Post-bankruptcy survival and signaling
Organizational Reorganization, Stakeholder Reaction, and Post-bankruptcy Outcome: An
Application of Signaling Theory
Jun Xia
College of Business and Economics
West Virginia University
PO Box 6025
Morgantown, WV 26506
Jun.Xia@mail.wvu.edu
304-293-7948
David D. Dawley
College of Business and Economics
West Virginia University
PO Box 6025
Morgantown, WV 26506
David.Dawley@mail.wvu.edu
304-293-7923
Rong Ma
Henry W. Bloch School of Management
University of Missouri-Kansas City
5100 Rockhill Road
Kansas City, MO 64110
mar@umkc.edu
816-235-6238
Kimberly Boal
Rawls College of Business
Texas Tech University
Lubbock, TX 79409
kim.boal@ttu.edu
806-742-2150
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Post-bankruptcy survival and signaling
ORGANIZATIONAL REORGANIZATION, STAKEHOLDER REACTION, AND POSTBANKRUPTCY OUTCOME: AN APPLICATION OF SIGNALING THEORY
Drawing on signaling theory, this study differentiates between firm reorganization and
stakeholder reaction as two distinct sources of information. It advances our knowledge by
presenting a stakeholder reaction approach to bankrupt firm survival in the stock market. The
results, based on a sample of U.S. bankrupt firms under Chapter 11 reorganization, show that
while a decrease in leverage and de-diversification are useful reorganization signals to predict
post-bankruptcy survival, post-bankruptcy reactions of external stakeholders (alliance partners,
institutional investors, and equity analysts) provide significant additional explanations.
Keywords: signaling theory, reorganization, stakeholder reaction, post-bankruptcy survival
INTRODUCTION
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Post-bankruptcy survival and signaling
Corporate bankruptcy is common given the fact that ‘hundreds of thousands of firms
around the world declare bankruptcy each year’ (Lee, Peng, and Barney, 2007: 257). As business
failure prediction is of great interest for both scholars and practitioners, a number of models have
been developed to assess a firm’s likelihood of survival (for a review, see Daubie and Meskens,
2002). However, most current models are developed to predict bankruptcy filing (Altman, 1993;
Hambrick and D’Aveni, 1988, 1992). Earlier studies have paid limited attention to the postbankruptcy outcomes of firms. As Daily (1994: 218) pointed out in her review of the bankruptcy
literature: ‘Emergence from bankruptcy is also an undeveloped area of research.’ To date, no
studies have examined whether or not these models, which predict bankruptcy filings, also
predict post-bankruptcy outcomes. This is an important question because investors want to know
if they should sell, hold, or perhaps, even buy in the face of bankruptcy.
Since 1993, a limited but growing body of literature has shifted our attention to the
prediction of the reorganization outcomes of firms in a crisis situation beyond the bankruptcy
filing date (Daily, 1995, 1996; Dawley, Hoffman, and Lamont, 2002; Denis and Rodgers, 2007;
Hotchkiss, 1995; Moulton and Thomas, 1993). While organizational failure prediction is difficult
because information asymmetry exists between the firm and market (Certo, 2003), signaling
theory (Spence, 1973; Connelly et al., 2011) provides a useful lens to deal with the information
asymmetry problem. It suggests that although firm quality is difficult to observe, signals that can
reflect the underlying quality of firms may help us differentiate between higher-quality and
lower-quality firms. Unfortunately, the potential of signaling theory to predict business failure
has not been fully realized or systematically explored.
In this study, we focus on post-bankruptcy survival in an effort to introduce signaling
theory into the field of bankruptcy. To tell apart bankrupt firms that will survive from those that
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Post-bankruptcy survival and signaling
will not, information that will signal the quality of the firms plays an important role. In general,
filing for bankruptcy sends a strong negative signal. The bankrupt firm is stigmatized with
spoiled organizational image (Sutton and Callahan, 1987) with reduced reliability, dependability,
trustworthiness, and legitimacy. In reality, however, not all bankrupt firms fail. Filing for
Chapter 11, some bankrupt firms are able to reorganize and revitalize successfully. Some higherquality bankrupt firms may gradually attract stakeholders’ attention and eventually survive.
Certain reactions of external stakeholders, which may send positive signals concerning the
survival of post Chapter-11 filing firms, are commonly overlooked. We contend that such
positive signals might include stock market buying activity by institutional investors, positive
comments by equity analysts, alliance formed with other firms, or even talk of a possible
acquisition.
In this study, we differentiate between organizational reorganization signals and
stakeholder reaction signals. Previous literature has traditionally relied on organizational signals
in predicting corporate bankruptcy. There are three important bankruptcy approaches
documented in the management literature: (1) the downward-spiral approach (Hambrick and
D’Aveni, 1988), (2) the dependability approach (D’Aveni, 1989b), and (3) the domain
restructuring approach (D’Aveni and Ilinitch, 1992; Johnson, 1996). All these approaches aim to
identify critical financial and structural indicators to predict why firms declare bankruptcy (Hill,
Perry, and Andes, 1996). In post-bankruptcy settings, changes in key organizational indicators
actually signal whether the firm’s reorganization process is in a positive direction. It is not clear,
however, whether organizational signals are sufficient in predicting post-bankruptcy outcomes.
As a point of departure from the traditional approach, our study introduces stakeholder
reaction signals after the bankruptcy filing whereas previous studies tend to ignore signals
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Post-bankruptcy survival and signaling
generated from external stakeholders. We argue that positive signaling from the external
signalers may help improve interpretation of the firm’s situation (Connelly et al., 2011). Building
on the premise that stakeholders tend to follow higher-quality firms (Gulati and Higgins, 2003;
Stuart, Hoang, and Hybels, 1999), we argue that the positive or negative reactions of
stakeholders to bankrupt firms may allow us to differentiate their quality to survive and hence to
predict their post-bankruptcy outcomes. Accordingly, we examine whether the post-bankruptcy
reaction of external stakeholders, including alliance partners, institutional investors and equity
analysts, predicts post-bankruptcy outcomes.
This study makes a contribution by introducing signaling theory to the bankruptcy
literature that has stressed the importance of understanding firms in crisis and subsequent
turnaround in recent decades (Daily, 1994; Hambrick and D’Aveni, 1988; McKinley, 1993; van
Witteloostuijn, 1998; Weitzel and Jonsson, 1989). Signaling theory is relevant because although
some bankrupt firms survive immediate delisting from the stock market, it is still difficult to
predict their long-term survival due to information asymmetry regarding their quality. As
organizational reorganization and stakeholder reactions are two qualitatively distinct sources of
signals in differentiating higher-quality from lower-quality bankrupt firms, they may have
different levels of predictive power. Our paper, in turn, enriches the signaling perspective by
identifying the relative importance of different types of signals in the post-bankruptcy context.
This study also contributes to the empirical literature by simultaneously testing
organizational reorganization and stakeholder reaction signals to predict post-bankruptcy
outcomes. Following existing studies (Daily, 1995; Moulton and Thomas, 1993), we define a
public firm’s delisting as post-bankruptcy reorganization failure. Using an event history
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Post-bankruptcy survival and signaling
methodology in a longitudinal setting, we test our hypotheses based on a sample of publically
traded U.S. bankrupt firms under Chapter 11 reorganization.
