Business Methods Patents and Firm Value: An Events Study
(Preliminary Draft; Do Not Quote.)
Todd Palmer
Associate Professor of Management Sciences
St. Bonaventure University
St. Bonaventure, NY 14778
716-375-4037
tpalmer@sbu.edu
Terrence Moran
Instructor of Management Sciences
St. Bonaventure University
St. Bonaventure, NY
1
Business Method Patents and Firm Value: An Events Study
Recent research has suggested that non-financial indicators such as patent
approvals can aid the financial analyst in assessing a company’s value. In State
Street Bank & Trust Company v. Signature Financial Group, the Federal Court of
Appeals found that business methods were patentable, opening the floodgates
for the issuance of these types of patents in the years since. Utilizing an eventstudy methodology we found abnormal market returns on days –16, -15, and –1
(with 0 being the event day of issuance and publication of the patent). These
days corresponded with days that the United States Patent and Trademark Office
(USPTO) releases information concerning issuance to the potential patent holders
or in the case of day –1, no further practical action can be taken by the USPTO
to withdraw patent approval. This research suggests that innovative business
processes may be linked to value creation.
2
I. Introduction
In July of 1998 the D.C. Circuit Federal Court of Appeals dramatically
challenged the traditional notion of what constitutes a patentable idea. In State
Street Bank & Trust Company v. Signature Financial Group, the court permitted
the patenting of a business process manifested as a unique software program,
overturning a lower court’s conclusion that business methods were merely
abstract ideas and as such could not receive patent protection.1 The decision
opened the door for other companies to apply for similar “business methods”
patents to protect their own financial and data processing innovations. Since that
ruling, patents have been granted to, among others:

British Telecom for “hyperlinks” technology (McIntosh, 2000);

Principals at New Frontier Advisors LLC for a portfolio optimization
program (Kovaleski, 2000);

Lincoln National Risk Management, Inc. for automated insurance
underwriting software (Reich-Hale, 2000).

Kernel Creations Ltd. for a technology for preparing a patent application
(Chartrand, 2000).
The decision and ensuing flurry of business method patent applications 2 have
resulted in a heated controversy among industry leaders, government regulators
1
Signature Financial Group developed a process and software for managing mutual funds that
pooled certain expenses in account management thus permitting certain federal tax benefits.
Signature obtained a patent that was challenged by State Street Bank, a competitor. The court,
in holding for Signature, effectively dismissed two rationales that were previously recognized
grounds for invalidating patents: that patents that were essentially mathematical algorithms were
not patentable and that methods for doing business were not patentable. See Updike (1998).
2
OHaver and Rodgers (2000) report that one “large commercial bank” has been granted more
than fifty patents for asset management processes and systems, while another “large nonfinancial” company has filed more than 300 business methods patents.
3
and the patent applicants; lawsuits have inevitably followed. AMEX, for example,
is suing two entrepreneurs who patented an “open-end mutual fund
securitization process”, the same process by which AMEX trades its exchange
traded funds (Lucchetti, 2000). Amazon.com is suing Barnesandnoble.com to
protect its “one-click” patent and Priceline.com is suing Microsoft to safeguard its
reverse auction patent (O’Haver and Rodgers, 2000).
A patent is a grant from the government that gives the innovator an exclusive
right to produce, use, and sell the innovation for a period of twenty years from
the date of filing for the patent. For a patent to be granted the innovation must
be genuine, novel, useful, and not obvious in the light of current technology.
What is a business method? Neither Congress nor the courts have adequately
defined the term.
The United States Patent and Trademark Office (USPTO)
classifies business method patents under Class 705 (Data Processing: Financial,
Business Practice, Management or Cost/Price Determination) and provides the
following definition:
This is the generic class for apparatus and corresponding methods for
performing data processing operations, in which there is a significant
change in the data or for performing calculations operations wherein the
apparatus or method is uniquely designed for or utilized in the practice,
administration, or management of an enterprise, or in the processing of
financial data…. The arrangements in this class are generally used for
problems relating to the administration of an organization, commodities or
financial transactions (USPTO, 2001).
Even a cursory examination of the definition reveals its vague nature.
While many of the business patents that have been granted since State
Street involve business methods that are intertwined with software, earlier
court cases have provided illustrations of business patents that are not
4
software based. Historical examples of these low-tech business methods
patents include: a method for parking autos at drive-in theatres in order
to maximize visibility (4th Circuit Court of Appeal, 1949) and an accounting
method thought to prevent fraud (2nd Circuit Court of Appeals, 1908).
