Published in Volume 3
(2002), Number 1 of the Journal
of Psychology and Financial Markets
Can Markets Learn to Avoid Bubbles?*
Ross M. Miller
2255 Algonquin Road
Niskayuna, NY 12309-4711 USA
Keywords: Market bubbles, learning and adaptation,
behavioral finance, signaling, asymmetric information
One of the most striking results in experimental economics
is the ease with which market bubbles form in a laboratory setting and the
difficulty of preventing them. This article re-examines bubble experiments in
light of the results of an earlier series of market experiments that show how learning occurs in markets characterized by an asymmetry of information
between buyers and sellers, such as found in Akerlof’s lemons model and
Spence’s signaling model.
Markets with asymmetric information are incomplete because
they lack markets for specific levels of product quality. Such markets either
lump all qualities together (lemons) or using external indications of quality
to separate them (signaling). Similarly, the markets used in bubble
experiments are incomplete in that they are lacking a complete set of forward
or futures markets, depriving traders of the information supplied by the
prices in those markets. Preliminary experimental results suggest that the
addition of a single forward market can sometimes mitigate bubble formation
and this article suggests more extensive research in this direction is
warranted. Market bubbles outside of the laboratory usually are found in
markets with forward and futures markets that are either legally restricted
or otherwise limited.
Experimentation in markets with asymmetric information also
indicates that the ability of subjects to learn how to send and receive
signals can be enhanced by changing the way that market information is
presented to them. We explore how this result might be used to help asset
markets learn to avoid bubbles.
The bubble in Internet-related stocks that formed in the late
1990s and then ultimately burst illustrates how markets can exhibit unstable
behavior that would appear to impugn both their rationality and efficiency.
While some defenders of the efficient-market theory, most notably Peter Garber
, claim that the great speculative bubbles of history—Dutch tulip mania,
the South Sea bubble, John Law’s Mississippi scheme, etc.—were not really
bubbles but rather reflect a rational market response to conditions of low
supply and high demand, the experimental examination of markets within a
controlled laboratory setting beginning with the Vernon Smith, Gerry Suchanek,
and Arlington Williams  study has demonstrated the ease with which bubbles
can form. While the true supply and demand in a naturally-occurring market can
often only be roughly approximated at best, within the confines of a market
laboratory, experimenters can control market conditions by the values they
induce with payments to their market subjects. With the parameters of supply and
demand under his or her control, the experimenter can determine the degree to
which market prices exceed the “rational” level of a competitive
equilibrium. One can reasonably conclude that a bubble exists when the observed
price greatly exceeds equilibrium and moves away from it rather than converging
Given the current body of experimental evidence it appears
that bubbles arise from a confluence of factors; no single factor appears to be
sufficient to generate a bubble in a given market. Of the many ingredients that
go into a market bubble the one that has captured the popular imagination is
“irrational exuberance,” a phrase made famous by Federal Reserve Board
Chairman Alan Greenspan and further popularized by Yale economist Robert
Schiller . Irrational exuberance can be viewed as a mass delusion in which
an item’s “true value” becomes irrelevant as market participants
increasingly believe that prices will continue to rise forever, or at least
until they can sell their holdings to someone else. While such beliefs may be
reinforced, the practical impossibility of fueling the bubble forever eventually
leads prices back in line with rational valuations.
Obvious forms of intervention aimed at preventing bubbles or
moderating their formation appear to be ineffectual in the laboratory. Daily
price limits, circuit breakers, and restrictions on short selling may not only
fail to inhibit bubbles, they can actually help to promote them by apparently
providing traders with a false sense of security that helps fuel the bubble.
While limitations on price declines can draw out the time it takes for the
bubble to burst, the absence of market liquidity during this extended decline
may make a bad situation even worse. It appears that the prevention of bubbles
cannot be externally imposed on the market mechanism in an effort to put limits
on its operation. Instead, a more fruitful approach might be to enhance the
market mechanism in a way that harnesses its naturally tendency towards
efficiency under normal circumstances as first demonstrated by Vernon Smith
 and reproduced countless times thereafter.
The experimental evidence pointing to the formation and
persistence of market bubbles did not arise in the vacuum, but is the result of
a logical progression of market experiments dating back to the original market
experiment conducted by Harvard University’s Edward Chamberlin in the 1940s.
