Market Reaction to E-Commerce ImpairmentsEvidenced by Website Outages Joseph H. Anthony*Wooseok Choi**Severin Grabski*

*Department of Accounting and Information SystemsMichigan State University **Department of Accounting, California State University at Los Angeles

Presentation

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

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”

Research Objective

Direct Measures of Loss Due to Website/ e-commerce Outages 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 TD Waterhouse fined by SEC (Simon 2001) TicketMaster - Prioritized business units

Ticketing Online Personals Cityguide

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

CIOs “overspent” on security (Yager 2002) 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

Hacker Attacks (Ettredge and Richardson 2002) Resulted in negative abnormal stock returns

BUT---

Firms examined were only in the same industry as “hacked” firms, they were not hacked!!

Other Costs of Website/e-commerce Outages

Security Breaches (Campbell et al. 2003) Resulted in negative abnormal stock returns Market discriminated between types of attacks

Significant negative reaction to unauthorized access to confidential data

No significant reaction when not involving confidential data

Other Costs of Website/e-commerce Outages

Software Vulnerabilities – Cost to software developers (Telang and Wattal 2005) 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

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

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 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 Measures of website usage are value

relevant, they provide incremental explanatory power for stock prices (Trueman et al. 2000) 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

H1: There will be a significant negative association between an outage announcement and a firm’s stock returns.

H2: Firms with a high percentage of on-line 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 (Barua et al. 1999, 2000) Infrastructure Providers

Provide the backbone and basic Internet services

Commerce Providers Provide goods and services to businesses (B2B)

and individuals (B2C) over the Internet (either as an intermediary or directly)

Outage Type

Two basic types of outages reported by the news services E-mail

The e-mail function of a website fails, but the website itself works well without shutting down

Non E-mail outage (Website) The website is completely shut down or some important

functions other than an e-mail failure (e.g., stock trading functions)

Website Outage “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

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

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

H4: Long outages (12+ hours) are more negatively associated with stock returns than short outages (1 hour or less)

H5: The frequency of outages is negatively associated with stock price changes.

“Traditional” Event Study

Event date = first day reported by press Used a 4-day window

-1 to +2 -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 Started with firms in “Internet 500” (ZDNet

Interactive Week Special Report 1999) Had at least one outage Eliminated firms not “primarily internet”

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)

Amazon.com (C) Excite, Inc.* (I) America Online (I) IBM (I)

Ameritrade Holding (C) Intuit (C) AT&T (I) MCI (I) At Home* (I) MindSpring Enterprises (I) CNet (C) Netcom (I) Dell Computer (C) Network Solutions (I) E*Trade Securities (C) Sprint (I) EBay (C) Schwab (C) Egghead.com (C) Yahoo! (C)

*Merged and treated as a single firm in this study

Selected OutagesCompany Date Problem Length Effect

Amazon.com 1/7/98 Internal technical problem 12 hrs Website down

  11/19/99   30 min Website down

Ameritrade 2/8/99Communication link to one of servers failure 28 min. Website down

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 1 hr Website down

AT&T WorldNet 12/1/99   8 hrs  

CNET 12/8/98 Black out   Disable severs

Dell 4/13/99 an affiliate's computer system delay 30 min Online trading system crashed

  11/23/99   hours Website down

EBay 11/2/98 H/W glitch 45 min Database server crashed

  6/10-11/99 Sever OS failure 22 hrs  

  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

(1) R*it = a + bRmt + eit

(2) ARit = Rit – R*it

(3) CAR = ARit

Market Model

Test of H1 – Outage - CARCumulative 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

p = 0.0171, 1-tail t-test

High50% or moreN=56

LowLess than 50%N=30

Mean CAR -0.0510 -0.0193

Median CAR -0.0404 -0.0212

Variance 0.0093 0.0014

Standard deviation 0.0967 0.0376

Test of H3 – Outage Type and Firm TypeCARit = 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

Length -0.0041 0.007

Type 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

Test of H3 – Outage Type and Firm Type

-0.4

-0.3

-0.2

-0.1

0

0.1

0.2

Outage Type

Mea

n C

AR

InfrastructureProvider

-0.027 -0.056

Commerce Provider 0.008 -0.263

E-Mail Outage WebSite Outage

Test of H4 – Long vs. Short OutagesOutage Mean CAR t-statistic p-value

Short (N=21) -0.0504 -2.2954 0.0327Long (N=13) -0.0579 -2.9726 0.0116

Statistical Significance of Difference

Statistics p-value t-test -0.2400 0.4069Wilcoxon z-test -0.4860 0.4859

Test of H5 – More Frequent Outages1 - 4 outages reported versus more than 7

Frequency Mean CAR t-statistic p-value

More (N=40) -0.0426 -3.0082 0.0023

Less (N=16) -0.0397 -2.8384 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 Examined if a firm has an outage within 3 days of

another firm experiencing an outage

Outage Mean CAR t-statistic p-value

First (N=61) -0.0319 -3.1332 0.0027

Subsequent (N=25) -0.0587 -3.3493 0.0027

Statistical significance of difference

Statistics p-value

t-test -1.3805 0.0856

Wilcoxon z-test -1.5673 0.0585

Post Hoc Economic Value Tests 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 Confounding Effects

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 Large firm might have greater market

reaction than small firms More users influenced by outage

Small firms might have greater market reaction than large firms 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

-0.015

-0.01

-0.005

0

0.005

0.01

-3 -2 -1 0 1 2 3 4 5 6 7

Trading Day

CA

R

Abnormal Return

Persistence of Losses – Long Outages

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

-3 -2 -1 0 1 2 3 4 5 6 7

Trading Day

CA

R

Abnormal Return

Limitations

Small sample size Was 100% for period under study

Limited to time period Consistent economic growth Avoid Y2K Avoid dotcom melt down 2001

Focus on B2C – for “commerce providers”

Discussion

Firms are (differentially) penalized for outages IT Governance Impact

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 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