 about here
To appreciate the extent of a performance shock necessary to motivate a restructuring
activity, the sample firms are partitioned into quartiles conditioned on their industry adjusted ratio
of EBITDA to total assets in the year of the performance shock. Overall, 30.3% of restructuring
activities occurred in the 1st quartile whilst 27.6% occurred in the 4th quartile. There are however,
some differences in the frequency of individual restructuring events across quartiles. Over 8% of
divestitures occurred in the 1st quartile whilst 66.7% occurred in the 4th quartile. Although asset
sales are common across quartiles, they are more pronounced in the 4th quartile. The appointment
of new CEOs mostly occurs in the 1st quartile. Whilst 37.5% of employee layoffs occur in the 1st 7 Although our research design does not allow us to directly test the impact of corporate control, it is
important to realise that the external takeover market plays an important role in restructuring Australian
firms that have experienced performance shocks. To provide information on this issue, we compiled a list
of takeovers from the SDC Platinum database. In all, 13 firms (3.3% of sample) were acquired in friendly
deals between year 0 and 1.
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quartile, none of this event occurs in the 4th quartile. Despite these differences, our Pearson’s χ2
test suggests that there is no difference in the frequency of restructuring activities among firms in
the 1st and 4th quartiles of performance declines, with the sole exception of divestitures. This
suggests that corporate restructuring activities are pursued by poorly performing firms
irrespective of the magnitude of the performance shock. This seems to indicate that self selecting
a sample from a list of poorly performing firms, which has characterised most previous studies, to
evaluate their restructuring activities is unlikely to add any value to the analysis.
5.1. Determinants of corporate restructuring activities following performance shocks
In view of the fact that over 80% of the sample firms did not engage in any restructuring
activity from year 0 to year +1, we are inclined to investigate the determinants of restructuring
likelihood following performance shocks. To pursue this issue further, we estimate a multinomial
logit model, testing the association between the likelihood of a restructuring choice and a set of
financial variables. The multinomial logit model specifies the probability Pij that firm i will select
outcome j following a performance shock (be a non-restructuring firm if j=0; be a takeover target
if j=1; layoff employees if J=2; divest if J=3; appoint a new CEO if J=4, and sell assets if j=5).
Hence, the dependent variable takes the values 0, 1, 2, 3, 4 and 5. Xij, is a vector of explanatory
variables and β is a vector of unknown parameters to be estimated. The vector of variables
include: return on assets, dividend ratio, financial leverage, operating expenses, revenue growth
and working capital. These variables measure the industry adjusted change in financial conditions
from year -1 to year 0. We also include firm size because large firms are more likely to
restructure following performance shocks. The model is specified as follows:
Pij = )'exp(1
)'exp(
ij
ij
XXβ
β
∑+
(2)
1818
In order to identify the parameters of the model, the normalisation β0 = 0 is imposed
(Maddala, 1983). The maximum likelihood technique is used to estimate the model’s parameters.
The estimation procedure yields five sets of coefficients, representing each of the restructuring
choices relative to the non restructuring firms.
Insert Table 9 about here
The results in column 5 of Table 9 indicate that the decision to sell assets following
performance shocks is negatively related to return on investments. This result is consistent with
the findings by Denis and Shome (2004) who document significant negative relationship between
prior operating performance and the likelihood to downsize. Furthermore, the results in column 1
and 3 of Table 9 indicate that the likelihood to divest or be acquired following a performance
shock is positively related to firm size. The coefficients indicate that divestitures are more
sensitive to firm size than takeovers. This result is consistent with prior evidence that suggest that
large firms coping with performance declines are more likely to divest in order to focus operation
on core business areas (eg. Lang et al., 1995; Berger and Ofek, 1999). Moreover, there is
evidence to indicate that large firms experiencing low revenue growth are more likely to replace
their CEOs following a performance shock. Note that there is no relevant variable that explains
the decision to layoff employees following performance declines. The general implication of
these findings is that, although firms respond to performance shocks with a variety of
restructuring activities, the likelihood of a restructuring event cannot be easily determined by the
change in financial conditions in the prior year. Perhaps ownership and strategic factors can better
explain choice of a restructuring activity following performance shocks. This is an avenue for
future research.
