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Bankruptcy Spillover in the Technology Channel
This version: November, 2014
Abstract We document strong spillovers in wealth effects of bankruptcy announcements through technological relatedness. On average, the value of an equal-weighted portfolio of firms that intensively cite technologies of the bankrupt firm decreases by 1% around the bankruptcy announcement. The effects remain strong after taking into account of other economic linkages such as industry rivalries, customer-supplier relationships and strategic alliances. The effects are more pronounced if the bankruptcy is likely to be attributable to aging technologies owned by the filing firm and among firms that have non-diversified technologies and greater growth options. Overall, our results identify technological relatedness as an important channel through which wealth effect of bankruptcy announcements spillover to other firms. Key words: bankruptcy, distress, technology, innovation, spillover JEL classification: G33, G14, O33
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1. Introduction
Firms are economically related to each other in many ways. Uncovering and understanding the
nature and extent of economic linkages between firms have important implications for corporate
financing policies and valuations—especially, when firms are in financial distress—as one firm’s
distress could have significant valuation implications and generate an economy-wide impact on
the wealth of investors of economically linked firms.
Studies on corporate bankruptcy have documented negative wealth spillover effects of
bankruptcies among firms that are competitors in the same industry (Lang and Stulz, 1995),
supplier and customers (Hertzel, Li, Officer, and Rodgers, 2008), partners such as strategic alliance
and joint venture (Boone and Ivanov, 2012), and borrowers whose collateral value could be
damaged by the bankruptcies of other industry participants (Benmelech and Bergman, 2011).
These studies provide significant insights on the contagion of bankruptcy wealth effect by focusing
on economic linkages that are explicit or contractual. In this paper, we uncover and analyze an
implicit but yet important economic linkage between firms, namely technological relatedness.
The stock price reaction of Irvine Sensors Corp. to the bankruptcy announcement of Polariod
Corp illustrates the contagious wealth effect among technologically related firms. On October 12,
2001, Polaroid Corp., a large consumer electronics and eyewear company (in the industry of
photographic equipment and supplies; SIC code 3861) most famous for its instant film cameras
introduced in the 1950s, filed for Chapter 11 petition. The company’s road to bankruptcy started
in the 1990’s when it decided to invest heavily in traditional dry film technology rather than digital
technologies. Irvine Sensors Corp., a firm in the industry of electronic components and accessories
(SIC code 3674), developed a technology in its “Silicon Film” subsidiary that merged the film
technologies and digital technologies – a digital sensor that could be put into the film slot of
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traditional non-digital cameras, essentially turning them into digital cameras. Not surprisingly, the
technologies of Polaroid and Irvine Sensors are highly related despite they are in different
industries. The cross citation ratio of Irvine Sensors’ patents with respect to Polaroid’s is at about
the 85th percentile of the distribution of our sample. During the [0, 2] event window surrounding
the Chapter 11 filing of Polaroid, the share price of Irvine Sensors decreased in value by
approximately 24% relative to the market index. The strong negative reaction of stock price of
Irvine Sensors in response to the bankruptcy of Polariod highlights that a firm could be affected
by the bankruptcy announcement of another firm that is in a different industry yet technologically
related.
In general, a bankruptcy announcement could have two opposing wealth effects on the filing
firm’s technological related firms (TRFs). One is the technology revaluation effect. The
announcement of a bankruptcy filing typically is associated with a significant drop in the value of
the filing firm’s stock, conveying negative information about the bankrupt firm’s future cash flows
or risk profiles. If the expected drop in future cash flows is is related to the underlying technologies
that the filing firm relies on to generate cash flows, the announcement will trigger a downward
revision in investors’ valuation of the underlying technologies, affecting TRFs that rely on the
same or related technologies. Such a downward revaluation could also affect the operation of TRFs
as they might face more difficulty in maintaining business with customers and suppliers and
retaining key employees if their stakeholders are wary of the future of these firms. Moreover, given
the significance of patents and intangible assets in the secured borrowing, the downward revision
of collateral value of these assets could increase TRFs’ financing constraints, resulting in lower
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firm values.1 Technological reevaluation effect, thus, predicts that a bankruptcy announcement has
a negative wealth effect on TRFs.
The other effect is the technology competition effect in that bankruptcies provide TRFs
opportunities to acquire proprietary technologies owned by the bankruptcy firm at discount prices.
Given inevitable asset sales and key employee departures associated with bankruptcy filing (e.g.
Pulvino, 1998 and Goyal and Wang, 2014), it may trigger the reallocation of assets and human
capital (e.g. scientists, technicians) from the bankrupt firm to TRFs. The bankruptcy events thus
allow TRFs, which presumably benefit most from bankrupt firms’ technologies, to have the
opportunity to acquire proprietary technologies and assets that are not available under normal
conditions. In addition, Shleifer and Vishny (1992) show that forced selling assets may yield
transaction prices that are significantly below fundamental values. TRFs thus have the opportunity
not only to acquire proprietary technologies but also to purchase them at discounted prices.
Moreover, TRFs are likely to be competitors in developing new technologies. One firm’s
bankruptcy could mean to TRFs that they will face less competition in technological developments.
Therefore, the technology competition effect suggests that a bankruptcy announcement is not
necessary a bad news to TRFs.
In this paper we first examine the overall wealth effect of bankruptcy announcement on TRFs.
We measure technological relatedness between the filing firms and its TRFs by patent cross-cite
ratio (CCR), i.e., the number of times that TRFs’ patent portfolios cite a bankrupt firm’s patent
portfolio over the three-year window preceding the year of Chapter 11 filing. Our results show that
the common stock value of TRFs that are in the highest CCR quartile decreases by 1% over a
1 Mann (2014) documents that 15% of patents are pledged collateral within five years of being granted. Loumioti (2012) reports that, from 1997 to 2005, the dollar proportion of a sample of Dealscan loans primarily collateralized by intangible assets increased from 11% to 24%.
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three-day window around bankruptcy announcement, and the decline is statistically significant.
There is a clear monotonic negative relationship between stock reaction and CCR; the stronger is
the technological linkage the greater is the spillover effect. To exclude the possibility that
technology linkages capture other economic linkages that have been studied in the bankruptcy
spillover literature, we identify industry competitors, customers and suppliers, and strategic
alliances partners, and find that the spillovers through technology linkages are not driven by these
channels. Moreover, spillovers are more pronounced if we remove bankruptcies that are likely
triggered by suboptimal leverage decisions rather than a declining economic fundamental. The
results, thus, show that the overall wealth effect of bankruptcy announcement on TRFs is negative,
i.e., the technology re-evaluation effect dominates the technology competition effect.
We then examine whether the wealth effect on TRFs is related to the future prospect of the
bankruptcy firm’s technologies. To capture the prospect of technologies, we build a technology
trend measure based on the growth of patent classes possessed by the Chapter 11 firm. We find a
much larger negative wealth effect of bankruptcy announcements on TRFs when the filing firm’
technologies face a negative citation trend. To the extent that the bankruptcy of a firm with
downward trending technologies is likely related to obsolescent technologies, the results are
consistent technology revaluation effect, suggesting that investors significantly devalue the
technologies that the bankrupt firm owns if they view that aging technologies is likely to be the
cause for the failure of the bankrupt firm.