It took 40 years for experimental economists to introduce the necessary market
features—order books, multiple periods with asset carryover, and speculative
traders—to create a laboratory setting sufficiently rich to produce (and
reproduce) bubbles.1 While the road to
isolating market bubbles in the laboratory followed an orderly and relative
direct progression, market experimentation has spun off several alternative
threads in the 1970s. One of the more notable threads, which received little
attention at first but has been the source of renewed interest among
experimental economists in recent years is how learning (in a collective sense)
occurs in a market setting.
A number of major advances in economic theory in the
1970s—recognized by the 2001 Nobel prize awarded to George Akerlof, Michael
Spence, and Joseph Stiglitz—concern markets in which information was
asymmetrical distributed, usually in such a way that the seller of an item knew
more about its value than a potential buyer and had no way to convey that
knowledge directly. Because
informational asymmetries can seriously undermine the market mechanism, it often
pays sellers to discover a way to signal
the value of an item to buyers and buyers had to learn how to decode this
This new thread of market experimentation explored the
specific kind of learning required to make signaling and related informational
transfer mechanisms work; however, all markets can be viewed as implicitly
involving learning. Even the simplest supply-and-demand experiments of Edward
Chamberlin  and Vernon Smith  require the market to learn the proper
price for an item that, in turn, determines the quantity traded at that price.
As Smith discovered when he modified Chamberlin’s multilateral bargaining
arrangement into an organized market patterned after the New York Stock
Exchange, the ability of the market to learn prices depends on how the market is
organized—a discovery that influenced the later experimental work of Smith and
his many collaborators.
re-examines the results from the experimental studies of market bubbles in light
of what we now know about how learning occurs in the context of asymmetric
information. This way of viewing market bubbles suggests several new lines of
bubble experiments that might be conducted to help determine what causes bubbles
and how to prevent them. (The exercise of actually running these experiments is
left to the reader: no new experimental results appear in this article.)
The basic answer to the question posed by the title of this
article is an unsatisfying “Yes.” Both inside and outside the laboratory
direct personal involvement in a bubble appears to be a potent means by which
the market participants can learn to avoid them in the future having, in effect,
undergone a collective form of aversion therapy. There is anecdotal evidence
that such learning goes on outside of the laboratory. For example, the stock
market crash of October 1929 changed attitudes about investing for an entire
generation, with exuberance only returning to the market in full flower during
the “Go-Go Years” of the Sixties. Experimental subjects who had been through
one or two experiments in which bubbles have formed and then burst similarly
learn to avoid them.
Following Ben Franklin’s advice that “experience keeps a
dear school, but the fool will learn in no other,” it would be highly
beneficial to find a way to prevent bubbles without having to experience the
deflation of even a single bubble first, much less the two or more that some
experimental subjects appear to need to experience. Merely accelerating the
existing learning process may reduce the number of crashes require to teach
subjects not to collectively start a bubble in motion, it appears that something
more is required if bubbles are to be eliminated entirely.
While learning to avoid bubbles poses an impressive challenge
for the market mechanism, experiments involving asymmetric information indicate
that very sophisticated learning is possible in a market environment. This type
of sophistication is demanded by economic theories that depend on extended
notions of rationality, such the “rational expectations” invoked in the
monetary theories of Robert Lucas . Nonetheless, sophisticated learning
does not occur automatically and how the markets are organized can play a
critical role in aiding or inhibiting the type of learning require to mitigate
bubbles. A careful reappraisal of the existing experimental evidence can aid in
the development of ways to avoid bubbles both inside and outside of the
How Markets Learn to Signal
Ross Miller and Charles Plott  conducted the earliest
market experiments that required any explicit learning by market participants.2
(The “learning” of the competitive price and quantity in standard market
experience is more an emergent property of the market—or “spontaneous
order” as F. A. Hayek referred to it—and can occur without any apparent
conscious effort of the part of subjects.) In contrast to the traditional market
experiments pioneered by Vernon Smith , in which buyers exchanged
identical items that could be viewed as perfect substitutes for one another, the
Miller-Plott experiments were the first market experiments in which items of
different “qualities” were traded in the same market. In these experiments,
only the seller knew the quality of an item at the point of sale, the buyer did
not discover the quality until after the trade had been consummated. Without any
loss of theoretical generality, these experiments limited sellers to two
specific quality levels, using low-quality items called “Regulars” and
high-quality items called “Supers.” Furthermore, in each period the quality
that a seller could produce was exogenously determined, so that sellers assigned
to produce Supers could not intentionally “rip off” buyers by delivering
Sellers of Supers were able to distinguish themselves by
“signaling” that their item for sale was a Super by attaching “stripes”
to it. The parameters for these signaling experiments were designed so that the
unit cost of stripes was sufficiently less for sellers of Supers than it was for
sellers of Regulars enabling the market to effectively separate the two groups,
with Regulars selling at a lower price and with fewer stripes than Supers. These
experiments were designed as a direct test of the signaling model developed by
Michael Spence , which has multiple Nash-like equilibria in which the
signal (stripes) are used to distinguish the two qualities. Labor markets in
which education was the signal inspired this model; however, the Miller-Plott
experiments changed the setting to a consumer product market in order to avoid
any prior associations or special expectations that subjects might attach to the
roles of employer and employee.
