intra-industry effects of negative stock price surprises
TRANSCRIPT
ORI GINAL RESEARCH
Intra-industry effects of negative stock price surprises
Aigbe Akhigbe • Jeff Madura • Anna D. Martin
� Springer Science+Business Media New York 2014
Abstract We find that a pronounced stock price decline of one firm yields negative
valuation effects for industry rivals, on average. We test whether the impact is conditioned
on a measure of default likelihood of rivals derived from the option pricing framework.
The stock price contagion effects are more pronounced for rivals with the greatest default
likelihood. The contagion effects are also conditioned on the degree of the surprise,
characteristics of the firm experiencing the negative surprise (such as its relative size),
characteristics of the rival firms (such as their similarity to the firm experiencing the
negative surprise), and characteristics of the corresponding industry (such as degree of
concentration). The sensitivity of industry rivals and portfolios to negative stock price
surprises changes during the 2007–2008 financial crisis, which may be because stocks had
already been priced to reflect pessimistic outlooks, or because the market anticipated
restructuring or government intervention that could prevent the collapse of firms with the
greatest default likelihood.
Keywords Negative surprise � Intra-industry � Default likelihood � Distress
JEL Classification G30 � G33
A. AkhigbeCollege of Business Administration, University of Akron, Akron, OH 44325, USAe-mail: [email protected]
J. MaduraCollege of Business, Florida Atlantic University, Boca Raton, FL 33431, USAe-mail: [email protected]
A. D. Martin (&)Tobin College of Business, St. John’s University, Jamaica, NY 11439, USAe-mail: [email protected]
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Rev Quant Finan AccDOI 10.1007/s11156-014-0446-4
1 Introduction
Research has investigated whether a firm’s financial distress can emit a negative signal
throughout the industry. Earlier studies provide evidence that corporate defaults are cor-
related (e.g., Lucas 1995; Pedrosa and Roll 1998). More recently, research shows that
default contagion may occur through firm-specific and industry variables (e.g., Chava and
Jarrow 2004; Lando and Nielsen 2010; Huang and Lee 2013). Das et al. (2007) offer three
explanations for default clustering that are also applicable to the proposition of intra-
industry stock price effects of distress, which is the focus of our study. First, firms in the
same industry are likely exposed to common risk factors. Second, firms within the industry
may have direct cash flow linkages. Third, when investors learn about the distress of one
firm, they may more closely scrutinize corresponding industry competitors that are exposed
to potential distress.
Lang and Stulz (1992), Ferris et al. (1997), Jorion and Zhang (2007) and Hertzel et al.
(2008) examine whether the negative signal associated with a bankruptcy can trigger stock
price effects for rival firms in the corresponding industry. These studies predominantly
show that the average stock price response of industry rivals to bankruptcies is negative
and significant, supporting an intra-industry contagion effect.
Jorion and Zhang (2007) mainly focus on the impact of bankruptcy filings and large
jumps in CDS spreads on the CDS spreads of rival firms, but they also evaluate the impact
on rival stock prices. They find that Chapter 11 (Chapter 7) bankruptcy events elicit
significant intra-industry contagion (competitive) CDS spread effects, but do not find
significant stock price effects from these bankruptcy events. They also find that large jumps
in CDS spreads result in strong contagion effects, in terms of both CDS spread and stock
price effects on rivals, and assert that they are due to the unanticipated nature of these
events.
Our study uniquely examines whether firms that experience a substantial decline in their
stock price generate intra-industry stock price effects. Certainly bankruptcies are more
severe, but a pronounced stock price decline may be an early signal of financial distress
and/or a signal that the firm is experiencing adversity, thus may also transmit industry
information. Based on the strong contagion effects in response to unanticipated jump
events documented in Jorion and Zhang (2007), we expect that negative stock price sur-
prises likely trigger significant contagion effects since these shocks are also unanticipated.
While negative surprise events are not as severe as bankruptcies, they do occur more
frequently. We identify 2,368 firms with a minimum of $1 billion market capitalization
between 1998 and 2011 that have experienced a one-day stock price drop of 15 % or more,
which is nearly one major stock price drop across relatively large firms every day. In
comparison, Hertzel et al. (2008) draw their sample of 250 firms from a population of
1,695 bankruptcy filings between 1978 and 2004, which equates to about five filings per
month on average. The events ultimately included in their sample occur less than once per
month. Furthermore, their sample has an average distress-day abnormal return of -26 %,
whereas we focus on a less stringent requirement of a -15 % nominal change in stock
price and do not require the firm to eventually file for bankruptcy. The typical information
content of a bankruptcy filing reveals a firm that has already been subject to very weak
performance and a declining stock price, and seeks a formal restructuring of its debt as a
desperate strategy to rebound. Conversely, the typical firms in our sample are not neces-
sarily even close to bankruptcy, and many of them were performing well prior to the
negative surprise. Yet, even though the negative surprise is not as severe as a bankruptcy
signal, we believe that it can transmit contagion effects throughout the industry. Our
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assessment of more common events offers insight on the transmission of stock price effects
that occur even without extreme events such as bankruptcies.
One explanation for the previously documented negative intra-industry stock price
effects due to bankruptcy filings is that the bankruptcy draws attention to the default
likelihood of all firms in the industry, causing a contagion effect. Furthermore, market
participants may reduce their projections of expected future cash flows for all firms in the
industry, as the bankruptcy announcement could signal a shrinking industry. Thus, we
hypothesize that rival firms of negative surprise firms experience significantly negative
valuation effects. Additionally, as the large stock price drop may prompt the market to
focus attention on the default likelihood of rivals and/or on adverse industry conditions, we
hypothesize that greater contagion stock price effects occur for those rivals with greater
default likelihood.
Of the previous studies that examine intra-industry stock price effects of distress, only
the study by Lang and Stulz (1992) provides empirical evidence that financial leverage (as
a proxy for default likelihood) has some influence on the contagion stock price effects.1
They report that rival portfolios with above median degrees of leverage experience sig-
nificantly negative abnormal returns, but do not detect leverage as a significant factor in
cross-sectional analyses on rival responses. Jorion and Zhang (2007) and Hertzel et al.
(2008) acknowledge that a small sample size can make it difficult to detect significant
cross-sectional factors. In our cross-sectional analyses, we address two key limitations of
these previous studies. First, by examining negative surprises that are not as severe as
bankruptcies, we assess a very large sample, which enables us to uncover significant cross-
sectional factors. Second, we estimate the default likelihood from an option pricing
framework and use it as a cross-sectional factor. While this volatility-adjusted leverage
measure is frequently used in default contagion literature (e.g., Das et al. 2007; Duffie et al.
2007; Lando and Nielsen 2010; Huang and Lee 2013), it has not yet been used to explain
intra-industry stock price effects of distress; we believe that this measure is advantageous
over a balance sheet measure such as financial leverage because of its reliance on infor-
mation embedded in equity prices and volatility.