Hence, due to the lack of a viable definition, many commentators and
businesspeople have espoused the broader view, one that has been
adopted in practice by the USPTO that has recently issued a white paper
that acknowledging the controversial nature of these patents (USPTO,
2000). Dreyfuss (2001) argues that business method patents are “bad for
business” because they reduce the quality of patents awarded, deter
competition among existing companies and deter new business start-ups.
The purpose of this research is to examine the market response to the
granting of these business methods patents providing a unique opportunity to
directly estimate the value of business innovations that up until now have
remained beyond the reach of traditional valuation methods.
The results will add to the growing body of finance literature on “nonfinancial” indicators of firm value. In a survey of financial analysts, Dempsey,
Gatti, Grinnell and Cats-Baril (1997) report that the investment community
recognizes the value of non-financial operating measures as leading indicators of
long-term financial success and are interested in using a broad range of nonfinancial information. That information, however, is not as readily available as
traditional financial data, so it is not used as often. In addition, Lev and Zarowin
5
(1999) find the usefulness of financial information like reported earnings, cash
flows and book values has declined over the past 20 years.
Recent research has suggested that non-financial indicators such as patent
approvals can aid the financial analyst in assessing a company’s value. Deng, Lev
and Narin (1999) find “patent measures reflecting the volume of companies’
research activity, the impact of companies’ research on subsequent innovations,
and the closeness of research and development to science are reliably associated
with the future performance of R&D-intensive companies in capital markets”.3
We extend this research by looking specifically at the market reaction to the
granting of business method patents. This is of interest to researchers in other
business disciplines besides finance, including law, management, human
resources, marketing and management information systems. The debate
between finance and these areas is whether highly touted marketing,
management and data processing innovations truly add value to the business
enterprise, as financial theory suggests they should in order to be justified.
Research in these fields is only beginning to attempt to make the link between
innovative business processes and value creation.
We use a traditional event-study methodology to examine the market
reaction to the granting of business method patents to publicly traded companies
over the past fifteen years. From a financial analyst’s perspective, we are
interested in whether business method patent grants are useful indicators of
future value.
3
For additional examples of analyses of other types of non-financial indicators, see Ittner and
Larcker (1998) and Amir and Lev (1996).
6
We find that cues as provided by the USPTO serve to alert the potential
patent holders as to the probable approval of the patent resulting in abnormal
returns on days -16, -15, and -1. The largest returns were on days –16 and –15
probably reflecting the issuance of official notification by the USPTO to the
potential patent holder. Smaller returns were recorded on day –1, the day prior
to publication of the official Patent Gazette and issuance of the patent.
II. Data and Methods
From a sample of 215 Class 705 patents applications approved by the USPTO
in subclass 35 (Finance) and subclass 39 (funds transfer or credit transaction)
through December 31, 2000, we identify 88 observations in which the assignee is
publicly traded company with data available on the CRSP tapes. In 5 cases in
which the assignee has two patent approvals with 20 days of one another, we
eliminate the second observation, yielding a final sample of 83 observations. One
additional firm was deleted because it had fewer than 50 days of returns in the
estimation period. Because some companies had multiple Class 705 approvals
during the sample period, the final sample of 82 observations includes 36
different companies (see table 1). Although the event dates range from 1988 to
2000, 73 of 83 occurred in 1998, 1999 or 2000, reflecting the recent rise in
popularity of Class 705 filings. The USPTO always releases patent approvals on
Tuesday; so all event days are Tuesdays.
We use a standard event-study methodology to estimate both marketadjusted and market-model abnormal returns around the event date using the
CRSP value-weighted index. We estimate a market model for each firm during a
7
120-day estimation period from day –141 to day –21, and we estimate abnormal
returns and cumulative abnormal returns over a 41-day event window from day
–20 to +20. For both models we use time series standard deviation method
(Brown and Warner, 1985) to generate test statistics and report both the
standard t-statistic and a generalized sign test. The null hypothesis for the sign
test is that the percentage of positive returns is the same in the event window as
the estimation period. See Cowan (1992) for details
III. Empirical Results
In general, we find a significant market response to the final approval of
business method patents on day –1, the Monday prior to the event date, and on
days –15 or –16, the Monday and Tuesday three weeks prior to the event date.
These results tend to be consistent across estimation models and statistical tests.
Market adjusted abnormal returns are reported in Table 2. We find
statistically significant average abnormal returns (at the 5% level using the
standard t-test) on days –16, -15 and –1, equal to 1.06%, 0.84% and 1.12%,
respectively. A number of days have statistically significant returns according to
the generalized sign test, including days –15 and –1. The results are similar for
market model abnormal returns in Table 3. Days –15 and –1 are significant at
the 10% and 5% levels, respectively, according to the parametric t-test.