Spence’s model and the later refinements of it do not
specify how sellers learn to send the signal and buyers learn to recognize it,
only that a signaling equilibrium is consistent with buyers associating the
amount of the signal transmitted by the seller with the ultimate quality of the
item purchased. Although the simplest form of Spence’s model provides the
signal with no intrinsic value, the stripes used in the signaling experiments
have enough value to buyers that sellers would provide them in a competitive
equilibrium even if they were unnecessary for signaling quality. The experiments
were parameterized so that the quantity of stripes required to distinguish
Supers from Regular significantly exceed the amount that would be provided in a
market where buyers could know the quality of an item at the point of sale.
As in the single-product market experiments, the signaling
experiments were run using a sequence of consecutive periods in order to see if
repetition was sufficient to allow the market to converge toward one of the
signaling equilibria. In the signaling experiments, it was necessary to assign
Regulars and Supers to sellers at random each period so that buyers could only
use the number of stripes and not which seller sold the unit as a signal of its
quality. Furthermore, while single-product experiments, such as those pioneered
by Vernon Smith, required the market to discover the values of two variables
(price and quantity), signaling experiments placed a heavier information burden
on the market, requiring not only that it determine the value of six variables
(price, quantity, and stripes for both Supers and Regulars) but also that it
establish the functional relationship between stripes and quality.
The first few signaling experiments were conducted using
California Institute of Technology (Caltech) undergraduates as subjects and
these initial experiments confirmed (or at least failed to refute) Spence’s
signaling theory. In these experiments, the market showed a strong tendency to
approach the most efficient of the signaling equilibria, i.e., the one that
required the least possible number of stripes that would allow sellers of Supers
to distinguish themselves from sellers of Regulars. This result was predicted by
later refinements of Spence’s theory.4
The Caltech subject pool, however, consisted largely of individuals—aspiring
scientists and engineers—who had been selected, in part, on the basis of their
pattern-recognition ability, which is an important element of effective market
signaling. In other subject pools, such as those recruited at community
colleges, the market had a more difficult time learning that stripes could serve
as a signal of quality.
The original signaling experiments were conducted in the early
1980s using blackboards and white chalk, these experiments were too complex for
the computer-based experimental software that was still under development. The
key technological innovation in these experiments was the use of intercoms and
citizen band radios to transmit orders between buyers and sellers who were
segregated from each other in different classrooms. This “channeling” of
information prevented the subjects from using their voices and physical gestures
to transmit information, so that any signaling that occurring was limited to
market orders and transactions. The other major difference from the market
experiments conducted with a single market was that instead of maintaining a
single “market book” on the blackboard a separate one was kept for each
possible level of stripes (usually the integers from zero to thirty). The
blackboard was arranged so that the number of stripes increased as one moved
from left to right. As in previous market experiments, when a transaction was
consummated it was indicated by circling the relevant bid or offer on the
There was a strong tendency for Supers to contain more stripes
that Regulars regardless of subject pool or experimental parameters. This did
not always reflect a conscious effort of the part of the sellers of Supers to
distinguish their items, but was instead a consequence of their cost advantage
for supplying stripes to the market. Furthermore, sellers did not know exactly
how buyers valued stripes—the only feedback that they received was that
supplied through the market mechanism. Indeed, the value of stripes to buyers
was structured so that the higher prices required for sellers to sell units with
several stripes at a profit would only be worthwhile to buyers if the unit was
almost certain to be a Super.
It required only a minor institutional change to get the
market to learn how to signal and converge toward equilibrium. At the end of
each period when the quality of each unit was revealed, Regulars and Supers were
circled with contrasting colors of chalk. (Previously, qualities had simply been
indicated using an “R” or “S” written next to the circled transaction in
white chalk.) Buyers who saw the Super color on the right side of the blackboard
and the Regular color on the left would often make the connection between
stripes and quality instantaneously. (This discovery was so dramatic that it
called to mind the image of a bulb lighting up above their heads.)
