Working Paper Series

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Market-Based Corrective Actions: An Experimental Investigation*

Douglas Davis, Virginia Commonwealth University

Edward Simpson Prescott, Federal Reserve Bank of Richmond†

Oleg Korenok, Virginia Commonwealth University

Working Paper Number 11-01

March 2011

Abstract

We report results from an experiment that evaluates the consequences of having a socially

motivated monitor use the market price of a bank’s traded assets to decide whether or not to

intervene in the bank’s operations. Consistent with predictions of a recent theoretical paper by

Bond, Goldstein, and Prescott (2009, “BGP”), we find that a possible value-increasing

intervention weakens the informational efficiency of markets and that the monitor commits

numerous intervention errors. Not anticipated by BGP, we find that a possible value-decreasing

action also affects market performance. Further, in both cases the active monitor undermines

allocative efficiency, particularly for market fundamentals close to the efficient intervention

cutoff.

Keywords: bank regulation; experiments; market discipline

JELs: C92; G14; G28

______________________________________________________

* Thanks for useful comments to Asen Ivanov, Edward Millner, Robert Reilly, and seminar participants at the Federal Reserve Bank of Richmond and the School of Business at Virginia Commonwealth University. The usual disclaimer applies. Financial assistance from the National Science Foundation (SES 1024357), the Federal Reserve Bank of Richmond, and the Virginia Commonwealth University Summer Research Grants Program is gratefully acknowledged.

† The views expressed in this paper do not necessarily reflect those of the Federal Reserve Bank of Richmond or the Federal Reserve System.

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1. Introduction

Designing a system in which regulators can reliably and accurately assess a financial firm’s

position so that they may, if needed, take corrective action represents a significant problem in

financial regulation. In bank regulation, pertinent information is traditionally gathered from a

mix of regulatory accounting reports and periodic on-site evaluations of a bank’s portfolio and

operations. Concerns about the limitations of these data sources have led some to propose using

market data to assess a bank’s financial position. The idea is that the price of a firm’s assets

reflects the information that traders gather and use to assess the firm’s quality, so regulators

could gain from this knowledge by looking at prices. Long-standing proposals, as in Stern

(2000), advocate supplementing the traditional regulatory sources of information with market

signals.1 More recent proposals, as in Flannery (2009), McDonald (2009), and Hart and Zingales

(2010), go further by advocating using prices as a trigger for mandatory changes to a bank’s

capital structure.2

This paper uses experimental evidence to assess policies in which a regulator bases decisions

on market prices. Specifically, we report an experiment in which traders have information on the

fundamental value of an asset that a regulator does not know. Traders trade the asset, the

regulator observes the price, and then the regulator uses the price to decide whether to take an

action that affects the asset’s payoff. We assess how much information is transmitted by the

prices, the quality of the regulator’s action, and the efficiency of the market allocation.

The idea that prices transmit information is an old one that is often attributed to Hayek

(1945). Empirically, the voluminous literature on the efficient markets hypothesis associated

with Fama (1970) finds that financial market prices incorporate all available information. A

famous example is in Roll (1984), who found that orange juice futures better predicted variations

in Florida weather than the National Weather Service. Similar empirical results are found in

prediction markets for electoral results. In over a decade of experience, prices in political stock

markets have consistently predicted ultimate vote counts more accurately than polls (see, e.g.,

Berg, Forsythe, Nelson, and Rietz, 2008). Finally, the empirical banking literature surveyed in

1Feldman and Schmidt (2003) and Burdon and Seale (2005) report that bank examiners do use market data in evaluating banks, but not as part of a formal process. 2 A related long-standing proposal is to require banks to issue subordinated debt with one advantage (among others) of being to infer bank risk from the price of the debt (see Evanoff and Wall, 2004, Herring, 2004).

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Flannery (1998) finds that bank security prices contain information not contained in supervisory

reports (and vice versa).

Recent theoretical work by Bond, Goldstein, and Prescott (2009, “BGP”), however, finds

that inducing regulators to react to market prices can destroy the very information that they want

to ascertain from those prices.3

To illustrate, consider an example of a bank examiner trying to assess the value of a publicly

traded bank (“the Bank”). The fundamental value of the Bank is $4. The regulator finds it in the

public interest to intervene if and only if this fundamental value is below a socially efficient

intervention value of $5. If the regulator intervenes, the Bank’s value increases by $2 (e.g., the

regulator steps in to “bail out” the Bank if it is close to failing).

When a regulator intervenes, the associated actions affect the

value of the financial firm. An intervention to replace an incumbent management team, for

example, can improve a firm’s future prospects. Similarly, extending central bank credit to a

bank will improve a bank’s liquidity position and can thus improve its value. These intervention

decisions will affect the price of the bank’s securities. In turn, however, if the intervention

decisions are based on the price, then there will be feedback between the traded prices and the

intervention decision. BGP find that for this environment, rational expectations equilibria do not

always exist. They interpret the non-existence as indicating a loss of information transmitted by

prices.

Now suppose that traders of the Bank’s assets collectively have information sufficient to

determine the fundamental value, but that the regulator has less precise private information. The

following question arises: “Do asset prices allow the regulator to infer the position of the

fundamental value relative to the socially efficient intervention value?” In BGP the answer is not

always yes. Specifically, when the regulator may make a value-increasing “positive” corrective

action, and when the regulator’s private information is sufficiently imprecise, a range of

fundamental values arises for which no rational expectations equilibrium exists. In our example,

the regulator cannot tell from the market price of $6 whether the fundamental value is $4 and the

price incorporates the value of the $2 intervention or whether the fundamental value is in fact $6.

Traders, in turn, have no basis for including or excluding the value of the intervention in the

asset price because they cannot tell what prices will trigger an intervention.

3 See also Birchler and Facchinetti (2007).

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The use of experimental methods to examine this sort of issue offers some important

advantages. Most prominently, in the laboratory the investigator can directly observe the

relationship between fundamentals and asset prices, a relationship that is inherently unobservable

in natural contexts. (Indeed, it is the unobservabilty of fundamentals that drives

recommendations that regulators use asset prices as a reflection of value.) Our ability to set

fundamentals and then observe trading prices allows us to assess directly the information loss in

prices associated with the possibility of intervention, as well as the extent to which regulators err.

To study the interactions between regulator and trader incentives, we constructed an

experimental design that features value heterogeneity among traders and a trading institution in

which asset prices are endogenously determined. This deviates from BGP, who use a rational

expectations framework and in this way avoid having to specify such market mechanics. For this

reason our experiment is not a direct test of the BGP model. Nevertheless, our experiment

provides insight into the problems that the presence of an “active” regulator can create on the

informational value of asset prices, and on the allocative efficiency of markets.

Our experimental results indicate that, as anticipated by BGP, inducing a regulator to base

intervention decisions on asset prices can undermine the informational efficiency of markets.

Further, when the market fundamental is sufficiently close to the cutoff for efficient intervention,

the distortionary impact of the regulator on asset prices is sufficient to cause intervention errors.

Not anticipated by BGP, we also find that the price distortions induced by a regulator can reduce

market trading efficiency, which we define as assets ending up with the traders who value them

the most. Further, we observe intervention errors not only when intervention is value-increasing,

but also when intervention is value-decreasing.

These results provide evidence that price-dependent policies should be evaluated with some

caution, because it is easily possible to do more harm than good. The remainder of this paper is

organized as follows. Section 2 reviews the pertinent theoretical predictions. Section 3 presents

the experimental design and procedures. Section 4 reports the results. Finally, Section 5

concludes.