5.2. Impact of corporate restructuring activities on firm performance
1919
We further investigate the extent to which corporate restructuring events contribute to
performance improvements. We relate restructuring activities from year 0 to year +1 to the
changes in firm performance from year 0 to year +3. Since we have no prior evidence on the
functional form, we assume based on the empirical literature that restructuring events will be
linearly related to performance improvement (eg., Kang and Shivdasani, 1997; Denis and Kruse,
2000). Hence, we estimate the following cross sectional equation;
titiit
titititiit
SIZEEBITDASALECEODIVLAYEBITDA
ελλλλλλλτ
++∆+
++++=∆
−
+
6,15
43210,
(3)
Insert Table 10 about here
where τ = years +1, +2 and +3 and ti captures the restructuring activities from year 0 to year +1.
Again, we estimate this regression for each of the three horizons. We also control for firm size
and change in performance in the previous period. As presented in Table 10, the only
restructuring activity that results in a performance improvement in year +1 is asset sales (t=2.13).
Asset sales have a negative contemporaneous impact on firm performance. This finding suggests
that an ad hoc decision to eliminate assets following a performance shock could result in
operating losses. Furthermore, the results show a positive and significant association between
employee layoffs and performance improvement in year +2 (t=2.06). Thus, employee layoffs
result in the firm achieving efficiency in operations through improved productivity of labour and
a reduction in labour cost. Furthermore, divestitures have a positive impact on firm performance
in year +3 (t=2.32). These results seem to suggest that employee layoffs and divestitures are
2020
strategic decisions that are taken with a long term corporate performance in view. Contrary to
expectations, the appointment of new CEOs has no significant impact on firm performance.8
6. Additional analysis and robustness checks 6.1 Impact of financial and corporate restructuring activities on firm performance
As pointed out in sections 4 and 5, both financial and corporate restructuring activities
play significant roles in dealing with performance shocks. Consequently, we model both
turnaround activities together to assess their complementary impact on firm performance. This
analysis also serves as a robustness check for the results reported in the previous sections. To
investigate this, we estimate the following cross sectional OLS regression where τ = years +1, +2
and +3 and ti measures financial and corporate restructuring activities from year 0 to year +1.
titiit
tititititi
tititititiit
SIZEEBITDASALECEODIVESTLAYWC
GROEXPLEVDIVYREBITDA
εβββββββ
βββββτ
++∆+++++∆+
∆+∆+∆+∆+=∆
−
+ ∑
11,110
98765
4321
93
030,
(4)
Insert Table 11 about here
8 One criticism of this approach is that it ignores restructuring activities that take place after year +1. To
overcome this problem, restructuring activities from year 0 to the year in which performance is measured
are taken into account. In results not reported, we find a significant positive association between employee
layoffs and performance improvement in year +2 but divestitures become insignificant in year +3. Assets
sales become negative and significant for both years +2 and +3.
2121
Since the Australian economy experienced a positive growth throughout the sample
period, an improvement in firm performance could be attributed to the general growth of the
economy and not necessarily to the turnaround actions pursued by management. We control for
this effect by including yearly dummies in the models. The variable YR is an indicative variable
that equals one if a performance improvement is recorded in year Y and zero otherwise. Table 11
presents OLS results from equation 3 for each of the three horizons. Consistent with the previous
results, financial leverage, operating expenses and asset sales are negatively and significantly
associated with performance improvement in year +1. The results suggest that firms that are able
to reduce their financial leverage probably from cash realised from asset reduction strategies and
the conversion of debt into equity instruments experience performance improvement. The results
for year +2 are essentially the same in signs and significance as those reported in Tables 8 and 10.
Two major differences should, however, be noted. First, the results show that CEOs have a
positive impact on firm performance in year +2, whereas working capital becomes negative and
significant (t=1.72 and 1.71). Thus consistent with theory, new CEOs improve performance but
there is a time lag for their impact, though marginal, to be felt. Similar to the previous models,
divestitures are positively and significantly related to performance improvement in year +3.9
Overall, the results suggest that financial and corporate restructuring strategies play
complementary roles in dealing with performance shocks.
6.2. Interaction effects
9 It is possible that the performance improvements achieved by the sample firms are influenced by the
manipulation of the asset structure by management. For example, management may use a substantial
amount of off balance sheet assets, which can potentially decrease the asset base, resulting in the
improvement in the ratio of EBITDA to total assets. As a further robustness check, we remove firm size
and replace it with change in size, but the results remain unchanged.