In the absence of colored chalk, some of the signaling
experiments failed to exhibit the stability that characterizes single-market
experiments. Rather than converge to a signaling equilibrium, the market would
simply move from one unstable and inefficient allocation to another without
converging. Although these signaling experiments were parameterized so that they
would each have a unique efficient signaling equilibrium and several inefficient
ones, some experiments exhibited behavior that suggested the nonexistence of
equilibrium. Such a situation is possible only when the signaling cost advantage
for sellers of Supers is sufficiently small.5
The stability issues that can arise in signaling experiments
can be traced to the additional burden that signaling places on the subjects.
In a single-market experiment, only the bare minimum of individual economic
rationality appears necessary for convergence to equilibrium—the auction
mechanism itself provides so much of the impetus towards equilibrium that
convergence can withstand significant deviations from rationality by the
Convergence to a signaling equilibrium requires that some of the sellers learn
how to send signals and that some of buyers learn how to receive them. While not
every buyer and seller needs to “get the signal” in order for the market to
approach a signaling equilibrium, without a critical mass of savvy buyers and
sellers driving the market it is more difficult to reach an equilibrium.
It can be useful to think of buyers and sellers in a signaling
market as looking for a path that uses the signal to connect them. By altering
the parameters that determine the cost of signaling and the value of quality,
the experimenter can make the path wider (and easier to discover) or narrower
(and more difficult to discover) or can even make it disappear entirely in the
case where no equilibrium exists. The Miller-Plott experiments found that the
market converged to a signaling equilibrium more quickly and with greater
frequency when the path was wider. In addition, the use of colored chalk aided
subjects who had become temporarily “lost” in ultimately finding the path to
The instability seen in the signaling experiments that arose
from the failure of an effective signaling mechanism to emerge spontaneously may
be less dramatic than the instability of a market bubble; however, the fact that
a minor institutional change—the use of colored chalk to distinguish Supers
from Regulars—can help stabilize the market and guide it towards an efficient
allocation indicates that relatively trivial changes in market institutions
might help prevent bubbles from forming. Neoclassical economic theory makes no
provision for colored chalk or any other representation method to affect
individual choices and the equilibrium allocations that are generated by them.
In fact, the learning induced by colored chalk may itself by viewed as
irrational because it significantly alters individual behavior without changing
any of the variables considered relevant to the economic choices faced by
individuals in the market.
In a signaling experiment, making the connection between
stripes and quality is in an individual’s rational best interest only when
the market collectively learns to make the connection. This learning can occur
even if the individual subjects never become consciously aware that signaling is
taking place. A seller of a Super-quality product who has figured out that
Regular-quality sellers cannot economically produce units enough stripes to
imitate his or her product will be unable to recoup the cost of the stripes if
none of the buyers is willing to pay a premium price. It is, in fact, irrational
for a seller to send a signal when there are no buyers capable of receiving it.
The instability caused by the failure of buyers and sellers to
learn how to use stripes as a means of distinguishing Supers from Regulars does
not appear to be related to individual deviations from rationality of the sort
documented by psychologists and economists dating back to the pioneering work of
Daniel Kahneman and Amos Tversky.7
While buyers may not be able to update the probability that a unit will be a
Super conditional on the number of stripes it contains in a properly Bayesian
manner, this failure comes not from shifting frames of reference, but rather
from a failure to notice the link between quality and stripes quickly enough to
provide sellers with the feedback necessary to transmit the signal.
Bubbles Under the Microscope
Markets that bubble to excess and then crash exhibit their
instability in a more dramatic way than markets in which signaling fails to take
hold, but the underlying problem may be quite similar. For asset markets to be
both efficient and stable, individuals must learn to make the connection between
an asset’s price and its expected future cash flows. For example, some of the
most basic bubble experiments allow trade in an asset that pays a dividend of
$0.24 at the end of each of 15 periods. At the beginning of the 15-period
experiment, the asset will generate a total cash flow of $3.60 with certainty,
which gives it a competitive market value of $3.60. After each period’s
dividend payment of $0.24, the “intrinsic value” of the asset based on its
cash flows declines by exactly the dividend amount until it becomes worthless
after the last dividend is paid and the experiment concludes. More intricate
versions of this basic bubble experiment make the dividend payment uncertain in
order to stimulate the natural trade that would arise between more and less
risk-averse subjects, but the basic outcome of the experiment is unchanged: a
bubble forms in virtually every instance.8
The path taken by the bubble usually follows the same general
pattern. In the early periods of the experiment, the asset trades at a
substantial discount to the value of its cash flows. Within a few periods,
competition among subjects drives the price up to its intrinsic value, which by
then has fallen from its initial level of $3.60. For example, the price of asset
might rise from $2.20 in the Period 1 to its intrinsic value of $2.88 in Period
4 ($3.60 minus three dividend payments of $0.24) at that same time that its
intrinsic value has dropped from $3.60 down to $2.88.
