Market Reaction to
E-Commerce Impairments
Evidenced by Website Outages
Joseph H. Anthony*
Wooseok Choi**
Severin Grabski*
*Department of Accounting and Information Systems
Michigan State University
**Department of Accounting, California State University at
Los Angeles
Presentation
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Introduction & Research Question
Research Approach
Prior Research Literature
Hypotheses
Regression Models
Results
Last summer, on-line auction site eBay Inc.
unwittingly became the latest poster child for
Web-site crashes, as it endured a host of
outages, the worst of which took the site offline
for nearly 22 hours on June 10. Bidders and
sellers were angry, and investors sent the
company’s stock down more than 25% in the
two business days after the problems began,
slashing nearly $6 billion off its market value.
Wall Street Journal: November 22, 1999
Research Objective

Systematically investigate the impact of
website and other e-commerce related
outages on economic returns as measured
by the stock market
“Self-Inflicted”, not
“Hacked”
Direct Measures of Loss Due to
Website/ e-commerce Outages
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Repeated outages resulted in loss of 10% of
customer base (McKnight 1997)
Hour of web downtime results in $50,000 in
lost sales (Woods 2000)
Unfortunately, data is generally not available
Alternative Costs of Website/
e-commerce Outages
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TD Waterhouse fined by SEC (Simon 2001)
TicketMaster - Prioritized business units
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Ticketing
Online Personals
Cityguide
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Lost revenues from ticketing is real
Might result in permanent loss of customer
(Fonseca 2001)
Again, data is generally not available
Alternative Costs of Website/
e-commerce Outages
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CIOs “overspent” on security (Yager 2002)
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Spend average of $3.6M on Security
Average cost of security breach - $193,000
Might be missing other costs, the potential
decline in the market value of the firm
Other Costs of Website/
e-commerce Outages
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Hacker Attacks (Ettredge and Richardson 2002)
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Resulted in negative abnormal stock returns
BUT---
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Firms examined were only in the same industry as
“hacked” firms, they were not hacked!!
Other Costs of Website/
e-commerce Outages
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Security Breaches (Campbell et al. 2003)
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Resulted in negative abnormal stock returns
Market discriminated between types of attacks
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Significant negative reaction to unauthorized access to
confidential data
No significant reaction when not involving confidential data
Other Costs of Website/
e-commerce Outages
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Software Vulnerabilities – Cost to software
developers (Telang and Wattal 2005)
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18 firms, 146 announcements (1999-2004)
Resulted in negative returns of .6% stock price per
disclosure
Average loss $.86B per vulnerability announcement
More negative impact w/o patch (.8%)
More severe flaws have more negative impact
Confidentiality breach resulted in greater
decline than other breach types (.75%)
Event Studies

Investors process information about expected and
unexpected events and consider these events in the valuation
of shares
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Event studies examine the residual price change of a sample
of firms for a window of time on either side of an identifiable
“event,” such as announcements of earnings, stock splits and
dividends, cash dividends, earnings forecasts, or changes in
accounting methods. The influence of economy-wide and, if
necessary, additional factors such as industry-wide
information on stock prices is extracted to obtain a residual
return. The expected value of the residual price changes, not
conditioning on the event, is zero. (Beaver 1998, p.133)
Prior Accounting & Finance
Research
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Beaver (1968) & Ball and Brown (1968) –
stock returns and accounting earnings
Aharony and Swary (1980) – quarterly
dividend announcements
Eccher et. al. (1996) & Barth et. al. (1996) –
fair value disclosures by banks
Anthony (1987) – expected and unexpected
news releases
Prior IT Research
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Dos Santos et al. (1983) - innovative uses of IT
rewarded
Subramani and Walden (1999) - E-commerce
initiatives
Krishnan and Sriram (2000) - estimates of Y2K
compliance costs
Chatterjee et al. (2001) - announcement of CIO
position creation
Im et al. (2001) – IT investment announcements
E-Commerce Firm Valuation
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Measures of website usage are value
relevant, they provide incremental
explanatory power for stock prices (Trueman
et al. 2000)
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Number of unique visitors
Number of page views
Bartove et al. (2002) and Rajgopal et al.
(2003), also provide research results related
to the valuation of internet/e-commerce firms.
Hypotheses
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H1: There will be a significant negative
association between an outage
announcement and a firm’s stock returns.
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H2: Firms with a high percentage of online revenue will have a significantly more
negative association in the stock returns
than firms with a low percentage of on-line
revenue.
