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Two Centuries of Innovations and Stock Market Bubbles

July 2018
Marketing Science 37(4)

DOI:10.1287/mksc.2018.1095

Authors:

Texas A&M University

The interplay between innovation and the stock market has been extensively studied by scholars across all business disciplines. However, one phenomenon remains understudied: the association between innovation and stock market bubbles. Bubbles—defined as rapid increases and subsequent declines in stock prices—have been primarily examined by economists who generally do not focus on individual characteristics of innovations or on the consequences of bubbles for their parent firms. We set out to fill this gap in our paper. Using a sample of 51 major innovations introduced between 1825 and 2000, we test for bubbles in the stock prices of parent firms subsequent to the commercialization of these innovations. We identify bubbles in 73% of the cases. The magnitude of these bubbles increases with the radicalness of innovations, with their potential to generate indirect network effects, and with their public visibility at the time of commercialization. Moreover, we find that parent firms typically raise new equity capital during bubble periods and that the amount of equity raised is proportional to the magnitude of the bubble. Finally, we show that the buy-and-hold abnormal returns of parent firms are significantly positive between the beginning and the end of the bubble, suggesting that these innovations add value to their firm and to the economy, in spite of the bubble. Our findings have important implications for managers interested in commercializing innovations and for policy makers concerned with the stability of the financial system. Data and the online appendix are available at https://doi.org/10.1287/mksc.2018.1095 .

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Bubbles Associated with the Stock Prices of Innovating Companies

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Buy-and-Hold Returns for the Beginning to End of the Bubble Period Buy-and-hold returns from beginning to end of bubble (%)

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Analysis of Net Capital Raised and Postbubble Visibility

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Two Centuries of Innovations and Stock Market Bubbles

Alina Sorescu, Sorin M. Sorescu, Will J. Armstrong, Bart Devoldere

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Alina Sorescu, Sorin M. Sorescu, Will J. Armstrong, Bart Devoldere (2018) Two Centuries of Innovations and Stock Market

Bubbles. Marketing Science

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MARKETING SCIENCE

Articles in Advance,pp. 1–23

http://pubsonline.informs.org/journal/mksc/ ISSN 0732-2399 (print), ISSN 1526-548X (online)

Two Centuries of Innovations and Stock Market Bubbles

Alina Sorescu,aSorin M. Sorescu,aWill J. Armstrong,bBart Devolderec, d, e

aMays Business School, Texas A&M University, College Station, Texas 77843; bRawls College of Business, Texas Tech University,

Lubbock, Texas 79409; cVlerick Business School, 9000 Ghent, Belgium; dNetherlands Organisation for Applied Scientiﬁc Research,

2595 DA The Hague, Netherlands; eTomorrowLab, 1800 Vilvoorde, Belgium

Contact:

asorescu@tamu.edu,http://orcid.org/0000-0002-3625-0656 (AS); ssorescu@tamu.edu,

http://orcid.org/0000-0002-9338-1927 (SMS); will.armstrong@ttu.edu,http://orcid.org/0000-0002-2605-5814 (WJA);

bart.devoldere@vlerick.com,http://orcid.org/0000-0001-6722-3805 (BD)

Received: April 22, 2016

Revised: October 12, 2017; January 27, 2018

Accepted: February 4, 2018

Published Online in Articles in Advance:

July 12, 2018

https://doi.org/10.1287/mksc.2018.1095

Abstract. The interplay between innovation and the stock market has been extensively

studied by scholars across all business disciplines. However, one phenomenon remains

understudied: the association between innovation and stock market bubbles. Bubbles—

deﬁned as rapid increases and subsequent declines in stock prices—have been primarily

examined by economists who generally do not focus on individual characteristics of inno-

vations or on the consequences of bubbles for their parent ﬁrms. We set out to ﬁll this gap

in our paper. Using a sample of 51 major innovations introduced between 1825 and 2000,

we test for bubbles in the stock prices of parent ﬁrms subsequent to the commercialization

of these innovations. We identify bubbles in 73% of the cases. The magnitude of these

bubbles increases with the radicalness of innovations, with their potential to generate

indirect network eﬀects, and with their public visibility at the time of commercialization.

Moreover, we ﬁnd that parent ﬁrms typically raise new equity capital during bubble peri-

ods and that the amount of equity raised is proportional to the magnitude of the bubble.

Finally, we show that the buy-and-hold abnormal returns of parent ﬁrms are signiﬁcantly

positive between the beginning and the end of the bubble, suggesting that these innova-

tions add value to their ﬁrm and to the economy, in spite of the bubble. Our ﬁndings have

important implications for managers interested in commercializing innovations and for

policy makers concerned with the stability of the ﬁnancial system.

History:

K. Sudhir served as the editor-in-chief and Harald van Heerde served as associate editor for

this article.

Funding:

The authors gratefully acknowledge ﬁnancial support from the Marketing Science Institute

and from Mays Business School at Texas A&M University. B. Devoldere acknowledges ﬁnancial

support from the Belgian Intercollegiate Centre for Management Science.

Supplemental Material:

Data and the online appendix are available at https://doi.org/10.1287/

mksc.2018.1095.

Keywords:

radical innovation

•

stock market bubbles

•

capital raised

•

diﬀusion

•

visibility

•

network eﬀects

1. Introduction

A sizable stream of research has examined the inter-

play between innovation and the stock market. In

marketing, the focus has been on short- and long-

term abnormal returns associated with new prod-

uct introductions. The marketing literature generally

ﬁnds a positive impact of innovation on stock prices

(e.g., Pauwels et al. 2004, Sood and Tellis 2009, Warren

and Sorescu 2017) and concludes that many new prod-

ucts and technological innovations have spurred the

growth of their parent ﬁrms and even of entire indus-

tries and countries (Tellis et al. 2009).

In turn, the literature in ﬁnancial economics tends

to take a more macro view of innovation. A signiﬁcant

part of this literature has studied the causes and con-

sequences of technological revolutions—periods when

technological advances bring abrupt and long-lasting

changes to our society (Lamoreaux et al. 2007, Perez

2002). A few authors working in this domain suggest

that the stock market plays a critical role in ﬁnancing

innovations launched during technological revolu-

tions, particularly beginning with the 1920s (Hsu et al.

2014, Neal and Davis 2007, O’Sullivan 2007, Perez

2002). Others observe that such revolutions tend to be

associated with stock market bubbles—a rapid increase

and subsequent decline in stock prices—citing, as

examples, radio stock prices during 1929–1930 and In-

ternet stock prices during 1999–2000 (DeMarzo et al.

2007, Pástor and Veronesi 2009, Shiller 2015).

Are certain types of innovations more likely to be

associated with stock market bubbles than others? If

an innovation produces a bubble, are ﬁrms that com-

mercialize it aﬀected by the bubble, and if so, in what

way? Can ﬁrms raise new capital during bubble peri-

ods and use it to accelerate the diﬀusion of the innova-

tion? Extant literature does not provide clear answers

to these questions. The lack of insight is due, in part, to

the fact that the bubble literature in ﬁnancial economics

tends to view innovation as an undiﬀerentiated out-

put generated by an aggregate production function,

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

rather than a collection of products with distinct char-

acteristics. Moreover, studies in this area rarely incor-

porate a formal statistical measurement of bubbles,

using instead ex post observations of sudden rises and

declines in stock prices, and little attempt is made

to link such price movements to speciﬁc innovations.

A notable exception is by Frehen et al. (2013), who show

that the South Sea Bubble of 1720—commonly thought

of as a single occurrence by historians—actually con-

sisted of two separate bubbles, each associated with

contemporaneous innovations in two distinct indus-

tries: insurance and the Atlantic trade. Finally, most

papers that study bubbles tend to focus on only one or

two salient innovations, such as rail (Campbell 2012) or

the Internet (Pástor and Veronesi 2009).

Overall, the academic literature in marketing and ﬁ-

nancial economics provides little systematic evidence

of the link between external ﬁnancing, innovation, and

bubbles. However, understanding the extent to which

the stock market plays a role in helping new indus-

tries develop and ﬂourish is a topic of high relevance

to managers seeking to ﬁnance innovation and to pol-

icy makers who seek to foster economic growth while

maintaining the stability of the ﬁnancial system. Man-

agers might also be interested in learning whether cer-

tain types of innovations are more likely to be associ-

ated with disruptive patterns in their ﬁrm’s stock price,

and whether such patterns impact the eventual diﬀu-

sion of the innovation.1

Our paper seeks to ﬁll this gap through a study

positioned at the interface of marketing, ﬁnance, and

economics. Using the list of radical innovations iden-

tiﬁed by Chandy and Tellis (2000) as a starting point,

we assemble a sample of 51 major innovations intro-

duced between the years 1825 and 2000. For each of

these innovations, we collect data on ﬁrms that com-

mercialized it—which we call parent ﬁrms—and test

whether these ﬁrms experienced bubbles in their stock

prices following commercialization.

To detect bubbles, we look for signiﬁcant diﬀerences

between stock market prices and intrinsic values, using

the econometric method proposed by West (1987). We

then estimate a number of cross-sectional models to

understand how these bubbles relate to various char-

acteristics of each innovation. Next, we measure the

amount of new equity capital raised by parent ﬁrms

during bubble periods. Finally, we estimate the relation

between this new capital raised and the subsequent

visibility of the innovation in the public domain.

We present six important ﬁndings. First, we detect

bubbles in approximately 73% of the innovations stud-

ied (37 out of 51). By contrast, the largest collection

of innovations previously associated with bubbles can

be found in the book by Shiller (2015), who describes

nine possible bubbles, and argues that they tend

to be associated with major shifts in technological

paradigms.2Second, we show that the magnitude of

these bubbles is positively related to the contempora-

neous level of visibility of each innovation. To measure

this visibility, we obtain from the Google Books Ngram

Viewer the annual frequency with which the innova-

tion is mentioned in public books, through time. Third,

we ﬁnd that bubbles are more likely to occur for inno-

vations that have a higher degree of radicalness and

for innovations that are more likely to generate indi-

rect network eﬀects. Fourth, we ﬁnd that parent ﬁrms

raise signiﬁcantly more equity capital during bubble

periods compared to the average ﬁrm in the market,

and the amount of this new capital is proportional to

the magnitude of the bubble. Fifth, we show that the

new capital raised during bubble periods is positively

associated with a faster and stronger increase in the

visibility of the innovation after the bubble. Finally, we

show that the buy-and-hold abnormal returns of par-

ent ﬁrms are signiﬁcantly positive between the begin-

ning and the end of the bubble, suggesting that these

innovations add value to their ﬁrms and to the econ-

omy, in spite of the bubble.

These ﬁndings have important implications for man-

agers and policy makers. Managers of ﬁrms commer-

cializing innovations that are radical (Chandy and

Tellis 1998) or innovations that can generate indirect

network eﬀects (Stremersch et al. 2007) should be

aware of a potentially disruptive pattern in their stock

prices in the form of bubbles. At the same time, these

bubbles may create a short window of opportunity

during which equity capital could be raised on favor-

able terms. If this capital is judiciously invested, it can

help accelerate the diﬀusion of the innovation. In terms

of policy implications, our results suggest that bub-

bles might allow for faster building of the infrastruc-

ture needed for innovation to achieve its full economic

potential. Therefore, at least from this narrow perspec-

tive, bubbles appear to act as an indirect, voluntary tax

that some people pay to invest in what is essentially a

public good. This implication is consistent with Olivier

(2000), whose mathematical model predicts that stock

market bubbles are beneﬁcial to value creation, invest-

ment, and growth.

Our results also support the views expressed by for-

mer Federal Reserve Chairman Alan Greenspan, who

opined that while bubbles are precipitated by exagger-

ated perceptions of economic growth, they also fuel the

diﬀusion of the underlying innovation by providing

the capital required for its commercialization (Lansing

2008). Without a bubble, he argued, a major innovation

might not take oﬀ or achieve its full potential. Observ-

ing that “there were no bubbles in the Soviet Union,”

Greenspan concluded that policy makers should not

attempt to quash bubbles since doing so could impede

innovation and lead to suppressed economic activity

over the long term (Guha 2008, p. 2).

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

We do not claim to establish causal relations in our

paper. Instead, our goal is to provide a number of sta-

tistical tests to study stock market bubbles across the

largest set of innovations used in this literature. As

is the case with most papers that focus on ﬁrst-order

research questions, we are only able to answer a sub-

set of questions that readers might deem important.

The data collection for this project was laborious and

extensive. Data for innovations from the 19th century

and from the earlier part of the 20th century were chal-

lenging to obtain, and in many cases extensive manual

searches had to be performed in newspapers and other

contemporaneous sources. We nevertheless hope that

our insights into the economic consequences of inno-

vation will serve as impetus for additional research in

this area.

The rest of the paper proceeds as follows. In Sec-

tion 2, we present an overview of the theory that

supports the existence of stock market bubbles, and

we review the literature that links innovations with

bubbles. We also present theoretical arguments that

support the relation between individual innovation

characteristics, stock market bubbles, and external ﬁ-

nancing. In Section 3, we discuss the economic con-

sequences of bubbles. In Section 4, we describe the

data collection process and empirical methodology.

Section 5presents the results, and Section 6concludes

with a discussion of the implications of our ﬁndings

for researchers, managers, and policy makers.

2. Innovation and Stock Market Bubbles

We begin this section with a discussion of the economic

theory of bubbles. We continue with a brief overview

of the literature that links innovation and bubbles, and

conclude with a set of empirical predictions that link

bubbles with speciﬁc innovation characteristics.

2.1. Asset Price Bubbles

Bubbles occur when the price of an asset exceeds its

intrinsic value by a signiﬁcant amount and for a suﬃ-

ciently long period of time. The literature in ﬁnancial

economics distinguishes between “behavioral” bub-

bles and “rational” bubbles (Scherbina and Schlusche

2014). Behavioral bubbles occur when investors deviate

from the rational expectations model, perhaps because

they overreact to news or fail to update their beliefs in

a Bayesian manner. By contrast, rational bubbles occur

despite all agents being perfectly rational and having

access to the same information set.

We adopt the rational bubble approach because it

is based on the rational expectations paradigm pre-

vailing in ﬁnancial economics. Fama (1998) argues that

researchers should not reject rational expectations until

(or unless) the alternative (behavioral) paradigm can

do a better job at explaining existing empirical obser-

vations within a uniﬁed body of knowledge. While the

literature in behavioral ﬁnance has evolved over the

past 20 years, to our knowledge, there is no uniﬁed

behavioral theory of bubbles. Indeed, Scherbina and

Schlusche (2014) discuss no fewer than 18 papers

grouped into four diﬀerent categories of behavioral-

based models of bubbles. In turn, each category is

based on a unique set of underlying cognitive biases

(such as self-attribution or conservatism). Collectively,

these 18 papers provide little guidance for our research

because there is no systematic way to determine, ex

ante, which cognitive biases apply to various innova-

tions or time periods. By contrast, the rational bubble

approach allows us to develop a parsimonious set of

testable predictions that apply uniformly to all innova-

tions in our sample.