We then examine whether there are differentiated market reactions around the bankruptcy
event between a bankrupt firm that emerges from reorganization and a firm that is ultimately
liquidated. One the one hand, liquidation reveals that the bankrupt firm has a low going concern
value. The ultimate liquidation decisions could lead to a stronger revision of market belief on the
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value of technologies possessed by the bankrupt firm. On the other hand, liquidations typically are
associated with substantial sale of assets. Technology competition effect suggests that TRFs are
more likely to benefit from the sale of technologies. Our results show that TRFs react more
negatively to bankruptcy filings by firms that end up being liquidated than those by firms that
emerged. The results are consistent with earlier findings that technology revaluation effects
dominate the technology competition effects in bankruptcy spillover through technology linkages,
To investigate the potential technological competition effect, we examine whether the impact
of bankruptcy announcements on TRFs is related to the number of TRFs. When the number of
TRFs is high, there are more firms competing for technological assets of the filing firm, resulting
in a less discount in asset liquidation. Moreover, when there are a greater number of TRFs, one
firm’s bankruptcy is unlikely to result in a significant reduction in technology competition. We
find that negative wealth effects are much stronger when the number of TRFs is higher, consistent
with the conjecture that positive technology completion effect is weaker when there are a greater
number of TRFs.
We further show that the magnitude of wealth effects depends on TRFs’ reliance on
technologies for growth. We capture the importance of technologies in a firm’s future growth by
its technological concentration, R&D expenditure, and growth opportunities (Tobin’s Q). TRFs
with high concentrated technologies tend to rely heavily on small set of technologies, making these
TRFs exposed to severe growth constraints after the failure of such technologies. Further, the
revenue generation of TRFs with high R&D expenditure or growth opportunity depends largely
on the competitiveness of their technologies. We find that TRFs with highly concentrated
technologies, high R&D intensity, or high Tobin’s Q experience greater negative spillover effects
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around bankruptcy announcements. These findings show that bankruptcy announcements have a
more severe wealth effects on TRFs whose growth is more dependent on technologies.
Lastly, we investigate the real performance of TRFs post-bankruptcy announcements. Our
regressions results show that the contribution of R&D to future sales and earnings is lower for high
CCR firms during the seven-year period around the bankruptcy filing. The results are consistent
with the evidence that stock market reaction of TRFs is more negative for those with stronger
technological relatedness to the bankrupt firm, suggesting that negative information revealed in
bankruptcy announcements indeed reflects the lower ability of technologies owned/utilized by the
filing firm in generating future cash flows. We further find evidence that higher CCR firms
experience a higher likelihood of default and/or bankruptcy in the one-year, two-year, or three-
year period after the bankruptcy filing than low CCR firms. TRFs’ inability to transform R&D
investment into revenue and profits and their higher likelihood of failure both provide further
support to the negative spillover effects through technological relatedness.
Our paper contributes to several strands of literatures. First, it adds to bankruptcy literature by
identifying a unique bankruptcy spillover channel that has not been examined in the prior literature.
Given the increasing importance of technology in defining a firm’s competitiveness and its
relations with peer firms, our analysis is important to understand the overall effect of bankruptcy
announcement on peer firms. Second, our study adds to the recent literature that examines the role
of technological linkage in corporate financing and investment decisions. For instance, Bena and
Li (2013) find that greater technological linkage between two firms increases the probability that
the two firms merge. Qiu and Wan (2014) find that firms tend to hold more cash in order to take
advantage of knowledge spillovers from technologically linked firms. Third, our study adds to
recent studies that show a firm’s financing policies could be influenced by their industrial peers’
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choices (MacKay and Phillips 2005; Rauh and Sufi, 2010; and Leary and Roberts, 2014). Our
results suggest that technological linkage is an important dimension that defines a firm’s peers.
Our findings have broad implications for corporate financing polices. For instance, a firm’s capital
structure decision could be affected by the choices of its TRFs given that financial distresses could
spillover through their technology linkages. The concern for potential negative contagions of
financial distress from TRFs could also increase a firm’s precautionary motive for cash holdings.
Finally, our study complements the economics literature on knowledge spillovers. Extant
Literature on economic growth and industrial organization show that, through technological
linkages, a firm’s R&D investments could generate positive externality in enhancing TRFs’
innovative capability and firm value (e.g., Jaffe, 1986; Bloom, Schankerman, and Van Reenen,
2013). Our results complement these findings by showing that negative externality could occur
when financial distress generates spillover through technological linkage.
The rest of the paper is organized as follows. Section 2 describes data and variable
construction. Section 3 investigates the spillover effects through technological relatedness and
provides empirical evidence on the real performance of TRFs post-bankruptcy announcements.
Section 4 concludes.
2. Data and variable construction
2.1. Data sample
Our initial bankruptcy sample draws all Chapter 11 filings by U.S. firms with at least $50
million assets at bankruptcy petition between 1981 and 2011 from New Generation Research’s
bankruptcydata.com. There are a total of 1,740 filings during the period. We first drop Chapter 11
filings with unknown outcomes and filings that were dismissed by courts. Next, we manually
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identify whether the Chapter 11 firm is public and merge the sample with Compustat. We require
that the Chapter 11 firm file with the SEC within one year prior to bankruptcy filing. This process
results in a total of 941 Chapter 11 filings by public U.S. firms during the 31-year period. All
bankruptcy characteristics and outcome variables are obtained from bankruptcydata.com and
supplemented by Lynn LoPucki’s Bankruptcy Research Database. We retrieve accounting
information of Chapter 11 firms as of the last fiscal year before filing from Compustat. Stock
returns are obtained from CRSP.
To measure firms’ innovating activities, we obtain data on firms’ patenting activity from the
National Bureau of Economics Research (NBER) Patent Citation Database. This database contains
annual information from 1976 to 2006 on patents and citations for U.S. publicly traded firms,
including patent ID, patent assignee, number of citations made and the cited patent IDs, number
of citations received and the citing patents IDs, patent application year, and the year in which a
patent is granted.2 After merging the initial bankruptcy sample of 941 cases with the Patent
Citation database, we end up with a final sample of 128 Chapter 11 cases during 1982-2007 for
this study. Next, to identify TRFs, we examine whether a firm cites the bankrupt firm’s patent at
the time of bankruptcy filing. This process yields a total of 1,944 TRFs for our study. We note
that our final sample of 128 Chapter 11 firms is comparable to prior studies on bankruptcy
spillovers. For example, Hertzel, Li, Officer, and Rodgers (2008) identify 118 bankruptcies with
supplier portfolio and 154 bankruptcies with customer portfolio at the time of filing. Boone and
Ivanov (2012) include 130 bankruptcies in their study on strategic alliances and partnership.
2.2. Variable construction
2 NBER Patent Citation Database is used by a number of prior studies. See Hall, Jaffe, and Trajtenberg (2001) for a detailed description of the database.
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We use the cross-cites ratio (CCR) to capture the technological link between the bankrupt firm
and its TRFs. CCR measures the extent to which one firm’s patent portfolio cites another firm’s
patent portfolio. Specifically, for a bankrupt firm A, we compute the number of firm B’s patents
with application years from t-3 to t-1 that cite any of firm A’s patents, where t indicates firm B’s
fiscal year that ends immediately after firm A’s bankruptcy filing. To calculate the CCR of firm B
with respect to firm A, we scale the total citations from the previous step by the number of firm
B’s patents with application years from t-3 to t-1. We deem firm B as technologically related to
firm A if its CCR on firm A is greater than zero.3
To examine how the spillover effects vary across different characteristics of bankrupt firms
and their TRFs, we consider the following firm characteristics: R&D (R&D expenses scaled by
total assets, which is set to zero if value is missing), Sales (net sales), Assets (book value of total
assets), Leverage (the ratio of total liabilities to book value of total assets), ROA (operating income
before depreciation scaled by book value of total assets), and Tobin’s Q (ratio of market value of
assets scaled by book value of assets). In addition, we consider number of patents (the total number
of patents the firm possessed up to year t-1), number of citations received (the number of citations
the firm received during the three-year period from t-3 to t-1), and number of TRFs for the bankrupt
firm.
