DOES THE MARKET HAVE A MIND OF ITS
OWN, AND DOES IT GET CARRIED AWAY WITH EXCESS CASH?
It has
become an article of faith that the “invisible hand” of the free market sets
prices with the collective wisdom of the body of traders. The success of the
free market in providing stable consumer prices has enhanced the confidence in
this idea. Within a very abstract
sense, the price theory of consumer goods is similar to that of financial
assets. Both are set by supply and demand for the item. Yet there is a profound
difference between the two in that financial assets are often bought with the
sole purpose of selling at a higher price later, while consumers rarely buy for
that reason. For the consumer, the market offers a local optimization problem.
A consumer must exercise a relative preference for different items each of
which provides some utility. Modern portfolio theory is based largely upon the
idea that one purchases a portfolio of stocks, bonds and other financial
instruments in a similar way, by using a utility function that balances reward
with risk, just as a consumer balances expenses with needs and desires.
Price
Evolution in the Asset Markets
For many
decades, consumer prices have been very stable in the
In the
equity markets, however, there is every reason to be concerned with the
expectations and motivations of others. One could sell short a high-flying
stock that is overvalued by a factor of ten only to see it soar to twenty times
its valuation. Thus a failure to anticipate others’ actions (and mistakes)
could be costly. Traders are often aware of this phenomenon so that overvalued
stocks draw more commentators than short sellers.
Experimental
asset markets offer an important perspective into price evolution (Davis and
Holt, [1993]), where participants trade an asset designed by the experimenter
using real money through a computer network.
In many of these asset experiments there is no uncertainty in the payout
of the asset, so that
any uncertainty necessarily involves the potential actions of the
other traders, as noted early on (Smith, Suchanek and Williams [1988]).
An
important issue that can be tested in experimental markets is the extent to
which a market can assimilate information from different groups and yield a
price that reflects all of that information. For example, suppose that one
group has information on the finances of a particular company, another has
information about its product reliability, and another on current sales levels.
If these groups do not share information freely, is it nevertheless possible
for that the stock price will accurately reflect all of the information that is
available, even though no one has access to the complete set of information?
Often in a competitive market situation, participants are not always eager to
share the information they have accumulated, and look to price movements for
hints on the information of others.
Market
Intelligence Experiments
A simple
test of this in the experimental setting can be performed by informing all
participants that the asset traded will have a single payout value of either $1, $2 or $3 at the end of the experiment. For
example, some participants are given the information that the payout is not $1
(so it must be $2 or $3), some participants are given
the information that it is not $2, and others are given no additional
information. If the participants could share their “private” information, they
would conclude that the payout would be $3. But if they cannot share their information,
will the market evolve toward this $3 price due to the aggregate knowledge that
the market assimilates intelligently? Will this happen under all conditions?
How quickly will the price evolve toward a steady state price? Will a surplus
or shortage of cash in the experiment influence the steady state price?
In order to
examine these questions, pilot experiments were designed and performed at a graduate student workshop attended mainly
by economics students from various universities (July 2001 at George Mason
University) . The setting provided an opportunity to test these ideas under the
following conditions most favorable to market efficiency. (i) The participants
were all knowledgeable about markets, game theory and economic strategy. (ii)
The auctions were oral double auction with prices cleared after each trade.
This allowed participants to discover the identity of the traders and note
persistency in trading patterns of specific individuals. (iii) There were four periods during each
experiment lasting approximately five minutes each, allowing substantial time
for implicit discovery of the information held by others. (iv)
Experiments with the identical design were repeated using the same group
of participants five times. Experience is known to be an important factor in
asset market experiments. Thus by the third experiment the group was highly
sophisticated in the strategy required for the experiment and eagerly looking
for clues from traders who were implicitly revealing their information. (v) There
was no uncertainty in the information given to each subset of the participants.
In each
experiment the eighteen participants were each given an endowment of six shares
of the asset and a number of laboratory francs that varied among experiments
but not among participants in any single experiment. All participants were told
that there would be a single payout of either one, two or three francs for each
share at the end of the experiment. They were also told not to disseminate the
additional “private” information. The laboratory francs were converted to
dollars at the end of all experiments.
During the first experiment six traders were given the private
information that the payout is not one franc, six others were given the
information that it was not two francs, and six were given no additional
information. Each participant received eight francs (see Table 1). The average
price during the fourth period was 2.56 francs, which is not far from the
midpoint between two and three. Hence, the relative scarcity of cash appears to
have resulted in a trading price that is considerably lower than the three
franc payout
incorporated into the aggregate information given to the
participants.
Table 1. Summary of Pilot Experiments
|
Experiment |
Value |
Liquidity
Price |
Avg.
Trading Price |
Avg.
Price in 4th Period |
|
1 |
3 |
1.33 |
2.6 |
2.56 |
|
2 |
2 |
2 |
2.2 |
2.04 |
|
3 |
2 |
4 |
2.06 |
2.05 |
|
4 |
1 |
4 |
1.88 |
1.87 |
|
5 |
1 |
1 |
1.6 |
1.3 |
In the second experiment the setup
was similar except that the payout was two francs and the cash endowment twelve
francs per participant. Defining the “liquidity” or “excess cash” price of the
asset as the number of francs per share (see Caginalp and Balenovich, [1999]), we see that the liquidity price is equal to the payout
price incorporated in the total information. Here, the resulting average
trading price in the fourth period was very close to two francs.