A common theoretical explanation for rational bub-

bles is the mathematical, discrete-time model of

Blanchard and Fischer (1989). Versions of this model

have been used in other papers, including those by

Blanchard and Watson (1982), Froot and Obstfeld

(1991), Rappoport and White (1993), White (1990), and

Scherbina and Schlusche (2014). The model explains

how bubbles may arise even if all economic agents act

rationally. Blanchard and Fischer (1989, pp. 213–223)

posit that the price of an asset in one period is equal

to the discounted value of its expected price and cash

ﬂows during the next period. They then seek to ﬁnd

the theoretical value of the price during the current

period, expressed only in terms of future cash ﬂows

and discount rates.

Blanchard and Fischer (1989) show that the solu-

tion to their model is not unique. There are, in fact,

an inﬁnite number of solutions. One of them (the fun-

damental solution) is the well-known present value of

future cash ﬂows, which deﬁnes the intrinsic value of

an asset. All other solutions include a “bubble” compo-

nent: a positive quantity that is artiﬁcially added to the

intrinsic value. We know very little about this bubble

component. Blanchard and Fischer’s (1989) theory tells

us only that it is expected to grow at a rate r, equal to

the discount rate of the asset. This property is not very

useful because it allows for an inﬁnite number of out-

comes. For example, any random number that grows

through time at rate ris a potential bubble. Ideally, we

would like to obtain more speciﬁc properties for this

bubble so that we may learn something about its mag-

nitude and determinants.

Fortunately, a special case of the rational bubble the-

ory allows us to narrow down the set of bubble solu-

tions and obtain useful testable implications. This spe-

cial case was proposed by Froot and Obstfeld (1991),

who introduced the concept of intrinsic bubbles, a subset

of rational bubbles. With intrinsic bubbles, the magni-

tude of the bubble component is proportional to the

contemporaneous value of the asset’s cash ﬂows (or

to the value of another proxy that is correlated with

cash ﬂows).

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

We mentioned earlier that the solution to the

Blanchard and Fischer (1989) model may or may not

include a bubble. While both scenarios are theoretically

possible, only one of them will be observed in reality—

either the asset price includes a bubble or it does not.

Mathematics alone cannot help us determine which of

these scenarios will prevail. For this, we need to know

more about the underlying economic conditions.

In Web Appendix A, we provide a simpliﬁed ver-

sion of the Blanchard and Fischer (1989) model, along

with an economic interpretation of their solution. We

also discuss the intuition behind Froot and Obstfeld’s

(1991) theory of intrinsic bubbles and explain how its

predictions relate to our paper. We argue that bubbles

are more likely to occur when the following four con-

ditions are met: (1) the asset is expected to live for an

indeﬁnite period of time (as is the case with stocks),

(2) no bubble-free close substitute exists for the asset,

(3) investors have exploding expectations about the

future cash ﬂows generated by the asset, and (4) short

selling is costly or subject to constraints. When we over-

lay the theory of intrinsic bubbles on the basic model of

Blanchard and Fischer (1989), we obtain the additional

prediction that the magnitude of a bubble is propor-

tional to a proxy for the cash ﬂows generated by the

asset at the time when the bubble occurs. These eco-

nomic implications form the basis for our predictions

about the determinants and consequences of bubbles

associated with innovations. We present these predic-

tions in Sections 2.3 and 3, respectively.3

2.2. Relation Between Innovation, External

Financing, and Bubbles

A review of the bubble literature reveals several aspects

that warrant further inquiry. First, a few authors note

that stock market bubbles occur during industrial rev-

olutions; however, this observation is made primarily

in case studies, books, and in the popular press (Gross

2009, Kindleberger and Aliber 2005, Perez 2002, Shiller

2015, Wood 2006).4Perhaps because of the tendency

to look for bubbles during these periods of intense

technological progress, only a limited set of bubbles

has been examined in academic studies. For instance,

Campbell (2012) studies the British railway bubble

of the 1840s, Ofek and Richardson (2003) study the

Internet bubble of 1999–2000, and Pástor and Veronesi

(2009) study the Internet bubble and the American rail-

roads before the Civil War, while Frehen et al. (2013)

establish a link between the stock market bubbles of

1720 and innovations that at the time were taking place

in the insurance and Atlantic trade industries.

Second, with the exceptions mentioned above, this

literature tends to focus on marketwide bubbles, rather

than on industry-speciﬁc bubbles spurred by individ-

ual innovations. Moreover, academic papers in this

stream tend to view innovation as an undiﬀerenti-

ated aggregate output, which precludes the study of

product-speciﬁc characteristics. For example, Nicholas

(2008) evaluates the extent to which the stock mar-

ket during the 1920s responded to contemporaneous

changes in the intellectual capital of U.S. ﬁrms, mea-

sured using citation-weighted patents assigned to each

ﬁrm. He ﬁnds that excess stock returns during 1928–

1929 are positively related to ﬁrms’ intellectual capi-

tal, but does not evaluate the relation between stock

returns and speciﬁc products.

Third, the literature does not provide a uniﬁed view

on why bubbles might be associated with innovations,

and why some bubbles might be larger than others.

This could be a consequence of diﬀerent philosoph-

ical perspectives regarding the underlying causes of

bubbles. For example, several authors take the view

that bubbles are caused by irrational investors (e.g.,

Ferguson 2008, Perez 2002, Shiller 2015). Others adopt

a more rational view of bubbles but tend to focus on

only one or two bubbles and, therefore, lack context to

systematically examine common patterns across inno-

vations. As a result, the rationale presented in support

of the bubble varies from investor myopia (Campbell

2012) to changes in the systematic risk of parent ﬁrms

(Pástor and Veronesi 2009).

A number of questions remain unanswered. Perhaps

the most salient relates to the scale of the relation

between innovation and bubbles: Does this relation

extend to all innovations or is it conﬁned to technolog-

ical revolutions? Because innovations vary on a contin-

uum in terms of technological advances and beneﬁts

they bring to consumers, it is important to understand

where on this continuum the boundary that could trig-

ger a bubble might lie.

What happens after the bubble is also of interest.

A number of authors have argued that the collapse

of bubbles is disruptive to economic activity (Perez

2002, Wood 2006), but are there other, more desirable

outcomes? Rapp (2014, p. 21) proposed that “when

stock prices increase, ﬁrms can raise cash from exist-

ing and new investors at lower costs.” Thus, bubbles

could create a favorable environment for ﬁrms to raise

new equity capital during the commercialization phase

of new products. However, there is no systematic evi-

dence that supports this assertion. Finally, we know

very little about the longer-term prospects of products

whose parent ﬁrms are subject to bubbles. Do bubbles

help or hurt the visibility and eventual diﬀusion of

innovations?

We address some of these open questions using a

novel data set that spans multiple industries across the

19th and 20th centuries. In Section 2.3, we leverage the

theories proposed by Blanchard and Fischer (1989) and

by Froot and Obstfeld (1991) to make predictions on the

determinants and consequences of bubbles associated

with innovation.

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

2.3. Which Innovations Are More Likely to Be

Associated with Bubbles?

We mentioned earlier that the literature in ﬁnancial

economics has not examined how diﬀerences in inno-

vation characteristics may impact stock prices. By con-

trast, research in marketing has examined the diﬀeren-

tial eﬀect of radical versus incremental innovation on

stock prices. When compared with incremental innova-

tions, radical innovations—deﬁned as “new products

that (1) incorporate substantially diﬀerent technology

from existing products and (2) can fulﬁll key customer

needs better than existing products” (Chandy and

Tellis 1998, p. 475)—are associated with stronger long-

term stock performance and also with higher ﬁrm risk

(Pauwels et al. 2004, Sood and Tellis 2009, Sorescu

and Spanjol 2008). The overarching conclusion from

this stream of research is that the superior beneﬁts

provided by radical innovation eventually result in a

broader adoption of these products, which translates

into higher revenues and proﬁts.

To understand the relation between bubbles and spe-

ciﬁc innovation characteristics, we return to the the-

oretical framework of intrinsic rational bubbles pre-

sented in Section 2.1. Recall that from Blanchard and

Fischer (1989) and Froot and Obstfeld (1991) we had

derived a set of conditions that are more conducive to

the emergence of bubbles. We now turn to interpret-

ing these conditions within the context of our current

research.

Two of the four conditions that underlie the for-

mation of bubbles are met by all innovations in our

sample. Speciﬁcally, the ﬁrst condition—indeﬁnitely

lived assets—is met by all stocks. Unless a ﬁrm is

in bankruptcy, the expectation is that it will con-

tinue to operate for the foreseeable future. The fourth

condition—costly short selling—is also likely to be met

by all stocks in our sample. Danielsen and Sorescu

(2001) provide several reasons why investors ﬁnd it dif-

ﬁcult to establish short positions in the stock market. For

example, investors do not immediately get access to the

proceeds from short selling, investors face high search

costs for ﬁnding a willing security lender, and there is

always a risk of a “short squeeze,” that is, of a premature

request to liquidate the short position before investors

can earn a proﬁt (also see Akbas et al. 2017).

The second and third conditions are met by some

but not all ﬁrms. The second condition—the absence of

a close substitute in the stock market—is more likely to

be met in the case of innovations whose characteristics

are very diﬀerent from those of existing products. By

deﬁnition, radical innovations meet these criteria (at

the product category level), since they provide signiﬁ-

cantly higher consumer beneﬁts and incorporate a new

technology relative to existing products. Because of

these characteristics, parent ﬁrms that commercialize

radical innovations are exposed to risks previously

unknown to investors, which cannot be easily hedged

using existing assets. The more radical the innova-

tion, the fewer close-substitute stocks are available in

the stock market.5Because close-substitute stocks are

necessary to arbitrage away mispricing and prevent

the bubble from growing, a lower number of close-

substitute stocks may lead to bigger bubbles. Thus, our

ﬁrst prediction is that the higher the level of radicalness

of innovations, the larger the magnitude of the innovation

bubble.

The third condition—exploding expectations of fu-

ture cash ﬂows—is more likely to be satisﬁed by in-

novations believed to oﬀer quasi-unlimited revenue

potential. We propose that network goods meet this

condition. Network goods and services are those

whose value to adopters increases as a function of the

number of other people who use them (e.g., Gupta

et al. 1999, Hall 2005). Direct and indirect network

externalities have been distinguished in the literature

(Katz and Shapiro 1986). The steam engine exempliﬁes

direct externalities, because the utility derived from

rail transportation increased along with its diﬀusion

(the size and geographic reach of the rail network).

A good example of indirect externalities is given by

the recent growth in the app industry, spurred by

smartphones and tablets. In turn, apps provide smart

devices with increased functionality, arguably broad-

ening those devices’ appeal and potentially strength-

ening their diﬀusion (e.g., Stremersch et al. 2007).

Assessing the future cash ﬂows of an innovation is

particularly diﬃcult if it requires an estimation of the

magnitude of network eﬀects. Network eﬀects could

exponentially increase the adoption of a technology

within and across industries, indicating that the inno-

vation has the potential to generate exploding future

cash ﬂows. Investors—particularly optimistic ones—

will factor this possibility into their valuation. There-

fore, we suggest that the magnitude of a bubble increases

with an innovation’s potential for network externalities.

Finally, we derive a third testable implication from

the intrinsic bubble model of Froot and Obstfeld (1991).

Their research suggests that the magnitude of an intrin-

sic bubble is proportional to the contemporaneous cash

ﬂows generated by the underlying innovation, or to

any exogenous variable that is correlated to these cash

ﬂows. In support of this prediction, Froot and Obstfeld

(1991) provide macro-level evidence of intrinsic bub-

bles obtained from aggregate stock returns and divi-

dends in the United States during the period from 1900

to 1988.

Recall that the intrinsic bubble theory requires that

we measure innovation-speciﬁc cash ﬂows that are con-

temporaneous with the bubble. In the absence of speciﬁc

information about cash ﬂows—which are not directly

observable—investors are likely to anchor their valua-

tions on metrics that are visible and that, in their opin-

ion, might be correlated to cash ﬂows. We propose that

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

the public visibility of the innovation around the time

of the bubble is a good exogenous proxy for the extent

to which it has been adopted by the public, and there-

fore for the cash ﬂows that it generates. To develop

this proxy, we use the Google Books Ngram Viewer, an

Internet search engine that provides annual frequen-

cies of any search terms that appear in print up to 2008.

We obtain from Google Ngram the annual frequencies

for each innovation at the time of the bubble.

While admittedly imperfect, the Google Ngram

proxy has the distinct advantage of not suﬀering from

retrospective bias because it contains information that

was publicly known at the outset of the bubble. In ad-

dition, Google Ngram data are available for all inno-

vations in our sample, which provides us with a con-

sistent proxy for cash ﬂows that is comparable across

several innovations and time periods. In turn, this con-

sistency facilitates cross-sectional analysis in a sample

that is otherwise quite heterogeneous. Thus, our third

prediction is that the magnitude of a bubble increases with

the public visibility of the innovation as captured by its

contemporaneous Google Ngram frequency of mentions in

printed books.

In sum, we predict that the magnitude of innovation

bubbles is increasing in the degree of radicalness, in

the potential for network externalities, and in the con-

temporaneous visibility of the innovation. In Section 3,

we examine the consequences of bubbles for the parent

ﬁrms and for the public visibility and eventual diﬀu-

sion of the innovation.

3. What Are the Consequences of

Bubbles?

3.1. The Economic Value Added of Bubbles

The intrinsic bubble theory of Froot and Obstfeld

(1991) is based on the premise that when bubbles oc-

cur, they do so because the underlying asset—in this

case, the stocks of parent ﬁrms—adds signiﬁcant value

to the economy. This prediction is corroborated by

Olivier (2000, p. 133), who argues that “the real impact

of a [rational] bubble depends on the type of asset

that is being speculated on. Speculative bubbles on

equity raise the market value of ﬁrms, thus encourag-

ing entrepreneurship, ﬁrm creation, investment, and

growth.” Therefore, we expect innovations that are sub-

ject to bubbles to have, on average, positive net present value

(NPV) despite the presence of bubbles. We test this predic-

tion in our paper, with NPV measured as the abnormal

returns earned by the stocks of parent ﬁrms, from the

beginning to the end of the bubble.

3.2. Capital Raised in the Stock Market

A critical question of interest to managers is how to

raise the capital needed to ﬁnance innovations. This

is particularly true for innovations that are fueled by

new technologies, which require signiﬁcant capital

investment. While it might be natural today to assume

that this new capital could be easily raised by issuing

new equity, historical evidence suggests that this was

not always the case. Indeed, prior to the mid-1920s,

innovations were ﬁnanced mostly with private capital

raised from the owners’ personal network (Lamoreaux

et al. 2007, O’Sullivan 2007). The stock market began

to play a more signiﬁcant role in ﬁnancing innovation

beginning with the aircraft and radio industries in the

1920s (O’Sullivan 2007). Since that time, ﬁrms have

increasingly relied on equity capital raised from public

sources.