To further identify whether bankruptcies are related to technology obsolescence and inability
to innovate, we build a technology trend measure based on the citation growth of patents possessed
by the Chapter 11 firm. First, for each patent class i of 426 patent classes that are classified by
3 We are not the first to use cross-cite ratio to measure the technological relatedness between two firms (see Bena and Li (2013)). An alternative measure of technological relatedness is technology proximity in Jaffe (1986), which captures the extent to which two firms’ patents are in the same patent classes. A high value in technology proximity between two firms, however, need not necessarily indicate that a firm’s technologies directly derive from the other’s.
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United States Patent and Trademark Office (USPTO), we calculate its average annual growth rate
in the number of patents over ten years before a firm’s bankruptcy, ,i tg .4 Specifically,
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,t i1
1,
10it ii
g g
(1)
,t ,t 1,t
,t 1
Patent number Patents number.
Patent numberi i
ii
g
(2)
where i is the patent class i, t is the year of bankruptcy filing. A higher growth rate indicates a
rising class of technology. Next, we calculate the weight of class i’s patents in a firm j’s patent
portfolio before bankruptcy at time t using patents applied and eventually awarded during the
period t-3 to t-1,
, ,, ,
,
Patents number
Total patent numberj i t
j i tj t
w (3)
where j is the firm j. Patent numberj,i,t is the number of class i’s patents applied and eventually
awarded during the period t-3 to t-1 by firm j; Total patent numberj,t is the total number of patents
applied and eventually awarded during the period t-3 to t-1 by firm j;
Finally, we create a firm-level measure of technology trend that captures the citation trend of
a firm’s patent portfolio using the weighted average of the citation growths for the firm’s patent
portfolio,
, , , ,Technology Trend .j t j i t i ti
w g (4)
A lower Technology Trendj,t suggests that firm j have more patents in classes that are having a
smaller growth rate in citations before the bankruptcy year t. The technology trend measure allows
us to capture whether a bankruptcy filing is related to the competitive edge of a firm’s technologies.
4 We require observations of at least three years for the calculation of average patent growth in the past.
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Firms experiencing a low technology trend before bankruptcy filing are likely to be those that
possess obsolete technologies and are incapable of generating new technologies through
innovation to replace aging technologies.
We build a measure on technology concentration of TRFs, Technology Concentration, to
capture their technology constraint. For each firm and patent class, we calculate the ratio of the
firm’s number of patent applications from t-3 to t-1 in that patent class to the total number of patent
applications in the same period. The firm’s technology concentration before the bankruptcy
announcement is the Herfindahl-Hirschman index of the ratio across all patent classes:
∑∑
, (5)
where nk is the number of patents applied in the patent class k during the period t-2 to t, and K is
the total number of patent classes.
We estimate cumulative abnormal stock returns (CARs) of both the Chapter 11 firm and TRFs
upon the bankruptcy announcements. Abnormal returns are computed using market-adjusted
returns following Hertzel, Li, Officer, and Rodgers (2008), where the daily abnormal stock return
is calculated as the daily stock return minus the CRSP value-weighted market return.5 We then
sum up the daily abnormal stock returns over a two-day or three-day event window to obtain
CARs.
2.3. Sample overview
Table 1 presents an overview of the sample. Panel A summarizes the data construction while
Panels B and C report the year and industry distribution of both Chapter 11 firms and TRFs,
5 Market adjusted abnormal returns are more desirable than market model adjusted abnormal returns for estimating abnormal returns for bankrupt firms due to the significant stock price drop and dried stock liquidity within the immediate months before the bankruptcy filing. Many prior studies use market index adjusted abnormal returns to study market reactions around bankruptcy filing (e.g. Dawkins, Bhattacharya and Bamber (2007), Jiang, Li, and Wang (2012)).
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respectively. Panel B shows that 62 out of the 128 bankruptcy filings occur between 2000 and
2003, a period subsequent to the burst of a technology bubble. There are 15 TRFs for each Chapter
11 firm over the sample period, suggesting that a large number of firms are related through
technology links.
Panel C shows that our Chapter 11 firms span over 29 different (two-digit SIC) industries.
Durable-goods industries (SIC 3400-3999), where specialized technologies are often required for
production processes, account for 46% of the bankruptcies in the sample. Service industries, on
the other hand, account for the least observations. Our TRFs are distributed in 45 (two-digit SIC)
industries, again with durable-goods industries making up half of the sample. However, the
evidence does not suggest TRFs are bankrupt firms’ direct industry rivals. In untabulated analyses,
we find that there are on average five different (two-digit SIC) industries in which TRFs are
identified for each Chapter 11 filing.
[Table 1 about here]
Table 2 presents the summary statistics on Chapter 11 firms and TRFs. Panel A shows that the
average CAR of bankrupt firms over the three-day window after Chapter 11 filing is -24%. The
evidence suggests that the bankruptcy filing has a significant valuation effect and is not fully
anticipated by investors. With respect to the outcomes of bankruptcy filings, 62% of the firms
emerge from reorganization, 12% are acquired while 25% are liquidated piecemeal in bankruptcy.
Less than 2% of the cases were still pending as of year 2012.
Chapter 11 firms are large, with size measured by assets and sales. The median values of the
two size measures are $305 million for assets and $343 million for sales (in constant year 2004
dollars). The median book leverage ratio of our sample firms is 92.9% and the median ROA is
3.7%. Both values are comparable to those reported in prior studies with large bankruptcies (e.g.
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Bharath, Panchapegesan, and Werner, 2007; Jiang, Li, and Wang, 2012), suggesting that our 128
Chapter 11 cases are representable of a typical large bankrupt firm. Our firms spend 1% of total
assets on R&D, owns 29 patents, and these patents gets cited 46 times over the three-year period
prior to the filing.
The average CCR is 0.07. TRFs are also large in size with a skewed distribution. The
combination of low leverage and high ROA suggest they are both financially and operationally
healthy. High Tobin’s Q and R&D investment indicate that TRFs are high growth firms and that
they rely on innovation for growth. The mean technology concentration ratio is at 0.20, indicating
TRFs’ technologies are concentrated in certain technology classes.
To further examine firm characteristics of TRFs by their technological relatedness to the
Chapter 11 firm, we divide TRFs into quartiles by CCR. Panel B of Table 2 presents the mean
values of our key control variables by CCR quartiles. The average CCR is fairly low in the first
three quartiles. For example, the average CCR in the third quartile is 0.026, indicating that lower
than three percent of patents that belong to the TRFs in this quartile cite the bankrupt firms’ patents.
However, the average CCR of the TRFs in the highest quartile is quite high (0.246), suggesting
that there is a significant level of technology overlap with the bankrupt firms. The mean values of
firm characteristics across CCR quartiles indicate a clear decreasing trend in firm size and the
number of patents applied as one moves from the lowest quartile to the highest quartiles. Since
larger firms tend to have more patents and thus are less likely to rely on a particular type of
technology, it is not surprising that the average size of TRFs decreases with CCR.
[Table 2 about here]
3. Empirical analysis
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In this section, we present the main empirical results of the effect of bankruptcy spillovers on
TRFs. We first investigate the overall wealth effects of bankruptcy filings on TRFs by CCR
quartiles over two different event windows. We then focus on TRFs in the highest CCR quartile
for the rest of analysis on market reactions. Finally, we present evidence on TRFs’ ability to
transform R&D investment to sales and earnings, and their probability of survival after the
bankruptcy event.
3.1. Wealth effects of bankruptcy filings for technological related firms
Table 3 shows the market reactions over event windows of [0, 1] and [0, 2] around bankruptcy
filings, where date 0 is the filing date. Panel A includes all Chapter 11 filings in the sample while
Panel B and Panel C consider subsamples after dropping bankrupt firms that are financially but
not economically distressed or that have positive value of technology trend, respectively. Column
(1) shows CARs for the full sample. Columns (2) to (5) show CARs of TRFs by CCR quartiles.