2. Theoretical Considerations

To further illustrate the incentive problems induced by a monitor who uses price as an

indication of underlying value, and to motivate our experimental design we use the following

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example.4

00.5$ˆ =θ

Suppose that the underlying fundamental value for a firm is randomly drawn from a

uniformly distributed range of values between $2.00 and $8.00. Assume that this fundamental

realization θ is known (collectively) by traders. Suppose further, however, that a monitor may

make a corrective action which increases θ by ω=$2.00. Also, unlike the traders, the monitor

cannot see the underlying fundamental, but can only observe transactions prices. This transaction

price depends on the extent to which traders incorporate into the price the value of the

intervention, but is bound from below by the fundamental value P(θi), and from above by the

fundamental value supplemented by the intervention, P(θi+ω). If, for example θi = $3.50, prices

might range between P(θi) =$3.50 and P(θi+ω)=$5.50. The monitor is driven to act out of

concern for social welfare, and intervention is desirable socially only if the (unaided)

fundamental value of the firm is below a critical value Thus, depending on the asset

price, the monitor might choose not to intervene, even though intervention is desirable.

Figure 1 illustrates this situation. At prices below $5.00 the monitor can unambiguously

infer that an intervention is welfare improving, since even if traders fully incorporate the value of

the intervention into the price the monitor can infer that θi $5.00. At prices between $5.00 and $7.00, however, the relationship between

asset prices and underlying fundamentals breaks down, because in that range the monitor cannot

assess the extent to which the price incorporates the value of the intervention. For example, a

price of $5.50 might indicate that the fundamental value θi = $3.50, and that the price fully

incorporates the market value of the monitor’s (socially desirable) expected action, that the

fundamental value is $5.50 and that (socially unnecessary) intervention will not occur, or that the

fundamental value is somewhere between $3.50 and $5.50 and the price only partially

incorporates the value of the monitor’s action.

Significantly, this ambiguity in price information is not solely a consequence of

introducing a monitor that may take a corrective action. Rather, the ambiguity arises only when

the potential corrective action increases the Firm’s value. Value-decreasing “negative”

corrective actions are certainly possible in contexts similar to those described above. Low asset

4 In what follows we will use the more neutral terms “monitor” and “firm” instead of “regulator” and “bank.”

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prices, for example, might trigger a bond rating reduction in a way that decreases a firm’s

fundamental value.

Given a negative corrective action, the possibility of an intervention does not undermine

the monotonic relationship between fundamentals and prices. Figure 2 illustrates the intuition

driving this result. When intervention reduces firm value, the price represents a lower rather than

an upper bound on value when intervention is welfare enhancing. Thus, any θθ ˆi individuals have no reason to suspect

an intervention and the price remains P(θi). Notice also that given a possible negative corrective

action, no prices should be observed in the range between )ˆ(θP and )ˆ( ϖθ +P ($3.00 to $5.00 in

Figure 1).

3. Experimental Design and Procedures

3.1 Background. A relatively large experimental literature examines pricing performance of asset

markets. The branch of this literature most pertinent to the present investigation examines the

capacity of traders to aggregate disparate information regarding a non-stochastically valued asset

in a repeated single-period design.5

5 Another branch of the experimental asset market literature, initiated by Smith, Suchanek, and Williams (1988) considers the capacity of traders to track the underling fundamental value of a relatively long-lived asset that yields stochastic returns. Results indicate a persistent propensity for speculative pricing bubbles. This result appears resilient to a variety of conditions, including brokerage fees, short selling or subjects drawn from subpopulations of corporate managers or professional stock traders (see, e.g., King et al., 1993, Lei, Noussair, and Plott, 2001). Common experience with the trading institution appears to minimize the propensity toward speculative pricing. However, recent research by Hassam et al. (2008) indicates that other factors, such as dividend uncertainty and a capacity to sell short can reignite bubbles even with very experienced traders. An important and to the best of our knowledge unexplored experimental question regards the relationship between these general speculative pricing tendencies and the capacity of markets to generate accurate relative price signals in a multi-asset context.

Plott and Sunder (1988) evaluate 12-trader markets in which

traders are uniformly endowed with a cash endowment and a number of asset units, the value of

which is determined at the end of the period by its fundamental. The traders are divided into

three groups, each of which is informed of one of the three values that the asset will not take on.

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Traders then buy and sell assets in a standard open book double auction. Plott and Sunder find

some evidence that trading does allow sellers to identify the underlying value. Nevertheless,

information aggregation is often incomplete in the sense that prices often deviate substantially

from the underlying fundamental. Using a similar design Forsythe and Lundholm (1990) find

that experience appears to improve information aggregation. More recently Hanson et al. (2006)

and Opera et al. (2007) examine variants of the Plott and Sunder design with the modification

that a subset of traders were motivated to attempt to bias market prices in a particular direction.

Results in both papers indicate that even a sizable number of manipulators find altering prices in

a desired direction difficult. Nevertheless, as in the related research, prices often failed to reflect

underlying value.

3.2. Experiment Design. Our research questions require some substantial deviations from these

previously used information-aggregation designs. We are interested in examining the effects of a

monitor who can intervene in a market context where the fundamental value is unknown to the

monitor and revealed only through trading. Further to avoid “no trade” predictions we must

induce some heterogeneity in asset values. At the same time, however, we seek a baseline

context where sellers aggregate information sufficiently to make market prices reflect reasonably

well the underlying fundamental.

To achieve these ends we implement the following three part design, which consists of

(a) an informationally efficient BASE condition (b) a Positive Corrective Action (PCA)

treatment, and (c) a Negative Corrective Action (NCA) treatment. We describe these in turn.

3.2.1. BASE condition. The baseline environment consists of 10 traders. Each period

traders are endowed with two units of an asset, and a cash endowment of E =$16 lab. Six of the

traders realize an underlying fundamental asset value θ1 drawn from a uniform distribution

U[$2.00,$8.00]. The remaining four traders also receive two units of an asset and E=$16 lab, but

the fundamental value of the asset to these traders is 60 cents less than the fundamental, e.g., θ2 =

θ1 -60¢. Aggregating value realizations generates an underlying supply and demand condition

shown in Figure 3. As seen in the figure, the substantial excess demand for units at prices

between θ2 and θ1 should provide considerable incentive for prices to rise to θ1, and for that

reason we term θ1 the “market” fundamental.

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The value distribution, the relation between high and low values, and the aggregate

number of high and low-value units are provided as common knowledge to traders. Traders,

however, do not know if their fundamental value draw for the period is low or high. Trade in this

market accomplishes a very simple information aggregation task: goods flow from the low to the

high-value traders. Given the excess demand for low-value assets, a result of this process is that

the price should approach market fundamental θ1.

The market is organized as a standard, open book double auction (similar to the rules

used on the NYSE), and traders may trade their endowments of cash and asset units as they see

fit.6

∑ ∑= =

+−×++−=b sn

i

m

jsbkjik mnppEPayoff

1 1

)2(θ

Trading periods last 110 seconds. At the end of each period, payoffs for each trader of type k

are determined as the sum of residual cash, and the fundamental value of all units owned at the

end of the period, or

(1)

where nb units are bought at prices pi, i={1,nb} and ms units are sold at prices pj, j={1,ms}.