We expect that the presence of bubbles could make

it easier for parent ﬁrms to raise equity capital because

one consequence of overvalued stocks is a temporary

reduction in the ﬁrm’s cost of equity during the bub-

ble period. In turn, this could make it attractive for

managers to raise new equity capital in the form of

initial public oﬀerings (IPOs) or seasoned equity oﬀer-

ings (SEOs). This new capital would be raised by issu-

ing overvalued shares, which is favorable to the ﬁrm’s

existing shareholders, but not to its new shareholders.

Although managers would normally want to issue

new equity when their stock is overvalued, most of the

time they are not able to do so without paying a penalty

because of adverse selection. Indeed, investors usually

view SEOs as a signal of overvaluation, and a mere

announcement of an SEO typically results in an imme-

diate decline in the market value of the ﬁrm (see, e.g.,

Myers and Majluf 1984). In a nonbubble environment,

this decline would oﬀset any beneﬁts obtained from

issuing overvalued stock. The bubble environment,

however, might be diﬀerent: it might provide a unique

opportunity for managers to raise cheap equity capi-

tal without worrying about sending negative signals

to the market. If this were true, we would expect par-

ent ﬁrms to raise an abnormally high amount of equity

capital during bubble periods. We provide empirical

evidence on this issue in our paper.

3.3. Postbubble Trajectory of Innovation

We have so far focused on the consequences of bubbles

for parent ﬁrms. We now turn to the potential conse-

quences of bubbles for the innovations themselves. Can

a bubble aﬀect the public visibility, and ultimately the

diﬀusion, of an innovation, and if so, in what way?

The diﬀusion literature has examined several mile-

stones in the life cycle of a new product. The ﬁrst mile-

stone is the takeoﬀ time, deﬁned by Golder and Tellis

(1997, p. 257) as “the point of transition from the intro-

ductory stage to the growth stage of the product life

cycle,” a point when a dramatic increase in sales occurs.

Determinants of the takeoﬀ time include a reduction in

the price of the innovation and the level of market pen-

etration (Golder and Tellis 1997). Another determinant

of takeoﬀ that is particularly relevant to our context is

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

the performance of the parent ﬁrm’s stock in the early

commercialization stages: a strong positive abnormal

stock performance has been found to predict takeoﬀ

in the subsequent year (Markovitch and Golder 2008),

suggesting that takeoﬀ could follow shortly after a bub-

ble has emerged. Finally, the parent ﬁrm’s ability to

raise cheap equity capital during a bubble essentially

reduces the cost of the innovation, which can poten-

tially reduce its price and shorten its time to takeoﬀ

(Golder and Tellis 1997). Therefore, we expect that the

magnitude of the bubble and the capital raised during the

bubble are positively related to how quickly takeoﬀ occurs.

We do not measure diﬀusion directly. Rather, we use

as a proxy the time series of visibility obtained from

Google Ngram, beginning with the commercialization

date of each innovation. Just as visibility could serve as

an ex ante proxy for the innovation’s cash ﬂows, it can

also serve as an ex post proxy for its diﬀusion. Using

this proxy, we estimate the takeoﬀ of each innovation as

the inﬂection point in the time series of Google Ngram

visibility. In Web Appendix D we show, for a subsam-

ple of 25 innovations, that the takeoﬀ estimated from

visibility correlates highly with the takeoﬀ estimated

from product adoption data.

The literature on new product diﬀusion also makes

predictions about the shape of the diﬀusion curve after

takeoﬀ. Initial models predicted a monotonic increase

in sales up to the peak of growth, followed by a

slowdown (e.g., Peres et al. 2010). Other authors have

argued that the diﬀusion curve follows a chasm and

saddle pattern, where sudden decreases in sales fol-

low the initial rise (Golder and Tellis 2004, Mahajan

and Muller 1998). In all cases, as newer technolo-

gies become available, the diﬀusion curve eventually

plateaus and starts declining. The point at which it

plateaus has been assumed to be the point of market

saturation, but researchers have focused less on this

saturation point compared to the takeoﬀ point.

We argue that the same characteristics of a bubble

that lead to an accelerated takeoﬀ are also associated

with a higher market saturation point. A lower inno-

vation cost obtained from the parent ﬁrm’s ability to

raise cheap capital will not only lead to a faster takeoﬀ

but also to a broader adoption of the innovation. Thus,

we expect that the magnitude of the bubble and the capital

raised during the bubble are positively related to the visibil-

ity that the innovation achieves after takeoﬀ. This visibility

is proxied by the maximum Google Ngram value that

the innovation attains in its lifetime.

4. Data and Methods

4.1. Selection of Innovations Included in

Our Sample

Assembling a comprehensive data set of innovations

introduced during the last two centuries is a challeng-

ing task. We start with the largest set of radical inno-

vations documented in the literature, a list of 64 new

products introduced between 1864 and 1998 provided

in Chandy and Tellis (2000). The authors used the his-

torical approach to data collection (Golder 2000) and

consulted “more than 250 books and 500 articles in peri-

odicals” to construct their sample (Chandy and Tellis

2000, p. 5). Therefore, we believe that this list consti-

tutes a good initial sampling frame for our study. Of the

64 innovations mentioned in Chandy and Tellis (2000),

we were able to identify and collect data for 43 innova-

tions commercialized between 1886 and 1998.6

In our review of the literature on technological rev-

olutions, we also encountered a few additional inno-

vations that have been singled out by economists

as being notable in their ability to spur economic

growth. For instance, Shiller (2015, p. 125) mentions

the steam engine train, motion pictures, and the Inter-

net (or, more speciﬁcally, the World Wide Web) as

major innovations that ushered what he calls “a new

era.” Through this archival process, we add eight addi-

tional innovations to the Chandy and Tellis (2000) list:

the steam engine train, the telegraph, motion pictures,

rayon, the airplane, the Internet, the GPS receiver, and

the smartphone. Table WA-B3 in Web Appendix B

details the sources used to identify these innovations.

Our ﬁnal sample consists of 51 innovations.

4.2. Identiﬁcations of Parent Firms and of

Financial Data Used for the Bubble Tests

After assembling our sample of innovations, we iden-

tify the date when each innovation was commercial-

ized, as well as the parent ﬁrms involved in the commer-

cialization process. To do so we use extensive archival

searches encompassing a large number of books and

articles on the history of technology, major U.S. and UK

journals that date back to the 19th century (such as the

Wall Street Journal and the London Times), and academic

articles that examine the diﬀusion of innovations (e.g.,

Agarwal and Bayus 2002, Golder and Tellis 1997).

From the set of parent ﬁrms, we select those with

available stock and dividend data. For most innova-

tions in the 20th century, company-speciﬁc data are

available from the Center for Research in Security

Prices (CRSP) beginning with the year 1925. For ear-

lier innovations, we turn to the Cowles Foundation

database (https://som.yale.edu/faculty-research/our

-centers-initiatives/international-center-ﬁnance/data/

historical-cowles), which covers the period from 1871

to 1938. Unlike the CRSP, Cowles provides data on

industry indices, not on individual companies. If we are

able to identify a clear industry index that corresponds

to a particular innovation (for example, “automobile”),

we use the Cowles price and dividend data for that

index, and the set of parent ﬁrms for that particular

innovation is deemed to be the set that the Cowles

researchers had chosen to include in their index.

If the Cowles index is too broad (for example, “appli-

ances” instead of “refrigerator”), we identify parent

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Table 1. Sources of Stock and Dividend Data

Data source Description

CRSP data set Monthly, stock-level data are available from the Center for Research in Security Prices at the

University of Chicago from December 1925 to the present. Stock-level data are used to construct a

price index and monthly dividend series for each innovation. The monthly price index is

constructed using the value-weighted average return (excluding dividends) of the stocks included

in the index. The monthly dividend series for each index is constructed by ﬁrst computing each

stock’s monthly dividend yield as the diﬀerence between its total monthly return and its monthly

return excluding dividends. The monthly dividend yield of the index is then computed as the

value-weighted average of the component stocks’ dividend yields. Monthly dividends in terms of

the price index are computed as the product of the index’s dividend yield and the value of the price

index at the end of the prior month.

London Stock Exchange (Old LSE

data set)

The Old LSE data set was produced by compiling hard copy entries from the Investors Monthly Manual,

a record of the London Stock Exchange for the period from 1871 to 1930. Common stock prices and

dividends from this data set are used to construct the required price indexes and dividend series in

a manner consistent with those that are based on CRSP data.

The Cowles Commission for

Research in Economics

The Cowles data set, produced from the Cowles Commission’s third monograph (1939), contains

industry-level common stock data series for the period 1871 to 1938. We use two price indexes from

these data: a price index that excludes dividends and a total return index that includes both

dividends and price appreciation. As we do with the CRSP data, we use the diﬀerence in the

returns of these two indexes to construct a monthly, industry-level dividend series denominated in

terms of the price index.

Compustat Global Monthly, stock-level data for foreign stocks are available from Compustat Global from January 1985 to

the present. Variables constructed from this data set are computed in the same manner as described

above for the CRSP data. This data set was used to collect stock price and dividend data for

Japanese ﬁrms that did not trade in the United States during the time period of interest.

New York Stock Exchange (Old

NYSE data set)

The Old NYSE data set was produced from data used in Goetzmann et al. (2001). Common stock

prices and dividends from this data set are used to construct the required price indexes and

dividend series in a manner consistent with those that are based on the CRSP data.

Note. This table presents the data sources used to collect stock and dividend data for the bubble tests.

companies from historical sources and collect data on

individual stocks from the Historical London Stock

Exchange and Historical New York Stock Exchange da-

tabases, both of which are available at Yale University

in electronic format. Finally, data not available in elec-

tronic format are hand collected from contemporane-

ous newspapers in the United States and the United

Kingdom. One exception is the data set on the UK rail-

road industry from the 19th century, which was pro-

vided to us by Gareth Campbell (Campbell 2012).

Table 1describes our data sources, and Table 2pro-

vides a list of the 51 innovations in our sample, along

with the years of commercialization and the corre-

sponding portfolios of parent ﬁrms. A full descrip-

tion of the history of these innovations is available on

request from the authors. For innovations not covered

by the CRSP or Cowles, we occasionally have to conﬁne

our analysis to a single ﬁrm, because of data availabil-

ity. For example, for the telegraph industry, we study

only the stock of Western Union, as we are unable to

identify data for other companies. Our results are not

likely to be biased by these single-ﬁrm portfolios.7

Our portfolios do not contain the complete set of

early entrants in each industry. While this is a limi-

tation caused by data availability, our goal here is to

create clean portfolios for the measurement of bub-

bles, rather than to extensively study the entry process

into new industries. As a result, we are primarily con-

cerned with building portfolios that have signiﬁcant

exposure to the focal innovation (and little exposure

to everything else), rather than to make the portfo-

lios as comprehensive as possible. To accomplish this

goal, we endeavor as much as possible to include in

our test portfolios only ﬁrms that are pure plays, in

the sense that their product portfolio includes primar-

ily the innovation and not much else. To the extent

to which our test portfolios may occasionally include

ﬁrms that are not pure plays (such as multiproduct

ﬁrms that commercialize other products in addition

to the focal innovation), the inclusion of such ﬁrms

would merely bias our results toward zero, because

these ﬁrms would have a smaller exposure to the eco-

nomic forces that ignite the bubble.8

4.3. Characteristics of Innovations That Are More

Likely to Be Associated with Bubbles

Previously, we identiﬁed three innovation characteris-

tics that increase the probability of a bubble: the inno-

vation’s degree of radicalness, its potential to generate

network eﬀects, and the extent of its public visibility.

Ideally, we would like to collect contemporaneous data

for each of these three characteristics to minimize the

risk of a retrospective bias. However, we could only do

so in the case of visibility. We measure contempora-

neous visibility using the Google Ngram data, which

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Table 2. Firms Included in the Bubble Analysis of Each Innovation

Innovation Comm. year Firms (and initial year) included in index

Steam engine train 1825 Campbell (2000) Railway Index

Telegraph 1845 Western Union (1865)

Incandescent vacuum lamp 1880 Cowles Electrical Equipment Index components: Edison General Electric/General Electric

(1890)

Automobile 1886 Cowles Automobiles and Trucks Index components: General Motors (1912), Chrysler (1914),

Studebaker (1912), Willys-Overland (1915), Chandler Motor Car (1916), Saxon Motor Car

(1916), Stutz Motor Car (1916), White Motor (1916), Pierce-Arrow Motor Car (1917), Fisher

Body (1918), Hupp Motor (1918), Mack Trucks (1920), Packard Motor Car (1920)

Portable camera 1888 Eastman Kodak (1904)

Disk phonograph 1894 Gramophone company (1904)

Motion pictures 1898 Cowles Theater and Motion Picture Index components: Famous Players-Lasky (1919, later

renamed to Paramount Publix), Loew’s (1920), Radio-Keith-Orpheum (1920)

Photoelectric scanning fax 1907 Cowles Utilities—Telephone and Telegraph Index Components: American Telephone and

Telegraph (1907), New York and New Jersey Telephone (1907), Central South America

Telegraph (1907), Mackay Companies (1907), Paciﬁc Telephone and Telegraph (1909), All

America Cables (1918)

Electric clothes washer 1908 Cowles Electrical Equipment Index components: General Electric (1908), Electric Storage

Battery Co. (1915), National Conduit and Cable (1917), Westinghouse Electric and

Manufacturing (1918)

Electric percolator 1908 Cowles Electrical Equipment Index components: General Electric (1908), Electric Storage

Battery Co. (1915), National Conduit and Cable (1917), Westinghouse Electric and

Manufacturing (1918)

Electric toaster 1908 Cowles Electrical Equipment Index components: General Electric (1908), Electric Storage

Battery Co. (1915), National Conduit and Cable (1917), Westinghouse Electric and

Manufacturing (1918)

Rayon 1910 Cowles index: Industrial Rayon Corp. (1926), Tubize Artiﬁcial Silk Co. of America (1926),

Snia Viscosa Co. (1926), Celanese Corp. of America (1927), Tubize Chatillon Corp. (1930)

Refrigerator 1916 General Electric (1925), Kelvinator (1926)

Airplane 1919 Bendix Aviation (1925), Curtiss Aeroplane (1927), Wright Aeronautical (1925), Curtiss

Wright Corporation (1929), National Air Transport (1929), United Aircraft and Transport

(1929)

Radio 1919 RCA (1925), AT&T (1925), Westinghouse Electric and Manufacturing (1925)

Electric typewriter 1920 IBM (1928)

Electric blanketa1930 General Electric (1928)

Electric dishwashera1930 General Electric (1928)

Electric shaver 1930 Remington Rand (1928)

Electric garbage disposer 1935 General Electric (1928)

