STOCK PRICE PERFORMANCE OF INTERNET FIRMS: IDENTIFICATION OF KEY DRIVERS
Kathleen
Seiders, Babson College
Elizabeth G. Riley, Babson
College
ABSTRACT
INTRODUCTION
RESEARCH BACKGROUND
DRIVERS OF
INTERNET STOCK PRICE PERFORMANCE
METHODOLOGY
DISCUSSION
EXHIBIT 1
TABLE 1
TABLE 2
TABLE 3
CONTACT
REFERENCES
Valuation of publicly held firms in the electronic commerce industry is volatile: the market recently has witnessed radical stock price increases and declines in newly issued Internet companies. What are the underlying causes of instability and lofty valuations in this market? This research takes initial steps toward identifying and examining the key drivers of Internet firm stock price performance. We propose a model that incorporates traditional and non-traditional variables, test our propositions, and discuss the implications of our results. Findings from this study underscore the need to develop future investigations of Internet sector performance using models that include strategic and marketing-related variables.
The stock price volatility of Internet firms recently has received significant attention in the business press. Because the market has witnessed radical increases and declines in the valuations of newly issued Internet companies, and industry experts have pronounced these stocks as consistently overvalued, some mystery surrounds this phenomenon. No known studies have systematically identified and examined the underlying causes for the lofty valuations given to Internet IPOs. This study takes initial steps toward determining the key drivers of stock price performance in the electronic commerce market.
A fundamental premise of this research is that Internet stock prices are influenced by factors that are unique to the sector. In addition, some factors have greater or lesser influence in this context than in traditional market valuations. In the sections that follow we identify the likely causes of these differences, propose a set of Internet stock price performance drivers, and discuss the results of a study designed to test the proposed relationships. Finally, we outline the limitations of our study and offer suggestions for future research.
The dynamics of Internet stock price performance are regarded by many in the investment industry as difficult to explain and/or predict. Three key characteristics distinguish the performance of Internet stock prices from the performance of traditional investments. These characteristics include 1) extreme price fluctuations; 2) substantial activity in individual on-line (and day) trading; and 3) shortage of supply for particular Internet stocks.
In November of 1998, Business Week reported that eBay Inc., a Web auctioneer, was valued at 773 times expected 1999 earnings and Yahoo, trading at 270 times forecast earnings, was valued three times higher than the New York Times Company (Himelstein 1998). In their first days of trading, Theglobe.com, Earthweb Inc., and Marketwatch.com closed at 606 percent, 247 percent, and 474 percent, respectively, above their initial offering prices (Abelson 1998; Gilpin 1999). In 1998, Amazon.com’s share price grew 966% in value; its market value surpassed $30 billion, exceeding that of all American bookstores (including Barnes & Noble and Borders) combined (The Economist 1999).
Analysts refer to the trading activity in Internet stocks with terms such as “feeding frenzy,” “mob rule,” “mania,” “the lottery,” and “trading on vapors.” Prices of Internet stocks have fluctuated dramatically—prices have risen so high so quickly that analysts have warned of a significant sector price fall (Hansell 1999).
The proportion of individual investors relative to institutional investors is growing rapidly, and the day trader phenomenon has had an important impact on big-cap Internet stocks (Caruso 1998). Fourteen percent of all equity trades were executed online in 1998, as Internet brokers attracted a deluge of new customers; the online brokerage industry had 7.3 million accounts and $420 billion in assets. On-line stock trading volume rose by a third in January of 1999; that month, E*trade handled an average of 42,000 trades a day (Hansell 1999). Analysts have noted that a shortage of supply of Internet stocks—a limited number of shares sold in IPOs—is serving to propel the prices (Abelson 1998). Because a number of Internet companies have had insufficient shares outstanding to meet institutional investor guidelines, mutual and pension funds have found it difficult to purchase large stakes without substantially moving share prices (Hansell 1999).