A SIGNALING THEORY PERSPECTIVE OF BANKRUPT FIRM SURVIVAL
Signaling theory (Spence, 1973) explains how decision makers rely on signals from
available sources to identify the quality of a potential candidate (e.g., an individual or a firm) in
situations of information asymmetry (Connelly et al., 2011). In Spence’s (1973) seminal work,
signaling theory explains how potential employers distinguish between high-quality and lowquality candidates in the labor market. Given that some qualities of the candidates cannot be
directly observed, potential employees with good credentials may distinguish themselves from
others via credible and observable indicators (e.g., education) as signals which reduce
information asymmetry and enhance their job opportunities.
Organization scholars have applied signaling theory in a wide array of research contexts
(see Connelly et al., 2011, for a review) from labor markets to stock markets (Marcus and
Goodman, 1991). For example, Zhang and Wiersema (2009) suggest that CEO background
signals the unobservable quality of the firm, which, in turn, affects stock market reaction.
Signaling theory has also been applied to explain the performance of initial public offerings
(IPOs) (Certo, 2003; Certo, Daily, and Dalton, 2001), in which IPO firms are potential
‘candidates’ that tend to send positive signals to influence the IPO stock price. Moreover,
previous research has applied signaling theory in the context of firms in crisis (Marcus and
Goodman, 1991) such as the reputational penalty of financial fraud (Kang, 2008).
An organizational crisis is often defined as ‘any event or condition that threatens the
survival of the organization’ (D’Aveni and MacMillan, 1990: 635). Bankruptcy filing represents
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Post-bankruptcy survival and signaling
a unique form of such event that is publically visible. Although previous studies have
demonstrated various applications of signaling theory, then tend to ignore the dynamics of
survival processes and do not explicitly explain changes from bankruptcy filing to public firm
delisting. Nonetheless, bankruptcy reorganization is a dynamic process and we know very little
about how internal and external changes as signals might predict the survival of bankrupt firms
in the stock market. As a departure from previous studies, our study adopts a dynamic approach
that considers changes in organizational reorganization and stakeholder reactions as a signal of a
bankrupt firm’s likelihood of survival.
Quality to Survive and Signaling
According to signaling theory, signals are emitted to reflect the underlying quality of the
candidate, either an organization or an individual. Connelly et al. (2011: 43) defines quality as
‘the underlying, unobservable ability of the signaler to fulfill the needs or demands of an outsider
observing the signal.’ In the context of bankruptcy, organizations must have some redeeming
characteristics to survive (Hannan and Freeman, 1984; Stinchcombe, 1965). The fundamental
premise of our study is that higher-quality bankrupt firms are more likely to emerge successfully,
while lower-quality bankrupt firms are more likely to fail (Lee, Peng, and Barney, 2007).
Due to information asymmetry, however, attributes possessed by each firm cannot be
directly observed, which makes it a challenging task to distinguish between high-quality and
low-quality firms (Connelly et al., 2011). Further complicating the issue, bounded rationality
(March, 1978) makes it difficult to identify all the possible attributes of the firm to make this
distinction. What lies at the heart of the signaling perspective is that visible signals provide
informational clues to assess a firm’s quality (Certo, 2003; Kang, 2008; Spence, 1973). The
organization literature has documented a number of criteria to identify a firm’s quality, such as
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Post-bankruptcy survival and signaling
reliability (continuity of a firm’s behavior) (Fischer and Pollock, 2004), dependability (financial
health) (D’Aveni, 1989b), trustworthiness (Stuart, Hoang, and Hybels, 1999), and legitimacy
(compliance with institutional norms) (Certo, 2003). In this study, we identify organizational
reorganization and stakeholder reaction signals that might predict post-bankruptcy outcomes.
Internal and External Changes as Sources of Information
Connelly et al. (2011) assert that the use of signaling theory requires identifying the
specific conditions when some signaling effects are more significant than others, which makes it
an important task to distinguish among different sources of signals. In this study we identify
organizational reorganization and stakeholder reactions as two important sources of signals.
(This organizational and stakeholder signal distinction is useful as different sources of
information may emphasize different aspects of the post-bankruptcy restructuring process and
the two sources of information may not have equal predictive power.
From a signaling theory perspective, higher-quality bankrupt firms are able to reorganize
by initiating important internal changes that lower quality firms are not capable of. We thus
argue that the direction and outcome of internal changes are important signals in predicting
turnaround. In particular, financial disclosure (Kang, 2008; Zhang and Wiersema, 2009) and
corporate restructuring (Hambrick and Schecter, 1983) are important sources of signals to
identify changes in a bankrupt firm’s reliability or dependability during the reorganization
process. We examine whether the increase in firm performance, decrease in debt-to-equity ratio,
and de-diversification are important reorganization signals of the survival of a bankruptcy firm.
Moreover, the actions of external stakeholders can also be important sources of signals
about a firm’s trustworthiness or legitimacy (Fischer and Pollock, 2004; Flynn and Farid, 1991;
Sutton and Callahan, 1987). An organization’s stakeholders include those individuals or
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Post-bankruptcy survival and signaling
organizations that have an interest in the actions of or outcomes produced by an organization and
have the ability to influence it (Freeman, 1984). The decisions and reactions of external
stakeholders also provide signals of a firm’s quality because they tend to be aligned only with
high-quality firms to avoid possible reputation damages or penalties (Certo, 2003), which means
that this type of signal is also costly or hard for lower quality companies to replicate. Hence, the
reaction of stakeholders sends a signal about the quality of the firm. In this study, we use
increases in alliance partners, institutional investors, and equity analyst coverage as stakeholder
reaction signals to predict post-bankruptcy survival.
SIGNALS OF ORGANIZATIONAL REORGANIZATION
Most existing bankruptcy models emphasize the importance of organizational signals.
There are two approaches. The accounting-based approach suggests that important financial
indicators such as a firm’s performance (e.g., ROA) and financial position (e.g., debt-to equity
ratio) predict bankruptcy (D’Aveni, 1989b; Hambrick and D’Aveni, 1988). In line with
economic approaches (Altman, 1983), existing findings support that key financial indicators (e.g.,
performance, leverage, or Z-score) provide signals of financial distress of firms in crisis
(Connelly et al., 2011). The aforementioned metrics are indeed signals that serve as credible
signals of a firm’s underlying quality (Certo, 2003).
Scholars have also highlighted the importance of non-financial signals in predicting
bankruptcy (Hill, Perry, and Andes, 1996). The strategy-based approach emphasizes a firm’s
ability to refocus on its core or promising businesses through de-diversification or portfolio
restructuring as a correction to a prior error of judgment by management (Brauer, 2006;
Moschieri and Mair, 2008) or a strategy to revitalize declining firms (Johnson, 1996). Drawing
on these insights, we develop hypotheses below in the context of post-bankruptcy.
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Post-bankruptcy survival and signaling
Increase in firm performance
Performance is the most straightforward indication of potential of a bankrupt firm to
survive. Organizational performance has been extensively examined in the organizational decline
literature (Daily, 1994). The well-known downward-spiral model (Hambrick and D’Aveni, 1988,
1992) suggests that bankruptcy is a function of performance decline, moving from deficient
performance to eventual bankruptcy. The majority of studies support the decline-bankruptcy
relationship (Wiseman and Bromiley, 1996), suggesting that decline is a process of decreasing
performance (profitability) over a prolonged period of time (Weitzel and Jonsson, 1989). This
process may lead to a liquidity crisis because poor performers will have a problem generating
sufficient cash flow to meet their immediate cash demands (Levinthal, 1991).