Our results demonstrate that a pronounced stock price decline of one firm yields neg-
ative valuation effects for industry rivals. This finding extends the results of previous
studies on intra-industry stock price effects of (1) bankruptcy announcements (e.g., Lang
and Stulz 1992; Ferris et al. 1997; Hertzel et al. 2008) and (2) unanticipated jumps in CDS
spreads in Jorion and Zhang (2007). Unlike previous studies, we are able to show that
default likelihood, derived from an option pricing framework, is an underlying reason for
the intra-industry contagion stock price effects. Leverage, Tobin’s Q, similarity between
rivals and negative surprise firms, degree of the surprise, relative size of the negative
surprise firm, industry concentration and industry performance are also found to explain
the contagion effects that accrue to industry rivals in response to negative surprises.
The remainder of the paper is organized as follows. In Sect. 2, we provide an overview
of previous literature on intra-industry stock price effects and discuss in more detail those
studies that specifically examine intra-industry effects from bankruptcies. In Sect. 3, we
describe the sample, data, and our methods for estimating valuation and default likelihood.
In Sect. 4, we describe: (1) the results for valuation effects that occur in response to
1 Jorion and Zhang (2007) find leverage to significantly explain the cross-sectional variation in the CDSspread effects that result from CDS jump events. They do not evaluate the cross-sectional variation in thestock price effects that result from CDS jump events or Chapter 11 and Chapter 7 bankruptcies.
Negative stock price surprises
123
negative stock price surprises, and (2) the analysis of factors that may influence the cross-
sectional variation in these valuation effects. We provide a summary in Sect. 5.
2 Literature on intra-industry stock price effects
Numerous studies have tested how negative information about a single firm could have an
impact on the stock prices of other firms in the industry. The general impetus for these
studies is that because of asymmetric information, the release of new public information
about one firm is used by market participants to make valuation inferences about corre-
sponding rival firms in the same industry. Foster (1981) and Clinch and Sinclair (1987) find
that negative earnings information about a firm can cause contagion effects within the
corresponding industry. Fenn and Cole (1994) find that negative news about First Exec-
utive and Travelers (leading to asset writedowns) has contagion effects that are most
pronounced for rivals with riskier assets. Aharony and Swary (1983; 1996) determine that
negative news about one bank causes contagion effects within the banking industry. Ak-
higbe et al. (1997) find that bond rating downgrades causes contagion effects within the
corresponding industry. Laux et al. (1998) and Kohers (1999) show that dividend reduc-
tions cause contagion effects throughout the corresponding industry.
More recently, Govindaraj et al. (2004) document favorable competitive effects in
response to a product recall of Firestone tires by the Bridgestone Corporation. Akhigbe
et al. (2006) find that analyst downgrades lead to negative industry effects. Xu et al. (2006)
show that an accounting irregularity at one firm can cause contagion effects within the
corresponding industry. Chen et al. (2007) find that delayed new product announcements
harm industry rivals. Hertzel et al. (2008) find that negative news about a firm can be
transmitted to other firms that are part of the supply chain.
In addition to the aforementioned studies, some studies have focused specifically on the
impact of a firm’s bankruptcy on the stock prices of rival firms. Lang and Stulz (1992)
explain that while the bankruptcy of a firm could signal problems for other rival firms, it
could allow rival firms an opportunity to increase market share that may be lost by the
bankrupt firm, and therefore cause competitive effects. They examine the intra-industry
effects of 59 bankruptcy filings between 1970 and 1989 by firms with more than $120
million in liabilities, and find industry rival portfolios react negatively to the bankruptcy
announcements on average. In their cross-sectional analyses, they find that the interaction
of leverage and industry concentration significantly influence the wealth effects of the
rivals. That is, firms that jointly have greater leverage and operate in more competitive
industries suffer to a greater extent when one of their competitors files for bankruptcy.
Ferris et al. (1997) study a sample of 279 bankruptcies between 1979 and 1989 without
restrictions on firm size, and also find the average intra-industry effects to be negative and
significant. They categorize rival firms as likely to default or not, based on whether the
rival firm actually files for bankruptcy within the next 3 years, and find greater contagion
effects for the rivals that end up in bankruptcy.
Using a more recent time period of 2001 through 2004, Jorion and Zhang (2007) find
their sample of 272 Chapter 11 bankruptcy events elicit intra-industry contagion effects,
and their sample of 22 Chapter 7 bankruptcy events elicit competitive effects. They also
assess 170 unanticipated distress events, as captured by jumps in credit default swap rates,
and find that these jumps generate contagion CDS spread and stock price effects. They
analyze the cross-sectional variation in the CDS spread effects and find larger contagion
effects for rival portfolios that are more strongly correlated with the event firm and have
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greater leverage, and when the distressed firm is larger. Their cross-sectional results also
show greater competitive effects for distress events in more concentrated industries. Using
a sample of 250 bankruptcy filings, Hertzel et al. (2008) evaluate valuation consequences
for rivals, suppliers, and customers. Because financial distress typically begins prior to the
bankruptcy filing, they also examine the largest abnormal drop in the market value of the
filing firm in the year prior to filing and document intra-industry stock price effects. They
find that larger bankruptcies and bankruptcies in more concentrated industries are asso-
ciated with greater contagion effects.
3 Sample and methodology
The sample of firms with negative stock price surprises is formed over the 1998–2011 time
period. We identify all firms traded on the NYSE, AMEX or NASDAQ stock exchanges
with a minimum market capitalization of $1 billion and that experience a stock price
decline of at least 15 % in a single day. Table 1 shows the sample distribution over time
and across industries for the 2,368 firms that meet these criteria. The yearly distribution in
Panel A shows that 17 % of the stock price drops occurred in 2000, which coincides with
the collapse of the technology sector. In the subsequent years, 2001 and 2002, 16 % of the
negative surprises occurred. It is in these years that the financial markets uncovered a
variety of frauds that led to the passage of the Sarbanes–Oxley legislation, and the 9/11
terror attacks occurred. Reflecting the turmoil of the 2007–2008 financial crisis period, we
have approximately 25 % of our negative surprises over 2007 and 2008. The industry
distribution presented in Panel B tabulates the number of negative surprise events for each
two-digit SIC industry that represents 5 % or more of the 2,368 observations. The business
services industry, with 19 % of the observations, has the largest proportion of negative
surprise events. Also, technology-related industries that suffered from the eruption of the
technology bubble are heavily represented; 13 % of the observations are in electronics and
other electric equipment and 8 % are in industrial and commercial machinery and com-
puter equipment.
3.1 Measuring abnormal returns
We use event study methodology to estimate the daily abnormal stock returns (ARs) for the
2,368 negative surprise firms, 21,936 individual event-rival firms, and 2,368 equally-
weighted rival portfolios.2 Daily abnormal returns are calculated for the period surrounding
the date of the negative surprise, t0:
ARkt ¼ Rkt � ak þ bkRmtð Þ ð1Þ
where ARkt is the daily abnormal return for firm, rival, or rival portfolio k, Rkt is the daily
return for firm, rival, or rival portfolio k, Rmt is the daily return on the CRSP equally-
weighted index, and the parameters ak and bk are obtained from the market model that is
estimated with daily returns over the period t-120 to t-21 relative to the date of the negative
surprise.
