dot com bubble

Technology Analysis & Strategic Management, Vol. 15, No. 1, 2003

Bursting the dot.com ‘Bubble’: A Case Study inInvestor Behaviour

PETER ROBERT WHEALE & LAURA HEREDIA AMIN

A The Austrian economist Joseph Schumpeter considered innovation to be the driving force

of economic growth and argued that innovations were also the main cause of cyclical fluctuations in the

economy, an idea now well established in the economic literature. In this paper, the authors attempt to

gain insights into the behaviour exhibited by investors before and after the market correction of the newly

established Internet sector—a technology with revolutionary potential—in the Spring of 2000 by

structuring their analysis around the psychological themes of heuristic-driven bias, frame dependence,

and inefficient prices. Linear regression models are constructed using data collected on publicly traded

Internet companies, market performance both before and after the collapse of the Internet sector stock

prices in an attempt to assess whether or not market returns were correlated with certain specific measures

of corporate internet performance. Finally, the authors draw inferences relating to the psychology of

investor behaviour during this period based upon their empirical analysis, and conclude by summarizing

the managerial implications of their findings.

1. Introduction

The Austrian economist Joseph Schumpeter1 considered innovation to be the drivingforce of economic growth and argued that innovations were also the main cause ofcyclical fluctuations in the economy, an idea now well established in the economicliterature. It is further argued that clusters of innovations conduce to the growth of newindustries such that when their diffusion takes place it can amount to the emergence ofwhole new technological systems and that the impetus to economic growth comes,therefore, not from the first innovations but from a pattern of change associated withdiffusion investment related to breakthroughs in fundamental science and technology,inventions, and the level of economic demand.2 It may be argued that recent developmentsand investment in electronics and computer technologies, new material technologies andtelecommunications comprise such a cluster of fundamental innovations consistent withthe ‘new technological system’ thesis and that over the next few years Internet-enablingtechnologies will have the capacity to radically transform business operations andstructure by facilitating business interfacing. Internet-enabling technologies certainlyappear to have all the characteristics of a fundamental technological innovation with thepower to transform global and economic development and thus, investors’ enthusiasmfor them is understandable. However, we appear to have recently witnessed an over-

Peter Wheale is Director of Postgraduate Research Studies at the Surrey European Business School, University of Surrey,Guilford GU2 7XH, UK and Laura Heredia is a Business Analyst at Future Electronics, Colnbrook, Berkshire, UK.

ISSN 0953-7325 print; 1465-3990 online/03/010117-20 © 2003 Taylor & Francis Ltd

DOI: 10.1080/0953732032000046097

118 P. R. Wheale & L. Heredia Amin

investment in them far greater even than that of the Tulip mania in 1637, the South Seabubble of 1720 and the British Railway euphoria of the 1840s.3

The classic theory of securities market equilibrium is based on the interaction ofcompletely rational investors. However, several recent studies4 have explored alternativesto the premise of full rationality. Behavioural finance is a burgeoning field that focuseson the psychological influences of investors’ behaviour. According to Shefrin5 certainpsychological phenomena pervade the entire landscape of investment and finance, andthese phenomena can be organized around three themes: namely, heuristic-driven bias,frame dependence, and inefficient prices. Using these three themes as a framework forour analysis, this study attempts to gain insights into the behaviour exhibited by investorsbefore and after the market correction of the Internet sector in the first quarter of 2000.

Section 2 describes the rise and fall of the Internet sector during the late 1990s.Section 3 briefly reviews the theoretical underpinnings of the psychology of investorbehaviour. Section 4 describes the methodology we have used for our empirical analysis.Section 5 reviews our finding and explores some of the reasons for investor overconfidence,and their overestimation of the quality of information signals about security values, beforethe market correction in spring 2000. Finally, in Section 6 we draw conclusions derivingfrom our empirical analysis and summarize some managerial implications of our findings.

2. The Rise and Fall of the Internet Sector

In the early period of the diffusion of a major innovation new firms tend to be formedto exploit the new technology, and investment and employment in the associated industriestend to expand. The demand for Internet technology deriving from the huge potentialof commercial Internet implementations by private and public organizations reinforcedthe favourable investment climate in the late 1990s for the newly created Internet firmsfunded by venture capital and it was claimed that Internet-enabling technologies wouldrapidly change the structure of the stock market and the corporate landscape.6 Enormousopportunities were considered to exist for those companies prepared to find creative andunique ways to use the Internet to solve problems and provide novel services andproducts, and investment literature such as the Investors Guide also encouraged highlyspeculative investment.7 Figure 1 summarizes the estimated global e-commerce growthfor business-business (B2B) and business-consumer (B2C) models.

Some innovation studies have postulated the so-called ‘push-pull’ models of innova-tion. The ‘push’ idea is that innovation is pushed by scientific and technologicalbreakthroughs—sometimes called ‘capabilities push’ and the ‘pull’ idea is that inventionand innovation are stimulated by some perceived social need or market demand.8

Although evidence from empirical studies do not support simple linear-sequential modelsof innovation,9 it appears that Internet technologies and the formation of dot.comcompanies have been ‘pushed’ by technological capabilities and to a much lesser degree‘pulled (by market forces), albeit in complex ways.

A technological advance affecting part of a production process increases the pressuresfor technological advances in other parts of the process and may shift the responsiveness(elasticity) of technological substitution upwards. Process innovation, usually responses toa shift in demand or to increased costs in a firm’s production function, are typicallyembodied in bought-in capital equipment. Rosenberg10 convincingly argues that, historic-ally, innovations affecting part of a production process lead to searches for innovationsaffecting other parts of the process11 but this process takes time, particularly in a sluggishglobal economy.

The provision of Internet technologies for commerce, industry and public organization

Bursting the dot.com ‘Bubble’ 119

8000

6000

4000

2000

0

Mill

ions

($)

2000 2001 2002 2003 2004

B2B

B2C

603.7

53.3

1137.6

96

2061.3

169.9

3693.8

285.8

6335.4

454.4

Source: www.chrisfoxinc.com

Figure 1. Estimated global e-commerce growth.

was the operating multiplier, or ‘cash flywheel’, that sustained the commercial momentumin the early phase of development and initial capital investment. The operating multipliereffect works in reverse when this derived demand falls and can have devastating affectson the demand for capital equipment. Internet firms have suffered from these effectsbecause they have been highly reliant on demand derived from industry and commerce(that is, not directly from individual consumers). This derived demand is illustrated inFigure 1, where the B2B market is estimated to be 12 times the size of the B2C market.One of the problems that Internet innovators have encountered is that the existing markethas inadequate knowledge and information with which to evaluate and embrace this newtechnology. As Mowery and Rosenberg12 argue, the market must learn how to acceptrevolutionary new innovations that fall outside of their present mindset. This appears tohave been the case with Internet technology, where much of established industrialmanagement have been slow to adopt the new technology. Furthermore, dot.comcompanies have found themselves having to invest heavily to provide a product (media/information) with a marginal revenue rapidly approaching zero. However, none of theabove factors, or combination of them, can be said to explain the extraordinary over-investment in the, so-called, new technology sector in the late 1990s, and its capitulationin 2000.

