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Market Reaction to E-Commerce Impairments Evidenced by Website Outages

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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Market Reaction to E-Commerce Impairments Evidenced by Website Outages

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  1. 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

  2. Presentation • Introduction & Research Question • Research Approach • Prior Research Literature • Hypotheses • Regression Models • Results

  3. 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

  4. 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”

  5. 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

  6. 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

  7. 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

  8. 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!!

  9. 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

  10. 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%)

  11. 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)

  12. 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

  13. 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

  14. 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.

  15. 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.

  16. 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)

  17. 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)

  18. 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).

  19. 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

  20. 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.

  21. “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

  22. 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

  23. 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

  24. Selected Outages

  25. Market Model • (1) R*it = a + bRmt + eit • (2) ARit = Rit – R*it • (3) CAR =  ARit

  26. 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

  27. p = 0.0171, 1-tail t-test Test of H2 – % Internet Revenue  - CAR

  28. CARit = 0 + 1WebOutageit + 2Ratioit + 3Lengthit + 4Typeit + 5(WebOutage*Type)it + 6(Ratio*Type)it + 7(Length*Type)it + it Test of H3 – Outage Type and Firm Type

  29. Test of H3 – Outage Type and Firm Type

  30. Test of H4 – Long vs. Short Outages OutageMean CARt-statisticp-value Short (N=21) -0.0504 -2.2954 0.0327 Long (N=13) -0.0579 -2.9726 0.0116 Statistical Significance of Difference Statisticsp-value t-test -0.2400 0.4069 Wilcoxon z-test -0.4860 0.4859

  31. Test of H5 – More Frequent Outages 1 - 4 outages reported versus more than 7 Frequency Mean CARt-statisticp-value More (N=40) -0.0426 -3.0082 0.0023 Less (N=16) -0.0397 -2.8384 0.0063 Statistical Significance of Difference Statisticsp-value t-test -0.1500 0.4425 Wilcoxon z-test -0.1179 0.4531

  32. Test of H5 – More Frequent Outages • Examined if a firm has an outage within 3 days of another firm experiencing an outage Outage Mean CARt-statisticp-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

  33. 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

  34. 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)

  35. 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

  36. Persistence of Losses – Short Outages

  37. Persistence of Losses – Long Outages

  38. 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”

  39. 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?

  40. 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

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