In the present context, organizational performance demonstrates managers’ ability to
revitalize the firm (Daily, 1994) as improved performance stems from managers’ better use of
resources. In the stock market, declining firms often suffer from a chronic failure – staying in the
market with negative profitability; after a certain period of losses, the firm may eventually leave
the market (van Witteloostuijn, 1998). Denis and Rodgers (2007) find that failure to achieve
improvements in profitability after filing Chapter 11 may lead to dissolution. In contrast, an
increase in profitability after bankruptcy filing may function as a positive signal of
reorganization, which demonstrates the firm’s financial and operational reliability to turn around.
Hypothesis 1: The performance turnaround of a bankrupt firm reduces its likelihood of
failure in the stock market.
Decrease in debt-to-equity ratio
Poor financial health (e.g., high leverage, high debt-to-equity ratio, or low liquidity) may
result in bankruptcy as the firm is viewed as an undependable exchange partner in the eyes of
creditors (D’Aveni, 1989b). Empirical observations show that WorldCom was heavily indebted
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Post-bankruptcy survival and signaling
to the tune of $28 billion in the two months before filing bankruptcy in 2002, and its stock price
had slid to $1.79 from its mid-1999 peak of $64.50 (Pandey and Verma, 2005). While Chapter
11 provides managers the right to retain control of the bankruptcy firm during the reorganization
process (Daily, 1995), a committee of creditors appointed by the judge will oversee the process.
To gain external support for survival, bankrupt firms are likely to restructure their debt to reflect
greater solvency and cater to creditors’ pressures such as the withdrawal of resources or support
(D’Aveni, 1989b).
If high leverage is a cause of Chapter 11 filing, then reduction in debt levels is an obvious
direction for successful reorganization. In the hope of recouping their losses, creditors may
emphasize a reduced debt-to-equity ratio as a positive signal of reorganization. Studies have
shown that the reduction in debt increases the likelihood that a firm achieves better performance
and re-emerges as an independent firm (Denis and Rodgers, 2007). However, only higher-quality
firms can afford to consistently repay debt and thus return to a state of dependability. Such
signals are difficult for lower-quality firms to imitate (Certo, 2003). Therefore, a decrease in the
level of leverage signals an increase in the dependability of the firm and may serve as an
indicator of a positive post-bankruptcy outcome.
Hypothesis 2: A decreased debt-to-equity ratio of a bankrupt firm reduces its likelihood
of failure in the stock market.
De-diversification
Alternatives to accounting-based approaches are strategy-based models of bankruptcy
(Stopford and Baden-Fuller, 1990).The domain restructuring approach suggests that overdiversification may lead to organizational decline and bankruptcy (Hambrick and D’Aveni, 1988;
Johnson, 1996; van Witteloostuijn, 1998). Complex patterns of vertical integration, a form of
diversification, have been found to be an important indicator of bankruptcy risk (D’Aveni and
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Post-bankruptcy survival and signaling
Ilinitch, 1992). As such, after bankruptcy filing, firms may reorganize to undergo some form of
portfolio restructuring, typically known as downscoping, refocusing, or de-diversification
(Brauer, 2006; Markides, 1995; Moschieri and Mair, 2008; Singh, 1993). It is expected that such
a post-bankruptcy reorganization will lead to ‘rejuvenation,’ a process that involves dediversification and concentration on a firm’s core business and capabilities (Stopford and BadenFuller, 1990).
Post-bankruptcy de-diversification is likely to signal a positive trajectory of
reorganization. Diversified firms can be valued less than focused firms in the stock market
(Zuckerman, 1999) because capital resources in a firm may flow toward less efficient segments
(Rajan, Servaes, and Zingales, 2000). De-diversification is likely to help a Chapter 11 firm
emerge as an independent entity because it reduces the firm’s scope and may lead to better
performance (Denis and Rodgers, 2007). As such, focusing on the promising business domains is
deemed as a legitimate action for a firm in crisis (Hambrick and Schecter, 1983; Johnson, 1996;
Pandey and Verma, 2005). Such a strategy signals that the firm is taking actions in response to
organizational crisis and building its core competence (Prahalad and Hamel, 1990), which may
eventually revitalize the firm.
Hypothesis 3: The de-diversification of a bankrupt firm reduces its likelihood of failure in
the stock market.
Critics of Organizational Signals
An implicit assumption of many studies based on signaling theory is that the focal actor
has a significant level of control over the signal (Pollock and Gulati, 2007). That is, signals as
observable characteristics of the focal actor are subject to manipulation by the focal actor
(Spence, 1973). Therefore, characteristics that are less subject to manipulation are more reliable
signals in reducing information asymmetry. While a bankrupt firm’s financial and strategic
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Post-bankruptcy survival and signaling
turnaround efforts are important sources of information, bankruptcy filing creates difficulties in
interpreting the firm’s signals and predicting its reorganization outcome. One important reason is
that although corporate executives obtain both positive and negative private information, they
‘generally do not send these negative signals to outsiders’ (Connelly et al., 2011: 44). In this
sense, some organizational signals may artificially inflate the firm’s reliability.
In contrast, signals not sent by the focal firm may be more effective in reflecting quality
and predicting its future prospects. A bankrupt firm often suffers not only from financial losses
but also from significant reputational losses (Lee, Peng, and Barney, 2007). Bankruptcy filing
can ‘catalyze a dominant negative perception and potentially spoil the image of the organization’
(Flynn and Farid, 1991: 67). Without considering external signals that capture a firm’s
reputational penalty, organizational signals alone do not fully capture all information regarding
the firm’s quality. The reaction of outside stakeholders provides additional sources of signal
which are difficult to be manipulated because these stakeholders are reluctant to pay attention to
a firm if they are uncertain about the firm’s quality (Certo, 2003; Stuart, Hoang, and Hybels,
1999; Rao, Greve, and Davis, 2001). For these reasons, we introduce a stakeholder reaction
approach to advance our understanding by explicitly assessing the effectiveness of the reactions
of multiple external stakeholders as signals of a bankrupt firms’ promise.
A STAKEHOLDER REACTION APPROACH
In many cases, the support of external stakeholders can be a critical condition for the
bankrupt firm’s survival (Bowie, 1988; Nasi, 1995). Successful turnaround is closely related to
whether the firm is able to maintain, renew, and even increase external support (Daily, 1996;
Hambrick, 1985; Slatter and Lovett, 1999). Bankrupt firms that are endowed with more external
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Post-bankruptcy survival and signaling
resources have a better chance of emerging from a bankruptcy filing (Dawley et al., 2002). Thus,
linkages with external stakeholders are of particular importance for bankrupt firms to turn around
because they are highly constrained by limited resources. Therefore, bankruptcy reorganization
needs to enhance the stakeholders’ perception of the firm’s prospects, which in turn will bring in
needed external support.
From a signaling theory perspective, we argue that higher-quality bankrupt firms are
more likely to establish external linkages than lower-quality ones. The reactions of external
stakeholders, in turn, may send signals that allow us to predict post-bankrupt outcomes. Positive
external stakeholder (e.g., equity analyst) reactions to a given bankrupt firm may reflect potential
investment benefits. They can be viewed as evidence that the firm is still reliable and thus worth
their investments, time and attention. Due to different levels of firm quality, bankruptcy
reorganization might result in positive stakeholder reactions (Campbell, Hilscher, and Szilagyi,
2008). For example, stakeholders, motivated by potentially higher returns, may react positively
to higher-quality bankrupt firms that are more likely to turn around (Morse and Shaw, 1988).