Since rival firm returns are estimated within the same industry and over the same period
of time, the rival firm returns may not be independent. Therefore, we also measure the
2 There are 2,438 unique rival firms included in the sample of individual event-rival firms. For the sample ofrival portfolios, the mean (median) number of rivals per portfolio is 9.3 (4.0).
Negative stock price surprises
123
abnormal return using portfolios of rivals in Eq. (1), where the portfolio consists of all
rivals with the same four-digit SIC code as the negative surprise firm and are assigned an
equal weight within the portfolio. This method is advantageous because it controls for
contemporaneous correlation, but it (1) implicitly allows greater weight to rival firms in
more concentrated industries, because each rival is assigned a relatively high weight within
an industry that contains a small number of firms and (2) can reduce the variation across
the sample of rival portfolios due to the averaging process. Given the advantages and
disadvantages of both approaches, we analyze both individual rivals and rival portfolios,
following Song and Walking (2000). Z-statistics from Mikkelson and Partch (1988) are
Table 1 Distribution of negative stock price surprises
Year Frequency Percent Cumulativefrequency
Cumulativepercent
Panel A: sample distribution by year
1998 146 6.17 146 6.17
1999 167 7.05 313 13.22
2000 403 17.02 716 30.24
2001 194 8.19 910 38.43
2002 188 7.94 1,098 46.37
2003 86 3.63 1,184 50.00
2004 108 4.56 1,292 54.56
2005 98 4.14 1,390 58.70
2006 98 4.14 1,488 62.84
2007 138 5.83 1,626 68.67
2008 447 18.88 2,073 87.54
2009 89 3.76 2,162 91.30
2010 68 2.87 2,230 94.17
2011 138 5.83 2,368 100.00
Total 2,368 100.00 2,368 100.00
Industry Frequency Percent Cumulativefrequency
Cumulativepercent
Panel B: sample distribution by industry
Business services 456 19.26 456 19.26
Electronics and other electrical equipment(excl. computer equipment)
312 13.18 768 32.43
Chemicals and allied products 265 11.19 1,033 43.62
Industrial and commercial machineryand computer equipment
193 8.15 1,226 51.77
Communications 127 5.36 1,353 57.14
Measuring, analyzing and controllinginstruments; photographic, medicaland optical goods; watches and clocks
123 5.19 1,476 62.33
All others (54 other two-digit SICs) 892 37.67 2,368 100.00
Total 2,368 100.00 2,368 100.00
This table reports the sample distribution of 2,368 firms with a minimum of 15 % daily decline in stockprice over 1998 to 2011. Panel A (Panel B) provides the number of observations by year (by industry)
A. Akhigbe et al.
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computed to test for statistical significance of the cumulative standardized average ARs for
the firms and rival portfolios.
3.2 Measuring default likelihood
The default likelihood measure used in this study is based on option pricing methodology
following previous studies (e.g., Vassalou and Xing 2004; Hillegeist et al. 2004; Duffie
et al. 2007; Huang and Lee 2013).3 This default likelihood measure (DL) uses information
embedded in equity data as a proxy for likelihood of default per firm per day. More
specifically, for each rival firm, we average the daily DL as described in Eq. (2) below over
the 60 days prior to the negative surprise event window:
DLt ¼ N �lnðVA;t=XtÞ þ ðlt � 1
2r2
A;tÞTrA;tT
!ð2Þ
where DLt = default likelihood measure on day t, VA,t = market value of assets on day t,
Xt = book value of total liabilities on day t, gathered for each year from Compustat,
lt = drift on day t (the daily rolling mean of the change in lnVA over the prior year),
rA,t = annualized standard deviation of asset returns on day t, T = time to maturity and is
set equal to 1, N = cumulative density function of the standard normal distribution. VA,t
and rA,t = unobservable variables estimated simultaneously using an iterative process.
Based on the notion that equity is like holding a call option on the value of the firm’s
assets, the Black–Scholes–Merton option pricing model can be used as the basis on which
to simultaneously estimate the two unobservable variables, VA,t and rA,t :
VE;t ¼ VA;tNðd1;tÞ � Xte�rtT Nðd2;tÞ ð3Þ
where, VE,t = market value of equity from CRSP at the end of day t, rt = risk-free rate on
day t (the daily nominal one-year U.S. T-bill rate), d1,t and d2,t = defined below in Eqs. (4)
and (5), respectively, and the remaining variables are previously defined.
d1;t ¼lnðVA;t=XtÞ þ rt þ 1
2r2
A;t
� �T
rA;tTð4Þ
d2;t ¼ d1;t � rA;t
ffiffiffiffiTp
ð5Þ
The iterative process we use to simultaneously estimate VA,t and rA,t applies a Newton
search algorithm to Eq. (3) and the optimal hedge equation, rE = (VA e-T N(d1) rA)/VE.
This process conforms to that in Hillegeist et al. (2004). For a starting value of rA,t we use
the volatility of equity on day t, rE,t. We calculate the annualized, rolling rE,t as the
standard deviation of daily equity returns over the previous year multiplied by the square
root of the number of trading days in the year. We then use the daily values of rE,t and VE,t,
to compute the initial daily value of rA,t, where rA,t = rE,t VE,t/(VE,t ? Xt). VA for each day
t is computed using Eq. (3) and the optimal hedge equation, where the Newton search
algorithm identifies values for VA and rA.
3 We acknowledge that the study by Bharath and Shumway (2008) shows the Merton distance to defaultmodel may not be a sufficient statistic for predicting default likelihood and argues that most of its marginalbenefit comes from its functional form. Nevertheless, the default likelihood measure has been shown tooutperform the traditional Altman-Z types of corporate default predictors that are based on accounting data(see Hillegeist et al. 2004).
Negative stock price surprises
123
Using the estimated daily values of VA and rA from this iterative process, l, T, and X,
we ultimately calculate the default likelihood measure, DL, per day for each rival firm in
our sample with Eq. (2). The default likelihood measure by construction is a function of
leverage, returns, and volatility. Regression analyses show the estimated default measures
are statistically significantly related to each of these three factors. Standardized regression
coefficients show that the most influential factor is leverage, followed by volatility, and
lastly returns.4 For brevity, these results are not tabulated.
4 Analyses and results
Results from estimating abnormal returns are reported in Table 2 for the 2,368 firms
experiencing the negative surprises in Panel A, along with the 21,936 individual event-
rival firms in Panel B and the 2,368 rival portfolios in Panel C. In Panel A, the 2,368 firms
experiencing the negative surprises have an average -19.00 % CAR for the [-1, 0]
window. The [? 1, ?2] window is slightly positive, 0.42 %, resulting in an average
-18.58 % CAR for the [-1, ?2] event window. The [? 3, ?20] post-event window does
not detect significant longer-term or persistent effects from the negative stock price surprises.