TV 1936 Admiral (1945), Crosley (1940), Emerson (1945), Farnsworth Television and Radio (1943),

General Electric (1940), Galvin Manufacturing/Motorola (1946), Philco (1940), RCA

(1940), Westinghouse Electric (1940), Zenith (1940)

Fluorescent light bulb 1938 General Electric (1936), Westinghouse Electric and Mfg (1936)

FM radio 1940 RCA (1935), Westinghouse Electric and Mfg (1935), Zenith Radio (1935), Philco (1940),

Galvin Manufacturing/Motorola (1946)

Ballpoint pen 1945 Eversharp (1946)

Magnetic tape player 1947 Minnesota, Mining and Mfg (1946)

Microwave oven 1947 Raytheon (1958), Litton (1958)

NTSC color TV 1954 General Electric (1952), Philco (1952), RCA (1952), Raytheon (1952), Hoﬀman Electronics

(1955)

Electric can opener 1956 Westinghouse Electric (1950)

Videocassette recorder 1956 North American Phillips (1950), RCA (1950), Magnavox (1950), Ampex (1959)

Electronic watch 1957 Bulova Watch (1955), Hamilton Watch (1955), Elgin National Watch (1955)

Cassette 1964 North American Phillips (1962), Memorex (1968), Sony (1970), Fuji (1972), Hitachi (1972),

TDK Electronics (1975)

Electronic desktop calculator 1964 Texas Instruments (1959), Hewlett-Packard (1959), Friden (1959), Wang Laboratories (1968)

Electronic pocket calculator 1968 Texas Instruments (1965), Hewlett-Packard (1965), Canon (1965), Bowmar Instrument

(1965), Craig (1969), Toshiba (1972)

Dot-matrix printer 1970 Centronix Data Computer (1972)

Single-player video game 1972 Magnavox (1969), General Instrument (1969), Centuri (1972), Bally Manufacturing (1972),

Williams Electronics (1981)

Digital LED watch 1975 Texas Instruments (1973)

Disposable shaver 1975 Gillette (1975), Schick (1975), Bic Pen (1975)

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Table 2. (Continued)

Innovation Comm. year Firms (and initial year) included in index

Personal computer 1975 Apple (1980), Commodore (1978), IBM (1978), Intel (1978), RadioShack (1978), Lotus

Development (1983), Ashton Tate (1983), Compaq (1983)

Laser printer 1976 Hewlett-Packard (1974), IBM (1974), Xerox (1974), Canon (1974)

Laser disc player 1978 MCA (1976), Pioneer (1976), Sony (1976), Phillips (1976)

Mobile phone 1979 AT&T (1980), Motorola (1980), Ericsson (1981), Interdigital (1981), SBC (1984), Nokia (1988),

Qualcomm (1991)

Camcorder 1983 Hitatchi (1983), JVC (1983), Panasonic (1983), Sony (1983)

Compact disc player 1983 Panasonic (1981), Philips (1981), Sony (1981), Toshiba (1981)

Portable computer 1983 IBM (1975), Radioshack (1975), Hewlett-Packard (1975)

Digital camera 1989 Fuji (1986), Nikon (1986), Casio (1986), Olympus (1986)

Internetb1991 List of Internet IPOs included in Loughran and Ritter (2004)

Minidisc player 1992 Sony (1990)

Digital video disc player 1997 Sony (1990)

Digital high-deﬁnition TV 1998 Zenith (1990), Sony (1990), Panasonic (1990), Hitatchi (1990), Technicolor (1999)

GPS receiver 2000 Garmin (2000), Lowrance (2000), Trimble (2000)

Smartphone 2000 Apple (2002), Ericsson (2002), Handspring (2002), Nokia (2002), Research in Motion (2002),

Google (2004)

Notes. This table presents the names of the ﬁrm(s) included in the test portfolio of each innovation. The year of each ﬁrm’s entry into the

portfolio is included in parentheses. For the Internet industry, we provide the source of the index employed. The ﬁrst column shows the name

of each innovation and the second column shows the year of commercialization.

aBecause the electric blanket and the electric dishwasher were introduced in the same year (1930), and because the test portfolio for both

products is limited to General Electric, we can only perform a joint bubble test for these two products. If a bubble is detected, it could be

attributed to either one of the two innovations or to both.

bWe use the term Internet to refer to the World Wide Web (WWW) enhancement to the Internet technology. Tim Berners-Lee invented the

WWW in 1990, and the concept was ﬁrst commercialized in 1991. Berners-Lee’s invention consisted of three new technologies that remain

in place today: HTML, URL, and HTTP. These technologies allowed for a user-friendly communication among computers, which made the

Internet accessible to the general public.

shows the frequency with which a search term appears

in published books through time. To measure radical-

ness and the potential for network eﬀects, we use rat-

ings obtained from participants in a Mechanical Turk

(MTurk) study. While these ratings are provided post

hoc (and could be aﬀected by memory bias), they pro-

vide a consistent measure across our diverse sample of

innovations. We describe below the MTurk and Google

Ngram measures and the two data sources used to

obtain them.

4.3.1. MTurk Study. We asked 100 participants in an

MTurk study to rate the 51 innovations included in our

sample on several dimensions relevant to our research.

We requested that the participants be U.S. based, have

a Human Intelligence Task (HIT) approval rate of 95%

or higher, and have “masters” qualiﬁcations in MTurk.

Participants were each paid $5 and took an average of

52 minutes to complete the survey.

At the beginning of the survey, we provided spe-

ciﬁc deﬁnitions for our characteristics of interest, along

with examples of innovations that were not part of the

list to be evaluated. We provided the most commonly

referenced deﬁnitions of radical innovation, direct and

indirect network eﬀects, and disruptive innovation. We

present these deﬁnitions in Web Appendix C. Dis-

ruptive innovations are initially inferior to existing

products. However, through time, they improve and

become the dominant players in the market (Sood and

Tellis 2011). The “disruptive” dimension has not been

studied previously in relation to stock prices, and is

not part of our theoretical framework that links inno-

vation with bubbles. We include this dimension in the

MTurk survey to control for the possibility that any new

product that is fundamentally diﬀerent from the status

quo might be associated with a bubble, not just radical

innovations. Disruptive innovations, when introduced,

do not provide higher consumer beneﬁts than exist-

ing alternatives (unlike radical innovations). Therefore,

comparing the eﬀects of disruptive and radical innova-

tion in relation to bubbles can yield interesting insights

on how investors value products that provide signiﬁ-

cant increases in consumer beneﬁts.

After providing the deﬁnitions and examples of rad-

ical, disruptive, direct network eﬀect, and indirect net-

work eﬀect, we instructed participants to evaluate each

innovation in relation to the existing technology at

the time of its commercialization. For instance, we

told participants that when they evaluate the color TV

on the radical dimension, they should do so in relation

to the black and white TV that was the standard when

the color TV was commercialized. To facilitate this task,

we listed innovations in the order of commercializa-

tion. For each characteristic, we presented respondents

with our list of innovations and asked them to rate their

agreement on a one-to-ﬁve scale (where 1 is “strongly

disagree” and 5 is “strongly agree”) that each innova-

tion has that particular characteristic. We also allowed

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

respondents to choose the following option: “I am not

able to make an assessment.” The survey included four

reading checks. Only respondents who correctly com-

pleted all reading checks were able to ﬁnish the survey

and obtain payment.

We use the average ratings across the 100 partici-

pants to measure the characteristics of the 51 innova-

tions in our sample. The sample average of these

average ratings is as follows: 4.10 for the radical di-

mension, 3.51 for disruptive, 3.44 for indirect net-

work eﬀects, and 3.25 for direct network eﬀects.9We

acknowledge that these post hoc ratings might not fully

capture the consumer perceptions of these products

at the time when they were introduced. However, we

hope that this preliminary eﬀort, which allows us to

present some cross-sectional evidence on the associa-

tion between innovation characteristics and stock mar-

ket bubbles, will stimulate additional research on the

valuation of distinct types of innovations.

4.3.2. Google Ngram Data. Given the span and scope

of our sample, data on a single measure of diﬀusion—

such as product-level sales—would be very diﬃcult

to obtain. Instead, to gain preliminary insight into the

adoption of each innovation, we measure its contem-

poraneous visibility in the public domain using the

annual frequency with which the innovation is men-

tioned in printed books. The numerator of this fre-

quency measure is the number of times that a given

word appears in print in all books digitized by Google

in a given year. The denominator includes the total

number of words in all books during the same year.

For example, the Ngram value for the word “radio” in

1929 is 1.92 ×10−5. Thus, the word “radio” represents

one in every 52,035 words across all books published

that year.

We use Google Ngram data to compute three mea-

sures of visibility of relevance to our study.10 First, to

test the intrinsic bubble model of Froot and Obstfeld

(1991), we use Ngram frequencies measured during

the year when the bubble is detected as a proxy for

contemporaneous cash ﬂows. Second, to measure the

magnitude of postbubble visibility, we use the maxi-

mum value of Ngram frequencies achieved during the

lifetime of the innovation, regardless of date, which

is a proxy for the market saturation point. Third, the

rate of increase in visibility is measured as the distance

in time between the commercialization date and the

inﬂection point in the time plot of Ngram frequencies.

The inﬂection point, a proxy for takeoﬀ, is the year with

the largest positive change in the Ngram frequencies.11

To evaluate the extent to which visibility is an appro-

priate proxy for diﬀusion, we collect product adop-

tion data for a subsample of 25 innovations. Adop-

tion data for 24 of these innovations are obtained from

the Statistical Abstract of the United States, published

annually by the Bureau of the Census. In addition, we

obtain adoption data for the Internet from the World

Bank, which publishes annual measures of Internet

usage per 100 inhabitants. We then compare the adop-

tion data with the visibility data previously obtained

from Google Ngram. The results are presented in

Web Appendix D and in Table WA-D1. Panel A of

Table WA-D1 shows our data sources and the partic-

ular measure of product adoption obtained for each

of these 25 innovations. Panel B of Table WA-D1 com-

pares the takeoﬀ dates computed from visibility data

with the takeoﬀ dates computed from product adop-

tion data. The two measures correlate very highly.

Overall, the results presented in Web Appendix D sug-

gest that the visibility measure computed from Google

Ngram data is a good proxy for product adoption.

4.4. Detecting Stock Market Bubbles

We use the statistical method developed by West (1987)

to detect the presence of bubbles in the time series

of stock prices. For each innovation, we form a single

test portfolio containing all ﬁrms shown in the right-

most column of Table 2. For each test portfolio, we

compute a monthly value-weighted price index and,

separately, a monthly value-weighted dividend index,

using as weights the market capitalization of each stock

at the end of the previous month. The Cowles data set

already comes in the form of value-weighted test port-

folios. In the end, this process produces 102 diﬀerent

time series, two series (dividends and prices) for each

of the 51 innovations.

We perform a bubble test for each innovation using

the price and dividend time series of its respective test

portfolio. We require a minimum of 24 months to esti-

mate the parameters of the model. After a bubble is

detected, we continue the process until the model indi-

cates that a bubble is no longer present. Our empirical

speciﬁcation is motivated by the model of West (1987),

which begins with the no-arbitrage condition that the

current-period stock price is equal to the discounted

value of the next-period stock price and dividends

PtρtEt[Pt+1+dt+1]+t,(1)

where Etis the expectation operator, ρt1/(1+kt),

and ktis the discount rate for the stock at time t. West’s

(1987) model further assumes that the aggregate divi-

dend of each test portfolio follows a zero-mean, AR(1)

process

dtϕtdt−1+νt.(2)

From Equations (1) and (2), we obtain F∗

t, the funda-

mental value of the stock

F∗

tδtdt,(3)

where

δtρtϕt

1−ρtϕt

.(4)

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

If no bubble is present, the price of the stock, Pt, equals

its fundamental value, F∗

t. Thus,

Ptδtdt.(5)

According to Equation (5), in the absence of bubbles,

the observed stock price, Pt, should equal to a multi-

ple δtof the current dividend dt, where δtdepends,

as shown in Equation (4), only on the fundamental

parameters that govern the stock’s law of motion: ρt

and ϕt.12 However, if bubbles are present, the observed

stock price Ptcould be signiﬁcantly higher. The bubble

test consists of comparing δtestimated in Equation (5)

with its intrinsic value estimated in Equation (4). If δt

exceeds this intrinsic value, the stock price Ptis higher

than the present value of its future dividends, implying

that a bubble is present.

Empirically, the bubble test examines whether ˆ

δt

[ˆ

ρtˆ

ϕt/(1−ˆ

ρtˆ

ϕt)]. This test requires that we compute

δt,ˆ

ρt, and ˆ

ϕt, the empirical estimates of parameters δt,

ρt, and ϕt, and that we do so for all values of t. We

estimate ˆ

ρtfrom Equation (1), ˆ

ϕtfrom Equation (2),

and ˆ

δtfrom Equation (4). The empirical speciﬁcation

for each of these equations includes an intercept and

an error term.

We deﬁne Ht, a test statistic for detecting bubbles, as

follows:

Htˆ

δt−ˆ

ρtˆ

ϕt/(1−ˆ

ρtˆ

ϕt)ˆ

σt,(6)

where ˆ

σt, the standard error of the Htstatistic, is com-

puted using the heteroskedasticity-adjusted method of

Newey and West (1987). This test statistic follows a chi-

square distribution with two degrees of freedom (West

1987). We estimate Hteach month, t, for each innova-

tion, beginning with the 25th month in the time series

of returns and dividends.

Detecting the Beginning and End of the Bubble. A bub-

ble is deemed to form in month tif the Htstatistic

is signiﬁcantly positive at the 10% level (two-tail test)

during months t,t+1, and t+2. The bubble is deemed

to continue beyond month t+2so long as the Ht

statistic remains signiﬁcantly positive. Once a bubble

is detected, we allow up to two consecutive months

of insigniﬁcant Hstatistics before concluding that the

bubble has ended. The bubble is deemed to end when

the Hstatistic is no longer signiﬁcantly positive and

remains so for at least two months.

Measuring the Magnitude of a Bubble. We deﬁne mis-

pricing as the diﬀerence between the observed price of

the test portfolio and the fundamental value predicted

by Equation (3). For each innovation, we compute mis-

pricing for each month, t, during the bubble’s exis-

tence. The peak of the bubble is deemed to occur when

mispricing is highest. We measure the magnitude of

the bubble as the ratio of mispricing to the fundamen-

tal value of the test portfolio, computed at the peak of

the bubble. Its statistical signiﬁcance is the p-value of

the contemporaneous Htstatistic.

5. Results

We begin by performing a bubble test for each of the 51

innovations in our sample. The results are presented

in Table 3. Our method identiﬁes bubbles in approx-

imately 73% of the innovations studied (37 out of 51

innovations). The results corroborate the presence of

bubbles in a number of cases that had been conjec-

tured in books and in the popular press. For example,

we identify bubbles in the rail, telegraph, automobile,

radio, airplane, and Internet industries. We also iden-

tify bubbles in innovations that had not been men-

tioned previously in the literature, such as the personal

computer or the microwave oven. Finally, we ﬁnd no

evidence of bubbles in the remaining 14 innovations,

which include the color TV and the electronic watch.