2222
An important consideration in implementing turnaround strategies is that a turnaround
action pertaining to one strategy may impact other types (Chowdhury and Lang, 1996) which
could potentially lead to an incremental effect on firm performance. This hypothesis is grounded
in the synergy effect theory, where the implementation of two strategies may result in a better
performance improvement. For example, if employee layoffs contribute to higher employee
productivity, it will reflect in revenue growth which could impact firm performance. Also, a new
CEO may pursue an aggressive revenue generating strategies, both of which could impact firm
performance. As hypothesised in table 1, corporate restructuring activities and financial strategies
(revenue growth and financial leverage) are predicted to have a positive impact on firm
performance. Hence, we expect the interaction of revenue growth and leverage with the
restructuring activities to provide a positive incremental effect on firm performance. 10
Specifically, we replicate equation 3, permitting the coefficients on corporate restructuring
variables to vary with revenue growth and leverage.
The regression results are reported in Panel B of table 11. Consistent with predictions, the
incremental coefficient on new CEO interacted with revenue growth is positive for all three
horizons but significant in years +1 and +3 only (t=3.86 and 1.65). This result suggests that firms
that appoint new CEOs and are able to increase their revenue have greater chance of achieving a
recovery. Surprisinly, layoffs interacted with revenue growth result in a negative incremental
impact on firm performance in year +1 (t=2.15). This finding is counterintuitive. However, layoff
interacted with revenue growth result in a positive incremental effect on firm performance in year
+2 (t=1.76). Furthermore, divestitures interacted with revenue growth results in a significant
positive incremental effect on firm performance in year +2 (t=1.86). Thus firms that are able to
streamline their operations by eliminating misfit subsidiaries and are also able to pursue revenue
growth strategies at the same time have a greater chance of recovery. Asset sales interacted with
10 We do not provide the interactive effects for all variables because there are no clear expectations when a
corporate restructuring event and a financial strategy result in an opposing effect on firm performance.
2323
revenue growth results in an incremental negative impact on firm performance. Thus, the
interaction of financial and corporate restructuring activities can result in an incremental effect on
firm performance.
7. Summary and concluding remarks
This paper provides evidence that both financial and corporate restructuring strategies
play important roles in dealing with performance shocks. Regarding financial strategies, the
results show a negative contemporaneous effect of financial leverage and operating expenses on
performance improvements. Growth in revenue has a one year positive lagged impact on firm
performance. These results reflect short term efficiency gained through prudent adjustments to
financial strategies (Chowdhury and Lang, 1996). Second, prior evidence suggests that corporate
restructuring events are common among firms that have experienced performance declines (Ofek,
1993; Kang and Shivdasani, 1997; Denis and Kruse, 2000). Consistent with prior studies, we find
asset sales, divestitures, new CEO appointments, employee layoffs and friendly takeovers to be
popular restructuring strategies pursued by Australian firms that have experienced performance
shocks. Whilst the determinant of a restructuring choice following performance shocks cannot
easily be determined by financial variables, there is evidence that lower return on investment
compared with non restructuring firms can determine the likelihood of asset sales. This is similar
to the findings by Denis and Shome (2004). Also, firm size is positively related to the likelihood
of divestiture and takeovers whilst CEO change is significantly related to lower revenue growth
and positive firm size. Further analysis indicates that asset sales have a contemporaneous negative
impact on firm performance. However, we find employee layoffs and new CEO appointments to
be positively associated with performance improvement in year +2. Divestitures are found to be
positively associated with performance improvements in year +3. These findings suggest that
employee layoffs, divestitures and new CEOs have long term impact on firm performance.
Furthermore, there is evidence that the interaction between corporate restructuring events and
financial strategies provides an incremental effect on performance improvement. More
interestingly, we find the appointment of new CEOs interacted with revenue growth to have a
2424
positive contemporaneous effect on firm performance, whilst layoffs and divestitures interacted
with revenue growth have a lagged impact on firm performance.
Our analysis offer several suggestions for corporate executives responsible for designing
effective turnaround strategies to reverse a performance decline. First and foremost, it is
important for management to make serious attempt to reduce financial leverage and operating
expenses when their firms encounter performance shocks in order to achieve a quick turnaround.