Panels B and C show that the individual rivals and rival portfolios experience negative
and significant abnormal returns of -1.15 and -3.63 %, respectively, over the two-day
window. Even though the rivals have significant and positive wealth effects over the [? 1,
?2] time period, the rival effects over the [-1, ?2] event window in both Panels B and C
are negative and significant, -0.47 and -3.39 %, respectively. These findings extend the
results of previous studies on intra-industry effects of bankruptcy announcements (e.g.,
Lang and Stulz 1992; Ferris et al. 1997; Jorion and Zhang 2007; Hertzel et al. 2008) by
providing evidence that large stock price declines transmit net contagion stock price effects
to the industry, even though negative surprises are not as drastic as bankruptcy.
As robustness checks, we evaluate whether the valuation effects are affected by using
alternative cutoffs instead of a 15 % negative stock return. Panels D and E report the
valuation effects when we instead use 10, 20 and 25 % as our cutoff. The individual rivals
and rival portfolios experience negative and significant abnormal returns over the [-1, 0]
and [-1, ?2] windows, consistent with the results when we use a 15 % cutoff. From this
point forward, we use the 15 % cutoff to define our sample, since a 15 % one-day stock
price drop is a substantial decline that clearly generates negative rival effects and it allows
us to examine a sufficiently large sample for our cross-sectional analyses.
We evaluate the cross-sectional variation in the rival valuation effects using ordinary
least squares regressions. The results of the simulations in Karafiath (2009) show that no
other estimators have clear advantages over ordinary least squares in the presence of
‘‘event clustering.’’ We estimate the following model using both the 21,936 individual
event-rivals and the 2,368 rival portfolios:
CARk ¼ h0 þ h1Defaultk þ h2Leveragek þ h3TobinQk þ h4Similarityk
þ h5PostPerformancek þ h6DegreeOfSurprisek þ h7RelativeSizek
þ h8Herfindahlk þ h9IndustryReturnsk þ x
ð6Þ
where the dependent variable is the rival CAR [-1, ?2], and each of the explanatory
variables is discussed and defined below. Table 3 reports summary statistics for the
4 Das et al. (2006) also report that leverage and volatility are the two largest factors explaining covariationin conditional default probabilities.
A. Akhigbe et al.
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Table 2 Valuation effects for negative stock price surprises
Day/Window N AR/CAR % z value % positive
Panel A: ARs and CARs for firms that experience negative surprises
-5 2,368 0.02 -1.34 45
-4 2,368 0.07 0.19 48
-3 2,368 -0.02 -1.59 45
-2 2,368 0.31 1.37 48
-1 2,368 0.06 -2.10** 45
0 2,368 -19.06 -349.68*** 0
?1 2,368 0.41 5.51*** 51
?2 2,368 0.00 1.16 48
?3 2,368 -0.13 -0.50 48
?4 2,368 -0.10 0.71 49
?5 2,368 -0.10 -0.80 49
[- 1, 0] 2,368 -19.00 -247.23*** 3
[? 1, ?2] 2,368 0.42 4.59*** 51
[- 1, ?2] 2,368 -18.58 -169.70*** 7
[? 3, ?20] 2,368 -1.57 0.30 47
Panel B: ARs and CARs for individual event-rivals
-5 21,936 -0.00 -1.32 48
-4 21,936 0.10 5.90*** 49
-3 21,936 0.05 2.94*** 48
-2 21,936 -0.08 -1.02 48
-1 21,936 -0.19 -7.58*** 46
0 21,936 -0.96 -53.66*** 38
?1 21,936 0.46 26.27*** 53
?2 21,936 0.22 10.02*** 50
?3 21,936 0.09 7.60*** 49
?4 21,936 0.04 4.00*** 49
?5 21,936 -0.09 -1.12 47
[- 1, 0] 21,936 -1.15 -43.68*** 39
[? 1, ?2] 21,936 0.68 24.64*** 53
[- 1, ?2] 21,936 -0.47 -13.46*** 46
[? 3, ?20] 21,936 0.59 9.45*** 52
Panel C: ARs and CARs for rival portfolios
-5 2,368 -0.25 -6.49*** 45
-4 2,368 -0.36 -9.69*** 43
-3 2,368 -0.44 -10.14*** 43
-2 2,368 -0.14 -4.13*** 46
-1 2,368 -0.06 -11.80*** 43
0 2,368 -3.03 -62.22*** 22
?1 2,368 0.21 4.28*** 49
?2 2,368 0.03 -1.96** 47
?3 2,368 -0.19 -4.30*** 47
?4 2,368 -0.07 -2.00** 47
Negative stock price surprises
123
Table 2 continued
Day/Window N AR/CAR % z value % positive
?5 2,368 -0.12 -2.57** 47
[- 1, 0] 2,368 -3.63 -54.01*** 24
[? 1, ?2] 2,368 0.24 1.63 51
[- 1, ?2] 2,368 -3.39 -37.22*** 32
[? 3, ?20] 2,368 -1.60 -7.95*** 47
Panel D: robustness tests on price drop cutoff for individual event-rivals
10 %
[- 1, 0] 54,985 -1.20 -70.49*** 40
[? 1, ?2] 54,985 0.27 20.31*** 51
[- 1, ?2] 54,985 -0.94 -35.18*** 44
[? 3, ?20] 54,985 -0.96 -3.75*** 49
20 %
[- 1, 0] 8,347 -1.19 -29.92*** 38
[? 1, ?2] 8,347 0.25 4.30*** 50
[- 1, ?2] 8,347 -0.95 -18.07*** 43
[? 3, ?20] 8,347 0.73 6.67*** 52
25 %
[- 1, 0] 4,061 -1.23 -19.85*** 39
[? 1, ?2] 4,061 0.12 1.45 49
[- 1, ?2] 4,061 -1.11 -13.07*** 42
[? 3, ?20] 4,061 -0.12 -0.90 51
Panel E: robustness tests on price drop cutoff for rival portfolios
10 %
[- 1, 0] 6,366 -4.38 -123.60*** 21
[? 1, ?2] 6,366 0.34 10.31*** 52
[- 1, ?2] 6,366 -4.03 -79.94*** 27
[? 3, ?20] 6,366 -1.92 -17.69*** 47
20 %
[- 1, 0] 897 -2.71 -26.56*** 29
[? 1, ?2] 897 -0.04 -1.65* 48
[- 1, ?2] 897 -2.75 -20.05*** 33
[? 3, ?20] 897 -0.61 -1.82* 49
25 %
[- 1, 0] 416 -2.50 -15.84*** 31
[? 1, ?2] 416 -0.41 -3.45*** 47
[- 1, ?2] 416 -2.90 -13.51*** 34
[? 3, ?20] 416 -0.98 -2.28** 48
This table shows daily abnormal returns (ARs) for 10 days around the negative stock price surprise as wellas mean cumulative abnormal returns (CARs) over various windows for the 2,368 negative surprise firmsalong with 21,936 individual event-rivals and 2,368 rival portfolios. AR is the difference between actual andexpected returns, where expected returns are based on the market model that is estimated over the periodt-120 to t-21 relative to the date of the negative stock price surprise
*, **, and *** denote significance at the 10, 5 and 1 % levels, respectively
A. Akhigbe et al.
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variables used in Eq. (6). The variable definitions below are described for the individual
rivals; the variable definitions for the rival portfolios are the equally-weighted averages of
the individual rivals that comprise the portfolio.
