Technological Change and Stock Return Volatility:

Evidence from eCommerce Adoptions

Deepak Agrawal∗ Sreedhar T. Bharath† Siva Viswanathan‡

March 2003§

∗K.M.V Corporation, 1620 Montgomery Street Suite 140 San Francisco, CA 94111†Assistant Professor, Finance Department, University of Michigan Business School, Room D6209, Davidson

Hall, 701 Tappan Street, Ann Arbor, MI 48109-1234. E-mail : sbharath@umich.edu.‡Assistant Professor, 4313, Van Munching Hall, Robert H. Smith School of Business, University of Mary-

land, College Park, MD-20742. E-mail : sviswana@rhsmith.umd.edu [Corresponding Address]§We thank M.P. Narayanan and Nagpurnanand Prabhala for helpful comments. All errors are our own.

Viswanathan acknowledges financial support from the Center for Electronic Markets and Enterprises, UMCP

Technological Change and Stock Return Volatility: Evidencefrom eCommerce Adoptions

Abstract

This paper is among the first to use a unique controlled empirical setting - traditional

firms’ adoption of the Internet for commerce - to investigate the impact of changes in firms’

technological environment on their stock return volatility. Using three distinct empirical

methodologies we detect a significant and corresponding increase in the idiosyncratic and

total stock return volatility when a firm initiates eCommerce. Interestingly, this increase in

volatility is observed only for firms that moved online post-June 1998, a period when Inter-

net growth reached critical mass. An increase in the implied volatility of at-the-money call

options of our sample firms reinforces our findings. We find that this increase in volatility

is attributable to changes in the firms’ product markets, specifically increased demand un-

certainty, resulting from the adoption of a new technology-driven channel. Relevant controls

rule out firm-specific characteristics as well as market microstructural factors as possible ex-

planatory variables. We also find that this surge in volatility is accompanied by a positive

abnormal return of stock prices. Overall, our results provide strong evidence of the impact

of real activity within a firm on its stock return volatility and highlight the importance of

understanding changes in firms’ technological environment.

Keywords: Technological Change, Stock Return Volatility, Product Markets, Structural

Break Analysis, Event Studies.

JEL Classification: D80, G12, G14, O33

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1 Introduction

The last few decades have witnessed significant technological developments, the most recent

and the most revolutionary being the advent of the Internet. Financial markets have also

been affected by these developments, particularly with technology firms increasingly domi-

nating these markets. For instance, it is widely believed that the dominance of technology

firms has made stock market returns more volatile; but the evidence in this regard has been

largely circumstantial. Two notable exceptions in this context are the recent work by Schwert

(2002) and Campbell et al (2001). Schwert (2002) identifies ’technological factors’ common

to computer, biotechnology as well as telecommunication portfolios, rather than firm size

or age, as the primary driver of the observed increase in Nasdaq volatility in the post-June

1998 period. Campbell et al (2001) also attribute the significant increase in the idiosyn-

cratic volatility of several industries that they observe, to the emergence of new technologies.

However, what is it about technology that affects firms’ volatility, is a question that remains

unanswered. This study is an attempt in that direction.

This study seeks to examine the relationship between technological change and changes

in a firm’s stock return volatility, and also investigate the drivers of these changes. Rather

than examining firms in the technology sector, we look at a sample of traditional ’brick-

and-mortar’ firms that have embraced a new technological environment, the Internet, for

commerce. The adoption of eCommerce by a traditional firm provides a ’clean’ natural

experimental setting to examine the impact of changes in a firm’s technological environment

on its stock return volatility. As we report in detail later, we do find evidence of a definitive

and substantial increase in both idiosyncratic as well as total firm volatility when a firm

adopts the Internet for commerce. To the best of our knowledge, this is among the first

papers to study the relationship between stock return volatility and real activity within the

firm. Studying this linkage is important because changes in stock return volatility due to

firm-level decisions may affect among other things, the stock valuation, cost of capital, capital

structure, the firm’s ability to use its stock in acquisitions and pay-for performance policies.

Also, increased volatility could increase risk of default and consequently exacerbate agency

problems between stockholders and bondholders. The effects of stock volatility on these

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issues are clearly of first order importance to the firm’s management and stake holders.

How does the adoption of the Internet by traditional firms change their technological

environment and where exactly lies the source of increased volatility? To take an extreme

example, while a company like Amazon.com (a pure-Internet retailer) is certainly a poster-

child for the ’new economy’, a traditional book retailer like Barnes and Noble (a good example

of the type of firms we study here) is by no means considered a technology-oriented firm.

However, adopting a technological channel like the Internet for commerce, catapults a tra-

ditional firm like Barnes and Noble into a new environment, with characteristics shared by

several ’technology firms’. While the Internet enables traditional firms to complement their

existing marketing channels, its impact on firms has been more profound. First and foremost,

a traditional firm moving online is faced with a new competitive landscape dominated by

online pure-plays1 like EBay, Amazon.com and E*Trade, to name a few. The Internet also

enables traditional firms to offer customized products and services2 and alters their value

creation activities. In addition, lower costs of entry to many businesses and the erosion of

the traditional barriers of space and time differentiate the online channel from traditional

ones. The intense competition fuelled further by lower search costs for consumers, ease of

price comparison, and reduced switching costs, have forced traditional firms moving online

to realign their pricing and product positioning strategies (Viswanthan, 2002). Several tra-

ditional firms including books, CDs, grocery, and consumer electronics retailers, as well as

brokerages, real-estate and insurance services have reduced their prices in the online channel,

faced with competition from Internet firms3. Thus, the initiation of eCommerce alters the

competencies of conventional firms and portends a transition from a ’low-tech’ to a ’high-tech’

environment.

As noted by Klein (1977), changes in technological regime result in greater demand uncer-1It is pertinent to note here that while online startups (’Internet pure-plays’) dominated the early phases

of eCommerce, traditional firms have established a significant presence online in several sectors including

apparel, books, music, computers, consumer electronics, brokerages(Bakos et al, 2003), and travel services.2P&G, Dell, Mattel, McGraw-Hill, Wells Fargo, and Nike are just a few of the diverse group of firms that

have used the Internet to provide highly customized offerings, not available through their traditional channels.3Brynjolfsson and Smith (2000) find that prices for books and CDs are 9-16% lower online compared to

traditional retailers. Brown and Goolsbee (2002) find that term life insurance prices fell by 8-15 percent from

1995 to 1997 due to the growth of online intermediaries.

3

tainty in the product markets due to a turbulent industry structure and changing consumer

tastes. In addition, new technologies trigger rampant experimentation, by both companies

and customers and more so in the initial stages of adoption and growth. Rapid technological

changes in the automobile industry in 1930’s as well as the PC industry in the 1980’s have

led to periods of industrial turbulence characterized by high entry and exit rates, as well

as rapidly falling prices (Mazzucato, 2002). Just as the ’competence-altering’ technological

developments in these industries led to market share instability, the adoption of the Internet,

a new technology-driven channel, increases turbulence in the product markets of traditional

firms. The reconfiguration of existing industry structure by the addition of a new marketing

channel to existing ones is thus expected to have a direct impact on the risk-return profile

of firms. These effects are more prominent in retailing sectors characterized by thin profit

margins. The adoption of eCommerce by traditional firms thus, provides us an excellent

setting in which to examine the link between real activity within the firm and stock return

volatility, a issue that has received little attention (Schwert, 2002).

In this paper, we study the effect of initiation of eCommerce by existing ’brick-and-mortar’

retailers on their total as well as idiosyncratic volatilities, after controlling for changes in

market volatility. We find a significant and long term increase in both volatility measures

associated with firms moving online. More interestingly, we find that this increase in volatility

occurs only for firms that moved online after the growth spurt in Internet related activities,

post-June 1998 - a phenomenon attributable to financial markets’ recognition of the potential

of the Internet to impact a traditional firm’s business. This post-June 1998 surge in firm-level

volatility parallels Schwert’s (2002) findings of an increase in market volatility of technology

intensive firms after mid-1998.

We use three distinct methodologies to examine the impact of the event on both the total

(systematic and idiosyncratic) as well as idiosyncratic volatility of a firm. The first method

involves comparing the post-event average volatility ratio to the pre-event average volatility

ratio4. Using this methodology, we find that firm-level volatility (after controlling for market

volatility) increases significantly immediately following the event. The second method applies4Volatility ration refers to the ratio of firm volatility to market volatility and is a simple device to control

for any changes in firm volatility due to changes in market volatility rather than the event.

4

the structural break analysis technique of Bai, Lumsdaine and Stock, (1998)5 to detect a break

in the total volatility series around the event. We find that the break occurs around the event

date, confirming our earlier results. The third method is a formal volatility event study and

closely follows the methodology of Hilliard and Savickas (2000). This analysis shows that the

event of announcing an eCommerce initiative leads to an unambiguous and sharp increase in

the idiosyncratic volatility of firms in our sample.

We then examine the source of this increased volatility and also perform a series of

robustness checks. We postulate that increased demand uncertainty (stemming from changes

to the firm’s technological environment) results in uncertain profit opportunities leading to

increases in stock return volatility. However, it is possible that that the increase in volatility

around the event date that we uncover is related to other firm specific characteristics such as

firm size, historical stock price performance and historical volatility, rather than to product

market initiatives. In order to control for these effects we construct two matched samples.

The first sample consists of firms matched on size and historical stock price performance

while, the second sample has firms matched on size and historical volatility performance.

We examine the volatility behavior of the two matched samples and find that firms in the

matched samples do not experience the increase in volatility that we document for our test

sample. This suggests that firm specific factors are not likely to be the driving influences of

our results.

A large body of empirical literature documents a positive relation between trade size or

number of transactions and stock return volatility (See Karpoff, 1987 for a survey). In order

to ensure that our results are not simply an artifact of increased trading volume and number

of transactions, we compare the post-event to pre-event trading volume and number of trans-

actions, for all the three samples. We find that while all three samples experience similar

increases in their trading activity measures over time, their volatility increases are substan-

tially different, as noted earlier. Thus, we conclude that the observed volatility increases in

our test sample cannot be attributed to market microstructure variables.