Profitability is normally considered to be the sine qua non of the firm, and as Koller13

remarks, the crucial drivers of value creation are, therefore, the potential revenue of acompany and its capability to translate that revenue into cash flows for shareholders.This ability can be best measured by its long-term return on invested capital. However,by the time the NASDAQ reached its peak in 2000, many financial analysts werebeginning to believe the idea that stock market valuations were no longer driven only bytraditional economic factors such as earnings growth, inflation, and interest rates. Instead,they began to suggest that new factors such as the value of intangible assets and brandswarranted the haughty stock prices. During this time there were just two retail Internetcommerce companies that were not over-investing in order to grow, namely, Bay andYahoo, whilst the rest of the companies, including Amazon, Pets.com and Buy.com, spentinvestors’ money trying to become big before they became profitable, and, with few


Figure 2. NASDAQ 100 (NASDAQ Stock Exchange).

+200

–20–40

–60

–80

–100May 2000 Sep 2000 Jan 2001 May 2001 Sep 2001 Jan 2002

Per

cent

IFX Corpas of 7–Mar–2002

Source: Yahoo Finance (http: // finance.yahoo.com)

SPDE

GEEK.OB

BIZZ.OBFUTR

CYCO

Splits

Figure 3. Web portal’s market performance March 2000–January 2002.

exceptions, these companies and their investors suffered the adverse consequences of thisstrategy.

Inevitably, growth without profitability could not be sustained indefinitely, and in thespring of 2000 there was a ‘capitulation’ of the Internet sector—a sudden and final waveof selling of Internet stocks that sent the market down far enough and quickly enough towipe out all investor optimism for the Sector—which eliminated all, so-called, ‘irrationalexhuberance’. Figure 2 illustrates the 39% fall in the NASDAQ-100 Index during theyear 2000, and Figure 3 traces five web portals, reflecting the dramatic collapse of stockprices from the spring of 2000 to January 2002.

3. The Psychology of Investor Behaviour

Conventional valuation techniques rely on the assumption that securities’ prices infinancial markets must equal fundamental values, both because all investors are deemed


to be rational and because arbitrage eliminates pricing abnormalities: this supposition isknown as the efficient market hypothesis (EMH). The EMH is defined by Fama14 as onein which security prices always reflect individuals acting rationally and considering allavailable information in the decision-making process. According to this theory, stockprices look like ‘random walks’ through time where price changes are unpredictable dueto the fact that they occur in response to new information. Fromlet15 explains that, infinancial economics, abnormality is usually used to describe a temporarily inexplicableobservation such as a stock market ‘bubble’.

The basic theoretical case for the EMH rests on the three following arguments. First,investors are believed to be rational and therefore they value securities rationally. Whenthey are rational, they assess each security for its fundamental value: the net presentvalue of its future cash flows, discounted using its risk characteristics. This means thatonce investors learn something about fundamental values of securities, they promptlyreact to new information. This reaction is entrenched by bidding up prices when reportsare good and bidding them down when reports are bad. Consequently, all availableinformation will be reflected in security prices and prices adjust to new levels correspond-ing to the new net present values (NPVs) of cash flows. Shiller16 remarks that smartinvestors, in terms of investment performance, will do no better than the least intelligentones. This is because their superior understanding is already incorporated in existingshare prices. In accordance with this theory, Chancellor17 emphasizes that bubbles ormanias cannot exist since market prices always reflect their intrinsic value. Second, someinvestors are not rational but when there are a large number of irrational investorstrading randomly, and when their trading strategies are uncorrelated, their trades aregoing to nullify each other and, in such a scenario, prices are going to be close tofundamental values. Third, when investors are irrational, rational arbitrageurs willeradicate their influence on prices. For example, if a stock is overpriced as a consequenceof the purchases by irrational investors, then this stock becomes a bad buy since its priceexceeds its properly risk-adjusted NPV of its cash flows or dividends. In this situation,arbitrageurs would sell this high-priced stock and simultaneously purchase other (essen-tially similar) securities to hedge their risks.

Specifically, the evidence from the market in Internet stocks during the late 1990sindicate significant deviation from economic efficiency and share prices in this sectorcould not be explained by economic ‘fundamentals’ such as inflation, economic growthand the cost of capital. In this respect, if we agree that fundamentals are unlikely to havechanged much during this period, then it must be that investors’ psychology has changed.Reviewing the evidence, some academics18 began to wonder whether traditional valuationapproaches were capable of explaining what determined security prices and began todevelop behavioural finance as an alternative approach to understanding financialmarkets. Behavioural finance can be defined as the study of how humans interpret andact on information to make informed investment decisions, and its findings suggest thatinvestors do not always behave in a rational, predictable and an unbiased manner asindicated by traditional finance models. From this perspective, financial markets are notexpected to be efficient, and systematic and significant deviations can be expected topersist for long periods of time.

As described above, in theory, when investors are rational, markets are efficient bydefinition, and any irrational trading is supposedly countervailing. However, there isplenty of evidence that investors frequently deviate from the standard decision-makingmodel in three broad categories: attitudes toward risk, non-Bayesian expectation forma-tion, and sensitivity of decision making to the framing of problems.19

Investors often do not look at the levels of final wealth they can attain but at gains


and losses relative to some reference point, which may vary from situation to situation,and display loss aversion. One notorious example of loss aversion is the reluctance ofinvestors to sell stocks that lose value.20 They are also likely to systematically violateBaye’s rule and other axioms of probability theory in their predictions of vague results,as Kahneman and Tversky observe.21 Investors, for example, may extrapolate pasthistories of rapid earnings growth too far into the future and therefore overprice thesecompanies. This overestimation reduces future returns as past growth rates are unlikelyto repeat themselves and prices adjust to more reasonable valuations. Investors’ prefer-ences and beliefs in buying securities conform to psychological evidence or heuristicsrather than the normative economic model or Bayesian rationality and is known as‘investor sentiment’. Investors sharing these preferences or sentiments have been called‘noise traders’.22 In this sense, there is evidence that people do deviate in the same way,and not randomly, from rationality; for example, ‘noise traders’ would follow each others’mistakes by listening to rumours or imitating others. Then, investor sentiment reflectsthe common judgement errors made by a significant number of investors, rather thanuncorrelated random mistakes. Furthermore, the theoretical case for efficient marketswill rely on the effectiveness of arbitrageurs. This efficacy will be dependent on theavailability of close substitutes for stocks whose price is potentially affected by noisetrading. Behavioural finance confronts the EMH hypothesis and therefore, below, wediscuss some behavioural principles that have direct implications for this hypothesis.

Shiller23 states that people do not know, to any degree of accuracy, what thefundamentally correct level of the market should be. Therefore, in order to understandwhat influences a market’s level on any given day, or what the market does to stay withina certain range for days at a time, it is necessary to comprehend psychological ‘anchors’in the market. Chancellor24 explains the stock market is composed of the actions ofindividual speculators; therefore during the bull or manic phase, activity is frenetic andexpectations become unrealistic. In his book, The Great Crash, J.K. Galbraith wrote:‘Speculation on a large scale requires a pervasive sense of confidence and optimism andconviction that ordinary people are meant to be rich.’25 In this context, overconfidenceappears to be a fundamental factor promoting the high volume of trade that is observedin speculative markets. Schumpeter26 observed that speculative manias commonly occurat the beginning of a new industry or technology when people misjudge the potentialgains and too much capital is attracted to new ventures.