4.1 Explanatory variables used in cross-sectional model
We hypothesize that the degree of the revision in the rivals’ stock prices is greater for those
with higher default likelihood. We expect that the negative surprise events would result in
additional scrutiny to the default likelihood of all members of the industry. With this added
focus on the likelihood of default, rival firms with greater default likelihood may be
penalized to a greater extent. The variable Default is calculated as the daily average of the
DL from Eq. (2) over the 60 days prior to the event [-61, -1], so it is not influenced by the
negative surprise event.
It can be seen in Table 3 that the default likelihood measure is zero for more than half of
the sample of 21,936 event-rival firms. As a result, we also create and utilize a high default
indicator variable to capture the rival firms with distance to default in the top quartile of the
sample to handle the nonlinearity in the data. High Default is set equal to one for rivals
with Default in the top quartile of the sample, and zero otherwise. For the rival portfolios,
High Default captures the proportion of the rivals in the industry portfolio with default
likelihood in the top quartile of the sample. As reported in Table 3, the mean (median)
value for High Default for the rival portfolio is 23 % (7 %).
Industry effects are expected to be worse for rival firms that have higher financial
leverage. Rival firms with lower leverage have more financial flexibility to deal with cash
flow pressure, and may even be able to capitalize during weak industry conditions by
seizing market share (e.g., Opler and Titman 1994). Leverage is measured as the ratio of
long-term debt to total assets measured at the end of the calendar year prior to the cor-
responding negative surprise, using Compustat data. The summary statistics in Table 3
report the average rival portfolio leverage is 17 % leverage and the median rival portfolio
leverage is 14 %.
Rival firms may be more susceptible to adverse stock price reactions in response to
negative signals when they have been performing strongly because the signal may reflect a
more pronounced shock. We use Tobin’s Q as a proxy for firm performance. Tobin Q is a
dummy variable that equals one when the market value of equity is greater than the book
value of equity, and zero otherwise.5 The market value of equity is measured at the time of
the negative surprise using CRSP data and book value of equity is the year end value
gathered from Compustat for the calendar year-end prior to the negative surprise. From the
summary statistics in Table 3, we see that the majority of the rivals and rival portfolios
have market value of equity greater than book value of equity.
Rival firms should experience greater contagion effects if they are more similar to the
negative surprise firm. Similarity is measured as the correlation in daily stock returns
between the negative surprise firm and the rival firm over the period t-120 to t-21 relative to
the date of the negative surprise. The summary statistics in Table 3 report the correlation in
stock returns for the mean and median rival portfolio is 50 %.
5 We also use the ratio of market value of equity to book value of equity in the regressions. Since thiscontinuous version of Tobin’s Q is not significant, we report the results when we include the dummyvariable. Lang et al. (1989) also convert Tobin’s Q into a categorical variable for part of their analysis. Forour analysis, the continuous variable may not be significant because the relationship between CARs and Qmay be nonlinear.
Negative stock price surprises
123
We expect that a larger negative surprise to the firm would elicit a more pronounced
negative market response for the firm’s rivals. Degree of Surprise is the percent drop in
the share price of the negative surprise firm. The average share price drop is approximately
21 %, and the median drop is approximately 18 %.
It can be argued that the contagion effects are more profound when the negative surprise
firm is relatively large. To the extent that the larger firms are market share leaders, rivals
are more likely to also suffer when these industry leaders experience a negative shock. The
Relative Size variable is measured using CRSP data as the ratio of the equity market value
of the negative surprise firm to the median industry equity market value one month prior to
the negative surprise (i.e., t-21 relative to the date of the negative surprise). As reported in
Table 3, the mean (median) value for Relative Size for the rival portfolio is 3.3749
(2.2201 %).
When a firm experiences a pronounced negative surprise, the negative effects on the
industry may depend on industry conditions. Rivals in less competitive industries may not
be adequately prepared to handle the added challenge and suffer to a greater extent. Thus,
it can be argued that greater contagion effects would result in less competitive industries.
An alternative argument is presented by Lang and Stulz (1992). Due to customer shifts
in demand as a result of bankruptcies, they argue that the strength of intra-industry
competitive effects would be greater in less competitive industries. However, we believe
that this competitive repositioning benefit is less likely to occur with our less severe
distress events than with bankruptcy events.
Table 3 Summary statistics for rival firm characteristics
Variables Individual rivals Rival portfolios
Mean Q1 Q2 Q3 Mean Q1 Q2 Q3
Rival CAR -0.0047 -0.0448 -0.0043 0.0029 -0.0338 -0.0658 -0.0215 0.0086
Default 0.0092 0.0000 0.0000 0.0006 0.0094 0.0000 0.0002 0.0050
High default 0.2500 0 0 0 0.2295 0.0000 0.0690 0.4000
Leverage 0.1550 0.0099 0.1042 0.2376 0.1674 0.0850 0.1366 0.2217
Tobin Q 0.6052 0 1 1 0.9134 1 1 1
Similarity 0.5419 0.3900 0.5526 0.6900 0.4971 0.3441 0.5083 0.6593
Degree ofsurprise
-0.2090 -0.1721 -0.1862 -0.2223 -0.2084 -0.2254 -0.1823 -0.1628
Relative size 1.9505 0.3299 0.5547 1.1956 3.3749 1.1069 2.2201 4.7090
Herfindahl 0.2147 0.1044 0.1713 0.2915 0.3862 0.1870 0.3045 0.5044
Industry returns 0.1349 -0.0400 0.0795 0.2309 0.1096 -0.0659 0.0664 0.2212
This table presents summary statistics for 21,936 individual event-rivals and 2,368 rival portfolios. RivalCAR is the event CAR [- 1, ?2]; Default is the daily average of the DL from equation (2) over the 60 daysprior to the event [- 61, -1]; High Default equals one for rivals with Default in the top quartile of thesample, and zero otherwise; Leverage is the long-term debt/total assets for the rival; Tobin Q is market valueof equity [ book value of equity; Similarity is the correlation in stock returns between the negative surprisefirm and the rival firm; Degree of Surprise is the percent share price drop for the negative surprise firm;Relative Size is the negative surprise firm market value/market value of median firm in the industry;Herfindahl is the sum of the squared market shares of all firms in the industry; Industry Returns is the %change in the value of all firms in the industry. For the rival portfolios, the variables are calculated asequally-weighted averages of the values for the individual rivals that comprise the portfolio, except HighDefault is the proportion
A. Akhigbe et al.
123
We use the Herfindahl index to reflect industry concentration, where an industry is
considered to be less competitive, or more concentrated, with a greater Herfindahl index.