Researchers have observed that Chapter 11 firms that signal asset and liability reductions are
more likely to successfully reorganize than firms that do not (Denis and Rodgers, 2007; Lee,
Peng, and Barney, 2007). For instance, WorldCom filed for bankruptcy protection under Chapter
11 in 2002 and immediately signaled changes through nurturing a new ethical work culture,
internal accounting systems, and providing reconsolidated financial statements; the company
successfully emerged two years later (Pandey and Verma, 2005). Some bankrupt firms that
signal improvements in operating margins (e.g., Toys “R” Us, WorldCom) also re-emerge as
revitalized entities (Denis and Rodgers, 2007; Flynn and Farid, 1991) and provide substantial
returns for investors.
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Post-bankruptcy survival and signaling
Because ‘different people know different things’ (Stiglitz, 2002: 469), we focus on
reactions of multiple external stakeholders, including alliance partners, institutional investors and
equity analysts, as they provide non-redundant information about the focal firm. Stakeholders
can exert influence on the focal firm for different reasons. Some stakeholders, such as alliance
partners and institutional investors, are primary in the sense that they have a formal, official, or
contractual relationship with the firm. Others, such as equity analysts, are secondary in the sense
that they are not directly engaged in the firm’s economic activities but are nonetheless able to
access important information about the firm. The signaling role of these stakeholders’ reactions
fulfills the criteria that credible signals must be both observable and difficult to manipulate
(Certo, 2003). These stakeholders tend to abandon low-quality firms and follow high-quality
ones. They are more capable of assessing a bankrupt firm’s quality because of their interactions
with the firm’s top managers. In addition, they have to carefully evaluate bankrupt firms’
prospects to avoid substantial losses of their investments.
Reaction of Alliance Partner
An important signal the current study identifies is alliance partnerships formed by the
firm after its bankruptcy filing. Although little research has explicitly examined the effect of
alliance partnerships on post-bankruptcy outcomes, a substantial body of research has shown
inter-organizational relationships as a signal that may reflect desired but unobservable firm
quality such as trustworthiness (Gulati and Higgins, 2003; Ozcan and Overby, 2008; Stuart,
Hoang, and Hybels, 1999). For example, Gulati and Higgins (2003) suggest that alliance
activities can be an informative signal of an IPO firm’s social capital, reputation, and
trustworthiness, leading to the IPO’s success. Stuart et al. (1999) examined young firms that are
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Post-bankruptcy survival and signaling
most often resource-constrained and found that alliances formed by these young firms were
positively associated with their subsequent performance.
Alliances are often formed by interdependent actors to stabilize their resource exchange
(Pfeffer and Nowak, 1976; Pfeffer and Salancik, 1978). Firms tend to form alliances with
competent, credible and trustworthy partners. As Stuart et al. (1999: 319) pointed out,
‘organizations in general will eschew relations with firms that may be unreliable.’ As higher
levels of uncertainty about firm quality and future prospects are present in bankrupt firms,
forming alliances with these firms is risky because alliance failure will have a disruptive effect
on organizational operations. Alliance formation is a complex process in which firms are likely
to exchange inside information about each other. More alliances formed by a bankrupt firm may
serve as an important indication of the prospect of the firm because multiple partners have
demonstrated their confidence in the bankrupt firm and are willing to take the risk and endow the
firm with exchange contracts. We therefore expect that:
Hypothesis 4: The increase in alliances formed by a bankrupt firm reduces its likelihood
of failure in the stock market.
Reaction of Institutional Investors
Investing in bankrupt firms’ securities has been a common and accepted practice since
the Bankruptcy Reform Act of 1978, especially once their reorganization plans have been
approved by the creditor committee (Morse and Shaw, 1988). Institutional investors control more
than half of all the equity in U.S. public firms (Certo, 2003), including many bankrupt firms
(Daily, 1996). However, bankruptcy is still a serious matter for institutional investors.
Institutional investments signal that the institutional investors have not abandoned the firm in
crisis due to their perceived potential to emerge (D’Aveni, 1989b), thereby enhancing the firm’s
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Post-bankruptcy survival and signaling
legitimacy in the stock market. We argue that an increase in institutional investors invested in the
bankrupt firm may send a strong signal of the firm’s quality for two reasons.
First, institutional investments hold ‘smart money’ managed by fund managers who are
investment experts (Sanders and Boivie, 2004). They do not randomly choose bankrupt firms to
invest in. Instead, they must carefully choose a higher-quality firm that is less likely to fail so as
to reduce the valuation uncertainty. More importantly, institutional investors are able to evaluate
and choose higher-quality firms and closely monitor their managers’ decisions. For these reasons,
research has shown that institutional investors are likely to enhance the performance of firms in
crisis (Daily, 1996; Daily and Dalton, 1994).
Second, although investing in bankrupt firms may return nothing to their investors, it may
also multiply the market value of the investors’ equity many times if the firm can revitalize.
Institutional investors prefer reorganization over liquidation because they may lose all their
investment with liquidation (Morse and Shaw, 1988). In this sense, institutional investors may
serve as a ‘sociopolitical shield’ (Fischer and Pollock, 2004) against failure during its
reorganization process. Taken together, we propose that:
Hypothesis 5: The increase in institutional investors attracted by a bankrupt firm reduces
its likelihood of failure in the stock market.
Reaction of Equity Analysts
Equity analysts play an important role of collecting and disseminating information on
public firms. Equity analysts often selectively evaluate public firms’ quality and provide
recommendations to the market. They typically ‘favor covering firms whose stock market
performance is expected to be good in the future and avoiding poor performers’ (Rao, Greve, and
Davis, 2001: 502). For example, Jain and Martin (2005) suggest that analyst coverage is useful in
explaining IPO firm survival; a firm will be delisted if it is unable to attract analyst attention.
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Post-bankruptcy survival and signaling
Previous studies have shown that analysts’ coverage, an important source of information, is able
to positively influence the market value of the firm’s stock price (Rao and Sivakumar, 1999;
Zuckerman, 1999).
Post-bankruptcy analyst coverage is highly visible, which may provide an important
signal of the firm’s quality to predict its reorganization outcome because analysts are also likely
to focus more on higher-quality bankrupt firms than lower-quality ones. While analysts’
forecasts tend to be optimistically biased (Chan, Karceski, and Lakonishok, 2003; Rajan and
Servaes, 1997), over time if a firm’s securities are overestimated, they will be subsequently
adjusted based on the actual performance disclosed (Womack, 1996). Empirical evidence has
shown that analysts tend to abandon their coverage on firms that fall short of their expectations
(Rao, Greve, and Davis, 2001; Welch, 2000). Following this reasoning, higher-quality bankrupt
firms are more likely to attract, sustain, and even increase the attention of analysts. Increased
research coverage thus signals that the firm’s reorganization is more likely to succeed.
Hypothesis 6: The increase in equity analysts’ coverage reduces a bankrupt firm’s
likelihood of failure in the stock market.