Methodologies used for valuation vary and large differences have been found to exist when valuation approaches are compared, particularly across industries (Houlihan Valuation Advisors 1998). Many variables examined in this literature stream (e.g., earnings record, dividend-paying capacity) are not applicable to our study context, and patterns found to affect traditional market capitalization may have limited relevance for the Internet sector. Measures of new venture success generally are not meaningful in developmental stage firms (Deeds, DeCarolis, and Coombs 1998). Valuation in new or emerging business areas is very difficult because of its intangibility, extreme uncertainty, and dependence on future prospects. The most common approach—to make comparisons with selected peer group companies (Mills 1998)—is not feasible in an Internet setting.
An analyst from Securities Data Inc. noted: “Internet valuations violate every type of traditional benchmark we’ve used to gauge the worth of a stock based on earnings or revenue.” We contribute to the extant literature by investigating the drivers of Internet stock price performance within this increasingly significant market.
DRIVERS OF INTERNET STOCK PRICE PERFORMANCE
This study examines perceptual and objective influences of stock price performance. Perceptual drivers include the quality of the management team, the attractiveness of the business concept, the firm’s visibility in the media, and the quality of the lead investment bank and its financial analyst. Objective drivers include level of risk based on initial public dilution, quarterly growth rate in revenues, and market capitalization relative to annual revenues. These drivers are presented in Exhibit 1.
Perceptual Drivers
The quality of an early-stage company’s management team (i.e., chief executive officer, chief financial officer, and chief technology officer) widely is regarded as a key indicator of the firm’s future success. Management quality has been studied as a factor in venture capital decisions (Shrader et al. 1997; Tyebjee and Bruno 1984) as well as in firm performance (Roure and Keeley 1990; Sandberg and Hofer 1987). Both individual and collective aspects have been investigated, including functional and related industry experience, education, team completeness, and prior joint experience (Schilit 1998; Keeley and Roure 1990). The more complete the team, the broader are contacts—with customers, suppliers, potential employees, consultants, and investors—likely to be (Roure and Keeley 1990).
Management quality in an Internet context is evaluated in this study as a function of prior general management and IPO experience and working history of the team. For the CEO, specific criteria include profit and loss experience in a high growth business, start-up expertise, and experience with public ownership. Criteria for the CFO include experience in revenue recognition models, reporting expertise to public owners (particularly institutions), and relationships with accounting firms and Wall Street analysts.
Proposition 1: Internet firms with a management team regarded to be high in quality are more likely to demonstrate strong stock price performance, i.e.: a) high stock price per share, and b) significant post-IPO price increase
The quality of the firm’s investment bank, including the quality of its leading financial analyst, is likely to be an important driver of stock price performance. Investment bank quality is related to status (e.g., top tier firm), national reputation, and distribution capability. Specifically, quality is a function of the bank’s track record in Internet IPO’s, relationships with institutional investors, ability to price relative to demand, and ability to expedite and add value to the IPO process. We evaluate the quality of a bank’s leading financial analyst as a function of that individual’s Internet specialization, publication record (including relative ratings), and following within the investment community. For example, a buy recommendation from a member of Institutional Investor’s All-American Team of analysts is more influential than a buy recommendation from an analyst not on that team. The analyst’s role particularly is significant for institutional investors.
Proposition 2: Internet firms with a lead investment bank and financial analyst regarded to be of high quality are more likely to demonstrate strong stock price performance, i.e.: a) high stock price per share, and b) significant post-IPO price increase
Product differentiation traditionally is regarded to be an important determinant of an entrepreneurial firm’s success (Carter et al. 1994) and is a key factor used in evaluating a firm’s business concept. A firm’s differentiation strategies and product distinctiveness are prominent in venture capitalists’ investment assessment criteria (Bygrave et al. 1997; Shrader et al. 1997). Product differentiation is related to first mover advantage, which allows firms to gain customer awareness, set product standards, establish switching costs, gain power in the distribution channel, and achieve experience curve effects (Deeds, DeCarolis, and Coombs 1998; Lieberman and Montgomery 1988; Robinson 1998).