Specifically, Herfindahl is defined as the sum of the squared market shares of all firms in
the industry measured at the calendar year-end prior to the negative surprise event, using
Compustat data. The summary statistics in Table 3 show the mean (median) Herfindahl for
the rival portfolios is 0.3862 (0.3045).
Rival firms may be more susceptible to adverse stock price reactions in response to a
negative surprise when the industry has been performing strongly. If the industry has been
performing strongly, the market may be less likely to expect and more shocked to see a
negative surprise within the industry. We use Industry Returns as a proxy for industry
performance. It is calculated using CRSP data as the percent change in the value of all the
firms in the industry over the period t-120 to t-21 relative to the date of the negative
surprise. Table 3 shows the mean (median) industry performance for the sample of rival
portfolios to be positive 10.96 % (6.64 %).
4.2 Results of cross-sectional analyses
In Tables 4 and 5, we present the regression results from estimating equation (6). We
organize the explanatory variables into three groups: (1) related to rival firms, (2) related to
negative surprise firms, and (3) related to industry. Table 4 presents the results using the
21,936 event-rivals, and Table 5 presents the results using the 2,368 rival portfolios. We
evaluate the regression model separately for negative surprise events that occur during the
2007–2008 financial crisis period; Models 1 and 2 analyze negative shocks that occur in
the non-crisis period and only differ by the proxy that is used to measure the default
likelihood whereas Model 3 analyzes the shocks that occur in the crisis period. Following
previous studies (e.g., Brunnermeier 2009; Fahlenbrach et al. 2012; Gorton and Metrick
2012), we define the 2007–2008 financial crisis period as July 2007 through December
2008.6
Also, these same three models are estimated using only the first observation of a
negative surprise to occur in an industry within 20 trading days. This subset of first
observations provides a robustness check on the possibility that event clustering is driving
our results. Indeed, the results for this subset are consistent, with few exceptions. Thus, the
following discussion related to Tables 4 and 5 primarily focuses on the full sample of
negative surprises. The variance inflation factors across the models range from 1.0 to 1.3,
which suggests that multicollinearity is not unduly influencing the statistical testing of the
coefficients.
In Table 4, when we analyze Models 1 and 2, the Default variable is not significant, but
High Default is negative and significant. This finding suggests that a negative shock has a
more pronounced and adverse valuation effect on industry rivals with the greatest default
likelihood. The significance and direction of influence of the remaining variables are
consistent across Models 1 and 2. The coefficient of Leverage is negative and significant. It
appears that rival firms with higher leverage are penalized to a greater extent, perhaps
because they do not have the financial flexibility to deal with cash flow pressure (Opler and
Titman 1994). The coefficient of Tobin Q is negative, indicating that rivals with strong
performance are more adversely affected by the negative surprise. The coefficient on
Similarity is found to be negative and significant, consistent with our argument that rivals
that are more similar to the negative surprise firm suffer greater contagion in response to
6 The results are essentially the same when January 2007 is used as the beginning of the crisis period.
Negative stock price surprises
123
Ta
ble
4C
ross
-sec
tional
resu
lts
for
rival
effe
cts
from
neg
ativ
esu
rpri
ses:
indiv
idual
rival
s
Fir
stO
bse
rvat
ion
On
ly
Mo
del
1M
od
el2
Mo
del
3M
odel
1M
odel
2M
odel
3V
aria
ble
sN
on
-Cri
sis
No
n-C
risi
sC
risi
sN
on
-Cri
sis
No
n-C
risi
sC
risi
s
Inte
rcep
t0
.02
34
(8.0
0**
*)
0.0
24
6(8
.41
**
*)
0.0
00
9(0
.09
)0
.018
7(5
.65
**
*)
0.0
19
7(5
.93
**
*)
-0
.024
6(-
2.4
2*
*)
Rel
ate
dto
riva
lfi
rms
Def
ault
-0
.005
2(-
0.4
5)
--
0.0
01
4(0
.12
)–
–
Hig
hd
efau
lt-
-0
.00
65
(-4
.77*
**
)0
.04
78
(9.7
7*
**
)–
-0
.004
6(-
2.9
2*
**
)0
.010
2(1
.73
*)
Lev
erag
e-
0.0
14
5(-
3.9
4*
**
)-
0.0
12
6(-
3.4
2*
**
)-
0.0
06
1(-
0.8
1)
-0
.00
90
(-2
.16*
*)
-0
.008
0(-
1.9
2*
)0
.018
6(2
.00
**
)
To
bin
Q-
0.0
06
0(-
4.7
5*
**
)-
0.0
06
4(-
5.0
4*
**
)-
0.0
09
0(-
2.7
7*
**
)-
0.0
03
8(-
2.5
5*
**
)-
0.0
04
3(-
2.9
0*
**
)-
0.0
07
6(-
2.0
2*
*)
Sim
ilar
ity
-0
.018
4(-
5.6
6*
**
)-
0.0
16
3(-
4.9
7*
**
)-
0.0
08
9(-
0.9
5)
-0
.02
59
(-7
.36*
**
)-
0.0
24
2(-
6.8
2*
**
)-
0.0
00
2(-
0.0
2)
Rel
ate
dto
neg
ati
vesu
rpri
sefi
rms
Deg
ree
of
surp
rise
0.0
54
2(6
.72
**
*)
0.0
56
4(7
.06
**
*)
-0
.069
2(-
2.6
5*
**
)0
.027
9(2
.97
**
*)
0.0
29
6(3
.16
**
*)
-0
.090
3(-
3.6
6*
**
)
Rel
ativ
esi
ze-
0.0
00
3(-
4.6
1*
**
)-
0.0
00
3(-
4.5
8*
**
)0
.00
16
(4.4
8*
**
)-
0.0
00
7(-
3.0
9*
**
)-
0.0
00
7(-
3.0
3*
**
)0
.003
1(1
0.2
8*
**
)
Rel
ate
dto
ind
ust
ry
Her
fin
dah
l-
0.0
15
7(-
4.3
1*
**
)-
0.0
16
0(-
4.3
9*
**
)-
0.0
56
7(-
5.6
7*
**
)-
0.0
11
7(-
3.2
2*
**
)-
0.0
11
8(-
3.2
5*
**
)-
0.0
25
0(-
2.7
4*
**
)
Ind
ust
ryre
turn
s0
.00
79
(4.1
3**
*)
0.0
07
4(3
.85
**
*)
0.0
07
6(0
.75
)-
0.0
13
7(-
3.8
4*
**
)-
0.0
13
7(-
3.8
3*
**
)-
0.0
32
1(-
3.0
6*
**
)
N1
7,5
76
17
,57
64
,36
08
,553
8,5
53
1,8
28
Ad
j.R
20
.01
09
0.0
11
70
.03
86
0.0
10
70
.011
70
.091
4
F-v
alu
e2
4.1
8*
**
27
.03
**
*2
2.8
7*
**
12
.56
**
*1
3.4
6*
**
23
.97
**
*
Th
ista
ble
repo
rts
reg
ress
ion
resu
lts
toan
alyze
the
cro
ss-s
ecti
on
alv
aria
tio
nin
the
CA
Rs
[-1
,?