Shiller27 conjectures that psychological research reveals that there are patterns ofhuman behaviour that imply ‘anchors’ for the market that would not be expected ifmarkets worked entirely rationally. In this way, investors are striving to do the correctthing, however, they have restricted capabilities and certain natural modes of behaviourexist that shape their investment decisions. Two important psychological ‘anchors’ willbe considered below, namely, quantitative anchors and moral anchors.

Quantitative anchors. Where investors are evaluating numbers against prices when theydecide whether stocks are priced correctly, they tend to apply the most recentlyremembered price and consequently this tendency enforces the likeness of stock pricesfrom one day to another. For individual stocks, price changes may tend to be anchoredto the individual changes of other stocks, and price–earnings ratios may be anchored toother firm’s price–earning levels. This kind of fixing may explain why individual stockprices move together as much as they do, and consequently the volatility of stock priceindices.

Moral anchors. With moral anchors, people contrast the instinctive or emotionalstrength of the reason for investing in the market, which has no quantitative aspect,against their financial wealth and their perceived need for immediate disposable income.


In judging the significance of these psychological anchors for the market, it isimportant to take into consideration the appearance of a persistent human tendencytoward overconfidence in one’s beliefs. In order to understand why it is that peopleseem to be overconfident, psychologists have theorized that people (in evaluating theirevaluations) tend to assess the probability that they are right on only the last step of theiranalysis, overlooking other elements that could imply they are wrong. Shiller28 citesevidence that individuals make probability judgements by looking for similarities to othercommon cases, however, they forget that there are many other possible observations toconsider. It is apparent that investors are often overconfident and tend to make judgmentsin unclear situations by looking for familiar patterns and assuming that future patternswill be similar to past ones, often without sufficient consideration of the reasons for thepattern or the probability of the pattern repeating itself.

4. Methodology

Because of differences between Internet and non-Internet firms various suggestions havebeen made about the relationships (or lack thereof ) between the stock market valuationof, so-called, ‘new economy’ companies and measures of performance. Copeland et al.29

suggest that the way to value new economy companies is to pay special attention to theirfuture performance and base valuation on their past or present performance. On theother hand, traditional notions such as profitability, cash flow and a healthy scepticismremain the key to success. Hand,30 for instance, explored the value relevance of certainfinancial statement data as features used by investors in the pricing of Internet stocks.However, the use of accounting-based measures is treated with extreme scepticismnowadays and, at the time of writing, allegations of fraud at big corporations rangingfrom Enron to WorldCom have further undermined trust in widely used accountingpractices, and created something of a crisis in corporate governance.

We investigated the extent of rationality exhibited by investors before and after themarket correction of spring 2000 by relating the market price of Internet stocks withdata on six of the most renowned measures of corporate performance. Our hope was togain some insights into the change of investor psychology after the market correction inthe spring of 2000 and the hypotheses we constructed and tested were, namely, that thereis a significant relationship between corporate performance and stock market returnsduring the period pre-market correction, and there is a significant relationship betweencorporate performance and stock market returns during the period post-market correction.Formally stated, the hypotheses are as follows:

H0: There is no relationship between corporate performance and stock market returnsduring the pre-or post-market correction periods.

H1: There is a significant relationship between corporate performance and stock marketreturns during the pre- and post-market correction periods.

The hypotheses are tested by constructing models, applying Multiple Regression Analysis(MRA), and assessing the reliability of the regression results by reference to the PearsonCorrelation Coefficient. As key variables we selected six measures as indicators ofcorporate performance, namely, return on assets, return on equity, price–sales ratio,price–earnings ratio, book value, and free cash flow. Quarterly data were collected forthe period January 1999 to June 2001 from a sample frame comprising the populationof 474 US publicly traded Internet companies which were identified from a comprehensivelist provided by Bloomberg Financial Markets via an interrogation terminal at the SurreyEuropean Management School, University of Surrey, from the subgroups described in


Table 1. Bloomberg’s subgroups

Subgroup Definition

E-commerce/products Includes companies which retail physical products via the Web. Excluded are brick-and- mortar companies that also retail over the Internet as a supplement to theiroperations.

E-commerce/services Includes businesses that sell services via the Internet and companies that facilitate thetransfer of products and services between the purchaser and seller. Included arecompanies that provide the forum for the exchange of goods, services and informationover the Internet and companies that operate as middlemen between the supplier ofthe goods and/or services.

E-marketing/information Included are companies that enable business decision makers to address criticalmarketing and merchandising questions concerning the effectiveness of their websites’activity data, such as user profiles and audience measurement.

Internet content— Included are companies that deliver via the World Wide Web media such as text,entertainment music, spoken word, radio, sports, games, and movie related information

Internet content—info/news Includes companies that provide information and news services such as financialinformation, online user forums, newsletters, resource directories, and commentary.

Internet finance services Comprise businesses offering online securities brokerage.Web hosting/design Included are companies which provide hosted websites as well as related e-commerce

services and applications.Web portals/Internet service Includes companies that offer access to the Internet. These companies offer online

providers content, search capabilities and directories, filtering on-line communities and e-mail,and enable e-commerce.

Source: Bloomberg Financial Markets.

Table 1. Specifically, Internet companies were included in the initial sample if they fellinto the range of internet business subgroups. Table 1 contains a list of these subgroupstogether with their Bloomberg definitions.

It was considered appropriate to exclude Internet companies from the population iftheir market capitalization was less than US $0.05 million. When the data were obtained,they were grouped in two sub-groups: pre-market correction (from first quarter year 1999to first quarter 2000) and post-market correction (from second quarter 2000 to secondquarter 2001). Consequently, this resulted in a base total corporate group of 169companies, which are listed in Appendix A. All data were analyzed using the statisticalsoftware Statistical Package for Social Sciences (SPSS) version 10.0 for Windows.Particular statistical tests were used to determine whether there is any significantrelationship between corporate performance and stock market return according to theprocedures discussed below.

First, it was necessary to explore the linear regression of the dependent variable (sharevalue) and each of the independent variables (return on assets, return on equity, price–sales ratio, price–earnings ratio, book value, and free cash flow). The Pearson correlationcoefficient is used to the test the statistical significance. However, in order to meet therequirements for the assumptions of normality, linearity and homoscedasticity, it wasnecessary to convert continuous numeric data to a discrete number of categories. Theprocedure creates new variables containing the categorical data. Data are categorizedbased on percentile groups, with each group containing approximately the same numberof cases. For example, a specification of 4 groups would assign a value of 1 to cases belowthe 25th percentile, 2 to cases between the 25th and 50th percentile, 3 to cases betweenthe 50th and 75th percentile, and 4 to cases above the 75th percentile.31

Having examined whether there is a correlation between the variables in the modelconstructed, the next step is to test the correlation coefficients between the independent


variables and the dependent variable through Multiple Regression.32 The null hypothesis(H0) states that the relationship predicted in the study does not exist and implies that anyrelationships are purely due to chance: statistical significance tests are used to eitheraccept or reject it. The significance test produces a numerical value that can be translatedinto a p-value that is expressed either as percentage or a decimal.33 Here, the significancelevel that would be applied is 0.05, thus the alternative hypothesis can be accepted if theprobability level is over 95% (i.e. pO0.05).