The third experiment was identical
to the second except that each participant was endowed with twenty-four francs,
resulting in a liquidity price of four, i.e., double the payout value.
Nevertheless, the average price near the end was once again near two francs.
The fourth experiment was identical
to the third except that the payout was one franc (with analogous information
given to participants). In this experiment there was a liquidity price of four
(24 francs/6 shares), so that there is a ratio of four between the liquidity
price and the fundamental value that is implicit in the given information. The
resulting average price near the end of the experiment is 1.87 francs, which is
nearly double the payout value and half the liquidity price.
Note that experiments three and
four, which are identical in the cash level, differ by a factor of two in
payout, are fairly close in terms of trading prices.
The effects of cash level are
manifest in the results of these experiments. In particular, even with
experienced and knowledgeable traders within a relatively transparent setting,
a high cash level that is four times the fundamental value of the shares
results in a trading price that is nearly double the value implicit in the
given information. In other words, when the cash level is comparable to
the value level of shares (as in Experiment 2), the trading price converges to
the payout value. However, when the cash level is quadrupled, with no change in
the information given, the trading price is nearly doubled. Note also that
there is almost no movement toward the fundamental value during Experiment 4 as
prices were only slightly higher during the first period.
The Role of Excess Cash in
Distorting the Market Intelligence
These pilot experiments suggest that
under the most favorable conditions (including a balanced cash level), the trading price ultimately reflects all of the
information. However, as more cash is added to the system, the prices become
inflated. If more extensive experimentation bears out this conclusion, the
implications would cast more doubt on the basis for efficient markets.
They would also suggest a much
stronger role for the excess cash argument in bubbles. Caginalp and Balenovich
[1999] noted that in addition to the fundamental value and the trading price,
there is an additional important quantity with units of price per share (within
a single asset model). In terms of the differential equations theory, this
liquidity price (i.e., L above) is a natural price that would be attained in
the absence of value and momentum (i.e., price trend) considerations.
The spectacular rise in high-tech
stocks during 1999 and 2000 may be viewed in this context. A large pool of
additional cash entered the stock market due to several coincidental events (i)
Participation by a wider segment of society that was drawn in by rapidly rising
prices; (ii) An easy monetary policy by the Federal Reserve, partly in response
to potential crises such as the Long Term Capital Management and the Year 2000
Problem; (iii) Tax changes and demographics that led to increased wealth for
the more affluent groups who are most likely to invest. In other words,
policies favoring the affluent tend to result in higher asset prices in the
same way that policies favoring the less affluent lead
to higher consumer inflation.
The crucial question that underlies
these experiments is the extent to which one can rely on market prices of
assets to reflect the aggregate of all known information. If the excess cash
argument is borne out in further experimentation and data analysis, it would
suggest, for example, that using a nation’s stock market index as a barometer
of economic health is almost circular reasoning, since high market prices may
be reflecting the effects of an easy monetary policy. Furthermore, there would
be serious implications at the more theoretical level if the level of cash
turns out to be as important as the content of the aggregate information.
The pilot experiments involve the
conditions (i)-(v) above that are highly favorable to market efficiency.
Variations on this design could include uncertainty in the information given to
participants. For example, the information that the payout is not two francs
could be stated as a 75% probability event. There could also be some
conflicting information with differing probabilities, as is often the case in
world markets.
Toward a Theory of Price Evolution
Classical economics is largely concerned
with equilibrium pricing. Yet information, valuation and cash positions of
investors change with time, and just as equilibrium is being restored, these
changes require an evolution to a different price. An important question involves the time scale
on which prices return to equilibrium (if they move in that direction at all),
even when the excess cash is not a significant factor. In particular, is this
time scale smaller than typical intervals between such events? Does the time scale increase significantly
with the uncertainty of the information given to subsets of participants? In
other words, if there is 75% certainty given to the participants, is there a
slower convergence to the equilibrium price compared to the complete certainty
case?
Experimental asset markets with
incomplete and asymmetric information may be the key to understanding
fundamental aspects of price evolution through a behavioral perspective (Davis
and Holt [1993], Richards and Hays [1998]). If one knows that other
participants have additional information, then the astute trader will pay
careful attention to trading patterns in order to obtain clues on the
additional information.
The data obtained from experimental
asset markets can be used in connection with differential equations or
statistical time series models. Using a variety of experimental settings, one
can understand, for example, how the uncertainty and asymmetry of the
information interact with the psychology and strategy of the participants.
Ultimately, a successful theory must incorporate the behavioral aspects as
manifested in the experiments. The theory can then be tested against world
market data.
What Is the Mechanism by Which
Excess Cash Yields Excess Prices?