METHOD
Sample Selection
Filings for Chapter 11 bankruptcy provide us an initial condition to examine the
hypothesized relationships above. For publicly listed firms, post-bankruptcy reorganization may
lead to one of three outcomes: the firm may (1) survive the stock market as an independent entity;
(2) be merged with or acquired by another firm; or (3) be delisted from the stock market for
negative reasons (Moulton and Thomas, 1993). The first two outcomes are desirable, whereas
delisting represents organizational failure (Daily, 1995).
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Post-bankruptcy survival and signaling
We use a sample of U.S. public firms that filed for Chapter 11 reorganization bankruptcy
to test the likelihood of their delisting. This setting is appropriate as public firms are highly
visible, which allows us to identify reliable disclosed information. Following the data collection
procedure described by previous studies (Denis and Rodgers, 2007; Hotchkiss, 1995; Moulton
and Thomas, 1993), we collected a sample of public firms that filed for Chapter 11
reorganization between 1991 and 2004 from two sources: the Security Data Corporation (SDC)
Bankruptcy database and the Sarbanes-Oxley (SOX) Bankruptcy database. All cases in our
sample were governed by the Bankruptcy Reform Act of 1978. Firms in financial industries were
excluded because their bankruptcies were subject to the Federal Deposit Insurance Corporation
(FDIC) regulations.
We tracked each firm for five years after its Chapter 11 filing. The short period of
reorganization following bankruptcy filing is critical to turnaround. Dawley and colleagues
(2003: 414) define this period of time as the ‘recovery time’ of bankrupt firms. Firms that were
delisted following their bankruptcy filings in the same year were excluded (Dawley et al., 2002;
Hotchkiss, 1995; Moulton and Thomas, 1993). We collected financial data from COMPUSTAT,
delisting information from both the Center for Research in Security Prices (CRSP) and
COMPUSTAT, diversification data from COMPUSTAT segment tapes, analyst coverage from
the Institutional Brokers Estimates System (I/B/E/S), and institutional investor data from the
Thomson-Reuters Institutional (13F) Holdings. Accordingly, we discarded filings made by firms
that were not covered by these databases. The final sample included 291 firms, of which 142
survived, 54 were acquired, and 95 were delisted from 1992 to 2004.
We adopt a dynamic approach and use an event history methodology (Hill, Perry, and
Andes, 1996). This approach is appropriate because post-bankruptcy reorganizations and
19
Post-bankruptcy survival and signaling
stakeholder reactions are dynamic processes that shift away from initial bankruptcy conditions
rapidly. Following the study of Fischer and Pollock (2004) on public firm delisting, we used the
discrete-time event history technique to model the likelihood of bankrupt firm delisting. The unit
of analysis is firm-year. The 291 firms resulted in 1,195 firm-year observations. This technique
allows us to cope with censored observations (Allison, 1999) and test the effects of both timevarying and time-invariant variables (Dawley et al., 2002; Hill, Perry, and Andes, 1996). The
annual time period for each firm in our study started one year after its Chapter 11 filing until it
was right censored. Right censoring occurred at the cut-off year (1) when a firm survived after
five years, (2) when a firm was acquired by another firm, and (3) when a firm was delisted for
negative reasons.
Dependent Variable
The dependent variable, delisting for negative reasons, was coded 1 in the year of
delisting, otherwise coded 0. Negative reasons to delist in our sample included dissolution,
liquidation, Chapter 7 filing, or failure to maintain minimum market capitalization or stock price
with CRSP delisting codes in the 400 range (liquidations) and 500 range (dropped). The 500
range indicates delisting by the current exchange due to a failure to meet requirements related to
minimal price, insufficient capital, or shareholder interest. For example, stocks that fail to meet
the minimum price per share of $1 on the NYSE or the NASDAQ are often delisted (Campbell,
Hilscher, and Szilagyi, 2008). For bankrupt firms, being acquired by other firms has been viewed
as a desired outcome. Following Fischer and Pollock (2004), we kept those acquired firms with
CRSP delisting codes in the 200 range (mergers) and 300 range (acquisitions) in the sample until
they were acquired. These firms were coded 0 as they are not considered corporate failure
(Demers and Joos, 2007; Fischer and Pollock, 2004).
20
Post-bankruptcy survival and signaling
Independent Variables
Most existing studies have used rather static research methods, but stakeholder reactions
can be swayed by changes and trajectories in declining firms (Hambrick and D’Aveni, 1988). To
avoid reverse causality, all time-varying, firm-level independent and control variables in our
empirical specification were lagged one year.
Increase in firm performance. We computed industry-adjusted performance by
subtracting the mean value of industry ROA (return on assets) at the two-digit SIC level from
firm-level ROA for each firm and year to control for potential industry effects. Following
Shimizu and Hitt (2005), increase in firm performance in year t was calculated as (performance
in year t) – (performance in year t-1). Thus, a positive value indicates an increase in performance,
and a negative value indicates a decline in performance. Negative values would be consistent
with the downward-spiral model of corporate bankruptcy.
Decrease in debt-to-equity ratio. Leverage was measured by the debt-to-equity ratio
(total debts divide total equities) for each firm and year. Decrease in leverage was calculated as
(leverage in year t-1) – (leverage in year t). A positive value indicates a more favorable debt
position, and a negative value indicates a less favorable debt position. Positive values would be
consistent with the dependability model of corporate bankruptcy.
De-diversification. The yearly product diversification of a bankrupt firm was measured
by a Herfindahl index (Palepu, 1985; Palich, Cardinal, and Miller, 2000), defined as:
N
H  1   si2
i 1
where si is the sales percentage of business segment i , and N is the number of business
segments of the firm. The index ranges in value from 0 to 1, and higher values indicate greater
product diversification. De-diversification was calculated as (diversification in year t-1) –
21
Post-bankruptcy survival and signaling
(diversification in year t). A positive value indicates a reduced level of diversification, and a
negative value indicates an increased level of diversification. Positive values would be consistent
with the domain restructuring model of corporate bankruptcy. The data for the three variables
above were collected from COMPUSTAT.
Increase in the number of alliance partners. We counted the number of all other firms
that formed alliances with the bankrupt firm after its bankruptcy filing. The count coding method
has been used in previous studies (Palmer and Barber, 2001). Accordingly, alliance partner
reaction was measured by (the number of alliance partners in year t) – (the number of alliance
partners in year t-1). A positive value indicates an increase in the number of alliance partners, and
a negative value indicates a decline in the number of alliance partners. We collected the data
from the SDC Alliances database.
Increase in the number of institutional investors. Institutional investor reaction was
measured by the number of institutional investors who invested in the firm. Positive institutional
investor reaction was calculated as (the number of institutional investors in year t) – (the number
of institutional investors in year t-1). A positive value indicates an increase in the number of
institutional investors, and a negative value indicates a decline in the number of institutional
investors. We used the data from Thomson Reuters that maintains the most up-to-date and
comprehensive collection of institutional investor data available, spanning 13F institutions,
mutual funds, pension funds, and insurance funds.
Increase in analyst coverage. Analyst coverage was measured by the number of analysts
who followed the firm in each year. Increase in analyst coverage was calculated as (the number
of analysts in year t) – (the number of analysts in year t - 1). A positive value indicates an increase
22
Post-bankruptcy survival and signaling
in analyst coverage, and a negative value indicates a decline in analyst coverage. Following Jain
and Kini (2008), we collected the analyst data from the I/B/E/S database.
