Submission to
11th Auckland Regional Conference, 30th November
2012, Auckland, New Zealand
Financial distress, earnings management and
market pricing of accruals during the global
financial crisis
Ahsan Habib
Associate Professor
Department of Accounting, AUT University
Auckland, New Zealand
Md. Borhan Uddin Bhuiyan
Lecturer
School of Accountancy, Massey University
Auckland, New Zealand
Ainul Islam
Senior Lecturer
School of Accounting and Commercial Law, Victoria University of Wellington
Wellington, New Zealand
Corresponding author
Md. Borhan Uddin Bhuiyan can be contacted at: m.b.u.bhuiyan@masssey.ac.nz
1
Abstract
Purpose - This paper empirically examines managerial earnings management practices for
financially distressed firms and whether these practices changed during the recent global
financial crisis.
Design - We investigate this topic in New Zealand, which was shaken by a series of finance
company collapses recently. We use three distress measures and use discretionary accruals, a
popular proxy for earnings management, to investigate the impact of distress on earnings
management.
Findings - We find that managers of distressed firms engage more in income-decreasing
earnings management practices compared to their healthy firm counterparts. We also
document that this effect became more pronounced during the financial crisis period. Finally,
we show some evidence of positive market pricing of discretionary accruals in the non-crisis
period but a substantial reduction in pricing coefficient during the global financial crisis
period.
Research limitations/Implications - We believe that the findings of this study would be
useful to current and prospective investors as well as to regulatory authorities who are
responsible for monitoring managerial financial reporting quality. Income-decreasing
earnings management by the distressed firms as reported in this study distorts the quality of
reported information and therefore makes it difficult for investors to adequately predict future
firm performance. The findings will also help the regulatory authorities to closely monitor the
financial reporting quality of distressed firms.
Originality - This study provides evidence that financially distressed firms manipulate
earnings downwards. This association, however, is not significantly changed during the GFC
period. New Zealand market considers discretionary accruals to be informative but only
during the non-crisis period. During the crisis period the market penalizes discretionary
accruals reported by firms
Keywords Financial distress, Earnings management, Global financial crisis, New Zealand
2
Financial distress, earnings management and
market pricing of accruals during the global
financial crisis
1. Introduction
The objective of this paper is to empirically examine managerial earnings management
behaviour of financially distressed firms in New Zealand and whether this behaviour changed
during the global financial crisis (hereafter GFC). We also examine the market pricing of
discretionary accruals, a popular proxy for earnings management. Financial distress in
enterprises has long been an issue of concern to governments and the investing public.
Corporate financial performance can deteriorate for a numbers of reasons and in the extreme
may cause companies to go bankrupt or be subject to acquisition by other firms. Corporate
bankruptcies have significant adverse consequences for an economy since investors and
creditors suffer considerable financial loss. If a firm is in financial distress, the company’s
managers can expect to have their bonuses cut, be replaced and suffer loss of reputation
(Liberty and Zimmerman 1986; Gilson 1989). Therefore, conventional wisdom suggests that
managers have incentives to conceal such a deteriorating performance by resorting to
accounting choices that increase income. Empirical evidence, however, is not conclusive.
Rosner (2003), for example, documents that firms that become bankrupt ex post but
do not appear distressed ex ante, engage in income-increasing earnings manipulation
practices. DeAngelo et al. (1994), on the other hand, find that managers reduce income via
negative abnormal accruals and discretionary write-offs, instead of inflating income. Other
studies that examine accounting policy choice of troubled companies include Burgstahler and
Dichev (1997), Charitou et al. (2007a), Charitou et al. (2007b), Charitou et al. (2011), Elliott
3
and Shaw (1984), and Lillien et al. (1988). Although these studies examine samples of firms
experiencing some kind of financial difficulty, their sample periods did not include the GFC.
Extant literature on the effect of economic crises on managers’ earnings management
behavior is not conclusive. One analytical model suggests that managers are more likely to
manipulate earnings during an economic boom as opposed to a recession (Strobl 2008).
However, empirical evidence from the 1997 Asian financial crisis and earnings management
studies provide some evidence that managers engaged in more income-decreasing earnings
management during the crisis period (Saleh and Ahmed 2005; Ahmed et al. 2008). During
the GFC, the global credit market experienced severe illiquidity, investors’ confidence
significantly declined, and most of the listed firms on world stock exchanges experienced
downward pressure on their stock prices (Bartram and Bodnar 2009). Following the
increasing uncertainties in business environment, a recent survey in Asia, Europe and North
America, documents that the Chief Financial Officers (CFOs) responded to the economic
crisis by reducing investments to avoid business risk and financial constraints (Campello et
al. 2010).
We use data from New Zealand to shed further light on this issue. Recently, New
Zealand experienced a spate of finance company collapses, making the country’s financial
system more fragile and vulnerable.1 Although these collapses come nowhere close to Enron
or WorldCom type of collapses, they somewhat contribute indirectly to financial distress
experienced by firms. Previous research on the association between financial distress and
earnings management has primarily been done in the US. Whether the findings from studies
of the accounting choices of troubled firms in the US can also be generalized to New Zealand
1
During the period 2006-2010, 49 finance companies collapsed or entered moratoriums, owing investors
in excess of $8 billion. The size of these companies varied from $1.7million in Kiwi Finance (by deposit
size) to $1.6 billion in South Canterbury Finance (Yahanpath and Cavanagh 2011).
4
is an empirical question. The presence of concentrated ownership2 (Prevost et al. 2001),
relaxed monitoring by New Zealand regulatory authorities (Habib 2008), and a very low
litigation threat may impact on New Zealand corporate managers differently with respect to
accounting choices during financial distress. Previous research on accounting choices for
troubled companies included firms that went bankrupt, generating possible selection bias. We
include firm-year observations that are stressed but non-bankrupt and firm-year observations
delisted from the stock exchange.3 We also contribute to the market pricing of accruals
literature by investigating capital market pricing of earnings components including
discretionary accruals of the distressed firms, and whether the GFC moderated such pricing.
Using a sample of NZX listed firms from 2000 to 2011, this research documents that
financially distressed firms engaged in income-decreasing earnings management strategies
which is unaffected by the GFC. With respect to market pricing of the discretionary accruals
component of earnings, the evidence shows that the market positively prices discretionary
accruals but such a pricing coefficient significantly reduces during the GFC period.
The paper proceeds as follows. Section two provides a brief review of the literature on
the association between financial distress and managerial discretionary reporting behaviour
and develops testable hypotheses. Section three explains the research methodology employed
2
There are two competing hypotheses regarding the monitoring role of concentrated ownership. On the
one hand, efficient-monitoring hypothesis claims that large shareholders have great expertise and can
monitor management at lower cost than individual shareholders. So, ownership concentration can prevent
managers from expropriating company resources for their personal benefit (Berle and Means, 1932;
Huddart, 1993; Maug, 1998; Shleifer and Vishny, 1986). On the other hand, conflict-of-interest and
strategic-alignment hypotheses contend that ownership concentration can also give rise to severe agency
conflicts between majority and minority shareholders since the former group has the opportunity and
incentives to work for management (Faccio, Lang, & Young, 2001; Shleifer & Vishny, 1997). We control
for the effect of ownership concentration on earnings management behaviour in our regression
specifications.
3 Our sample includes a total of 107 firm-year observations coming from 24 unique firms that were
delisted from New Zealand Stock Exchange (NZX) during our sample period. However, the majority of
these delistings occurred because of mergers and acquisitions. This prevented us from separately
examining the effect of bankruptcy on earnings management strategies and the market perception of
earnings components.
5
in this study. Descriptive statistics and regression analysis results are presented in section
four, and section five concludes.
2.
Literature review and development of hypotheses
Financial distress in enterprises has long been an issue of concern to governments and the
investing public. A significant and persistent decline in a company’s financial performance
may eventually result in insolvency, making investors and creditors suffer considerable
financial loss. Financial distress is costly “… because it creates a tendency for firms to do
things that are harmful to debtholders and nonfinancial stakeholders …, impairing access to
credit and raising costs of stakeholder relationships. In addition, financial distress can be
costly if a firm’s weakened condition induces an aggressive response by competitors seizing
the opportunity to gain market share” (Opler and Titamn 1994, 1015). If a firm is in financial
distress, the company’s managers can expect to have their bonuses cut, be replaced and suffer
loss of reputation (Liberty and Zimmerman 1986; Gilson 1989).
However, financial distress does not necessarily lead to corporate bankruptcy.
McKeown et al. (1991), Hopwood et al. (1994), and Mutchler et al. (1997), study bankrupt
and nonbankrupt firms and classify them as (i) stressed/bankrupt (SB), (ii) nonstressed/
bankrupt (NSB), (iii) stressed/nonbankrupt (SNB), and (iv) nonstressed/nonbankrupt
(NSNB). The bankrupt/nonbankrupt classification was easily determined by observing firms’
ex post bankruptcy status. The stressed/nonstressed classification was based on ex ante signs
in the financial statements of impending bankruptcy. They assigned the SB status to a
bankrupt firm that exhibited any one of four symptoms: (i) negative working capital in the
current year; (ii) a loss from operations in any of the three years prior to bankruptcy; (iii) a
6
retained earnings deficit in year 3 (where year 1 is the last financial statement date preceding
bankruptcy); or (iv) a bottom-line loss in any of the last three pre-bankruptcy years.
