ISSN (Print) 0473-453X

Discussion Paper No. 1147

ISSN (Online) 2435-0982

THE IMPACT OF ASSET PURCHASESIN AN EXPERIMENTAL MARKET
WITH CONSUMPTIONSMOOTHING MOTIVES
Jieyi Duan
Nobuyuki Hanaki

November 2021

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

The impact of asset purchases in anexperimental market with consumption
smoothing motives∗
Jieyi Duan†

Nobuyuki Hanaki‡

November 10, 2021
Abstract
We investigate the effect of preannounced market intervention on
an asset price as well as participants’ welfare in an experimental framework where participants have consumption smoothing motives to trade
the asset. The results show that, on one hand, the preannounced intervention results in significantly larger overpricing of the asset relative
to the rational expectations equilibrium level in periods prior to theintervention compared with the treatment without it. The participants’ welfare, measured by the discounted sum of the payoffs at the
beginning of the experiment, on the other hand, are not significantly
worsened by the intervention.

JEL Code: C90, D84
Keywords: Asset Pricing, Consumption Smoothing, Bounded Rationality,
Quantitative Easing∗

The experiments reported in this paper has been approved by IRB of the Instituteof Social and Economic Research, Osaka University. We gratefully acknowledge financial
support from grant-in-aid for scientific research (KAKENHI, grant number: 18K19954,
20H05631) from the Japan Society for the Promotion of Science (JSPS) as well as support
from the Joint Usage/Research Center at ISER, Osaka University. We thank Daiki Maeda
and participants at the international workshop on lab and field experiments (Osaka), 2021
ESA Global Online Meeting for comments and discussion.
†
Institute of Social and Economic Research, Osaka University.
E-mail:duan@iser.osaka-u.ac.jp‡
Institute of Social and Economic Research, Osaka University.
E-mail:nobuyuki.hanaki@iser.osaka-u.ac.jp1

1

Introduction

The biggest monetary policy experiment in recent years has been quantitativeeasing (QE). Between 2008 and 2017, seven central banks (Mexico, England,
Japan, Europe, US, Switzerland, and Sweden) have employed this policy(Committee on the Global Financial System, 2019). Furthermore, QE has
been extended in duration in many countries in response to the economic
downturn resulting from the spread of Covid-19. For example, since March
2020, the US Federal Reserve has implemented an unlimited QE policy.
However, from the perspective of the standard textbook model with complete markets without frictions and infinitely lived rational decision makers,
this policy should have no effects on any macroeconomic variables (Eggertsson and Woodford, 2003). Nevertheless, a number of empirical studies provide evidence that QE has indeed affected the market price of financial assets.
For instance, Corbet et al. (2019) find the Federal Open Market Committee announcement has significantly and substantially increased stock market
volatility, McLaren et al. (2014) and Grosse-Rueschkamp et al. (2019) indicate that the QE announcements from central banks has reduced bond yields,
implying there exists a QE announcement effect. In particular, D’Amico andKing (2013) and McLaren et al. (2014) observe a “local supply” effect, i.e.,
the yield curve within a particular maturity sector has responded more tochanges in the total value of outstanding bonds in that sector than to similar changes in the other sectors, indicating a segmentation or an imperfect2

substitution within the Treasury bond market.
Penalver et al. (2020) offer, based on an experimental analysis, a perspective based on the limited rationality of investors a la level-K (Nagel, 1995)
or the cognitive hierarchy (Camerer et al., 2004) model; namely, a possibilityof some market participants (maybe naively) believing that QE will raise theprices of bonds because QE is a commitment by the central bank, who is anunusual participant to the market, to buy a large amount of bonds. Sucha possibility would indeed result in QE raising the prices of bonds because
other, more sophisticated, participants optimally respond to the existence ofsuch naive participants.1
The experiment of Penalver et al. (2020) is similar to that of Bostian andHolt (2009) and extends the framework of Smith et al. (1988).2 In the Benchmark setting, participants trade a set of risk-free bonds using experimental
currency (cash) for a known finite number of periods. Bonds and cash, when
carried over to the next period, generate a dividend and an interest income,respectively. The bond matures at the end of the final trading period andis converted into cash. The interest on cash, the dividend, and the valueof the bond at maturity are set so that the fundamental value of the bondis constant, and equal to the value of the bond at maturity in all trading
1

Farhi and Werning (2019) analyze, theoretically, an implication of similar limited rationality on the effects of monetary policy in the New Keynesian setup. Namely, the authors
show that level-k thinking together with incomplete credits markets, idiosyncratic risks
that cannot be insured, and borrowing constraints that bind occasionally, can potentiallyexplain the “forward guidance puzzle”; i.e., monetary policy being too effective.
2
See, among others, Palan (2013) and Powell and Shestakova (2016), for surveys of thelarge literature on the experimental asset market pioneered by Smith et al. (1988).

3

periods. In the treatment with QE intervention, after a preannounced number of periods, the computer (acting as a QE operator) intervenes to buy
a prespecified quantity of bonds from participants through a discriminatory
auction. Penalver et al. (2020) find that such a market intervention not only
raises the postintervention prices of bonds, but also, after participants experience it once, significantly raises preintervention market prices when theexperiment is repeated.3
The effect of market intervention observed in the experiments by Penalver
et al. (2020) and Haruvy et al. (2014) are observed in an environment where
no trade is predicted under a risk neutral rational expectations equilibrium(REE, for short). In such an environment, asset prices tend to deviate fromtheir fundamental value because the main rationale for participants to trade
the asset is to speculate, compared with alternative environments where participants have nonspeculative motives, such as a consumption smoothing
motive, to trade (Crockett et al., 2019). Hence, the effect of intervention observed by Penalver et al. (2020) and Haruvy et al. (2014) may be exaggerated
compared with situations where participants have nonspeculative motives totrade the asset.
In this study, therefore, we reexamine the effect of preannounced market intervention considered by Penalver et al. (2020) in a new experimen3

Haruvy et al. (2014) also show, in a similar but a different experimental design, that assets being (re-)purchased by computer from market participants raises their prices. Haruvy
et al. (2014) did not investigate, however, how participants experiencing the effect of (repurchase) intervention once influences the preintervention price by repeating the experiment.