Furthermore, management should consider disposing of misfit subsidiaries in order to concentrate
on core business areas to achieve organisational efficiency. The board can also appoint a new
CEO who can competently facilitate the transition from the performance shock to corporate
recovery. These specific actions are consistent with the proposals in the turnaround literature, and
are essential for firms to recover from performance shocks. It is worth noting that performance
shocks are reversible through proper application of financial and corporate restructuring strategies.
However, management should be aware that turnaround actions could have either
contemporaneous or lagged impact on operating performance. Consequently, we recommend
management and the board to have constructive debates to enable them design effective
turnaround strategies with the view to achieving a recovery.
References Aboody, D., Barth, M., and Kasznik, R., 1999. Revaluation of fixed assets and future firm
performance: Evidence from the UK. Journal of Accounting and Economics 26, 149-178.
Arogyaswamy, K., Barker, V. L., and Yasai-Ardekani, M. 1995. Firm turnarounds: An interactive
two stage model. Journal of management Studies, 32, 493-525.
Barker III, V. L., and Duhaime, I. M., 1997. Strategic change in the turnaround process: theory
and empirical evidence. Strategic Management Journal 18, 13-38.
Berger, P., Ofek, E., 1999. Causes and effects of corporate refocusing programs. Review of
Financial Studies 12, 311-345.
2525
Bhattacharya, S., 1979. Imperfect information, dividend policy, and the “bird in the hand fallacy”.
Bell Journal of Economics 10, 259-270.
Chen, P., Mehrotra, V., Sivakumar, R., and Yu W., 2001. Layoffs, shareholders’ wealth and
corporate performance. Journal of Empirical Finance 8, 171-199.
Chowdhury, S. D., 2002. Turnarounds: A stage theory perspective. Canadian Journal of
Administrative Sciences 19, 249-266.
da Silva Rosa, R., Lee, P., Skott, M., and Walter, T., 2004. Competition in the market for
takeover advisers. Australian Journal of Management 29, 61992.
Chowdhury, S. D., and Lang, J. R., 1996. Turnaround in small firms: An assessment of efficiency
strategies. Journal of Business Research 36, 169-178.
Denis, D. J., and Denis D. K., 1995. Performance changes following top management dismissals.
Journal of Finance 50, 1029-1057.
Denis, D. J. and Kruse, T. A., 2000. Managerial discipline and corporate restructuring following
performance decline. Journal of Financial Economics 55, 391-424.
Denis, D. K. and Shome, D. 2004. An empirical investigation of corporate asset downsizing.
Forthcoming, Journal of Corporate Finance.
Fama, E. F. 1980. Agency problems and the theory of the firm. Journal of Political Economy 88,
288-307.
Fama, E. F., and French, K., 1995. Size and book-to-market factors in earnings and returns.
Journal of Finance 50, 131-155.
Grullon, G., Michaely, R., Swaminathan, B., 2002. Are dividend changes a sign of firm maturity?
Journal of Business 75, 387-424.
Iverson, R. D., and Pullman, J. A. 2000. Determinants of voluntary turnover and layoffs in an
environment of repeated downsizing following a merger: An event history analysis. Journal
of Management 26, 977-1003.
Jensen, M. C., 1989. Eclipse of the public corporation. Havard Business Review 67, 60 74.
2626
Jensen, M. C., and Meckling, W. H., 1976. Theory of the firm: managerial behaviour, agency
costs and ownership structure. Journal of Financial Economics 3, 308-360.
John, K., Lang, L., and Netter, J., 1992. The voluntary restructuring of large firms in response to
Performance decline. Journal of Finance 47, 891-918.
John, K., and Ofek, E. 1995. Asset sales and increase in focus. Journal of Financial Economics 37,
105-126.
Kahl, M., 2002. Economic Distress, financial distress, and dynamic liquidation. Journal of
Finance 57, 135-168.
Kang, J. and Shivdasani, A. 1997. Corporate restructuring during performance declines in Japan.
Journal of Financial Economics 46, 29-65.
Kim, A. K., Kitsabunnarat, P. and Nofsinger, J. R. 2004. Ownership and operating performance
in an emerging market: evidence from Thai IPO firms. Journal of Corporate Finance 10,
355-381.
Lang, L., Poulsen, A., and Stulz, R. 1995. Asset sales, firm performance and the agency costs of
managerial discretion. Journal of Financial Economics 37, 3-37.
Lie, E. 2004. Operating performance following dividend decreases and omissions. Journal of
Corporate Finance. Article in press.