Rosner (2003) employed this classification and finds that firms that become bankrupt
ex post but do not appear distressed ex ante (NSB), use income-increasing earnings
management techniques. DeAngelo et al. (1994), on the other hand, find that managers use
income-decreasing earnings management techniques via negative abnormal accruals and
discretionary write-offs, rather than attempts to inflate reported income. Charitou et al.
(2007a) use a sample of 859 US bankruptcy-filing firms over the period 1986-2004, and find
evidence that managers of highly distressed firms shift earnings downwards prior to the
bankruptcy filing. The findings may be attributed to earnings bath choices adopted by new
management teams during the distress period. This evidence is particularly pronounced for
companies with low levels of institutional ownership. Charitou et al. (2007b) also find
evidence of downwards earnings management, one year prior to the bankruptcy-filing. When
a specific context of debt covenant violations is examined, results are consistent with
managers increasing reported income via positive abnormal accruals in the year prior to
covenant violation (DeFond and Jiambalvo 1994; Sweeney 1994). Chen et al. (2010) find
that distressed firms in China employ income-increasing earnings management techniques to
avoid a delisting threat and special monitoring treatment from the government. Given the
inconclusive evidence on the association between firm distress and earnings management
strategies we develop the following null hypothesis:
H1: There is no association between financial distress and earnings management
proxied by discretionary accruals.
Our second set of analysis examines whether the GFC has any incremental impact on
the association between firm distress and earnings management. An analytical model
7
proposes that earnings management is most prevalent during economic booms (Strobl 2008).
Cohen and Zarowin (2007) find empirical support for this proposition. When business
conditions are good, most firms have high earnings. Investors thus correctly believe that few
firms have an incentive to manipulate their accounting statements. However, this is exactly
when the incentive for a manager of a low-value firm to issue an upwardly biased report is
highest since investors, in general, are not questioning the integrity of the reporting system.
In bad times, on the other hand, incentives for earnings management are low because
investors expect a large number of firms to manipulate their earnings and, hence, put less
emphasis on the observed reports.
However, empirical evidence from research using the 1997 Asian financial crisis
suggests otherwise. For example, Saleh and Ahmed (2005) find that during an economic
downturn in Malaysia, managers undertook income-reducing earnings management during
debt renegotiation, perhaps hoping to benefit from government support or improved
borrowing terms. Alternatively, managers may have recognized that the market tolerates poor
performance during an external shock (crisis) environment, so they may have depressed
earnings further, via accruals, to enable greater post-shock performance improvements to the
benefit of managers’ reputations (a ‘big bath’ argument). Chia et al. (2007) find that serviceoriented companies in Singapore engage in income-decreasing earnings management during
the crisis period.
Conventional wisdom suggests that an economic crisis should encourage managers to
adopt big bath accounting for at least two reasons. First, since investors expect companies to
report losses during bad times, big bath accounting is a rational managerial response during
bad times, and second, big bath accounting during the crisis period allows companies to
report positive earnings in the post-crisis period since accruals reverse. Whether such an
association will be more pronounced for distressed firms during the crisis is open to empirical
8
examination since non-distressed firms, too, can engage in income-decreasing earnings
management practices. The following hypothesis, therefore, is developed in null form:
H2: There is no incremental association between firm distress and earnings
management during the crisis period.
Finally, we examine the market pricing of earnings components for distressed firms
and whether the relation is changed during the GFC. The accruals component of earnings is
composed of discretionary accruals and non-discretionary accruals. The discretionary
accruals component of accruals is under managerial discretion and managers can use such
discretion to convey useful information or they can use it opportunistically. If managers use
discretionary accruals to convey private information about firm value then such accruals are
perceived positively by the market (informativeness view). On the other hand, if managers
use discretionary accruals for opportunistic reasons, then rational managers discount such
accruals (opportunistic view). Contrary to the conventional wisdom that discretionary
accruals represent managerial opportunism, empirical findings from the market pricing test
suggests an informative role of discretionary accruals (Subramanyam 1996; Guay et al. 1996;
Krishnan 2003; Chung et al. 2004).
However, empirical evidence on the ‘opportunism versus informativeness’ role of
discretionary accruals during an economic crisis is very limited. To the best of our
knowledge, Ahmed et al. (2008) and Choi et al. (2011) are the only published studies that
have examined the effect of the 1997 Asian financial crisis on the pricing of earnings
components including discretionary accruals. Ahmed et al. (2008) find that negative
discretionary accruals for debt renegotiating firms are associated with higher market values of
equity and are not related to a firm’s future earnings. These findings imply that investors
place a positive value on the probability that negative accruals increase the likelihood that
concessions can be extracted from lenders during renegotiation. In contrast, discretionary
9
accruals for a control sample of non-debt renegotiating firms are not significantly associated
with stock prices but are positively associated with future earnings. Investors’ confidence on
financial reporting quality during a crisis naturally declines, as they tend to associate
discretionary accruals more with managerial opportunism rather than efficient signaling.
Managerial opportunism-induced discretionary accruals have lower predictability with
respect to future cash flows and hence investors attach a negative information value to this
discretionary earnings component (Choi et al. 2011). However, Jenkins, Kane, and Velury
(2009) report that accounting conservatism and value relevance of earnings are higher during
economic contractions because firms generally report more conservatively to avoid sharp
increases in litigation risk and regulatory scrutiny during recession. In addition, investors
place great reliance on firms’ current earnings in their prediction of future earnings during a
period characterized by increased uncertainty. Whether firm distress has any moderating
effect on the market pricing of discretionary accruals during economic crisis has not been
examined before. We, therefore, develop the following null hypothesis:
H3: The market pricing of discretionary accruals for the distressed firms during the
GFC are not different from their non-distressed counterparts.
3.
Research design and measurement of variables
3.1
Sample selection
We begin with an initial sample of 1,200 firm-year observations from 1999 to 2011 with
required information to estimate the regression equations. We delete 302 firm-year
observations pertaining to finance, investment, and equity trust and funds because of the
different regulatory environments for which the standard accruals estimation technique is not
useful. We also lost 85 firm-year observations because we need lagged total assets data to
deflate the accruals variables. This leaves us with 813 firm-year observations. We then
10
eliminated industries with less than six observations in a year as we ran industry-year
discretionary accruals regressions. The choice of six observations is consistent with Rosner
(2003) and also allows us to retain more observations. We derive a final usable sample of 767
firm-year observations from 2000 to 2011. It should be noted that our sample includes a
broad group of firms that are SNB as well as NSNB observations, as per Hopwood et al.’s
(1994) and Mutchler et al.’s (1997) classifications. The sample selection procedure is
explained in Table 1.
[TABLE 1 ABOUT HERE]
3.2
Measurement of financial distress
We now explain the measurement of financial distress, our independent variable of primary
interest. We adapt the distress/non-distress classification of McKeown et al. (1991),
Hopwood et al. (1994), and Mutchler et al. (1997), and classify a company as stressed if it
exhibits at least one of the following financial distress signals:



Negative working capital in the most recent year;
A bottom line net loss in the most recent year;
BOTH negative working capital and net loss experienced in the most
recent years.
We assign a value of 1 for firm-year observations that meet any one of the above three
criteria, and 0 otherwise. Rosnser (2003) also uses a classification similar to this one although
her focus was on failing firms. Rosner (2003, 373), however, expresses concern about these
classifications schemes. Rosner argues that:
“…the stressed / nonstressed state can change from year to year. For example, a firm that
overstates earnings three years prior to bankruptcy … may not appear stressed and thus
should be classified in that year as [non-stressed bankrupt]. However, as the firm approaches
bankruptcy, the auditor may discover the firm’s distressed state and misstatements in year -2
or -1, insist on their reversal, and render a going- concern opinion. Thus, using the [McKeown
et al. (1991) classification] criteria, the firm would likely be classified as [stressed bankrupt],
11
and the nonstressed state and earnings overstatements occurring in year -3 would be
ignored.
Despite this we believe that the financial distress signals mentioned above are the most
indicative of financial distress.
3.3
Measurement of earnings management
We use Dechow et al.’s (1995) discretionary accruals (DA) model to estimate earnings
manipulation. We define accruals (ACC) as the difference between net income (NI) and
operating cash flows (OCF) and estimate equation (1) below for all firms in the same
industry (using five broad industry classifications) in each year to derive the nondiscretionary component of total accruals (NDA).
ACCt   0 (1/ Assetst 1 )  1 (Sales t  Debtorst )   2 PPEt   t
(1)
Where, Δ Salest is the change in operating revenue from t-1 to year t, ∆DEBTORS is the
change in debtors from year t-1 to year t and PPE is property, plant and equipment. DA is the
residual from equation (1), i.e., DA= ACC-NDA. All variables are winsorized at the top and
bottom 1 percent of their distributions to control for outliers and have also been deflated by
lagged total assets to control for heteroscedasticity.