4

tal framework introduced by Asparouhova et al. (2016) and Crockett et al.
(2019), where participants trade assets to smooth their consumption acrossperiods. This new experimental framework is designed based on the Lucas asset pricing model (Lucas Jr, 1978), and has the following characteristics: (1)there are an indefinite number of periods, instead of a known finite numberof periods; (2) participants receive incomes, which can be used to consume
or to trade the assets, that fluctuate across periods; and (3) participants arepaid based on their consumption at the end of periods, instead of the final
value of their portfolio at the end of the last period. In this setting, the asset
will be traded to smooth consumption in the REE.
We investigate, in this new framework, the effect of preannounced market intervention (which we call QE operations below), as well as the effectof the existence of an additional method of consumption smoothing; i.e., thepossibility to directly save cash. Our data show that the existence of QE
operations significantly increase the magnitude of overpricing of the asset(which we call bonds below) relative to the REE level regardless of the existence of a saving possibility even before participants having experienced theeffect of the QE operation. However, the participants’ payoffs, measured by
the discounted sum of per period payoffs, are not significantly worsened by
the larger mispricing of assets caused by the preannounced intervention.
The rest of the paper is organized as follows. Section 2 describes the modeland experimental design. Section 3 outlines the theoretical predictions andhypotheses. Section 4 provides an analysis of the data. Finally, Section 55offers some conclusions.

2

Experiments

2.1

Basic experimental design

Our experimental design in the Benchmark (B) treatment builds upon theprevious experiments by Crockett et al. (2019) and Asparouhova et al. (2016).
We consider an indefinite horizon economy with a nonstorable consumption
good and an infinitely lived asset. In the experiment, the nonstorable consumption good and the infinitely lived asset are represented by the experimental currency (francs) and the bond, respectively. Time is discrete. At
the beginning of the economy (t = 1), each trader is endowed with someunits of bond and some francs, which they can use to trade among themselves. Subsequently, at the beginning of period t ≥ 2, each trader receives
yti francs of income as well as D = 2 francs per unit of bonds they hold asPthe dividend payment. While the aggregate income, i yti , is constant acrossperiods, individual income, yti , fluctuates between odd- and even-numberedperiods. Let yoi and yei denote trader i’s income in odd- and even-numberedperiods, respectively.
At the end of each period (t ≥ 1), each trader consumes the francs he/shehas. Once consumption takes place, the economy continues to the next periodwith probability π = 5/6 or ends with probability 1 − π = 1/6. Recall thatwhile francs are nonstorable and cannot be carried over to the next period,
6the bond is storable and can be carried over to the next period. However, if
the economy ends, all the bond holdings are lost without any compensation.
In the experiment, participants, who are acting as traders, are paid based on
their final consumption before the economy ends according to a prespecified
conversion rate we specify below. The risk neutral fundamental value of aunit of bonds in period t is F Vt = Dπ/(1 − π) = 10 francs for all t.
In the experiment, there are eight traders in an economy. These eight
traders are equally divided into two types of traders, Type 1 and Type 2,
who differ in their endowment and income streams. The endowments are 92
francs and one unit of bonds for Type 1 traders and 32 francs and four units ofbonds for Type 2 traders, respectively. Incomes are set so that yo1 = ye2 = 90
francs and ye1 = yo2 = 24 francs, where superscripts denote trader’s type.
Consumption, c, in francs is converted into JPY according to the following
increasing and strictly concave function:uJP Y (c) = 3573.50 − 64872.01 × (c + 40.5)−0.7478 .

(1)Under this experimental setting, traders can obtain a higher expected payoffby smoothing their consumption across periods. Note, however, to smooththe consumption, traders need to trade bonds. Thus, unlike many experiments employing the framework of Smith et al. (1988) including Haruvy
et al. (2014) and Penalver et al. (2020), trade will take place in the current
experimental setup under the REE. Crockett et al. (2019) demonstrated that7

such a consumption smoothing motive of trading reduces the price deviationfrom the fundamental value.

2.2

Trading mechanism

The experiment adopts an open-book continuous double-auction mechanism.
The trading period lasts 120 seconds. As noted above, at the beginning of
period t, before the market opens, trader i who hold Kti units of bonds
receives yti + 2Kti units of cash (francs) as his/her income and the dividend
payment.
Once trading begins, traders can submit a bid order (a buy order) and/or
an ask order (a sell order) for a unit of bonds in continuous time. Traders
can trade as many units of bonds as they wish during the 120 seconds within
their budget constraint. No borrowing of cash or short-selling of bonds ispermitted. Orders are sorted according to price and the time of the ordersubmission. A transaction takes place when the best bid and the best ask
cross, at the price determined by whichever is submitted earlier. Once atransaction takes place, cash and bond holdings are immediately updated
and all the outstanding orders submitted by the two traders who just traded
are automatically canceled.

8Table 1: Four treatments
Without saving With saving
without QE
BBSwith QE
QE
QES

2.3

Treatments

We use a two-by-two between-subjects design, in which we vary the existenceof QE operations (with v.s. without QE operations), and the existence of anadditional way of smoothing consumption (with v.s. without the possibilityof saving francs). Table 1 summarizes these four treatments.
Regarding the QE operations, before the experiment begins, participants
are informed that, just before the trading in period 4 and after participantsreceive their income and dividend, a maximum of six units of bonds will
be bought by the computer through a uniform price auction. During thisintervention, participants who want to sell their bonds to the computer,
place an order by specifying the minimum price at which they wish to sell
their bonds and the number of units they wish to sell.
Then, the computer buys up to six units of bonds from the lowest-pricedorders from the participants in each group. The transaction price of theintervention is the highest price among those accepted orders in the group.
In the treatments with saving (BS and QES), francs become storable.
Namely, just after the trading in each period, participants can determine
how much of their remaining francs to save and to carry over to the next
period. The saved francs generate an interest earning from a 20% interest

9rate, which is paid at the beginning of the next period. If the economy ends,
however, just as the bond is lost, the saved francs are lost. Interest earnings
are rounded down to the nearest integer. We set the interest rate at 20% tomake it equal to the return of bonds at the rational expectations equilibrium.