A second robustness check is to use a completely different metric of volatility and see if

our results survive. Following Schwert(1990), we use the implied volatility of at-the-money5This method is also used by Bekaert et al. (2002) to date the integration of world equity markets

5

call options as an alternative volatility measure. We track the monthly series of these implied

volatilities for firms in our test sample over a period of 4 years around our the event dates.

Many studies have shown that close-to-the-money option prices convey the most information

about the expectations of the options market concerning future volatility (Day and Lewis,

1988). We find that implied volatility increases after the event for firms in our test sample,

confirming our earlier results. We substantiate our earlier claim that these volatility increases

are driven by increased demand uncertainty in the product markets by an empirical analysis

of this relationship. We construct two alternative measures of demand uncertainty (based on

quarterly sales data of sample firms in the 1990-2002 period) and find that these measures

are strongly associated with increases in stock return volatility. Based on this cumulative

evidence, we conclude that increased demand uncertainty in firms’ product markets stemming

from changes in their technological environment due to eCommerce initiatives is the driving

force behind the observed volatility increase.

We also perform a traditional event-study analysis controlling for event-induced variance,

and find that the firms under consideration also experience significant positive abnormal

returns attributable to their adoption of eCommerce. More interestingly, we find that these

positive abnormal returns only for firms that moved online after June 1998, a finding that is

similar to the results of our earlier volatility analysis.

The rest of the paper is organized as follows. In Section 2, we review the literature directly

related to this study. In section 3, we briefly note the growing importance of eCommerce

in today’s economy. We state our null and alternative hypotheses and advance economic

arguments to support them. Section 4 describes our test data sample. Section 5 describes

the empirical methodologies used. Section 6 describes the main results. Section 7 concludes.

2 Literature Review

Our study is primarily concerned with firm-level (total and idiosyncratic) volatility6. To-

tal stock return volatility can basically be decomposed into two components: systematic

volatility - the component of volatility that can be explained by common market or industry-6A related stream of research (for instance, see French, Schwert and Stambaugh (1987) and Schwert (1990))

examines aggregate stock market volatility and finds that aggregate volatility varies over time.

6

specific factors; and unsystematic or firm-level idiosyncratic volatility. Firm-level idiosyn-

cratic volatility, in particular, is important for large holders of individual stocks (who are

restricted from diversifying), and arbitrageurs who trade to exploit the mis-pricing of an

individual stock. Firm-level volatility is also important for pricing options and in event stud-

ies, as the significance of abnormal event-related returns is determined by the volatility of

individual stock returns relative to the market or industry (Campbell et al., 2001).

As noted by Campbell et al.(2001), there is surprisingly little research on firm-level volatil-

ity, in contrast to research on market volatility. Their study documents a dramatic increase

in firm-level idiosyncratic volatility over the last three decades (compared to industry and

market volatility), phenomena they believe is attributable to technological changes. How-

ever, they highlight the need for a more detailed investigation of the drivers of increased

volatility. They also note that most volatility studies present a statistical description rather

than a structural economic model. In keeping with these observations, our paper focuses pri-

marily on the impact of firms’ adoption of eCommerce, an economically significant event, on

total and firm-specific stock volatility. Also, as noted earlier our study complements Schw-

ert (2002), who finds that the value-weighted portfolio of Nasdaq stocks shows unusually

high volatility since mid-19987. Of particular interest, is Schwert’s (2002) conclusion that

the unusual volatility of large Nasdaq firms since mid-1998 is attributable to the fact that

these firms belong to the technology sector, rather than to firm size. Our paper adds to

these findings, by identifying changes in firms’ product markets (that stem from changes in

the firms’ technological environment) as being the primary source of the increased volatility.

The results of our study are also in line with the recent findings of Mazzucato (2002), that

stock prices are the most volatile during periods of high market-share instability and radical

technological change using data from automobile and Computer industries. Likewise, the

adoption of eCommerce with its potential to radically change the technological environment

of well-established traditional firms and consequently their product markets, also significantly

affects their financial volatility.

Our paper is also closely related to a number of studies that examine the impact of various7This finding contradicts earlier research, where smaller NYSE stocks were found to be more volatile than

larger NYSE stocks.

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corporate events on firms’ stock return volatility. Clayton et al (2000) analyze the impact

of CEO turnover on firm’s return volatility and find that forced turnovers lead to greater

volatility increases compared to voluntary ones. In contrast to the traditional volatility

studies’ focus on capital structure or management changes within the firm, our primary

concern here is the effect of changes in a firm’s product market resulting from changes to its

technological environment, on its risk-return profile. Mazzucato and Semmler (1999) consider

changes in industry life-cycle in the US auto industry and relate it to stock price volatility.

In particular they relate changes in market shares of firms aggregated at the industry level

to the excess volatility implied by a dividend discount model and find them to be correlated.

Another related stream of research is on the interaction between financial structure and

product market (real activity) decisions, an issue that has received little attention. Recent

theoretical models by Brander and Lewis (1986), Maksimovic (1990,1995), Bolton and Scharf-

stein (1990) have formalized ways in which industry product markets may be influenced by

corporate financial decisions. Phillips(1995), and Chevalier (1994) have also attempted to

establish the relationship between financial structure choices of firms and their correspond-

ing product market outcomes at the industry level. Demers and Lewellen (2003) show the

marketing benefits of IPO underpricing for a sample of Internet firms. These papers are

concerned with the effect of financial decisions on product markets. This paper complements

this growing body of literature by providing evidence of the reverse interaction - viz. the

influence of product market decisions of firms (the adoption of eCommerce) on their financial

structure, more specifically on their stock return volatility . In particular, we use the advent

of eCommerce as an unique exogenous event that impacts traditional firms in multiple ways.

The primary issue in the context of asset price volatility relating to the Internet and tra-

ditional brick-and-mortar firms, is “excess volatility”. Excess volatility is generally defined

as the volatility in prices that cannot be explained by the fundamentals alone. It is tradition-

ally considered synonymous with the market irrationality. Shiller (1981) and Black (1986)

are two notable papers in this area. A large empirical literature followed and it broadly

concludes that volatility is primarily driven by trading. In particular, changes in trading

patterns, increasing institutionalization of equity ownership, increase in day-trading, among

other factors affecting investors’ discount rates are also believed to influence idiosyncratic

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volatility (Campbell et al, 2001). In contrast, this paper argues that fundamental factors

such as adoption of a new technology-driven marketing channel, that increase uncertainty

in product markets, rather than market microstructure variables, can also lead to increased

volatility. This rational explanation for increased volatility has not been empirically analyzed

before.

Finally, another related branch of literature is one that studies the impact of information

events in the Internet sector on asset prices. Most of the existing studies in this context

are returns event-studies i.e., they examine abnormal returns attributable to specific events.

While we find that firms in our sample experience significant positive abnormal returns when

they move online, ours is the first study to investigate the impact of moving online on the

firms’ stock price volatility. Cooper, Dimitrov and Rau (2001) investigate the stock price

response to an event in which a firm just changes its name to an Internet related name such

as a “dotcom”. They find that relative to an Internet matched portfolio, these firms earn, on

average, 53% excess returns around the event. A few papers, e.g. Conell and Liu (2000) and

Lamont and Thaler (2000) look at the stock price response to equity carveouts where the

subsidiary is a public company in the “new economy” (i.e. the Internet sector). They find

that in over 10 such cases, the marked to market value of the shares of the subsidiary held by

the parent exceeded the entire parent’s market value! Ofek and Richardson (2001) provide

a comprehensive survey of how the fundamentals and events in the Internet sector impact

asset prices. In addition to the common context of eCommerce, our study is related to Ofek

and Richardson (2001) to the extent that they investigate, among other areas, (1) volatility

of asset prices and (2) response of stock price to information-based events. Our study is also

related to Perotti and Rossetto (2000) who theoretically investigate the impact of demand

uncertainty in product markets on firm’s profits for the purpose of valuation of Internet

portal firms as a portfolio of entry options. However, a significant point of departure is that

most of the above Internet-related studies focus primarily on ’pure Internet’ firms. While

the dominance of pure Internet firms in the early phases of eCommerce generated a lot of

interest, we believe that the Internet’s impact on traditional brick-and-mortar firms is much

more profound. Consequently, we study traditional brick-and-mortar retailing firms that

have chosen to embrace the Internet as an additional marketing channel for their products

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and services and the impact of such choices on their risk-return profiles.

3 The Hypotheses

While several online start-ups failed to live up to the hype, online sales have nevertheless

been growing steadily. According to the government Department of Census, online sales in

the US accounted for $5.5 billion, $9.4 billion and $11.2 billion in revenues in the fourth

quarter of 1999, 2000 and 2001 respectively, a 43% cumulative annual growth rate. Recent

surveys by Ernst and Young8 indicate that by the year 2005, online retailing would account

for more that 10% to 12% of sales in categories such as apparels, accessories and toys and as

much as 20% to 25% of total sales in categories such as books, music, software and consumer

electronics. Also, nearly two-thirds of consumers surveyed had purchased products online in

the last 12 months and more importantly, more than half of these purchases would normally

have been made in retail outlets.

Although online sales have been growing steadily, the launch of Internet retailing oper-

ations may or may not be a significant event for an existing traditional retailer. It can be

argued that the revenues from Internet retailing are likely to be just a fraction of the total

revenues from retail sales (more so in the early phases of eCommerce). Consequently, share-

holders’ expected returns and risks may not be significantly affected by a traditional firm’s

adoption of eCommerce, and the event under consideration may be inconsequential to the

stock volatility, at least in the short run. Thus, our Null hypothesis in this study is as follows:

The commencement of eCommerce operations by a traditional firm has no significant impact

on its stock return volatility.

While Internet retailing is still in its infancy, it is expected to grow quickly keeping pace

with the rapid growth of the Internet and consequently affording new profit opportunities for

firms. However, as highlighted earlier online retailing in an emerging technology-driven chan-

nel is also fraught with risks. Although the volume of online retailing is small compared to

traditional retailing, even a small shift of sales to the online channel in the future is expected

to have a very significant impact on the revenues and profitability of traditional firms (Ernst8Source: Global Online Retailing: An Ernst and Young Special Report, 2001.

10

and Young Global Online Retailing, 2001). As noted by Lusch (1995), retailers usually face

a break-even point of 85 to 92 percent of their sales which suggests that even a modest drop

in sales volume due to increased competition online, a retailer would incur significant losses.