Because the Internet represents a new commercial frontier, a firm’s business concept is likely to be both more difficult to evaluate and more important to investors. Therefore, the perceived strength of business concept is a driver of stock price performance as well as a critical determinant of future commercial success. Future earnings potential of Internet firms is difficult to gauge, and a great deal of speculation about success factors in this channel has been published. Our analysis incorporates frameworks from industry reports, specifically those of Forrester Research (Kadisson 1998) and Morgan Stanley (Meeker 1997).
An Internet firm’s business concept is compelling when it offers the promise of first mover advantage (and brand leadership), leverages technology and database capability, and is designed to capture economies of scale. A compelling Internet concept targets a large and growing market of identifiable and solvent customers with products and services that are relatively simple, readily comprehensible, and reasonably involving. Ease of use, speed, and accessibility (i.e., aspects of convenience) characterize delivery of the product or service. The appeal to the buyer is likely to be price- or efficiency-driven, or to offer a significant service enhancement.
Proposition 3: Internet firms with a compelling business concept—relative to Internet-specific characteristics—are more likely to demonstrate strong stock price performance, i.e.: a) high stock price per share, and b) significant post-IPO price increase
Marketing-related factors have been minimally discussed in the new venture strategy and firm performance literatures. New ventures entering markets with an aggressive marketing strategy have been found to be more successful than those targeting narrow segments (Carter et al. 1994). Sandberg and Hofer (1987) identify marketing innovation as a type of venture strategy; Carter et al. (1994) discuss the importance of “market sensitivity” as a counterpart to product distinctiveness. Brand-name recognition is reported to be a predictive factor of post-IPO stock market performance (Schilit 1998).
Many electronic commerce firms have engaged in heavy spending to gain brand recognition and acquire customers. E*Trade, for example, spent three times as much on marketing as on technology in 1998; the company spent $150 million in 1999 on advertising to gain new accounts (Glasgall 1999). Customer acquisition costs are rising rapidly. Advertising and press generated by Internet companies act as a catalyst for sales in all electronic commerce product and service sectors (Meeker 1997). Just as this awareness factor drives sales, it is also likely to drive investment activity (Lucchetti 1998).
The retail market plays an increasingly important role: higher proportions of investments are under retail, rather than institutional, ownership. This is especially true for Internet stocks, which have enjoyed a certain vogue with individual investors, who recently have become “empowered” (Glasgall 1999). The retail market, characterized by a short-term orientation, is more likely than the institutional market to be sensitive to and act on the intensity of media coverage and exposure. Whereas we would be less likely to use a marketing-driven awareness factor in a traditional model, or in an environment where the institutional market drives the retail market, we suggest that firm visibility via the media is a driver in an Internet context. Firm visibility is a function of media (general and business) attention and investment in marketing communications.
Proposition 4: Internet firms with significant visibility—as indicated by intensity of media attention—are more likely to demonstrate strong stock price performance, i.e.: a) high stock price per share, and b) significant post-IPO price increase
Objective Drivers
A factor likely to influence stock price performance in the Internet sector is dilution per share to new investors—the gap between the last private round and the first public trade. This represents the risk transferred from private investors to the public: higher relative dilution represents strong return for the early stage investor but signifies higher risk for the public investor. Higher dilution levels indicate a window of opportunity—the potential to maximize return. Analysts have noted that Internet stocks exhibit a relatively high range of dilution. (NASDAQ recently lowered the market cap parameters for an IPO.) The apparent relationship of a firm’s risk profile to its perceived future growth suggests that dilution is a driver of stock price performance. This indicator is related to the “market value added” measure of new venture success used by Deeds, DeCarolis, and Coombs (1997).