5.1. Economic Value Added

To measure the economic value added of each innova-

tion, we compute the buy-and-hold abnormal returns

(BHARs) of each innovation’s test portfolio from the

beginning to the end of the bubble. BHARs are obtained

by subtracting the buy-and-hold returns of the stock

market from the buy-and-hold returns of the test port-

folio.13 We compute this measure for each innovation.

The statistical signiﬁcance is obtained from the monthly

time series variation using a t-test of paired diﬀerences.

The results are presented in Table 4. The ﬁrst column

in Table 4lists all 37 innovations for which we have

previously identiﬁed bubbles, and the second column

provides the year of commercialization. The third col-

umn in Table 4shows the buy-and-hold returns of par-

ent ﬁrms from the beginning to the end of the bubble.

The fourth column shows the buy-and-hold returns of

the market during that same period. The ﬁfth column

shows the BHARs, computed as the diﬀerence between

the previous two columns. The last column shows the

p-value for all positive values of BHAR.

Despite the presence of bubbles, we ﬁnd evidence

of positive economic value added for innovations in

our sample. On average, ﬁrms in our test portfolios did

well during the bubbles, both in absolute terms and

by comparison to the market. A hypothetical investor

who purchased a typical test portfolio immediately

prior to the start of the bubble and remained invested

throughout the bubble would have earned, at the end

of the bubble, an average return of 65.01%, compared

to an average market return of only 20.42%. The aver-

age diﬀerence of 44.58% is statistically larger than zero

(p0.0054). In approximately 76% of the cases studied

(28 out of 37), the test portfolio was worth more (in

market-adjusted terms) at the end of the bubble than

it was at the beginning. This fraction is statistically dif-

ferent from 1

2(p0.0233).

5.2. Do Bubbles Lead or Lag Visibility

We compare the start date of each innovation bubble

with the takeoﬀ in visibility (computed as the inﬂection

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Table 3. Bubbles Associated with the Stock Prices of Innovating Companies

Bubble magnitude

Bubble Bubble Bubble Bubble

Innovation Comm. year detected? start date peak date Value (%) p-value end date

Steam engine train 1825 Yes 8/1843 3/1844 31.10.0001 2/1845

Telegraph 1845 Yes 12/1870 2/1873 75.60.0123 9/1873

Incandescent vacuum lamp 1880 Yes 6/1892 10/1892 16.30.0410 4/1893

Automobile 1886 Yes 3/1919 11/1919 119.50.0166 9/1920

Portable camera 1888 Yes 7/1918 4/1921 101.10.0753 4/1921

Disk phonograph 1894 Yes 7/1906 4/1907 102.60.0014 1/1908

Motion pictures 1898 Yes 9/1921 9/1922 49.80.0002 5/1923

Photoelectric scanning fax 1907 No

Electric clothes washer 1908 No

Electric percolator 1908 No

Electric toaster 1908 No

Rayon 1910 Yes 2/1928 4/1928 24.40.0001 7/1928

Refrigerator 1916 Yes 4/1928 8/1929 369.30.0009 9/1930

Airplane 1919 Yes 1/1928 2/1929 183.90.0024 9/1929

Radio 1919 Yes 1/1928 8/1929 86.50.0252 11/1930

Electric typewriter 1920 Yes 4/1933 7/1935 51.80.0063 12/1935

Electric blanket 1930 Yesa11/1938 11/1938 41.20.0972 10/1939

Electric dishwasher 1930 Yesa11/1938 11/1938 41.20.0972 10/1939

Electric shaver 1930 No

Electric garbage disposer 1935 Yes 11/1944 5/1945 12.00.0931 7/1945

TV 1936 Yes 2/1943 1/1946 50.30.0012 9/1946

Fluorescent light bulb 1938 No

FM radio 1940 Yes 11/1947 5/1948 26.40.0871 3/1949

Ballpoint pen 1945 Yes 1/1955 3/1956 47.70.0001 9/1957

Magnetic tape player 1947 Yes 2/1960 6/1960 62.90.0001 8/1960

Microwave oven 1947 Yes 8/1965 2/1966 59.20.0002 4/1966

NTSC color TV 1954 No

Electric can opener 1956 No

Videocassette recorder 1956 Yes 12/1965 4/1966 79.10.0065 6/1966

Electronic watch 1957 No

Cassette 1964 Yes 8/1969 11/1969 72.90.0001 3/1970

Electronic desktop calculator 1964 Yes 12/1971 10/1973 50.30.0001 6/1974

Electronic pocket calculator 1968 Yes 12/1971 1/1973 46.90.0001 11/1973

Dot-matrix printer 1970 No

Single-player video game 1972 Yes 3/1979 11/1981 171.20.0029 1/1984

Digital LED watch 1975 No

Disposable shaver 1975 Yes 6/1979 8/1979 20.10.0001 8/1979

Personal computer 1975 Yes 7/1982 5/1983 86.20.0031 8/1983

Laser printer 1976 No

Laser disc player 1978 Yes 9/1983 12/1989 186.10.0454 6/1990

Mobile phone 1979 Yes 7/1986 12/1989 101.90.0001 8/1991

Camcorder 1983 Yes 1/1988 7/1988 50.70.0001 6/1990

Compact disc player 1983 Yes 10/1985 6/1987 93.50.0711 6/1987

Portable computer 1983 No

Digital camera 1989 No

Internet 1991 Yes 2/1998 2/2000 54.10.0001 3/2000

Minidisc player 1992 Yes 4/1992 1/1994 43.60.0010 8/1994

Digital video disc player 1997 Yes 12/1997 12/1997 58.50.0665 7/1998

Digital high-deﬁnition TV 1998 Yes 3/1999 6/1999 35.70.0936 6/1999

GPS receiver 2000 Yes 4/2007 9/2007 96.70.0001 12/2007

Smartphone 2000 Yes 8/2004 9/2005 134.90.0275 9/2005

Notes. The table shows the extent to which ﬁrms associated with each innovation experience stock price bubbles. Each bubble is identiﬁed

using West’s (1987) model, described in this paper. When the model detects a bubble, we show the start, peak, and end dates (year/month)

of each bubble. The bubble peak date is the month with the largest diﬀerence between the observed price index of the test portfolio and its

predicted price index from the model. At each peak date, we show the bubble magnitude as the percentage diﬀerence between observed prices

and predicted values, and the corresponding p-value of the H-statistic from the bubble test.

aBecause the electric blanket and the electric dishwasher were introduced in the same year (1930), and because the test portfolios for both

products consist of the same ﬁrm (General Electric), we are only able to perform a joint bubble test for these two innovations. The bubble

detected here could be attributed to either one of the two innovations or to both. We chose to attribute the bubble in the analysis to both

innovations, but the results remain substantively the same if we attribute it to either one of the two innovations.

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Table 4. Buy-and-Hold Returns for the Beginning to End of the Bubble Period

Buy-and-hold returns from beginning to end of bubble (%)

Innovation Comm. year Innov. stocks Market Diﬀerence (BHAR) p-value

Steam engine train 1825 45.20 13.20 32.00 0.0135

Telegraph 1845 54.70 −22.90 77.60 0.0037

Incandescent vacuum lamp 1880 −4.33 −4.69 0.36 0.4599

Automobile 1886 38.80 0.00 38.80 0.1275

Portable camera 1888 88.10 −3.70 91.80 0.0144

Disk phonograph 1894 80.20 2.20 78.00 0.0264

Motion pictures 1898 35.40 34.50 0.90 0.2734

Rayon 1910 −14.60 7.90 −22.50

Refrigerator 1916 54.30 −13.30 67.60 0.0321

Airplane 1919 128.90 54.30 74.60 0.1757

Radio 1919 18.50 −18.80 37.30 0.0232

Electric typewriter 1920 145.00 140.50 4.50 0.4992

Electric blanket 1930 −14.21 0.78 −14.99

Electric dishwasher 1930 −14.21 0.78 −14.99

Electric garbage disposer 1935 12.62 14.66 −2.04

TV 1936 18.90 54.00 −35.10

FM radio 1940 −0.16 −4.65 4.49 0.3254

Ballpoint pen 1945 1.80 15.30 −13.50

Magnetic tape player 1947 34.47 2.94 31.52 0.1003

Microwave oven 1947 75.60 9.50 66.10 0.0216

Videocassette recorder 1956 3.02 −5.90 8.92 0.2777

Cassette 1964 10.10 −1.90 12.00 0.3073

Electronic desktop calculator 1964 53.04 −15.40 68.44 0.0065

Electronic pocket calculator 1968 97.21 −2.79 100.00 0.0021

Single-player video game 1972 36.84 80.74 −43.90

Disposable shaver 1975 16.04 11.77 4.27 0.1837

Personal computer 1975 105.20 54.00 51.20 0.0797

Laser disc player 1978 264.44 92.06 172.38 0.0649

Mobile phone 1979 55.10 43.80 11.20 0.3858

Camcorder 1983 24.38 40.27 −15.89

Compact disc player 1983 97.50 58.70 38.80 0.2398

Internet 1991 570.60 54.60 516.00 0.0018

Minidisc player 1992 50.86 18.22 32.65 0.2105

Digital video disc player 1997 12.75 13.43 −0.68

Digital high-deﬁnition TV 1998 42.63 11.52 31.11 0.1258

GPS receiver 2000 64.80 3.90 60.90 0.0707

Smartphone 2000 115.70 15.90 99.70 0.0157

Average 65.01 20.42 44.58 0.0054

Notes. For each innovation where a bubble has been detected, the table shows the buy-and-hold returns realized by ﬁrms associated with

the innovation, the buy-and-hold returns of the stock market during the same period, and the buy-and-hold abnormal returns (BHARs),

consisting of the diﬀerence between the previous two variables. These abnormal returns provide a measure of the economic value added by

the innovation during the bubble period. The last column provides the p-value of a test that each BHAR is signiﬁcantly higher than zero (for

positive BHARs). The last row in the table shows the sample mean of all variables, as well as the p-value of a test that the sample mean BHAR

is equal to zero.

point in the Google Ngram data). On average, bubbles

lead the takeoﬀ in visibility, which is what we would

expect if market investors are forward looking.

Of the 37 innovation bubbles in our sample, 28 start

no later than the takeoﬀ in visibility. For a formal statis-

tical test, we calculate the diﬀerence (in years) between

the year when the bubble starts and the year when

the visibility takes oﬀ. We then average this diﬀerence

across the 37 innovation bubbles in our sample. On

average, bubbles lead visibility by approximately 4.5

years, and this diﬀerence is statistically diﬀerent from

zero (t2.90).

5.3. Determinants of Bubbles

We now turn to examining the determinants of bub-

bles. Table 5presents the results. The dependent vari-

able in Table 5is the magnitude of the bubble for

innovations where bubbles were detected. For the

remaining innovations, the dependent variable is set

to zero. We use a Tobit model to account for the fact

that the magnitude of the bubble is left-censored to

zero. The ﬁrst four independent variables in Table 5

are the characteristics of innovations previously men-

tioned: radical, disruptive, indirect network eﬀects, and

direct network eﬀects. These variables represent the

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Table 5. Factors Related to the Magnitude of Innovation Bubbles

Dependent variable: Bubble_Mag (Magnitude of the Innovation Bubble)

Independent variables Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Radical 82.92 83.60 86.82 87.43

2.562∗∗ 2.322∗∗ 2.826∗∗∗ 2.862∗∗∗

Disruptive −86.92 −119.8−77.04 −76.42

−2.097∗∗ −2.476∗∗ −1.954∗−1.951∗

Indirect Network Eﬀects 55.33 87.93 58.96 58.20

1.800∗2.243∗∗ 2.014∗∗ 2.002∗∗

Direct Network Eﬀects −21.06 −44.14 −37.91 −38.53

−0.859 −1.388 −1.533 −1.57

Ngram_Start 3.70 4.14

2.057∗∗ 2.161∗∗

Ngram_Peak 3.93 4.37

2.182∗∗ 2.275∗∗

Intercept −112.7−42.79 −140.2−142.4 20.98 18.74

−0.945 −0.316 −1.234 −1.261 1.314 1.152

AIC 457.02 366.59 454.98 454.49 459.71 459.25

No. obs. 51 43 51 51 51 51

Notes. The table shows the results of six models that relate the magnitude of the innovation bubble (Bubble_Mag) to two diﬀerent categories

of independent variables. The ﬁrst category evaluates the extent to which the innovation (i) is Radical, (ii) is Disruptive, (iii) has Direct Network

Eﬀects, and (iv) has Indirect Network Eﬀects. This evaluation is performed through an MTurk survey of 100 qualiﬁed respondents. The second

category is the extent to which the innovation has been featured in books—as captured by Google Ngram frequencies—(i) up to the time when

a bubble is detected (Ngram_Start) and (ii) up to the year of the peak of the bubble (Ngram_Peak). When no bubbles are detected, we compute

estimated start and peak dates for the bubble based on the year of product commercialization. In such cases, Ngram_Start and Ngram_Peak

are computed using Ngram frequencies based on the estimated (rather than actual) start and peak years of the bubble. Models 1 and 3–6 are

estimated using the entire sample of 51 innovations. Model 2 is conﬁned to the sample of radical innovations included in Chandy and Tellis

(2000). The dependent variable is the Bubble_Mag when a bubble is detected. When a bubble is not detected, Bubble_Mag is set to zero. All

models are estimated using a Tobit speciﬁcation, which accounts for the magnitude of the bubble being censored at zero. The t-statistics are

shown in italics under each coeﬃcient.

∗,∗∗, and ∗∗∗ denote coeﬃcients that are statistically signiﬁcant at the 10%, 5%, and 1% levels, respectively.

average ratings provided by MTurk respondents on

each characteristic. The remaining independent vari-

ables are two alternative proxies for the contempora-

neous visibility of the innovation. They are obtained

using the Ngram frequencies at the start of the bubble,

and, alternatively, using the Ngram frequencies at the

peak of the bubble.

In Model 1 we show that the magnitude of the bubble

is positively related to the radical innovation measure

(β82.92,p<0.05) and negatively related to the dis-

ruptive innovation measure (β−86.92,p<0.05). The

negative eﬀect of the disruptive characteristic suggests

that it is diﬃcult for investors to forecast whether a new

technology that is initially inferior to existing alterna-

tives might one day improve and surpass these alterna-

tives in performance. In regard to network eﬀects, we

ﬁnd a signiﬁcant positive association between the mag-

nitude of the bubble and the presence of indirect net-

work eﬀects (β55.33,p<0.10). However, the relation

with direct network eﬀects is insigniﬁcant. This could

be due in part to the high correlation between indirect

and direct network eﬀects, but it is also possible that

indirect network eﬀects might be more closely aligned

with the concept of exploding expectations mentioned

in Section 2. Investor enthusiasm will be heightened

if an innovation not only has the potential to gener-

ate signiﬁcant growth in its own industry, but could

also gain additional functionality through complemen-

tary products that would enhance its consumer ben-

eﬁts. Accurately determining all categories in which

complementary products may be introduced, as well

as the demand for these complementary products,

could be even more diﬃcult than assessing the primary

demand for the focal innovation; optimism toward the

innovation’s potential could be compounded by opti-

mism about complementary products. In Model 2 we

show that these results also hold for the subset of

radical innovations listed in Chandy and Tellis (2000),

which constitute a more conservative subsample for

our study.