2]
of
indiv
idual
rival
sof
firm
sth
atex
per
ience
neg
ativ
esu
rpri
ses.
The
subse
t,F
irst
Ob
serv
ati
on
On
ly,in
clu
des
just
the
firs
tn
egat
ive
surp
rise
too
ccu
rin
anin
du
stry
wit
hin
20
trad
ing
day
s.D
efa
ult
isth
ed
aily
aver
age
of
the
DL
from
equ
atio
n(2
)o
ver
the
60
day
sp
rio
rto
the
even
t[-
61
,-
1];
Hig
hD
efa
ult
equ
als
on
efo
rri
val
sw
ith
Def
ault
inth
eto
pq
uar
tile
of
the
sam
ple
,an
dze
roo
ther
wis
e;L
ever
age
isth
elo
ng
-ter
md
ebt/
tota
las
sets
for
the
riv
al;
To
bin
Qis
mar
ket
val
ue
of
equ
ity
/boo
kv
alue
of
equ
ity
;S
imil
ari
tyis
corr
elat
ion
inst
ock
retu
rns
bet
wee
nth
en
egat
ive
surp
rise
firm
and
the
riv
alfi
rmp
rio
rto
the
neg
ativ
esu
rpri
se;
Deg
ree
of
Su
rpri
seis
the
per
cen
tsh
are
pri
ced
rop
for
the
neg
ativ
esu
rpri
sefi
rm;
Rel
ati
veS
ize
isth
en
egat
ive
surp
rise
firm
mar
ket
val
ue/
mar
ket
val
ue
of
med
ian
firm
inth
ein
du
stry
;H
erfi
nda
hl
isth
esu
mo
fth
esq
uar
edm
ark
etsh
ares
of
all
firm
sin
the
indu
stry
;In
du
stry
Ret
urn
sis
the
%ch
ang
ein
the
val
ue
of
all
firm
sin
the
ind
ust
ry
*,
**,
and
***
den
ote
signifi
cance
atth
e10,
5an
d1
%le
vel
s,re
spec
tivel
y
A. Akhigbe et al.
123
Ta
ble
5C
ross
-sec
tional
resu
lts
for
rival
effe
cts
from
neg
ativ
esu
rpri
ses:
rival
port
foli
os
Fir
sto
bse
rvat
ion
on
ly
Mo
del
1M
odel
2M
odel
3M
odel
1M
odel
2M
odel
3V
aria
ble
sN
on-c
risi
sN
on-c
risi
sC
risi
sN
on-c
risi
sN
on-c
risi
sC
risi
s
Inte
rcep
t0
.019
5(1
.87
*)
0.0
29
1(2
.74
**
*)
-0
.022
8(-
1.0
8)
0.0
27
6(2
.50
**
)0
.03
39
(3.0
1**
)-
0.0
23
9(-
1.0
2)
Rel
ate
dto
riva
lfi
rms
Def
ault
0.0
17
8(0
.38
)–
–0
.00
47
(0.1
0)
––
Hig
hd
efau
lt–
-0
.000
2(-
3.0
2*
**
)0
.00
01
(0.8
4)
–-
0.0
00
1(-
2.2
1*
*)
-0
.000
0(-
0.0
7)
Lev
erag
e-
0.0
22
3(-
1.6
2)
-0
.019
6(-
1.4
3)
-0
.117
3(-
4.3
5*
**
)-
0.0
31
5(-
2.2
4*
*)
-0
.029
2(-
2.0
8*
*)
-0
.108
2(-
3.7
2*
**
)
To
bin
Q0
.002
5(0
.35
)-
0.0
04
9(-
0.6
6)
0.0
22
0(2
.23
**
)0
.00
08
(0.1
1)
-0
.004
4(-
0.5
9)
0.0
15
4(1
.34
)
Sim
ilar
ity
-0
.060
3(-
7.3
8*
**
)-
0.0
55
6(-
6.7
5*
**
)-
0.0
56
0(-
2.9
3*
**
)-
0.0
63
5(-
7.1
7*
**
)-
0.0
59
8(-
6.7
0*
**
)-
0.0
33
7(-
1.6
2)
Rel
ate
dto
neg
ati
vesu
rpri
sefi
rms
Deg
ree
of
surp
rise
0.0
05
8(0
.28
)0
.00
84
(0.4
1)
-0
.137
7(-
2.4
8*
**
)0
.02
94
(1.2
8)
0.0
30
0(1
.31
)-
0.1
61
8(-
2.8
3*
**
)
Rel
ativ
esi
ze-
0.0
01
2(-
2.8
1*
**
)-
0.0
01
2(-
2.7
9*
**
)0
.00
04
(0.2
8)
-0
.000
3(-
0.5
9)
-0
.000
3(-
0.5
6)
0.0
01
2(0
.83
)
Rel
ate
dto
ind
ust
ry
Her
fin
dah
l-
0.0
18
8(-
3.1
1*
**
)-
0.0
19
2(-
3.2
0*
**
)-
0.0
31
3(-
2.3
3*
*)
-0
.021
8(-
3.4
4*
**
)-
0.0
21
9(-
3.4
7*
**
)-
0.0
39
3(-
2.8
0*
**
)
Ind
ust
ryre
turn
s-
0.0
58
8(-
11
.63
**
*)
-0
.059
4(-
11
.77
**
*)
-0
.025
0(-
1.3
7)
-0
.038
5(-
5.3
6*
**
)-
0.0
38
6(-
5.3
8*
**
)-
0.0
31
0(-
1.6
2)
N1
,806
1,8
06
56
21
,22
51
,22
53
45
Ad
j.R
20
.088
70
.09
32
0.0
71
80
.05
16
0.0
55
40
.099
0
F-v
alu
e2
2.9
5*
**
24
.19
**
*6
.42
**
*9
.32
**
*9
.97
**
*5
.72*
**
This
table
report
sre
gre
ssio
nre
sult
sto
anal
yze
the
cross
-sec
tional
var
iati
on
inth
eC
AR
s[-
1,?
2]
of
riv
alp
ort
foli
os
of
firm
sth
atex
per
ience
neg
ativ
esu
rpri
ses.
