RETURNS TO INVESTORS IN STOCKS IN NEW INDUSTRIES

I. INTRODUCTION One thousand dollars invested in America Online's initial public offering (IPO) in March 1992 became $295,545 by the end of 2000 when AOL was purchased by Time-Warner. This is an extraordinary payoff for shareholders: over 5% per month. While this investment has an outstanding return, new industries have high risk and their stocks are likely to have high expected returns. On the other hand, the extraordinary behavior of dot-corn stocks makes this conclusion less than obvious. The stratospheric prices of dot-corn stocks and returns to some shareholders suggest that expected returns in a new industry may well be dominated by forces not common in more mature industries. One possibility is euphoria or craziness, an explanation given more plausibility by the boom and bust in dot-corn stock prices. Another explanation is that such stocks are similar to options and lottery tickets. This possibility has occurred to many observers, among them Alan Greenspan who observed that When you are dealing with stock the possibilities of which are either it's going to be valued at zero or some huge number--you get a premium in that stock price which is exactly the same sort of price evaluation process that goes on in the lottery. --(Blumberg Capital 1999) Perhaps stocks in new industries have a distribution of returns that includes the possibility of large gains and small losses? If so, then investors may buy stocks for the same reasons that people are willing to pay for lottery tickets. This explanation could be consistent with high valuations and even negative-expected returns because investors are willing to pay on average to have a small probability of a large gain. (1) Indeed, the limited history of dot-corns suggests realization of substantial negative returns. The geometric return to shareholders averaged across dot-coms is --4.3% per month. Even so, the history of dot-coms probably is too short to deliver very reliable evidence on the expected return in new industries. This average return to stockholders is based on the same firms as in the study by Hand (2001), but there are only five firms in the Center for Research in Security Prices (CRSP) data for these firms as recently as 1995 and the financial crisis of 2007 and 2008 would suggest ending the data in 2006. We estimate expected returns in new industries by examining new industries with longer experiences than dot-coms. Many analogies have been drawn between dot-coms and earlier industries as far back as railroads and telegraphs, for example, in Business Week (Mandel 1999; Hof and Hamm 2002), the Economist (2000), Nairn (2002), and Perez (2002). (2) Among these analogies is the importance of network effects. Network effects are important for dot-coms and they are important for some of the industries examined in this paper. Industries examined in this paper include network industries if a network industry is defined as one in which a standard for the product or the availability of complementary inputs has high marginal value for the industry. One such industry is the automobile industry, which standardized on gasoline engines instead of electricity or steam. Another is the telegraph industry, for which interconnection is as crucial as for the Internet. While it is less obvious, interconnection is an important issue for railroads as well. When differences in track width--gauge--are large enough, connections across lines can require unloading and reloading railroad cars. Gauge was not standardized in the United States until the 1880s and even today, gauge in Spain and Portugal is different than in the rest of Europe. In this paper, we examine the returns in major, new industries in the United States and summarize the distribution of returns over the period of the industry's development. Our paper is related to papers by Fama and French (2001) and Gompers and Lerner (2003). Fama and French (2001) examine the returns on newly listed firms in the CRSP data set from 1926 to 2000 and find that these firms' returns are similar to their benchmark returns. …