3.4
Regression specifications
We estimate the following regression equation to examine the effect of financial distress on
earnings management after controlling for the known determinants of earnings management
(test of H1):
DAi ,t   0  1 DISTRESS i ,t   2 SIZEi ,t   3 LEVERAGEi ,t   4OCFi ,t   5 AUDi ,t 
 6OWNCON i ,t   7GROWTH i ,t   i ,t
(2)
Where:
12
DA
DISTRESS1
=
=
DISTRESS2
=
DISTRESS3
=
SIZE
LEVERAGE
OCF
AUD
=
=
=
=
OWNCON
=
GROWTH
=
signed DA calculated using Dechow et al. (1995) model;
a dummy variable coded 1 if the current year net income is negative,
zero otherwise;
a dummy variable coded 1 if the current year working capital is
negative, zero otherwise;
a dummy variable coded 1 if both the current year net income and
working capital are negative, zero otherwise;
firm size measured as the log value of total assets;
the ratio of long term debt to total assets;
operating cash flows divided by total assets;
audit quality coded 1 if the firm observations are audited by Big 4
auditors, zero otherwise;
a dummy variable coded 1 if the percentage of shareholding by the top
shareholder exceeds 50%, zero otherwise;
the ratio of market value of equity over book values of assets.
All the continuous variables are winsorized at the top and bottom 1 percent of their
distributions. Our coefficient of primary interest is β1. A positive (negative) coefficient on β1
will imply income-increasing (income-decreasing) earnings management, respectively by the
distressed firms.
A brief explanation of the control variables used in regression analysis follows. There
are competing arguments regarding the effect of firm size on earnings management. On the
one hand firm size may be negatively associated with earnings management because of more
sophisticated internal control systems, being audited by high quality auditors, and the risk of
losing more in the event of being detected for manipulating accounting information. In
contrast, larger firms may be more likely to manage earnings than small-sized firms, since the
former faces more pressures to meet or beat the analysts’ expectations (Barton and Simko
2002). Large-sized firms have greater bargaining power with auditors, and auditors are more
likely to waive earnings management attempts by large clients (Nelson et al. 2002), and such
firms have more room to manipulate given the wide range of accounting treatments available
(Hodgson and Stevenson-Clarke 2000). LEVERAGE is expected to be positively associated
with DA as Defond and Jiambalvo (1994) find that firms manage earnings prior to the debt
13
covenant violations. We expect a negative relationship between OCF and DA (Subramanyam
1996). Audit quality (AUD) takes a value of 1 for firm-year observations audited by Big 4
auditors and 0 otherwise, and we expect a negative coefficient following the argument that
high quality auditors constrain earnings management (Becker et al. 1998). The association
between OWNCON, the proxy for ownership concentration, and DA is ambiguous. From an
entrenchment perspective, more concentrated ownership is likely to exacerbate earnings
management for private benefits (Leuz et al. 2003; Bolton et al. 2006). An efficient
contracting argument for concentrated ownership, on the other hand, predicts a negative
association between ownership concentration and earnings management (Claessens and Fan
2002; Maug 1998). MB represents firm growth opportunities and is calculated as the ratio of
market value of equity to book value of equity. MB is expected to have a positive association
with DA as growth firms are found to use DA to signal private value-relevant information
(Skinner and Sloan 2002).
To test the incremental effect of GFC on the earnings management of distressed firms
(test of H2), we expand equation (2) as follows:
DAi ,t   0  1 DISTRESS i ,t   2GFCi ,t   3GFC * DISTRESS i ,t   4 SIZEi ,t   5 LEVERAGEi ,t 
 6OCFi ,t   7 AUDi ,t   8OWNCON i ,t   9GROWTH i ,t   i ,t
GFC
=
(3)
a dummy variable coded 1 if the firm-year observations come from GFC period
(2008-11), 0 otherwise. Clinch and Wei (2011) argue that the crisis started in
the last quarter of 2007 and therefore we assume the 2007 crisis effect will be
shown in 2008 annual reports. All other variables are previously defined.
The incremental effect of GFC on DA behaviour conditional on firm distress is
captured by (β1+β3).
Our final set of regression equations is designed to test the market pricing of different
components of earnings including DA conditional on firm distress (H3), and whether GFC
moderates this association. We first estimate the standard pricing test in equation (4) below
where return is the dependent variable and earnings components are independent variables:
14
RETit   0  1 NDAit   2 DAit   3OCFit   it
(4)
where RET is stock return calculated over a 12 month window beginning after the third
month of fiscal year end and ending on 3 months after the fiscal-year end. All other variables
have been previously defined. The coefficient on β2 will be positive (negative) if DA is
perceived to be informative (opportunistic) by the market participants.
Equation (4a) below extends the basic pricing equation by incorporating firm distress
and GFC:
RETit   0   1 NDAit   2 DAit   3 OCFit   4 GFC it   5 GFC it * NDAit   6 GFC it * DAit   7 GFC it * OCFit 
 8 DISTRESS it   9 DISTRESS it * NDAit   10 DISTRESS it * DAit   11 DISTRESS it * OCFit 
 12 GFC it * DISTRESS it   13GFC it * DISTRESS it * NDAit   14 GFC it * DISTRESS it * DAit 
 15GFC it * DISTRESS it * OCFit .  . it .......... .......... .......... (4a)




The coefficient on β2 captures market pricing of DA during the sample period;
the combined coefficient (β2+ β6) captures the market pricing of DA during the GFC;
the combined coefficient of (β2+ β10) captures the market pricing of DA of distressed
firms and finally;
the combined coefficient of (β2 + β14) captures the market pricing of DA of distressed
firms during the GFC.
All variables have been previously defined.
4.
Empirical results
Panel A of Table 2 contains the descriptive statistics of the variables used in the regression
analyses. Average signed DA are -0.8% of lagged total assets. About 22% and 29% of the
sample observations fall into distress categories following the definition of negative net
income and negative working capital respectively. However, when the very restrictive
definition of both negative net income and negative working capital is used as a distress
measure (DISTRESS 3), the proportion drops down to 10%. Thirty-six percent of the firmyear observations pertain to GFC. Sample observations are not highly-leveraged, average
15
growth, and are predominantly audited by Big 4 audit firms. About 26% of the sample
observations have a single shareholder holding more than 50% of the outstanding stocks.
Sample firms report an average (median) stock return of 0.12 (0.10) respectively with a
standard deviation of 0.44.
Panel B reports the correlation analysis. DA is negatively correlated with all three
distress measures (correlation coefficients significant at better than the 1% level, two-tailed
test), implying that distressed firms engage in income-decreasing DA practices. OCF is
significantly negatively correlated with DA consistent with the well-known negative
correlation between OCF and ACC. A positive correlation between OWNCON and DA
(correlation coefficient of 0.07) suggests that firms with concentrated ownership engaged in
income-increasing earnings management practices.4 Consistent with expectations, all three
distress measures are positively and significantly correlated with GFC. Interestingly the
negative correlation between financial distress and AUD implies that the distressed firms
were audited by non-Big 4 firms.
[TABLE 2 ABOUT HERE]
Table 3 presents a univariate test of difference in mean values for some select
variables. Distressed firms are smaller in size, have lower leverage, produce negative OCF,
have higher growth opportunities but have lower stock returns compared to their nondistressed counterparts. All these differences are statistically significant at better than the 5%
level. DA reported by distressed firms are significantly more negative compared to their nondistressed counterparts. This is primarily attributed to significantly larger income-decreasing
This finding suggests an ‘entrenchment’ as opposed to ‘efficient monitoring’ perspective of concentrated
ownership in New Zealand. Some recent research evidence from New Zealand also appears to support the
entrenchment perspective. For example, ownership concentration is associated with excessive managerial
pay and less pay-for-performance sensitivity (Jiang and Habib, 2009) and higher bid-ask spreads (Jiang et
al., 2010), compared to their low concentrated ownership firms.
16
4
DA reported by the distressed firms (about twice as large as their non-distressed counterparts
under all three definitions of firm distress). This evidence demonstrates that managers of
distressed firms use income-decreasing earnings management techniques. Non-distressed
companies report slightly higher income-increasing DA, compared to stressed companies, for
the second distress measure only (0.058 versus 0.046, t-statistic for the difference in mean is 1.69). There is no such significant difference for the other two firm distress measures.
We also provide univariate differences among some variables of interest in the pre
and during GFC. The mean DA do not show any significant difference between this two
periods (-0.51% and -1.2% of lagged total assets respectively). Not surprisingly, all the
DISTRESS measures are significantly higher during the GFC. OCF is positive during the
GFC but is significantly lower compared to pre-GFC (0.042 versus 0.09 respectively, the
mean difference is statistically significant at better than the 1% level).
[TABLE 3 ABOUT HERE]
4.1
Multivariate results
We now turn our attention to multivariate analysis. Although univariate analysis reveals some
interesting insights, the results do not control for other known determinants of DA choice.
Columns (2-4) of Table 4 presents regression results for the effect of financial distress on
earnings management (equation 2) whilst columns (5-7) present regression estimations of
equation (3) that tests the moderating effect of GFC. Results from Table 4 reveals that the
coefficient on all the three DISTRESS measures are negative and statistically significant at
better than the 1% level (coefficient values of -0.13, -0.03, and -0.09 respectively) suggesting
that distressed firms engage in income-reducing earnings management activities. Bulk of the
earnings management research considers income-increasing earnings management as a red
flag in evaluating the integrity of financial reporting by firms. However, income-decreasing
17
earnings management practices also obfuscate the underlying economic performance of the
firms and provide misleading pictures to corporate stakeholders.