Cumulative abnormal returns for both methods are reported in Tables 4 and
5. None of the cumulative returns over the three intervals [(-20,-2), (-1,0), (+1,
+20)] are significant with the market-adjusted model. However, cumulative
8
returns are significantly positive over the interval (-1, 0) and significantly
negative over the interval (+1,+20) using the market model.
The results in Tables 4 and 5 suggest that the market model may be upward
biased in the estimation period, resulting in a negative post-event drift. If the
securities in the sample are generating positive abnormal returns during the
estimation period, the use of an estimation period prior to such a run-up will
result in upward biased alphas in the market model regressions. This leads to
downward-biased abnormal returns in the event window, because this abnormal
performance is “expected” to continue through the event window. When the
downward biased returns are cumulated over the event window, the results
show a continued downward drift.4 Evidence from the parameter estimates of
the individual firms supports this contention that the pre-event model
parameters are biased upward. On average, 46% of the residuals from all the
models are greater than 0, and only 17 of the 83 individual models had more
than 50% positive residuals.
There are a number of ways to avoid this biased benchmark problem. One is
to use a post-event estimation period; this is not practical in our case since many
of the event dates are in 2000, and post event returns are not available. A
second solution is to use a market adjusted model with the CRSP value-weighted
index, which avoids the pre- and post-benchmarking problem entirely. We
suggest that this is appropriate for our sample, since the average beta parameter
from the market models is 1.06 versus the CRSP value-weighted index.
4
See Albert and Smaby (1996) for a discussion of the biased benchmark problem in the context
of analyst recommendations.
9
IV. Discussion
Our empirical results suggest that something is happening on day –1 and
days –15 and –16. Is the market actually responding to new information in the
form of business method patent approvals, or are these results an artifact of the
data and/or the methodology? We argued in the previous section that the
market-adjusted model is a reasonable approach to use to avoid the apparent
biased benchmark problem inherent in our sample, so we are confident that we
are detecting a market response to these events. But why are days –1, -15 and –
16 important?
Conversations with patent attorneys and the press officer of the USPTO
suggest possible explanations. Patent approvals can take years from the time of
the initial filing to the final approval. However, as time goes on the likelihood
that a patent will ultimately be approved increases. Until the patent is actually
published in the Patent Gazette, the official publication of the USPTO, it has not
been officially issued. Three weeks prior to the issuance of that particular
Gazette, the publishing office starts to work on that issue, sending an "issue
notification" to the patent holder. On it will be the patent number and the date
that it is expected that the patent will be published. This is also the date on
which the materials are actually transmitted to the printer. The day prior to the
issuance date, Monday, is actually the last day the approval can be withdrawn.
Once the patent reaches this point in the approval process, however, it is
extremely rare to have an approval withdrawn.
10
The abnormal returns we found are consistent with the procedure as set forth
by the USPTO. The holder of the potential patent receives various cues from the
USPTO that indicate an increasing certainty that the patent will be published and
hence, approved.
On days –15 and –16 the potential holder is sent official
notification that the patent will probably be published. Since the USPTO reserves
the right until the last minute to withdraw the patent from publication, it is not
until the Gazette has been printed and awaiting delivery for the next day that the
potential patent holder feels that the news is certain enough to warrant further
disclosure to the market. Hence we found a reaction on day –1, not day 0, the
day the Gazette is published.
These findings suggest that the market perceives
business patent approvals as having value for the business.
11
References
1st Circuit Court of Appeals, 1949, Loews Drive-In Theatres v. Park-In Theatres,
Federal Reporter 2nd 174, 547.
2nd Circuit Court of Appeals, 1908, Hotel Checking Co. v. Lorraine Co., Federal
Reporter 160, 467.
Albert, R. L. and T. R. Smaby, Market response to analyst recommendations in
the “Dartboard” column: The information and price pressure e0ffects, Review
of Financial Economics 5, 59-74.
Amir E. and B. Lev, 1996, Value-relevance of non-financial information: The
wireless communications industry, Journal of Accounting and Economics 22,
3-30.
Anason, D., 1999, Financial firms given legal defense against lawsuits over
patents, American Banker 164, 2:1.
Brown, S. and J. Warner, 1985, Using daily stock returns: The case of event
studies, Journal of Financial Economics 14, 3-31.
Chartrand, S., May 1, 2000, A patent lawyer wins protection for a product
intended to help other win protection for theirs, New York Times late edition,
C.2.