When all Chapter 11 cases are included, the number of bankruptcy cases is 102, 93, 84, and 86
across the four CCR quartiles, respectively. This rather uniform distribution suggests that most
bankrupt firms have technologically related firms in all of the four CCR quartiles.
Panel A shows, on average, TRFs do not react negatively to bankruptcy filings.6 CARs over
the two event windows are both at -0.1%, which is not statistically significant. However, TRFs in
the highest CCR quartile experience large negative stock returns around the bankruptcy filings.
CAR over the three-day window around the bankruptcy event is -1%, which is statistically
significant at the 1% level. Figure 1 plots CAR [0, 2] by CCR quartiles. The graph clearly indicates
a negative monotonic relationship between stock market reaction and CCRs. Thus, bankruptcy
6 Because a small number of TRFs in Compustat and NBER Patent Citation Database do not have stock return data in CRSP around the bankruptcy announcement dates, the number of observations in each quartile is slightly lower than that reported in Table 2 Panel B.
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events seem to have the largest impact on TRFs that intensively cite technologies of Chapter 11
firms.
In general, bankruptcy filings can be attributed to either financial distress or economic distress,
or a combination of the two. Financial distress, which is often triggered by suboptimal leverage
decisions rather than operation risks, is likely to be idiosyncratic to the filer. Bankrupt firms that
have both high leverage and high ROA at the time of filing are more likely to suffer from a financial
distress rather than economic distress (e.g. Andrade and Kaplan (1998) and Lemmon, Ma, and
Tashjian (2009)). Bankruptcies as a result of financial distress are unlikely to result in spillovers
to peer firms in the sense that the filing has no indication on the loss of competitiveness of a firm’s
underlying technology. In order to focus on those bankruptcies that are likely to be caused by
economic distress, we remove bankruptcies that are in the highest quartile of leverage ratio and
the highest quartile of ROA. This results in a sample that has 20% fewer observations. Panel B
shows that the stock market reactions for the subsample are negative and statistically significant
at the 5% level. The negative monotonic relationship between market reactions and CCR sustains,
with TRFs in the highest quartile experiencing the largest decline in stock prices.
To further identify bankruptcy drivers that are possibly related to technology obsolescence and
firm’s inability to innovate, we keep those bankrupt firms that experience a negative technology
trend over the three years preceding bankruptcies. We expect to see more pronounced stock market
reactions by TRFs for this subsample of bankrupt firms than those bankrupt firms that experience
a positive technology trend. Results in Panel C confirm our prior. The CARs for both the two-day
and three-day window around bankruptcy filings are -0.8% and -0.9%, respectively. They are
statistically significant at the 1% level. Firms that are in the highest CCR quartile in this subsample
experience a CARs of as large as -1.4%.
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[Table 3 about here]
Table 4 shows regressions of CAR [0, 2] on CCR quartiles. Column (1) includes three CCR
quartile dummies for the 2nd, 3rd and 4th quartiles in the CCR distribution, where the omitted
category is the 1st quartile. Column (2) includes only the dummy variable for the highest CCR
quartile. In essence, columns (1) and (2) confirm the univariate analysis presented in Panel A of
Table 3 while correcting standard errors for heteroscedasticity and clustering at the Chapter 11
firm level. The magnitude of coefficient estimates is consistent with that presented in Table 3.
Column (3) further controls for characteristics of TRFs in addition to the dummy for the highest
CCR quartile. We find our results unchanged. To mitigate the effect of industry level unobservable
characteristic that affect market reaction of TFRs, we add (2-digit SIC) industry fixed effects in
column (4). To further mitigate the existence of unobservable Chapter 11 characteristics that drive
the returns of the TRFs we conduct Chapter 11 firm fixed effects estimations in column (5). We
find that neither inclusion affects our results.
The evidence presented in Tables 3 and 4 shows strong spillover effects from bankruptcy
filings to TRFs that intensively cite the bankrupt firm. The spillover effects are stronger for those
bankruptcies that are likely caused by economic distress, and for those associated with
technologies that have a downward trending in citations. Overall, the findings show that the
negative technology re-evaluation effect dominates the positive technology competition effect in
the spillovers of wealth effect in bankruptcy announcement through technological relatedness.
[Table 4 about here]
3.2. Product market competition, customer-supplier relationship, and strategic alliance
It is possible that TRFs overlap with firms that compete in the same product market (Lang and
Stulz (1995), along the supply chain (Hertzel, Li, Officer, and Rodgers (2008)), or in a strategic
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alliance (Boone and Ivanov (2012)). In this section, we examine whether the observed spillover
effects are, in fact, due to these channels rather than the technology link.
Although firms competing in the same industry are likely to possess similar technologies, firms
that have similar technologies do not necessarily compete in the same product market. In fact,
many firms that are not industry rivals are related through technology citations. For example,
International Business Machine Corp. (SIC code 7370 – computer programming, data processing,
and other computer related services) and AT&T Corp. (SIC code 4813 – telephone
communications) do not compete directly in the same product market. However, IBM is close to
AT&T in the technology space, evidenced by their highly similar patent-filing patterns with a
cross-citation ratio of 16% from AT&T to IBM and 21% from IBM to AT&T in year 1990. This
is not surprising given that both firms develop computer network hardware and software. On the
other hand, firms in the same industry could have a weak technology links. For example, Pfizer
and Genentech, two leading pharmaceutical companies firms, strive against each other head to
head in the product market with similar products. However, they are relatively distant in
technology space with a cross-citation ratio of 0.34% from Pfizer to Genentech and 0.32% from
Genentech to Pfizer in year 2002. This is due to that Pfizer mainly relies on traditional
pharmaceutical research and works with chemical based compounds; while, Genentech, on the
other hand, uses advances in genetics research and manufactures products in living organisms.
Recent papers by Bloom et al (2013) and Qiu and Wan (2014) provide large sample evidence that
firms could have distinctive relations in product market space and in technology space.
Nevertheless, to mitigate the concern that the technology link may be driven by the product
market rival, we examining market reactions by TRFs that carry the same four-digit, three-digit,
or two-digit SIC. Lang and Stulz (1995) suggest that the four-digit SIC better captures industry
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rivals than the broader two-digit SIC classification. For example, two-digit SIC code 73 designates
the business service industry which includes advertising, consumer credit service, mailing,
services to buildings, equipment rental, personnel supply services, and software. Clearly, firms in
the business service industry do not compete in one common market. Even within the same three-
digit SIC, firms could compete in different product markets. For example, both SAP (business
software) and EA Games (gaming software) are both within the same software industry (three-
digit SIC 737). However, by no means should they be considered rivals, even though they could
possibly rely on related technologies.
Table 5 presents market reactions of TRFs in the highest CCR quartile by whether they are
industry competitors to the bankrupt firm. Of 443 TRFs, 45 are in the same four-digit SIC industry
as the bankrupt firm. If we use three-digit SIC or two-digit SIC instead, 74 and 132 TFRs,
respectively, are in the same industry as the filing firm. We find that TRFs that are also competitors
in two-digit SIC industry react more negatively to the bankruptcy filing. However, the stock
reactions by TRFs are not statically significant when we use four-digit or three-digit SIC to classify
industry rivals. This is most likely due to the small sample size of TRFs being direct industry
rivals. More importantly, we find that the market reactions of TRFs that are not in the same four-
digit, three-digit or two-digit SIC industry remain statistically significant and have similar
economic magnitude as those presented earlier. Our results show that the spillover effects we
document are not likely caused by industry contagion as documented by Lang and Stulz (1995);
the spillover effects through technological relatedness transcend industries.