[TABLE 4 ABOUT HERE]
Our second hypothesis examines whether the association between financial distress
and earnings management changed during the GFC. To accomplish this we estimate
regression equation (3) and report the results in columns (5-7). The coefficients on all three
DISTRESS variables are negative and statistically significant at better than the 1% level. The
incremental effect of GFC on distressed firms’ DA choices can be found by adding the
coefficients on [DISTRESS+GFC*DISTRESS]. The combined coefficients for the three
distress measures are -0.011 (a reduction of almost 21% during the GFC period)5, -0.02 (a
reduction of 50%) and -0.05 (a reduction of almost 62%), respectively.
Among the control variables, the coefficient on OCF is found to be consistently
negative and significant across all the three DISTRESS measures. This relationship is not
surprising since there is a strong negative correlation between OCF and ACC. The
coefficients on LEVERAGE and SIZE are negative and positive respectively but not
consistent across the distress measures. The negative coefficient on firm leverage is contrary
to expectation because highly-leveraged firms are more likely to manipulate earnings
upwards to avoid debt covenant violations (DeFond and Jiambalvo 1994; Sweeney 1994). A
plausible explanation for this contrary finding may be attributed to the monitoring role
exercised by the banks themselves. The positive coefficient on SIZE implies that larger firms
employ more income-increasing DA choices.6 The positive coefficient on ownership
5
The coefficient on DISTRESS1 is -0.14 and that of the interactive coefficient GFC*DISTRESS1 is 0.03.
The incremental effect of GFC is calculated as (-0.14+0.03) = -0.11 a reduction of 21%. Similar procedure
is followed to calculate the change in interactive coefficients for two other distress measures.
6 Two opposing views exist on the role of firm size in earnings management. Larger firms are less likely to
engage in earnings management practices because of (i) a better internal control system; (ii) high quality
auditing; and (iii) concern for lost reputation in the event of earnings management being detected. In
contrast, an opposing view suggests that large-sized firms are more likely to manage earnings than small18
concentration implies that earnings management is more prevalent in firms with concentrated
ownership. Adjusted R2s of the models vary from 23% to 7%.
Taken together the regression results provide evidence that managers of distressed
firms employ an income-decreasing earnings management technique. This finding is
consistent with DeAngelo et al. (1994) who find that managers engage in downward earnings
management via negative abnormal accruals and discretionary write-offs, rather than attempt
to inflate reported income. The incremental effect of the GFC on the association between
financial distress and earnings management is insignificant.
4.2
Informational value of earnings components during the GFC in New Zealand
It is well know that earnings consist of accruals and cash flows. Accruals can again be
decomposed into DA and NDA. The former reflects the results of managers’ discretionary
accounting choices. Unlike OCF and NDA, the information value of DA depends critically on
managerial incentives for discretionary accounting choices and the market’s interpretation of
the incentives. Managers could use DA for either informative or opportunistic purposes. To
the extent that DA reflect managerial opportunism, rational investors are likely to attach a
negative value to DA. Previous research indicates that managers use DA as a signaling device
to communicate private information to the market, and the market takes into account the
information contained in DA as a credible signal when assessing firm performance (e.g.
Subramanyam 1996; Guay et al. 1996; Krishnan 2003; Choi et al. 2011). We examined the
effect of firm distress on the pricing of earnings components and whether this association is
moderated by GFC. Choi et al. (2011) argued that investors’ confidence on financial
sized firms. First, as Barton and Simko (2002) indicate, large-sized firms face more pressures to meet or
beat the analysts' expectations; (ii) large-sized firms have greater bargaining power with auditors and can
negotiate with auditors in waiving earnings management attempts (Nelson et al. 2002) (iii) large-sized
firms have more room to maneuver given wide range of accounting treatments available, and (iv) largesized firms may manage earnings to decrease political costs.
19
reporting quality during a crisis naturally declines, as they tend to associate DA more with
managerial opportunism rather than efficient signaling. Managerial opportunism-induced DA
have lower predictability with respect to future cash flows and investors, therefore, attach a
negative information value to this discretionary earnings component.
[TABLE 5 ABOUT HERE]
Table 5 reports regression results of equations (4) and (4a). Consistent with prior
research, we document a positive and statistically significant coefficient on DA in column (2)
(coefficient value 0.97, t-statistic 4.18, significant at better than the 1% level). We also find
that the coefficient on OCF is positive and significant but of much lower magnitude,
compared to the coefficient on DA (0.52 versus 0.97). This evidence supports Sloan’s (1996)
‘accrual anomaly’ proposition where investors seem to attach a much higher weight to
accruals despite it being less persistent than the cash flow component of earnings. Columns
(4-9) present regression results of equation (4a). In all three distress proxies, the coefficient
on DA remains positive and statistically highly significant implying that DA is considered to
be informative rather than opportunistic. The coefficient on OCF, too, is positive and
significant.
The coefficient on two-way interaction variable, GFC*DA, is negative suggesting that
the market perceives DA reported during the GFC to be opportunistic, which is consistent with
Choi et al. (2011). The coefficients on DA for the DISTRESS1 measure in the pre-GFC and
during GFC period are, 0.96 and 0.59 [0.96 + (-0.37)], respectively in column (1). Thus, the
magnitude of change in its information value is -0.39 [(0.59-0.96)/0.96)]. For the other two
distress measures, the change in the magnitude of the information value of DA is 0.48 and
0.77 respectively. Neither of the interactive coefficients, DISTRESS*DA and GFC*
DISTRESS*DA, is significant. The coefficient on interactive variable OCF*DISTRESS enters
20
the regression with a negative and significant coefficient in the first and third distress
measures, implying that poor OCF reported by distressed firms is discounted by the market.
4.3 Sensitivity tests:
(a) Asset write-offs versus earnings management
The inference that distressed firms engage in income-decreasing DA choices (opportunistic
choice) may not be valid if such DA are influenced by legitimate asset write-offs by
distressed firms. About 30% of the sample observations reported some form of asset writeoffs. The correlation between financial distress and asset write-off (proxied by a dummy
variable coded 1 for non-zero asset write-offs and zero otherwise) is significantly positive for
all three distress measures (0.11, 0.17 and 0.25 for DISTRESS1, DISTRESS2 and DISTRESS3,
all statistically significant at better than the 1% level). We then re-run our regression
equations
(2)
and
(3) including asset-write off dummy as an additional explanatory variable. We report the
results in Panel A of Table 6. We find the coefficient on write-off dummy to be negative and
statistically significant at better than the 5% level across all the specifications suggesting that
asset write downs reduces DA component of ACC (the write-off could be legitimate or
discretionary). However, our coefficients of primary interest, i.e., DISTRESS, continue to be
negative and significant implying that the income-decreasing DA of the distressed firms are
opportunistic rather than caused by asset write-offs.
We further examine the behaviour of these firms with respect to asset sales as a
strategy for improving financial health. Sales of fixed assets are negligible and amount to
only 0.95% of total assets and do not differ significantly between income-increasing and
income-decreasing DA sub-groups (0.95% versus 0.97% respectively). For a robustness
analysis we include the sale of fixed assets as an additional independent variable in equations
21
(2) and (3). The untabulated result reveals negative but insignificant coefficients on this
variable across both specifications.
(b) Income-increasing versus income-decreasing DA analysis
When equations (2) and (3) are performed splitting the DA sample into income-increasing
and income-decreasing DA observations, we find that firms with financial distress
predominantly use income-decreasing DA strategies (coefficient values of -0.08 and -0.06
respectively for DISTRESS1 and DISTRESS3, both significant at better than the 1% level).
[TABLE 6 ABOUT HERE]
For the income-decreasing DA test in equation (3) we find that the primary
coefficients on firm distress are negative and statistically significant for DISTRESS1 and
DISTRESS3 measures during the non-GFC period (coefficients of -0.10 and -0.07, significant
at better than the 1% and 5% level respectively). The incremental effect of GFC on distressed
firms’
DA
behaviour
can
be
obtained
by
adding
the
coefficients
on
[DISTRESS+GFC*DISTRESS]. The combined coefficients for the three distress measures are
-0.06 (a reduction of almost 40% during the GFC period7, significantly different from zero at
the 1% level), 0.016 (insignificant), and -0.02 (a reduction of almost 72%, significantly
different from zero at the 10% level), respectively.
(c) Lagged DA as an explanatory variable for the regression analyses
As a robustness check, we include lagged DA in both equations (2) and (3) as an additional
independent variable. Accruals by definition reverse through time and are less persistent than
cash flows. However, some proportion of accruals is predictable based on last year’s accruals
7
The coefficient on DISTRESS is -0.10 and that of the interactive coefficient GFC*DISTRESS is 0.04. The
incremental effect of GFC is calculated as (-0.10+0.04) = -0.06 a reduction of 40%.
22
and hence merits the inclusion of lagged DA as a control. Regression results reveal that the
coefficients on all the DISTRESS measure are virtually unchanged from the main results
reported in Table 4. The coefficient on lagged DA is insignificant in all the regression
specifications.
(d) Performance-adjusted DA model
We check the robustness of our main findings by using an alternative DA model,
performance-matched model, as suggested by Kothri, Leone, and Wasley (2005). To control
for extreme performance that may have biased the calculation of DA, we include an
additional independent variables, return-on-assets (ROA) calculated as net profit divided by
lagged total assets. We report the findings in Panel C of Table 6. Our primary findings remain
robust to this alternative DA model. The coefficient on DISTRESS continues to be negative
and statistically significant for all three distress measures. The coefficient on the interactive
variable GFC*DISTRESS is significant only for the third distress measure.