Internet Firm Types
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Infrastructure Providers
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(Barua et al. 1999, 2000)
Provide the backbone and basic Internet services
Commerce Providers
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Provide goods and services to businesses (B2B)
and individuals (B2C) over the Internet (either as
an intermediary or directly)
Outage Type
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Two basic types of outages reported by the
news services
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E-mail
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The e-mail function of a website fails, but the website
itself works well without shutting down
Non E-mail outage (Website)
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The website is completely shut down or some important
functions other than an e-mail failure (e.g., stock trading
functions)
Website Outage
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“The suit, filed in the Santa Clara County Superior Court by
the Alexander law firm of San Jose, Calif., is seeking
unspecified damages for investors who claim they missed
out on making money in the stock market because of the
outage.
"E*Trade customers were unable to trade or obtain access
to their online accounts on Feb. 3, 1999 for in excess of
one hour, on Feb. 4, 1999, for in excess of two and one half
hours, and for approximately one-half hour on Feb. 5,
1999," the Alexander suit said. "As a result of this 'virtual'
lockout, class members lost potentially millions of dollars in
damages."  One E*Trade customer, Dar Hay of Memphis,
Tenn., said he lost close to $12,000 last week when he was
unable to cancel an order to buy 350 shares of brokerage
firm Siebert Financial Corp.” (Reerink 1999b).
Hypotheses
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H3a: The negative stock market impact of
an e-mail type outage will be greater than
that of a non e-mail type outage (website)
for infrastructure providers
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H3b: The negative stock market impact of
a non e-mail type outage (website) will be
greater than that of an e-mail type outage
for commerce providers
Hypotheses
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H4: Long outages (12+ hours) are more
negatively associated with stock returns
than short outages (1 hour or less)
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H5: The frequency of outages is negatively
associated with stock price changes.
“Traditional” Event Study
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Event date = first day reported by press
Used a 4-day window
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-1 to +2
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-1 because the outage could have occurred past trading
but was picked up prior to the opening of trade the next
day
+2 since some outages were longer than 24 hours
Similar results obtained for 2 and 3-day windows
Sample Selection
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Started with firms in “Internet 500” (ZDNet
Interactive Week Special Report 1999)
Had at least one outage
Eliminated firms not “primarily internet”
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Internet revenues > 50% total revenues for 1997, 1998 or
1999 (retained 4 infrastructure firms – ATT, IBM, MCI, Sprint)
Stock return data available for 240 day
estimation period
19 firms, 86 outages
Sample Firms (Internet
Classification)
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Amazon.com
America Online
Ameritrade Holding
AT&T
At Home*
CNet
Dell Computer
E*Trade Securities
EBay
Egghead.com
(C)
(I)
(C)
(I)
(I)
(C)
(C)
(C)
(C)
(C)
Excite, Inc.*
(I)
IBM
(I)
Intuit
(C)
MCI
(I)
MindSpring Enterprises (I)
Netcom
(I)
Network Solutions
(I)
Sprint
(I)
Schwab
(C)
Yahoo!
(C)
*Merged and treated as a single firm in this study
Selected Outages
Company
Date
Problem
Amazon.com
1/7/98
Internal technical problem
11/19/99
Length
Effect
12 hrs
Website down
30 min
Website down
28 min.
Website down
Ameritrade
2/8/99
Communication link to one of servers
failure
AOL
1/15/97
A router device failure
3 hrs 45 min
e-mail
3/24/98
Electronic malfunction
30 min
e-mail
9/21/98
S/W malfunction
AT&T
WorldNet
12/1/99
CNET
12/8/98
Black out
Dell
4/13/99
an affiliate's computer system delay
11/2/98
6/10-11/99
Website down
8 hrs
Disable severs
30 min
Online trading system crashed
hours
Website down
H/W glitch
45 min
Database server crashed
Sever OS failure
22 hrs
11/23/99
EBay
1 hr
12/8/99
H/W problem
2 hr 46 min
Website down
Egghead
2/28/98
System maintenance
48 hrs
Website closed
E*trade
7/25/97
S/W application error
40 min
Website down
2/3/99
S/W change
1 hr 15 min
Trading function inaccessible
2/4/99
S/W change
1 hr
Trading function inaccessible
Market Model