2.4

Termination, timing, and payment

To secure sufficient data and to ensure that market intervention occurs in the
QE and QES treatments, we adopted the method of block random termination (BRT, Fréchette and Yuksel, 2017). Similarly to the standard random
termination, under BRT, the computer rolls a die at the end of each period
to determine whether an economy (called a round) ends or not. However,participants must at least experience the round for a fixed number of periods(one block). Whether the round has ended or not is disclosed only at the endof each block. If the round has ended during the current block, participants
are told in which period it has happened, and their corresponding payoffs
at that period. Recall that participants’ payoff (for the round) is computed
based on their consumption at the final period of the round. Otherwise, they
are told that the round has not ended yet, and they start a new block. In
this experiment, at the end of each period, the round ends with a probability
1/6, and each block consists of six periods.
At the end of a round, if fewer than 35 minutes have passed since thebeginning of the first round, a new round begins; otherwise, the experimentends. Therefore, each session of the experiment consists of at least one round.
10

Furthermore, each round consists of at least one block of six periods. At thestart of each round, the endowments of all participants are reset. After theexperiment ends, the computer randomly selects one round to calculate theparticipant’s earning. Participants are paid in cash based on their earnings
in the chosen round in addition to the 1000 JPY show-up fee.
To exclude the effect from variations in duration of rounds, and to control
the length of the experiment, we use a computer program to randomly determine the number of periods (thus, the number of the block) in each roundin advance.4 Then, we used this predetermined number of periods acrossrounds for all the sessions in our experiment. Therefore, each round has
the same number of periods (blocks) in all the sessions, and all the sessions
consist of at least two rounds in our experiment. In particular, all of therounds in the four treatments consist of one block of six periods, and eachsession of all the treatments except for B consists of two rounds, while eachsession of B consists of three rounds. We did not provide participants withthis information.5
4Although there is so far no evidence showing that variations in duration of rounds
affect asset pricing in the macroeconomics experiment, some studies document that suchvariations indeed affect the experimental results. For instance, Bo and Fréchette (2011)and Engle-Warnick and Slonim (2006) report that such variations can affect the extent
of cooperative behavior in repeated prisoner’s dilemma game experiments and repeatedtrust game experiments.
5
Such a design is also adopted by Duffy and Puzzello (2014), Duffy and Puzzello (2020),
and Fréchette and Yuksel (2017).

11

3

Hypothesis

Let us derive the REE bond price under the representative agent framework.
At period 0, the agent faces the following maximization problem.

max{ct ,kt+1 }∞
t=0

[

∞
X

π t v(ct )]

(2)

t=0

subject to
yt + (D + Pt )kt = ct + Pt kt+1 ,(3)

where ct is the consumption of francs, Pt is the bond price, and kt is thenumber of the bonds that the agent owns at the beginning of period t. π ∈(0, 1) is the continuation probability. v(ct ) = (1 − π)uJP Y (ct ) + π0 = (1 −π)u(ct ) is the expected monetary reward in period t by “consuming” ct .
We define the Lagrangian as follows:

L=

∞
X


π t v(ct ) + λt (yt + (D + Pt )kt − ct − Pt kt+1 ) .

(4)

t=0

The first order condition with respect to ct is∂L= π t v 0 (ct ) − λt = 0 for all t.
∂ct

(5)

The first order condition with respect to kt+1 is∂L= −λt Pt + λt+1 (D + Pt+1 ) = 0 for all t.
∂kt+1
12

(6)

Combining Eq. 6 and Eq. 5 for ct and ct+1 gives us
π t v 0 (ct )Pt = π t+1 v 0 (ct+1 )(D + Pt+1 ).

Thus,
Pt∗ =

v 0 (ct+1 )π(Pt+1 + D),
v 0 (ct )(7)

where Pt∗ is the equilibrium price of the bond at period t. By applying thelaw of iterated expectations, Eq. 7 can be rewritten as

Pt∗ =

∞
X
τ =1

πτ

v 0 (ct+τ )D,
v 0 (ct )(8)with v 0 (ct ) = (1 − π)u0JP Y (ct ) for all t. As uJP Y (ct ) is strictly concave, in
0)treatment B, consumption satisfies c1 = c2 = ... = ct = .... Thus, v v(c0 (ct+τ= 1.
t)Therefore,
Pt∗,B =∞Xτ =1

πτ D =πD= F V for all t.
1−π

(9)

We now consider the REE price of the QE treatment. In the experiment, theQE operation occurs at the beginning of period 4. Because up to six unitsof bonds are purchased during the QE operation, the dividend loss of thepurchased bonds leads to a decrease in consumption after period 4. Thus,

c4 > c1 = c2 = c3 > c5 = c6 = ...

13

(10)

and
v 0 (ct|t≥5 ) > v 0 (ct|t≤3 ) > v 0 (c4 ).

(11)

Given that consumption will be constant, in the equilibrium, after period 5,
we have∗,QE
Pt|t≥5= F V.

(12)

The equilibrium prices in period 4 are
P4∗,QE =v 0 (c5 )v 0 (c5 )
π(F
V
+
D)
=
F V,
v 0 (c4 )v 0 (c4 )(13)which depends on c4 ; i.e., the outcome of the QE operation. The equilibrium
price in period 3 isv 0 (c4 ) ∗,QE
v 0 (c5 )v 0 (c4 )P3∗,QE = π 0(P4+ D) = π 0
FV + π 0D.
v (c3 )v (c3 )v (c3 )

(14)

In period 4, if the agent responds to the QE operation, the expected payoff
must be no less than when the agent can expect from not doing so. Thismeans that the consumption stream must satisfy the following inequality∞Xπ τ [v(c3 ) − v(c∗∗ )] ≤ v(c4 ) − v(c3 ),

(15)

τ =1

where c∗∗ is the optimal per period consumption from period 5 on.
The left part of the above inequality is the discounted future loss frompermanently lower income (because of lost dividend income) when the agent

14

responds to the QE operation, and the right part is the short-term gain in
welfare from doing so. If the competition among agents drives the QE pricedown to the point of indifference, this impliesv(c3 ) − πv(c∗∗ )= v(c4 ).
1−π

(16)

This identifies the equilibrium QE intervention price. Although we could
not derive the relationship between P3∗,QE and F V under general conditions,
we can at least numerically derive the specific value of P3∗,QE for the specificparameter used in our experiment. Assuming, consistent with the representative agent assumption, that all the agents have the same level of consumption
in the REE, when the computer purchases six units of bonds in the QE operation, we ct|t≤3 = 62 and ct|t≥5 = 60.5. Moreover, substituting these values
in Eq. 16, we have c4 = 70.1216 under the representative agent assumption(so that each agent sells 6/8 units of bonds to the computer during the QE
operation), with the equilibrium intervention price being
6p∗,QE
inter = (70.1216 − 62)/ = 10.82.
8(17)With uJP Y (ct ) and P5∗,QE = F V = 10, we obtain
∗,QE
∗,B
.
Pt=3
= 10.00959 > F V = Pt=3