When trying to measure the level of the relationship between measures of corporateperformance and stock market returns it is of vital importance to create valid and reliablemeasures. The reliability of the measures used depends on various factors. First, the datacollected have to be accurate and correct. In addition to that, the firms included in thesample have to be from different sub-sectors in order to create a sample that actuallyrepresents the business population and not just a specific category of firms. It was decidedto use Bloomberg Financial Markets as the main source for collecting all the secondarydata needed for the statistical analysis. The next section reviews our findings from thisstatistical analysis.

5. Review of Findings

Internal validity refers to the extent to which a causal relationship between two variablescould be inferred34 and discriminant validity suggests to what extent a construct wasdistinguishable from another construct, which could be examined by the level of measuresbeing correlated. The acceptable level of correlation from Table 2, such as ROA andFCF suggests the acceptance of discriminant validity for this study. Some importantmeasures in this study were correlated, including FCF with ROA, ROE, BV and PS but

Table 2. Correlation matrix of the measures of Internet corporate performance

ROA ROE PE BV PS FCF

ROA Pearson correlation 1.000 0.700 ñ0.050 ñ0.100 ñ0.200 0.300p (2-tailed) — 0.000 0.530 0.208 0.011 0.000N 160 160 160 160 160 160

ROE Pearson correlation 0.700 1.000 ñ0.250 0.025 ñ0.075 0.275p (2-tailed) 0.000 — 0.001 0.754 0.346 0.000N 160 160 160 160 160 160

PE Pearson correlation ñ0.050 ñ0.250 1.000 ñ0.200 ñ0.375 0.050p (2-tailed) 0.530 0.001 — 0.011 0.000 0.530N 160 160 160 160 160 160

BV Pearson correlation ñ0.100 0.025 ñ0.200 1.000 0.400 ñ0.500p (2-tailed) 0.208 0.754 0.011 — 0.000 0.000N 160 160 160 160 160 160

PS Pearson correlation ñ0.200 ñ0.075 ñ0.375 0.400 1.000 ñ0.275p (2-tailed) 0.011 0.346 0.000 0.000 — 0.000N 160 160 160 160 160 160

FCF Pearson Correlation 0.300 0.275 0.050 ñ0.500 ñ0.275 1.000p (2-tailed) 0.000 0.000 0.530 0.000 0.000 —N 160 160 160 160 160 160

Source: SPSS output.


Table 3. Correlation matrix for companies before market correction


PRICE Pearson Correlation ñ0.025 0.075 ñ0.275 0.525 0.325 ñ0.350p (2-tailed) 0.754 0.346 0.000 0.000 0.000 0.000N 160 160 160 160 160 160

Source: SPSS Output.

the correlation metrics summarized in Table 2 indicate an acceptable level of convergentvalidity for our purposes.35

The data collected from the 169 companies selected according to the predeterminedprocedure, were analyzed by means of linear regression models. The first equation wastested using a cross-sectional linear regression equation and the results are tabulated inTable 3.

First, in the correlation matrix for Internet companies, before the market correctiona strong correlation between stock returns and book value (BV) (ró0.525, p\0.05) wasfound. With high levels of book value, high values of stock market return are associated.These findings are congruent with the market inefficiency findings of Basu36 andStatman,37 where book value appears to represent the fundamentals to which someinvestors refer when buying Internet stocks. Second, a positive correlation is revealedbetween stock price and price–sales ratio (PS) (ró0.325, p\0.05), implying an associationbetween high levels of price–sales ratios and high values of stock market returns. Theseresults support the argument that, since many Internet companies had not earned anyprofits, investors seemed to value revenue as a proxy for market acceptance and marketshare. Third, the results summarized in Table 3 suggest a negative correlation betweenquarterly stock returns and quarterly measures of free cash flow (FCF) (róñ0.350,p\0.05) and price–earnings ratio (róñ0.275, p\0.05), thus indicating that high valuesof stock market return are associated with low levels of free cash flow and low values ofprice–earnings (PE) ratio. This result suggests that the market’s pricing of Internet stocksis such that larger losses translate into higher stock prices. This surprising phenomena isconsistent with the findings of Hand38 who found that many investors appear to assumethat losses incurred by Internet companies reflect strategic expenditures by management,not poor performance! This heuristically driven bias reflects judgements based onpredictions too far from the performance mean—a form of ‘Gamblers’ fallacy’.

It is important to note, that during this period return on assets (ROA) and return onequity (ROE) do not reflect a statistically significant association with stock prices. Thisfinding suggests that during the period pre-market correction, Internet investors did notevaluate companies’ efficiency in earning profits in terms of the capital provided by theowners of the company.39 Such overconfidence represents a ‘frame dependence’—a formof decision making in which the investor is highly selective of available data and cultivatesa high tolerance for risk. Gross40 raises this issue when discussing investors’ preferencefor some frame dependences over others, behaviour know as ‘hedonic editing’.

It was necessary to determine how well the four measures of corporate performance(book value, price–sales ratio, free cash flow and price–earnings ratio) predict marketreturns and how much variance in these returns can be explained by scores in these fourvariables. To explore these issues, standard linear multiple regression was used.

The results of this regression equation are summarized in Table 4. The R2 of 32.2%suggests that the independent variables explain 32.2% of the stock returns. The analysisof variance shown in Table 5 illustrates that the results are of statistical significance, i.e.p\0.05.


Table 4. Variance explained by the model (Share price and FCF, PE, PS and BV)

Std error ofModel R R square Adjusted r square the estimate

1 0.567a 0.322 0.304 0.42

aPredictors: (constant), FCF, PE, PS, BV.bDependent variable: PRICE.Source: SPSS output.

Table 5. Regression output (ANOVAb )

Sum ofModel squares df Mean square F Significance

1 Regression 12.868 4 3.217 18.377 0.000a

Residual 27.132 155 0.175Total 40.000 159

aPredictors: (constant), FCF, PE, PS, BV.bDependent variable: PRICE.Source: SPSS output.

Table 6. Results of single cross-sectional linear regression equationa

Unstandardized Standardizedcoefficients coefficients

Model B Std error � t Significance

1 Constant 1.213 0.275 4.408 0.000PE ñ0.162 0.072 ñ0.162 ñ2.257 0.025BV 0.405 0.081 0.405 5.004 0.000PS 6.922Eñ02 0.077 0.069 0.899 0.370FCF ñ0.121 0.077 ñ0.121 ñ1.564 0.120

aDependent variable: PRICE.Source: SPSS output.