Subjects who participate in this experiment for the first time
appear to be learning the lesson that the price of the asset always increases by
generalizing from how prices behave in the first few periods, and so they
continue to bid its price up even as its intrinsic value declines steadily
The result is a bubble in which the price of the asset greatly overshoots its
intrinsic value until it finally crashes below it when just a few periods are
remaining in the experiment and subjects must finally face up to the reality of
holding an overvalued asset. The initial rise in the asset price serves to mask
the subjects’ ability to learn that the market price of the asset should be
near its intrinsic value. In the typical bubble experiment, it takes one or more
15-period repetitions before a given pool of subjects learns to avoid a bubble.
The instability found in bubble experiments, like that of the
signaling experiments, cannot readily be traced to any inconsistency or
irrationality in individual choices. The bubble experiments are designed so that
the intrinsic value of the asset is never in doubt. Regardless of whether the
dividend payments are deterministic or drawn from a known random distribution,
most bubble experiments are conducted so that the experimenter knows that every
subject has complete knowledge of the asset’s intrinsic value throughout the
experiment by posting the expected total payoff on each subject’s computer
monitor. Additionally, subjects may be asked to estimate the total value of the
remaining dividend payments to make sure that they are receiving the message
about the asset’s value.
At the same time that subjects are learning about the
asset’s intrinsic value, the market teaches them two things that can undermine
that knowledge. First, as the asset price moves towards equilibrium in the early
periods, subjects see that prices tend to increase over time. Second, because
this increase occurs as the intrinsic value is decreasing, subjects learn that
the market price does not need to track the intrinsic value, at least over the
short run. Until the markets crashes as the experiment nears its conclusion,
subjects who learn to ignore the asset’s intrinsic value are rewarded by
speculative profits, while those who follow it are quickly priced out of the
market. Indeed, in experiments that allow selling short, subjects who sell the
asset short may not only lose money, should they liquidate their short positions
too soon their purchases can help sustain the bubble.10
Filling Holes in the Market
A notable similarity between the signaling and bubble
experiments is that both involve incomplete market systems. Although the
signaling market facilitates trade in items with every possible number of
stripes, it does not allow trade in markets for Supers and Regulars directly,
creating a gap in the market system. In the presence of a mechanism that would
enforce or guarantee quality, the necessary markets could exist and the
signaling value of stripes would vanish. Signaling emerges as the market’s way
of dealing with incomplete markets for quality.
The bubble experiments are also missing keys markets, those
for future delivery of the asset. In a perfect world of complete markets,
subjects would be able to trade not only in the “spot” market that provides
direct ownership of the asset, but also in forward markets that provide for the
buyer of the contract to receive the asset from its seller in a specified future
period of the experiment.11
Hence, at the beginning of the experiment there would be 14 additional forward
markets, one each for Period 2 through Period 15.
It is worth examining how prices in the forward markets should
behave in the simple case of a fixed dividend of $0.24 at the end of each
period. Consider the price of a forward contract for delivery in Period 2 that
trades during Period 1. Although prices in bubble experiments usually move
higher during the early periods, it is unlikely that the Period 2 forward
contract will exceed the spot price at any time during Period 1. Were such an
opportunity to present itself, a subject could purchase the asset on the spot
market and simultaneously sell a forward contract at a higher price, yielding
not only an immediate profit, but also the $0.24 dividend that is paid at the
end of Period 1. When one fully takes the dividend into account, not only must
the Period 2 forward contract be priced at least $0.24 less that the spot price
in Period 1, but also the Period 3 forward contract must be at least $0.24 less
than the Period 2 forward contract, and so on to Period 15. If all 14 futures
contracts are actively traded—as we will see below this is a very big
“if”—then the fact that the Period 15 forward contract cannot have a
negative price means that the spot price during Period 1 must stay above $3.36.