15

(18)

Given Eq. 7, this means
∗,QE
∗,QE
∗,QE
Pt=3
> Pt=2> Pt=1
> 10;

(19)

∗,QE
∗,QE
in particular, Pt=2
= 10.005, Pt=1
= 10.0042. Thus, the announced QE

operation slightly raises the prices in preintervention periods under the REE.
In the BS and QES treatments, because participants can carry over theirfrancs into the next period by saving with an interest rate of 20%, a 10 francs
saving can perfectly substitute for holding one unit of bonds when the market
price is F V . Thus, there is no reason that participants buy or sell the bonds
at a market price higher or lower than F V under the REE. Furthermore, in
the QES treatment, for the same reason, the intervention price converges to
FV when the QE operation is fully competitive.
While the REE bond prices are (slightly) different across the four treatments, we hypothesize, based on the REE,
Hypothesis 1 The magnitudes of mispricing are the same across the four
treatments.
Alternatively, if, as proposed by Penalver et al. (2020), some participants
naively anticipating profits to be made during the QE operation causes prices
to deviate from the REE, the magnitude of mispricing would be larger in thetreatments with QE than those without, especially, in the periods prior to
the QE operation.
Furthermore, because of the competition during the QE operation, we
16

expect, based on the REE,
Hypothesis 2 The ex ante expected payoffs are the same across the four
treatments.
Alternatively, while QE operations may raise the consumption level inperiod 4 (and if saving is possible in the later periods) and thus make participants better off, speculative trades in periods prior to it may disturb consumption smoothing and thus may make participants worse off. The overall
effect is, however, not clear and the ex ante expected payoff may differ acrosstreatments.

4

Results and Discussions

The experiment was programmed using z-Tree (Fischbacher, 2007) and was
run at the experimental laboratory of the Institute of Social and Economic
Research at Osaka University from October 2020 to June 2021. All participants were students enrolled in the school and recruited by the ORSEE
recruiting system (Greiner, 2015).
In total, 304 students participated in the experiment across 13 sessions.
In all the sessions, the instruction movie was played to the participants. The
participants had a printed handout at hand. The participants’ understanding of the rules of the experiment, including how their payoffs are computed,
was checked with a quiz. To ensure that participants understood the rules,

17

the experiment started only after all the participants had answered all thequestions correctly. There are nine groups in Benchmark, QE, and BS treatments and 11 Groups in the QES treatment.6 The sessions lasted between
one and a half to two hours.7 The average payoff was 2456 JPY (≈ 22.99
USD, based on the exchange rate at the time experiments were conducted).

4.1

Price dynamics and mispricing

Did the intervention affect the bond transaction prices? Figures 1 and 2
show the dynamics of mispricing observed in each treatment in Rounds 1
and 2, respectively. The mispricing in period t of round r of group g is
P g,r g,r,n ∗,r ∗,r(Pt −pt )/pt , where Ntg,r is the number
calculated by M Ptg,r = N1g,r Nnt

of transactions in the group for period t of round r, Ptg,r,n is the realized priceof the n-th transaction in period t of round r for the group, and p∗,r
is thet
REE price in period t of round r for the treatment as derived in the previous
section.
Each solid line shows the dynamics of the within period median mispricing
of a group. The dashed lines correspond to the dynamics of the across-groupmedian for the treatments. As mentioned in Section 2.4, because all sessions
adopted the same sequence of predetermined dice numbers, the duration of
6
In the QES treatment, however, the computer failed to purchase the bonds in threegroups in one session because of a programming mistake. Subsequently, the error in the
program was corrected, and we excluded the data of these three groups from the analyses.
Thus, data from 35 groups (nine groups each in the Benchmark, QE, and BS treatments,and eight in the QES treatment) are used for subsequent analysis.
7
We recruited participants for two hours, but our sessions all ended within 1.5 hours,
so as to avoid any possible end game effects.

18

Figure 1: Dynamics of mispricing: Round 1
BS

5

5

4

4

3

3

Mis−pricing

Mis−pricing

B

2
1
0

2
1
0

−1

−1
1

2

3

4

5

6

1

2

3

Period

QE

5

6

4

5

6

QES

500

5

400

4

300

3

Mis−pricing

Mis−pricing

4
Period

200
100

2
1
0

0

−1

−100
1

2

3

4

5

1

6

2

3
PeriodPeriodNote: Each line represents the dynamics of the normalized median prices of a group. The dashedlines represent the dynamics of the median normalized prices for the treatment in the round.

19

Figure 2: Dynamics of mispricing: Round 2
BS

5

5

4

4

3

3

Mis−pricing

Mis−pricing

B

2
1
0

2
1
0

−1

−1
1

2

3

4

5

6

1

2

3

Period

QE

5

6

4

5

6

QES

500

5

400

4

300

3

Mis−pricing

Mis−pricing

4
Period

200
100

2
1
0

0

−1

−100
1

2

3

4

5

1

6

2

3
PeriodPeriodNote: Each line represents the dynamics of the normalized median prices of a group. The dashedlines represent the dynamics of the median normalized prices for the treatment in the round.

20each round is the same. Precisely, each round consists of one block (sixperiods).
Figure 1 reveals that, in Round 1, mispricing tends to be lower in B compared with QE, and in BS compared with QES. In particular, this tendency
is observed not only after the QE operation that takes place in period 4(periods 4–6), but also before it takes place (periods 1–3). Furthermore, themispricing in QES tends to be smaller than that in QE. Between B and BS, apart from the latter half of the round, the mispricings are similar. A similartendency is observed in Round 2, shown in Figure 2, except that mispricing
in BS tends to be smaller than the one in B.
To formally compare the degree of mispricing, we compute the geometricdeviation (GD) introduced by Powell (2016).8 For group g in round r, GDg,ris defined as

g,r

GD

g,r

= exp

N
1 X pg,rng,rN n=1 p∗n

!
− 1,

(20)

where N g,r is the number of transactions that occurred in round r, pg,rn is
the realized price of the n-th transaction, and p∗n are the REE prices in the
period when the n-th transaction occurred. As GD takes the direction ofprice deviation into account, the bonds are overpriced (underpriced) relative
to the REE prices if GD is significantly higher (lower) than 0.
We calculate the GDs for two subperiods separately: periods 1–3 denotethe preintervention periods, and periods 4–6 denote the postintervention periods. Table 2 summarizes the median GDs in Round 1 for each treatment
8

See Appendix A for results based on other measures of mispricing.