Table 6 indicates which of the independent variables included in the MRA modelcontributed to the prediction of the dependent variable. From Table 6, it is possible toinfer that tangible book value (bó0.405) has the most explanatory value, when thevariance explained by all other variables in the model is controlled for. Tangible bookvalue makes a unique, and statistically significant, contribution to the prediction of equitymarket values; whereas, price–sales ratio, free cash flow and price–earnings ratios do notappear to make statistically significant contributions to the estimation of the dependentvariable.

The inferences that may be drawn from these results are that investors’ corporatevaluations seem to be strongly influenced by tangible book value that appears to representthe company’s fundamentals. The second equation was tested using a cross sectionallinear regression equation. Table 7 summarizes the correlation matrix for Internetcompanies after the market correction and indicates that quarterly stock returns arepositively correlated with contemporaneous measures of return on assets (ró0.273,p\0.05), return on equity (ró0.295, p\0.05), book value (ró0.438, p\0.005) andprice–sales ratios (ró0.405, p\0.05). As these results suggest, investors’ conceptions of


Table 7. Correlation matrix for companies after market correction


PRICE Pearson correlation 0.273 0.295 ñ0.477 0.438 0.405 ñ0.113p (2-tailed) 0.000 0.000 0.000 0.000 0.000 0.031N 363 363 363 363 363 363


valuation towards Internet companies at, and after the market correction have changed.Return on assets and return on equity reflect a relationship with stock price that suggeststhat during this period investors have started to evaluate companies’ efficiency in earningprofits, where profitability is interpreted as the net result of a number of company’spolicies and decisions, as Besley and Brigham41 suggest. The results summarized in Table7 suggest a negative correlation between quarterly returns and free cash flow (róñ0.113,p\0.05) and price–earnings ratios (róñ0.477, p\0.05). It can be inferred that theframe dependence had changed, because the strength of the relationship between freecash flow and the dependent variable has decreased in comparison with the value beforemarket correction, and, on the other hand, the strength of the correlation between price–earnings and market returns has increased.

Furthermore, we considered it was necessary to determine how well the six measuresof performance (free cash flow, tangible book value, price–earnings ratio, price–salesratio, return on assets, return on common equity) are associated with Internet marketvalues, and then to try and establish which of these six elements seems to bestpredict stock returns. For this purpose, a standard linear multiple regression model wasconstructed.

The results of this model are summarized in Table 8 and suggest a 37.2% strengthin explaining of the variance in stock returns. The analysis of variance shown in Table 9

Table 8. Variance explained by the model (Share price and FCF, PS,ROA, PE, BV, ROE)

Std error of theModel R R Square Adjusted R square estimate

1 0.610a 0.372 0.362 0.40

aPredictors: (constant), FCF, PS, ROA, PE, BV, ROE.bDependent variable: PRICE.Source: SPSS output.

Table 9. Regression output (ANOVAb )

Model Sum of squares df Mean square F Significance

1 Regression 33.793 6 5.632 35.203 0.000a

Residual 56.957 356 0.160Total 90.749 362

aPredictors: (constant), FCF, PS, ROA, PE, BV, ROE.bDependent variable: PRICE.Source: SPSS output.


Table 10. Results of single cross-sectional linear regression equationa

StandardizedUnstandardized coefficients coefficients

Model B Std. error � T Significance

1 (Constant) 1.039 0.161 6.466 0.000ROA 0.131 0.066 0.131 1.976 0.049ROE 8.856Eñ03 0.068 0.009 0.130 0.897PE ñ0.292 0.048 ñ0.292 ñ6.121 0.000BV 0.256 0.049 0.256 5.190 0.000PS 0.205 0.047 0.205 4.369 0.000

FCF ñ6.408Eñ04 0.046 ñ0.001 ñ0.014 0.989

aDependent variable: PRICE.Source: SPSS output.

illustrates that the result reaches statistical significance i.e. p\0.05. Table 10 indicateswhich of the variables included in the model contributed to the prediction of thedependent variable. As can be seen from Table 10, price–earnings ratio (bóñ0.292)appears to have the strongest explanatory power in explaining the dependent variablefollowed by tangible book value (bó0.256) and price–sales ratio (bó0.205), when theeffects of all other variables in the model are controlled. The inference that may bedrawn from these results is that Internet investors during the post-market correctionperiod seem to consider companies’ profits in their corporate valuations. Thus, price–earnings ratio, tangible book value and price–sales ratio make a unique, and statisticallysignificant, contribution to the prediction of market returns; whereas, the rest of thevariables in the model (return on assets, free cash flow, return on common equity) do notmake a significant unique contribution to the prediction of the dependent variable.

The results obtained for the pre-market correction period suggest that the alternativehypothesis, namely, that there is significant correlation between corporate performanceand stock market returns, cannot wholly be rejected (see Table 11). Further examinationof the hypothesis indicates that except for ROA and ROE, all the variables of corporateperformance proposed by the model are correlated with the dependent variable. Examina-tion of findings from the post-market correction period, however, suggest that there is asignificant correlation between corporate performance and stock market returns, (seeTable 12). Further inspection of these results suggest that all the variables of corporateperformance proposed by the model are correlated with the dependent variable, indicatinga sea-change in investors’ attitudes to loss aversion and an increase in reduction of therisk premium required on Internet stocks.

Table 11. Summary of SPSS output (pre-market correction)

Pearson correlationRelationships coefficient Accept/reject null hypothesis

Stock returns—return on assets ñ0.025 Accept H0

Stock returns—return on equity 0.075 Accept H0

Stock returns—price–earnings ratio ñ0.275 Reject H0 in favour of H1

Stock returns—book value 0.525 Reject H0 in favour of H1

Stock returns—price–sales ratio 0.325 Reject H0 in favour of H1

Stock returns—free cash flow ñ0.350 Reject H0 in favour of H1



Table 12. Summary of SPSS output (post-market correction)

Pearson correlationRelationships coefficient Accept/reject null hypothesis

Stock returns—return on assets 0.273 Reject H0 in favour of H1

Stock returns—return on equity 0.295 Reject H0 in favour of H1

Stock returns—price to earnings ratio ñ0.477 Reject H0 in favour of H1

Stock returns—book value 0.438 Reject H0 in favour of H1

Stock returns—price to sales ratio 0.405 Reject H0 in favour of H1

Stock returns—free cash flow ñ0.113 Reject H0 in favour of H1

Source: SPSS.

The hypothesis tested by means of the statistical analysis of secondary data providesevidence that apart from the return on assets and return on equity, all the variables(price–sales ratio, price–earnings ratio, book value and free cash flow) of Internetcorporate performance proposed by the model are correlated with the dependent variableduring the pre-market correction phase. Conversely, all basic measures of performanceincluded in the model, namely, return on assets, return on equity, price–sales ratio, price–earnings ratio, book value and free cash flow are value-relevant during the post marketcorrection phase.

First, during the pre-market correction period, the association between stock returnsand return on assets indicates that there is no statistically significant relationship betweenthese variables. However, the results reveal that for these relationships during the post-market correction period there is a weak positive correlation. These findings, that highervalues of stock returns are associated with higher values of return on assets and returnon equity in the post-correction period, suggests that during the pre-market correctionperiod Internet investors did not evaluate companies’ efficiency in earning profits interms of the capital provided by the owners of the company, evidence of overconfidence,that is, predictions too far from the mean, but that investors’ conception of valuationappear to have changed in the spring of 2000.