Cowan,A., 1992, Nonparametric event study tests, Review of Quantitative
Finance and Accounting 2, 343-358.
Dempsey S., J. Gatti, D. Grinnell, and W. Cats-Baril, 1997, The use of strategic
performance variables as leading indicators in financial analysts’ forecasts,
Journal of Financial Statement Analysis, Summer, 61-79.
Deng, Z., B. Lev, and F. Narin, 1999, Science and technology as predictors of
stock performance, Financial Analysts Journal, May/June, 21-32.
Dreyfus, R., 2001, Santa Clara Computer and High Technology Law Journal,
forthcoming.
12
Ittner, C. and D. Larcker, 1998, Are nonfinancial measures leading indicators of
financial performance? An analysis of customer satisfaction, Journal of
Accounting Research 36 supplement, 1-35.
Kovaleski, D., 2000, Patents for investment processes still unusual, Pensions and
Investments 28, 3.
Lev, B. and P. Zarowin, 1999, The boundaries of financial reporting and how to
extend them, Journal of Accounting Research 37, 353-385.
Lucchetti A., September 21, 2000, Patent poses problem for Amex exchangetraded funds, The Wall Street Journal, C1.
McIntosh, N., June 21, 2000, E-finance: BT wakes up to chance of making hyperprofit, The Guardian, 1.27.
O’Haver, R. and E. Rodgers, 2000, Financial service companies should protect
their core intangibles, Corporate Finance April, 22-24.
Reich-Hale, D., 2000, Suit raises question: Can patents protect business
processes?, American Banker 165, 7.
Schwartz, J., March 30, 2000, Online patents to face tighter review, The
Washington Post, E1.
United States Patent and Trademark Office, 2000, Automated financial or
management data processing methods (business methods), USPTO White
Paper, http://www.uspto.gov/web/menu/busmethp/index.html.
United States Patent and Trademark Office, 2001, Manual of Classification,
http://www.uspto.gov/web/offices/ac/ido/oeip/taf/def/705.html.
Updike, E., October 26, 1998, What’s next—a patent for the 401(k)?, Business
Week, 104.
13
Table 1. Sample of firms granted a Class 705 patent approval in class 35 and class 39 through December 31, 2000 with data
available on CRSP tapes.
Issue
Patent # Date
Assignee
5,884,28 16-Mar8
99
Sun Microsystems, Inc. (Palo Alto, CA)
6,018,72 25-Jan4
00
Sun Microsystems, Inc. (Palo Alto, CA)
6,073,11
3
6-Jun-00 Sun Microsystems, Inc. (Palo Alto, CA)
5,852,81 22-Dec2
98
Microsoft Corporation (Redmond, WA)
5,872,84 16-Feb4
99
Microsoft Corporation (Redmond, WA)
5,883,81 16-Mar0
99
Microsoft Corporation (Redmond, WA)
5,918,21 29-Jun6
99
Microsoft Corporation (Redmond, WA)
5,870,72
2
9-Feb-99 AT&T Wireless Services Inc (Middletown, NJ)
5,950,17
4
7-Sep-99 AT&T Corp. (Middletown, NJ)
5,963,62
5
5-Oct-99 AT&T Corp (New York, NY)
5,991,38 23-Nov0
99
AT&T Corp. (New York, NY)
6,052,67
5
18-Apr-00 AT&T Corp. (New York, NY)
6,064,97 16-May- AT&T Corp (New York, NY)
File Date Permno Industry
9-Dec-96 10078
3570
30-Jun-97 10078
3570
29-Jun-98 10078
3570
23-Aug-95 10107
7370
18-Nov-96 10107
7370
24-Sep-97 10107
7370
22-Aug-96 10107
7370
22-Sep-95 10401
6711
25-Apr-97 10401
6711
30-Sep-96 10401
6711
21-Oct-97 10401
6711
21-Apr-98 10401
17-Sep-97 10401
6711
6799
1
Issue
Patent # Date
Assignee
2
00
6,125,34 26-Sep9
00
AT&T Corp. (New York, NY)
5,120,94
4
9-Jun-92 Unisys Corp. (Detroit, MI)