21

Table 2: Median GDs in Round 1
Median GDs
B
QE
BS
QES
Preintervention-0.052 0.467 -0.208 0.101
Postintervention
0.166 0.871 -0.054 0.252∗
p-values (H0 : Pre = Post) 0.004 0.074 0.910 0.547
p-values+ for treatment comparisons
Preintervention periods
Postintervention periods
B
QE
QES
B
QE
QES
QE
0.047
QE
0.969
QES 1.000 0.833
QES 0.164 1.000
BS
1.000 0.034 0.047 BS
0.113 0.011 1.000∗: p − values from the Wilcoxon signed-rank (WSR) test are reported.
+: p−values from the pairwise Mann–Whitney U (MW) test are reported. Bonferroni
method is used for correcting p-values for multiple comparisons.

in the preintervention and postintervention periods (top panel), as well as
the p − values of the pairwise treatment comparisons based on the Wilcoxon
rank-sum test (bottom panel). These p-values are corrected for multiple
comparisons using the Bonferroni method.
Table 2 shows that, in Round 1, the preintervention GDs in the QE treatment are significantly higher than those in the B treatment (p = 0.047), andsimilarly, the preintervention GDs in the QES treatment are significantly
higher than in the BS treatment (p = 0.047). Thus, unlike the results reported in Penalver et al. (2020), the announced intervention raises the degreeof mispricing even before the intervention actually takes place, regardless ofthe existence of the saving possibility.
Observation 1 In Round 1, the announcement of the QE intervention re22sults in significantly larger overpricing of the bond in the preintervention
periods than the treatments without the intervention.
Moreover, although the median postintervention periods GDs are higher
in the QE treatments than in the B treatments (and also in the QES treatments than in the BS treatments), as reported in the bottom panel of Table 2,these differences are not statistically significant. Thus, unlike in Penalver
et al. (2020), the intervention does not significantly affect the magnitude ofmispricing in the postintervention periods compared with the cases withoutit.
Observation 2 In Round 1, the intervention does not result in a statisticallysignificant difference in the magnitude of mispricing in the postintervention
periods compared with the cases without the intervention.
A potential reason for the absence of the significant differences in thepostintervention GDs between the B and QE treatments is the significant
increase in the GDs in the postintervention periods compared with the preintervention periods observed in the B treatment. We have, however, no clear
explanation for the upward trend in the mispricing observed in this treatment.
Let us now turn to the outcomes of Round 2. Penalver et al. (2020) reportthat once the effect of intervention is experienced in Round 1, the bond issignificantly overpriced in the preintervention periods in the treatment withpreannounced intervention in Round 2.
23

Table 3: Median GDs in Round 2
Median GDs
B
QE
BSPreintervention
0.275 0.860 -0.056
Postintervention
0.264 0.764 -0.331∗
p-values (H0 : Pre = Post) 0.654 0.027 0.129

QES
0.198
-0.0590.742

p-values+ for treatment comparisons
Preintervention periods
Postintervention periods
B
QE
QES
B
QE
QES
QE
0.969
QE
0.815
QES 1.000 1.000
QES 1.000 0.278
BS
0.047 0.002 0.355 BS
0.011 0.005 1.000∗: p − values from the Wilcoxon signed-rank (WSR) test are reported.
+: p−values from the pairwise Mann–Whitney U (MW) test are reported. Bonferroni
method is used for correcting p-values for multiple comparisons.

Table 3 shows the median preintervention and postintervention GDs in
Round 2 for each treatment. While the median preintervention GD is higher
in the QE than in the B treatments (higher in the QES than in the BS
treatments) as shown in the top panel, there is no statistically significant
difference in preintervention or postintervention GDs between the QE andthe B treatments (and in the QES treatments and BS treatments) in Round 2.
Thus, unlike the results of Penalver et al. (2020), the effect of intervention
does not persist when participants repeat the same experiment with the samegroup of participants under the same market conditions.
It should be noted that preintervention GDs are significantly higher inRound 2 than in Round 1 for the QE treatment (p = 0.008, WSR test)
suggesting that having experienced the high preintervention and postinter24

vention market prices in Round 1, participants in the QE treatment traded
the bond at an even higher price before the intervention in Round 2 than inRound 1. However, as noted above, probably because of the upward pricetrend observed in the B treatment in Round 1, its preintervention GDs in
Round 2 are also significantly higher than those in Round 1 (p = 0.004,
WSR test).9 In Penalver et al. (2020), and many other experiments using
the Smith et al. (1988) paradigm, the magnitude of mispricing decreases as
participants gain experience in the baseline treatment. We believe that theincrease in the magnitude of mispricing in Round 2 under the B treatment is
the reason for the absence of the persistence of the QE intervention when thesaving possibility is absent. We also note that in Round 2, the GDs are significantly higher in the B treatment than in the BS treatments both for the
preintervention (p = 0.047, using the MW test) and for the postintervention(p = 0.011, using the MW test). Thus, in the absence of the intervention, thesaving possibility significantly reduces the magnitude of overpricing amongexperienced participants.
9

There is no statistically significant difference in the preintervention GDs between
Round 1 and Round 2 for the BS and QES treatments (p = 0.496 in the BS treatment andp = 0.641 in the QES treatment, using the WSR test). Furthermore, there is no significant
difference (at the 5% significance level) in the postintervention GDs between Rounds 1
and 2 for all the treatments (p-values are 1.000, 0.652, 0.098, 0.461, for the B, QE, BS,
and QES treatments, respectively, using the WSR test.