(If the Period 15 forward contract trades at its intrinsic value of $0.24, then
Period 1 spot price is pushed up to at least its intrinsic value of $3.60).
With a properly functioning set of forward contracts, the
downward pressure on prices from period to period will be apparent to subjects
from the beginning of the experiment. It is still possible for a bubble to form
that raises the price of the asset in the spot market and all of the forward
markets above their intrinsic values; however, if the pattern of declining
forward prices that is easily generated by the arbitrage activities of even a
single rational subject is detected by other subjects, the illusion that prices
will increase from period to period, which appears necessary for the formation
of bubbles, will be difficult to maintain.
Bubble experiments are already among the most complex
experiments that are conducted on a regular basis and adding a full complement
of forward markets further complicates them. David Porter and Vernon Smith
 have conducted bubble experiments in which they have added a single
forward market for the asset with delivery at the midpoint of the experiment in
Period 8. While this additional forward market still leaves the market system
substantially incomplete, it does appear to attenuate the bubble that forms in
experiments with inexperienced subjects.
The difficulty with using forward markets to prevent bubbles
in both experimental and naturally-occurring markets is that these markets can exert a stabilizing influence only if they transact enough business to
generate meaningful prices. In their current design, bubble experiments already
provide extremely limited economic incentives to trade on the spot market
because the asset has the same intrinsic value of everyone; trade in the forward
markets, especially a multitude of them, has even less motivation. Indeed, on
the major futures markets of the world, many contracts go for days or weeks
without a trade and provide no useful price information to the market system.
The potential for futures contracts to limit asset-pricing
bubbles works somewhat differently in naturally occurring markets than it does
in the laboratory. In contrast to the experimental market, in which the asset
price should decline from period to period in a competitive equilibrium, the
futures price of an asset that pays little or no dividends can be expected to
increase over time so that the capital gains from holding it provide a suitable
return on the capital invested in it. Hence, futures prices for such assets (or
indexes that consist of them) increase as the delivery date moves further into
the future. If the future prospects of an asset are sufficiently promising, any
increase in the futures prices will tend to drag the spot price up with it.
Consider, for example, the stock of a company, let us call it
hightech.com, that currently trades at $50/share and that the market believes
will trade at $200/share in six months. (Such highly optimistic projections were
common during the run-up in Internet-related stocks.) If a futures contract for
delivery of hightech.com in six months were publicly available, a uniform belief
that a share would be worth $200 then would drive the futures price up towards
Such a move would be inconsistent with a current stock price of $50 because the
simultaneous purchase of the stock on the spot market and the sale of it on the
futures market would give a return of $150/share for an investment of $50/share
made over six months, which is vastly beyond any realistic cost of capital for
this investment. In such a situation, the existence of the futures contract
means that either the present or future assessment of the stock value must be
reappraised until the spot and futures prices are brought into equilibrium. As
in the experimental market, there is still the possibility that a bubble would
form in which all these prices simultaneously exceed the stock’s intrinsic
value, but this would presumably be more difficult than generating a bubble in
the spot market alone. Furthermore, to the extent that a temporary scarcity of
stock issues that provide speculators with a “technology play” helps fuel
the bubble, the existence of stock futures provides an inexhaustible outlet for
speculation that does not carry the time premium associated with other
alternatives, such as options. While many stocks would not warrant trade in
futures contracts, it is likely that speculative issues would attract sufficient
volume to maintain a full complement of them.
During the Internet boom years, there was no organized
mechanism for trading forward contracts in individual U.S. stocks, the best that
one could do was to trade in relatively illiquid “equity swaps” created by
investment banks. Trade in standardized futures or forward contracts on
individual shares had been made temporarily illegal by the 1982 Shad-Johnson
Accord that delineated the securities under SEC and CFTC jurisdiction and left
stock futures in limbo until their status was finally resolved in 2000. Some
astute financial observers believe that futures on individual Internet stocks
might have prevented the Internet bubble from forming at all.13
To the extent that futures contracts on stock indexes were
available as an alternative way to cash in on the Internet boom, they appear to
have been ineffectual in bringing the bubble under control. Futures contracts on
the Nasdaq Composite Index and the Nasdaq 100 were heavily traded during the
period; however, because they averaged the returns from many stocks, including a
significant proportion with no connection to the Internet boom, they lacked the
excitement and potential stratospheric returns of individual Internet companies.
Internet index futures, such as the ISDEX contract on the Kansas City Board of
Trade, appeared only as the boom was reaching its crest and they failed to
attract much attention from the financial media.