Proposition 5: Internet firms with a high risk profile—as defined by post-IPO dilution—are more likely to demonstrate strong stock price performance, i.e.: a) high stock price per share, and b) significant post-IPO price increase
Growth in revenue is a variable traditionally used in models that measure or predict firm performance. “Change in sales” is one of the seven most frequently used measures of economic performance (Robinson 1998). This variable is important in the Internet sector, where profitability growth is likely to be nonexistent. In our context, quarter-to-quarter growth measures increase in the number of customers and/or transactions, thus representing consumer acceptance and utilization. Quarterly revenue growth is used in the technology sector to determine the degree to which a firm is accelerating or declining.
Proposition 6: Internet firms with strong quarterly revenue growth are more likely to demonstrate strong stock price performance, i.e.: a) high stock price per share, and b) significant post-IPO price increase
The expectation of future stock performance is likely to be captured by the ratio of market capitalization to annual revenues. Use of this measure allows us to consider a standardized variable that represents the degree of market optimism (or pessimism) associated with an individual firm’s stock. Market capitalization-to-sales can serve as an earnings multiple proxy in a sector void of earnings. Although the ratio might function in some contexts as an indicator of performance, in an Internet setting it may most importantly serve as information related to long-term expectations.
Proposition 7: Internet firms with strong market capitalization-to-sales ratios are more likely to demonstrate strong stock price performance, i.e.: a) high stock price per share, and b) significant post-IPO price increase
Studies have found that IPOs handled by the most prestigious lead underwriters produce superior returns (e.g., Stein and Bygrave 1990). The quality of the venture capital firm that provided initial seed money was not tested here due to insufficient data: many of the public Internet firms were not funded by venture capital. Growth in profitability was not considered because few of the public Internet firms are profitable.
Interviews with analysts and electronic commerce consultants were conducted to identify variables likely to influence the valuation of Internet firms. Using our findings and the results of previous studies, we generated a set of multi-item measures designed to assess the effects of relevant constructs.
Research Approach
We used two measures to capture our dependent variable, stock price performance. The first is a moving average, calculated from stock prices at the close of three consecutive months: November (1998), December (1998), and January (1999). The second measure is the percentage change in stock price since the initial public offering. This was calculated using the IPO price and the three-month closing average. (The IPOs occurred from August of 1995 to December of 1998.) Efficient market theory suggests that our first measure (average stock price), which is determined by daily market events, is superior to our second measure (price change since IPO), which is, to some degree, a function of IPO price.
The independent variables in this study (drivers of stock price performance) include: management team quality, investment bank/analyst quality, business concept potential, firm visibility, dilution to new public investors, quarterly revenue growth, and market capitalization-to-sales. Insight gained from exploratory research was used in construct operationalization and measurement design. We use a combination of objective measures and rating scales established to assess more complex and/or perceptual constructs. These variables are rated on 7-point Likert- type scales. Multiple expert raters, selected for their specific knowledge fields, were used in all cases. Inter-rater reliabilities range from 0.84 to 0.93.
Drivers of stock price performance—introduced in Exhibit 1—are described at length above. The constructs of management team quality, investment bank/analyst quality, and business concept attractiveness were operationalized using a multi-dimensional approach. Firm visibility is captured by a proxy: number of mentions in the general and business press. Headline and/or lead paragraphs were searched in both business and general periodicals (e.g., The New York Times, Forbes, Newsweek, USA Today, Business Week, The LA Times, The Wall Street Journal). The measured set included over one thousand periodicals targeted to the general public; the search covered a 24-month period (1997 and 1998). Dilution and IPO price were calculated on the close of the first day of trading. Quarterly revenue growth is an average of growth in the third and fourth quarters of fiscal 1998. The market capitalization-to-sales ratio is based on shares outstanding current to our study period and annual revenues for fiscal 1998.
A sample of forty Internet companies was selected for analysis; data collection was completed for 38 of the 40 firms. Companies included in the sample were required to be pure Internet, NASDAQ- traded firms with a market capitalization minimum of 50 million dollars. Additionally, the sample companies were required to be followed by multiple research analysts at bulge bracket firms. Industry statistics suggest that our sample included approximately 90 percent of all public Internet companies at the time of data collection.