Maddala, G. S. 1983. Limited Dependent and Qualitative Variables in Econometrics.
Econometrics Society Monographs, Cambridge University Press, Cambridge.
Morck, R., Shleifer, A. and Vishny, R. W., 1989. Alternative mechanisms for corporate control.
American Economic review, Vol. 79: 842-852.
Myers, S., and Majluf, N. S., 1984. Corporate financing and investment decisions when firms
have information that investors do not have. Journal of Financial Economics 13, 187-221.
Ofek, E., 1993. Capital structure and firm response to poor performance: An empirical analysis.
Journal of Financial Economics 34, 3-30.
2727
Pant, L. W., 1987. Fuelling corporate turnaround through sales growth. Journal of Commercial
Bank Lending 70, 25-32.
Penman, S., 1991. An evaluation of accounting rate of return. Journal of Accounting, Auditing
and Finance 6, 233-255
Pourciau, S., 1993. Earnings management and nonroutine executive changes. Journal of
Accounting and Economics 16, 317-336.
Pound, J., 1992. Beyond takeovers: politics comes to corporate control. Harvard Business Review
70, 83-93.
Powell, R. G., and Yawson, A., 2004. Industry aspects of takeovers and divestitures: evidence
from the UK. Journal of Banking and Finance, Forthcoming.
Slatter, S. 1984. Corporate recovery: successful turnaround strategies and their implementation,
first edition. Penguin Books, New York.
Stulz, R. M. 1990. Managerial discretion and optimal financial policies. Journal of Financial
Economics 26, 3-28.
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Table 1 List of variables and their expected signs
Variable Definition Expected
sign Dividend payout ratio (DIV) Dividend/Net income - Financial leverage (LEV) Total Debt/Total share capital and reserves +/- Operating expense (EXP) Operating Expense/Operating income - Working capital (WC) Current Assets less current liabilities - Growth (Growth) Average change in total revenue + Size Log of total assets +
New CEO (CEO) Dummy equals 1 if a new CEO is appointed, otherwise 0 +
Sale of assets (Sale) Cash received from sale of assets/Total assets +
Divestitures (DIVEST) Dummy equals 1 when a firm sells a subsidiary, otherwise 0 +
Employee layoffs (LAY) Dummy equals 1 if a firm announces employee layoffs, otherwise 0 +
This table reports the variable proxies used for the main hypotheses and their expected signs. A positive sign indicates that the variable increases performance improvements and a negative sign implies the opposite.
2929
Table 2 Annual distribution of sample firms Year Total % Mean % Median % 1992 13 3.26 -0.32 19.75 -0.11 22.00 1993 36 9.02 -0.08 4.94 -0.05 10.00 1994 44 11.03 -0.13 8.02 -0.05 10.00 1995 56 14.04 -0.14 8.64 -0.05 10.00 1996 44 11.03 -0.12 7.41 -0.04 8.00 1997 43 10.78 -0.24 14.81 -0.05 10.00 1998 50 12.53 -0.10 6.17 -0.04 8.00 1999 45 11.28 -0.36 22.22 -0.05 10.00 2000 68 17.04 -0.13 8.02 -0.06 12.00 Total 399 100 - 100 - 100 Mean 44 11.11 -0.18 11.11 -0.05 11.11 Median 44 11.03 -0.13 8.12 -0.05 9.91
Annual distribution of sample firms in the year of the performance shock is reported. The table also reports mean and median performance recorded in the year of the performance shock.
3030
Table 3 Analysis of industry adjusted operating performance Operating performance Mean Median t-ratio U Panel A Performance in year -1 0.099 0.036 8.078*** 8.802*** Performance in year 0 -0.166 -0.047 5.203*** 7.937*** Performance in year +1 -0.139 -0.031 7.200*** 5.336*** Performance in year +2 0.087 -0.033 0.237 9.338*** Performance in year +3 -0.123 -0.021 5.933*** 4.008*** Panel B Change in performance year -1 to year 0 -0.265 -0.083 7.690*** 25.634***Change in performance year 0 to year +1 0.027 0.016 0.748 4.623*** Change in performance year 0 to year +2 0.253 0.014 0.685 4.368*** Change in performance year 0 to year +3 0.043 0.026 0.715 4.429***
Mean and median industry adjusted ratio of EBITDA to total assets for the years -1 to +3. Panel A reports industry adjusted operating performance from year -1 to year +3. Panel B reports mean and median changes in operating performance from year -1 to 0, and from year 0 to each of the three years following performance declines. Statistical significance of the mean (median) difference is based on a two sided paired sample t-test (Mann Whitney U) under the null hypothesis of mean (median) difference of zero. ***,**,* denotes statistical significance at the 1%, 5% and 10% level, respectively, using a two tailed test.