5,884,29 16-Mar0
99
Unisys Corporation (Blue Bell, PA)
5,949,88
0
7-Sep-99 Dallas Semiconductor Corporation (Dallas, TX)
5,696,90
7
9-Dec-97 General Electric Company (Schenectady, NY)
5,613,11 18-Mar3
97
International Business Machines Corporation (Armonk, NY)
5,878,23
3
2-Mar-99 International Business Machines Corporation (Armonk, NY)
5,991,41 23-Nov1
99
International Business Machines Corporation (Armonk, NY)
6,026,37 15-Feb4
00
International Business Machines Corporation (Armonk, NY)
6,064,99 16-May0
00
International Business Machines Corporation (Armonk, NY)
5,262,94 16-Nov1
93
ITT Corporation (New York, NY)
5,911,13
7
8-Jun-99 Motorola, Inc. (Schaumburg, IL)
6,144,94
9
7-Nov-00 Motorola, Inc. (Schaumburg, IL)
5,970,15 19-Oct-99 Pitney Bowes Inc. (Stamford, CT)
File Date Permno Industry
30-Jun-98 10401
6799
10-Oct-89 10890
3573
22-Oct-96 10890
3573
26-Nov-97 11761
3674
27-Feb-95 12060
3634
7-Jun-95 12490
3573
7-Aug-95 12490
5410
8-Oct-96 12490
30-May96
12490
5410
31-Mar-98 12490
3573
30-Mar-90 12570
3662
15-Jul-96 22779
3663
12-Feb-98 22779
19-Dec-96 24459
3663
3579
3573
2
Issue
Patent # Date
Assignee
0
5,634,01 27-May2
97
Xerox Corporation (Stamford, CT)
5,839,11 17-Nov9
98
Xerox Corporation (Stamford, CT)
6,021,39
9
1-Feb-00 Xerox Corporation (Stanford, CT)
6,032,13 29-Feb5
00
Diebold, Incorporated (North Canton, OH)
5,717,86 10-Feb8
98
Huntington Bancshares Inc. (Columbus, OH)
5,787,40
3
28-Jul-98 Huntington Bancshares, Inc. (Columbus, OH)
5,899,98
2
4-May-99 Huntington Bancshares Incorporated (Columbus, OH)
5,902,98 11-May3
99
International Game Technology (Reno, NV)
6,167,38 26-Dec5
00
The Chase Manhattan Bank (New York, NY)
5,659,73 19-Aug1
97
Dun & Bradstreet, Inc. (Mary Hill, NJ)
5,765,14
4
9-Jun-98 Merrill Lynch & Co., Inc. (New York, NY)
5,826,24
3
20-Oct-98 Merrill Lynch & Co., Inc. (New York, NY)
6,016,48 18-Jan2
00
Merrill Lynch & Co., Inc. (New York, NY)
5,424,93 13-Jun- First Chicago Corporation (Chicago, IL)
File Date Permno Industry
23-Nov-94 27983
3861
27-Sep-96 27983
3861
25-Nov-96 27983
3861
27-Apr-98 40440
3499
7-Mar-95 42906
6020
8-Mar-95 42906
6020
25-Jun-98 42906
6020
29-Apr-96 45277
3990
30-Nov-98 47896
6025
19-Jun-95 48506
7392
24-Jun-96 52919
6211
3-Jan-94 52919
6211
11-Jan-96 52919
13-Oct-92 53858
6211
6025
3
Issue
Patent # Date
8
95
5,991,74 23-Nov8
99
4,794,53 27-Dec0
88
4,947,47
9
7-Aug-90
5,754,65 19-May6
98
5,880,44
6
9-Mar-99
5,963,92
6
5-Oct-99
5,991,74 23-Nov7
99
6,044,36 28-Mar3
00
6,058,38
2
2-May-00
6,155,48
4
5-Dec-00
5,907,83 25-May2
99
5,991,41 23-Nov2
99
6,061,66
4
9-May-00
6,076,07 13-Jun-
Assignee
File Date Permno Industry
American Express Travel Related Services Company, Inc. (New
York, NY)
6-Dec-96 59176
Hitachi, Ltd. (Tokyo, JP); Japanese National Railways (Tokyo,
JP)
7-May-86 64231
6052
3569
Hitachi, Ltd. (Tokyo, JP)
25-Apr-89 64231
3569
Hitachi, Ltd. (Tokyo, JP)
1-Aug-96 64231
3569
Hitachi, Ltd. (Tokyo, JP)
29-Jan-97 64231
3569
Hitachi, Ltd. (Tokyo, JP)
4-Mar-97 64231
3569
Hitachi, Ltd. (Tokyo, JP)
26-Jul-96 64231
3569
Hitachi, Ltd. (Tokyo, JP)
2-Sep-97 64231
3569
Hitachi, Ltd. (Tokyo, JP)
10-Sep-97 64231
3569
Hitachi, Ltd. (Tokyo, JP)
10-Nov-98 64231
3569
Koninklijke PTT Nederland N.V. (The Hague, NL)
15-Nov-96 76616
5410
Koninklijke KPN N.V. (Groningen, NL)