25

4.2

Welfare and Consumption Smoothing

Note that in our experiment, the participants’ expected payoffs do not directly depend on the transaction price in each period but on their consumption paths across several periods. Here, we first compare participants’ payoffs,
measured by the discounted sum of the payoffs over six periods (for particiPpant i, it is defined as wi = 6t=1 π t−1 uJP Y (cit )), across four treatments. We
use the within-group mean of wi , W g , as an independent observation.
Table 4 shows the across-group median W g in the four treatments in two
rounds. In Round 1, the W g s are similar between the B and QE treatments(p = 1.000), but the W g in the BS treatment is significantly smaller thanthat in the QES treatment (p = 0.047). Thus, despite the significantly
larger preintervention mispricing observed in the QE and QES treatments
than in the B and BS treatments, the discounted payoffs are not significantly
worsened. On the contrary, in the presence of a saving possibility, the QE
intervention increased welfare.
Table 4 also shows that the W g s are significantly higher in Round 2 thanin Round 1 at the 5% significance level in all the treatments except for QES.
In Round 2, there is no longer significant differences in the W g s between the
B and QE treatments or between the BS and QES treatments.10
As noted above, the QE intervention may lower the payoffs in the preintervention period because of increased mispricing, while improving the postin10

In Appendix C, we separately analyze discounted payoffs for two types. The results
show that significant treatment differences in the discounted payoffs are mainly becauseof the variation in the discounted payoff of Type 2 players.

26

Table 4: Median W g
Median W g
B
QE
Round 15723.84 5747.33
Round 25851.31 5892.67∗
p-values (H0 : R1 = R2) 0.008
0.004

BS
5507.735838.080.004

QES
5798.265955.09
0.055

p-values+ for treatment comparisons
Round 1Round 2
B
QE
QES
B
QE
QES
QE
1.000
QE
1.000
QES 0.560 1.000
QES 1.000 1.000
BS
0.240 0.302 0.047 BS
1.000 1.000 0.560∗: p − values from the Wilcoxon signed-rank (WSR) test are reported.
+: p − values from the pairwise Mann–Whitney U (MW) test are reported. Bonferroni
method is used for correcting p-values for multiple comparisons.

tervention payoffs because of the increased consumption induced by the cash
injection. However, as reported in Appendix D, the anticipated intervention
does not result in a significant change in (nondiscounted) mean payoffs either
in the preintervention or postintervention periods at the 5% significance levelin both rounds.
Observation 3 The intervention does not significantly affect welfare, measured by the discounted payoffs, in Round 2. It improves it in the presenceof a saving possibility in Round 1.
We also compute the Gini coefficient of wi for each group to investigate
whether the QE intervention has increased the within-group inequality inthe discounted payoffs. Table 5 shows the median Gini coefficient in each27

Table 5: Median Gini coefficient based on wiMedian Gini coefficientB
QE
Round 10.030 0.044
Round 20.030 0.041∗
p-values (H0 : R1 = R2) 0.91 0.652

BS
0.068
0.032
0.012

QES
0.050
0.037
0.641

p-values+ for treatment comparisons
Round 1Round 2
B
QE
QES
B
QE
QES
QE
1.000
QE
0.970
QES 1.000 1.000
QES 1.000 1.000
BS
0.110 0.460 1.000 BS
1.000 0.970 1.000∗: p − values from the Wilcoxon signed-rank (WSR) test are reported.
+: p − values from the pairwise Mann–Whitney U (MW) test are reported.
Bonferroni method is used for correcting p-values for multiple comparisons.

treatment. While there is no statistically significant difference between any
relevant pairs of treatments, we do observe significant reduction in the Gini
coefficient in Round 2 compared with Round 1 for the BS treatment. This reduction in the Gini coefficient in the BS treatment is because of the improvement in the discounted payoff of Type 2 participants in Round 2 comparedwith Round 1.11
Observation 4 The intervention does not significantly affect within-groupinequality in terms of their discounted payoffs.
11

In fact, while the mean discounted payoffs are significantly higher in Round 2 than inRound 1 for Type 2 players at the 5% significance level in all the treatments except for
QES, there is no significant increase for Type 1 participants. See Appendix C.

28

Table 6: Comparison of the mispricing during the intervention between QE
and QES treatments and rounds.
QE
QES
p − values∗∗∗∗∗∗
Round 1 1.032
0.850
0.726
∗∗∗
∗∗
0.525
Round 2 1.309
0.300
p − values
0.195
0.125
Note: The median mispricing of each treatment in eachround is reported. The fourth column reports the p-values ofthe differences between treatments from the Wilcoxon ranksum test. The third row reports the p-values of the differences between rounds within treatments from the Wilcoxon
signed-rank test.
**, and *** indicate a significant difference from 0 at the
5 and 1% significance levels using the Wilcoxon rank-sum
test.

4.3

Prices in the intervention

As reported in Section 4.1, the magnitudes of the overpricing in the preintervention periods are significantly larger in the presence of intervention thanin the absence of it. This indicates that participants expect the bonds tobe sold at high prices during the intervention, pushing the preintervention
prices up. Are their expectations fulfilled? Here we investigate the following
two questions: (1) do participants sell the bonds to the computer at a pricehigher than the competitive equilibrium intervention price? and (2) is theintervention price influenced by the possibility of saving?
Table 6 compares the magnitude of mispricing during the intervention
between the QE and QES treatments for Rounds 1 and 2. The mispricing
g,rg,ris computed as (Pinter− p∗inter )/p∗inter , where Pinteris the realized computerpurchasing price for group g in round r, and p∗inter is the competitive equilibrium price derived in Section 3. The fourth column reports the p-values

29

of differences between treatments. The last row reports the p-values of differences between rounds within treatments. The “*”s indicate whether themispricing is significantly different from zero.
As shown in Table 6, the mispricings during the intervention are significantly higher than 0 in both treatments in both rounds. This result is
consistent with Penalver et al. (2020), who suggest that the price competition
was not strong enough during the QE operation.12
Observation 5 The intervention prices are significantly higher than thecompetitive equilibrium intervention prices, regardless of the existence of thesaving possibility.
Besides, Table 6 (the fourth column) shows that the magnitude of mispricing is not significantly different between the QE and QES treatments.
Thus, the saving condition does not influence the intervention prices. Table 6 (the last row) also fails to show any statistically significant difference
between Round 1 and Round 2 within both treatments, implying participants observing a high intervention in Round 1 do not promote the pricecompetition during the QE operation of Round 2.
12

In Bertrand price competition experiments, for example, conducted by Dufwenberg
and Gneezy (2000) and Baye and Morgan (2004), overpricing is also observed. Dufwenberg
and Gneezy (2000) explain the reason for participants not competing aggressively enougha la the level-k model.