Thus the adoption of eCommerce places traditional firms in a new and uncertain environ-

ment, exposing them to fresh competitive forces9. This leads to our alternative hypothesis

viz. launching of eCommerce operations is a significant event in a firm’s life because (i) it

places the firm in a new environment that offers a lot of potential for growth and (ii) it

increases risks stemming from increased competition and price-wars. This implies that the

adoption of eCommerce should have an immediate impact on stocks’ expected returns and

risks, as shareholders re-evaluate the firm’s risk-return tradeoff.

We thus postulate that adopting a new technology-driven channel and operating in new,

unexplored markets is associated with higher uncertainty of product demand. Higher demand

uncertainty leads to higher variance of profits and higher perceived risk by the stockholders,

resulting in higher volatility of stock returns. A short formal derivation (Refer Appendix

A) illustrates how increased demand uncertainty in the product markets leads to a higher

volatility of profits.

To sum up, there are two competing hypotheses about the impact of Internet-retailing

on the firm’s stock price volatility and their resolution is an empirical matter. We use a

combination of event study methodology, structural break methodology and the study of

event effects on unsystematic volatility to examine this question.

4 Data

An event in the context of this study is the announcement of an online retailing initiative by a

traditional brick-and-mortar firm. The online retailing initiative is defined as the launch of a

Web-site that enables consumers to conduct online retail transactions. Thus, we disregard the

firms that launched just an informational Web-site without any transaction capabilities. The9As noted by Porter(2001), the paradox of the Internet is that its very benefits - making information widely

available; reducing the difficulty of purchasing, marketing and distribution; allowing buyers and sellers to find

one another and transact business with one another more easily - also make it more difficult for existing

companies to capture those benefits as profits, due to heightened competition.

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announcement dates were collected from leading news sources viz. PR Newswire, Business

Wire, Hoover’s Online and the Lexis/Nexis database. All the firms included in the sample

are those whose stocks are publicly traded and listed on NYSE or NASDAQ. The sample

of firms so obtained was further restricted to satisfy several criteria. We excluded all firms

which did not have a two years history of publicly listed stock price prior to the event date.

We also omitted firms whose announcements coincided with other events with potentially

confounding effect on stock prices, e.g. earnings announcements or announcements about

alliances and mergers. Further, since the focus of the study is on business-to-consumer

segment of eCommerce activity as the end consumer of the product is clearly defined, we

omitted announcements regarding business-to-business eCommerce. Our final sample consists

of 166 firms with event dates spread over the years 1995 to 2000. The year-wise breakup of

these dates is given in Table I, Panel A. This table shows that event dates are sufficiently

spread out over time and that clustering of events is unlikely to be a significant issue in the

study. However, two thirds of the firms announced their online initiatives post June 1998.

Table I, Panel B shows the industry wide distribution of the firms in the sample. The largest

group of firms belong to the Computer Hardware and Consumer Electronics sector, followed

by Speciality Retailers. The daily stock price data for each firm in the sample is taken from

Center for Research in Security Prices (CRSP) daily database. Our proxy for the market

return is the equally weighted return on S&P500 index and the data for this index also comes

from CRSP files.10 In order to analyze the implied volatilities of at the money call options

for our sample firms, we use the Option Metrics database.

5 Empirical Methodology

In this paper, we use three distinct methodologies to detect if the event under consideration

has an impact on firm-specific volatility. The first methodology involves comparing the

pre-event and post-event volatilities of stock returns, after controlling for the overall market

volatility in the corresponding periods. Next, we use the methodology of Bai, Lumisdaine and

Stock (1998) to detect a structural break in the average volatility series. The average volatility10Using value weighted return on the index, produced qualitatively similar results

12

series is constructed by taking the cross-sectional average of stock volatilities in event time.

The formal test for structural break uses a Wald statistic to search for the break in the series

around event date zero, and is used to confirm the above event-study results. The third

methodology is designed to measure the impact of an event on the unsystematic volatility

of a firm. Similar to a traditional returns event-study, it involves estimating the parameters

of a market model of security returns over an estimation window. The market model is

augmented by assuming a parametric model for the evolution of volatility. The estimated

parameters are then used over an event window to determine the impact of the event on

unsystematic volatility. The following paragraphs describes these different methodologies.

5.1 Comparison of pre-event and post-event volatilities

Following the standard event study methodology, we realign the stock returns of the sample

firms in event time. We estimate the firm’s stock return volatility σ2i over two different

windows viz. (1) the pre-event window [−L, 0] and the (2) post-event window [0, L], where L

is the length of each window and 0 represents the event date. Four different window lengths

are considered (three months, six months, one year and two years). Market volatility σ2m is

also estimated over the same two windows. The simplest way to estimate the volatilities is

by the sample standard deviation of daily returns over the relevant windows. This method of

estimating volatility of returns has been used in a number of studies, e.g. Schwert (1989).11.

Next, we compute the volatility ratio λ, defined as,

λ =

√σ2

i

σ2m

(1)

The impact of the event on firm volatility, after controlling for any changes in the market

volatility, can be studied by comparing the volatility ratio λ over the pre-event and post-event

windows. Similar to standard event studies, the firms in the sample are stacked together in11French, Schwert and Stambaugh (1987) consider an alternative estimator for volatility, which adjusts

for autocorrelation in returns that includes sum of the products of adjacent returns, and apply it on stock

portfolios. As they note, this modification has little effect on their results. However in our case if the

autocorrelation of individual stocks is less than -0.5, it causes the volatility estimate to become negative and

hence we use the simpler estimator. This effect never happens with portfolio volatility.

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event time and the tests are then performed on cross-sectional average λs. This methodology

has been used in earlier volatility event studies. For instance, Clayton et al. (2000) use this

methodology to examine the impact of CEO turnover on equity volatility12.

Any differences in the average λ between the pre-event and post-event windows can be

formally examined with a simple t-test or a non parametric Wilcoxon test. In addition, we

also confirm our results using a more sophisticated econometric technique viz. the test for

the location of structural break in a time series. The methodology for detecting a structural

break in a time-series was pioneered by Banerjee, Lumsdaine, and Stock (1992) (BLS1) and

Bai, Lumsdaine, and Stock (1998) (BLS2). These papers contain two key observations viz.

(i) that tests can be constructed to determine whether or not a structural break occurred

in a given time series, (ii) that confidence intervals can be computed enabling inference

about the break date. They demonstrate this for both stationary vector autoregressions and

cointegrated systems. The details of this methodology are provided in Appendix B.

Under our null hypothesis, there should be no structural break in the total volatility series

around the event date, while such a break will be expected under the alternative hypothesis.

In order to conduct this test, we calculate a time series of average monthly volatility,(defined

as the sample standard deviation of daily returns over the relevant month) for two years prior

to and after the event date. The firm volatilities are then averaged cross-sectionally for each

month in event-time. The BLS methodology is then used to identify a structural break in

the average volatility series.

5.2 Event Induced Unsystematic Volatility

We also study the event’s effect on the firm’s unsystematic volatility using the methodology

proposed in Hilliard and Savickas (2002) (hereafter HS). HS specify a market model for

security returns, to separate the systematic and unsystematic components of volatility. They

postulate the following diffusion processes for the instantaneous market return m and its12The volatilities themselves may be estimated in alternative ways. One possible approach is to use option

implied volatilities. Mayhew (1995) reviews event studies which use implied volatility. We present results using

implied volatility later. Another possible approach to volatility estimation is to use a parametric technique

like GARCH. We also repeated our analysis using GARCH(1,1) volatilities with very similar results.

14

volatility Vm,

dm = µmdt +√

VmdZm; dVm = (ωm −ΘmVm)dt + bmVmdZvm (2)

where dZm ∼ N(0, dt), dZvm ∼ N(0, dt) and Corr(dZm, dZvm) = 0. The instantaneous

security return p of a firm is given by the market model,

dp = αdt + βdm +√

VεdZε, dVε = (ωε −ΘεVε)dt + bεVεdZvε (3)

where dZε ∼ N(0, dt), dZvε ∼ N(0, dt) and Corr(dZε, dZvε) = 0.√

VεdZε is the unsystematic

volatility of the firm.

The estimation of model parameters follow traditional event studies. Unlike the usual

return models, the estimation of above volatility models is complicated by two factors (a)

the volatilities are unobserved and (b) the models are in continuous time, while the data

are observed in discrete time. HS overcome these problems by using a discrete stochastic

volatility model (a filter) that converges to the above continuous model as time interval

shrinks to zero. Their filter is based on the general result in Nelson and Foster (1994) who

show that the optimal filter for the diffusion,

dx = µdt + σdW1,

dσ2 = (ω −Θσ2)dt +√

2aσ2VmdW2, (4)

where

dW1 ∼ N(0, dt), dW2 ∼ N(0, dt)

and

Corr(dW1, dW2) = 0

is,

xt+∆ − xt = µ∆ +√

∆ξt+∆; ξt+∆ ∼ N(0, yt),

yt+∆ = ω∆ + (1−Θ∆−√

∆a)yt +√

∆aξ2t+∆. (5)

15

Nelson and Foster (1994) prove that the discrete time model in (5) converges in distribu-

tion to the continuous time model in (4) as ∆ shrinks to zero. The discrete equations above

can be written as the following GARCH(1,1) model,

Xt+1 −Xt = c + ηt+1; ηt+1|Ωt ∼ N(0, ht+1);

ht+1 = a0 + a1η2t+1−i + b1ht+1−i (6)

with c = µ∆, a0 = ω∆2, a1 =√

∆a, b1 = (1−Θ∆−√∆a) and ht+∆ = ∆yt.

The parameters of the GARCH model over a window can be easily estimated using the

method of maximum likelihood. Based on these estimates, we can then derive the estimates

of the parameters of the original diffusion process.

Following the above methodology, we first estimate the parameters of the diffusion process

(2) for market returns over the estimation window. We then estimate the parameters of the

continuous time market model for each firm (3). The betas in (3) are estimated separately by

running OLS regression of firm’s returns on the market returns over the estimation window.

