bankruptcy spillover in the technology channel · 2017-02-16 · bankruptcy spillover in the...

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.


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 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.


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.


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.


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.


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.


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.


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

Jiang, Wei, Kai Li, and Wei Wang, 2012, Hedge funds and Chapter 11, Journal of Finance 67, 513-560.

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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 TRFs

Low High

CAR [0,2] -0.001 -0.015***

p-value (0.81) (0.00)


Number of citations

Low High

CAR [0,2] -0.002 -0.014***

p-value (0.64) (0.00)


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)


Tobin’s q

Low High

CAR [0,2] -0.006 -0.014***

p-value (0.21) (0.00)


Technology concentration

Low High

CAR [0,2] -0.001 -0.012***

p-value (0.85) (0.00)


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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%

40

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

41

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)

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