5 Conclusion
This study examines the effect of financial distress on earnings management choices and
whether this effect changed during the GFC period. Financial distress of a firm has long been
considered a significant threat for the viability of the organization and poses significant risk
for outside investors. It is therefore important to understand managerial response to financial
distress in terms of accounting information quality. This study provides evidence that
financially distressed firms manipulate earnings downwards. This association, however, is
not significantly changed during the GFC period. We also examine the market pricing of
earnings components and whether such pricing is affected by financial distress and economic
23
crisis. We find that the New Zealand market considers discretionary accruals to be
informative but only during the non-crisis period. During the crisis period market appears to
perceive DA as opportunistic and attaches less weight on this component of earnings. The
association between DA and financial distress during the crisis period is insignificant. Our
result remains robust after conducting a number of sensitivity tests.
Financial distress of firms has received considerable research attention because of the
significance of financial distress for the overall economy. This situation is particularly acute
in New Zealand where the economy is severely affected because of the recent spate of
finance company collapses. Although not part of our sample, these finance company
collapses significantly affected the financial condition of many other companies who relied
on finance companies for funding. We believe that the findings of this study would be useful
to current and prospective investors as well as to regulatory authorities who are responsible
for monitoring managerial financial reporting quality. Income-decreasing earnings
management by the distressed firms as reported in this study distorts the quality of reported
information and therefore makes it difficult for investors to adequately predict future firm
performance. The findings will also help the regulatory authorities to closely monitor the
financial reporting quality of distressed firms.
Future research would benefit from a cross-country comparison of the earnings
management behavior of financially distressed firms. Such an approach would allow the
researchers to consider the cross-country variation in regulatory enforcement. We also
encourage future research on this topic using alternative financial reporting constructs. The
extant literature offers a number of alternative financial reporting quality proxies, like
reporting conservatism, stock price informativeness, and earnings persistence, and it would
be interesting to see the response of distressed firms’ managers to alternative reporting
quality constructs.
24
References
Ahmed, K., Godfrey, J.M. and Saleh, N.M. (2008), “Market perceptions of discretionary
accruals by debt renegotiating firms during economic downturn”, The International Journal
of Accounting, Vol. 43 No. 2, pp. 114-138.
Altman, E.I. (1968), “Financial ratios, discriminant analysis and the prediction of corporate
bankruptcy”, The Journal of Finance, Vol. 23 No. 4, pp. 589-609.
Argenti, J. (1976), Corporate Collapse: Causes and Symptoms, McGraw Hill, London.
Barton, J. and Simko, P. (2002), “The balance sheet as an earnings management constraint”,
The Accounting Review, Vol. 77 (Supplement), pp. 1-27.
Bartram, S.H. and Bodnar, G.M. (2009), “No. place to hide: the global crisis in equity
markets in 2008/2009”, Journal of International Money and Finance, Vol. 28 No. 8, pp.
1246-1292.
Beaver, H. (1966), “Financial ratios as predictors of failures”, Journal of Accounting
Research, Vol. 4 (Supplement), pp. 71-102.
Becker, C.L., Defond, M.L., Jiambalvo, J. and Subramanyam, K.R. (1998), “The effect of
audit quality on earnings management”, Contemporary Accounting Research, Vol. 15 No. 1,
pp. 1-24.
Berle, A. A., and Means, G. C. (1932), The Modern Corporation and Private Property, New
York: MacMillan.
Bolton, P., Scheinkman, J. and Xiong, W. (2006), “Executive compensation and short-termist
behavior in speculative markets”, Review of Economic Studies, Vol. 73 No. 3, pp. 577-610.
Burgstahler, D. and Dichev, I. (1997), “Earnings management to avoid earnings decreases
and losses”, Journal of Accounting and Economics, Vol. 24 No. 1, pp. 99-126.
Campelo, M., Graham, J.R. and Harvey, C.R. (2011), “The real effects of financial
constraints: evidence from a financial crisis”, Journal of Financial Economics, forthcoming.
Charitou, A., Lambertides, N. and Trigeorgis, L. (2007a), “Earnings behaviour of financially
distressed firms: the role of institutional ownership”, Abacus, Vol. 43 No. 3, pp. 271-296.
Charitou, A., Lambertides, N. and Trigeorgis, L. (2007b), “Managerial discretion in
distressed firms”, The British Accounting Review, Vol. 39 No. 4, pp. 323-346.
Charitou, A., Lambertides, N. and Trigeorgis, L. (2011), “Distress risk, growth, and earnings
quality”, Abacus, Vol. 47 No. 2, pp. 158-181.
Chen, Y., Chien, C. and Huang, S. (2010), “An appraisal of financially distressed companies'
earnings management: evidence from listed companies in China”, Pacific Accounting Review,
Vol. 22 No. 1, pp. 22-41.
Chia, Y.M., Lapsley, I. and Lee, H-W. (2011), “Choice of auditors and earnings management
during the Asian financial crisis”, Managerial Auditing Journal, Vol. 22 No. 2, pp. 177-196.
25
Choi, J-H., Kim, J-B. and Lee, J.J. (2011), “Value relevance of discretionary accruals in the
Asian financial crisis of 1997-1998”, Journal of Accounting and Public Policy, Vol. 30, pp.
166-187.
Chung, R., Ho, S. and Kim, J-B. (2004), “Ownership structure and the pricing of
discretionary accruals in Japan”, Journal of International Accounting, Auditing & Taxation,
Vol. 13, pp. 1–20.
Claessens, S. and Fan, J.P. (2002), “Corporate governance in Asia: a survey”, International
Review of Finance, Vol. 3 No. 2, pp. 71-103.
Clinch, G. and Wei, Z. (2011), “The association between earnings and returns and
macroeconomic performance: evidence from Australia, the US and China”, Australian
Accounting Review, Vol. 21 No. 1, pp. 54-63.
Daily, C. (1992), “An empirical examination of ownership structure in family and
professionally managed firms”, Family Business Review, Vol. 5 No. 2, pp. 117-136.
DeAngelo, H., DeAngelo, L. and Skinner, D. (1994), “Accounting choice in troubled
companies”, Journal of Accounting and Economics, Vol. 17 No. 1-2, pp. 113-143.
Dechow, P.M., Sloan, R. and Sweeney, A. (1995), “Detecting earnings management”, The
Accounting Review, Vol. 70, pp. 193–225.
DeFond, M. (2010), “Earnings quality research: advances, challenges and future research”,
Journal of Accounting and Economics, Vol. 50, pp. 402-409.
DeFond, M. and Jiambalvo, J. (1994), “Debt covenant violation and manipulation of
accruals”, Journal of Accounting and Economics, Vol. 17, pp. 145-76.
DeFond, M. and Park, C. (1997), “Smoothing income in anticipation of future earnings”,
Journal of Accounting and Economics, Vol. 23 No. 2, pp. 115-139.
Elliott, J. and Shaw. W. (1988), “Write-offs as accounting procedures to manage
perceptions”, Journal of Accounting Research, Vol. 26 (Supplement), pp. 91-126.
Faccio, M., Lang, L., and Young, L. (2001), "Dividends and expropriation", American
Economic Review, Vol. 91 No. 1, pp. 54-78.
Gilson, S. (1989), “Management turnover and financial distress”, Journal of Financial
Economics, Vol. 25 No. 2, pp. 241-262.
Graham, J. R., Harvey, C.R. and Rajgopal, S. (2005), “The economic implications of
corporate financial reporting”, Journal of Accounting and Economics, Vol. 40 No. 1-3, pp. 373.
Guay, W.R., Kothari, S.P. and Watts, R.L. (1996), “A market-based evaluation of
discretionary accrual models”, Journal of Accounting Research, Vol. 34 (Supplement), pp.
83-105.
Habib, A. (2008), “The role of accruals and cash flows in explaining security returns:
evidence from New Zealand”, Journal of International Accounting, Auditing and Taxation,
Vol. 17 No. 1, pp. 51-66.
26
Hodgson, A. and Stevenson-Clarke, P. (2000), “Earnings, cash flows and returns: functional
relations and the impact of firm size”, Accounting and Finance, Vol. 40, pp. 51-73.
Huddart, S. (1993), "The effect of a large shareholder on corporate value", Management
Science, Vol. 39, pp. 1407-1421.
Hopwood, W., McKeown, J. and Mutchler, J. (1994). “A reexamination of auditor versus
model accuracy within the context of the going-concern opinion decision”, Contemporary
Accounting Research, Vol. 10 No. 2, pp. 409–31.
Jenkins, D. S., Kane, G. D., and Velury, U. (2009). “Earnings conservatism and value
relevance across the business cycles”, Journal of Business Finance and Accounting, Vol. 36,
Nos. 9&10, pp. 1041-1058.
Jiang, H., Habib, A. and Hu, B. (2011). “Ownership structure, voluntary disclosure
and
information asymmetry in New Zealand”, British Accounting Review, Vol. 43, No. 1, pp. 3953.
Jiang, H., Habib, A. and Smallman, C. (2009). “Ownership concentration and CEO
compensation pay-for-performance relationship in New Zealand”, Pacific Accounting
Review, Vol. 21, No. 1, pp. 104-131.