Analysis and Results
Regression analysis was used to estimate the relationships between the independent variables and the performance (dependent) variables and to test our research propositions. In separate models, we estimated the regression coefficients using the technique of least squares. Initially, data was checked to verify that assumptions of linearity, constant error variance, independence, and normality were not violated. Normal probability and partial regression plots were generated for these analyses. Because the calculation of tolerance and variance inflation factors found collinearity and multicollinearity to be insignificant, we are confident in our assessments of each independent variable’s unique contribution.
Table 1 shows results of the first model, with the dependent variable measured as a three-month average of the firm’s stock price. An F test is used to test the null hypothesis that there is no linear relationship between the dependent variable and the set of independent variables. Model goodness of fit is evaluated using R2, the coefficient of determination. Regression results show that 70 percent (adjusted R2) of the variation in stock price performance is explained by the model, which has an F value of 13.37 and is significant at p< 0.000.
Standardized beta coefficients are used for interpretation of individual effects and for assignment of relative importance to each independent variable. Student’s t is the distribution of the statistic used to test the linear relationship between each variable and stock price performance. Five of the seven independent factors we tested contributed significantly (p<0.05) to the model as proposed. In order of importance, these include firm visibility, market cap-to-sales, dilution, business concept, and revenue growth. Results of this analysis support propositions 3, 4, 5, 6, and 7. Quality of investment bank/analyst (proposition 2) is significant, but not in the proposed direction. Propositions 1 (quality of management team) and 2, therefore, are not supported. (The correlation matrix for this model is shown in Table 2.)
Results of the second model, with the dependent variable measured as change in stock price after IPO, are shown in Table 3. The F-value is 7.68, and the model is significant at p<0.000. In this model, 56 percent (adjusted R2) of the variation is explained by the predictor variables. Three of the seven independent factors—market cap-to-sales, firm visibility, and quarterly revenue growth—play a significant role (p<0.05) in explaining this performance variable. Results of the analysis support propositions 4, 6, and 7; propositions 1, 2, 3, and 5 are not supported. (This model’s correlation matrix is shown in Table 2.)
Although no specific propositions relating to venture capital firm funding are offered, the effects of venture capital versus other sources of private funding were tested. Effects were insignificant for both model one (p = .42) and model two (p = .40).
In this study, we propose and test a mix of non-traditional (e.g., firm visibility) and traditional (e.g., quarterly growth rate) variables posited to influence Internet stock price performance. The results of our analysis suggest that the measurement and evaluation of these drivers and their individual effects is a contribution to the literature. Our research indicates that shifts in market dynamics, such as growth in individual on-line (and day) trading and earlier public offerings, may have affected the firm valuation process in the electronic commerce sector. Promotion in the popular press may be bringing electronic commerce users who previously were light or non-traders into the market, increasing demand and consequently price.
Results from our first analysis suggest that the proposed set of key drivers of Internet stock price performance provide a strong explanatory model. Five of our seven independent variables make a significant, unique contribution to the model. These findings are particularly interesting because our set of proposed drivers differs from explanatory variables used in traditional models.
The importance of firm visibility and business concept (as operationalized) is notable, as these two factors are highly Internet relevant. Deeds, DeCarolis, and Coombs (1997) argue that uncertainty about future profitability creates an informational barrier for financial markets, generating a need for indicators that are signals of the future performance of firms in emerging industries. The Internet firms we studied are likely to be using aggressive marketing and advertising as a signaling mechanism, sending messages to the marketplace and to potential investors about the capabilities and the future value of the firm.
Market capitalization-to-revenues is both company- and market-driven: a major uptick in the market for one stock may trigger the buying of other Internet stocks to avoid missed opportunity. Dilution’s strong performance may be related, to some degree, to higher quality current market conditions. Companies that need to fund extensive growth to maintain first mover advantage leverage the ability to execute early IPOs.