Finally, in the BASE condition the three monitors are shown the median transaction price

at the close of each period and then guess θ1.7

6 Organizing the market as a simultaneous move institution, such as a call market, would be procedurally simpler. Overall, call markets perform quite favorably relative to double auctions (see, e.g., Cason and Friedman, 2008, and Kagel, 2004). However, a number of experimental studies indicate that simultaneous move institutions like the call market are susceptible to information cascades (Anderson and Holt, 1997) and the winner’s curse (Kagel and Levin, 1986), and are thus less desirable as information aggregation mechanisms. See, e.g., Plott (2001), or, for information aggregation problems in a context that is in some respects related to the one examined here, Duffy and Fisher (2005).

Monitors make decisions simultaneously, and once

all decisions are complete the actual θ1 is revealed. Monitors earn $3 lab if their guess is within

20¢ of θ1, $1 lab if their guess is within 50¢ of θ1, and zero otherwise. Absent the possibility of

intervention, markets in BASE periods should aggregate information effectively. Operationally,

this should mean that the deviation between median prices and θ1 should be consistently small.

Defining informational efficiency as the extent to which the median price reflects the underlying

7 We elected to provide a single price-based measure of market activity both for simplicity and for purposes of parallelism with relevant natural contexts, where market assessments focus primarily on summary price measures. Alternative possible price-based measures include the closing price and the average of the final several contracts in a period. Concerns about traders trying to manipulate prices in order to convince monitors to intervene or not guided our decision to use the median price. We observe that other measures of trading activity such as contract volumes or the standard deviation of contract prices may also potentially indicate when the underlying fundamental in the ambiguous range. However, as shown in Appendix A1, little in our data suggests that the closing price, contract volumes, or the standard deviation of contract prices help provide a more accurate guide to θ1 than the median price alone.

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market fundamental, and allocative efficiency as the percentage of total available gains extracted

from exchange we form a first conjecture:

Conjecture 1: In the BASE condition markets are both informationally and allocationally

efficient.

3.2.2. The PCA treatment. Conditions for the PCA treatment duplicate those for the BASE

condition with the following differences. First, in addition to assessing θ1 for the period the

monitor must also make an intervention decision, under the condition that intervention is

desirable if the market fundamental θ1 is less than 5$ˆ =θ . The monitor earns $10 lab for a

correct decision. The relatively large payment for correct intervention decisions was imposed to

reflect incentives for a regulator, the bulk of whose returns are determined by making socially

optimal decisions.

Monitors make decisions simultaneously. After all decisions are complete, θ1 is revealed

to the monitors, and the action of one of the three monitors is selected at random and

implemented. If the selected monitor decides to intervene, then the value of assets increases by

$2.00, so for high-value traders the value is θ1+2.00 and for low-value traders it is θ1-0.60+2.00.

Over the range of market fundamental realizations θ1∈[$3.00, $7.00] no equilibrium set

of intervention decisions and asset prices exists.8

8 The range of ambiguity corresponds to the limit case in BGP, where the monitor has no information about asset value other than the asset price. As mentioned above in the introduction, our analysis differs from the BGP development in that rather than using a rational expectations framework, we induce trade by creating a gap between θ1 and θ2. Importantly, however, the gap in fundamental values does not affect the range of predicted ambiguity in the PCA treatment. To see this consider first a θ1 realization slightly below $3, say $2.70. In this case the offer range for a high-value trader (with a value of θ1 and who assumes that an intervention will occur) is between $4.70 and $5.30. The bid range for this same trader will be $4.10 to $4.70. For the low-value trader (with a value of θ2 = $2.10) the offer range will be $4.10 to $4.70 and the bid range $3.50 to $4.10. Bid and offer ranges overlap at prices between $4.10 and $4.70. The first contract in this range will indicate to all traders that θ1=$2.70. Further, given excess demand, the market price should approach $4.70. Similar reasoning applies for any θ1 $7.00, say $7.30. For low-value traders, the range of rational overlapping bids and offers will be between $6.70 and $7.30, and the first trade in this range will indicate that θ1 = $7.30. Given the excess demand for low-value units, the market price should exceed $7.00 and tend towards $7.30.

We term this the “ambiguous range.” In the

ambiguous range, prices should range between $5.00 and $7.00 and from these prices the

monitor should not be able to make reliable inferences. Further, we anticipate that uncertainty

about the likelihood of intervention may also affect the allocative efficiency of markets. In the

ambiguous range traders, with heterogeneous perceptions about the asset values, may forego

many surplus-increasing contracts. On the other hand, for fundamental realizations outside

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ambiguous range, the presence of a monitor who can intervene should affect neither

informational nor allocative efficiency. The effects of variations in fundamental realizations on

informational and allocative inefficiencies in the PCA treatment represent our second and third

conjectures.

Conjecture 2. In the PCA treatment, fundamental realizations outside the ambiguous

range generate informational and allocative outcomes similar to those observed in the BASE

condition. Monitors will commit no intervention errors.

Conjecture 3. In the PCA treatment market, fundamental realizations in the ambiguous

range, e.g,. θ1 ∈ [$3,00, $7.00] will result in informational efficiency losses. As a consequence,

the allocative efficiency of traders will fall and monitors will make errant intervention decisions.

3.2.3 The NCA treatment. The NCA treatment is structured in exactly the same way as the

PCA treatment except the intervention reduces rather than increases the value of the firm. As

discussed above, traders should fully incorporate the value of a negative corrective action if and

only if doing so is socially beneficial. For this reason the possibility of a negative corrective

action should not affect the informational efficiency of markets, regardless of the fundamental

realization. Thus monitors should commit no intervention errors, and since traders can

consistently infer monitor actions from fundamental realizations, allocative efficiency should not

differ from the BASE condition.9

Conjecture 4: No “ambiguous” zone exists in the NCA treatment. Regardless of the fundamental

realization informational and allocative efficiency levels should be similar to those observed in

the BASE condition. Monitors should commit no intervention errors.

This is a fourth conjecture.

9 As in the PCA treatment, the gap between θ1 and θ2 in our experiment design does not affect our prediction that no ambiguous zone exists in the NCA treatment. We do observe, however, that in the NCA treatment when θ1 and θ2 are

split by the efficient intervention value θ

, the terms of trade shift in favor of the low-value traders. To see this,

suppose that θ

=$5.00, and consider the fundamental realizations θ1=$5.30 and θ2 =$4.70. At the outset of the period a majority of traders (those with θ1=$5.30) know that intervention is not socially desirable. With the first contract, which should occur in the range between $4.70 and $5.30, the remaining traders (those with θ2 = $4.70)

can also infer that they have the low value and that intervention is not socially desirable. At the same time, θ

becomes an effective lower bound on offers to sell since the high-value traders wish not to signal to the monitor that an intervention is warranted. This limit on the offer range may disadvantage high-value traders. In fact, as discussed in the results, realizations in this range did create some problems.

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3.3 Experiment Procedures. To evaluate the above four conjectures we conducted an experiment

consisting of a series of 16 twenty-period market sessions. At the outset of each session

participants were randomly seated at visually isolated PCs. An experiment administrator then

read aloud a common set of instructions, which explained incentives for traders and for monitors

in the BASE condition, as well as how to make decisions on the computer interface used in the

experiment.10

In total 208 undergraduate student volunteers participated in the experiment, with eight

sessions each in the PCA and NCA treatments. Participants were upper-level math, science,

engineering, and business students enrolled in courses at Virginia Commonwealth University in

the spring 2010 semester. No one participated in more than one session. Lab earnings were

converted to U.S. currency at $12 lab =$1 U.S. rate. Participant earnings for the 90-105 minute

sessions ranged from $14 to $28 and averaged $23.50 (inclusive of a $6 appearance fee).