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(1) R*it = a + bRmt + eit
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(2) ARit = Rit – R*it
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(3) CAR =  ARit
Test of H1 – Outage  - CAR
Cumulative Abnormal Returns (CAR)
from Day –1 to Day +2
All observations (N=86)
Mean CAR
-0.0392
Median CAR
-0.0363
t-statistic
-4.4487
p-value
0.0001
Number of positive CARs: 28
Number of negative CARs: 58
Test of H2 –
% Internet Revenue  - CAR
Mean CAR
Median CAR
Variance
Standard deviation
High
50% or more
N=56
Low
Less than 50%
N=30
-0.0510
-0.0404
0.0093
0.0967
-0.0193
-0.0212
0.0014
0.0376
p = 0.0171, 1-tail t-test
Test of H3 – Outage Type and Firm Type
CARit = 0 + 1WebOutageit + 2Ratioit + 3Lengthit +
4Typeit + 5(WebOutage*Type)it + 6(Ratio*Type)it +
7(Length*Type)it + it
Variable
Coefficient
p-value
WebOutage
0.0050
0.891
Ratio
0.5582
0.845
-0.0041
0.007
0.1230
0.658
WebOutage*Type
-0.1052
0.025
Ratio* Type
-0.1122
0.697
Length*Type
0.0037
0.059
Adj. R2
0.1421
Length
Type
Test of H3 – Outage Type and Firm Type
0.2
0.1
Mean CAR
0
-0.1
-0.2
-0.3
-0.4
E-Mail Outage
WebSite Outage
Infrastructure
Provider
-0.027
-0.056
Commerce Provider
0.008
-0.263
Outage Type
Test of H4 – Long vs. Short Outages
Outage
Short (N=21)
Long (N=13)
Mean CAR t-statistic
-0.0504
-0.0579
-2.2954
-2.9726
p-value
0.0327
0.0116
Statistical Significance of Difference
Statistics
t-test
-0.2400
Wilcoxon z-test -0.4860
p-value
0.4069
0.4859
Test of H5 – More Frequent Outages
1 - 4 outages reported versus more than 7
Frequency Mean CAR t-statistic
More (N=40) -0.0426
-3.0082
Less (N=16) -0.0397
-2.8384
p-value
0.0023
0.0063
Statistical Significance of Difference
Statistics
p-value
t-test
-0.1500
0.4425
Wilcoxon z-test
-0.1179
0.4531
Test of H5 – More Frequent Outages
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Examined if a firm has an outage within 3 days of
another firm experiencing an outage
Outage
Mean CAR
First (N=61)
-0.0319
Subsequent (N=25) -0.0587
t-statistic
-3.1332
-3.3493
Statistical significance of difference
Statistics
p-value
t-test
-1.3805
0.0856
Wilcoxon z-test
-1.5673
0.0585
p-value
0.0027
0.0027
Post Hoc Economic Value Tests
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Measure the loss (gain) per share from day
–1 to day +2
Are a “crude” measure, but are in dollars
Per-share changes in stock prices around
outage announcements (using winsorized
data) :
Mean –$ 1.710
Median –$ 0.9375
Sensitivity Analysis
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Confounding Effects
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dividends and earnings,
mergers and acquisitions, alliances, joint ventures,
and partnerships,
law suits,
important news releases related to technologies
None in 4-day window
Also used 11-day window, eliminated 7 Obs.
Results were consistent (and stronger)
Firm Size Effects
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Large firm might have greater market
reaction than small firms
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Small firms might have greater market
reaction than large firms
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More users influenced by outage
Less publicly available information – information
asymmetry (Atiase 1985, 1987)
Regressed CAR on Total Assets (Sales,
Market Value of Equity, Working Capital)
No Significant Effect
Persistence of Losses – Short Outages
0.01
0.005
CAR
0
Abnormal Return
-0.005
-0.01
-0.015
-3
-2
-1
0
1
2
3
Trading Day
4
5
6
7
Persistence of Losses – Long
Outages
0.03
0.02
CAR
0.01
0
Abnormal Return
-0.01
-0.02
-0.03
-3 -2 -1 0
1
2 3
4
Trading Day
5
6
7
Limitations
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Small sample size
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Limited to time period
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Was 100% for period under study
Consistent economic growth
Avoid Y2K
Avoid dotcom melt down 2001
Focus on B2C – for “commerce providers”
Discussion
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Firms are (differentially) penalized for outages
IT Governance Impact

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COSO ERM – Identification and assessment of
risks affecting achievement of business objectives
Evaluate from
Future revenue stream
Firm market value
Focused on B2C; impact on B2B?
Summary
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Mean CAR surrounding outages is negative and
statistically significant
Non e-mail outages result in significant negative CAR
for commerce providers; e-mail outages not
significantly different than 0
No difference due to length of outage
Firms earning more than 50% internet revenues had
significantly more negative CAR
Repeat outages by same firm were not penalized
more heavily
Two or more outages in the same window resulted in
the second firm more heavily penalized
Cost of outage $ 1.71/share