3131
Table 4 Analysis of survivorship bias Survivors Non survivors Year Number Mean Median Number Mean Median t-ratio U Year 0 399 -0.166 -0.047 - - - - - year +1 395 -0.169 -0.047 4 -0.101 -0.059 1.334 0.016 Year +2 388 -0.170 -0.047 11 -0.086 -0.053 2.136** 0.226 Year +3 367 -0.175 -0.047 32 -0.085 -0.039 2.294** 0.549
Mean and median industry adjusted ratio of EBITDA to total assets for firms that survived the performance shock and those that could not survive. Statistical significance of the mean (median) difference is based on a two sided paired sample t-test (Mann Whitney U) under the null hypothesis of mean (median) difference of zero. ** denotes statistical significance at the 5% level, using a two tailed test.
3232
Table 5 Changes in the financial characteristics of sample firms Change in means Change in median
Financial strategy Year -1 to year 0
Year 0 to year +1
Diff. in means t-ratio
Year -1 to year 0
Year 0 to year +1
Diff. in medians U
Dividend ratio 0.036 -0.069 -0.105 2.501** 0.000 0.000 0.000 0.355Financial leverage
0.014 0.002 -0.012 0.536 0.028 0.011 -0.018 1.327Operating expense 0.185 0.346 0.161 1.844* 0.211 -0.015 -0.226 8.542***Growth in revenue -0.809 -0.082 0.727 2.252** -0.06 0.034 0.094 5.701***Working capital 0.085 -0.255 -0.34 2.167** -0.158 -0.110 0.048 0.806 Size -0.181 -0.127 0.054 0.446 -0.306 -0.257 0.049 0.245
Mean and median changes in industry adjusted financial variables for year -1 to year 0, and year 0 to year +1. The definitions of the variables are provided in Table 1.The sample consists of firms that have experienced substantial decline in their industry adjusted ratio of EBITDA to total assets for the period 1992 to 2000. Statistical significance of the mean (median) difference is based on a two sided paired sample t-test (Mann Whitney U) under the null hypothesis of mean (median) difference of zero. ***,**,* denotes statistical significance at the 1%, 5% and 10% levels, respectively, using a two tailed test.
3333
Table 6 Correlation analysis
Variable Dividend
ratio Financial leverage
OperatingExpense
Revenue growth
Working capital
Employee layoffs Divest
New CEO
Asset sales Size
Dividend ratio 1.000 Financial leverage 0.043 1.000 Operating expense 0.014 -0.026 1.000 Revenue growth -0.014 0.075 -0.176*** 1.000 Working capital -0.033 -0.191*** 0.087* -0.018 1.000 Employee layoffs 0.004 -0.023 -0.048 0.012 0.008 1.000 Divestitures
0.029 -0.015 -0.047 0.014 0.026 -0.025 1.000New CEO 0.042 0.030 -0.049 0.015 0.026 0.043 0.086 1.000 Asset sales 0.025 -0.001 0.038 -0.034 0.065 0.025 -0.052 -0.030 1.000Size 0.021 0.007 -0.023 -0.045 0.050 -0.001 0.360*** 0.117** -0.099** 1.000
Correlation coefficients for the independent variables used in this paper. The definitions of the variables are provided in Table 1. ***,**,* denotes statistical significance at the 1%, 5% and 10% levels, respectively, using a two tailed test.