The experiment was programmed and conducted with the software z-Tree

(Fischbacher, 2007). To facilitate participant understanding, screen shots were projected onto a

wall at the front of the lab. Following the instructions, participants completed a short quiz of

understanding, which the lab monitor reviewed publicly. Finally, participants completed a

practice period for which they were not paid. At any time during the instructions, quiz, and

practice period, participants were encouraged to ask questions by raising their hands. Questions

were answered privately. Following completion of the practice period the session commenced.

After five periods in the BASE condition the session was paused and additional instructions for

either the PCA or the NCA treatments were distributed to participants, which a lab monitor read

aloud. Following a second short quiz of understanding the second 15-period portion of the

session commenced. Following period 20 the session ended, participants were paid privately and

dismissed one at a time. To facilitate the comparison of outcomes across treatments, a common

set of fundamental realizations were used in all sessions. These values are displayed in Table 1.

4. Results

4.1 Overview. The median contract prices for the BASE condition as well as for the active

monitor periods of the PCA and NCA treatments, shown respectively as panels (a), (b), and (c) of

Figure 4 provide an overview of experiment results. In examining the panels, observe first that

for the initial five BASE condition periods of each treatment, median prices cluster tightly about

10 Instructions are available at http://www.people.vcu.edu/~dddavis.

http://www.people.vcu.edu/~dddavis�

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θ1. Turning to the treatment periods for the PCA markets, shown in panel (b), notice that given

an active monitor, price dispersion increases markedly, particularly for market fundamentals less

than $5.00. In this range, prices “bubble up” from the ex ante fundamental to the value inclusive

of the intervention. Notice further, however, that for fundamental realizations below $3.00,

despite the loss of informational content, prices still do not send ambiguous information

regarding intervention to monitors, since the ensuing median transaction prices remain below

$5.00. As the fundamental approaches $5.00, however, median prices increasingly exceed $5.00.

This in turn complicates the monitor’s task of distinguishing the relationship of θ1 to 00.5$ˆ =θ .

This ambiguity of price information is largely as anticipated.

Turning to median prices for the NCA treatment, shown in panel (c), observe that

contrary to our expectations, substantial informational efficiency losses also arise here. For

market fundamentals less than $5.00 (where intervention should be certain) median prices

incompletely “drip down” from the ex ante fundamental to the ex post efficient level.

Nevertheless, when the fundamental is less than $5.00 the price variability does not result in

misleading monitor signals, since the price accurately indicates that intervention is warranted.

However, as indicated by the dotted box in panel (c), for market fundamental realizations close

to, but above $5.00, median prices frequently fall below $5.00, errantly suggesting to monitors

that intervention is warranted. Notice in particular the market fundamental realization closest to,

but above $5 ($5.31). In this case, in each of the eight market session median contract prices

uniformly remain at $5 or less.

The informational efficiency losses associated with active monitors in the PCA and NCA

treatments have consequences both on allocative efficiency and on intervention errors. Figures 5

and 6 illustrate. The bar clusters in Figure 5 plot mean allocative efficiencies (e.g., the

percentage of possible gains from the exchange of asset units in a trading period) for the BASE

condition as well as for the PCA and NCA treatments.11

11 Maximum gains from portfolio reallocations are $4.80 lab: 60¢ each from the movement of the eight units held by “low-value” traders (with values of θ2) to “high-value” traders (with value of θ1).

Three observations follow from

inspection of Figure 5. First, markets in the BASE condition are allocatively quite efficient. In the

BASE condition allocative efficiency is invariant to the fundamental realization and averages

about 94%, a level comparable to efficiency extraction rates in most standard double auction

markets. Second, the presence of an active monitor causes allocative efficiency to fall

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significantly, even for fundamental realizations that fall outside the ambiguous [$3.00, $7.00]

range in the PCA treatment. In this “unambiguous” range, allocative efficiencies for both the

PCA and NCA treatments average 82% and 84%, respectively. Third, for fundamental

realizations within $2.00 of the intervention cutoff allocative efficiency falls still further, with

allocative efficiencies of 74% and 77% in the respective PCA and NCA treatments. The

extremely low levels of trading efficiency observed over this range of fundamentals in our

markets merits some emphasis. In a long history of experimental double auctions, markets very

consistently tend to extract nearly all the possible gains from exchange. Trading efficiencies on

the order of 75% represent an atypically large efficiency loss.12

As the intervention error rates for the PCA and NCA treatments, shown in Figure 6,

illustrate, the informational efficiency losses caused by an active monitor also result in

intervention errors. Notice in particular that for the PCA treatment fundamental realizations in

the “ambiguous” range generate an error rate of about 20%, as is consistent with the ambiguous

median price signals shown in Figure 4(b). Observe further, however, that intervention error

rates of about 10% are also observed in the NCA treatment. The nontrivial intervention error rate

in the NCA treatment was unexpected, but is consistent with the median price results for the

NCA treatment shown in Figure 4(c). Taken together, these results for our NCA treatment

suggest that, at least for some fundamental realizations, prices send ambiguous signals to

monitors, even when the possible corrective action reduces the value of the firm.

4.2 Evaluation of Conjectures. To evaluate formally the above four conjectures, we conduct a

series of mixed effects regressions. Our primary analysis focuses on three variables:

informational efficiency, which we measure as the absolute deviation of median price from the ex

post efficient fundamental; allocative efficiency, measured as the percentage of gains from

exchange extracted from exchange; and intervention error rates, which are the percentage of

instances where a monitor either intervened when the ex ante fundamental exceeded $5.00, or

failed to intervene when the ex ante fundamental was less than $5.00.

For the PCA treatment we estimate informational and allocative efficiency with

12 For example, in a double auction design with inexperienced traders where each period supply and demand receive random shocks and where relative cost and value assignments are reshuffled among sellers and buyers, respectively, Cason and Friedman (1999) observe mean efficiencies of 88.4%. In a similar eight-seller design, also with inexperienced traders Kagel (2004) observes average trading efficiencies of 95%.

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itiAmbigAMActiveAoit euDDy ++++= βββ , (2)

where ity denotes the pertinent efficiency measure for market i in period t. In equation (2) DActive

is an indicator variable that takes on a value of 1 in treatment periods 6-20 (when the monitor can

actively intervene in markets), 0 otherwise, and DAmbig is an indicator variable that takes on a

value of one if the fundamental realization for the period is in the [$3.00, $7.00] range and a

treatment period is between 6 and 20. The error term ui denotes a market-specific random effect

that is included to control for within-market effects. Also, we use a robust (White “sandwich”)

estimation technique to control for possible unspecified autocorrelation or heteroskedasticity.

Notice from (2) that for informational and allocative efficiency the BASE condition is the

omitted variable, and that Ambig is a subset of Active. Thus, the intercept estimates efficiency

levels in the BASE condition periods, Active estimates the incremental effects of an Active

monitor when the fundamental market realization is outside the [$3.00,$7.00] range, and Ambig

estimates incremental effects of an Ambig fundamental realization given an Active monitor.13

To estimate intervention error rates in the PCA treatment, we conduct a regression

identical to (2), except that we include only DAmbig as an independent variable, and use only data

from Active periods 6-20 for estimation. In this case the intercept estimates the pertinent

intervention error rate for Active periods, while the Ambig coefficient estimates the incremental

effects of a fundamental realization in the ambiguous range, given an active monitor.