Kothari, S. P., Leone, A. J., and Wasley, C. E. (2005). “Performance matched discretionary
accrual measures”, Journal of Accounting and Economics, Vol. 39 No. 1, pp. 163–197.
Krishnan, G.V. (2003), “Audit quality and the pricing of discretionary accruals”, Auditing: A
Journal of Practice and Theory, Vol. 22 No. 1, pp. 109-126.
Leuz, C., Nanda, D. and Wysocki, P.D. (2003), “Earnings management and investor
protection: an international comparison”, Journal of Financial Economics, Vol. 69 No. 3, pp.
505-527.
Liberty, S. and Zimmerman, J. (1986), “Labor union contract negotiations and accounting
choice”, The Accounting Review, Vol. 61 No. 4, pp. 692-712.
Lilien, S., Mellman, M. and Pastena, V. (1988), “Accounting changes: successful versus
unsuccessful firms”, The Accounting Review, Vol. 63, pp. 642-656.
Maug, E. (1998), “Large shareholders as monitors: is there a trade-off between liquidity and
control?”, Journal of Finance, Vol. 53, pp. 65-98.
McKeown, J., Mutchler, J. and Hopwood, W. (1991), “Towards an explanation of auditor
failure to modify the audit opinion of bankrupt companies”, Auditing: A Journal of Practice
and Theory, Vol. 10 (Supplement), pp. 1-13.
Mutchler, J., Hopwood, W. and McKeown, J. (1997). “The influence of contrary information
and mitigating factors on audit opinion decision on bankrupt companies”, Journal of
Accounting Research, Vol. 35 No. 2, pp. 295–310.
27
Nelson, M.W., Elliott, J.A. and Tarpley, R.L. (2002), “Evidence from auditors about
managers' and auditors' earnings-management decisions”, Accounting Review, Vol. 77
(Supplement), pp. 175-202.
Prevost, A.K., Rao, R.P. and Hossain, M. (2002), “Determinants of board composition in
New Zealand: a simultaneous equations approach”, Journal of Empirical Finance, Vol. 9 No.
4, pp. 373-397.
Rosner, R.L. (2003), “Earnings manipulation in failing firms”, Contemporary Accounting
Research, Vol. 20, pp. 361-408.
Saleh, N.M. and Ahmed, K. (2005), “Earnings management of distressed firms during debt
renegotiations”, Accounting and Business Research, Vol. 35 No. 1, pp. 69-86.
Shleifer, A., and Vishny, R. (1997), "A survey of corporate governance", Journal of Finance,
Vol. 52, 737-783.
Shleifer, A., and Vishny, R. (1986), "Large shareholders and corporate control", Journal of
Political Economy, Vol. 94, pp. 461-448.
Skinner, D.J. and Sloan, R. (2002), “Earnings surprises, growth expectations, and stock
returns or don’t let an earnings torpedo sink your portfolio”, Review of Accounting Studies,
Vol. 7 No. 2-3, pp. 289-312.
Sloan, R. (1996), “Do stock prices fully reflect information in accruals and cash flows about
future earnings?”, The Accounting Review, Vol. 71, pp. 289-315.
Strobl, G. (2008), “Earnings manipulation and the cost of capital”, working paper, University
of North Carolina at Chapel Hill.
Subramanyam, K.R. (1996), “The pricing of discretionary accruals”, Journal of Accounting
and Economics, Vol. 22, pp. 249-281.
Sweeney, A. (1994), “Debt-covenant violations and managers’ accounting responses”,
Journal of Accounting and Economics, Vol. 17 No. 3, pp. 281-308.
Yahanpath, N., and Cavanagh, J. (2011), “New Zealand finance company collapses and
subsequent blame game”, working paper, Eastern Institute of Technology.
28
Table 1: Sample selection procedures
Initial firm-year observations (1999-2011)
Less: firm year observations representing financial institutions
Finance and other services
Investment
Equity trust and funds
Observations lost due to the requirement of lagged TA
Available firm-year observations for DA
Less: Industry-year eliminated with less than 6 observations*
Final sample
1,200
(110)
(140)
(52)
(85)
813
(46)
==========
767
==========
* Rosner also uses 6 observations in the estimation of DA (2003, 376).
29
Table 2: Descriptive statistics and correlation analysis
Panel A: Descriptive statistics
Variables
NDA
DA
DISTRESS1
DISTRESS2
DISTRESS3
GFC
SIZE
LEVERAGE
OCF
AUD
OWNCON
GROWTH
RET
Mean
-0.045
-0.008
0.222
0.291
0.095
0.355
5.114
0.174
0.062
0.860
0.257
1.17
0.12
Median
-0.034
-0.003
0.000
0.000
0.000
0.000
5.119
0.153
0.070
1.000
0.000
0.86
0.10
SD
0.102
0.102
0.416
0.454
0.294
0.479
0.874
0.161
0.144
0.347
0.437
1.05
0.44
25%
-0.065
-0.050
0.000
0.000
0.000
0.000
4.552
0.004
0.020
1.000
0.000
0.54
-0.16
75%
-0.007
0.039
0.000
1.000
0.000
1.000
5.706
0.286
0.133
1.000
1.000
1.43
0.35
Panel B: Correlation analysis
Variables
NDA(1)
DA(2)
DISTRESS1 (3)
DISTRESS 2 (4)
DISTRESS3 (5)
GFC (6)
SIZE (7)
LEVERAGE (8)
OCF (9)
AUD (10)
OWNCON (11)
GROWTH (12)
RET (13)
(1)
1
-0.110***
-0.226***
-0.130***
-0.273***
-0.055
0.204***
0.071**
0.007
0.053
-0.020
-0.150***
-0.004
(2)
(3)
(4)
(5)
(6)
(7)
1
-0.261*** 1
-0.140*** 0.163*** 1
-0.205*** 0.608*** 0.507** 1
-0.039
0.169*** 0.078**
0.140*** 1
*
1
0.015
-0.361*** 0.050
-0.215*** 0.063*
-0.054
-0.191*** 0.195** -0.025
-0.015
0.431***
-0.217*** -0.489*** -0.049
-0.257*** -0.097*** 0.317***
*
-0.001
-0.292*** 0.017
-0.126*** -0.032
0.413***
0.066***
-0.199*** -0.008
-0.109*** -0.055
0.046
** and *
-0.059
0.115*** 0.090** 0.163*** -0.095*** -0.233***
represent
0.159***
-0.198*** -0.041
-0.065*
-0.194*** 0.037
*
significanc
e at the
***. ** and * represent significance
at the 1%, 5%, and the 10% level respectively (two-tailed test).
1%, 5%,
and the
10% level
respectivel
y (twotailed test)
(8)
1
0.137***
0.219***
-0.059
0.071*
-0.014
(9)
(10)
1
0.202**
0.122**
*
-0.022
*
0.162**
*
1
0.125**
*
-0.005
0.036
(11)
1
-0.11***
** and *
0.066*
represent
and
*
significan
represent
ce at the
significan
1%,at5%,
ce
the
and the
1%,
5%,
10%the
level
and
respective
10%
level
ly (tworespective
tailed
ly
(two-
(12)
(13)
1
0.019
1
30
Note:
Sample consists of 767 firm-year observations from 2000 to 2011 except for RET for which we have 642 firm-year observations because of missing return
data. RET is stock return calculated over a 12 month window beginning after the third month of fiscal year end and ending on 3 months after the fiscal-year
end.
We use Dechow et al.’s (1995) discretionary accruals (DA) model to estimate earnings manipulation. We define accruals (ACC) as the difference between net income
(NI) and operating cash flows (OCF) and estimate equation (1) below for all firms in the same industry (using five broad industry classifications) in each year to
derive the non-discretionary component of total accruals (NDA). We delete industries without at least 6 observations in a particular year consistent with Rosner
(2003).
ACCt   0 (1/ Assetst 1 )  1 (Sales t  Debtorst )   2 PPEt   t
(1)
Where, Δ Salest is the change in operating revenue from t-1 to year t, ∆DEBTORS is the change in debtors from year t-1 to year t and PPE is property, plant and
equipment. All variables are winsorized at the top and bottom 1 percent of their distributions to control for outliers and have also been deflated by lagged total assets
to control for heteroscedasticity. DA is the residual from equation (1), i.e., DA= ACC-NDA.
DISTRESS1= a dummy variable coded 1 if the current year net income is negative, zero otherwise;
DISTRESS2= a dummy variable coded 1 if the current year working capital is negative, zero otherwise;
DISTRESS3= a dummy variable coded 1 if both the current year net income and working
capital are negative, zero otherwise;
GFC = a dummy variable coded 1 if the firm-year observations come from GFC period (2008-2011), 0 otherwise;
SIZE = firm size measured as the log value of total assets;
LEVERAGE = the ratio of long term debt to total assets;
OCF= operating cash flows divided by total assets;
AUD = a dummy variable coded 1 if the firm observations are audited by Big 4 auditors, zero otherwise;
OWNCON= a dummy variable coded 1 if the percentage of shareholding by the top shareholder exceeds 50%, zero otherwise;
GROWTH= the ratio of market value of equity over book values of assets; and
RET= stock return calculated over a 12 month window beginning after the third month of fiscal year end and ending on 3 months after the fiscal-year end.