The second part of this methodology involves hypotheses testing over the event window

using parameter estimates from the estimation window. HS introduce a parameter, the

multiplicative abnormal volatility to measure the impact of a given event on unsystematic

volatility Vε.(To avoid notational confusion we use ψ instead of the λ used by HS). The

parameter ψ measures the multiple by which unsystematic volatility increases from its no

event level, due to the event. Thus, if ψ = 1, the event has no effect on Vε; ψ > 1 implies

a volatility increase due to the event and ψ < 1 implies a volatility decrease due to the

event. A particular event may have a different value of ψ for each day in the event window

which is denoted by a time subscript t on ψ. HS show that the estimate of ψt for each

day t in the event window can be obtained by computing the cross-sectional variance of the

standardized GARCH(1,1) residuals, ηt in equation (6). The cumulative abnormal volatility

Cψk,m between event days k and m is the sum of daily abnormal volatilities over these days.

Cψk,m =m∑

t=k

ψt (7)

16

As HS illustrate, the null hypothesis regarding the effects of an event on volatility can be

expressed in terms of either ψt or Cψk,m. In terms of ψt, it is,

H0 : ψt = 1 (8)

and, in terms of Cψk,m, it is,

H0 : Cψk,m = m− k + 1 (9)

HS show that this hypothesis (8) can be tested using a test statistic st ≡ (N −1)ψt which

is distributed χ2N−1 under the null. Similarly, hypothesis (9) can be tested using the test

statistic Csk,m ≡ (N − 1)Cψk,m which is distributed χ2(N−1)(m−k+1) under the null. If the

observed value of the test statistic exceeds the critical value, the null hypothesis is rejected

and we can conclude that the event has a statistically significant (daily or cumulative) impact

on the (daily or cumulative) unsystematic volatility of stock returns.

6 Results

6.1 Comparison of pre-event and post-event volatilities

First we present the results of our analysis using the simple methodology of comparing the

pre-event and post-event volatility ratios λ ≡√

σ2i

σ2m

. We use the pre-event and post-event

windows of 3 months, six months, one year and two years. The resulting pre-event and

post-event average λs are reported in Table II, Panel (A). It is interesting to note that the

average λ jumps significantly from pre-event to post-event period. The ratio of post-event

to pre-event average λ’s varies between 1.28 to 1.07. The jump is statistically significant at

the 1% level using a standard t-test and a wilcoxon test. This indicates that moving online

is associated with a significant volatility increase. We then split the sample into two parts,

one with event dates prior to June 1998 and the other with event dates post June 1998. The

analysis of the two split samples is presented in Table II, Panels (B) and (C). Results show

that firms with event dates prior to June 1998 have no increase in the volatility associated

with the event and the volatility increase for the overall sample can be attributed to the firms

which moved online post June 1998.

17

The results highlight the changing perceptions of the financial markets about the potential

impact of the Internet on a firm’s business. This shift in market perceptions coincides with

the spurt in the growth of the Internet. The growth of the Internet is significantly higher in

the post-June 1998 period compared to the pre-June 1998 period as measured by the growth

in the number of Websites in existence. Figure 1A illustrates the growth of Web-sites over

the past several years13. We observe an exponential growth in the number of Web-sites in the

post-June 1998 period. The difference between the two periods is also seen in Figure 1B which

shows the evolution of AMEX Internet index over the two sample periods. Schwert(2002)

shows the unusual increase in Nasdaq volatility in the post June 1998 period. Based on all

this evidence, we analyze our sample separately for the pre and post June 98 sample periods.

Figure 2 graphically shows the changes in abnormal volatility around the event date. We

compute the monthly λ of each stock (using daily returns) for 24 months before and after the

event. Individual λs in each month were then averaged cross-sectionally in the event time.

The time-series of average λ are plotted in Figure 2. The notable result is the spike in λ at

the event date, which can be seen in Figure 2(A). This again confirms our results in Table

II that the event is associated with a surge in volatility. The corresponding graphs for two

sub-samples and the results are in Figure 2(B) and 2(C). These figures confirm that firms

which moved online prior to June 1998 did not experience a volatility increase, in marked

contrast to firms that moved online post June 1998.

Next, we use the methodology of Bai, Lumisdaine and Stock (1998) to detect a structural

break in the average firm volatility series. The volatility of a stock is computed as the 12-

month rolling sample standard deviation of monthly returns following Officer (1973) and

Merton (1980). The average volatility series is constructed by taking the cross-sectional

average of stock volatilities in event time. The average volatility series for the full, pre-June

1998 and post-June 1998 samples are shown in Figures 3(A),(B) and (C) respectively. The

pattern here is similar to the one seen in Figure 2 with the λ series. In the overall sample,

we observe a surge in volatility at event date, which is caused solely by the events after June

1998. The formal test for structural break using a Wald statistic confirms this result (Table

III). Figure 3 (D),(E) and (F) show the time series of the Wald statistic for the full sample,13 c©Robert H. Zakon, adapted with permission from http://www.zakon.org/robert/internet/timeline/

18

pre-June 1998 sample and post-June 1998 sample respectively. In the overall sample, as in

the post-June 1998 sample, we see a sharp rise in the Wald statistic around the event date.

The 1% confidence band of the break date is around the event date and is quite narrow,

covering a period of only about 3 months and includes the event date (month 0) for the full

sample and the post-98 sample. This is very clear evidence of a break around the event date

for the average volatility series. It can be seen that the pre-1998 sample shows a structural

break in average volatility 3 months before the event and thus, does not have any change in

volatility associated with the event. This analysis confirms the above event study results.

It is possible that our sample of firm experience increases in volatility for reasons unrelated

to their launch of eCommerce operations and our tests detect just these increases. In other

words, the volatility increase and the event of moving online could both be jointly caused by

other unobservable factors. To rule out this possibility, we repeat the volatility analysis in a

matched sample of firms.

We construct a return-matched sample of firms as follows. For each firm in the test

sample, we find a matched firm satisfying two criteria viz., (a) it is in the same NYSE

market capitalization decile as the firm in the test sample and (b) in this size decile, it is

the firm which has a compounded return in the two years prior to the event date which is

the closest to that of the test firm. This methodology closely follows that recommended by

Barber and Lyon (1996). If the matching firm thus selected is found in our sample or if it is a

’pure Internet’ firm or has operations in an Internet-related domain, we select the next-best

match. Size and past returns are well known to be among the most important influences on

the volatility of individual stocks (see, Duffee, 1995). Thus, a control by size and past returns

should be able to account for volatility changes not related to the event under consideration.

We also construct a volatility-matched sample of firms as follows. For each firm in the

test sample, we find a matched firm which satisfies two criteria viz., (a) it is in the same

NYSE market capitalization decile as the firm in the test sample and (b) in this size decile,

it is the firm which has a natural logarithm of the ratio of the sample standard deviation for

year -1 in event time to the sample standard deviation for year -2 (where 1 year consists of

250 trading days) that is the closest to that of the test firm. If the matching firm is found

to be in our sample or if it is a ’pure Internet’ firm or has operations in an Internet-related

19

domain, we select the next-best match as earlier.

The results of the above analysis for the matched sample of firms appear in figures 2 and

3. Figure 2 depicts the monthly average λs for the return and volatility matched samples. No

spike in average λ is seen at the event date for these two samples either in the pre-98 or the

post-98 period. Figure 3 confirms this pattern by constructing a rolling volatility series, even

though the general level of volatility is higher in the post-event period than in the pre-event

period for all the 3 samples. 14.

The contrasting results found with our test sample of firms and the matched sample

confirm that the volatility surge around the event cannot be attributed to other well known

determinants of stock return volatility viz. past returns, past volatility run up and firm size.

Since we have already omitted other firm specific announcements and events which could

have a confounding effect on the stock volatility, we conclude that the observed surge in

volatility can be attributed solely to the commencement of eCommerce operations. We now

proceed using the HS methodology to confirm our above findings.

6.2 Event Induced Unsystematic Volatility-Results

The results for this analysis are summarized in Figures IV (A),(B) and (C), which show

the estimated cumulative abnormal volatility Cλt over the event window for the test sample,

return matched sample and the volatility matched sample. They also illustrate the cumulative

abnormal volatility that would be expected under the null hypothesis of no event induced

volatility, given the values of the parameter estimates. The same results are summarized

in a tabular form in Table IV, which shows the values of abnormal volatility λ cumulative

abnormal volatility Cλ for the event window. The event window covers the period [-26,+25]

days around the event date.

Figure IV(A) shows the results for the entire sample. First we note that the cumulative

abnormal volatility drifts up (compared to its expected value under the null) for the test

sample as well as for the two matched samples. This is also reflected clearly in Table IV(A).

Note that under the null hypotheses (8) and (9) above, the value of λ should be 1 on each day14The individual stock volatility has increased much more in recent years compared to the market volatility.

See Campbell, Lettau, Malkiel and Xu (2001) for a detailed analysis.

20

and the value of Cλ should start at 1 on day -26 and increase by 1 for each day over the event

window. The p-values of the test statistic for testing the null hypothesis (9) of no event effect

on cumulative abnormal volatility is also reported. The null is resoundingly rejected for all

the three samples on all days except day -26. This reflects that, in general, the idiosyncratic

volatility of stocks has risen over time in the past few years. This is also consistent with the

findings in Campbell et al.(2001) that the unsystematic volatility of individual stocks have

risen over the last few decades.

Although all three samples show an increase in unsystematic volatility over time, the

impact of the event is evident from figure IV(A). The cumulative abnormal volatility surges

significantly at the event date for the test sample only, but not for the two matched samples.

The deviation of the cumulative abnormal volatility from its value under null is also the

maximum for the test sample. These results indicate that while the statistical tests proposed

by HS show a significant increase in unsytematic volatility for all three samples on the event

date, the event clearly has the strongest effect on unsystematic volatility for the test sample.

The test sample is then split into two - sample 1, consisting of firms with event dates

prior to June 1998 and sample 2 consisting of firms with event dates after June 1998. The

results for these 2 samples are shown in Figures IV(B) and IV(C)respectively15 and also in

Panels (B) and (C) of Table IV. We find in sample 1, that the event had no effect on the

unsystematic volatility a result that confirms our earlier analysis. The entire effect that we

observe for the full sample is due to sample 2.