Finally for the NCA treatment we estimate informational and allocative efficiency in a

manner identical to (2), except we exclude DAmbig because in that treatment theory predicts no

ambiguous zone. 14

Consider first in Table 2 the informational and allocative efficiency of BASE condition

periods, the subject of Conjecture 1. Looking at results for the absolute median price deviations,

summarized in row (i), columns (1) and (4) observe that informational efficiency is imperfect.

Absolute median price deviations average 20¢ in the BASE PCA periods and 21¢ in the BASE

Similarly, we estimate intervention error rates for the NCA treatment by

excluding both DAmbig and DActive and by confining estimate only over “active” periods 6-20.

Table 2 summarizes regression results.

13 The coefficient on the Active variable estimates the incremental effect of having an active monitor and a fundamental draw outside the ambiguous range because Ambig nets out the incremental effect of a draw inside the ambiguous range for the monitor. 14 We replicated the regressions for allocative efficiency and intervention error rates by imposing the constraint that dependent variable is a fraction taking on values between zero and one using the approach in Papke and Wooldridge (1996). Our findings were robust to this approach and are available upon request.

14

NCA periods. Using the gap between θ1 and θ2 as a reference measure of informational

efficiency, our BASE markets thus extract roughly just two-thirds of the underlying information.

Nevertheless, relative to other information aggregation experiments (e.g., Plott and Sunder,

1988, Forsythe and Lundholm, 1990, Hansen et al., 2006, or Oprea et al., 2007) performance in

our BASE periods is fairly impressive.15

Finding 1. Baseline markets are both informationally and allocationally quite efficient:

the median prices deviate from the fundamental by an average of 20¢ and trading extracts 94%

of the available surplus.

Observe similarly from the allocative efficiency

estimates in columns (2) and (5) of row (i) that units flow from low- to high-value traders quite

effectively in the BASE condition periods: Mean allocative efficiency is 95% in the BASE

periods that preceded PCA treatment sessions, and 93% in those periods that preceded NCA

treatment sessions. These observations constitute a first finding, which is largely a calibration

result.

Now consider the performance in the PCA treatment relative to the BASE condition.

Pertinent statistical evidence appears in columns (1), (2), and (3). As can be seen from

coefficients on DActive in columns (1) and (2) of row (ii), the presence of an active monitor

significantly reduces both allocative and informational efficiency even when the fundamental

draw is such that price is not predicted to send an ambiguous signal about interventions to the

monitors. Relative to the BASE condition absolute median price deviations for the Active periods

more than double, increasing by 25¢, and thus reducing information transmission to roughly one-

third of the spread between θ1 and θ2. Similarly, given an active monitor, allocative efficiency

falls by 11 percentage points. As indicated by the asterisk aside the coefficients in columns (1)

and (2) of row (ii), both effects are significant at p

15

Finding 2. Relative to the BASE condition, fundamental realizations outside the ambiguous

range in the PCA treatment result in statistically significant and substantial informational and

allocative losses. Despite these losses, monitors tend not to make intervention mistakes.

Next we evaluate for the PCA treatment the effects of fundamental realizations in the

ambiguous range. Here we start with intervention error comparisons in column (3) of Table 2. As

seen in row (iii) for fundamental realizations in the Ambiguous range intervention error rates

increase by 20%. Columns (1) to (3) of row (iii) similarly indicate that informational and

allocative efficiency also suffer for fundamental realizations in this range. Absolute median price

deviations for the ambiguous range increase by 32¢ over the active/non-ambiguous periods for a

cumulative total of 77¢ for that range, a difference which exceeds the 60¢ difference between θ1

and θ2. Similarly, allocative efficiency falls another 9 percentage points below those in the Active

periods, falling to 75%. These observations form a third finding.

Finding 3: In the PCA treatment, fundamental realizations in the ambiguous range induce

intervention errors. Further, in these periods informational and allocational efficiencies are both

substantially lower and significantly different from periods where the monitor is active but where

fundamental realizations are outside the ambiguous range.

Entries in columns (4), (5), and (6) of Table 2 list results for the NCA treatment that

parallel results for the PCA treatment, and allow evaluation of Conjecture 4. Looking at the

entries in row (ii) of columns (4) to (6), one immediately apparent result is that, contrary to

predictions, the presence of an active monitor in the NCA treatment does in fact significantly

impact both informational and allocative efficiency. Median absolute price deviations for active

monitor/unambiguous fundamental periods increase by 39¢ over the BASE condition, which

represents a more than doubling of observed absolute price deviations. Similarly allocative

efficiencies fall by 14 percentage points. Both changes are statistically significant (p

16

4.3 Additional Analysis. In broad terms, the analysis in the preceding section supports the notion

suggested by BGP, that the presence of a socially motivated monitor who uses asset prices as a

basis for intervention can importantly affect market performance. The analysis of BGP, however,

gives little guidance as to the expected behavior of traders and monitors in the ambiguous range.

In this section, we attempt to shed some light on this issue by describing how monitors respond

to price signals and how the presence of an active monitor affects trading activity.

4.3.1 Monitor decisions and price signals: Consider as a first issue the relationship

between price signals and monitor decisions. A priori, this relationship is most interesting for

prices in the “ambiguous” $5.00 - $7.00 range of the PCA treatment, because in this range theory

is silent about the relationship between prices and monitor actions. Columns (2) and (3) of Table

3 list intervention frequencies by median price ranges for the PCA treatment. Notice that

monitors find median price signals between $5.00 and $7.00 to be less clearly informative than

prices outside this range. Given median prices of $4.99 or below, monitors intervene almost with

certainty (in 132 of 138 instances). Similarly for the 48 instances where monitors observed

median prices of $7.00 or more, monitors never intervened. However, prices in the $5.00 - $7.00

range sent no clear message. Of the 174 instances where monitors saw a median price between

$5.00 and $7.00, they intervened in 54 instances and did not intervene in 120 instances.

Interestingly monitors did not treat all median price signals in the $5.00 - $7.00 range as

equally uninformative. To the contrary, as the distance of the median price from $5.00 increased,

monitors increasingly concluded that the underlying market fundamental exceeded $5.00. For

example, in the $6.00 - $6.49 range, monitors intervened in 5 of 39 instances (12.8%). Similarly

in the $5.50 - $5.99 range, monitors intervened in only 9 of 66 instances (13.6%). Only in the

$5.00 - $5.49 range did monitors regard median price signals as truly uninformative. In this

range monitors intervened in 40 of 69 instances (58%).

Columns (4) to (6) of Table 3 summarize intervention error information for the PCA

treatment. Notice that the monitors’ tendency to regard only median prices in the $5.00 - $5.49

range as ambiguous was quite efficient ex post. In fact, had monitors uniformly followed an

“intervene if pmed< $5.00 and don’t intervene if pmed> $5.50” rule, they would have avoided

nearly one-third of the observed intervention errors. As can be seen in columns (4) and (5) for

median prices less than $5.00, monitors committed a total of six intervention errors, each of

which was a failure to enter. As indicated by the bolded parenthetic entries, none of these errors

17

would have occurred had monitors mechanistically intervened when pmed< $5.00. Similarly, for

fundamental realizations of $5.50 and above, monitors committed a total of 21 intervention

errors. By mechanistically employing a “don’t intervene if pmed> $5.50” rule monitors would

have avoided 13 of these errors (62%). As was seen from the incomplete “bubbling up” of prices

to the ex post efficient value in Figure 4(b), traders’ reticence to incorporate fully the value of an

intervention into their prices when market fundamentals are in the $3.00 to $5.00 range drives

the observed narrowing of the range of effectively ambiguous price signals in the PCA treatment.