Second, the association between stock prices and price–earnings ratios pre-marketcorrection period suggests a weak, negative correlation between the two variables. Thus,as expected, these results indicate that relatively higher values of stock returns areassociated with lower values of price–earnings ratio.

Third, our findings suggest a strong positive correlation between Internet stock returnsand their book value. However, during the post-market correction period the strength ofthis relationship significantly diminishes, indicating that higher values of stock returns areassociated with higher levels of book value but that investors were more favourablydisposed towards Internet companies’ book values in 1999, and thus appeared to adopta more critical view after the market correction.

Fourth, the findings concerning the association between stock returns and price–to-sales ratio reveal that there is a positive correlation amongst these two variables duringboth periods under study, implying that higher values of stock returns are associated withhigher values of price–sales ratio, suggesting that investors seem still to value revenue asa proxy of market acceptance and market share.

Finally, the association between stock prices and free cash flow pre-market correctionexhibits a negative correlation between the two variables (that is, that higher values ofstock returns are associated with lower values of free cash flow). However, the strengthof this relationship diminishes throughout the post-market correction period, implyingthat investors appear to assume that losses incurred by Internet companies reflect strategic


expenditures by management, not poor performance. The model, which includes thebook value, price–sales ratio, free cash flow and price–earnings ratio, may explain 32.2%of the variance in stock returns during the pre-market correction phase. In this MRAmodel, tangible book value makes a unique, and statistically significant, contribution tothe prediction of equity market values, implying that tangible book value appears torepresent the company’s fundamentals. During the post-market correction period, theMRA model included free cash flow, tangible book value, price earnings ratio, price–sales ratio, return on assets, return on common equity and appears to explain 37.2% ofthe variance in stock returns. This model suggests that the price–earnings ratio has thestrongest explanatory contribution, although tangible book value and price–sales ratioalso made a statistically significant contribution to the prediction of market returns. AsShefrin42 asserts, investors often do not appreciate the concept of regression to the meanof stock market prices—that predictions tend to be too far from the mean. The inferencethat may be drawn from these results is that Internet investors during the period post-market correction seem to emphasise companies’ profits in their corporate valuations,suggesting that although some of the basic measures of performance were value-relevantbefore the market correction, that all basic measures of performance included in theMRA model are significantly correlated after the market correction.

6. Conclusions

Schumpeter43 observed that speculative manias commonly occur at the beginning of anew industry or technology when people misjudge the potential gains and too muchcapital is attracted to new ventures. Internet enabling technologies appear to have all thecharacteristics of a fundamental technological innovation with the power to transformglobal and economic development, but it is now clear that this is an instance of suchinvestor misjudgement: investors were irrationally over-optimistic about the prospects ofthe, so-called, new technology sector. In the ‘push-pull’ terms referred to above, thecapabilities-push led to over-investment in this technology on an unprecedented scaleand far outweighed market demand. It should also be noted too, however, that investorswere encouraged by often biased reporting by investment analysts that had a vestedinterest in recommending certain types of investment even though they were not sound—a conflict of interests relating to the imperfect governance of the financial system itself.44

Structurally, the new technology sector was highly susceptible to contraction becauseit is highly reliant on demand derived from industry and commerce and therefore whenthe economy in general is not expanding, the operating multiplier tends to work inreverse. Furthermore, it seems that businesses and consumers have been slow to adoptthe new technology. However, just as the potential of the new technology sector cannotexplain the over-investment and unsustainably high stock prices of the late 1990s, noneof the factors referred to above could explain the extent of the sudden capitulation of thestocks. For such an explanation we explored the psychology of investors during the pre-and post-stock market capitulation in the spring of 2000. In taking a behaviouralapproach, we have attempted to gain insights into this apparently irrational investmentbehaviour exhibited by investors by structuring our analysis around the themes ofheuristic-driven bias, frame dependence, and inefficient prices.

Our methodological approach has been to explore the possible relationships betweenvarious measures of market performance using data on publicly traded Internet compan-ies, and involved assessing whether or not market returns were correlated to certainspecific measures of corporate performance both before and after the market correctionin the Spring of 2000. To test our hypotheses, secondary data on corporate performance


and Internet stock returns were collected and MDA techniques were employed toanalyze them.

The results of our analysis suggest that only some of the basic measures of performance,namely, price–sales ratio, price–earnings ratio, book value and free cash flow are value-relevant over the period before market correction, whereas, all basic measures ofperformance included in the model, namely, return on assets, return on equity, price–sales ratio, price–earnings ratio, book value, and free cash flow are value-relevant overthe post-market correction period.

These findings are consistent with those who suggest that investors believed that ithas been more important for Internet companies to produce revenue rather than profits,and therefore, revenue is treated as a proxy for market acceptance and market share.45

We provide evidence that there is a stronger negative correlation among the stock pricesof Internet companies and their free cash flows prior to the market correction. Thisfinding is in accordance with Hand and King,46 who explain that the common claimmade about the market’s pricing of Internet stocks is that larger losses translate intohigher stock prices, the idea here being that losses incurred by these companies reflectstrategic expenditures by management, not poor performance! Managers of these Internetcompanies invested substantially in intangible marketing assets in order to expand theirmarket share quickly in the expectation of obtaining future profits. However, by January2000 it was clear that the availability of cash would determine the destiny of manyInternet companies and corroborating this assertion, the strength of the relationshipbetween stock prices and free cash flow diminishes to weak over the post-market correctionperiod. This reasoning is supported by our study where the results suggest that the marketappears to have adopted a more critical view of Internet companies’ ROE and ROArates. In other words, the market treatment of past winners and losers had changed (frommental accounting that rated historical equity premiums too high relative to the underlyingfundamentals)—and the market had become more economically efficient. According toconvention, shares are valued with reference to a company’s profits. In other words, it iscrucial that the ability of the company to generate future profits from their operationsunderlies the valuation to inspire investor confidence: in a sense, it is the view of onegroup of investors of the willingness of another set of investors to believe in the company’sability to make profits.

Because of lack of data availability, it has not been possible to include non-financialweb traffic metrics in the models, nor was it feasible to incorporate certain other financialvariables that could be of interest for the research, for example, marketing expenses andR&D expenditures, although Internet companies have tended to capitalize the costs ofboth marketing expenditures and product development thereby artificially inflating profitsor (more probably) reducing losses.

Innovation is an activity that has the potential to provide information helpful tosound investment decisions and we conclude this paper by providing some managementimplications deriving from our analysis.

One obvious lesson to be learned is that financial managers need to keep in mindthat fundamental technological innovation does not necessarily translate into greatinvestment opportunities.47 We have noted above that process innovation, usuallyresponses to a shift in demand or to increased costs in a firm’s production function, aretypically embodied in bought-in capital equipment. As Rosenberg48 asserts, innovationsaffecting part of a production process lead to searches for innovations affecting otherparts of the process but this process takes time, particularly in a sluggish global economy.