6.3 Additional Tests for Robustness

A large body of empirical literature documents a positive relation between trade size or the

number of transactions and stock return volatility (See Karpoff(1987) for a survey). In order

to ensure that our results are not simply an artifact of increased trading volume and number

of transactions for the test firms, we compare the post-event to pre-event trading volume and

number of transactions for all three samples. The results in Table V suggest that all three

samples in our study had statistically significant and the same order of magnitude increases15In the estimation, we dropped the firms whose volatility process parameters showed the presence of

non-stationarities. Hence, the number of firms are unequal across the samples.

21

in their trading activity measures over time, whether measured as volume or number of

transactions, while their volatility experience as noted above, was very different. We therefore

conclude that increases in trading activity cannot be an explanation for increases in volatility

in our eCommerce sample.

Finally, as corroborative evidence, following Schwert(1990) we track the monthly series

of implied volatility of at-the-money call options for firms in our eCommerce sample over

a period of 4 years around our event date. We obtain the data from the Optionmetrics

database. Many studies have shown that close-to-the-money option prices convey the most

information about the expectations of the options market concerning future volatility (Day

and Lewis, 1988). The results are presented in Table VI and figure 5. We find that implied

volatility increased for our eCommerce sample after the event of moving online, confirming

our earlier results. This increase is statistically significant at the 5% level or higher.

We also provide additional evidence for higher demand uncertainty relating to the Internet

and more specifically to eCommerce. In addition to earlier discussions, the evidence that

eCommerce activity is associated with a higher uncertainty in product demand comes from

several sources. First, the US Census Bureau data of Internet-retail sales versus overall retail

sales are reproduced in Table VII, Panel A . These data show that Internet sales have been

far more volatile compared to traditional sales. The coefficient of variation was 22.7% for

Web-sales and only 5.6% for the total retail sales16. Table VII, Panel B shows the changes in

annual sales (both total retail sales as well as eCommerce sales) across different sectors. On

an average, Internet-driven sales rose by 89% between 1999 and 2000, compared to a 7.1%

increase in overall sales for the same time period. In addition, we also find that the agencies

which forecast eCommerce activity (such as online advertising spending by US firms) have

widely different views about eCommerce, reflecting the inherent uncertainties. Table VII,

Panel C provides estimates made in June 2000 by various consulting firms for the period

2000-2002 of Internet advertising spending by US firms to promote their online operations.

While this is not direct evidence on product demand, advertising is certainly an important

component of a firm’s marketing plan that stimulates product demand. As can be seen, there

is wide variation in their estimates reflecting the diversity of expectations about the potential16Coefficient of variation is the standard deviation normalized by the mean.

22

of eCommerce operations. The cross-sectional standard deviation of these projections is 35%

of the mean projection for the year 2000, 52% for the year 2001 and 66% for the year 2002.

17. Taken together, panels A-C of table VII suggest that eCommerce activity is associated

with a higher demand uncertainty compared to traditional retail channels.

Finally we attempt to relate demand uncertainty in the product markets to stock return

volatility. Ideally we would like to construct a measure of demand uncertainty using quar-

terly sales data before and after the introduction of eCommerce operations (using eCommerce

sales in the latter period) and relate it to the increase in stock return volatility. Due to data

limitations we are unable to obtain a long enough time series of sales after the adoption of

eCommerce operations at the firm level to undertake this test. Thus we adopt an alternative

approach and show that the measures of demand uncertainty we construct are positively

related to stock return volatility. This evidence taken together with the evidence from Ta-

ble VII suggests that the increased demand uncertainty due to introduction of eCommerce

operations is associated with increases in stock return volatility.

We use quarterly sales data for firms in our sample for the period 1990-2002 from COM-

PUSTAT (data item 2) to measure demand uncertainty. We posit an AR(1) process to

estimate quarterly sales for each firm in our sample. For each firm the following AR(1)

process for quarterly sales is estimated.

ln(Sales(i, t)) = αi + βi ∗ ln(Sales(i, t− 1)) + εit

where Sales (i,t) refers to quarterly sales for firm i in quarter t. The measure of demand

uncertainty for firm i, UNC1i is the time-series standard deviation of residuals εit from the

above specification. In the second stage a cross sectional regression is run between λi and

UNC1i as follows18.

λi = a + b ∗ UNC1i + ei

We also calculate another measure of demand uncertainty UNC2i. The calculation of UNC2i

follows exactly the same procedure as before except that the AR(1) specification in the first17Source : www.eMarketer.com18λi is calculated for each firm for eight years around the event date, corresponding roughly to the same

period for which AR(1) process is estimated using daily returns. Note that this definition of lambda is different

from the definition used in the rest of the tables.

23

stage is estimated without a constant term for each firm. For the regression White (1980)

corrected standard errors are reported.

Table VIII, Panel A provides the summary statistics of the 2 demand uncertainty measures

UNC1, UNC2and λ respectively. As can be seen, demand uncertainty measures are positively

correlated with stock return volatility. Panel B provides the results of the regression. The

results are shown to be statistically significant. Taken together, the results of tables VII and

VIII indicate a linkage between demand uncertainty stemming from eCommerce initiatives

and stock return volatility.

6.4 Volatility and Abnormal Returns

While the focus of this paper is on examining the impact of firms’ adoption of eCommerce on

their stock price volatility, a natural question that arises is the impact of the event on firms’

stock returns. To this end, we perform a traditional return-event study using a market-model

and a value-weighted index, ensuring to correct for non-synchronous trading using Scholes

William beta as well as for increases in event-induced variance using the method of Boehmer

et al. (1991). The results of the event-study are provided in Table IX. Panel A presents the

event study results for the full sample of 164 firms, for four different event windows, while

Panel B and C present the results for the pre-1998 sample and post-1998 sample of firms,

respectively. As illustrated in Table IX, it can be seen that while firms experience a positive

abnormal return on moving online, these abnormal returns are more pronounced for firms that

moved online post-June 1998, compared to firms that moved online prior to June 1998. These

findings parallel those of our volatility study and suggest a positive relationship between

firm-specific idiosyncratic volatility and abnormal returns. This is particularly interesting in

light of the recent findings by Duffee (2002) who finds a significant positive contemporaneous

relation between stock returns and firm-level idiosyncratic volatility; this positive relationship

being stronger for firms with higher betas and book-to-market ratios. Duffee (2002) theorizes

that shocks to firm value are caused by shocks to asset values, with riskier assets having larger

absolute shocks - a positive (negative) shock to firm value being accompanied by an increase

(decrease) in the value of the firm’s risky assets leading to higher idiosyncratic volatility. In

our study, the onset of eCommerce can be considered to be an exogenous event that is shown

24

to increase both the value of the firm (based on the results of the return event study) and

its idiosyncratic volatility - findings that reinforce Duffee’s (2002)arguments.

7 Conclusions

In this paper, we study the effect of real activity with in the firm on its stock return volatility.

Specifically, we focus on the impact of traditional firms’ adoption of eCommerce on the

volatility of their stock returns. We find that stock return volatility increases when the

traditional firms announce eCommerce initiatives. This increase in firm-level volatility is

detected using three different methodologies and similar abnormal volatility is absent in

samples of firms matched with the test sample. We corroborate this evidence by studying

the implied volatility of at-the-money call options of our test sample which are also found to

increase around the event date. More importantly, we find that the market’s perception of

the significance of the Internet for traditional firms plays a very important role in determining

the impact of the event on risk-return profile of firms. Only firms that moved online post-

June 1998, the period when Internet-related activity reached critical mass, experience a

significant surge in volatility, an effect that is absent for firms that moved online prior to June

1998. This coincides with the findings by Schwert (2002), of an increase in market volatility

of ’technology-intensive’ firms after mid-1998. The adoption of the Internet for commerce

constitutes a significant change in a traditional firm’s technological environment. We find

that the consequence of these developments is increased turbulence in firms’ product markets

as reflected in a higher demand uncertainty, leading to higher volatility of firm’s stock returns.

We also find that the behavior of event-induced positive abnormal returns closely parallels

that of the event-induced volatility increase and suggests a positive relationship between

the two. Further research would be required to examine the contemporaneous relationship

between abnormal returns and volatility increases and to identify factors that moderate the

strength of this relationship. Overall, our results show strong evidence of the impact of real

activity within the firm on its stock return volatility and highlight the need for a better

understanding of the financial impacts of firms’ changes in technological environment and

product market initiatives.

25

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9 Appendix A : A Simple Model of Demand Uncertainty

We begin with the premise that a firm faces a more uncertain product market demand online

than in traditional retailing. The objective of this simple model is to show that such an

uncertainty in the product market can lead to a higher volatility of profits.

The firm faces a stochastically varying inverse demand process θt in the product market,

which is modeled as a diffusion process in continuous time. It maximizes the profit function

π = (θ − Q)Q, where Q is the quantity sold. The optimal profit for a monopoly firm are

π1 = θ2

4 . Let xt = θ2t4 . We can express its evolution as a Geometric Brownian Motion, i.e.

dxt = xt[µ1dt + σ1dWt] (10)

so that,

xt = x0 exp((µ1 − 12σ2

1)t + σ1Wt) (11)

Thus, expected profit is,

π1 = E[∫ ∞

0xt exp(−rt)dt] =

x0

r − µ1(12)

and variance of the profit can be shown to be,

V ar(π1) = E[π21]− [E[π1]]2 = x0

12σ2

1

(r − µ1)2(r − µ1 − 12σ2

1)(13)

Now consider the same firm with eCommerce operations. We postulate two changes to

occur when the firm moves online viz. (1) there is a fixed setup cost I and (2) the demand

function becomes more uncertain, which is captured by a stochastically evolving multiplier

γ. The firm therefore maximizes the objective function γ(θ − Q)Q − I and the maximized

profit process is π2 = γx− I. The evolution of γ is assumed to be given by the process,

dγ = γ[µ2dt + σ2dBt] (14)

where corr(Bt,Wt) = 0.