Efforts to prevent bubbles by making markets more complete are
further complicated by the difficulty of establishing an objective intrinsic
value for securities that have speculative appeal. In the signaling experiments,
both Supers and Regulars had well-defined intrinsic values, all the market had
to do was figure out how to encode and decode that information with stripes.
While the expected future cash flows from highly speculative investments may be
highly variably and subject to vast differences of opinion, an objective market
consensus is still possible. What is troubling in the case of the Internet
bubble was a popular line of analysis that appeared to refute the fundamental
economic belief that the value of an asset should be equal to the sum of its
future discounted cash flows. The need to ground valuation in tangible future
returns was often dismissed as “old economic thinking” that was irrelevant
to the “new economy.” While such analysis constitutes a serious departure
from rationality, the lack of a valuation methodology for new technology
ventures that is truly objective helps to facilitate such flights of fancy.
It is also possible for bubbles to form in which the prices of
assets can be individually rationalized, but their aggregate valuation is
inconsistent with any plausible market outcome. When many companies are
competing for dominance in the same market and only one or two winners are
likely to emerge, it is possible that a bubble will form in which every firm is
valued as if it will emerge as the ultimate victor. The development of markets
that will not only highlight such inconsistencies but also allow arbitrageurs to
profit from them and nip any nascent bubble in the bud requires better models
than those currently available. Robert Shiller has championed the use of
“macro markets,” such as future contracts based on a country’s GDP, as a
way of helping to promote more rational and informative markets. Given the
difficulty of correlating macro variables with specific financial assets, it is
difficulty to see how a market-generated prediction of aggregate economic
activity could control any bubble that was less than universal in scope.
Just as colored chalk could be used to help guide the market
to a signaling equilibrium, it is possible that a similar method could be
employed to guide the value of an asset toward its intrinsic value and away
from bubbly excess. Exactly what mechanism might be able to contain the market
has yet to be determined; however, all possible methods are likely to face
similar challenges. Existing methods that provide information about the
intrinsic value of an asset to experimental subjects has not been successful at
A possible way of providing rational guidance to the financial
markets is to report the intrinsic value of an asset along with its market price
in the listings provided in newspapers and over the Internet. There is
substantial precedent for this practice in closed-end mutual funds, whose
intrinsic value can be explicitly determined from their asset holdings. While
the tendency for the price of closed-end mutual funds to diverge from their
intrinsic (or net asset) value has been acknowledged by even the most ardent
advocates of the efficient-market theory, such disparities are usually so small
as to not approach constituting a bubble.
An assortment of relative valuation measures, such as book
value and the ratio of price to earnings, are readily available to investors;
however, from the end of the 1990s and into the 2000s indications that stocks in
general, and technology stocks in particular, were historically overvalued
seemed to have little impact on prices. The ability of major stock indexes such
as the S&P 500 Stock Index and the Nasdaq Composite Stock Index to sport new
record price/earnings ratios with each passing month tended to reward investors
who ignored the early signs of overvaluation and punished those who heeded it. A
similar phenomenon appeared during the Japanese stock market bubble that lasted
into the early 1990s. In that bubble, U.S. brokers were known to reassure their
customers that the absurdly high price/earnings ratios seen on Japanese stocks
merely reflected differences in accounting practices and not a reflection of a
market bubble that had gone out of control.
If a respected financial publication were to institute its own
version of colored chalk by highlighting overvalued stocks (e.g., ones with
price/earnings or price/sales above a specified cutoff) much as it highlights
stocks with unusually high volume or price movement, it would be purely a matter
or chance whether its efforts would be sufficient to prevent bubbles in those
issues. Certainly, if a pattern emerged where any stock that rated a warning
would immediately succumb to selling pressure that would drop its price back in
line with its value, this mechanism could effective limit stock prices. Indeed,
given an objective and open method for determining overvaluation, anticipation
of the selling that being a highlighted issue would bring might pre-empt most
stocks from achieving this distinction.
Unfortunately, this heavy-handed approach to bubble prevention
is likely to be ineffectual. There are almost certain to be stocks that might
appear objectively overvalued, but deserve their high prices because of
outstanding growth prospects or other special situations. Such issues could come
to dominate the highlighted list since they would be able to survive the
automatic selling that would materialize as they approach overvaluation. When a
sufficient number of issues continued to rise once they had been highlighted,
likely aided by the purchases of short-sellers covering their positions, the
market would then learn that it should no longer avoid shares that appear to be
overvalued. This sequence of events would effectively discredit the
overvaluation method and provide the opening for truly overvalued shares to
escape the discipline of the market.
