Bubbles and Crashes

The chapter describes the dot-com bubble and the narrative that fueled it, as well as the uncertainty that faced early dot-com start-ups. The introduction also describes the methodological challenge of studying bubbles, which requires identifying comparable assets that are and are not associated with bubbles. The chapter then provides a list of fifty-eight technologies that meet this criterion. The factors that cause bubbles are introduced, including uncertainty, the existence of pure-play investment, the susceptibility of technologies to narratives, and novice investors. The relevance of the factors is illustrated through the example of arc lighting-the first commercialized electric lighting technology.

The chapter begins with an in-depth definition of boom and bust cycles, as well as bubbles. It then compares the commercialization and financial histories of four technologies: the telephone, insulin, radio, and television. The chapter introduces a methodology and measure of speculative activity, frothiness. Stock index movements across time are compared, and boom and bust periods defined and identified. Booms and busts are found in radio, and to a lesser degree in television and telephone. No speculative activity is found in insulin, despite its dramatic effects in children's health care especially.

The chapter begins with a description of narratives and storytelling and their role in imagining development paths for new technologies. Theories of options, biases and expectations and belief coordination all contribute the construction of narratives when new technological paths are uncertain. The chapter then unbundles the various types of uncertainty into specific categories: technological, competitive, business model, value chain and regulatory, and demand uncertainty. Each type of uncertainty is explored through examples, including aviation, rubber, the telephone, the automobile, and radio, to understand how and why each can form a contextual foundation of a bubble narrative.

The chapter describes the host of biases that make novices more likely to believe speculative narratives through stories of stock market speculation in both recent and distant past. The biases are contextualized into the uncertainty associated with particular technology, and theories of sticky ideas and idea contagion are invoked to understand which narratives are more likely to emerge. The chapter then explores the history of financial innovations that have made the stock market more accessible to the public. In particular, technological, legal, and institutional innovation in the stock market is followed from the 1850s through the 1970s, as is the role these innovations had in democratizing US capital markets.

The chapter brings together the frameworks developed in this book to rank technologies according to their likelihood of producing a bubble and to evaluate whether market speculation did indeed occur. The usefulness of the framework is explored through the historical accounts of the automatic watch, phototypesetting, antibiotics, nylon hosiery, the jet engine, commercial aviation, the rotary (Wankel) engine, and the transistor. The chapter concludes with a discussion of technologies that theory predicted would lead to bubbles but for which no bubbles were observed.

The chapter applies the model of bubbles derived from historical cases of technological innovation to a set of thirty recent technologies. This test evaluates the generalizability of the model by determining whether it can be applied beyond the training set of contexts in which it was developed. Examples include the internet, personal computers, liquid crystal displays, and laparoscopic surgery. Additional examples from outside the domain of technological innovation include the housing bubble and the Great Recession of 2007-2009, as well as Tesla and the future of the electric vehicle. Readers are invited to engage with the data to provide additional interpretations.

The chapter considers the public and private policy implications of the model of speculative bubbles advanced in this book. Using a series of questions intended to help potential investors identify when they may be part of a bubble, the chapter proposes several policy ideas proceeding from first principles.