This implies that,

γt = γ0 exp((µ2 − 12σ2

2)t + σ2Bt) (15)

The expected profit and the variance of profits can be calculated as follows:

π2 = E[∫ ∞

0γtxt exp(−rt)dt]− I =

γ0x0

r − µ1 − µ2− I (16)

V ar(π2) = E[π22]− [E[π2]]2 = γ0x0

12(σ2

1 + σ22)

(r − µ1 −−µ2)2(r − µ1 − 12σ2

1 − µ2 − 12σ2

2)+ 2I2 (17)

30

The ratio of the variances in the two cases is,

V ar(π2)V ar(π1

= γ0[1 +σ2

2

σ21)

][1 +µ2

(r − µ1 − µ2)]2[1 +

µ2 + 12σ2

2

(r − µ1 − µ2 − 12σ2

1 − 12σ2

2)] +

2I2

V ar(π1)(18)

This ratio is always greater than or equal to 1. Thus, the twin effects of higher demand

uncertainty and lower fixed cost of entry implies that the introduction of online operations

by a firm would always lead to an increase in the volatility of firm’s profit or net cash flow

each period. This will translate into a higher equity price volatility as well.

31

10 Appendix B : Structural Break Analysis

Under our null hypothesis, the event has no effect on the volatility σ, i.e. the process gener-

ating the time-series of σ remains structurally unchanged. Under the alternative hypothesis

of event-induced abnormal volatility, the process generating σt time series will have a struc-

tural break at the event date. Banerjee, Lumsdaine, and Stock (1992) (BLS1) and Bai,

Lumsdaine, and Stock (1998) (BLS2) provide an approach for detecting such a break. They

show (i) that tests can be constructed to determine the occurrence of structural break in a

set of time-series data, and (ii) that confidence intervals can be computed enabling inference

about the break date. They demonstrate this for both stationary vector autoregressions and

cointegrated systems. This paper focuses on the stationary case.

BLS2 postulate the following general form of regression relationship among dependent

variable y and vector of independent variables X (equation 2.2 from BLS2):

yt = (G′tIn)θ + dt(k)(G′

tIn)S′Sδ + εt (19)

where yt is n by 1, G′t is a row vector containing a constant, lags of yt, and row t of

the matrix of exogenous regressors, X, In is an n by n identity matrix, dt(k) = 0 for t < k

and dt(k) = 1 for t >= k. θ and δ are parameter vectors with dimension r. For example,

for a first-order vector autoregression with a vector of constants µ and parameter matrix A,

(yt = µ+Ayt−1+εt), θ = vec[(µ,A)] and r = n(n+1). S is a selection matrix containing zeros

and ones, with column dimension r and full row rank (equal to the number of coefficients

which are allowed to change). Note that S′S is idempotent with non-zero elements only

on the diagonal. If S = Ir, then the above model is a full structural change model. For

S = s⊗

In where s = (1, 0, ..., 0) a row vector, the model allows for a mean shift only (See

empirical examples in BLS2). The model (19) allows any or all of the coefficients to change.

More compactly,

yt = Z ′t(k)β + εt, (20)

where Z ′t(k) = ((G′tIn), dt(k)(G′

tIn)S′) and β = (θ′, (Sδ)′)′. If we let R = (0, I) be the

selection matrix associated with β, so that Rβ = Sδ and Σ is the covariance matrix of the

errors εt, then the F-statistic testing Sδ = 0 is

FT (k) = TRβ(k)′R(T−1T∑

t=1

ZtΣ−1Z ′t)−1R′−1Rβ(k), (21)

where β(k) and Σ(k) denote the estimators of β and Σ, respectively, evaluated at k, obtained

as described above. BLS2 show that FT (k) converges in distribution via the functional central

limit theorem to F∗, where F∗ = τ(1 − τ)−1‖W (τ) − τW (1)‖2, where ‖ · ‖ represents

32

the Euclidean norm and now W (.) is a vector of independent standard Brownian motion

processes whose dimension is equal to q, the rank of S. The corresponding distribution can

be approximated by partial sums of normal random variables for each dimension. We use the

table with corresponding critical values for the rank up to q = 50 as given in the appendix

of Bekaert, Harvey and Lumsdaine(2002). By the continuous mapping theorem, the limiting

distribution of maxkFT (k) converges to max F∗. As noted above, we focus on this test

statistic in the empirical work.

The dimension of the test statistic increases with both the dimensionality of the system

and with the number of regressors in the model whose coefficients are allowed to break. As

an example, consider an n by 1 VAR. If the order of the VAR is p and we allow for a break

in all of the coefficients, the relevant dimension of the F-statistic will be n(np + 1).

To conduct inference about the break date, theorem 4 of BLS2 shows that

[δ′T S′S(QΣ−1)S′SδT ](k − k0) ⇒ V ∗, (22)

where V ∗ has limiting density given by equation below (to be filled) and Q = plim 1T

∑Tt=1 GtG

′t.

Similarly, we can invert the limiting distribution to construct confidence intervals for the es-

timated break date, based on allowing any or all of the coefficients to experience a break.

The confidence interval is

k ± α 12π[(SδT )′S(QΣ−1

k )S′(SδT )]−1, (23)

where Q = 1T

∑Tt=1 GtG

′t and k, Σk are estimated values.

Finite sample properties of these test statistics are investigated in BLS2. The tests

are shown to have good size and power properties under the null hypothesis of no break

and the alternative of a breaking mean, respectively. In addition, simulations regarding

confidence intervals confirm that, for fixed parameters, increasing the sample will not affect

the precision of the MLE of k0 but increasing the number of series that experience the same

break does improve precision. In addition, this precision depends on the true value of the

break magnitude.

33

Table I, Panel A : Distribution of Event Dates

This table shows the temporal distribution of the event dates in the sample of 166 firms. The sample consists

of the firms which announced an online trading initiative in business to consumer segment of the market.

Firms with other events in addition to the announcement, which might have potentially confounding effect

on stock prices were omitted.

Year Quarter Number of Firms Year Quarter Number of Firms

1995 Q1 1 1997 Q4 3

1995 Q2 1 1998 Q1 17

1995 Q3 1 1998 Q2 0

1995 Q4 1 1998 Q3 15

1996 Q1 3 1998 Q4 11

1996 Q2 1 1999 Q1 29

1996 Q3 4 1999 Q2 15

1996 Q4 8 1999 Q3 28

1997 Q1 3 1999 Q4 17

1997 Q2 2 2000 Q1 3

1997 Q3 3

Total 166

Table I, Panel B : Distribution of firms by Industry

This table shows the industry wide distribution of the 166 firms in the sample. The sample consists of the

firms which announced an online trading initiative in business to consumer segment of the market. Firms

with other events in addition to the announcement, which might have potentially confounding effect on stock

prices were omitted.

Industry No. of Firms

Software 20

Computer Hardware, Consumer Electronics 29

Banking, Brokerage 21

Media, Publishing, Entertainment, Services 20

General Merchandise Retailers 20

Speciality Retailers 27

Auto, Motorcycles, Hardware, Home Improvement 7

Catalog, Direct Marketing, Auctions, Travel 12

Pharma, Conglomerates, Real Estate 10

Total 166

34

Table II : Pre and Post Event Abnormal Volatility

This table shows the Pre event and Post event abnormal volatility for the firms in the sample. Abnormal

volatility, Average λ is computed as the square root of the ratio of the firm’s stock return volatility σ2i to the

market volatility (S and P 500 equally weighted return) σ2m and averaged over all firms in the sample. This

is computed for (1) the pre-event window [−L, 0] and the (2) post-event window [0, L], where L is the length

of each window and 0 represents the event date. Four different window lengths are considered (three months,

six months,one year and two years). Post/Pre λ is the ratio of Post λ to Pre λ. t-stat and Wilcoxon are the

t-test and Wilcoxon test statistic for the 1-tail test of Post/Pre λ = 1.0 vs. Post/Pre λ > 1.0 respectively

Panel (A) : Full Sample Results (166 firms)

Firms that moved online

Window Pre-event Post-event Post/Pre λ t-stat Wilcoxon

Avg λ Avg λ Mean Median Max Min Std.Dev

3 months 6.43 6.66 1.23 1.09 5.65 0.11 0.71 4.14∗∗∗ 3807∗∗∗

6 months 5.74 6.54 1.25 1.14 3.83 0.14 0.62 5.21∗∗∗ 5215∗∗∗

1 year 4.99 6.16 1.28 1.21 4.05 0.26 0.58 6.16∗∗∗ 7003∗∗∗

2 years 5.24 5.48 1.07 1.00 3.79 0.29 0.39 2.19∗∗ 1247

Panel (B) : First half sample (before June 1998) results (56 firms)

Firms that moved online before June 1998

Window Pre-event Post-event Post/Pre λ t-stat Wilcoxon

Avg λ Avg λ Mean Median Max Min Std.Dev

3 months 6.01 6.26 1.27 1.18 3.03 0.30 0.76 2.70∗∗∗ 502∗∗

6 months 5.75 5.62 1.08 0.92 2.96 0.42 0.54 1.08 44

1 year 5.61 5.36 0.99 0.89 2.38 0.44 0.42 −0.21 -222

2 years 5.74 5.25 0.93 0.90 1.65 0.49 0.22 − 2.26∗∗ − 582∗∗∗

Panel (C) : Second half sample (after June 1998) results (110 firms)

Firms that moved online after June 1998

Window Pre-event Post-event Post/Pre λ t-stat Wilcoxon

Avg λ Avg λ Mean Median Max Min Std.Dev

3 months 6.64 6.87 1.20 1.07 5.65 0.11 0.69 3.13∗∗∗ 1667∗∗∗

6 months 5.74 7.00 1.34 1.20 3.83 0.14 0.64 5.54∗∗∗ 3379∗∗∗

1 year 4.68 6.56 1.42 1.33 4.05 0.26 0.59 7.47∗∗∗ 4749∗∗∗

2 years 4.99 5.60 1.13 1.05 3.79 0.29 0.43 3.24∗∗∗ 1871∗∗∗

∗ ∗ ∗, ∗∗, ∗ - Significant at 1%,5%,10% level respectively

35

Table III: Structural Break Analysis of Volatility

This table reports the estimated break dates k for the structural relation of equity return volatility

σt = µ + Aσt−1 + dt(k)(λ + βσt−1) + εt

Equity return volatility is computed as the 12 month rolling volatility following Officer(1973)and Merton

(1980). The median column in the table shows the estimated break date k in event time for the time series of

average monthly volatility for the corresponding sample. Break dates are estimated using the Wald Statistic

F described in Appendix B. We test the null hypothesis that the post-break coefficient changes are not

significantly different from zero, i.e., that no break occurred in the sample period by comparing the maximum

value in the estimated time series F(k) to the 5% quantile of its limiting distribution. The null hypothesis is

rejected when the maximum value for F(k), reported in the Max-Wald column of the table is higher than the

critical value for the selected significance level. We use the critical values from Bekaert et.al (2002), Table

10 in the Appendix who approximate the limiting distribution of the F process with partial sums of normal

random variables for each possible dimension of the test statistic which is the dimension of S. From that table

we use the asymptotic 1% critical value of 11.81 corresponding to a rank of 1 for vector S. the 2.5th and

the 97.5th percentile columns display the estimated lower and upper bands respectively for the confidence

intervals for the ”true break dates” as per equation (23) with quantiles of the Picard (1985) distribution.