9-Dec-96 76616
5410
Koninklijke PTT Nederland N.V. (NL)
Koninklijke KPN N.V. (Groningen, NL)
9-Oct-96 76616
1-Mar-99 76616
5410
5410
4
Issue
Patent # Date
Assignee
3
00
6,154,72 28-Nov9
00
First Data Corporation (Hackensack, NJ)
5,797,13 18-Aug3
98
Strategic Solutions Group, Inc (Atlanta, GA)
5,889,86 30-Mar2
99
Nippon Telegraph and Telephone Corporation (Tokyo, JP)
5,926,54
8
20-Jul-99 Nippon Telegraph and Telephone Corporation (Tokyo, JP)
6,023,68
7
8-Feb-00 Capital One Financial Corporation (Glen Allen, VA)
6,049,78
4
11-Apr-00 Capital One Financial Corporation (Glen Allen, VA)
5,897,62
1
27-Apr-99 Cybercash, Inc. (Reston, VA)
6,092,05
3
18-Jul-00 Cybercash, Inc. (Reston, VA)
5,870,72
1
9-Feb-99 Affinity Technology Group, Inc. (Columbia, SC)
5,940,81 17-Aug1
99
Affinity Technology Group, Inc. (Columbia, SC)
6,105,00 15-Aug7
00
Affinity Technology Group, Inc. (Columbia, SC)
5,678,01
0
14-Oct-97 CompuServe Incorporated (Columbus, OH)
5,909,49
2
1-Jun-99 Open Market, Incorporated (Cambridge, MA)
6,049,78 11-Apr-00 Open Market, Inc. (Burlington, MA)
File Date Permno Industry
19-Jun-98 77546
7373
3-Feb-97 79852
7370
15-Jul-96 80863
20-May97
80863
4813
30-Dec-98 81055
6141
16-Dec-97 81055
6141
14-Jun-96 83128
7370
7-Oct-98 83128
7370
15-Oct-96 83348
7370
15-Oct-96 83348
7370
5-May-99 83348
7370
7-Jun-95 83367
7374
18-Jun-97 83539
2-Mar-98 83539
7370
7370
4813
5
Issue
Patent # Date
Assignee
File Date Permno
5
5,832,46
3
3-Nov-98 Electronic Data Systems Corporation (Plano, TX)
28-Mar-96 83596
6,049,78
1
11-Apr-00 Electronic Data Systems Corporation (Plano, TX)
18-Apr-96 83596
5,878,40
4
2-Mar-99 Mechanics Savings Bank (Hartford, CT)
8-Oct-96 83675
5,757,91 26-May7
98
First Virtual Holdings Incorporated (San Diego, CA)
1-Nov-95 84312
5,835,60 10-Nov3
98
NCR Corporation (Dayton, OH)
23-Sep-96 84372
5,987,43 16-Nov7
99
NCR Corporation (Dayton, OH)
23-Sep-96 84372
6,021,40
0
1-Feb-00 NCR Corporation (Dayton, OH)
2-Jan-97 84372
5,715,39
30-May9
3-Feb-98 Amazon.Com, Inc. (Seattle, WA)
95
84788
5,727,16 10-Mar3
98
Amazon.Com, Inc. (Seattle, WA)
30-Mar-95 84788
5,950,17
9
7-Sep-99 Providian Financial Corporation (San Francisco, CA)
3-Dec-96 85073
5,905,97 18-May- France Telecom (Paris, FR); La Poste (Boulogne Billancourt, FR);
6
99
Sintef Delab (Trondheim, NO)
17-Jul-96 85425
6,014,64 11-Jan6
00
France Telecom (Paris, FR)
5-Jun-96 85425
6,105,86 22-Aug2
00
France Telecom (Paris, FR); La Poste (Boulogne Billancourt, FR) 29-Oct-98 85425
5,893,08 6-Apr-99 Bottomline Technologies, Inc. (Portsmouth, NH)
25-Jul-95 86717
Industry
7379
7379
6030
7370
3574
3574
3577
7370
7370
6311
3663
3663
3663
7370
6
Issue
Patent # Date
0
Assignee
File Date Permno Industry
7
Table 2. Market adjusted average abnormal returns. We use a standard event-study
methodology to estimate market-adjusted abnormal returns around the event date using the
CRSP value-weighted index. We estimate abnormal returns over a 41-day event window from
day –20 to +20 using the time series standard deviation method (Brown and Warner, 1985) to
generate test statistics and report both the standard t-statistic and a generalized sign test. The
null hypothesis for the sign test is that the percentage of positive returns is the same in the
event window as the estimation period.