An interesting result in this model is the lack of significance of management team and investment bank/analyst quality. In general, management quality is high across firms in our sample, suggesting that top tier individuals were recruited for their ability to manage IPO and post-IPO issues and processes. The constraint in variability across firms for this factor likely contributed to our result. This result is consistent with those of Keeley and Roure (1990) and Sandberg and Hofer (1987), who were unable to link firm performance with the background of the founding management team. Roure and Keeley (1990) found no individual effects to be significant, proposing that because most firms were qualified in the measures and above a threshold, additional qualifications added no value.
Our findings relative to investment bank/analyst also may be explicable. Because of Internet market attractiveness, firms are able to choose top-ranked investment bankers and financial analysts. These analysts are broadly quoted in the popular press so their perspectives are shared beyond the institutional investor community—a trend that may dilute their influence. If a higher proportion of retail than institutional shares are traded daily, the relationship we detected could be related to day traders who do not have access to investment research. This phenomenon also may be related to the strong performances of the firm visibility and business concept variables in our model.
Overall, we present a robust and parsimonious model. Model two, as anticipated, provides a somewhat less optimal test for our proposed model. This form of our dependent variable is linked to the firm’s initial price (which falls within a tight range) and thus provides a less suitable framework for analysis, as mentioned above. For example, in the time period between the IPO and our price calculation (November 1998 through January 1999), a successful new entrant could impact the price of an early pioneer.
The strength of our primary model supports our fundamental research premise: that the drivers of Internet stock performance differ from traditionally recognized factors. Traditional models are asset or earnings based; Internet firms have low asset intensity and few have earnings. Historically, companies without quarter-to-quarter profitability or financial robustness are unable to go public and gain access to capital. Internet firms are moving to IPO status more rapidly and with fewer constraints.
Developing industries offer greater opportunities: profits and sales are higher when firms enter at an introductory lifecycle stage, especially when an industry has higher levels of differentiation (Robinson 1998; Sandberg 1986).Traditional valuation theories assume an efficient market: the price of a share should at all times reflect rational expectations about the future (Leleux, Lange, and Matthys 1995). However, the expectation of the market relative to future performance of firms operating in this sector is formidably high, indicating that the “electronic revolution” concept has gained wide acceptance.
Limitations and Directions for Future Research
This research takes initial steps toward addressing important questions about the determinants of Internet stock performance; however, it is characterized by certain limitations. The first is sample size. To meet our qualifying criteria, we were constrained to a relatively small set of sample companies. The strictness of our sample selection process reinforces the validity of our findings. The size limitation affected the number of independent variables we legitimately could test (using a minimum ratio of five observations per explanatory variable) in our model (Hair et al. 1998). We offer a well-specified and comprehensive initial model, and a productive direction for future research would be the application of more fine-grained variables to a larger sample of Internet firms. The cross-sectional nature of our study can be viewed as a limitation; time series analysis will be a valuable future approach as the Internet market ages. In addition, dynamics outside of a bull market need to be examined.
Sample size parameters influenced the operationalization of certain model constructs. For example, our original objective for firm visibility was to evaluate the degree to which the sample firms are marketing driven. We are confident that we have captured the essence of the awareness factor, and future studies in this area would benefit from in-depth examinations of firms’ marketing strategies, i.e., of resources dedicated to traditional media, to Internet media, to Internet alliances, to co-branding, and to brand building in general. Our measurement of business concept, investment bank, and management quality would be enhanced by disaggregation—by the evaluation of specific aspects of these constructs. In summary, this is a research area that warrants ongoing, systematic investigation. The prominence of entrepreneurial firms and venture capital activity in the Internet sector underscores the need for close examination and multiple perspectives.
CONTACT: Kathleen Seiders, Babson College, Wellesley, MA 02457; (T) 781-239-4522; (F) 781-239-5020; seiders@babson.edu
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