In the $5.00 - $5.49 price range, however, no obvious mechanistic rule reduces the

intervention error rate over that which was observed. As indicated by the bolded entries in

columns (4) and (5) of the $5.00 - $5.49 range monitors would have committed a total of 45

intervention errors, had they never intervened if the median price was $5.00 or above,

considerably higher than the observed error total of 27 for this range. Similarly, monitors would

have committed a total of 24 errors had they always intervened, a rate that essentially equals the

observed error total of 27 for this range. We summarize this observation as a first comment.

Comment 1: In the PCA treatment traders’ reticence to fully incorporate the value of an

expected intervention when the market fundamental is between $3.00 and $5.00 narrows the

range of price signals that are ambiguous from $5.00 - $7.00 to $5.00 - $5.49.

Consider now monitor decisions in the NCA treatment. Table 4, formatted like Table 3,

summarizes monitor intervention decisions and errors for the NCA treatment. The monitor

decision and intervention information summarized in columns (2) and (3) of Table 4 reveals that

monitors regard median price signals below $3.00 as indicating that an intervention is warranted.

Monitors intervened in 149 of the 150 instances where pmed$5.00. Notice further that the monitors’

implied decision rule for median prices in these ranges was efficient ex post. As shown in

column (4) of Table 4, monitors committed only two errors when pmed$5.00, and

neither of these errors would have occurred had the monitors consistently followed an “intervene

if pmed>$5.00 and don’t intervene if pmed

18

signals. If traders respond efficiently to their fundamentals, these median price signals should not

be observed. For market fundamentals less than $5.00 the median price should fall below $3.00

as traders incorporate the expected intervention into the market price. For market fundamentals

in excess of $4.99, price should be driven to the market fundamental, since traders do not

anticipate an intervention.

The unpredicted median price signals in the $3.00 -$5.00 range caused the vast bulk of

intervention errors. As the intervention error data in column (4) of Table 4 indicates, for $3.00<

pmed

19

ambiguity for traders. Higher value traders know that θ1 >$5.00 at the outset of the period, and

lower value traders should readily be able to discern that intervention is not warranted from the

strike price of an initial contract.17

Comment 2: In the NCA treatment, median prices between $3.00 and $5.00 send

ambiguous signals to monitors. Over this range a market fundamental is sometimes less than

$5.00, but prices remain above $3.00 because some traders fail to completely incorporate the

value of the intervention into their trading price. In other instances the market fundamental

exceeds $5.00, but traders, concerned that the monitor may intervene, drive prices below $5.00.

We do observe, however, that high-value traders have only a

limited capacity to drive prices up, since they can affect prices only by purchasing units at the

ask prices of low-value traders. In several instances a number of initial contract prices below

$5.00 caused high-value traders to despair and incorporate the value of an intervention into their

asset values, even though intervention was not ex post efficient. We summarize these

observations into a second comment.

Prior to considering allocative efficiency we offer one additional observation regarding

median prices and monitor decisions. As noted in the introduction, some recent proposals for

financial reform include provisions that would require banks to include as part of their portfolio a

class of “contingent capital” bonds that convert to equity shares if the value of a bank falls

sufficiently. The bond/equity conversion rule employed importantly affects whether the PCA or

the NCA environment most closely parallels such a situation. The PCA environment is most

relevant in the case that the conversion rule is generous to incumbent equity owners (who receive

a boost in bank equity value from the conversion at the cost of only a small dilutive effect), while

the NCA environment is more pertinent to the case of a very dilutive conversion rule.

Independent of the conversion rule, our experimental environments differ from the Flannery

(2009) and McDonald (2009) proposals in that these authors propose a fixed-price rule as a basis

for intervention. Thus, the regulator makes no decision, and the market does not have to guess

how the regulator will react to prices. The monitors in our experiment did not use a fixed

intervention rule, so we cannot directly evaluate how the application of a fixed rule would affect

17 With θ1>$5.00, six traders know that the market fundamental exceeds $5.00. The remaining four traders can infer that they are the low-value traders and thus θ1>$5.00 with the first (rational) contract, which will occur at a price between θ1 and θ2.

20

trading behavior. Nevertheless we observe that the ex post application of such a rule would not

reduce the intervention error rates. Even in the NCA treatment, where no ambiguity between

prices complicates trader pricing incentives we observe a substantial potential for inefficiency

from a fixed-price intervention rule. The parenthetical entries in columns (4), (5), and (6) of

Table 4 illustrate: If monitors mechanistically intervened whenever the asset price fell below

$5.00, the number of intervention errors would actually increase from 24 to 30. While additional

experimentation is needed to isolate such a finding, our results suggest a possibly substantial cost

of a fixed-price intervention rule: As asset prices approach the level that triggers an intervention,

traders may start to sell assets in a way that inefficiently triggers an intervention that is not

socially warranted.

4.3.2. The allocative efficiency costs of an active monitor. Next we consider the effects of

an active monitor on allocative efficiency, which in this context is a measure of optimal ex post

portfolio adjustment. The disaggregated allocative efficiency outcomes shown in Figure 7

illustrate two primary efficiency consequences of an active monitor. First, comparing NCA and

PCA bars with those for the BASE condition, notice that in both treatments the active monitor

generally reduces allocative efficiency for all ranges of market fundamentals. Second, looking at

the fundamental realizations in the $3.00- $5.00 range, observe that in this range allocative

efficiency losses are particularly pronounced in both treatments.

Standard theories of decision-making provide plausible motivations for the general

propensity for an active monitor to undermine optimal portfolio adjustment. For all fundamental

realizations a “decision-making with errors” effect of the sort typically assumed in the quantal

response equilibrium literature likely affects portfolio adjustment.18

18 See, e.g., Palfrey and McKelvey (1995) or Anderson, Goeree, and Holt (1998). The essentially continuous price space in our markets forces us to confine our comments to an informal descriptive analysis of errors in decision-making.

Even in the $7.00 - $8.00

range of market fundamentals, for example, some traders may incorrectly calculate the likelihood

of an intervention. Still other traders may be less than completely certain that all monitors will

act in a consistently rational fashion. This uncertainty will cause perceived fundamental values to

differ from the induced θ1 and θ2 values, and will thus impede the flow of low-value units to

high-value traders. Similarly, the generally larger efficiency losses for fundamental realizations

below the $5.00 optimal intervention level may be attributable to a reference point effect. Some

traders may presume that the no intervention state (effective in the BASE condition) represents a

21

status quo.19

“Intervention uncertainty” and “reference point” effects may well explain some of both

the overall loss in allocative efficiency in the active monitor treatments, and a tendency for

allocative efficiencies to be still lower when market fundamental realizations are less than $5.00.

However, we suspect that a third factor, strategic considerations, likely combines with

intervention uncertainty and reference point effects to drive the extremely low levels of

allocative efficiencies in the $3.00 - $5.00 range, particularly in the PCA treatment and for

market fundamentals close to $5.00. In this case traders become reluctant to strike contracts at

prices above $5.00, because doing so sends a message to the monitor that intervention is

unnecessary.

These traders will be reluctant to focus on ex intervention payoffs even when an

intervention should be fully anticipatable, and thus may make ex post inefficient bids and offers.