Day
-20
-19
-18
-17
-16
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
Average
Abnormal
Return
-0.27%
-0.04%
0.29%
0.03%
1.06%
0.84%
0.19%
-0.22%
-0.29%
-0.24%
0.51%
-0.39%
0.76%
-0.05%
0.19%
-0.09%
-0.55%
0.11%
-0.46%
1.12%
-0.03%
0.15%
-0.08%
-0.13%
-0.13%
0.53%
0.34%
-0.20%
-0.18%
0.00%
-0.17%
-0.26%
-0.38%
-0.16%
0.41%
-0.44%
0.10%
Median
Abnormal
Return
-0.47%
-0.21%
-0.35%
-0.42%
0.11%
0.47%
-0.26%
-0.42%
-0.48%
0.12%
-0.17%
-0.26%
0.28%
-0.28%
0.52%
-0.15%
-0.75%
-0.22%
-0.49%
0.50%
-0.12%
-0.38%
-0.38%
-0.10%
0.19%
-0.10%
0.50%
-0.31%
-0.12%
0.04%
-0.30%
-0.38%
-0.66%
-0.18%
0.34%
-0.36%
-0.33%
t-statistic
-0.45
-0.06
0.47
0.05
1.74**
1.39*
0.31
-0.37
-0.48
-0.40
0.83
-0.64
1.26
-0.08
0.31
-0.15
-0.90
0.18
-0.75
1.84**
-0.05
0.25
-0.13
-0.21
-0.21
0.87
0.56
-0.34
-0.30
-0.01
-0.28
-0.42
-0.62
-0.27
0.68
-0.72
0.17
N
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
81
81
81
81
81
81
81
80
80
80
80
80
80
Positive:
Negative
38:44
38:44
38:44
36:46
42:40
48:34
37:45
35:47
31:51
44:38
38:44
38:44
47:35
38:44
48:34
40:42
29:53
37:45
36:46
49:33
39:43
37:45
34:48
37:45
44:37
40:41
48:33
36:45
36:45
41:40
38:43
37:43
28:52
35:45
48:32
34:46
33:47
Generalize
d
Sign Test
Z-statistic
-0.06
-0.06
-0.06
-0.51
0.82
2.15**
-0.28
-0.73
-1.61*
1.27
-0.06
-0.06
1.93**
-0.06
2.15**
0.38
-2.05**
-0.28
-0.51
2.37***
0.16
-0.28
-0.95
-0.28
1.38*
0.49
2.27**
-0.40
-0.40
0.71
0.04
-0.08
-2.10**
-0.53
2.39***
-0.75
-0.97
1
17
18
19
20
0.04%
0.10%
-0.23%
0.11%
-0.18%
-0.21%
-0.08%
-0.90%
0.06
0.16
-0.38
0.18
80
79
79
79
36:44
34:45
38:41
30:49
-0.30
-0.65
0.25
-1.55*
*** Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.
2
Table 3. Market model average abnormal returns. We use a standard event-study
methodology to estimate market-model abnormal returns around the event date using the
CRSP value-weighted index. We estimate a market model for each firm during a 120-day
estimation period from day –141 to day –21, and we estimate abnormal returns over a 41-day
event window from day –20 to +20. We use the time series standard deviation method (Brown
and Warner, 1985) to generate test statistics and report both the standard t-statistic and a
generalized sign test. The null hypothesis for the sign test is that the percentage of positive
returns is the same in the event window as the estimation period.