The Ngram visibility variables are introduced in Mo-

dels 3 through 6. As conjectured previously, we ﬁnd a

signiﬁcantly positive relation between the bubble mag-

nitude and the visibility of the innovation measured

at the beginning of the bubble (β3.70,p<0.05 in

Model 3 and β4.14,p<0.05 in Model 5) and at the

peak of the bubble (β3.93,p<0.05 in Model 4 and

β4.37,p<0.05 in Model 6). These results are con-

sistent with the intrinsic bubble theory of Froot and

Obstfeld (1991).14

5.4. Capital Raised and Postbubble

Visibility of Innovations

In our next set of tests, we measure the amount of

new equity capital raised during the bubble period and

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Table 6. Analysis of Net Capital Raised and Postbubble Visibility

Panel A: Univariate statistics

Variable

Name Description Number of obs. Mean value t-stat. p-value

DCapRaised Market-adjusted capital raised during bubble 38 0.1857 1.65 0.1064∆

DCapRaised2 Market-adjusted capital raised before and during

bubble

37 0.2795 1.94 0.0602∗

Ngram_Max Highest value of Ngram for innovations with

bubbles minus that of innovations without

bubbles

38 2.1622 2.33 0.0256∗∗

Ngram_Inﬂex Time to inﬂection point for innovations with

bubbles minus that of innovations without

bubbles; time to inﬂection point is the diﬀerence,

in years, between the commercialization date

and the date when variable Ngram reaches its

inﬂection point through time

38 4.5929 1.11 0.2727

Panel B: Cross-sectional analysis

Dependent

variable CapRaised DCapRaised Ngram_Max Ngram_Inﬂex

Indep. Model Model Model Model Model Model Model Model Model Model Model Model

variables 7 8 9 10 11 12 13 14 15 16 17 18

Bubble_Mag 0.00523 0.00575 0.0224 0.0168 0.0169 −0.0399 −0.0212 −0.0210

2.48∗∗ 2.82∗∗∗ 2.46∗∗ 1.91∗1.80∗−1.28 −0.79 −0.73

CapRaised 1.5275 1.0576 −4.1631 −3.5719

5.50∗∗∗ 3.14∗∗∗ −2.26∗∗ −2.22∗∗

DCapRaised 1.4806 0.9467 −3.9418 −3.2794

5.68∗∗∗ 2.89∗∗∗ −2.19∗∗ −2.04∗∗

Intercept −0.1240 −0.1237 5.5365 5.5018 4.5729 4.7040 4.6900 12.9706 13.0479 14.4609 14.0179 14.0552

−0.80 −0.82 13.53∗∗∗ 13.09∗∗∗ 8.82∗∗∗ 9.62∗∗∗ 9.47∗∗∗ 7.02∗∗∗ 6.97∗∗∗ 5.52∗∗∗ 5.24∗∗∗ 5.19∗∗∗

R-square 0.1458 0.1813 0.1614 0.1475 0.1847 0.2508 0.2341 0.0676 0.0590 0.0331 0.0756 0.0665

Number of obs. 38 38 38 38 38 38 38 38 38 38 38 38

Notes. The table shows an analysis of net capital raised and postbubble visibility for the 38 innovations in our sample that were commercialized

in 1919 and later, beginning with the radio and the airplane. CapRaised is the percentage of capital raised during the bubble period divided by

the market value of equity before the bubble. DCapRaised is the diﬀerence between CapRaised and the percentage capital raised by the entire

stock market during that same period. DCapRaised2 is the same as DCapRaised except that it is measured beginning with the commercialization

date through the end of the bubble. All three values are winsorized at the 99th percentile to mitigate the eﬀect of outliers. The postbubble

visibility is measured with Ngram_Inﬂex (the diﬀerence, in years, between the commercialization date and the date when the Ngram time

series reaches its inﬂection point) and with Ngram_Max (the maximum value of the Google Ngram frequency during the lifetime of an

innovation). Bubble_Mag is the magnitude of the innovation bubble; when a bubble is not detected, Bubble_Mag is set to zero. Panel A shows

univariate descriptive statistics, and panel B presents cross-sectional regressions. Standard errors in Models 9 through 18 are corrected for

heteroskedasticity using White’s method (1980). The t-statistics are shown in italics in panel B.

∗,∗∗, and ∗∗∗ denote statistical signiﬁcance at the 10%, 5%, and 1% levels, respectively (two-tailed test); ∆denotes a coeﬃcient that is marginally

insigniﬁcant (p0.1064).

examine how this new capital relates to the postbubble

visibility of innovations. The results are presented in

Table 6.

Panel A of Table 6provides univariate statistics for

the dependent variables. The ﬁrst two variables in panel

A are measures of market-adjusted equity raised by

parent ﬁrms around the bubble period. The ﬁrst of

these variables, DCapRaised, measures equity raised

during the bubble period only. The second variable,

DCapRaised2, measures the equity raised between the

commercialization date and the end of the bubble. Both

variables are computed as the amount of new equity

raised by parent ﬁrms divided by the market value of

equity of these parent ﬁrms at the beginning of the

bubble, minus the equivalent ratio of equity capital

raised by the broader stock market during the same

period.15 On average, ﬁrms in our sample are able to

raise new equity capital during the bubble correspond-

ing to 18.57% of these ﬁrms’ market value of equity at

the beginning of the bubble. The result is borderline

insigniﬁcant (p0.1064), perhaps because of the low

power of the small sample size (N38). Better statistical

signiﬁcance is achieved when the new equity raised is

measured over a longer period—from the commercial-

ization date until the end of the bubble. In this case, the

average equity capital raised is 27.95% (p0.0602).16

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Turning now to the postbubble visibility of the inno-

vation, the third variable in panel A, Ngram_Max,

shows the diﬀerence between the average of the highest

value of Ngram frequencies achieved by innovations

with bubbles and the same average achieved by inno-

vations without bubbles. As conjectured, this diﬀer-

ence is positive and statistically signiﬁcant (p0.0256),

suggesting that innovations with bubbles become sig-

niﬁcantly more visible during the postbubble period.

The fourth variable in panel A, Ngram_Inﬂex, mea-

sures the length of time between commercialization

and the Ngram inﬂection point computed for innova-

tions with bubbles, minus that for innovations with-

out bubbles. As conjectured, innovations with bubbles

have a higher rate of increase in postbubble visibility:

the Ngram inﬂection point is achieved 4.59 years ear-

lier, on average, when compared to innovations with-

out bubbles. This result, however, is not statistically

diﬀerent from zero, perhaps because of the low power

of the test. Another possibility is that the capital raised

during the bubble may be the factor that leads to a

faster takeoﬀ rather than the magnitude of the bubble

itself. We examine this possibility in our cross-sectional

analysis.

The results of the cross-sectional analysis are shown

in panel B of Table 6. The ﬁrst two models in panel B

examine the relation between new equity capital raised

during the bubble (the dependent variable) and the

magnitude of the bubble. In Model 7, the dependent

variable is the actual amount of equity capital raised

(as a fraction of prebubble capital) without any mar-

ket adjustment. In Model 8, the capital raised is market

adjusted. The independent variable in both models is

the magnitude of the bubble. As discussed previously,

the magnitude of the bubble could be inversely pro-

portional to the cost of equity during the bubble. The

results in Models 7 and 8 show that, on average, ﬁrms

raise more equity capital when the magnitude of the

bubble is higher (β0.00523,p<0.05 in Model 7 and

β0.00575,p<0.01 in Model 8), perhaps taking advan-

tage of temporarily lower funding costs.

In Models 9 through 13 of panel B, the dependent

variable is the maximum level of visibility achieved by

each innovation subsequent to the bubble (measured

using Ngram frequencies). In Models 14 through 18,

the dependent variable is the time distance between

commercialization and the inﬂection point of the

Ngram time series. This time distance is the inverse of

the rate of increase in postbubble visibility. The inde-

pendent variables in Models 9 through 18 are the mag-

nitude of the bubble and the two measures of equity

capital that had been previously used as dependent

variables (in Models 7 and 8).

The results from Models 9 through 13 show that the

maximum postbubble visibility is positively related to

the new capital raised during the bubble (β1.5275,

p<0.01 in Model 9 and β1.4806,p<0.01 in Model 10)

and to the magnitude of the bubble (β0.0224,p<0.05

in Model 11). When both independent variables are

included (Models 12 and 13), the coeﬃcients remain

statistically signiﬁcant.

The results from Models 14 through 18 show that the

time lag between commercialization and the Ngram

inﬂection point is negatively related to the amount of

new capital raised during the bubble, but not to the

magnitude of the bubble. The coeﬃcient of the new

capital raised is β−4.1631,p<0.05, in Model 14; β

−3.9418,p<0.05, in Model 15; β−3.5719,p<0.05,

in Model 17; and β−3.2794,p<0.05, in Model 18.

This suggests that the eﬀect of the bubble magnitude

on the rate of increase in visibility is channeled through

the new capital raised during the bubble, rather than

through the bubble itself.

Overall, the results in panel B of Table 6suggest that

parent ﬁrms faced with larger stock market bubbles

raise more capital, on average. In turn, this capital is

positively related to the maximum visibility achieved

by each innovation (postbubble) and to the rate of

increase in this visibility.

5.5. Macroeconomic Factors Impacting Bubbles

A common hypothesis in the popular press is that

bubbles are fueled by the availability of cheap credit

(Kindleberger and Aliber 2005, Wood 2006) and by the

collective frenzy aﬄicting investors during good eco-

nomic times (Perez 2002). We ﬁnd no support for this

hypothesis. We use two diﬀerent approaches to deter-

mine whether macroeconomic conditions explain the

occurrence of innovation bubbles. First, we estimate a

negative binomial model in which the dependent vari-

able is the number of innovation bubbles encountered

each year during the period from 1800 to 2010.17 Inde-

pendent variables include the level of interest rates

that year, the growth rate in gross domestic product

(GDP) over the previous year, the ratio of national debt

to GDP, and the ratio of the monetary base to GDP.

None of these independent variables are statistically

signiﬁcant (the results are available from the authors

on request).

Second, we evaluate the possibility that innovation

bubbles in our sample might be caused by contempo-

raneous marketwide bubbles. To do so, we use West’s

(1987) model to determine whether marketwide bub-

bles occur during the same time periods as the inno-

vation bubbles detected in Table 3. The results are pre-

sented in Table 7. We detect simultaneous stock market

bubbles in 50% of the innovations studied (18 out of

36).18 This fraction is signiﬁcantly diﬀerent from unity

(p<0.0001). Moreover, in 83% of the cases (15 out of

18), the magnitude of the marketwide bubble is smaller

than the magnitude of the innovation bubble. This

fraction is signiﬁcantly diﬀerent from 1

2(p0.0365).

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Table 7. Contemporaneous Market Bubbles

Bubble magnitude (%)

Innovation Comm. year Market bubble detected? Innov. stocks Market Diﬀerence

Steam engine train 1825 Yes 31.10 15.04 16.06

Telegraph 1845 Unknown 75.60 n/a n/a

Incandescent vacuum lamp 1880 No 16.25 16.25

Automobile 1886 No 119.50 119.50

Portable camera 1888 No 101.10 101.10

Disk phonograph 1894 No 102.60 102.60

Motion pictures 1898 Yes 49.80 24.93 24.87

Rayon 1910 No 24.40 24.40

Refrigerator 1916 No 369.30 369.30

Airplane 1919 Yes 183.90 19.38 164.52

Radio 1919 Yes 86.50 19.38 67.12

Electric typewriter 1920 Yes 51.80 291.30 −239.50

Electric dishwasher 1930 Yes 41.24 7.10 34.14

Electric blanket 1930 Yes 41.24 7.10 34.14

Electric garbage disposer 1935 Yes 11.97 29.80 −17.83

TV 1936 Yes 50.30 29.76 20.54

FM radio 1940 No 26.41 26.41

Ballpoint pen 1945 Yes 47.70 27.86 19.84

Microwave oven 1947 No 59.20 59.20

Magnetic tape player 1947 No 62.90 62.90

Videocassette recorder 1956 No 79.14 79.14

Cassette 1964 No 72.90 72.90

Electronic desktop calculator 1964 Yes 50.31 20.85 29.46

Electronic pocket calculator 1968 Yes 46.90 20.85 26.05

Single-player video game 1972 No 171.16 171.16

Personal computer 1975 Yes 86.20 24.46 61.74

Disposable shaver 1975 No 20.10 20.10

Laser disc player 1978 No 186.11 186.11

Mobile phone 1979 Yes 101.90 20.86 81.04

Compact disc player 1983 Yes 93.50 15.35 78.15

Camcorder 1983 Yes 50.69 91.35 −40.65

Internet 1991 Yes 54.10 8.27 45.83

Minidisc player 1992 No 43.60 43.60

Digital video disc player 1997 No 58.50 58.50

Digital high-deﬁnition TV 1998 No 35.68 35.68

GPS receiver 2000 Yes 96.70 9.96 86.74

Smartphone 2000 No 134.90 134.90

Average 79.33 18.99 60.44

Notes. For each innovation where a bubble has been detected, the table shows the result of West’s (1987) bubble test applied to the entire stock

market at the time of the innovation bubble. When a marketwide bubble is detected, the table provides the magnitude of the market bubble

(in percentage points). We are unable to conduct a market bubble test for the telegraph because of the absence of market dividend data during

that period. The last column of the table shows the diﬀerence between the magnitude of the innovation bubble and the magnitude of the

marketwide bubble. When no marketwide bubble is detected, the magnitude of the marketwide bubble is assumed to be zero for the purpose

of computing this diﬀerence. The last row in the table shows the sample mean of the following bubble magnitudes: (i) all innovation bubbles,

(ii) all market bubbles (including the values of zero previously mentioned), and (iii) the diﬀerence between the innovation bubble and the

market bubble.