31
Table 3: Univariate test
DISTRESS1
ACC
DA
DA+
DA SIZE
LEVERAGE
OCF
GROWTH
RET
Yes (Y)
No (N)
(N-Y)
Diff
-0.15
-0.06
0.06
-0.13
4.52
0.12
-0.07
2.82
-0.05
-0.03
0.007
0.058
-0.05
5.28
0.19
0.10
2.11
0.17
17.37***
5.59***
0.09
4.43***
9.03***
5.47***
10.67***
-2.36**
4.34***
(Y)
-0.10
-0.03
0.046
-0.12
5.18
0.22
0.05
2.63
0.10
DISTRESS2
(N-Y)
(N)
Diff
-0.04
0.002
0.058
-0.06
5.09
0.15
0.07
2.12
0.14
5.01***
3.66***
1.69*
3.06***
-1.21
-5.23***
1.26
-2.50**
1.06
(Y)
DISTRESS3
(N-Y)
(N)
Diff
-0.20
-0.07
0.057
-0.12
4.53
0.16
-0.05
3.55
0.03
-0.04
-0.001
0.05
-0.06
5.18
0.17
0.07
2.14
0.13
6.47***
3.49***
-0.21
2.63**
4.72***
0.70
5.04***
-2.68***
1.26
DISTRESS1
DISTRESS2
DISTRESS3
ACC
DA
OCF
NI
RET
(a)
2000-07
(b)
2008-11
0.17
0.25
0.06
-0.02
-0.0051
0.087
0.0540
0.20
0.30
0.32
0.15
-0.10
-0.012
0.0423
-0.0018
0.03
(b-a)
t-test of mean
difference
3.91***
1.97***
3.51***
-7.59***
-0.93
-2.15**
-4.19***
-4.61***
Notes: Sample consists of 767 firm-year observations from 2000 to 2011. We use Dechow et al.’s (1995) discretionary accruals (DA) model to estimate earnings
manipulation. We define accruals (ACC) as the difference between net income (NI) and operating cash flows (OCF) and estimate equation (1) below for all firms in
the same industry (using five broad industry classifications) in each year to derive the non-discretionary component of total accruals (NDA). We delete industries
without at least 6 observations in a particular year consistent with Rosner (2003).
ACCt   0 (1/ Assetst 1 )  1 (Sales t  Debtorst )   2 PPEt   t
(1)
Where, Δ Salest is the change in operating revenue from t-1 to year t, ∆DEBTORS is the change in debtors from year t-1 to year t and PPE is property, plant and
equipment. All variables are winsorized at the top and bottom 1 percent of their distributions to control for outliers and have also been deflated by lagged total assets
to control for heteroscedasticity. DA is the residual from equation (1), i.e., DA= ACC-NDA.
SIZE = firm size measured as the log value of total assets;
LEVERAGE = the ratio of long term debt to total assets;
OCF= operating cash flows divided by total assets;
GROWTH= the ratio of market value of equity over book values of assets; and
RET= stock return calculated over a 12 month window beginning after the third month of fiscal year end and ending on 3 months after the fiscal-year end.
DISTRESS1= a dummy variable coded 1 if the current year net income is negative, zero otherwise. A total of 170 firm-year observations fall into this category;
DISTRESS2= a dummy variable coded 1 if the current year working capital is negative, zero otherwise. A total of 223 firm-year observations satisfy this
condition; and
DISTRESS3= a dummy variable coded 1 if both the current year net income and working capital are negative, zero otherwise. Seventy three (73) out of
767 observations satisfy this very restrictive criterion. Forty three (43) of the 73 observations pertain to 2008-11 crisis period. The
rest is from 2000-07 pre-crisis period.
***. ** and * represent significance at the 1%, 5%, and the 10% level respectively (two-tailed test).
32
Table 4: Financial distress, GFC and DA
DAi ,t   0  1 DISTRESSi ,t   2 SIZEi ,t   3 LEVERAGEi ,t   4OCFi ,t   5 AUDi ,t   6OWNCONi ,t   7 GROWTHi ,t   i ,t
(2)
DAi ,t   0  1 DISTRESSi ,t   2 GFCi ,t   3 GFC * DISTRESSi ,t   4 SIZEi ,t   5 LEVERAGEi ,t   6 OCFi ,t   7 AUDi ,t   8 OWNCONi ,t 
 9 GROWTH i ,t   i ,t
(3)
(1)
Variables
(2)
DISTRESS1
Equation (2)
(3)
DISTRESS2
(4)
DISTRESS3
(5)
DISTRESS1
Equation (3)
(6)
DISTRESS2
(7)
DISTRESS3
Constant
0.04
(1.21)
-0.13***
(-10.15)
0.0046
(0.80)
-0.06***
(-2.85)
-0.33***
(-7.89)
-0.02
(-1.15)
0.004
(0.58)
0.0034
(0.18)
-0.05
(-1.37)
-0.03***
(-3.71)
0.013**
(2.16)
-0.02
(-0.91)
-0.20***
(-5.28)
0.0017
(0.11)
0.02**
(2.26)
-0.00084
(-0.46)
-0.03
(-0.10)
-0.09***
(-4.36)
0.0089*
(1.62)
-0.03
(-1.42)
-0.22***
(-5.89)
0.00013
(0.008)
0.02**
(2.11)
0.00
(0.02)
0.04
(1.36)
-0.14***
(-7.42)
0.0027
(0.25)
0.03*
(1.62)
0.0043
(0.76)
-0.06***
(-2.85)
-0.33***
(-7.86)
-0.02
(-1.08)
0.0041
(0.59)
0.00027
(0.14)
-0.04
(-1.25)
-0.04***
(-3.13)
-0.03*
(-1.94)
0.02
(1.22)
0.013**
(2.16)
-0.02
(-0.82)
-0.19***
(-5.27)
0.00091
(0.06)
0.02**
(2.52)
-0.00084
(-0.42)
-0.01
(-0.47)
-0.13***
(-3.31)
-0.016
(-1.42)
0.08*
(1.82)
0.0073
(1.42)
-0.025
(-1.24)
-0.22
(-5.88)
-0.00025
(-0.02)
0.014**
(1.99)
0.00
(0.02)
Yes
0.23
767
Yes
0.08
767
Yes
0.11
767
Yes
0.23
767
Yes
0.08
767
Yes
0.13
767
DISTRESS
GFC
GFC*DISTRESS
SIZE
LEVERAGE
OCF
AUD
OWNCON
GROWTH
Year dummies
Adjusted R2
Observations
33
Notes: Sample consists of 767 firm-year observations from 2000 to 2011. We use Dechow et al.’s (1995) discretionary accruals (DA) model to estimate earnings
manipulation. We define accruals (ACC) as the difference between net income (NI) and operating cash flows (OCF) and estimate equation (1) below for all firms in
the same industry (using five broad industry classifications) in each year to derive the non-discretionary component of total accruals (NDA). We delete industries
without at least 6 observations in a particular year consistent with Rosner (2003).
ACCt   0 (1/ Assetst 1 )  1 (Sales t  Debtorst )   2 PPEt   t
(1)
Where, Δ Salest is the change in operating revenue from t-1 to year t, ∆DEBTORS is the change in debtors from year t-1 to year t and PPE is property, plant and
equipment. All variables are winsorized at the top and bottom 1 percent of their distributions to control for outliers and have also been deflated by lagged total assets
to control for heteroscedasticity. DA is the residual from equation (1), i.e., DA= ACC-NDA.
DISTRESS1= a dummy variable coded 1 if the current year net income is negative, zero otherwise;
DISTRESS2= a dummy variable coded 1 if the current year working capital is negative, zero otherwise;
DISTRESS3= a dummy variable coded 1 if both the current year net income and working
capital are negative, zero otherwise;
GFC = a dummy variable coded 1 if the firm-year observations come from GFC period (2008-2011), 0 otherwise;
SIZE = firm size measured as the log value of total assets;
LEVERAGE = the ratio of long term debt to total assets;
OCF= operating cash flows divided by total assets;
OWNCON= a dummy variable coded 1 if the percentage of shareholding by the top shareholder exceeds 50%, zero otherwise;
GROWTH= the ratio of market value of equity over book values of assets; and
***. ** and * represent significance at the 1%, 5%, and the 10% level respectively (two-tailed test). t-statistics are in parentheses.