30

5Conclusions

In this study, we conducted an experiment to examine the effect of market
intervention in the presence of a consumption smoothing motive to trade.
Existing experimental studies that investigate the impact of market intervention (Haruvy et al., 2014; Penalver et al., 2020) have employed variantsof the Smith et al. (1988) paradigm where no trade is expected under the risk
neutral rational expectations equilibrium, and a large mispricing has been observed. By employing the new experimental framework based on the Lucas
asset pricing model (Lucas Jr, 1978) proposed by Asparouhova et al. (2016)
and Crockett et al. (2019), we reexamine the effect of market intervention in
the framework where assets are traded to smooth consumption. In this newframework, Crockett et al. (2019) report that the magnitude of mispricing
tends to be smaller than those observed in the Smith et al. (1988) framework.
To the best of our knowledge, this study is the first study that examines theeffect of market intervention under the Lucas asset pricing model in a lab.
Furthermore, we investigated the effect of the market intervention under
the two conditions: with and without the possibility to save. Under a nosaving condition, participants need to smooth consumption only by trading
assets, and under the saving condition, they can smooth consumption not
only by trading assets, but also by saving.
The results show a significant effect of market intervention on overpricingof the asset before the intervention actually occurs, regardless of the existence31

of the saving possibility. This result is consistent with that of Penalver
et al. (2020). However, contrary to Penalver et al. (2020), which showsthe effect becomes larger as participants repeat the same experiment, in
our experiment, the effect of market intervention on the mispricing becomes
statistically insignificant in the second round. Surprisingly to us, despite
the significant effect on the overpricing of the asset, the intervention did not
significantly worsen participants’ payoffs. On the contrary, in the presenceof a saving possibility, it improved it, although the effect was observed onlyin the first round.
Future research may investigate how the market intervention on one asset may influence the pricing of other assets in a multiple assets market
experiment by extending the multi-asset markets. Because experiments withmultiple assets markets are still scarce, and they mainly use the Smith et al.
(1988) framework (Charness and Neugebauer, 2019; Duffy et al., 2021) except for Asparouhova et al. (2016), we believe such an exercise will be very
fruitful.

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36

A

Other measures of market outcomes
Table A.1: Definitions of the measures of mispricing.
PN pn −p∗t1
| p∗ |
Relative absolute deviation (RAD)NnPn=1
pn −p∗tN
1
Relative deviation (RD)n=1 p∗nN
P
pn
Geometric absolute deviation (GAD) exp( N1 N
n=1 |ln p∗n |) − 1Ppn
Geometric deviation (GD)
exp( N1 N
n=1 ln p∗ ) − 1nThis section presents the comparison results for mispricing among treatments based on the four measures proposed in the literature, including thegeometric deviation (GD) reported in the main text. Table A.1 summarizes
the definition of each measure. Table A.2 displays the median value of eachmeasure of each treatment in each round and reports the results of comparisons between the treatments with and without intervention.
In Table A.2, consistent with the GD results, in the preintervention periods of the first round, we observe that there also exists a significant difference
in relative deviation (RD) between the B and QE treatments (p − value =0.047) and between the BS and QES treatments (p−value = 0.047) at the 5%
significance level. However, we do not find any difference in relative absolutedeviation (RAD) or geometric absolute deviation (GAD) in the preintervention periods. The reason is that RAD and GAD do not differentiate between
overpricing and underpricing. While in the preintervention periods, bonds
tend to be underpriced in the B and BS treatments, and they tend to be
overpriced in the QE and QES treatments.

37

38

a: p-values are based on the pairwise Wilcoxon rank-sum test among the four treatments

Table A.2: Median value (standard deviation) of each measure of bond mispricing.
B
QE
BS
QES
B = QEa BS = QESa
Round 1, Periods 1 - 3. Preintervention periods
RAD 0.180 (0.154) 0.661 (0.523) 0.261 (0.177) 0.197 (0.316) p = 0.460
p = 1.000
RD -0.046 (0.259) 0.581 (0.523) -0.200 (0.323) 0.117 (0.354) p = 0.047 p = 0.033
GAD 0.221 (0.237) 0.620 (0.450) 0.388 (0.319) 0.201 (0.303) p = 0.680
p = 1.000
GD -0.524 (0.243) 0.487 (0.517) -0.208 (0.318) 0.101 (0.328) p =0.047
p = 0.033
Round 1, Periods 4 - 6. Postintervention periods
RAD 0.233 (1.061) 0.957 (1.918) 0.164 (0.133) 0.479 (0.432) p = 0.680
p = 0.450
RD 0.170 (1.096) 0.950 (1.934) -0.050 (0.232) 0.285 (0.565) p = 0.681
p = 0.684
GAD 0.237 (0.105) 0.940 (1.453) 0.232 (0.243) 0.591 (0.494) p = 0.820
p = 0.680
GD 0.166 (1.091) 0.940 (1.476) -0.054 (0.233) 0.252 (0.596) p = 0.681
p = 0.684
Round 2, Periods 1 - 3. Preintervention periods
RAD 0.277 (1.024) 0.942 (0.867) 0.200 (0.138) 0.251 (0.420) p = 1.000
p = 1.000
RD 0.276 (1.041) 0.936 (0.867) -0.053 (0.227) 0.200 (0.482) p = 0.969
p = 0.355
GAD 0.276 (1.102) 0.891 (0.842) 0.248 (0.213) 0.272 (0.415) p = 1.000
p = 1.000
GD 0.276 (1.036) 0.884 (0.843) -0.055 (0.227) 0.198 (0.483) p = 0.969
p = 0.355
Round 2, Periods 4 - 6. Postintervention periods
RAD 0.333 (0.643) 0.643 (0.684) 0.392 (0.255) 0.507 (2.173) p = 0.560
p = 1.000
RD 0.268 (0.648) 0.846 (0.701) -0.240 (0.358) -0.048 (2.34)
p = 0.564
p = 1.000
GAD 0.331 (0.602) 0.839 (0.595) 0.686 (1.260) 0.702 (1.291) p = 0.970
p = 1.000
GD 0.264 (0.607) 0.836 (0.607) -0.331(0.376) -0.595 (1.314) p = 0.564
p = 1.000