In addition, the diﬀerence in magnitude between mar-

ketwide bubbles and innovation bubbles is signiﬁ-

cantly negative (p0.0699). Finally, innovation bub-

bles tend to lead market bubbles, on average. Of the 18

market bubble cases, the innovation bubble leads the

market bubble in 10 cases and lags the market bubble

in 4 cases. In the remaining four cases, the innovation

bubble begins at the same time as the market bub-

ble. These results suggest that innovations are the ones

spurring bubbles in the overall market, not the other

way around.19

In short, we ﬁnd no evidence that innovation bub-

bles are driven by low interest rates, by expansion in

the monetary base, by high GDP growth, or by bub-

bles in the broader stock market. While it is entirely

possible that other types of bubbles could be ignited

by such macroeconomic conditions, innovation bub-

bles appear to be ignited by the innovation per se. This

conclusion is consistent with the ﬁndings of Frehen

et al. (2013), who show that the Dutch wind trade and

South Sea Bubbles of 1720 were driven by innovation.

The conclusion is also corroborated by the fact that we

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

are detecting two innovation bubbles during the Great

Depression of the 1930s, a period known for tight mon-

etary policy and low economic growth.

6. Discussion and Conclusion

We explore a new facet of the interaction between the

stock market and the consumer product market—the

association between innovation and stock market bub-

bles. We examine 51 major innovations that were com-

mercialized during the 19th and 20th centuries. In 37 of

these 51 cases, we detect the presence of bubbles in the

stock price of parent ﬁrms. We show that these bub-

bles are more likely to occur for innovations that are

radical and that have the potential to generate indirect

network eﬀects. Moreover, the magnitude of these bub-

bles is proportional to the visibility of the underlying

innovation at the time of the bubble, as measured by

the contemporaneous frequency with which the inno-

vation appears in books, obtained from Google Ngram.

Parent ﬁrms that experience bubbles are shown to raise

additional equity capital during the bubble period. In

turn, this new equity capital is positively related to the

magnitude and to the rate of increase in the visibility of

the innovation after the bubble. Below we summarize

our paper’s contribution to theory, practice, and pol-

icy making; discuss the limitations of our research; and

provide a set of implications and directions for future

research.

6.1. Contributions to Theory, Practice, and

Policy Making

To our knowledge, our study presents the most com-

prehensive evidence of a systematic association be-

tween innovations and bubbles. The popular press has

long conjectured that technological revolutions and

bubbles seem to go hand in hand, often attributing the

cause of these bubbles to investor irrationality. How-

ever, there is insuﬃcient academic evidence to sup-

port this conjecture. While a few studies in ﬁnancial

economics evaluate the presence of bubbles around

innovations, these studies tend to focus on one or two

industries and generally treat innovation as an undif-

ferentiated output of the aggregate production func-

tion. Moreover, most studies do not provide formal

statistical tests for bubbles, and there is still a signiﬁ-

cant degree of disagreement among authors about the

causes and economic consequences of bubbles.

On the other hand, marketing studies do a better

job at recognizing heterogeneity among various types

of innovation and their impact on shareholder value

(e.g., Pauwels et al. 2004, Sood and Tellis 2009, Sorescu

and Spanjol 2008). However, the marketing literature

does not explore the association between innovation,

bubbles, and capital raised, or, more generally, the

manner in which the new product market interacts

with the stock market. Our paper seeks to bridge the

gap between the innovation literatures in marketing

and in ﬁnancial economics by illustrating one way in

which the marketing and ﬁnance functions come to-

gether to create and support economically signiﬁcant

innovation.

The ﬁnancial consequences we document in this

paper are noteworthy additions to the marketing litera-

ture and provide managers with actionable guidelines

when major innovations appear on their radar screens.

While these innovations are expected to be associated

with stock price bubbles, they are also expected to

add signiﬁcant value to the parent ﬁrm after bubbles

deﬂate. Thus, investing in the commercialization of

innovations appears to be worthwhile, especially since

bubbles seem to create a favorable environment for

ﬁrms to raise cheap equity capital without sending

negative signals to the market.

Our results challenge some of the views expressed

in the popular press about the association of innova-

tions with bubbles. We show that innovation bubbles

are not conﬁned to “technological revolutions.” Table 3

shows a number of bubbles surrounding radical inno-

vations (such as the microwave oven) in time periods

that are not normally associated with technological

revolutions. Moreover, we ﬁnd no evidence that bub-

bles are fueled by strong GDP growth or by loose mon-

etary policy. For example, we document two bubbles

during the 1930s, a period of declining GDP and tight

monetary policy. In short, innovation bubbles appear

to be intrinsic to the innovation per se, and seem to be

unrelated to aggregate macroeconomic conditions.

While more research is needed to fully understand

the relation between bubbles, issuance of new equity

capital, and the diﬀusion of innovations, the evidence

presented in this paper suggests that bubbles could

actually foster economic growth by facilitating the

development of new infrastructure. This ﬁnding is con-

sistent with the predictions of Olivier (2000). In spite

of bubbles, innovating ﬁrms have higher stock market

gains at the end of a bubble compared to the gains real-

ized by the stock market as a whole during the same

period. The negative consequences of bubbles are most

likely conﬁned to new investors who buy overvalued

stocks close to the height of the bubble. These ﬁnd-

ings suggest that economic policies designed to sup-

press bubbles might be misguided. These implications

are limited to bubbles that are spurred by important

technological breakthroughs and cannot be extended

to other types of bubbles, such as bubbles on intrinsi-

cally useless assets (e.g., tulip bulbs), bubbles caused

by fraud (e.g., Enron), or bubbles possibly fueled by

loose monetary policy.

6.2. Limitations

6.2.1. A Caveat on the Econometric Measurement of

Bubbles. All econometric models that attempt to detect

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

the presence of bubbles (including West 1987) are test-

ing a joint null hypothesis that (i) no bubble is present

and (ii) the correct dividend expectation model is used.

Rejecting the null hypothesis implies either that a

bubble is present or that we are using the wrong div-

idend expectations model. While this problem does

not have an econometric resolution, we oﬀer a con-

ceptual argument in favor of the bubble interpreta-

tion: the alternative interpretation—the wrong divi-

dend model—carries the embedded assumption that

the 37 innovations for which we detect bubbles could

have generated even higher cash ﬂows, but did not

do so because of bad luck; that is, from an ex ante

perspective, these innovations must have oﬀered a

nonzero probability of a subsequent increase in earn-

ings at a rate signiﬁcantly higher than what was actu-

ally observed in reality. Yet, by bad luck, this possibility

never materialized. By contrast, the bubble explanation

does not require this type of assumption and appears,

at least to us, to be more credible. We acknowledge,

however, that despite the weight of the evidence pre-

sented, one can never be certain that the price patterns

shown in this paper are true bubbles as opposed to

bubble-free valuations in anticipation of extraordinary

events that never materialized.

6.2.2. Discussion of Causality. We do not claim that

we can unequivocally establish a causal relation be-

tween innovation and bubbles. We mainly aim to pro-

vide formal statistical tests that document stock market

bubbles across the largest set of innovations identi-

ﬁed to date. It is diﬃcult to establish a causal relation

between innovation and bubbles because the nature

of our data—rare historical events—is not conducive

to a formal statistical test of causality. Thus, we can-

not exclude the alternative interpretation that bubbles

and innovations might be jointly caused by a third,

unknown factor, such as periodic changes in social

norms that could foster both creativity in the product

market and optimism in the stock market.

We note, however, that our paper’s managerial impli-

cations do not change under this alternative hypothe-

sis. To the extent that ﬁrms can take advantage of bub-

bles to raise equity capital on favorable terms, and to

the extent that this new capital can facilitate the dif-

fusion of the innovation, it does not matter whether

bubbles are caused by innovation or whether both are

jointly caused by a third factor, so long as both phe-

nomena occur simultaneously. In both cases, our rec-

ommendations to policy makers and managers remain

the same.

6.2.3. Data Limitations. Despite the signiﬁcant eﬀort

invested into assembling our data, the sample is not

without limitations. First, our sampling scheme con-

sists of a set of commercially successful innovations.

Over the time span of our sample, there could have

been other new products introduced that had the

potential to be radical and generate network eﬀects but

eventually failed in the marketplace. However, we note

that this type of selection bias is a limitation that is

common in the radical innovation and diﬀusion liter-

atures, whereby most authors take a post hoc view in

assembling the sample. Second, we were not able to

control for the sales of each innovation as a percentage

of the parent ﬁrm sales. We used archival data to quali-

tatively determine the extent to which each innovation

was an important part of the ﬁrm’s product portfolio at

the time of commercialization. We endeavored to limit

our test portfolios, as much as possible, to pure-play

ﬁrms. However, we recognize that, despite our best

eﬀorts, there will be some variation in our sample in

terms of the importance that the focal innovation plays

in the product portfolio of its parent ﬁrm. This vari-

ation leads to an error in measurement problem that

attenuates the coeﬃcients in Table 6toward zero. Thus,

the results presented in Table 6should be viewed as

conservative.

6.3. Directions for Future Research

Our paper suggests ﬁve promising avenues for future

research. First, our results imply that publicly traded

ﬁrms might have a resource advantage when it comes

to commercializing innovation because of the ease of

access to new equity capital, particularly during bubble

periods. If so, are privately held corporations at a dis-

advantage? Does the pattern of venture capital invest-

ment in private ﬁrms follow the patterns observed in

the public equity markets?

Second, while we provide ﬁrst-order evidence of the

association between bubbles, external ﬁnancing, and

innovation, additional studies are needed to under-

stand speciﬁcally how this new capital is actually de-

ployed and how it aﬀects the diﬀusion of the new

product.

Third, the theory on intrinsic bubbles suggests that

marketing metrics may play a critical role in deter-

mining the market value of ﬁrms in new industries.

An important implication of this theory is that in the

absence of accurate forecasts about long-term cash

ﬂows, investors are likely to anchor their valuation on

observable short-term metrics of performance, and val-

uations are likely to exceed intrinsic values for a period

of time. These short-term metrics most likely come

from the product market, as opposed to the ﬁnancial

market, because information from the product market

is typically the ﬁrst to become available to investors.

This is evidenced by examples in the dot.com era,

where valuations were multiples of website traﬃc, or

from more recent examples in the social media domain,

where valuations are multiples of the number of users.

Therefore, marketing metrics could play a critical role

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

in determining the market value of companies in new

industries. While our ﬁndings are certainly consistent

with this interpretation, additional research is needed

to provide a more direct link between marketing met-

rics and the valuation of companies that pursue a sig-

niﬁcant innovation strategy.

Fourth, the extent to which ﬁrms can appropriate

gains from innovations associated with bubbles re-

quires additional study. While we show in our paper

that these innovations have a positive economic value

added, we have not controlled for variations that may

arise from ﬁrms’ diﬀerential ability to protect their

innovations through patents. Should a ﬁrm be able,

through a strong patenting eﬀort, to prevent competi-

tors from commercializing the same technology, the

ﬁrm might obtain monopolistic proﬁts, but might also

miss out on the network eﬀects that arise when com-

petitors leverage the same technology. These trade-oﬀs

are a fruitful area of future research.

Fifth, the social beneﬁts of cheap equity ﬁnancing

provided by innovation bubbles must be evaluated

against their social costs. The magnitude of these social

costs depends, in part, on how these bubbles aﬀect

the aggregate disparity of wealth in the society. Shiller

(2015) and Perez (2002) both hypothesize that bubbles

have contributed to a signiﬁcant increase in the dispar-

ity of wealth observed during the past 100 years, and

Piketty (2013) presents evidence consistent with this

prediction. If true, there is perhaps a signiﬁcant hid-

den social cost of innovation bubbles, as no stable soci-

ety appears to have survived over the long term when

the levels of wealth became extremely concentrated.20

Future research is needed to understand the relation

between innovation bubbles and wealth inequality.

We hope that our research provides an impetus for

others to further study the eﬀects of innovation at the

macro level, including their relation to the stock mar-

ket, to economic growth, and to economic policy. At

the micro level, we hope that our research can help

managers better navigate through the perils of major

shifts in technological paradigms and even learn how

to proﬁt from periods of temporary overvaluation that

might provide windows of opportunity to obtain exter-

nal ﬁnancing on favorable terms.

Acknowledgments

The authors thank Rajesh Chandy and participants at

the Marketing Strategy Meets Wall Street conference at

Singapore Management University, the 2013 Strategic Man-

agement Society Lake Geneva Special Conference on Big

Bang Innovation, and the 2012 INFORMS Marketing Science

Conference, as well as participants in research seminars at

Bocconi University, Tilburg University, the University of Ari-

zona, and the University of Washington, for their valuable

feedback. The authors also thank Gareth Campbell for stock

prices and dividend data for all ﬁrms listed in the Railway

Times.

Endnotes

1As we describe in detail in Section 4, we do not measure diﬀusion

directly; rather, we use a contemporaneous measure of the public

visibility of innovations that is based on Google Books Ngram data.

For a subsample of innovations, we show in Section 4that the Ngram

visibility data correlate highly with the actual diﬀusion data based

on product adoption.

2The nine innovations mentioned by Shiller (2015) are the phono-

graph, electricity, trains, the automobile, radio, electriﬁcation, mo-

tion pictures, TV, and the Internet. However, Shiller (2015) does not

conduct formal statistical tests to document the existence of the bub-

bles associated with these nine innovations.

3We do not focus here on the exact mechanism that might trig-

ger a rational bubble; we only study boundary conditions that are

conducive to the emergence of rational bubbles. The literature in

ﬁnancial economics discusses several trigger mechanisms for ratio-

nal bubbles (e.g., Allen et al. 1993, Zeira 1999). Common among these

papers is the idea that when the cost of entry into the stock mar-

ket is low and there is asymmetric information among investors, an

excessive number of investors will enter the stock market, driving

the price of the stock temporarily above its intrinsic value. We note a

very interesting parallel between this mechanism and the one that in

the marketing literature has been shown to trigger excessive entry of

ﬁrms into a new industry. Speciﬁcally, Shen and Villas-Boas (2010)

and Shen (2014) show that under similar conditions of low entry

costs into the product market and information asymmetry among

ﬁrms, an excessive number of ﬁrms will enter a new industry, driv-

ing economic rents to become negative. Interestingly, both actions

are perfectly rational.

4For example, The Economist (2000) observed that “technological rev-

olutions and ﬁnancial bubbles seem to go hand in hand,” while

Gross (2009) is featured on CNN Money under the title “The bub-

bles that built America” (see http://money.cnn.com/galleries/2007/

news/0705/gallery.bubbles/index.html.).

5Even when several ﬁrms start commercializing a major innovation

around the same time, their stocks are likely to be perceived as hav-

ing a similar level of risk. Therefore, risk cannot be diversiﬁed away

by investing in a portfolio of ﬁrms that manufacture the same radical

innovation.

6We excluded 21 innovations from Chandy and Tellis (2000) for the

following reasons: 12 innovations were excluded because none of

the parent ﬁrms were publicly traded at the time of commercializa-

tion. Four additional innovations were excluded because dividend

or price data were not available. Three innovations were considered

duplicates of innovations already in our sample. One innovation was

excluded because none of the parent ﬁrms were pure plays. The ﬁnal

innovation (telephone) was excluded because of the lack of data for

Bell at the time of commercialization and because Bell’s successor

(AT&T) became a regulated utility in 1910. Firms in regulated indus-

tries typically face a cap on revenues that rules out the bubble solu-

tion in the Blanchard and Fischer (1989) model. A full explanation

for each of these exclusions is presented in Web Appendix B.