3434
Table 7 Cross sectional OLS regressions results for financial turnaround activities Year +1 Year +2 Year +3 Variable coeff. t-ratio coeff. t-ratio coeff. t-ratio Constant 0.049 2.060** -0.013 0.540 0.012 0.400 Dividend ratio 0.013 0.910 -0.021 1.470 -0.008 0.390 Financial leverage -0.578 2.450** -0.164 1.840* -0.040 0.200 Operating expense -0.005 2.190** 0.002 0.990 0.001 0.650 Growth in revenue -0.005 1.480 0.018 2.320** 0.000 1.620 Working capital -0.001 1.420 -0.001 1.430 0.001 0.970 Change in EBITDA -0.400 2.080** 0.886 12.710*** 0.782 6.110***Size 0.047 2.870*** 0.018 1.330 -0.031 1.870* F statistics 3.11*** 36.56*** 7.48*** Adjusted R2 0.49 0.69 0.56 Observation 395 388 367
Cross sectional OLS results for each of the three horizons. The dependent variable in each of the regression is the change in the industry adjusted ratio of EBITDA to total assets from year 0 to years +1, +2 and +3, respectively. The independent variables are changes in the industry adjusted variable in the prior to the year of performance measurement. The reported t statistics are based on White (1980) robust standard errors. See Table 1 for the definition of variables. ***, **, * denotes statistical significance at the 1%, 5% and 10% levels, respectively, using a two tailed test.
3535
Table 8 Frequency of corporate restructuring activities
Restructuring event Year -1
Year
0 to +1 % of
sample
1st
Quartile %
2nd Quartile
%
3rd Quartile
%
4th Quartile
%
Pearson χ2
Employee layoffs 0 8 2.01 37.50 25.00 37.50 0.00 3.00Divestitures 7
12 3.01 8.33 0.00 25.00 66.67 5.44*New CEO 4 22 5.51 45.45 9.09 22.73 22.73 1.66Asset sales 24 31 7.77 29.03 25.81 22.58 22.58 0.25Successful takeovers 4 13 3.26 15.38 23.08 30.77 30.77 0.66Overlapping events -1 -10 -2.51 20.00 10.00 40.00 30.00 0.20Total restructuring 38 76 19.05 30.26 18.42 23.68 27.63 0.09Non restructuring
361 323 80.95 - - - -
Number of firms 399 399 100.00 - - - - The Table reports the corporate restructuring events of firms following performance shocks for a sample of firms listed on the ASX. Information on divestitures and takeovers are compiled from Thomson Financials Securities Data Collection Platinum database. Divestitures represent the number of firms that sold subsidiaries to third parties. The information on the appointment of new CEOs and employee layoff announcements is provided by the Securities Industry Research Centre of Asia-Pacific (SIRCA). Asset sale is defined as sale of plant property and equipment with value of at least 5% of the total book value of assets. Overlapping events records firms that engaged more than on restructuring activity in the three years following the performance shock. The Pearson χ2 test the hypothesis that the frequency of restructuring events between the 1st and the 4th quartiles are equal. * denotes statistical significance at the 10%, level, using a two tailed test.
3636
Table 9 Determinants of corporate restructuring activities
Variable Takeovers Layoffs Divestitures New CEOs
Asset sales
Constant -3.372 -3.659 -5.049 -2.713 -2.469 (9.948)*** (9.448)*** (6.562)*** (10.895)*** (11.141)***Return on assets -0.712 -0.427 -0.157 -0.544 -1.151 (1.131) (0.468) (0.231) (0.997) (3.024)***Dividend ratio -0.040 0.107 0.654 0.662 0.224 (0.087) (0.156) (0.620) (0.883) (0.488) Financial leverage -0.158 -0.591 -1.257 0.485 -0.222 (0.139) (0.594) (1.357) (0.494) (0.336) Operating expense -0.002 -0.107 -0.159 -0.065 -0.013 (0.114) (1.174) (1.069) (1.569) (0.884) Revenue growth -0.020 -0.009 -0.039 -0.043 -0.020 (0.988) (0.206) (0.485) (2.241)** (1.324) Working capital -0.002 0.004 -0.001 0.008 0.014 (0.255) (0.251) (0.055) (0.650) (1.396) Size 0.399 0.052 1.166 0.345 -0.165 (2.468)** (0.226) (5.131)*** (2.627)*** (1.319) Likelihood Ratio (df=35) 85.020 McFadden R2 0.130
A mmultinomial logit regression testing the association between financial variables and the likelihood of restructuring. The definition of the variables is provided in Table 1. ***,**,* denotes statistical significance at the 1%, 5% and 10% level, respectively, using a two tailed test
37
Table 10 Cross sectional OLS regressions results for corporate restructuring activities Year +1 Year +2 Year +3 Variable coeff. t-ratio coeff. t-ratio coeff. t-ratio Constant 0.061 2.260** -0.013 0.580 -0.002 0.050 Employee layoffs -0.042 0.320 0.310 2.060** 0.020 0.350 Divestitures 0.113 0.540 -0.027 0.560 0.165 2.320**New CEO -0.031 0.270 0.049 0.920 0.058 0.860 Asset sales -0.276 2.130** 0.074 0.640 -0.027 0.310 Change in EBITDA -0.480 2.040** 0.913 14.510*** 0.780 6.080***Size 0.043 2.740*** 0.017 1.100 -0.042 2.100**F statistics 2.3** 37.9*** 9.2*** Adjusted R2 0.42 0.68 0.55 Observation 395 388 367
Cross sectional OLS results testing the relationship between performance improvement and corporate restructuring activities for each of the three horizons. The dependent variable in each of the regression is the change in the industry adjusted ratio of EBITDA to total assets from year 0 to years +1, +2 and +3, respectively. The reported t statistics are based on White (1980) robust standard errors. See Table 1 for the definition of variables. ***,**,* denotes statistical significance at the 1%, 5% and 10% levels, respectively, using a two tailed test.
