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The dot-com era of the late 1990s produced hundreds of publicly traded organizations whose principle product or service was, in one way or another, tied to information technology. Brokerage houses, venture capitalists, and investors in the secondary markets plunged billions of dollars of capital into tech stocks. For a period of almost five years, dot-coms, bolstered by massive market capitalization, buoyed common stock indices to never-before-seen highs. Many of these information technology-based companies participated in public stock offerings without having concise business plans, workable operating models, or even a hint of profitability. In light of the explosion of the dot-com bubble in the spring of 2001, many analysts and investors asked the same question, “What happened?” In this study, three forecasting tools, two Artificial Neural Networks (ANN) and a Multiple Linear Regression (MLR), are utilized to approximate the first day closing price of public stocks of Information Technology-Based Firms (ITBFs). Each tool utilizes only quantitative financial data to prepare estimates. A comparative analysis of the estimates provides the foundation from which inferences are drawn regarding 1) the value of the financial data with respect to prediction of first day closing stock price of ITBFs and 2) the relative robustness of the predictive tools used in the study.

What is a Dot-Com Worth? an exploration of the Value Estimates of Information Technology-Based Firms.