7All of our bubble tests are performed on single time series, even

those that are based on a large portfolio of ﬁrms. Speciﬁcally, if an

innovation portfolio contains two or more ﬁrms, we begin by aggre-

gating their prices and dividends into single industry-speciﬁc time

series and perform the tests on these two aggregate time series as

if the entire portfolio were a single ﬁrm. Moreover, the theory that

underlies the formation of bubbles posits that ﬁrms commercializ-

ing the same innovation have the same probability of experiencing

a bubble, as the bubble arises exogenously to ﬁrm characteristics,

being driven instead by the characteristics of the innovation that

meets the four conditions described in Section 2.1. Consequently, our

results should not be aﬀected by the number of ﬁrms included in

each portfolio.

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

8A multiproduct ﬁrm can be viewed as a conglomerate of smaller,

pure-play ﬁrms. Assuming that only one of the ﬁrm’s products is

innovative—and therefore subject to a potential bubble—the stock

price of the multiproduct ﬁrm is likely to experience a smaller bubble

than the stock of a pure play ﬁrm in the same industry. Therefore, the

inclusion of multiproduct ﬁrms in our test portfolios creates an error

in the measurement of the magnitude of innovation bubbles. In some

of the tests we perform in Section 4, this measurement error is incon-

sequential because the bubble magnitude is used as a dependent

variable. In other tests, where the bubble magnitude is an indepen-

dent variable, the consequence of the measurement error is to reduce

the coeﬃcients toward zero, resulting in a more conservative test.

9Moreover, the ratings vary signiﬁcantly across innovations to allow

for a meaningful cross-sectional analysis. For the radical dimension,

average ratings vary from 3.00 to 4.80; for the direct network dimen-

sion, they vary from 2.04 to 4.94; for the indirect network dimension,

they vary from 2.09 to 4.65; and for the disruptive dimension, they

vary from 2.63 to 4.35. The list of average ratings for all innovations

for each of the four characteristics is presented in Web Appendix C.

The correlations between these ratings range from 0.55 (correlation

between disruptive and direct network eﬀects) to 0.81 (correlation

between direct and indirect network eﬀects). The full correlation

matrix is also presented in Web Appendix C.

10 Ngram frequencies vary signiﬁcantly in terms of order of mag-

nitude from 10−5to 10−9, and, in some cases the Ngram count is

identically zero. Therefore, all Ngram frequencies are adjusted using

the natural log of (1+(RawNgram ×10+9)), where RawNgram is the

actual, raw Ngram frequency downloadedfrom Google. We do this to

improveinterpretability and mitigate the eﬀects of heteroskedasticity.

11 This is the discrete-time equivalent of setting the second derivative

equal to zero.

12 Parameter ρtis related to the discount rate for the stock, kt, as fol-

lows: ρt1/(1+kt). Parameter ϕtis related to the annual growth rate

of dividends,gt, as follows: ϕt(1+gt). This estimation is performed

each month, t, over periods of time that start 24 months after the

beginning of the time series. Progressively, each period of time is

nested within the time period used during the subsequent month.

For example, when we attempt to detect a bubble in month t30, we

estimate parameters using data from months t1to t30. The fol-

lowing month, when attempting to detect a bubble in month t31,

we reestimate parameters with data from months t1to t31,

so the ﬁrst time interval is nested within the second. Thus, the test

allows for estimates of growth and discount rates to change over

time; each month, the test produces an intertemporal average esti-

mate of discount rates and growth rates, which takes into account all

information available prior to that month.

13 For example, for a bubble that starts in March 1887 and ends in

June 1888, we would compute the buy-and-hold return of the test

portfolio from March 1887 through June 1888 and subtract from it

the buy-and-hold return of the market portfolio during that same

period. If the buy-and-hold return of the test portfolio is 20% and the

buy-and-hold return of the market is 5%, we would conclude that

the focal innovation adds 15% in terms of economic value, even after

accounting for the collapse of the bubble.

14 The results remain substantively the same if Rayon is excluded

from the sample. These results are presented in Web Appendix E.

15 The amount of new equity raised is computed as the product of

the number of shares issued times the price per share, adjusted for

stock splits and stock dividends.

16 However, not all industries appear to take advantage of overvalued

prices to issue stock. Most of the new equity capital was issued by

the airplane industry, Internet ﬁrms, and smartphone ﬁrms. To a

lesser extent, ﬁrms in the radio, microwave oven, video game, laser

disc player, camcorder, and GPS sectors also did issue more stock

compared to the market.

17 We also restrict this sample to the period from 1925 to 2010 and

the results are unchanged.

18 We are not able to perform a market bubble test associated with the

telegraph bubble because of the lack of marketwide dividend data

during the telegraph bubble period (1870–1873). Therefore, market

bubble tests are conducted for only 36 of the 37 innovations for which

we detect bubbles in Table 3.

19 We also repeat the analysis presented in Table 5after subtract-

ing the magnitude of the market bubble from the magnitude of the

innovation bubble. The results remain substantively similar.

20 For example, Piketty (2013) oﬀers wealth inequality as one of the

leading causes of the French Revolution.

References

Agarwal R, Bayus BL (2002) The market evolution and sales takeoﬀ

of product innovations. Management Sci. 48(8):1024–1041.

Akbas F, Boehmer E, Erturk B, Sorescu S (2017) Short interest,

returns, and unfavorable fundamental information. Financial

Management 46(2):455–486.

Allen F, Morris S, Postlewaite A (1993) Finite bubbles with short

sale constraints and asymmetric information. J. Econom. Theory

61(2):206–229.

Blanchard OJ, Fischer S (1989) Lectures in Macroeconomics (MIT Press,

Cambridge, MA).

Blanchard OJ, Watson M (1982) Bubbles, Rational Expectations and

Financial Markets, in Crises in the Economic and Financial Structure

(Lexington Books, Lexington, MA).

Campbell G (2012) Myopic rationality in a mania. Explorations

Econom. Hist. 49(1):75–91.

Chandy RK, Tellis GJ (1998) Organizing for radical product innova-

tion: The overlooked role of willingness to cannibalize. J. Mar-

keting Res. 35(4):474–487.

Chandy RK, Tellis GJ (2000) The incumbent’s curse? Incumbency,

size, and radical product innovation. J. Marketing 64(3):1–17.

Cowles A III (1939) Common stock indexes. Cowles Commission

monograph no. 3.

Danielsen BR, Sorescu SM (2001) Why do option introductions

depress stock prices? A study of diminishing short sale con-

straints. J. Financial Quant. Anal. 36(4):451–484.

DeMarzo P, Kaniel R, Kremer I (2007) Technological innovation and

real investment booms and busts. J. Financial Econom. 85(3):

735–754.

Economist, The (2000) A survey of the new economy: All technolog-

ical innovations carry risks as well as rewards. (September 21),

http://www.economist.com/node/375561.

Fama E (1998) Market eﬃciency, long-term returns, and behavioral

ﬁnance. J. Financial Econom. 49(3):283–306.

Ferguson N (2008) The Ascent of Money:A Financial History of the World

(Penguin Press, New York).

Frehen RGP, Goetzmann WN, Rouwenhorst KG (2013) New evidence

on the ﬁrst ﬁnancial bubble. J. Financial Econom. 108(3):585–607.

Froot KA, Obstfeld M (1991) Intrinsic bubbles: The case of stock

prices. Amer. Econom. Rev. 81(4):1189–1214.

Goetzmann WN, Ibbotson RG, Peng L (2001) A new historical

database for the NYSE 1815 to 1925: Performance and pre-

dictability. J. Financial Markets 4(1):1–32.

Golder PN (2000) Historical method in marketing research with new

evidence on long-term market share stability. J. Marketing Res.

37(2):156–172.

Golder PN, Tellis GJ (1997) Will it ever ﬂy? Modeling the take-

oﬀ of really new consumer durables. Marketing Sci. 16(3):

256–270.

Golder PN, Tellis GJ (2004) Growing, growing, gone: Cascades, dif-

fusion, and turning points in the product life cycle. Marketing

Sci. 23(2):207–218.

Gross D (2009) Pops! Why Bubbles Are Great for the Economy, Kindle ed

(HarperCollins).

Sorescu et al.: Two Centuries of Innovations and Stock Market Bubbles

Guha K (2008) Greenspan urges policymakers to focus on banks’

capitalization. Financial Times (May 27). https://www.ft.com/

content/c78994f0-2b65-11dd-a7fc-000077b07658.

Gupta S, Jain DC, Sawhney MS (1999) Modeling the evolution of

markets with indirect network externalities: An application to

digital television. Marketing Sci. 18(3):396–416.

Hall BH (2005) Innovation and diﬀusion. Fagerberg J, Mowery D,

Nelson RR, eds. The Oxford Handbook of Innovation (Oxford Uni-

versity Press, Oxford, UK), 459–485.

Hsu PH, Xuan T, Xu Y (2014) Financial development and innovation:

Cross-country evidence. J. Financial Econom. 112(1):116–135.

Katz M, Shapiro C (1986) Technology adoption in the presence of

network externalities. J. Political Econom. 94(4):822–841.

Kindleberger CP, Aliber R (2005) Manias, Panics, and Crashes: A His-

tory of Financial Crises, 5th ed. (John Wiley & Sons, Hoboken, NJ).

Lamoreaux NR, Levenstein M, Sokoloﬀ KL (2007) Financing innova-

tion during the second industrial revolution: Cleveland, Ohio,

1870–1920. Lamoreaux NR, Sokoloﬀ KL, eds. Financing Innova-

tion in the United States: 1870 to the Present (MIT Press, Cam-

bridge, MA), 39–84.

Lansing KJ (2008) Speculative bubbles and overreaction to technolog-

ical innovation.Economic Letter 2018-18, Federal Reserve Bank

of San Francisco, San Francisco.

Loughran T, Ritter J (2004) Why has IPO underpricing changed over

time? Financial Management 33(3):5–37.

Mahajan V, Muller E (1998) When is it worthwhile targeting the

majority instead of the innovators in a new product launch? J.

Marketing Res. 35(4):488–495.

Markovitch DG, Golder PN (2008) Using stock prices to predict mar-

ket events: Evidence on sales takeoﬀ and long-term ﬁrm sur-

vival. Marketing Sci. 27(4):717–729.

Myers SC, Majluf NJ (1984) Corporate ﬁnancing and investment deci-

sions when ﬁrms have information that investors do not have.

J. Financial Econom. 13(2):187–221.

Neal L, Davis LE (2007) Why did ﬁnance capitalism and the sec-

ond industrial revolution arise in the 1890s? Lamoreaux NR,

Sokoloﬀ KL, eds. Financing Innovation in the United States: 1870 to

the Present (MIT Press, Cambridge, MA), 129–161.

Newey WK, West KD (1987) Hypothesis testing with eﬃcient

method of moments estimation. Internat. Econom. Rev. 28(3):

777–787.

Nicholas T (2008) Does innovation cause stock market runups? Evi-

dence from the great crash. Amer. Econom. Rev. 98(4):1370–1396.

Ofek E, Richardson M (2003) DotCom mania: The rise and fall of

Internet stock prices. J. Finance 58(3):1113–1138.

Olivier J (2000) Growth-enhancing bubbles. Internat. Econom. Rev.

41(1):133–151.

O’Sullivan MA (2007) Funding new industries: A historical perspec-

tive on the ﬁnancing role of the U.S. stock market in the 20th

century. Lamoreaux NR, Sokoloﬀ KL, eds. Financing Innovation

in the United States: 1870 to the Present (MIT Press, Cambridge,

MA), 163–216.

Pástor L, Veronesi P (2009) Technological revolutions and stock

prices. Amer. Econom. Rev. 99(4):1451–1483.

Pauwels K, Silva-Risso J, Srinivasan D, Hanssens DM (2004) New

products, sales promotions, and ﬁrm value: The case of the auto-

mobile industry. J. Marketing 68(4):142–156.

Peres R, Muller E, Mahajan V (2010) Innovation diﬀusion and new

product growth models: A critical review and research direc-

tions. Internat. J. Res. Marketing 27(2):91–106.

Perez C (2002) Technological Revolutions and Financial Capital: The

Dynamics of Bubbles and Golden Ages (Edward Elgar Publishing,

Northampton, MA).

Piketty T (2013) Le Capital au XXIeSiècle (Editions du Seuil, Paris).

Rapp D (2014) Bubbles, Booms, and Busts: The Rise and Fall of Financial

Assets (Springer, New York).

Rappoport P, White EN (1993) Was there a bubble in the 1929 stock

market? J. Econom. Hist. 53(3):549–574.

Scherbina A, Schlusche B (2014) Asset price bubbles: A survey. Quant.

Finance 14(4):589–604.

Shen Q (2014) A dynamic model of entry and exit in a growing

industry. Marketing Sci. 33(4):712–724.

Shen Q, Villas-Boas JM (2010) Strategic entry before demand takes

oﬀ. Management Sci. 56(8):1259–1271.

Shiller RJ (2015) Irrational Exuberance, 3rd ed. (Princeton University

Press, Princeton, NJ).

Sood A, Tellis GJ (2009) Do innovations really pay oﬀ? Total stock

market returns to innovation. Marketing Sci. 28(3):442–456.

Sood A, Tellis GJ (2011) Demystifying disruption: A new model for

understanding and predicting disruptive technologies. Market-

ing Sci. 30(2):339–354.

Sorescu A, Spanjol J (2008) Innovation’s eﬀect on ﬁrm value and

risk: Insights from consumer packaged goods. J. Marketing 72(2):

114–132.

Stremersch S, Tellis GJ, Franses PH, Binken JL (2007) Indirect network

eﬀects in new product growth. J. Marketing 71(3):52–74.

Tellis GJ, Prabhu JC, Chandy RK (2009) Radical innovation across

nations: The preeminence of corporate culture. J. Marketing

73(1):3–23.

Warren NL, Sorescu A (2017) Interpreting the stock returns to new

product announcements: How the past shapes investors’ expec-

tations of the future. J. Marketing Res. 54(4):799–815.

West KD (1987) A speciﬁcation test for speculative bubbles. Quart. J.

Econom. 102(3):553–580.

White EN (1990) The stock market boom and crash of 1929 revisited.

J. Econom. Perspect. 4(2):67–83.

White H (1980) A heteroskedasticity-consistent covariance matrix

estimator and a direct test for heteroskedasticity. Econometrica

48(4):817–838.

Wood C (2006) The Bubble Economy: Japan’s Extraordinary Speculative

Boom of the ’80s and Dramatic Bust of the ’90s(Solstice Publishing,

Jakarta, Indonesia).

Zeira J (1999) Informational overshooting, booms, and crashes.

J. Monetary Econom. 43(1):237–257.

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Abstract and Figures