The Bid and Offer distributions for PCA periods 12 and 11, shown in Figure 8 illustrate

in a more direct way how market fundamentals just below $5.00 undermine allocative efficiency.

In period 12, shown in the upper portion of the figure, the ex ante market fundamental realization

is $6.33. Although this is in the theoretically “ambiguous” range, traders have little incentive to

make inefficient bids and offers. From the bid density, shown in the leftmost panel, observe that

almost all bids fall below the upper bound of the efficient contract zone (e.g., $5.73- $6.33).

Further, within the efficient contract zone, almost all of the bids are made by high-value traders

(shown as the white area, cumulative to bids from low-value traders shown as a gray area).

Turning to the offer distribution shown in the upper right panel of the figure, notice similarly that

virtually all offers exceed the lower bound of the efficient contract zone ($5.73), and that

essentially all offers in the efficient contract zone are from low-value traders. The combination

of bids from high-value traders and offers from low-value traders in the efficient contract zone

promote the relatively high allocative efficiency of 89% observed for this period, a level that

essentially matches the average PCA allocative efficiency for market fundamentals the $5.00 -

$7.00 range.

In period 11, shown in the lower panel of Figure 8, the ex ante market fundamental is

again in the ambiguous zone. This time, however, the market fundamental, at $4.73, is just below

the $5.00 intervention cutoff, making intervention socially efficient and yielding an ex post

19 The “no intervention” state is also the upper of the two lines on the payoff portion of participants’ computer screens. This line is printed in black. Payoffs in the case of intervention appear as a second line, printed in blue that appears below payoffs for the “no intervention” state.

22

efficient contract zone of $6.13 - $.6.73. In contrast to period 12, little bid or offer density weight

is in the efficient contract zone. In particular, as can be seen from the bid density in the lower left

panel of Figure 8, traders submit very few bids in excess of $5.00, since units are worth $6.73

only in the case that the monitor believes intervention is warranted, and since traders evidently

believe that the monitors will not perceive intervention as warranted unless the median contract

price is less than $5.00.

The reluctance of traders to submit bids in excess of $5.00 creates considerable scope for

allocative inefficiency. Many low-value traders simply forego exchange, since they hold units

worth $6.13 in the case the monitor intervenes, and no trader is willing to offer more than $5.00

for them. Moreover, considerable scope for inefficient exchange arises, as both high- and low-

value traders, with heterogeneous perceptions regarding the likelihood of intervention submit the

bulk of offers in an “inefficient offer” range in which either a high- or a low-value trader could

profitably make a purchase in the event that the monitor intervenes. The incapacity of bids and

offers to organize the transfer of units from low- to high-value traders in this zone impedes

allocative efficiency.20

As recognized in the previous subsection, the incentive for traders to truncate price

postings at $5.00 advantageously reduces the range of median price signals that are potentially

ambiguous to monitors. However, the sizable allocative efficiency losses observed when the

market fundamental approaches $5.00 represents a potentially important associated cost of

sellers’ truncating prices: As the market fundamental approaches $5.00, prices lose their capacity

to organize an efficient transfer of asset units, since units are worth more than $5.00 to both low-

and high-value traders in the event that the monitor intervenes.

In the period illustrated allocative efficiency is 67% about the average

allocative efficiency in the PCA treatment for market fundamentals in the $3.00- $5.00 range.

The extremely high allocative efficiency costs of an active monitor, just in the range

where the active monitor might intervene represents a consequence of using asset prices as a

basis for intervention decisions that has not to our knowledge been considered. We summarize

this observation as a third and final comment.

20 Generally speaking allocative efficiency losses are split roughly evenly between “reverse exchanges” and “foregone trades.” This is true in both treatments for all fundamental realizations. Both of these reasons for efficiency losses are consistent with the impaired capacity of prices to organize the efficient transfer of units from low- to high-value traders shown in Figure 8.

23

Comment 3: Regardless of the fundamental realization, allocative efficiency falls in the

active monitor treatments relative to that observed in the BASE condition. A combination of

intervention uncertainty, reference point effects, and strategic incentives to manipulate prices

make the allocative efficiency losses particularly high in the $3.00-$5.00 range of PCA

treatment.

5. Concluding Comments

This paper reports results from an experiment conducted to examine the effects of

incorporating socially motivated but uninformed “monitors” into the market for units of a traded

asset. Our experiment is inspired by a theoretical model recently reported by BGP and in several

critical respects results are as would be anticipated by this development. Most prominently we

find that an “ambiguous range” of market fundamental realizations exists around a critical cutoff

value for socially desirable intervention. θ̂ . In this ambiguous range monitors find it difficult to appropriately associate prices with underlying fundamental values, and consequently, monitors

frequently make errant intervention decisions. Also as predicted we observe considerably fewer

intervention errors in a “negative corrective action” treatment, where a monitor intervention

reduces firm value than in a “positive corrective action” treatment, where an intervention

increases firm value.

We also observe, however, two prominent results that we did not anticipate. The first such

result is that in the positive corrective action treatment the range of prices that is effectively

ambiguous for the monitors is much smaller than predicted. This occurs primarily because the

traders tend to truncate their prices at (or near) θ̂ when the market fundamental is less than θ̂ . A

negative consequence of this tendency is an often substantial loss in allocative efficiency when

market fundamental realizations are close to, but below θ̂ . These efficiency losses arise because prices lose their capacity to organize the transfer of units from low- to high-value traders as

traders truncate bids and offers.

A second unanticipated result is the sizable number of intervention errors in a negative

corrective action treatment. These intervention errors occur primarily for fundamental

realizations slightly above θ̂ . Here traders either know or should be able to infer from the first

contract that intervention is undesirable for monitors. However, if a few initial low contracts

24

occur at prices below θ̂ , traders sometimes incorporate the (negative) value of the intervention into their unit value. We also observe that the use of a fixed-price intervention rule (as has been

proposed as a rule for triggering conversion of contingent capital bonds) may actually increase

the incidence of intervention errors. Both the unanticipated outcomes discussed above depend on

the relative magnitude of portfolio heterogeneity, so these results may not be applicable to all

situations. That said, our results do quite clearly suggest that socially motivated but imperfectly

informed monitors who rely heavily on asset prices as a signal of market value may distort these

price signals in a way that leads to inefficient intervention and, as a byproduct, market

inefficiency.

Future work along these lines will focus on methods that might reduce the socially costly

intervention errors. Two directions seem most appealing. The first direction is to evaluate

markets using fixed-price intervention rule. Our suspicion is that such a rule would not improve

performance in a positive corrective action treatment and may actually harm performance in a

negative corrective action treatment. However, we cannot verify such an outcome absent the

direct observation of trading behavior given a fixed-price rule (which eliminates intervention

uncertainty).

A second direction is to evaluate markets where the monitor’s information set is

supplemented with the results of a prediction market conducted prior to the commencement of

trading each period. This treatment is suggested by BGP, who show that in a rational

expectations framework a prediction market that elicits the probability that a monitor will

intervene corrects the underlying informational problem that drives intervention errors in the

ambiguous range. As mentioned in the introduction to this paper, prediction markets have an

impressive record of predicting election outcomes, and such markets are increasingly used in

business and policy contexts to assess event probabilities within firms.21

The potential for

prediction markets to resolve the informational problems associated with intervention observed

here is an important issue for future investigation.