Next, we identify whether TRFs that are in the highest CCR quartile are in either customer-
supplier relationships or strategic alliances with the bankrupt firm. We find that out of the 443
TRFs in the highest CCR quartile only six firms are in customer-supplier relationships and 14 are
19
in a strategic alliance. In fact, out of our whole sample of 1,944 TRFs, only 20 firms are either
customers or suppliers to the bankrupt firm and 47 are in a strategic alliance. In untabulated
analysis, we exclude these 20 firms and find that the results remain virtually unchanged. Our
results indicate that the negative spillover effects on TRFs are not caused by customer-supplier
relationship or strategic alliance relationship channels.
[Table 5 about here]
3.3. Exploring Firm Characteristics
In this section, we explore the role of characteristics of both Chapter 11 firms and TRFs in the
spillovers effects to shed lights on the underlying mechanisms of the spillover effect through
technological linkages.
First, we examine if the spillover effect is related to the magnitude of bankruptcy firm’s stock
returns around announcement days. We divide our 128 Chapter 11 cases into two groups based on
the median stock return around bankruptcy filings of about -21% according to Table 2: firms with
higher-than-median and firms with lower-than-median stock returns. Within the high CCR
quartile, 160 TRFs are in the below-median bankrupt firm return group while 124 are in the above
median group. Large filing-period negative abnormal returns of bankrupt firms often indicate
bankruptcy filings are not fully anticipated by the market. They may also suggest low going-
concern values of the filing firm and large expected bankruptcy costs. We expect to observe larger
spillover effects to TRFs through the reevaluation channel for bankruptcy firms with more negative
stock returns around announcement days. Table 6 shows that TRFs experience significantly -2.1%
abnormal returns when the bankrupt firms’ stocks returns are below the median, while abnormal
returns for TRFs are not statistically significant when the bankruptcy firms’ stock returns are above
20
the median. The results suggest that stronger negative announcement returns of the bankruptcy
firm could generate a greater negative wealth spillover effects to TRFs.
Second, we examine whether TRFs react differently to various Chapter 11 outcomes.7 We are
interested in whether TRFs react more negatively to a bankruptcy filing by a firm that is ultimately
liquidated than one that emerges. One the one hand, firms with low going concern values are more
likely to liquidate. The ultimate liquidation decisions could lead to a stronger downward
reevaluation of market belief on the value of technologies possessed by the bankrupt firm. On the
other hand, liquidations typically are associated with substantial sale of assets. Technology
competition effect suggests that TRFs are more likely to benefit from the sale of technologies.
Table 6 shows that TRFs react more negatively to bankruptcy filings by firms that are eventually
liquidated, consistent with early findings that technology reevaluation effect dominates technology
competition effect.
To explore the potential technology competition effect, we examine if the spillover effect is
related to the number of TRFs that a bankruptcy firm has. The number of TRFs serves as a possible
proxy for the technology competition effect. When the number of related firms is high,
technological assets of the Chapter 11 firm are less likely to be sold at large discounts due to a
greater demand from TRFs which could be potential buyers of these technological assets. This
lowers the potential benefits received by TRFs from the fire sales of technologies of bankruptcy
firms. Further, when the number of TRFs is higher, a firm’s bankruptcy should have a smaller
impact on the market structure and the completion faced by individual firms. Table 6 shows that
negative market reactions are stronger when the number of TRFs is high, consistent with the notion
7 Note bankruptcy outcomes are measured ex post and therefore, are not observed at the time of Chapter 11 filing. Since investors often form expectations on the possible outcomes of Chapter 11 at the time of filing, it is plausible to use the ex post outcome as a proxy to measure the ex ante expectation of the outcomes.
21
that greater number of TRFs reduces the positive technology completion effect, resulting in a
higher overall negative spillover effect.
Further, we consider if the spillover effect is related to the influence of the bankruptcy firm’s
technologies. We proxy the influence of technologies using the number of citations received by a
bankrupt firm in the three-year period prior to the bankruptcy announcement. The higher is the
number of citations, the more influential are the bankrupt firm’s technologies. Firms with more
influential technologies are more likely to be in the center of a technological network. The
bankruptcy of a firm with influential technologies could reveal the bleak outlook of certain key
technologies and trigger significant downward reevaluation of firms that have related technologies.
We, thus, expect that the negative wealth effect of the bankruptcy event on TRFs to be more
pronounced for bankrupt firms with more influential technologies. Consistent with this
expectation, TRFs’ average CAR is significantly negative (-0.014) when the bankrupt firms are
highly cited. In contrast, the average CAR is insignificant (-0.002) when the bankrupt firms receive
low citations.
[Table 6 about here]
Finally, we examine if the reactions of TRFs to bankruptcy announcements are related to their
reliance on technologies for growth. We consider a set of variables that measure the extent to
which the peer firms rely on technology for growth. Our first set of measures builds on peer firms’
growth options. The value of high growth firms largely derives from growth options embedded in
the firm’s innovative activities. Such firms are expected to experience larger negative spillover
effects if the technologies on which they develop their growth opportunities fail. Our two measures
on whether TRFs rely on technology or innovation for growth are R&D intensity and Tobin’s Q.
The second measure we adopt – technology concentration ratio, a direct measure on technology
22
constraint of TRFs – estimates how diversified the peer firm is in its technology reliance on the
Chapter 11 firm. If a firm’s technology is highly concentrated, a larger proportion of the firm value
derives from a particular type of technology. We expect to observe stronger spillover effects in
firms whose technologies are less diversified, as these firms are less able to utilize alternative
technologies when existing technologies fail. Table 7 shows that TRFs that invest substantially in
R&D, rely on growth options for value, or have less diversified technology portfolio experience
larger declines in stock prices.
[Table 7 about here]
In sum, the results in this section show that there are heterogeneities in the bankruptcy spillover
effects through technological relatedness, depending on characteristics of both bankruptcy firms
and TRFs. The spillover effects of bankruptcy announcements are stronger for bankruptcy firms
that have more negative announcement returns, are eventually liquidated, have more TRFs and
have technologies with greater citations. They are also stronger when TRFs rely more on
technologies for growth. Our results are consistent with technology reevaluation effect and suggest
that unanticipated bankruptcy events, low going concern values of the bankrupt firm, and high
technological reliance of TRFs could trigger greater downward reevaluation of TRFs.
3.4. The performance of TRFs post-bankruptcy announcements
We have shown that significant negative wealth effects exist for TRFs surrounding the
bankruptcy announcements, suggesting that one firm’s bankruptcy has valuation implications for
the stocks of firms that are technological related. In this section we investigate if the negative
wealth effects are consistent with the real performance of TRFs post-bankruptcy announcement.
We first examine whether TRFs in the highest CCR quartile are less capable of converting
R&D investments into future value. If bankruptcy announcements reveal the doom prospective of
23
technologies that the bankruptcy firm has, R&D investments by TRFs that use highly related
technologies are likely to yield less value. Following the extant literature (e.g. Banker, Huang, and
Natarajan (2011), and Huson and Wier (2014)), we adopt regression specifications to study the
relation between lagged R&D and future earnings. We use lagged R&D because it takes time for
firms to transform R&D investments into innovations and reap the benefits. We assemble a panel
sample of our TRFs from year t-3 to t+3 around the year of bankruptcy filing. The dependent
variables are net sales, scaled by book assets, and ROA. Since both R&D and firm performance
measures are ratios scaled by total assets, we scale the constant term by total assets as well to
control for the size effect. In Table 8 we investigate how TRFs’ performance is related to their
R&D expenses surrounding the bankruptcy years.