34
Table 5: Firm distress, GFC and market pricing of earnings components
RETit   0  1 NDA it   2 DAit   3 OCF it   it
(4)
RETit   0   1 NDAit   2 DAit   3 OCFit   4 GFC it   5 GFC it * NDAit   6 GFC it * DAit   7 GFC it * OCFit 
 8 DISTRESS it   9 DISTRESS it * NDAit   10 DISTRESS it * DAit   11 DISTRESS it * OCFit 
 12 GFC it * DISTRESS it   13GFC it * DISTRESS it * NDAit   14 GFC it * DISTRESS it * DAit 
 15GFC it * DISTRESS it * OCFit .  . it .......... .......... .......... (4a)
(1)
Variables
(2)
Basic
(3)
DISTRESS1
(4)
DISTRESS2
(5)
DISTRESS3
Intercept
GFC(β4)
0.09***
(3.35)
0.03
(0.10)
0.97***
(4.18)
0.52***
(2.79)
-
GFC*NDA(β5)
-
GFC*DA(β6)
-
GFC*OCF(β7)
-
DISTRESS(β8)
-
DISTRESS*NDA(β9)
-
DISTRESS*DA(β10)
-
DISTRESS*OCF(β11)
-
GFC*DISTRESS(β12)
-
GFC*DISTRESS*NDA (β13)
-
GFC*DISTRESS*DA (β14)
-
GFC*DISTRESS*OCF(β15)
-
0.13***
(3.59)
-0.22
(-0.46)
0.96**
(2.14)
0.67**
(2.01)
-0.19***
(-2.80)
1.45
(0.80)
-0.37
(-0.31)
1.13
(1.21)
-0.06
(-0.66)
0.53
(1.06)
0.27
(0.49)
-1.26***
(-3.20)
0.09
(0.71)
-2.15
(-1.15)
0.29
(0.20)
-0.35
(-0.33)
0.12***
(3.38)
-0.26
(-0.54)
1.03**
(2.34)
0.74**
(2.33)
-0.17***
(-2.75)
1.48
(0.81)
-0.49
(-0.42)
0.99
(1.08)
0.00
(0.10)
0.72
(1.41)
0.29
(0.53)
-1.26***
(-3.25)
0.08
(0.69)
-2.35
(-1.27)
0.27
(0.18)
-0.31
(-0.29)
0.18***
(5.22)
0.40
(1.00)
1.02***
(2.96)
0.40
(1.48)
-0.19***
(-3.05)
-0.39
(-0.54)
-0.79*
(-1.66)
0.46
(1.49)
-0.07
(-0.90)
-0.81
(-0.87)
0.53
(1.18)
-0.79
(-1.40)
0.19
(1.33)
0.85
(0.64)
1.67
(1.16)
-0.64
(-0.57)
0.12
642
0.12
642
0.11
642
NDA (β1)
DA(β2)
OCF(β3)
Adjusted R2
N
Note:
We use Dechow et al.’s (1995) discretionary accruals (DA) model to estimate earnings manipulation. We
define accruals (ACC) as the difference between net income (NI) and operating cash flows (OCF) and
estimate equation (1) below for all firms in the same industry (using five broad industry classifications) in
each year to derive the non-discretionary component of total accruals (NDA). We delete industries without
at least 6 observations in a particular year consistent with Rosner (2003).
35
ACCt   0 (1/ Assetst 1 )  1 (Sales t  Debtorst )   2 PPEt   t
(1)
Where, Δ Salest is the change in operating revenue from t-1 to year t, ∆DEBTORS is the change in debtors
from year t-1 to year t and PPE is property, plant and equipment. All variables are winsorized at the top
and bottom 1 percent of their distributions to control for outliers and have also been deflated by lagged
total assets to control for heteroscedasticity. DA is the residual from equation (1), i.e., DA= ACC-NDA.
Where, ACC equal net income minus operating cash flows Δ Sales is the change in operating revenue from
t-1 to t; ∆DEBTORS is the change in debtors from year t-1 to year t; and PPE represents total fixed assets.
All other variables are previously defined.
***. ** and * represent significance at the 1%, 5%, and the 10% level respectively (two-tailed test). tstatistics are in parentheses.
36
Table 6: Sensitivity tests
Panel A: Legitimate asset write-offs versus earnings management
(1)
Variables
(2)
DISTRESS1
Equation (2)
(3)
DISTRESS2
(4)
DISTRESS3
(5)
DISTRESS1
Equation (3)
(6)
DISTRESS2
(7)
DISTRESS3
Constant
0.03
(1.05)
-0.12***
(-12.25)
-0.07***
(-2.74)
-0.03***
(-3.54)
-0.04*
(-1.66)
-0.08***
(-6.25)
ASSETWO
-0.019**
(-2.60)
-0.03***
(-3.81)
-0.02**
(-2.50)
0.03
(1.16)
-0.13***
(-10.68)
-0.0080
(-1.00)
0.03
(1.64)
-0.02**
(-2.62)
-0.06**
(-2.55)
-0.04***
(-3.55)
-0.02**
(-2.32)
0.02
(1.44)
-0.03***
(-3.95)
-0.03
(-1.26)
-0.12***
(-6.41)
-0.016**
(-2.16)
0.07***
(3.04)
-0.02**
(-2.56)
Year dummies
Adjusted R2
Yes
0.23
Yes
0.10
Yes
0.12
Yes
0.23
Yes
0.10
Yes
0.14
DISTRESS
GFC
GFC*DISTRESS
Note: ASSETWO is a dummy variable coded 1 for firms-year observations that reported some kind of asset write downs, and zero otherwise.
37
Panel B: Income-increasing versus income-decreasing DA choices, financial distress and global financial crisis
(1)
DISTR1
Income-increasing DA (DA+)
Test of H1
Test of H2
(2)
(3)
(4)
(5)
DISTR2
DISTR3
DISTR1
DISTR2
GFC
0.11***
(3.98)
-0.05***
(-2.75)
-
0.08***
(3.46)
-0.013
(-1.27)
-
0.08***
(3.24)
0.0062
(0.29)
-
GFC*DISTRESS
-
-
-
Adjusted R2
Observations
0.07
363
0.07
363
0.08
363
Variables
Constant
DISTRESS
Panel C: Lagged DA, financial distress and GFC
Equation (2)
(1)
(2)
(3)
Variables
DISTRESS1
DISTRESS2
Constant
DISTRESS
GFC
GFC*DISTRESS
DAt-1
Year dummies
Adjusted R2
Observations
(6)
DISTR3
(7)
DISTR1
Income-decreasing DA (DA-)
Test of H1
Test of H2
(8)
(9)
(10)
(11)
(12)
DISTR2
DISTR3
DISTR1
DISTR2
DISTR3
0.11***
(4.13)
-0.05***
(-3.09)
0.0021
(0.34)
0.018
(0.96)
0.09***
(3.42)
-0.016
(-1.11)
-0.02**
(-2.54)
0.0075
(0.48)
0.08***
(3.23)
0.04
(0.52)
-0.02**
(-2.52)
-0.05
(-0.81)
-0.06
(-1.48)
-0.08***
(-5.66)
-
-0.14***
(-2.96)
-0.01
(-1.29)
-
-0.13***
(-2.70)
-0.06***
(-3.14)
-
-
-
-
0.08
363
0.08
363
0.08
363
0.16
408
0.05
408
0.09
408
(4)
DISTRESS3
(5)
DISTRESS1
Equation (3)
(6)
DISTRESS2
(7)
DISTRESS3
0.01
(0.36)
-0.12***
(-9.47)
-0.03
(-0.58)
-0.07**
(-2.17)
-0.04
(-4.20)
-0.01
(-0.22)
-0.05
(-1.59)
-0.10***
(-4.80)
-0.02
(-0.42)
0.01
(0.45)
-0.13***
(-6.43)
-0.0054
(-0.93)
0.03
(1.08)
-0.03
(-0.66)
-0.07**
(-2.07)
-0.05***
(-3.32)
-0.02**
(-2.18)
0.02
(1.44)
-0.01
(-0.21)
-0.03
(-1.08)
-0.15**
(3.42)
-0.03**
(-2.25)
0.09**
(1.98)
-0.03
(-0.63)
Yes
0.23
709
Yes
0.09
709
Yes
0.11
709
Yes
0.23
709
Yes
0.09
709
Yes
0.15
709
-0.06*
(-1.75)
-0.10***
(-5.05)
-0.005
(-0.90)
0.04*
(1.74)
-0.12***
(-3.62)
-0.014
(-0.94)
-0.04***
(-3.16)
0.03
(1.42)
-0.11***
(-3.20)
-0.07**
(-2.55)
-0.03***
(-2.86)
0.05
(1.55)
0.14
408
0.04
408
0.06
408
38
Panel D: Alternative DA model (Performance-matched DA model of Kothari et al., 2005), financial distress and GFC
(1)
Variables
(2)
DISTRESS1
Equation (2)
(3)
DISTRESS2
(4)
DISTRESS3
(5)
DISTRESS1
Equation (3)
(6)
DISTRESS2
(7)
DISTRESS3
Constant
-0.02
(-0.50)
-0.06***
(-3.73)
-
-0.06
(-1.52)
-0.02**
(-2.69)
-
-0.05
(-1.30)
-0.05***
(-4.35)
-
-0.012
(-0.39)
-0.05**
(-2.28)
0.0067
(0.37)
-0.01
(-0.50)
-0.05
(-1.59)
-0.02***
(-2.72)
-0.01
(-0.73)
0.017
(1.48)
-0.03
(-1.14)
-0.07***
(-5.26)
-0.0085
(-0.47)
0.05***
(2.87)
Yes
0.19
767
Yes
0.15
767
Yes
0.16
767
Yes
0.18
767
Yes
0.15
767
Yes
0.16
767
DISTRESS
GFC
GFC*DISTRESS
Year dummies
Adjusted R2
Observations
Note: The following regression equation is estimated to determine the performance-matched DA.
ACC t   0 (1 / Assets t 1 )   1 (Sales t  Debtors t )   2 PPEt   3 ROAt   t
(1a)
Where ROA is return-on-assets calculated as net income divided by total assets. Other variables are previously defined.
Coefficients on the control variables for all the sensitivity tests are not reported for the sake of brevity.
***. ** and * represent significance at the 1%, 5%, and the 10% level respectively (two-tailed test). t-statistics are in parentheses.
39