Day
-20
-19
-18
-17
-16
-15
-14
-13
-12
-11
-10
-9
-8
-7
-6
-5
-4
-3
-2
-1
0
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
Average
Abnormal
Return
-0.39%
0.02%
0.14%
-0.05%
0.91%
0.75%
0.13%
-0.34%
-0.42%
-0.32%
0.42%
-0.33%
0.69%
-0.19%
0.01%
-0.37%
-0.59%
-0.09%
-0.66%
1.03%
-0.07%
0.14%
-0.23%
-0.09%
-0.22%
0.44%
0.20%
-0.46%
-0.24%
-0.43%
-0.28%
-0.35%
-0.62%
-0.23%
0.39%
-0.54%
Median
Abnormal
Return
-0.44%
-0.07%
-0.11%
-0.29%
-0.16%
0.34%
-0.36%
-0.46%
-0.52%
0.04%
-0.26%
-0.14%
0.40%
-0.19%
0.35%
-0.32%
-0.75%
-0.22%
-0.57%
0.28%
-0.17%
-0.12%
-0.44%
-0.18%
0.29%
0.06%
0.52%
-0.20%
-0.14%
-0.15%
-0.25%
-0.53%
-0.98%
-0.16%
0.42%
-0.56%
t-statistic
-0.66
0.04
0.24
-0.09
1.51*
1.25
0.22
-0.57
-0.70
-0.54
0.70
-0.54
1.16
-0.32
0.01
-0.62
-0.98
-0.15
-1.10
1.71**
-0.12
0.23
-0.38
-0.15
-0.36
0.73
0.34
-0.76
-0.40
-0.72
-0.47
-0.57
-1.04
-0.39
0.65
-0.90
N
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
82
81
81
81
81
81
81
81
80
80
80
80
80
Positive:
Negative
36:46
38:44
38:44
38:44
40:42
45:37
36:46
36:46
30:52
43:39
38:44
38:44
45:37
38:44
44:38
37:45
31:51
37:45
34:48
44:38
36:46
38:44
37:45
36:46
44:37
42:39
47:34
32:49
37:44
38:43
38:43
35:45
22:58
37:43
47:33
32:48
Generalize
d
Sign Test
Z-statistic
-0.46
-0.02
-0.02
-0.02
0.42
1.53*
-0.46
-0.46
-1.79**
1.09
-0.02
-0.02
1.53*
-0.02
1.31*
-0.24
-1.57*
-0.24
-0.91
1.31*
-0.46
-0.02
-0.24
-0.46
1.42*
0.97
2.09**
-1.25
-0.14
0.08
0.08
-0.48
-3.40***
-0.04
2.21**
-1.16
3
16
-0.01%
-0.44%
-0.01
80
17
0.06%
0.01%
0.11
80
18
0.10%
-0.12%
0.16
79
19
-0.35%
-0.30%
-0.57
79
20
0.11%
-0.79%
0.18
79
*** Significant at the 1% level. **Significant at the 5% level.
33:47
40:40
37:42
37:42
32:47
*Significant
-0.93
0.64
0.07
0.07
-1.06
at the 10% level.
4
Table 4. Market adjusted cumulative average abnormal returns. . We use a standard
event-study methodology to estimate market-adjusted abnormal returns around the event date
using the CRSP value-weighted index. We estimate cumulative abnormal returns over three
multi-day intervals using the time series standard deviation method (Brown and Warner, 1985)
to generate test statistics and report both the standard t-statistic and a generalized sign test.
The null hypothesis for the sign test is that the percentage of positive returns is the same in
the event window as the estimation period.
Days
(-20, -2)
(-1,0)
(+1,+20)
Cumulative Cumulative
Average
Median
Abnormal Abnormal
Return
Return
t-statistic
1.36%
-0.09%
0.52
1.08%
0.27%
1.27
-0.56%
-0.41%
-0.21
Positive:
Negative
41:41
44:38
37:45
Generalize
d
Sign Test
Z-statistic
-0.46
1.27
-0.28
*** Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.
Table 5. Market model cumulative average abnormal returns. We use a standard
event-study methodology to estimate market-model abnormal returns around the event date
using the CRSP value-weighted index. We estimate a market model for each firm during a 120day estimation period from day –141 to day –21, and we estimate cumulative abnormal returns
over three multi-day intervals. We use the time series standard deviation method (Brown and
Warner, 1985) to generate test statistics and report both the standard t-statistic and a
generalized sign test. The null hypothesis for the sign test is that the percentage of positive
returns is the same in the event window as the estimation period.
Days
(-20, -2)
(-1,0)
(+1,+20)
Cumulative Cumulative
Average
Median
Abnormal Abnormal
Return
Return
t-statistic
-0.68%
-0.61%
-0.26
0.95%
0.34%
1.13
-2.59%
-1.32%
-0.97
Positive:
Negative
40:42
44:38
30:52
Generalize
d
Sign Test
Z-statistic
-0.46
1.31*
-1.79**
*** Significant at the 1% level. **Significant at the 5% level. *Significant at the 10% level.
5
Figure 1. Plot of cumulative abnormal returns for the market-adjusted and market model.
4.0%
3.0%
1.0%
Mkt Adj
Mkt Mod
20
18
16
14
12
10
8
6
4
2
0
-2
-4
-6
-8
-1
0
-1
2
-1
4
-1
6
-1
8
0.0%
-2
0
CAR (%)
2.0%
-1.0%
-2.0%
-3.0%
Day
6