We use two-year lagged R&D expenses to predict future sales and ROA in regressions (1) and
(2) while using three-year lagged R&D expenses in regressions (3) and (4). We find that the
coefficient estimates on lagged R&D are positive for both sales and ROA, and are statistically
significant at the 1% level for ROA. More importantly, the interaction between the dummy for
high CCR and lagged R&D is negative and statistically significant at the 1% level for both sales
and ROA, suggesting that the contribution of R&D expenses to sales and operating income is lower
for high CCR TRFs during the years around the bankruptcy events.
[Table 8 about here]
We then investigate whether TRFs in higher CCR quartiles are more likely to fail after
bankruptcy filing than TRFs in lower CCR quarters. To assess bankruptcy probability, we resort
to three different data sources – Chapter 11 bankruptcies of U.S. firms with assets above $50
million from New Generation Research, Chapter 11 and Chapter 7 bankruptcies from SDC, and
S&P defaults and rating migrations. We identify the unique default and bankruptcy dates from all
24
sources and merge the information with our TRFs to calculate the probability of default/bankruptcy
within one year, two-years, and three-years after Chapter 11 filings. Table 9 shows the
default/bankruptcy likelihood of TRFs by CCR quartiles over different time windows after
bankruptcy filings. TRFs in the lowest CCR quartile experience zero likelihood of failure. There
is a monotonic increasing relationship between CCR and probability of failure, with TRFs in the
highest CCR quartile experiencing the highest likelihood of failure. The evidence on higher
likelihood of default/bankruptcy of TRFs with high CCR suggests that the large declines in stock
prices of TRFs observed around bankruptcy filings are consistent with the future likelihood of
survival of these firms.
[Table 9 about here]
Overall, the findings that firms with high technological relatedness to bankrupt firms are less
capable in creating value from R&D investments and are more likely to fail after the bankruptcy
filings suggest that the negative wealth contagion in the stock market through technological
relatedness reflects the poor future real performance of TRFs. The results highlight that
information revealed in bankruptcy announcement through the technological channel has real
valuation implications.
4. Conclusion
In this paper, we document large negative spillover effects of a bankruptcy event on
technologically related firms, which intensively cite the patents of the bankrupt firm. The negative
stock market reactions are more pronounced if we remove bankruptcies that are likely triggered
by financial distress rather than economic distress or bankruptcies that are not likely caused by
obsolete technologies. Further, we show technological related firms react more negatively to
25
bankruptcy filings by firms that liquidated piecemeal or experience large negative stock returns
around filings. Moreover, we find that the spillover effects depend on technology concentration
and reliance on innovation for growth of technological related firms. There is also evidence that
technologically related firms that cite bankrupt firm the most are more likely to fail after the
bankruptcy event.
The spillover effects in the technology channel documented in this paper are new to the
literature. This unique technology spillover channel suggests that technological relatedness is an
important economic linkage between firms. Further investigation of the natural and extent of
technological linkages could be a fruitful area for future research.
26
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27
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28
Figure 1
CAR by CCR Quartiles
This graph shows the equal-weighted stock return of the sample firms that are technologically related to the bankrupt firms. A firm is deemed to be technologically related to the bankrupt firm if it has patents applied within the 3-year period prior to the bankruptcy date and the patents cite the bankrupt firm’s patent. The sample firms are sorted into four groups according to their cross-cite ratio (CCR) to the bankrupt firm, where cross-cite ratio is defined as the percentage of the sample firm’s patents applied during the 3-year period prior to the bankruptcy year that cite the bankruptcy firm’s patents over the sample firm’s total number of patents applied during the 3-year period.
-0.012
-0.01
-0.008
-0.006
-0.004
-0.002
0
0.002
0.004
0.006
1 (low) 2 3 4 (high)
CAR[0,2]
29
Table 1 Sample Overview
This table provides an overview of the construction of Chapter 11 bankruptcy sample, annual distribution of the bankruptcy sample, and industry distribution of both the bankruptcy sample and TRFs. A firm is deemed to be technologically related to the bankrupt firm (TRFs) if it has patents applied within the 3-year period prior to the bankruptcy date and the patents cite the bankrupt firm’s patent. Panel A: Sample construction
Number of Chapter 11 filings by U.S. firms with $50 million assets or more at filing between 1981-2011
1,740
Number of bankruptcies after dropping cases that were dismissed or have unknown outcomes 1,514
Number of bankruptcies after dropping cases that do not file with the SEC within one year of bankruptcy filing
941
Number of bankruptcies cases that appear in NBER Patent Citation Database 128
Number of TRFs 1,944
Panel B: Annual distribution of bankruptcy filings
Year Number of Chapter 11 cases Number of TRFs
1982 2 28
1984 1 16
1985 1 3
1986 3 32
1987 1 4
1988 2 24
1990 6 43
1991 6 43
1992 5 114
1993 5 52
1994 2 9
1995 1 14
1996 4 17
1997 2 6
1998 4 15
1999 7 135
2000 11 256
2001 17 494
2002 20 230
2003 14 115
2004 4 17
2005 6 143
2006 3 133
2007 1 1
All 128 1,944
30
Panel C: Industry distribution of Chapter 11 firms and technology related firms
Industry Two-digit SIC
# of Chapter 11 cases
% of all Chapter 11 cases
# of TRFs
% of all TRFs
Agricultural Production 1 1 0.05 Coal Mining 12 1 0.81 Oil and Gas Extraction 13 19 0.98 Mining and Quarrying of Nonmetallic Minerals (No Fuels) 14 4 0.21 Building Construction General Contractors and Operative Builders 15 1 0.05 Heavy Construction Other than Building Construction Contractors 16 5 0.26 Construction Special Trade Contractors 17 1 0.81 Food and Kindred Products 20 1 0.81 23 1.18 Textile Mills Products 22 8 6.45 12 0.62 Apparel and Other Textile Products 23 3 2.42 1 0.05 Lumber and Wood Products (No Furniture) 24 11 0.57 Furniture and Fixtures 25 21 1.08 Paper and Allied Products 26 2 1.61 60 3.09 Printing and Publishing and Allied Products 27 1 0.81 5 0.26 Chemicals and Allied Products 28 6 4.84 198 10.19 Petroleum Refining and Related Industries 29 10 0.51 Rubber and Miscellaneous Plastics Products 30 6 4.84 37 1.90 Leather and Leather Products 31 1 0.84 1 0.05 Stone, Clay, Glass, and Concrete Products 32 4 3.23 25 1.29 Primary Metal Industries 33 7 5.65 30 1.54 Fabricated Metal Products, Except Machinery 34 4 3.23 28 1.44 Industrial and Commercial Machinery and Computer Equipment 35 21 16.13 413 21.24 Electronic and Other Electrical Equipment 36 18 14.52 406 20.88 Transportation Equipment 37 11 8.87 215 11.06 Measuring, Analyzing, and Controlling Instrument 38 5 4.03 165 8.49 Miscellaneous Manufacturing Industries 39 22 1.13 Motor Freight Transportation and Warehousing 42 1 0.81 4 0.21 Communications 48 5 4.03 39 2.01 Electric, Gas, and Sanitary Services 49 3 2.42 3 0.16 Wholesale-durable Goods 50 3 2.42 2 0.11 Wholesale-non-durable Goods 51 1 0.81 4 0.22 Building Materials, Hardware, and Garden Supply 52 1 0.81 1 0.05 Apparel and Accessory Stores 56 1 0.81 1 0.05 Home Furniture, Furnishings, and Equipment Stores 57 1 0.05 Miscellaneous Retail 59 1 0.81 1 0.05 Insurance Institutions 63 1 0.05 Real Estate 65 1 0.05 Patent Owners, and Real Estate Investment Trusts 67 2 0.11 Personal Services 72 1 0.05 Business Services 73 8 4.03 125 6.43 Automotive Repair, Services and Parking 75 1 0.05 Miscellaneous Repair Services 76 1 0.05 Motion Pictures 78 1 0.81 1 0.05 Amusement and Recreation Services 79 1 0.81 1 0.05 Health Services 80 1 0.05 Engineering, Accounting, Research, Management, and Related Services