Sample Firms 2.5th Median 97.5th Max-Wald p-value

Percentile Percentile

Full Sample 166 -1 0∗∗∗ 1 54.86 < 0.01

Pre 98 Sample 56 -4 −3∗∗∗ -2 29.24 < 0.01

Post 98 Sample 110 0 1∗∗∗ 2 32.78 < 0.01

∗ ∗ ∗ - Significant at 1% level

36

Table IV : Unsystematic Volatility Induced by the Event

This table shows the estimates of the unsystematic volatility induced by the event and their statistical signif-

icance following the method of Hilliard and Savickas (2002).

Panel (A) : Full Sample Results

Test Sample Matched Sample (Returns) Matched Sample (Volatility)

DAY ψ Cψ p-value ψ Cψ p value ψ Cψ p value

-26 0.861 0.861 0.891 0.889 0.889 0.829 1.076 1.076 0.249

-2 1.350 30.508 0.000 1.136 38.475 0.000 1.112 33.276 0.000

-1 1.311 31.819 0.000 1.948 40.423 0.000 1.173 34.449 0.000

0 21.251 53.071 0.000 1.164 41.587 0.000 1.455 35.903 0.000

1 3.305 56.376 0.000 1.437 43.023 0.000 1.145 37.048 0.000

2 0.889 57.265 0.000 1.225 44.249 0.000 1.300 38.348 0.000

25 1.049 83.290 0.000 1.217 75.682 0.000 1.247 67.031 0.000

Panel (B) : Pre-98 Sample Results

Test Sample Matched Sample (Returns) Matched Sample (Volatility)

DAY ψ Cψ p value ψ Cψ p value ψ Cψ p value

-26 1.109 1.109 0.277 0.820 0.820 0.815 1.139 1.139 0.229

-2 1.065 25.594 0.274 1.177 29.942 0.000 1.271 31.134 0.000

-1 0.841 26.434 0.331 0.710 30.651 0.000 0.729 31.863 0.000

0 1.997 28.431 0.086 1.489 32.141 0.000 1.143 33.006 0.000

1 1.114 29.545 0.074 1.364 33.505 0.000 1.381 34.387 0.000

2 1.147 30.691 0.060 1.256 34.761 0.000 1.375 35.762 0.000

25 0.875 55.888 0.004 0.959 58.796 0.000 1.441 63.622 0.000

Panel (C) : Post-98 Sample Results

Test Sample Matched Sample (Returns) Matched Sample (Volatility)

DAY ψ Cψ p value ψ Cψ p value ψ Cψ p value

-26 0.747 0.747 0.973 1.098 1.098 0.239 1.296 1.296 0.026

-2 1.493 33.122 0.000 1.255 74.087 0.000 1.372 45.972 0.000

-1 1.546 34.668 0.000 2.618 76.705 0.000 1.734 47.706 0.000

0 30.187 64.855 0.000 1.004 77.709 0.000 4.311 52.017 0.000

1 4.489 69.343 0.000 1.723 79.433 0.000 1.587 53.603 0.000

2 0.794 70.137 0.000 1.844 81.277 0.000 1.323 54.926 0.000

25 1.135 96.447 0.000 1.578 136.129 0.000 1.487 100.279 0.000

37

Table V : Robustness Checks- Volume and Number of Transactions

This table shows the ratio of the Pre event and Post event trading volume and number of transactions

for the firms in the sample for which data is available. This is computed for (1) the pre-event window [−L, 0]

and the (2) post-event window [0, L], where L is the length of each window and 0 represents the event date.

Four different window lengths are considered (three months, six months,one year and two years) and 3 dif-

ferent samples are considered in Panels A, B and C: Online sample, Return-matched sample and volatility

matched sample. Firms with extreme observations and errors in volume, transactions data are dropped from

the sample. t-stat is the t-test statistic for the 1-tail test of Ratio = 1.0vs.Ratio > 1.0 respectively

Panel (A) : Online Sample Results

Firms that moved online

Window Post/Pre Volume Post/Pre Transactions

Mean Std.Dev t-stat No. of firms Mean Std.Dev t-stat No. of firms

3 months 1.26 0.82 4.01∗∗∗ 160 1.28 0.99 2.53∗∗ 79

6 months 1.36 0.98 4.63∗∗∗ 154 1.56 1.33 3.66∗∗∗ 75

1 year 1.76 1.52 5.92∗∗∗ 141 2.73 3.72 3.69∗∗∗ 63

2 years 2.07 1.54 7.58∗∗∗ 118 3.24 3.22 5.05∗∗∗ 53

Panel (B) : Return Matched Sample

Firms matched by size and past return

Window Post/Pre Volume Post/Pre Transactions

Mean Std.Dev t-stat No. of firms Mean Std.Dev t-stat No. of firms

3 months 1.40 1.37 3.61∗∗∗ 153 1.43 1.75 2.00∗∗ 66

6 months 1.40 0.93 5.06∗∗∗ 140 1.69 2.43 2.21∗∗ 61

1 year 1.58 1.11 5.80∗∗∗ 123 1.86 1.70 3.62∗∗∗ 51

2 years 1.98 1.51 6.06∗∗∗ 86 2.76 2.55 3.84∗∗∗ 31

Panel (C) : Volatility Matched Sample

Firms matched by size and past volatility

Window Post/Pre Volume Post/Pre Transactions

Mean Std.Dev t-stat No. of firms Mean Std.Dev t-stat No. of firms

3 months 1.22 1.12 2.43∗∗ 160 1.07 0.78 0.75 77

6 months 1.26 1.28 2.56∗∗ 157 1.30 1.29 2.00∗∗ 75

1 year 1.44 1.91 2.80∗∗∗ 151 1.57 1.57 3.08∗∗∗ 71

2 years 1.38 1.16 3.46∗∗∗ 112 2.01 2.08 3.53∗∗∗ 53

38

Table VI : Robustness Checks- Implied Volatility from the options market

This table shows the implied volatility (expressed as % per month) of at the money call options for the

sample of firms that moved online to commence their eCommerce operations. The ratio of the Pre event and

Post event implied volatility is also presented. This is computed for (1) the pre-event window [−L, 0] and

the (2) post-event window [0, L], where L is the length of each window and 0 represents the event date. Four

different window lengths are considered (three months, six months,one year and two years) and the (3) The

ratio of post to pre implied volatility is also presented in Panels A, B and C respectively. t-stat is the t-test

statistic for the 1-tail test of Ratio = 1.0vs.Ratio > 1.0 respectively

Panel (A) : Pre Event, Implied Volatility Results

Implied Volatility before firms moved online

Window Implied Volatility

Mean Median Std.Dev No. of firms

3 months 15.17% 14.46% 5.46% 86

6 months 15.06% 14.53% 5.44% 86

1 year 14.81% 14.17% 4.86% 86

2 years 14.35% 13.55% 4.70% 88

Panel (B) :Post Event, Implied Volatility Results

Implied Volatility after firms moved online

Window Implied Volatility

Mean Median Std.Dev No. of firms

3 months 15.75% 14.18% 5.98% 89

6 months 15.67% 14.57% 5.52% 90

1 year 16.54% 15.66% 5.78% 95

2 years 17.23% 16.27% 6.18% 97

Panel (C) : Ratio of Post/Pre Event, Implied Volatility

Window Ratio of Implied Volatility Post/Pre

Mean Median Std.Dev No. of firms t-stat

3 months 1.05 1.02 0.29 86 1.50

6 months 1.06 1.03 0.24 86 2.39∗∗

1 year 1.12 1.12 0.20 86 5.27∗∗∗

2 years 1.21 1.20 0.22 88 8.75∗∗∗

∗ ∗ ∗, ∗∗, ∗ - Significant at 1%,5%,10% level respectively

39

Table VII : Demand Uncertainty in retail and eCommerce markets

Panel A : Estimated Quarterly US Retail Sales (Total and eCommerce)

This table shows the estimated quarterly U.S. retail sales (Total and eCommerce) as estimated by the U.S.

Census Bureau. Data are in millions of dollars and are not adjusted for seasonal, holiday and trading-day

differences. eCommerce sales refer to sales of goods and services on the internet, an extranet, Electronic Data

Interchange or other online system. Payment may or may not be made online. Source : US Department of

Commerce News, August 22, 2002.

Period Retail Sales eCommerce as a

Total eCommerce percentage of total

Q4,1999 784,278 5481 0.70%

Q1,2000 711,600 5814 0.82%

Q2,2000 771,691 6346 0.82%

Q3,2000 765,536 7266 0.95%

Q4,2000 810,311 9459 1.17%

Q1,2001 724,224 8256 1.14%

Q2,2001 805,245 8246 1.02%

Q3,2001 782,088 8236 1.05%

Q4,2001 856,285 11178 1.31%

Q1,2002 743,810 9880 1.33%

Q2,2002 825,532 10243 1.24%

Average 780,054.5 8218.6 1.05%

Standard Deviation 43,415.1 1866.8

Coefficient of variation 5.6% 22.7%

Table VII : Panel B : Annual US Retail Sales By Sector(Total and eCommerce)

This table shows the annual U.S. retail sales (Total and eCommerce) as measured by the U.S. Census Bureau.

Data are in millions of dollars and are not adjusted for seasonal, holiday and trading-day differences. eCom-

merce sales refer to sales of goods and services on the internet, an extranet, Electronic Data Interchange or

other online system. Payment may or may not be made online. Source : US Department of Commerce News,

August 22, 2002.