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Table 11 Cross sectional OLS regressions results for financial and corporate restructuring activities Panel A: Main effects Panel B: Interaction effects Year +1 Year +2 Year +3 Year +1 Year +2 Year +3 Variable Coeff t-ratio Coeff t-ratio Coeff t-ratio Coeff t-ratio Coeff t-ratio Coeff t-ratioDividend ratio 0.014 0.940 -0.031 1.410 -0.003 0.130 0.025 1.540 -0.031 1.310 -0.002 0.110 Financial leverage -0.577 2.400** -0.138 1.550 -0.028 0.140
-0.629 2.190** -0.168 1.820* -0.029 0.120Operating expense -0.005 2.230** 0.003 1.450 0.001 0.840 -0.006 2.520** 0.003 1.390 0.001 0.600 Growth -0.006 1.550 0.018 2.550** 0.003 1.730* -0.008 1.880* 0.013 1.730* 0.009 1.970** Working capital -0.001 1.220 -0.001 1.710* 0.001 1.230 -0.001 0.920 -0.001 1.710* 0.001 1.320 Employee Layoffs
-0.054 0.680 0.334 2.160** 0.021 0.300 0.035 0.410 0.216 1.540 0.041 0.530
Divestitures 0.031 0.170 -0.093 1.500 0.168 2.100** 0.199 0.990 -0.041 1.160 0.158 1.820*New CEO -0.043 0.440 0.109 1.720* 0.038 0.540 0.028 0.310 0.109 1.970** 0.084 1.110 Asset sale -0.264 2.140**
0.078 0.660 0.000 0.000 -0.287 2.130** 0.069 0.600 -0.021 0.220
CEO*Leverage - - - - - - -0.207 0.610 0.228 0.850 -0.991 1.720*CEO*growth - - - - - - 0.016 3.860*** 0.019 0.500 0.004 1.650*
Layoff*Leverage - - - - - - -0.178 0.400 -0.500 1.210 0.111 0.300Layoff*growth - - - - - - -0.052 2.150** 0.083 1.760* 0.019 0.740Divest*Leverage - - - - - - -1.077 0.930 0.246 0.610 0.429 1.100Divest*growth - - - - - - -0.387 1.300 0.140 1.860* 0.051 0.900Asset sale*Leverage - - - - - - 0.222 0.590 0.166 0.340 0.086 0.280Asset sale*growth
- - - - - - 0.004 0.920 0.020 0.850 -0.001 2.290**
EBITDA -0.386 2.130** 0.906 13.020*** 0.787 6.420*** -0.383 2.130** 0.900 12.570*** 0.785 6.240***Size 0.028 2.950***
0.023 1.450 -0.042 1.960** 0.028 2.970*
0.007 0.480 -0.042 1.870*
F-stat 2** 16*** 7*** 15*** 29*** 107***Adjusted R2 0.52
0.71 0.57 0.53 0.43 0.57
Observations 395 388 367 395 388 367Cross sectional OLS results for both financial and corporate restructuring strategies and a series of interacting effects. Panel A reports the main effects only. Panel B permits the coefficients on both financial and corporate restructuring strategies to differ depending on the interaction between them. The dependent variable is the change in operating performance following the performance shock. The definitions of the independent variables are provided in Table 1. ***,**,* denotes statistical significance at the 1%, 5% and 10% levels, respectively, using a two tailed test. The regression equations include untabulated year-specific intercepts.
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