Th
esu
bse
to
fF
irst
Ob
serv
ati
on
sin
clu
des
just
the
firs
tn
egat
ive
surp
rise
too
ccu
rin
anin
du
stry
wit
hin
20
trad
ing
day
s.D
efa
ult
isth
ed
aily
aver
age
of
the
DL
fro
meq
uat
ion
(2)
ov
erth
e6
0d
ays
pri
or
toth
eev
ent
[-6
1,
-1
];H
igh
Def
ault
equ
als
on
efo
rri
val
sw
ith
Def
ault
inth
eto
pq
uar
tile
of
the
sam
ple
,an
dze
roo
ther
wis
e;L
ever
age
isth
elo
ng
-ter
md
ebt/
tota
las
sets
for
the
riv
al;
To
bin
Qis
riv
alm
ark
etv
alu
eo
feq
uit
y/b
oo
kv
alu
eo
feq
uit
y;
Sim
ila
rity
isth
eco
rrel
atio
nin
stock
retu
rns
bet
wee
nth
en
egat
ive
surp
rise
firm
and
the
riv
alfi
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Negative stock price surprises
123
the negative surprise. The coefficient of Degree of Surprise is positive, implying that rivals
experience more pronounced adverse valuation effects in response to larger negative
surprises.
The coefficient of Relative Size is negative and significant. This result indicates that
rivals are more adversely affected when the firm experiencing the negative surprise is
relatively large. Industry concentration is also found to be an important factor. The
coefficient of Herfindahl is negative and significant, consistent with the argument that
rivals in less competitive industries suffer to a greater extent.
Lastly, the coefficient of Industry Returns is positive and significant. However, when we
examine Models 1 and 2 for the subset of first observations, we find the coefficient of
Industry Returns is negative and significant. These results imply that the rivals are more
adversely affected in response to the first negative surprise within the industry when the
industry return was more favorable. These results imply that when the industry is per-
forming well, the rivals experience more pronounced adverse effects in response to the first
negative surprise observation, but less pronounced adverse effects in response to sub-
sequent negative surprise observations in the same industry.
In Table 4, when we analyze Model 3 that is focused on negative surprises that occur
during the 2007–2008 financial crisis period, the results are quite similar to Model 2. One
notable exception is that the coefficients on High Default and Leverage are no longer
negative and significant, but are now positive and significant.7 This suggests that a negative
shock has a less pronounced adverse effect on industry rivals with the greatest default
likelihood. During the financial crisis, stock prices of most firms were systematically
depressed, even without the force of negative industry-specific signals. Industry rivals with
the greatest default likelihood might have already been priced during the crisis to reflect a
pessimistic outlook, so that the effects of an additional negative industry-specific shock
were diluted. Conversely, other industry rivals with lower default likelihood might not
have been priced as low in response to the general outlook of the crisis, thus these rivals
may be more exposed to negative industry-specific shocks.8 An alternative argument is that
the market may have expected beneficial restructuring or government intervention aimed at
preventing the collapse of firms with the greatest default likelihood, which could reduce
the adverse effects on these firms during the crisis. These arguments offer an explanation
for a positive relationship between the default likelihood and valuation effect among rival
firms.
In addition, the coefficients on Degree of Surprise and Relative Size also change signs
when we examine the sample of negative surprises during the crisis period. These coef-
ficients show that industry rivals benefit to a greater extent with larger negative surprises
and when the firms experiencing the negative shocks are relatively large. During the crisis,
valuations were already depressed. Therefore, the negative news may have triggered a
flight to quality within the industry, rather than a complete sell-off of all rival stocks in
response to a negative surprise.
In Table 5, when Models 1 and 2 are estimated for the sample of rival portfolios, the
results for the default variables are consistent with those shown in Table 4 for the indi-
vidual rivals. As with Table 4, the Default variable is not significant, while the coefficient
7 The coefficient on Leverage for Model 3 is positive and significant when examining the subset of firstobservations of negative surprises within an industry within 20 trading days.8 A comparison of CARs between rivals with high default and those without high default during the crisisperiod shows that the CARs are significantly lower for rivals without high default likelihood. This com-parison lends support to this interpretation.
A. Akhigbe et al.
123
of High Default is negative and significant. However, there are some differences in results
when applying Models 1 and 2 to the rival portfolios (Table 5) as compared to the indi-
vidual rivals (Table 4). The main differences are that the coefficient for Tobin Q is no
longer significant, while the Industry Return coefficient is negative and significant. These
results imply that the recent industry stock price performance may substitute for the firm-
specific past performance (as measured by Tobin Q) when models are applied to the rival
portfolios in the corresponding industry. In addition, the coefficients on Leverage and
Degree of Surprise are no longer significant during the non-crisis period.
When we examine the negative surprises that occur within the 2007–2008 financial
crisis period (Model 3), High Default is not significant. As previously discussed, it is
plausible that during these extreme economic conditions the resulting restructurings and
bailouts interferes with market sensitivity to default conditions. However, the coefficient of
Leverage is negative and significant when applying Model 3, which suggests weaker
effects for more highly levered rival portfolios in response to negative surprises during the
2007–2008 financial crisis period. In addition, the rival portfolios with strong performance
experience more favorable effects and those that are more similar to the firm experiencing
the negative surprise experience more unfavorable effect.
These same three models are estimated again using only the first negative surprise for a
firm within 20 trading days, as a robustness check on the possibility that event clustering is
driving our results. The results are consistent, with one exception that the coefficient of
Relative Size becomes insignificant.
5 Summary
Studies have determined that specific news about financial distress such as bankruptcy
announcements of one firm can emit a negative signal about its corresponding industry.
We extend the literature by examining a large sample of negative surprises to determine
whether a large decline in a stock’s price transmits net contagion effects to the industry.
While large stock price declines are not as severe as bankruptcy announcements, they are
unanticipated negative surprises that may transmit industry information. Our analyses
show that a pronounced stock price decline of one firm yields significant negative valuation
effects for industry rivals, on average.
We evaluate various factors to explain the cross-sectional variation in the rival valuation
effects. A negative surprise about one firm likely compels market participants to scrutinize
the default likelihood of rivals and/or industry conditions and penalize them accordingly.
We focus on a measure of default likelihood of rivals that has not yet been used to explain
the intra-industry stock price effects of distress. A key contribution of our cross-sectional
analyses is that we find negative shocks have more pronounced adverse effects on rivals
with the greatest default likelihood. The impact of the negative surprise on rivals is also
conditioned on the degree of the surprise, characteristics of the firm experiencing the
negative surprise (such as its relative size), characteristics of the rival firms (such as their
similarity to the firm experiencing the negative surprise), and characteristics of the cor-
responding industry (such as degree of concentration).
We also find that the sensitivity of industry rivals and portfolios to negative surprises
changes during the 2007–2008 financial crisis. The rivals with the greatest default likeli-
hood do not suffer more pronounced negative effects during this period, which may be
because their stocks had already been priced to reflect pessimistic outlooks, so that the
effects of an additional shock were diluted. Alternatively, the market may have expected
Negative stock price surprises
123
restructuring or government intervention that could prevent the collapse of firms with the
greatest default likelihood.
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