Industry / Sector Total Retail Sales eCommerce Sales

Year 2000 Year 1999 %Change Year 2000 Year 1999 % Change

Speciality Retailing 182211 173216 5.2% 2279 962 136.9%

Computers, Electronics 120969 110008 10.0% 8821 5657 55.9%

Media, Entertainment, Services 86833 81778 6.2% 3777 2654 42.3%

Retailing, Toys, Cosmetics 535293 504242 6.2% 3556 1627 118.6%

Auto, Motorcycles, Hardware 1436872 1344278 6.9% 5912 2079 184.4%

Catalog, Auctions, Travel 167080 139619 19.7% 22749 12082 88.3%

Pharma, Real Estate, Food 632926 600717 5.4% 1239 498 148.8%

Total 3162184 2953858 7.1% 48333 25559 89.1%

40

Table VII : Panel C : Comparative estimates of Internet Advertising Spending in the

US,2000-2002 (in millions)

This table shows the estimates made as of June 2000 by various consulting firms on internet advertising

spending by US firms for the period 2000-2002. Data are in millions of dollars. Source : eMarketer.com

Name of Forecaster Estimate for Year

2000 2001 2002

IDC 3,300 n.a n.a

Giga Info. Group 3,950 5,770 8,000

Myers Group 4,320 6,480 10,368

Veronia Suhler and Assoc 4,500 5,700 6,900

Jupiter Communications 5,000 6,700 8,800

Global Internet Project 5,000 n.a n.a

Aberdeen Group 5,100 n.a n.a

Forrester Research 5,400 8,700 12,600

Lazard Freres 5,493 8,028 11,057

eMarketer 6,100 9,500 13,500

Simba 6,500 7,100 n.a

Internet Advertising Bureau 7,740 12,487 18,350

Internet Stock Report 8,100 11,300 15,900

Meckler-Media n.a 16,300 22,900

ActivMedia 11,200 23,500 43,300

Average 5,835.9 10,130.4 15,606.8

Standard Deviation 2,044.7 5,252.8 10,340.9

Coefficient of variation 35.0% 51.9% 66.3%

41

Table VIII : Demand Uncertainty and Stock Return Volatility

This table shows the estimates of demand uncertainty estimated for each firm using its quarterly sales data

(obtained from COMPUSTAT) for the period 1990-2002. For each firm the following AR(1) process for

quarterly sales is estimated

ln(Sales(i, t)) = αi + βi ∗ ln(Sales(i, t− 1)) + εit

Where Sales (i,t) refers to quarterly sales for firm i in quarter t. The measure of demand uncertainty for firm

i, UNC1i is the time series standard deviation of residuals εit from the above specification. In the second

stage, a cross sectional regression is run between λi and UNC1i as follows

λi = a + b ∗ UNC1i + ei

λi is calculated for each firm for the same period for which AR(1) process is estimated (i.e.) 1990-2002 using

daily returns. The following table reports the distribution of UNC1i and the results of the cross sectional

regression. The calculation of UNC2i follows exactly the same procedure except that the AR(1) specification

in the first stage is estimated without a constant term for each firm. For the regression White (1980) corrected

standard errors are reported.

Panel A : Summary Statistics for Demand Uncertainty and λ

Variable N Mean Std. Deviation Min Max

UNC1 166 0.24906 0.20176 0.0482 1.31

UNC2 166 0.285 0.23924 0.05 1.43843

λ 166 5.35159 2.62666 2.22567 13.30

Correlation (UNC1, λ): 0.37871 (significant at 0.01% level.)

Correlation (UNC2, λ): 0.33256 (significant at 0.01% level.)

Panel B : Regression Results

λi = a + b ∗ UNC1i + ei

Variable Coefficient Std. Error t Value (white)

Intercept 4.12043 0.2662 15.48

b 4.94333 0.99082 4.99

Adjusted R-Squared: 0.139. F-Value: 27.63

λi = a + b ∗ UNC2i + ei

Variable Coefficient Std. Error t Value (white)

Intercept 4.31097 0.26576 16.22

b 3.65127 0.88257 4.14

Adjusted R-Squared: 0.105

F-Value: 20.39

42

Table IX : Event Study Results

This table reports the results of an event study on the announcement by firms to move online. The event

study results take into account non-synchronous trading by using Scholes William Beta in the market model

estimation and event-induced variance increases for assessing test Statistics. The event study uses a market

model with a value-weighted index. SCS Z is the standardized cross section Z statistic that corrects for event

induced variance along the lines of Boehmer, Musumeci and Poulsen, (1991).The symbols $,*,**, and ***

denote statistical significance at the 10%, 5%, 1% and 0.1% levels, respectively, using a 1-tail test.

Panel A :Event Study Results - Full Sample

Window N CAR Wtd. CAR SCS Z

(-8,+2) 162 4.50% 2.52% 2.204∗

(-4,+2) 162 3.98% 2.35% 2.258∗

(-2,+2) 162 2.80% 1.46% 1.474$

(0,0) 162 2.21% 1.49% 2.955∗∗

Table IX : Panel B :Event Study Results - Pre 98 Sample

Window N CAR Wtd. CAR SCS Z

(-8,+2) 56 3.33% 1.30% 0.633

(-4,+2) 56 3.55% 1.59% 0.801

(-2,+2) 56 3.23% 1.32% 0.678

(0,0) 56 1.24% 0.51% 0.928

Table IX : Panel C :Event Study Results - Post 98 Sample

Window N CAR Wtd. CAR SCS Z

(-8,+2) 106 5.12% 3.37% 2.551∗∗

(-4,+2) 106 4.21% 2.90% 2.576∗∗

(-2,+2) 106 2.58% 1.55% 1.568$

(0,0) 106 2.72% 2.19% 2.849∗∗

43

Figure 1

The Hobbes’ Internet Timeline and the Amex Internet Index

The top panel (Figure 1A) of this figure shows that the growth rate of number of Websites,

which was approximately linear till about June 1998, became exponential in the later period.

This shows that the importance and size of the Internet, as measured by this metric increased

dramatically after this date. The bottom panel (Figure 1B) of this figure shows the evolution

of AMEX internet index in periods corresponding to Pre and Post June 1998. Copyright :

BigCharts.com.

44

Figure 2Time Series of Average Monthly Volatility RatioThis Figure shows the time-series of average monthly volatility ratio over a four year interval around the

event date, covering the period [-2,2] years in event time. Volatility ratio, λ for each firm is computed as

the ratio of the square root of the firm’s stock volatility to the market volatility over the same monthly

period. Market volatility is computed using the return on the equally weighted index. These ratios are

then averaged across firms in the event time. The panels show the average ratios for the net sample (Firms

that moved online) and the same ratios for 2 samples in which each net sample firm is matched to a corre-

sponding firm by size and 2 year past return (return sample) and size and 2 year past volatility (volatility

sample). Pre98 and Post98 samples are firms that moved online before and after June 1998 respectively.

Average lambda

3

4

5

6

7

8

9

-24

-22

-20

-18

-16

-14

-12

-10

-8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24

month

Net Sample returnsample volatility sample

Average lambda - Pre 98 Sample

3

4

5

6

7

8

9

-24

-22

-20

-18

-16

-14

-12

-10

-8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24

month

Net Sample returnsample volatility sample

Average lambda - Post 98 Sample

3

4

5

6

7

8

9

-24

-22

-20

-18

-16

-14

-12

-10

-8 -6 -4 -2 0 2 4 6 8 10 12 14 16 18 20 22 24

Net Sample returnsample volatility sample45

Figure 3Structural Break methodologyThe left panels of this figure show the volatility defined and computed as the Average of the monthly %

standard deviation of returns across firms in event time. The panels show the average for the net sample

(Firms that moved online) and the same for 2 samples in which each net sample firm is matched to a

corresponding firm by size and 2 year past return (return sample) and size and 2 year past volatility (volatility

sample). Pre98 and Post98 samples are firms that moved online before and after June 1998 respectively.

The right panels show the Wald Test Statistics computed for the purpose of testing for a structural break

in the volatility series of the actual net sample in the corresponding left panel. The date of structural

break is the date at which the Wald Statistics peaks. The 1% confidence intervals are shown in the boxes.

! " ! #!

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Figure 4

Cumulative Abnormal Volatility

This figure shows the behavior of the Cumulative Abnormal Volatility (CAV) over the event

window of [-26,25] days. Each panel shows four graphs which correspond to the CAV plots for

(a) the test sample of firms (b) the size-returns matched sample of firms (c) the size-volatility

matched sample of firms and (d) under the null hypothesis of no event effect on volatility.

Panel (A) shows the results for the full sample period, Panel (B) for pre-June 1998 sample

and Panel (C) for post-June 1998 sample.

!"# !"$ !"" !"% !&' !&# !&$ !&" !&% !' !# !$ !" % " $(# ' &% &" &$ &# &' "% "" "$

) * + , - . / 0 1 + 2 - 354 . 1

6 7 8 1 9 0 , . . : ; 4 < + = 1 > ? >

5 @ A BC AD EF G

!"# !"$ !"" !"% !&' !&# !&$ !&" !&% !' !# !$ !" % " $H# ' &% &" &$ &# &' "%("" "$

) * + , - . / 0 1 + 2 - 354 . 1

6 7 8 1 9 0 , . . : ; 4 < + = 1 > ? >

I 1 + , 9 7J- + * = 1 82 - 354 . 1

ABC G D EF AG

!"# !"$ !"" !"% !&' !&# !&$ !&" !&% !' !# !$ !" % " $(# ' &% &" &$ &# &' "% "" "$

6 7 8 1 9 0 , . . : ; 4 < + = 1 > ? >

) * + , - . / 0 1 + 2 - 354 . 1

I 1 + , 9 7J- + * = 1 82 - 354 . 1

I 1 + , 9 7J- + * = 1 82 - 354 . 1 K < . - + ? . ? + ;5J- + * = 1 82 - 354 . 1

K < . - + ? . ? + ;5J- + * = 1 82 - 354 . 1

K < . - + ? . ? + ;5J- + * = 1 82 - 354 . 1

47

Figure 5

Implied Volatility from the options market

This figure shows the behavior of the average monthly implied volatility of at the money call

options for the firms that moved online, and for which data is available. This is plotted for

4 years around the event date. The graph also shows the number of firms in each month for

which options data was available.

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