Control Variables
In keeping with existing research, we controlled for both financial and strategic
explanations that are particularly relevant for bankruptcy research (D’Aveni, 1990; Daily and
Dalton, 1994, 1995; van Witteloostuijn, 1998). For financial considerations, we controlled for
each firm’s yearly net income (loss) since most bankrupt firms suffered from negative return. We
also controlled for yearly assets (logged) as a proxy of firm size. Smaller firms may suffer from
liabilities of smallness, resulting in organizational failure (Aldrich and Auster, 1986). Larger
firms may possess more slack that can be drawn upon during difficult times (Flynn and Farid,
1991; Moulton and Thomas, 1993).
Tobin’s q indicates how existing and potential investors value the public firm (i.e.,
market reaction): the higher the ratio, the more it is valued (Welbourne and Andrews, 1996). We
thus controlled for this variable. In this study, we adopted the approach suggested by Baker,
Stein, and Wurgler (2003), where Tobin’s q = (outstanding shares x share price + book value of
long-term debt + debt in current liabilities + carrying value of preferred stock)/total assets. This
variable was measured for each firm and year.
Previous studies suggest diversification may affect the probability of public firm survival
(Jain and Kini, 2008) and post-bankruptcy outcomes (Dawley et al., 2002). Swaminathan and
Delacroix (1991) suggest that product and geographic diversifications are two forms of
adaptation, which may affect failure rates. We controlled for both product and geographic
diversification using time-variant measures. Product diversification was measured by a
Herfindahl index, as shown above; geographic diversification was measured by foreign sales as a
23
Post-bankruptcy survival and signaling
percentage of total sales (Sullivan, 1994). The data were collected from the COMPUSTAT
Business and Geographic Segment databases, respectively.
CEO change has been viewed as a way of declining firms ‘to signal that they are taking
actions to deal with the organization’s troubles’ (Hambrick and D’Aveni, 1992: 1461), which
may result in ‘an improved perception of the organizational image and a renewed confidence in
organizations’ futures’ (Daily and Dalton, 1995: 394). However, frequent CEO change may also
demonstrate instability in a firm’s leadership (Alexander, Fennell, and Halpern, 1993). CEO
change in our study was measured by the number of CEO turnovers occurring at a given firm in
previous years after the bankruptcy filing. We collected the data from the COMPUSTAT
Executive Compensation, Corporate Library, Risk Metrics databases, and annual 10-K reports
filed by public firms with the Securities and Exchange Commission (SEC). The effect of
downsizing is of some controversy in the organizational decline literature (McKinley, 1993). We
controlled for downsizing, as measured by a decrease in the number of employees since a
previous year.
Since bankruptcy filing is a significant event of organizational transformation,
subsequent reorganization may be time-dependent, and the amount of time elapsed since
bankruptcy may non-monotonically affect long-term firm survival (Amburgey et al., 1993).
Following Fischer and Pollock (2004), we controlled for years since bankruptcy and added a
squared term of this measure.
Related regulation change in the stock market may also affect public firm delisting. The
Sarbanes-Oxley Act (SOX) of 2002 mandates management to disclose internal control
effectiveness. The mandate reduces the information asymmetry and uncertainty of investors. We
thus controlled for the post-SOX period from 2002 to 2009 in our sample.
24
Post-bankruptcy survival and signaling
Industry membership may systematically affect post-bankruptcy outcomes. We
controlled for high-tech industry based on a firm’s four-digit SIC codes, which is defined by the
American Electronics Association (AEA) -- a nationwide non-profit trade association for
professionals in technology industries. To guard against other unobserved heterogeneity, we
fixed industry effects based on firms’ two-digit SIC codes, following the procedure described by
Amburgey and Miner (1992). We incorporated the fixed industry effect in all regression analyses,
but omitted their coefficients from the tables to preserve space.
Method of Analysis
We estimated the dichotomous outcomes for the pooled time series data using logit
models (Allison, 1999). This model has been widely used in previous studies on public firm
delisting (Demers and Joos, 2007; Fischer and Pollock, 2004; Jain and Kini, 2008). Since the
observations of the same firm were not independent across firm-year spells, we accounted for the
longitudinally clustered nature of the data using a generalized estimating equation (GEE)
approach (Liang and Zeger, 1986). The GEE method accounts for correlation within the same
cluster (i.e., each bankrupt firm in our study), thus providing conservative tests of our hypotheses
(Allison, 1999). The model was estimated by using the SAS GENMOD procedure with the
command of repeated measures.
RESULTS
Table 1 provides means, standard deviations, and correlations, which suggests no critical
multicollinearity problem for regression analysis. Since the variable of years since bankruptcy
and its squared term are highly correlated. We mean-centered this variable, as recommended by
Aiken and West (1991). For ease of interpretation, its untransformed measure is reported in
25
Post-bankruptcy survival and signaling
Table 1. Diagnostic tests based on the variance inflation factor (VIF) value provided additional
evidence as the largest VIF value was below 2.0 in all models.
---------------------------------------------------------------Insert Table 1 and Table 2 about here
---------------------------------------------------------------Table 2 reports the results of the logit regression. Model 1 shows the control variables
only. Model 2 adds the control variables to test the extended pre-bankruptcy models, i.e., the
downward spiral model, (hypothesis 1), the dependability model, (hypothesis 2), and the domain
restructuring model, (hypothesis 3). Model 3 adds the control variables to test the stakeholder
reaction model, (hypotheses 4-6). While Model 4 is the full model adding all independent
variables. We used the Akaike information criterion (AIC) to evaluate model fit.
Turning to our variables of interest regarding organizational reorganization signals,
examining model 2, Hypothesis 1 was not supported, nor was it supported in model 4. We found
no relationship between increase in firm performance (or failure to improve performance) and
organizational failure (delisting for negative reasons). The non-significant result might be a
function of including other variables, such as net income and Tobin’s q, which also proxy firm
performance. In a sensitivity test, we removed these two measures of firm performance, but the
results remained the same. Hypothesis 2 was supported in both Model 2 and Model 4 (p < 0.05),
suggesting that decrease in debt-to-equity ratio increases a firm’s dependability and thus reduces
the likelihood of organizational failure. Hypothesis 3 was also supported by both Model 2 and
Model 4 (p < 0.01), suggesting that de-diversification by focusing on core businesses reduces the
likelihood of delisting from the stock market.
To test the stakeholder reaction model, Hypotheses 4, 5, and 6 predict that the likelihood
of delisting is negatively related to the positive reactions of alliance partners, institutional
26
Post-bankruptcy survival and signaling
investors, and equity analysts, respectively. As can be seen in both Model 3 and Model 4, these
hypotheses were all supported, suggesting that increase in the number of alliance partners (p <
0.01), institutional investors (p < 0.01), and equity analysts (p < 0.05) following a given bankrupt
firm demonstrates the firm’s potential quality to emerge as an independent entity. However, we
note that the AIC was lowest for the stakeholder reaction model when compared to either the
pre-bankruptcy models or the full model combining the pre-bankruptcy models with the
stakeholder reaction model. Thus, parsimony would favor the use of the stakeholder reaction
model for predicting post-bankruptcy outcomes.
Among control variables, the statistically significant contribution is from diversification
and high tech industry. As expected, delisting is positively related to a highly diversified firm but
negatively related to a firm in high tech industry. Delisting has an inverted-U relationship over
time since bankruptcy. Regulatory change (Sarbanes-Oxley Act) in the stock market since 2002
reduced the likelihood of bankrupt firm delisting.