Suppose we consider the situation in
which some participants know that the payout (of $1, $2 or $3) is not $2 while
others know that it is not $3. Initially, the trader with information that the
payout is not $2 knows that the payout is either $1 or $3, so that the
expectation is $2. The information that it is not $2 is not especially helpful
at the outset. However, as trading
begins, the trader with this information can augment it with the hints obtained
from the trading patterns. For example, if there are some eager sellers at
$2.20, that may be an indication that others have information that the payout
is not $3. Once they make this observation, the trader with the “Not $2"
information can become more confident that the payout is in fact just $1. The
group with the “Not $3" information initially would have an expectation of
$1.50 payout, but would be more confident that the payout is just $1 as the
sellers dominate the market near $2. There is a complicated interaction between
the two groups as each group takes its clue from unknown traders from the other
group. A theory of price dynamics based upon behavior and psychology must
describe this complex interaction between the reliance one’s own information
and others’ information that is suggested from price movement. For the group
that is given no additional information, all of the conclusions must be drawn
from the trading prices. These traders are similar to day traders in US
markets.
In an experimental design such as
the one described above, how can a higher level of cash ultimately mislead
traders to trading the asset at much higher prices that the aggregate
information would indicate? We suppose
that there is a large amount of excess cash in the system with the same
information structure (i.e., payout is again $1). The group with no additional
information will have some distribution of bidding prices that will range from
below $2 to above $2. Some of these will be on the higher end. If there is an
excess of cash, there will be enough high bids to balance the
asks (from the “Not $3" group) at a price that is on the high end
of this range. In other words the distribution of bids might have an average
of, $2 for example, but the top 25% of the bids might have an average of, say,
$2.50. With a cash level that is four times the asset value, the top 25% of the
bids may be adequate to meet the sellers, so that $2.50 becomes the relevant
price rather than $2. In other words, in a cash rich situation it is the high
end of the bidders that are relevant as the average becomes irrelevant.[1] A trader with the “Not $2” information is
then misled by prices trading above $2.
The high-tech sector of the stock
market in the late 1990's can be viewed within this perspective[2].
The average assessment of the value of a stock became increasingly irrelevant
since the excess cash increased to the point that only the buyers on the
highest fringe were needed to meet the sellers. According to classical
economics, the price should reflect the aggregate knowledge of the
participants. However, the excess cash in the marketplace (as discussed above)
means that only the highest bidders are needed for the transactions. Several knowledgeable
people made the clear and convincing case that the earnings, sales and other
parameters of these companies did not merit the soaring prices. However, the
existence of a sufficiently large pool of cash (controlled by people with
little experience) meant that the knowledgeable investors were outbid and
became nonparticipants since their bids were so much lower than the trading
range. Similarly, any analyst questioning the quality of earnings and
accounting could easily be ignored since those unconcerned had ample cash to
bid prices higher.
As the high-tech market collapsed,
some were surprised by the speed with which former giants (in terms of market
capitalization) were reduced to penny stocks within months. Of course, this may
also be attributed to the fact that the value-oriented investors would not be
bidding on these stocks until the prices were less than one-tenth of the highs.
As prices dropped, the available cash for investment in these companies
declined very rapidly and further aggravated the decline in prices.
Furthermore, some using momentum strategies may have joined the sellers due to
the trend alone. One reason that aggressive accounting became an important
issue early in 2002 (led by Enron) may be that the cash represented by the
investors unconcerned with value became inadequate to meet the supply of
shares. As shares of stocks and bonds sank in price, the focus on the details
of the accounting became sharper. This aftermath of a speculative boom repeated
the cycle observed in previous bubbles, most recently in
An important link between available
cash and the price inflation can be studied retrospectively in terms of this
period, and presumably linked to the concept of excess cash as in the
experimental markets. In this way one can test the hypothesis that excess cash
allows the fringe to dominate the market and thereby allow the most exaggerated
psychological characteristics to set market prices. For example, the effects of overreaction may
be difficult to see on the upside when there is little cash but become dominant
when the market is flush with cash. Experimental asset markets with asymmetry
can be useful in understanding the effect of excess cash on the behavior and
strategy of participants. The effect of excess cash may be important in terms
of understanding psychological effects since
Theories can be carefully constructed through repeatable experiments and
then tested with the data of this historic period.
References:
Caginalp, G. and
D. Balenovich.
“Asset flow and momentum: deterministic and stochastic equations.” Phil. Trans. Royal Soc.
Davis, D. and
Holt, C. Experimental Economics.
Dreman, D.
“The Role of Psychology in Analysts’ Estimates”.
J. of Psychology and Financial Markets (2001), 2(2) 66-68.
Miller, E. “Risk,
uncertainty, and difference of opinion”. J. of Finance (1977) 32, 1151-1168.
Richards, D. and Hays, J.
“Navigating a nonlinear environment: an experimental study of decision making
in a chaotic setting,”J. of Economic Behavior and Organization (1998) 35,
281-308.
Shiller, R. Irrational
Exuberance,
Smith, V.L., Suchanek, G.L. and
Willams, A.W.. “Bubbles, crashes and
endogenous expectations in experimental spot asset markets.”
Econometrica, (1988), 56, 1119-1151.
Gunduz Caginalp,
Editor