Internet company valuations must ultimately be based on corporate viability, implyinga reasonable business plan where logical expectations of generating a profit at some


future date feature. These future profits will generate the added future value that musttranslate into increased net cash flows. Discounted cash flow (DCF) is the dominantvaluation methodology, but there is a need to incorporate uncertainty and managerialflexibility into the DCF calculations. In order to hope to value a company accurately,several valuation methodologies need to be used.

Behavioural finance has been introduced as a valuable supplement to classical financialtheory. Psychological factors were considered important inputs to investment decision-making. In this way, reactions on financial markets that seem to be in opposition totraditional theory, that is, they appear irrational, may be explained and, as Fromlet49

asserts, appreciation of the psychological inputs to investment decisions may assistfinancial managers in avoiding serious mistakes and enable them to construct more viableinvestment strategies.

We have no doubt, that in the course of time, Internet-enabling technologies willgreatly benefit the economy and consumers in a multitude of ways. As a result of theinvention of the World Wide Web, the rules by which businesses operate are beingaltered. For consumer goods companies, the Internet allows them to gain direct access tosuppliers around the world. Potentially, at least, the web can put consumers in control,giving them access to information about products when they want it and how they wantit. This kind of ‘consumer empowerment’ means downward pressure on prices becauseinformed consumers can shop around in ways that could never have been imaginedbefore. But until the global economy expands and established industrial managementadopt this new technology with greater enthusiasm than hitherto we must wait for theInternet-enabling technologies to have the revolutionary transforming consequences of a‘new technological system’.

Notes and References

1. J. Schumpeter, Business Cycles: A Theoretical, Historical and Statistical Analysis of the Capitalist Process

(New York, McGraw-Hill, 1939).2. J. Clark, C. Freeman & S. Soele, ‘Long Waves, inventions, and innovations’, in: Long Waves in the

World Economy (London, Frances Pinter, 1984); C. Freeman, The Economics of Industrial Innovation

(London, Frances Pinter, 1982); J. Schumpeter, Innovation and Economic Growth (Cambridge, MA,Harvard University Press, 1966).

3. E. Chancellor, Devil Take the Hindmost: A History of Financial Speculation (London, Macmillan, 1999).4. R. Shiller, Irrational Exuberance (Princeton, Princeton University Press, 2000); A. Shleifer, Inefficient

Markets—An introduction to Behavioral Finance (Oxford, Oxford University Press, 2000).5. H. Shefrin, Beyond Greed and Fear (Boston, Harvard Business School Press, 2000).6. P. Oppenheimer, Chambers & R. Batty, The Anatomy of Stock Market Valuation (London, HSBC

Capel, 1996).7. A. King, ‘Valuing Red-Hot Internet Stocks, Strategic Finance, 81, 10, 2000, pp. 28–34.8. K. Pavitt, ‘Technical Innovation and Industrial Development—the New Causality’, Futures, 11, 6,

1979, pp. 458–470.9. See, for example, Freeman, op. cit., Ref. 2.

10. N. Rosenberg, Perspectives on Technology (Cambridge, UK, Cambridge University Press, 1976).11. See also, R. Nelson & S.G. Winter, ‘In Search of a Useful Theory of Innovation’, Research Policy, 6,

1977, pp. 36–76.12. D. Mowery & N. Rosenberg, ‘The Influence of Market Demand Upon Innovation: A Critical

Review of Some Recent Empirical Studies’, Research Policy, 8, 1979, pp. 102–153.13. T. Koller, ‘Valuing dot-coms After the Fall, The McKinsey Quarterly, 2, 2001, pp. 103–106.14. E.F. Fama, ‘Efficient Capital Markets: A Review of Theory and Empirical Work’, Journal of Finance,

25, 2, 1970, pp. 383–417.


15. H. Fromlet, ‘Behavioral Finance-Theory and Practical Application’, Business Economics, 36, 3, 2001,pp. 63–70.

16. Shiller, op. cit., Ref. 4.17. Chancellor, op. cit., Ref. 3.18. For a review of this burgeoning field, see R.H. Thaler (Ed.), Advances in Behavioural Finance (New

York, Russell Sage Foundation, 1993).19. D. Kahneman & M.W. Riepe, ‘The Psychology of the Non-Professional Investor’, Journal of Portfolio

Management, 24, 4, 1998, pp. 52–65.20. See, for example, Shleifer, op. cit., Ref. 4.21. D. Kahneman & A. Tversky, ‘Prospect Theory: An Analysis of Decision Making Under Risk’,

Econemetrica, 47, 2, 1979, pp. 263–291.22. See, for example, A. Kyle, ‘Continuous Auctions and Insider Trading’, Econemetrica, 53, 1, 1985,

pp. 1315–1336.23. Shiller, op. cit., Ref. 4.24. Chancellor, op. cit., Ref. 3.25. J.K. Galbraith, The Great Crash, 10th edn (London, Penguin Books, 1977), p. 72.26. Schumpeter, op. cit., Ref. 1.27. Schiller, op. cit., Ref. 4.28. Ibid.29. T. Copeland, T, Koller & J. Murrin, Valuation- Measuring and Managing the Value of Companies, 3rd

edn (New York, Wiley, 2000).30. J. Hand, ‘The Role of Accounting Fundamentals, Web Traffic, and Supply and Demand in the

Pricing of U.S. Internet Stocks’, North Carolina, Working paper, University of North Carolina, 2000.31. J. Pallant, SPPS Survival Manual (Milton Keynes, Open University Press, 2001).32. See for example, P. Ghauri, K. Gronhaug & I. Kristianslind, Research Methods in Business Studies

(London, Prentice Hall, 1995).33. See, for example, J. Foster, Data Analysis using SPSS for Windows, (London, Sage) 1999.34. Ghauri et al., op. cit., Ref. 32.35. See M. Saunders, P. Lewis P & A. Thornhill, Research Methods for Business Students, 2nd edn (London,

Pearson Education, 2000).36. S. Basu, ‘The Relationship Between Earnings Yield, Market Value, and Return for NYSE Common

Stocks: Further Evidence’, Journal of Financial Economics, 12, 1983, pp. 129–156.37. M. Statman, ‘How Many Stocks Make a Diversified Portfolio’, Journal of Financial and Quantitative

Analysis, 22, 3, 1987, pp. 353–364.38. Hand, op. cit., Ref. 30.39. M. Pendlebury & R. Groves, Company Accounts, Analysis, Interpretations and Understanding, 5th edn,

(London, International Thomas Business Press, 2001).40. L. Gross, The Art of Selling Intangibles: How to Make Your Million Dollars by Investing Other People’s Money

(New York, New York Institute of Finance, 1982).41. S. Besley & E. Brigham, Essentials of Managerial Finance, 12th edn (New York, The Dryden

Press, 2000).42. Shefrin, op. cit., Ref. 5.43. Schumpeter, op. cit., Refs 1 and 2.44. S. Cleland, & J. Eade, ‘Follow the Money to Wall Street’s Big Secret’, Financial Times, 9 October

2002, p. 23.45. For example, Hand, op. cit., Ref. 30; King, op. cit., Ref. 7.46. Ibid.47. W. Jahnke, ‘Valuing New Economy stocks’, Journal of Financial Planning, 13, 6, 2000, pp. 46–49.48. Rosenberg, op. cit., Ref. 10.49. Fromlet, op. cit., Ref. 15.