CONCLUSION AND IMPLICATION
Given the difficulty to directly evaluate the quality of bankrupt firms to successfully
turnaround, this study shows that signaling theory provides a useful foundation to explain how
different sources of information predict a post-bankruptcy survival. First, it has examined the
effects of key organizational reorganization signals drawing insight from the bankruptcy
literature. The findings suggest that a decrease in debt-to-equity ratio and de-diversification are
useful signals to predict the likelihood of public firm survival after bankruptcy. Second, it has
offered a new stakeholder reaction approach to advance our understanding of this topic. The
results indicate that positive reactions of alliance partners, institutional investors, and equity
27
Post-bankruptcy survival and signaling
analysts to a bankrupt firm not only contribute additional significant predictive power, but are
the most parsimonious predictors in their own right. Thus, reactions of external stakeholders
cannot be ignored as they also generate valuable signals to predict the fate of the firm once
bankruptcy has been declared.
Implications for Post-bankruptcy Research
This study adds to the organizational crisis and bankruptcy literatures by extending the
research domain of signaling theory (Connelly et al., 2011; Spence, 1973) to the post-bankruptcy
context. To our knowledge, there is no known published research that has systematically
examined post-bankruptcy outcomes through the lens of signaling theory. Traditional approaches
have been limited to a time frame prior to bankruptcy or Chapter 11 filings (Altman, 1983;
D’Aveni, 1989b; Hambrick and D’Aveni, 1988, 1992). As such, these studies are unable to
examine post-bankruptcy outcomes. As bankruptcy filing is an important event that triggers
subsequent reorganization, it calls for researchers to probe the generalizability of existing
findings.
Most previous studies have focused on the information emitted by the firm itself (e.g.,
financial disclosure or restructuring strategy) to signal a firm’s quality. Our study adds to the
literature by showing that the generalizability of previous findings varies across different
organizational indicators in the post-bankruptcy context. We find no relationship between
performance improvements and delisting, suggesting that enhanced performance alone is not
sufficient to differentiate the firm’s quality. However, post-bankruptcy de-diversification and
improvement in debt position reduce the likelihood of public firm failure. These findings are
useful in understanding the relative importance of various reorganization signals.
28
Post-bankruptcy survival and signaling
Complementing organizational signaling approaches, this study emphasizes signals
emitted from external stakeholders about bankrupt firms. Previous studies suggest that these
stakeholders are likely to withdraw their support for bankrupt firms, increasing the likelihood of
failure (Daily, 1995; Moulton and Thomas, 1993). Our stakeholder reaction approach is useful in
advancing a theory of firms in crisis, suggesting that post-bankruptcy reactions of external
stakeholders including alliance partners, institutional investors, and equity analysts are useful
predictors of a firm’s quality to survive.
More broadly, this study extends the literature on transformation outcomes of public
firms. Scholars have argued that external support may serve as transformational ‘shields’ by
providing continued access to financial resources and/or legitimacy for firms undergoing
transformation (Fischer and Pollock, 2004; Miner, Amburgey, and Stearns, 1990). Such a shield
refers to ‘an organizational trait that insulates an organization against the probability of failure
resulting from transformation’ (Miner, Amburgey, and Stearns, 1990: 695). These studies
suggest that the survival of a firm in the stock market is essentially a function of stakeholder
support. From a signaling theory perspective, external support may also function as signals of
firm quality, which offers an additional explanation for the positive relationship between external
linkages and firm survival. We reach a parallel conclusion when we examine the survival of
bankrupt firms in the stock market due to stakeholders’ positive reactions. For a bankrupt firm’s
shares to continue to trade, the positive reactions of external stakeholders demonstrate a firm’s
quality such as trustworthiness and legitimacy to survive. This information is useful in
distinguishing between higher-quality and lower-quality bankrupt firms, which represents an
extension of existing studies on transformation outcomes.
Implications for Signaling Theory Research
29
Post-bankruptcy survival and signaling
This study also contributes to signaling theory (Spence, 1973) by differentiating the
effectiveness of different types of signals in terms of signal fit, i.e. ‘the link between a signal and
the underlying quality’ (Connelly et al., 2011: 59). While prior literature has emphasized the role
of signals in predicting firm behavior (Gulati and Higgins, 2003; Janney and Bolta, 2003;
Pollock and Gulati, 2007; Sanders and Boivie, 2004), our understanding of the impact of
different types of signals is still limited. This study has distinguished between organizational
reorganization and stakeholder reaction signals as important indicators of firm quality in
predicting post-bankruptcy outcomes. While organizational signals have been a major focus in
prior research examining the antecedents of bankruptcy (Connelly et al., 2011), our study
complements prior literature by showing that signaling theory also provides a useful foundation
in predicting post-bankruptcy survival. It also responds to the call for research that examines
which signal represents a more valid measure of the underlying quality and in what conditions
signals are aligned with the underlying characteristics (Connelly et al., 2011).
Implications for Firms in Crisis and Their Stakeholders
Findings from this study have useful practical implications for firms in crisis and external
stakeholders. On the one hand, as D’Aveni (1989b) noted, the stigma of bankruptcy is likely to
reduce a firm’s dependability or reliability and signal stakeholders to withdraw support from the
firm. On the other hand, filing for Chapter 11 bankruptcy can be used as a strategic option for
many firms in decline (Flynn and Farid, 1991; Moulton and Thomas, 1993) because it provides
time and opportunities for the firms to revitalize. According to our findings, for firms in crisis,
what is important, as implied by our results, is to disseminate organizational reorganization
signals and to gain external support for two reasons. First, it is important for the bankrupt firm to
demonstrate its quality (e.g., through the timely disclosure of their financial turnaround plans and
30
Post-bankruptcy survival and signaling
strategies) to the market because external decision makers do not have the information
management has to directly assess the reliability and prospects of the firm.
Second, and more importantly, higher-quality firms must make substantial efforts to gain
support from external stakeholders to enhance their legitimacy. For example, management could
use conference calls to relevant stakeholders (e.g., exchange partners, institutional investors,
equity analysts, or journalists) to highlight particular criteria to show that the firm deserves their
positive support. Our findings suggest that signals from stakeholder sources of information are
more likely to generate trustworthy signals and thus reduce uncertainties in the stock market.
Especially, if higher-quality firms in temporary decline are able to attract some external
stakeholders’ attention and support, other firms may follow as such signals to the market that
may restore the confidence of other decision makers.
Traditional wisdom suggests that stakeholders tend to avoid firms that are perceived to be
less reliable or dependable. Although bankruptcy demonstrates the firm is problematic, our
results show that it is still useful for stakeholders to differentiate between higher-quality and
lower-quality firms, as informed by signaling theory. Higher-quality firms are more likely to
revitalize. For better returns, investors should evaluate the firm carefully by interacting with the
firms’ top managers. For many wholesale investors who often have incomplete information
about the quality of a troubled firm, existing studies have largely emphasized the importance of
organizational transformation signals. Our study suggests that the post-bankruptcy reaction of
other external stakeholders to a firm in a crisis situation is also an important source of credible
information to identify a firm’s quality, which cannot be ignored.
Limitations and Future Research
31
Post-bankruptcy survival and signaling
Several limitations of this study can be identified, and further research suggested. First,
we focus on the likelihood of stock market failure for Chapter 11 reorganization firms, but
recognize that bankruptcy is only one form of organizational crisis. In a broader sense,
organizational crises can be demonstrated in many different forms, such as the disclosure of
financial fraud (Kang, 2008; Pandey and Verma, 2005) and various adverse environmental
changes or shocks. It is useful to extend the stakeholder reaction approach in different crisis
situations to identify stakeholders whose reactions should be considered.