While the financial media never explicitly drew the chalk
lines that distinguished overvalued companies during the stock market boom of
the late 1990s, the extreme overvaluation of many companies were repeatedly
highlighted in only slightly less extreme manners. The ability of blatantly
overvalued shares to move even higher provided positive feedback to speculative
holders that masked any considerations of intrinsic value.
The experimental evidence compiled to date indicates that
under normal market circumstances market bubbles may be difficult to eliminate.
This article has examined experiments in which market efficiency relies entails
learning at an aggregate level to try to gain insight into how markets might
learn to avoid bubbles. Two of the more promising approaches involve patching
holes in the market system with the appropriate forward or futures markets and
supplementing the market with information (colored chalk) that guides prices in
the right direction. These two remedies, either alone or in combination, are
especially appealing because if they are shown to work in the laboratory the
development of real-world policies that incorporate them can be straightforward,
as opposed to the elimination of bubbles through past experience with comparable
In both the business press and the legal system, much of the
blame for the Internet bubble has been heaped on the stock analysts who placed
exorbitant valuations and price targets on the stock of Internet-related
companies. While laboratory experiments have yet to incorporate these analysts
into their design, it is clear for existing experimental results that their
presence is not necessary for the creation of bubbles and that they may well
simply make convenient deep-pocketed scapegoats after the fact. Although
subjects in a laboratory setting can eventually learn not to get involved in
bubbles, it is not so clear whether the legal and political system in which our
markets exist can so easily learn to find a cure for bubbles rather than simply treat
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Ross Miller  contains a detailed account of the path from Edward
Chamberlin’s  first experiments to the Vernon Smith, Gerald
Suchanek, and Arlington Williams  bubble experiments. Milestone
experiments in development of bubble experiments are described by Vernon
Smith , Ross Miller, Charles Plott, and Vernon Smith , and
Robert Forsythe, Thomas Palfrey, and Charles Plott .
Game-theoretic experiments in which a small group of subjects (often just
two) would learn to cooperate predated experiments in which any learning was
mediated by the market mechanism.
This moral hazard problem was examined in several experiments based on
George Akerlof’s  lemons model by Michael Lynch, Ross Miller,
Charles Plott, and Russell Porter  that employ the same basic design
as the signaling experiments.
See John Riley  and Charles Wilson  for more advanced version of
the signaling model that eliminate many of the inefficient signaling
Michael Rothschild and Joseph Stiglitz  prove a theoretical
demonstration of the possible nonexistence of a signaling equilibrium under
normal circumstances. Although Miller and Plott had considered running such
experiments, the observed behavior of markets in which a signaling
equilibrium existed but could not be determined pointed out the likely
futility of this avenue of investigation.
Dhananjay Gode and Shyam Sunder  show that the rules of the standard
auction-based market can guide a single market to equilibrium even with
robot traders programmed to place random orders. Such results cannot be
expected to carry over to markets where learning or the formation of
expectations is required of the traders.
Amos Tversky and Daniel Kahneman  provide a summary of this research
and its relation to theories of economic rationality.
The general properties attributed to experimental bubble markets in this
article drawn heavy on the papers by Vernon Smith, Gerald Suchanek, and
Arlington Williams , Porter and Smith , and Gunduz Caginalp,
David Porter, and Vernon Smith [1998 and 2000].
See G. Caginalp and D. Balenovich  for a formal model of how momentum
might drive an asset market bubble.
The “limits of arbitrage” issues facing short-sellers analyzed by Andrei
Shleifer and Robert Vishny  do not arise in these experiments.
The main difference between forward contracts and futures contracts is that
futures contracts provide for periodic settlement of gains and losses in the
value of the contract in advance of the delivery date as a way to reduce the
possibility that the contract will be breached. The futures markets used in
Porter and Smith  are technically forward markets because settlement
in cash occurs only at the end of the experiment.
In a world with relatively efficient futures markets, analysts’
projections that a stock will increase by 300% in six months might become
pointless. Such a proclamation can be recast to the spot price by simple
discounting; hence, any analyst’s statement that the market believes and
is reflected in the futures price automatically implies a specific spot
Holman Jenkins  advocates futures on individual stocks as a way of
preventing market bubbles.
Copyright 2001 by Ross M. Miller. All rights reserved.