Appendix A: List of tickers and names for the 169 Internet companies usedin this study

E-Commerce/Products1. ASFD ASHFORD.COM INC2. BFLY BLUEFLY INC3. BGST BIGSTAR ENTERTAINT4. BNBN BARNESANDNOBLE5. BUYX BUY.COM INC6. BYND BEYOND.COM CORP7. COOL CYBERIAN OUTPOST8. CTAC 1-800 CONTACTS9. EGGSQ EGGHEAD.COM INC

10. FASH FASHIONMALL.COM11. FLWS 1-800-FLOWERS12. GSPT GLOBAL SPORTS13. HITS MUSICMAKER.COM14. IGOC IGO CORP15. KTEL K-TEL INTL16. MBAY MEDIABAY INC17. NWKC NETWORK

COMMERCE18. PDEN PET QUARTERS INC19. PPOD PEAPOD INC20. SPNT SHOPNET.COM INC21. STMP STAMPS.COM INC22. THIN NUTRI/SYSTEM INC

E-Commerce/Services23. BPNT BARPOINT.COM24. CYTY CYTATION CORP25. EBAY EBAY INC26. EBNX EBENX INC27. ELOT ELOT INC28. ESTM E-STAMP CORP29. EXPE EXPEDIA INC—A30. GEPT GLOBAL E-POINT31. HHNT HEADHUNTER.NET32. HLTH WEBMD CORP33. HOMS HOMESTORE.COM34. HOTJ HOTJOBS.COM LTD35. IPRT IPRINT TECH36. MCYC MCY.COM INC37. MDLI MEDICALOGIC/MEDS38. MED E.MEDSOFT.COM39. PCLN PRICELINE.COM40. RTRN RETURN ASSURED41. SWBD SWITCHBOARD INC42. TMCS TICKETMASTER-B43. TMPW TMP WORLDWIDE

E-Marketing/Information44. ACRU ACCRUE SOFTWARE45. APTM APTIMUS INC46. AVEA AVENUE A INC47. BFRE BE FREE INC48. CBLT COBALT GROUP49. CLAC CLICKACTION INC50. DCLK DOUBLECLICK INC51. DRIV DIGITAL RIVER52. DTAS DIGITAS INC53. EPNY E.PIPHANY INC54. FIRE FIREPOND INC55. JMXI JUPITER MEDIA56. LNTY L90 INC57. LVWD LIVEWORLD INC58. MMPT MODEM MEDIA INC59. MPLX MEDIAPLEX INC60. NCNT NETCENTIVES INC61. NETP NET PERCEPTIONS62. PRMO PROMOTIONS.COM63. TFSM 24/7 MEDIA INC64. THDSE 3DSHOPPING.COM65. VCLK VALUECLICK INC

Internet Content—Entertainment66. ADBL AUDIBLE INC67. AHWYQ AUDIOHIGHWAY.COM68. ALOY ALLOY ONLINE69. HOLL HOLLYWOOD MEDIA70. MDM MEDIUM4.COM INC71. MPPP MP3.COM INC72. NETRC NETRADIO CORP73. NMUS NET4MUSIC INC74. NTXY NETTAXI INC75. PNJA PANJA INC76. POPM POPMAIL.COM INC77. SALN SALON MEDIA GRP78. SDRV STARDRIVE SOLUTI79. SPLN SPORTSLINE.COM80. SSTR SILVERSTAR HLDGS81. TEEE GOLF ROUNDS.COM82. TGLO THEGLOBE.COM INC83. VDAT VISUAL DATA CORP

Internet Content—Information/News84. ABTL AUTOBYTEL.COM85. ADAM ADAM INC


86. AMEN CROSSWALK.COM87. ARTD ARTISTDIRECT INC88. ASKJ ASK JEEVES INC89. CLKS CLICK2LEARN INC90. CNET CNET NETWORKS IN91. EDGR EDGAR ONLINE INC92. GLBN GLOBALNET FINL93. HCEN HEALTHCENTRAL.CO94. HGAT HEALTHGATE DATA95. HGRD HEALTH GRADES96. HMSK HOMESEEKERS.COM97. HOOV HOOVERS INC98. HPOL HARRIS INTERACT99. HSTM HEALTHSTREAM INC

100. IIIM I3 MOBILE INC101. IMPV IMPROVENET INC102. INFO INFONAUTICS INC103. INSP INFOSPACE INC104. INTM INT MEDIA GROUP105. IVIL IVILLAGE INC106. KNOT KNOT INC (THE)107. KOOP DRKOOP.COM INC108. LOOK LOOKSMART LTD109. LTWO LEARN2.COM INC110. MKTW MARKETWATCH.COM111. MLTX MULTEX.COM INC112. ONES ONESOURCE INFORM113. PASA QUEPASA.COM114. SNOWC SNOWBALL.COM INC115. SRCH US SEARCH.COM116. TSCM THESTREET.COM

Internet Financial Services117. EBDC EBANK.COM INC118. EELN E-LOAN INC119. ESPD ESPEED INC-CL A120. FNCM FINET.COM INC121. INSW INSWEB CORP122. NTBK NET.B@NK INC123. ORCC ONLINE RES CORP124. QUOT QUOTESMITH.COM

Web Hosting/Design125. ATHY APPLIEDTHEORY126. CPTH CRITICAL PATH

127. DIGX DIGEX INC128. ECLG ECOLLEGE.COM INC129. EGOV NATL INFO CONSOR130. EXDS EXODUS COMM INC131. FNT FRONTLINE COMM132. GBIX GLOBIX CORP133. ICCX INTERNET COMM &C134. INIT INTERLIANT INC135. INLD INTERLAND INC136. ISLD DIGITAL ISLAND137. MACR MACROMEDIA INC138. MIGS MCGLEN INTERNET139. NAVI NAVISITE INC140. NNCI NETNATION COMM141. NPSC NEW PARADIGM SOF142. PSIX PSINET INC143. RCOM REGISTER.COM144. SITN SITI-SITES.COM145. TSCN TELESCAN INC146. VRSO VERSO TECH

Web Portals/ISP147. ARDT ARDENT COMM148. ATHM AT HOME CORP149. BIZZ BIZNESSONLINE.CO150. CYCO CYPRESS COMM INC151. ELNK EARTHLINK INC152. EWEB EUROWEB INTL153. FSST FASTNET CORP154. FUTR IFX CORP155. GEEK INTERNET AMERICA156. GOTO GOTO.COM INC157. HEAR HEARME158. HYPD HYPERDYNAMICS CP159. JWEB JUNO ONLINE SERV160. LOAX LOG ON AMERICA161. NTWO N2H2 INC162. NZRO NETZERO INC163. PRGY PRODIGY COMM-A164. PSCO PROTOSOURCE CORP165. SOFN SOFTNET SYSTEMS166. SPDE SPEEDUS.COM INC167. STRM STARMEDIA NETWRK168. TZIX TRIZETTO GROUP169. WGAT WORLDGATE COMM

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