Second, although we broadly categorize signals regarding a firm’s quality into
organizational reorganization and stakeholder reaction signals, we have investigated a limited
number of observable indicators for each category. Alternative sources of information (e.g., a
firm’s mergers and acquisitions, divestitures) are available. In addition, stakeholders may differ
in their power, urgency, legitimacy, or salience (Mitchell, Agle, and Wood, 1997), which, in turn,
affects organizational outcomes (Agle, Mitchell, and Sonnenfeld, 1999). Future research could
extend our findings to examine whether other important signals could also predict organizational
transformation outcomes.
Finally, although we have examined signal effects on post-bankruptcy outcomes, the
survival of firms under organizational crises in different situations require further investigation
(Hotchkiss, 1995; Daily, 1995; Dawley et al., 2002). The extra value provided by signals sent
from external stakeholders may lie in the extra cost associated with this type of signal if inferior
signalers want to imitate the signal. That is, it is more difficult to imitate signals emitted from the
stakeholders than from inside the organization. This discussion leads to interesting future
research on how different types of stakeholders may influence the quality of their signals and
32
Post-bankruptcy survival and signaling
signal cost. For example, the reputation of the alliance partners of the focal firm may positively
influence the signal quality and cost to imitate.
Conclusion
The primary conclusion of this research is that the likelihood of a bankrupt firm to
survive the stock market can be predicted by organizational reorganization and stakeholder
reaction signals. As a departure from most existing studies on firms in crises, this paper
represents an effort drawing research attention from firm-based signals to stakeholder-based
signals using a dynamic approach. Our study shows that it is fruitful to examine the relevance of
signaling theory in predicting the delisting of bankrupt firms, which has emerged as an important
research topic (Daily and Dalton, 1995; Hotchkiss, 1995). The further development of a theory
of organizational crisis could stimulate a new stream of research in predicting organizational
adaptation outcomes.
33
Post-bankruptcy survival and signaling
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38
Table 1: Descriptive Statistics and Correlations*
Variable
Mean
s.d.
1
2
3
4
5
6
7
1
Firm assets
4.97
(2.18)
2
Net income (loss)
0.00
(0.13)
-0.04
3
Tobin’s q
0.30
(4.80)
-0.13
0.00
4
Diversification
0.00
(0.21)
0.33
0.00
-0.03
5
Foreign sales ratio
0.13
(0.24)
0.26
0.05
-0.03
0.15
6
CEO change
0.21
(0.42)
-0.03
-0.03
-0.03
0.00
-0.02
7
Downsizing
0.31
(2.28)
0.11
-0.06
-0.01
0.13
-0.04
0.05
8
High tech industry
0.19
(0.39)
-0.15
0.05
-0.02
-0.19
0.13
0.03
-0.05
9
8
9
10
11
12
13
14
15
Year since bankruptcy
3.67
(1.40)
0.00
0.03
0.00
0.03
0.06
-0.07
-0.08
0.02
10
Regulatory change
0.50
(0.50)
0.14
0.00
0.06
0.17
0.21
0.00
-0.02
0.16
0.18
11
Increase in firm performance
0.00
(0.74)
-0.03
0.00
0.49
-0.01
-0.01
0.00
0.00
0.03
0.01
0.01
12
Decrease in debt-to-equity ratio
0.00
(0.12)
-0.02
0.00
0.00
0.00
0.01
-0.01
-0.01
0.01
-0.03
-0.01
0.00
13
De-diversification
0.00
(0.10)
0.04
0.00
0.00
-0.18
-0.02
-0.02
-0.01
0.00
-0.02
0.00
0.00
0.04
14
Increase in alliance partners
0.00
(0.54)
0.14
-0.09
-0.01
0.03
0.10
0.03
0.07
0.10
-0.02
-0.04
0.00
0.01
-0.03
15
0.03
(33.07)
0.10
0.12
0.00
0.04
0.05
-0.05
-0.15
-0.02
0.27
0.07
0.00
0.00
-0.02
0.00
16
Increase in institutional investors
Increase in equity analyst
coverage
0.01
(8.18)
0.03
0.18
0.00
0.01
0.04
-0.08
-0.17
0.03
0.19
0.08
0.00
0.12
-0.01
0.08
0.39
17
Delisting
0.08
(0.27)
-0.06
-0.01
-0.02
-0.07
-0.09
0.01
0.02
-0.04
0.01
-0.08
0.00
-0.07
-0.01
-0.04
-0.04
* Correlations with absolute values greater than or equal to 0.05 are significant at the 0.05 level.
16
-0.08
40
Table 2: Results from Logit Regression Analysis
Variable
Model 1
Control variables
Firm assets
-0.06
Net income (loss)
-0.41
Tobin’s q
-1.05
Diversification
1.47*
Foreign sales ratio
0.33
CEO change
0.17
Downsizing
-0.01
High tech industry
-0.75*
Year since bankruptcy
0.23**
Years since bankruptcy squared
-0.14**
Regulatory change
-1.17***
Organizational reorganization signals
Increase in firm performance
Decrease in debt-to-equity ratio
De-diversification
Stakeholder reaction signals
Increase in alliance partners
Increase in institutional investors
Increase in equity analyst coverage
Intercept
-0.40
Fixed industry effect
yes
Log Likelihood
-311.74
AIC
645.47
Likelihood ratio tests
Standard errors are in parentheses.
†
p< 0.10
* p< 0.05
** p< 0.01
*** p< 0.001
Model 2
(0.05)
(0.38)
(0.79)
(0.60)
(0.55)
(0.19)
(0.03)
(0.37)
(0.08)
(0.05)
(0.29)
(0.43)
Model 3
-0.05
-0.42
-1.68
0.72
0.05
0.04
-0.01
-0.65†
0.17*
-0.13*
-0.97***
(0.05)
(0.40)
(1.23)
(0.62)
(0.58)
(0.21)
(0.03)
(0.38)
(0.07)
(0.05)
(0.29)
0.39
-1.24**
-2.16**
(0.26)
(0.47)
(0.69)
-0.69
yes
-308.86
645.72
5.75
40
(0.44)
-0.05
-0.68
-0.83†
1.86***
0.47
0.15
-0.06
-0.73*
0.32***
-0.19***
-1.29***
-1.00**
-0.01**
-0.02*
-0.36
yes
-306.99
641.98
9.49*
Model 4
(0.06)
(0.58)
(0.50)
(0.46)
(0.47)
(0.20)
(0.04)
(0.36)
(0.08)
(0.05)
(0.23)
(0.32)
(0.00)
(0.01)
(0.33)
-0.04
-0.67
-1.18
1.41**
0.36
0.12
-0.05
-0.70†
0.28***
-0.17***
-1.20***
(0.05)
(0.56)
(0.73)
(0.51)
(0.48)
(0.20)
(0.04)
(0.36)
(0.08)
(0.05)
(0.24)
0.26
-0.93*
-1.49**
(0.17)
(0.45)
(0.57)
-0.92**
-0.01**
-0.02*
-0.49
yes
-305.27
644.54
12.93*
(0.31)
(0.00)
(0.01)
(0.34)