21 In discussing an internal prediction market conducted by Google, Cowgil, Wolfers, and Zietwitz (2009) observe that a host of firms have begun using prediction markets to predict events pertinent to the firm. In addition to Google, examples include Abbott Labs, Arcelor Mittal, Best Buy, Chrysler, Corning, Electronic Arts, Eli Lilly, Frito Lay, General Electric, Hewlett Packard, Intel, InterContinental Hotels, Masterfoods, Microsoft, Motorola, Nokia, Pfizer, Qualcomm, Siemens, and TNT.

25

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Roll, Richard (1984) “Orange Juice and Weather,” American Economic Review 74, 861-80. Smith, Vernon L., Gerry L. Suchanek, and Arlington W. Williams (1988) “Bubbles, Crashes and

Endogeneous Expectations in Experimental Asset Markets.” Econometrica 56, 1119-51. Stern, Gary H. (2001)“Taking Market Data Seriously,” The Region, Federal Reserve Bank of

Minneapolis, September. Warner, Jerold, Ross Watts and Karen Wruck (1998) “Stock Prices and Top Management

Changes” Journal of Financial Economics 20, 461-492. Wolfers, Justin and Eric Zitzewitz, (2004) Prediction Markets” Journal of Economic

Perspectives 18, 107-126.

28

Figure 1. Prices and Fundamentals Given the Possibility of a Positive Corrective Action.

Figure 2. Prices and Fundamentals, Given the Possibility of a Negative Corrective Action.

29

Figure 3. Market Supply and Demand, Given a Market Fundamental θ1.

30

Figure 4. Median Contract Prices vs. Fundamental Realizations, (a) BASE condition, (b) PCA treatment and (c) NCA treatment.

31

Figure 5. Allocative Efficiency in the BASE condition and in the PCA and NCA treatments.

Figure 6. Intervention Error Rates, PCA and NCA Treatments.

32

Figure 7. Allocative efficiencies by fundamental realization in the BASE Condition, NCA treatment and PCA treatment.

Figure 8. Bid and Offer Distributions for PCA periods 12 and 11.

Bids Offers

(a) Period 12: Ex Ante Market Fundamental: $6.33

(b) Period 11: Ex Ante Market Fundamental: $4.73

0

10

20

30

40

$2.00 $3.00 $4.00 $5.00 $6.00 $7.00 $8.00

Num

ber

Offer ValueLow Value Trader High Value Trader

Intervention Cutoff

Inefficient OffersEfficient Contract Zone

0

10

20

30

40

50

60

$2.00 $3.00 $4.00 $5.00 $6.00 $7.00 $8.00

Num

ber

Offer Value

Low Value Trader High Value Trader

Intervention Cutoff

Inefficient Offers

EfficientContract Zone

0

10

20

30

40

$2.00 $3.00 $4.00 $5.00 $6.00 $7.00 $8.00

Num

ber

Bid ValueLow Value Trader High Value Trader

Intervention Cutoff

InefficientBids

Efficient Contract Zone

0

10

20

30

40

$2.00 $3.00 $4.00 $5.00 $6.00 $7.00 $8.00

Num

ber

Bid ValueLow Value Trader High Value Trader

Intervention Cutoff

InefficientBids

Efficient Contract Zone

33

Table 1. Sequence of Fundamental Value Realizations Baseline Condition

Period 1 2 3 4 5 Fundamental $2.94 $7.33 $4.76 $2.61 $6.50

Active Monitor Condition

Period 6 7 8 9 10 11 12 Fundamental $5.73 $3.77 $2.61 $7.39 $5.99 $3.49 $5.74

Period 13 14 15 16 17 18 19 20

Fundamental $4.54 $7.69 $2.82 $4.73 $6.33 $2.53 $5.31 $4.54

Table 2. Informational Efficiency, Allocative Efficiency and Intervention Errors,

PCA

NCA

(1) |Pmed – Pfx|:

(2) Allocative Efficiency

(3) Intervention Error Rate

(4) |Pmed – Pfx|:

(5) Allocative Efficiency

(6) Intervention Error Rate

(i) Cons 20¢* 95%*

21¢* 93%*

(ii) Active 25¢* -11% * 2%

39¢* -14%* 6%*

(iii) Ambig 32¢* -9%* 20%*

N 160 160 120

160 160 120

Wald χ 2 153.15* 6298.7* 34.85*

70.63* 8208.6* 0.00

Key: * indicates rejection of the null hypothesis., p

34

Key: Parenthetic entries in columns (5) and (6) indicate intervention errors using a fixed-price intervention rule. Bold indicates use of an “intervene if pmed

35

Appendix A1. Market Information and Intervention Errors

To facilitate implementation of the market environment, we only allowed the monitors to

see the median price. Additional or other information may improve an active monitor’s capacity

to identify the underlying fundamental, or at least to identify when an intervention is or is not

socially desirable. This appendix considers the possible corrective effects of providing monitors

with additional information regarding market activity.

In our experiment we presented the monitor only with the median contract price for each

trading period. Three pieces of information seem potentially most useful. First, we might allow

the monitor to observe the closing price each trading period rather than the median price. As is

well known, in many double auction contexts the closing price in a trading period is a better

indicator of the underlying equilibrium than the average (or median) price (see, e.g., Davis,

Harrison and Williams,1993).22

On the other hand, it may be the case that more information rather than different

information helps the monitor to better identify underlying value. For this reason we consider the

informative value of contract volumes and the standard deviation of contract prices. These

represent two other potentially useful bits of information. High contract volumes or highly

variable contract prices may indicate uncertainty about the likelihood of an intervention that may

help interpret price information. Unusually high contract volumes or a large standard deviation

of contract prices may supplement an observed median price of $5.10, for example, and allow a

monitor to conclude that θ1

36

add one at each of these independent variables on at a time and reevaluate the model. Formally,

we estimate

1 , 2

( 1)ln

1 ( 1)

sit

med it io ittsit

P Ip ex

P Iββ β = = + + + − =

, (3)

where sitI is a socially desirable intervention by monitor i in period t, and xit ∈{ pcit, Qit, σpit,},

with pct, the closing price in period t, Qit, is the contract volume in period t and σpit is the

standard deviation of contracts in period t. Table A1 reports regression results.

Looking at regression results, reported in Table A1, observe that in the NCA treatment

none of the additional variables differ significantly from zero, indicating that none substantially

improves the forecast from the median price. In the PCA treatment, we do observe that the

coefficient on one additional variable, the standard deviation of contracts does differ

significantly from zero, and in fact this variable has a marginal effect comparable to the effect of

the median price. Still, none of the variables changes the model’s fit substantially.

Table A1. Factors Affecting the Likelihood of Socially Optimal Intervention.

Dependent Variable: Likelihood of Socially Optimal Intervention

PCA NCA

(1) (2) (3) (4) (5) (6) (7) (8)

Constant 27.84* 28.14* 24.96* 25.50* 12.09* 11.81* 9.29* 12.18*

Median Contract Price -5.21*** -4.95* -5.19* -5.10* -3.07* -2.04* -3.11* -3.08*

Closing Price

-0.31

-1.09

Contract Volume

0.17

0.15

St. Dev. of Contracts

4.08*

-0.15

Pseudo R2, % 68 68 70 70 83 84 84 83

Marginal effects at $5

Median Contract Price -0.64* -0.66* -0.58* -0.64* -0.11 -0.17 -0.12 -0.11

Closing Price

-0.04

-0.09

Contract Volume

0.02

0.01

St. Dev. of Contracts 0.51* -0.01

Key: * indicates rejection of Ho , p