87 2 1.61 8 0.41
Non-classifiable Establishments 99 31 1.59 All 128 100 1,944 100
31
Table 2 Summary Statistics on Characteristics of Chapter 11 Firms and TRFs
This table reports the financial conditions (as of last fiscal year prior to bankruptcy filing) and bankruptcy characteristics of Chapter 11 firms, and the key financial variables of technology related firms. A firm is deemed to be technologically related to the bankrupt firm (TRFs) if it has patents applied within the 3-year period prior to the bankruptcy date and the patents cite the bankrupt firm’s patent. Panel A: Summary Statistics of all Chapter 11 Firms and TRFs
Variables # of obs. Mean Standard dev. Median
Chapter 11 firms
Stock return around filing CAR[0,2] 70 -0.237 0.481 -0.214
Emergence 128 0.617 0.488 1.000
Acquisition 128 0.117 0.323 0.000
Liquidation 128 0.250 0.435 0.000
Pending 128 0.016 0.125 0.000
Assets 128 1,087 3,550 305
Sales 128 1,208 5,352 343
Leverage 128 0.958 0.358 0.929
ROA 124 -0.014 0.216 0.037
R&D 128 0.048 0.100 0.011
Tobin’s Q 126 1.421 0.877 1.159
Number of patents 128 157 429 29
Number of citations received 127 283 882 46
Number of related firms 128 15.188 25.977 5.000
Technology trend 110 0.093 0.126 0.089
TRFs
CCR 1,944 0.070 0.168 0.011
Assets 1,944 14,306 39,587 2,694
Sales 1,944 9,445 18,489 2,571
Leverage 1,941 0.542 0.239 0.560
ROA 1,937 0.123 0.140 0.138
R&D 1,944 0.066 0.069 0.047
Tobin’s Q 1,938 2.286 1.891 1.608
Number of patents 1,944 3,266 6,183 730
Technology concentration 1,855 0.200 0.262 0.094
32
Panel B: Mean Characteristics of TRFs by CCR Quartiles
(1) (2) (3) (4)
CCR Quartile
1 (low) 2 3 4 (high)
CCR 0.001 0.006 0.026 0.249
Assets 38,519 11,157 4,800 2,748
Sales 23,904 7,770 4,121 1,983
Leverage 0.600 0.572 0.521 0.476
ROA 0.141 0.148 0.138 0.066
Tobin’s Q 2.300 2.208 2.240 2.397
R&D 0.066 0.062 0.059 0.077
Number of patents 8,987 2,687 1,051 338
Technology concentration 0.058 0.128 0.217 0.434
# of obs. 486 486 486 486
33
Table 3 Market Reactions of TRFs around Chapter 11 Filing
This table reports the equal-weighted cumulative abnormal returns (CARs) of sample firms that are technologically related to the bankrupt firms during the period 1981-2006. A firm is deemed to be technologically related to the bankrupt firm (TRFs) if it has patents applied within the 3-year period prior to the bankruptcy date and the patents cite the bankrupt firm’s patent. The cumulative abnormal return (CAR) is calculated over days [0, 1] or [0, 2]. In the first column, the whole sample is used. In columns (2)-(5), the sample firms are sorted into quartile according to their cross-cite ratio to the bankrupt firm, where cross-cite ratio (CCR) is defined as the percentage of the sample firm’s patents applied during the 3-year period prior to the bankruptcy year that cite the bankruptcy firm’s patents over the sample firm’s total number of patents applied during the 3-year period. Market reactions for the whole Chapter 11 sample are reported in Panel A. Panel B drops Chapter 11 firms that more likely suffers financial distress than economic distress. Panel C keeps only those Chapter 11 firms with a negative technology trend. (1) (2) (3) (4) (5) All CCR Quartile 1 (low) 2 3 4 (high) Panel A: All Chapter 11 firms # of obs. 1,862 479 474 466 443 CAR [0,1] -0.001 0.002 0.001 0.001 -0.006** p-value (0.64) (0.30) (0.48) (0.86) (0.03) CAR [0,2] -0.001 0.004 0.002 0.001 -0.010*** p-value (0.71) (0.12) (0.29) (0.76) (0.01) Panel B:Drop financially but not economically distressed firms
# of obs. 1,476 359 360 378 379 CAR [0,1] -0.003** -0.000 0.000 -0.003 -0.009*** p-value (0.02) (0.84) (0.98) (0.24) (0.01) CAR [0,2] -0.003** 0.002 0.002 -0.003 -0.012*** p-value (0.05) (0.50) (0.56) (0.40) (0.00) Panel C: Keep firms with negative technology trend
# of obs. 304 51 66 94 93 CAR [0,1] -0.008*** 0.003 -0.007** -0.012*** -0.011** p-value (0.00) (0.08) (0.08) (0.05) (0.01) CAR [0,2] -0.009*** 0.007 -0.006 -0.013*** -0.014*** p-value (0.00) (0.29) (0.55) (0.16) (0.00)
34
Table 4 Regressions of Market Reactions of TRFs
This table reports the results from cross-sectional regressions. The dependent variable is the equal-weighted cumulative abnormal returns (CARs) of sample firms that are technologically related to the bankrupt firms during the period 1981-2006. A firm is deemed to be technologically related to the bankrupt firm if it has patents applied within the 3-year period prior to the bankruptcy date and the patents cite the bankrupt firm’s patent. The cumulative abnormal return (CAR) is calculated over days [0, 2]. In column (1), dummies for the 2nd, 3rd, and 4th (the highest) cross cite ratio (CCR) are included as control variables. In column (2), dummy for the highest CCR quartile is included as a control variable. Column (3) augments column (2) by including an additional set of characteristics of the sample firms. Column (4) augments column (2) by including industry dummies as control variables, where industries are measured by 2-digit SIC codes. Column (5) augments column (2) by including dummies for each bankruptcy case. P-values reported in parentheses are based on heteroscedasticity-robust standard errors that allow clustering at the Chapter 11 case level. All variables are defined in Appendix Table.
(1) (2) (3) (4) (5)
Dummy for highest CCR quartile -0.012*** -0.011* -0.012*** -0.009** (0.00) (0.06) (0.00) (0.01)
Dummy for 2nd CCR quartile -0.001 (0.71)
Dummy for 3rd CCR quartile -0.003 (0.57)
Dummy for 4th CCR quartile -0.013*** (0.00)
Logarithm of # of patents -0.002 (0.39)
Logarithm of total assets 0.002 (0.33)
ROA 0.034 (0.17)
Leverage -0.002 (0.85)
Tobin’s q 0.001 (0.53)
Interception 0.004 0.002 -0.006 0.002 0.002* (0.32) (0.40) (0.68) (0.41) (0.06)
N 1,862 1,862 1,856 1,862 1,862
R-squared 0.01 0.01 0.01 0.03 0.10
Industry FE No No No Yes No
Chapter 11 case FE No No No No Yes
35
Table 5 Market reactions of TRFs by industry competitors
This table reports the equal-weighted cumulative abnormal returns (CARs) of sample firms with the highest technological relatedness (i.e., in the highest cross-cite ratio (CCR) quartile) with the bankrupt firms during the period 1981-2006. The cross-cite ratio is defined as the percentage of the sample firm’s patents applied during the 3-year period prior to the bankruptcy year that cite the bankruptcy firm’s patents over the sample firm’s total number of patents applied during the 3-year period. The sample firms are further divided into two groups. In the first two columns, the technologically related firms are also product market competitors of the bankrupt firms, whereas in the last two columns, the firms are not competitors. Two firms are deemed as product market competitors if they share the same 4-digit SIC code.
Competitors based on 4-digit SIC Industry
Competitors based on 3-digit SIC industry
Competitors based on 2-digit SIC industry
Yes No Yes No Yes No
CAR -0.013 -0.010*** -0.012 -0.009** -0.013* -0.008**
p-value (0.39) (0.01) (0.22) (0.01) (0.06) (0.04)
Number of observations
45 398 74 369 132 311
36
Table 6 Wealth effect spillovers in bankruptcy announcements: the role of filing firms’ characteristics
This table reports the equal-weighted cumulative abnormal returns (CARs) of sample firms with the highest technological relatedness (i.e., in the highest cross-cite ratio (CCR) quartile) with the bankrupt firms during the period 1981-2006. The sample firms are further divided into subgroups according to the bankrupt firm’s characteristics, including cumulative abnormal stock return, bankruptcy outcome, number of TRFs, and number of citations. Bankrupt firms’ abnormal stock return is measured over days [0,1] around the bankruptcy date. All other variables are defined in Appendix Table. For a particular firm characteristic, the firm is categorized into “high” (“low”) if its value for that characteristic is above (below) sample median.
(1) (2)
Bankrupt firm return
Low High
CAR [0,2] -0.021*** -0.010
p-value (0.00) (0.12)
Number of observations 160 124
Bankruptcy Outcome
Liquidated Emerged
CAR [0,2] -0.015* -0.011***
p-value (0.27) (0.01)
Number of observations 37 370
Number of TRFs
Low High
CAR [0,2] -0.001 -0.015***
p-value (0.81) (0.00)
Number of observations 161 282
Number of citations
Low High
CAR [0,2] -0.002 -0.014***
p-value (0.64) (0.00)
Number of observations 150 289
37
Table 7 Wealth effect spillovers in bankruptcy announcements: the role of TRFs’ characteristics
This table reports the equal-weight cumulative abnormal returns (CARs) of sample firms with the highest technological relatedness (i.e., in the highest cross-cite ratio (CCR) quartile) with the bankrupt firms during the period 1981-2006. The cross-cite ratio is defined as the percentage of the sample firm’s patents applied during the 3-year period prior to the bankruptcy year that cite the bankruptcy firm’s patents over the sample firm’s total number of patents applied during the 3-year period. The sample firms are further divided into two groups according to the TRFs’ characteristics. All variables are defined in Appendix Table. For a particular firm characteristic, the firm is categorized into “high” (“low”) if its value for that characteristic is above (below) sample median.
(1) (2)
R&D
Low High
CAR [0,2] -0.006 -0.014***
p-value (0.23) (0.01)
Number of observations 217 226
Tobin’s q
Low High
CAR [0,2] -0.006 -0.014***
p-value (0.21) (0.00)
Number of observations 239 204
Technology concentration
Low High
CAR [0,2] -0.001 -0.012***
p-value (0.85) (0.00)
Number of observations 84 359
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Table 8 Contribution of R&D investments to future earnings for TRFs
This table reports results from regressions in which lagged R&D expenditure is used to predict sales and profits in subsequent years. The sample is a seven-year panel that covers all TRFs around the bankruptcy year. In columns (1) and (3), the dependent variable is the sales, scaled by total assets; in columns (2) and (4), the dependent variable is operating income before depreciation, scaled by total assets. In columns (1) and (2), R&D expenditure is measured at 2-year lag, whereas in columns (3) and (4), it is measured at 3-year lag. P-values reported in parentheses are based on heteroscedasticity-robust standard errors that allow clustering at the TRF level. All variables are defined in Appendix Table.
(1) (2) (3) (4)
Lag=2 year Lag=3 year
Sales/assets ROA Sales/assets ROA
Dummy for high CCR*lagged R&D -0.352*** -0.238*** -0.471*** -0.339***
(0.01) (0.00) (0.00) (0.00)
Dummy for high CCR 0.082*** 0.020*** 0.106*** 0.030***
(0.00) (0.00) (0.00) (0.00)
Lagged R&D 0.117 0.192*** 0.138 0.268***
(0.19) (0.00) (0.23) (0.00)
Lagged sales/assets 0.925*** 0.896***
(0.00) (0.00)
Lagged ROA 0.755*** 0.677***
(0.00) (0.00)
Inverse of total assets 0.383** -0.443*** 0.566** -0.618***
(0.05) (0.00) (0.02) (0.00)
N 5,632 5,596 5,364 5,325
R-squared 0.95 0.68 0.94 0.62
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Table 9 Cumulative likelihood of bankruptcy and/or defaults for TRFs after chapter 11 filing
This table reports the frequency of bankruptcy or default of TRFs over the one-, two-, and three-year periods subsequent to the bankruptcy announcements during the period 1981-2006. A firm is deemed to be technologically related to the bankrupt firm if it has patents applied within the 3-year period prior to the bankruptcy date and the patents cite the bankrupt firm’s patent. CCR Quartiles
1 (low) 2 3 4 (high)
Within 1 year 0 0.21% 1.65% 1.65%
Within 2 years 0 0.72% 2.67% 3.09%
Within 3 years 0 1.65% 3.09% 3.81%
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Appendix: Variable definition
Variable Description Technology link and announcement return
Cross-cite ratio (CCR) Cross-cites ratio measures the extent to which one firm’s patent portfolio cites another firm’s patent portfolio. Specifically, for a bankrupt firm A, we compute the number of firm B’s patents with application years from t-3 to t-1 that cite any of firm A’s patents, where t indicates firm B’s fiscal year that ends immediately after firm A’s bankruptcy filing. To calculate the cross-cites ratio of firm B with respect to firm A, we then scale the number from the previous step by the number of firm B’s patents with application years from t-3 to t-1. We deem firm B as technologically related to firm A if its cross-cites ratio on firm A is greater than zero.
Cumulative abnormal return (CAR)
The sum of abnormal return, where abnormal return is the daily stock return minus the CRSP value-weighted market return.
Technology Number of patents The number of patents applied by the firm and eventually awarded to the firm before
bankruptcy filing. Number of citations received The number of citations a firm’s existing patents received during the three-year
period from before bankruptcy filing. Number of related firms The number of firms that cite the bankrupt firm’s existing patents during the three-
year period before bankruptcy filing. Technology trend Technology trend measures the extent to which a firm has pursued innovation in
technology classes that have recently experienced fast growth. First, for each patent class of a Chapter 11 firm we calculate its average annual growth rate over ten years before bankruptcy, git , i.e.
git 1
10g
i ,tii1
10
gi ,t
# patentsi ,t # patents
i ,t1
# patentsi ,t1
where i is the patent class i, t is the year of bankruptcy filing. A higher growth rate indicates a rising class of technology. Next, we calculate the weight of each class in a firm’s patent applications before bankruptcy (i.e., patents applied and eventually awarded during the period t-3 to t-1),
wj ,i ,t
# patents
j ,i ,t
Total # patentsj ,t
Finally, we create a firm-level measure of the technology trend that captures whether a firm’s patent portfolio on average is increasing or decreasing.
Tech Trendj ,t w
j ,i ,tg
i ,ti
Technology concentration For a particular patent class, define s as the ratio of a firm’s # of patents applied in the three-year period prior to the bankruptcy year over the total number of patents applied in the period. Technology concentration of the firm in the period is the Herfindahl-Hirschman index based on s across all patent classes.
Firm characteristics Assets Book value of total assets Sales Net sales Leverage Total liabilities scaled by the book value of total assets
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ROA Operating income before depreciation scaled by the book value of total assets R&D R&D expenses scaled by the book value of total assets Tobin’s Q (Market value of equity + book value of debt)/(book value of equity + book value
of debt)