B

Analysis of trading volume

Besides the mispricing and consumption, we also consider whether the intervention influences the trading volume. Intuitively, in the QES treatment,
if some participants expect to sell bonds to the computer at a high market
price during the QE operation, they have an incentive to trade the bonds.
That means the trading volume in the QES treatment may be higher thanthat in the BS treatment in the preintervention periods. Moreover, because
participants have to smooth consumption by trading in the B and QE treatments, the trading volume in the B and QE treatments should be higher
than in the BS treatment.
Table B.1 reports the median of Turnover in each treatment (top panel),as well as the results of comparisons among treatments (bottom panel).
Turnover of group g, T Og , is defined asT

1 X qt,TO =T t=1 OStg

(B.1)

where qt is the trading volume at period t, and OSt is the total number of
the tradable bonds at period t.
Contrary to our intuition, from Table B.1, we observe that while Turnoveris higher in the B treatment than in the QE treatment, and also in the
B treatment than in the BS treatment, there is no statistically significant
difference in Turnover among treatments either in the preintervation or the
postintervention periods. This may be because the intervention may not only
39

Table B.1: Median of Turnover in each treatment.

Round 1Round 2Round 1Round 2Preintervention periods
B
QE BS QES
0.50 0.40 0.40 0.38
0.43 0.38 0.37 0.30

Postintervention periods
B
QE BS QES
0.42 0.40 0.38 0.35
0.40 0.48 0.33 0.48

The comparison across treatments.
Preintervention periods
Postintervention periods
Benchmark QE
QES
Benchmark QE
QES
QE
1.000
QE
1.000
QES
0.600
1.000
QES
1.000
1.000
BS
0.250
1.000 1.000 BS
1.000
1.000 1.000
Benchmark QE
QES
Benchmark QE
QES
QE
1.000
QE
0.790
QES
0.540
1.000
QES
0.550
1.000
BS
1.000
1.000 1.000 BS
1.000
0.150 0.180

Note: The p − values from the pairwise Wilcoxon rank-sum test are reported. Bonferroni method is used forcorrecting p-values for multiple comparisons.

increase the participants’ incentive to buy the bonds, but also increase the
incentive to keep bonds, and neutralizes the variation of the trading volume.

40

C

Discounted payoffs of each typeIn this appendix, we compare discounted payoff, wi =

P6

t=1 π

t−1

uJP Y (cit )),for two types (1 and 2) separately across four treatments. Here, we use thegifor each type (∈ {1, 2}) , Wtype, as an independent
within-group mean of wtype

observation.
Table C.1 shows the across-group medians W1g and W2g in the four treatments for Rounds 1 and 2. It shows that W1g and W2g are significantly
different in the BS treatment in both rounds. Furthermore, it shows thatsignificant differences across treatments are observed only for Type 2 in bothrounds. Finally, a statistically significant increase in W2g in Round 2 compared with Round 1 is observed at the 5% significance level in all the treatments except for QES, but not for W1g .
The significantly lower discounted payoffs for Type 2 compared withType 1 in the BS treatment is because of the difficulty Type 2 players hadin trading the bond. When saving is possible, Type 1 players whose initialendowment consists of a larger amount and one unit of bonds can easily
carry their surplus cash to the next period without purchasing the bondfrom Type 2 players who are initially endowed with four units of bonds anda smaller amount of cash. As a result, Type 2 players’ consumption becomes
lower in early periods, and thus the lower discounted payoffs.

41

Table C.1: Median W1g and W2g
The median W1g and W2g in Round 1
B
QE
BS
Type 1 (W1g )5621.52 5769.30 5764.60
5714.31 5672.95 5240.40
Type 2 (W1g )gg∗p-values (H0 : W1 = W2 ) 0.359
0.496
0.039

QES
5960.915949.800.945

p-values+ for treatment comparisons for Round 1
Type 1
Type 2
B
QE
QES
B
QE
QES
QE
1.000
QE
1.000
QES 1.000 1.000
QES 1.000 0.684
0.034 0.024 0.033
BS
1.000 1.000 1.000 BSMedian W1g and W2g in Round 2
B
QE
BS
QES
g
Type 1(W1 )5647.32 5775.84 5931.09 6112.07
Type 2 (W2g )6054.54 6017.76 5671.41 6123.77
gg∗
p-values (H0 : W1 = W2 ) 0.039
0.129
0.008
0.945
p-values+ for treatment comparisons for Round 2
Type 1
Type 2
B
QE
QES
B
QE
QES
QE
1.000
QE
1.000
QES 1.000 1.000
QES 0.091 0.556
BS
0.189 0.969 1.000 BS
0.001 0.085 0.047
p-values∗ comparing Round 1 and Round 2
B
QE
BS
QES
Type 1(R1=R2) 0.820 0.734 0.074 0.148
Type 2 (R1=R2) 0.020 0.004 0.004 0.055∗: p − values from the Wilcoxon signed-rank (WSR) test are reported.
+: p − values from the pairwise Mann–Whitney U (MW) test are reported. Bonferroni
method is used for correcting p-values for multiple comparisons.

42

D

Nondiscounted payoffs in preintervention
and postintervention periods

In this section, we investigate further the effect of intervention by separately
considering the preintervention and postintervention periods. Namely, wei=compare nondiscounted payoffs in preintervention (periods 1 to 3), w
epre
P3
P
1
i
i
epost
= 31 6t=4 uJP Y (cit ).
t=1 uJP Y (ct ), and in postintervention (periods 4 to 6), w
3

We use within-group means as an independent observation. Table D.1 reports
these measures for the four treatments in Rounds 1 and 2.
iare not significantly different
Table D.1 shows that in Round 1, w
epre

(at the 5% significance level) between the B and QE treatments, as well asibetween the BS and QES treatments. The same is true for w
epost. Thus,the QE intervention does not significantly affect the nondiscounted payoffs.
ii
isis significantly lower (p = 0.034), while w
epost
Table D.1 also shows that w
epre

significantly higher (p = 0.003), in the BS treatment than in the B treatment.
Thus, the saving possibility, in the absence of QE intervention, results insignificantly lower payoffs in periods 1 to 3, while it results in significantly
higher payoffs in periods 4 to 6. In Round 2, contrary to Round 1, the saving
possibility does not significantly lower the payoffs in periods 1 to 3. However,it results in significantly higher payoffs in the postintervention periods both in
the presence (QE vs QES, p = 0.022) and in the absence (B vs BS, p < 0.001)
of the intervention.

43

i
i
and w
epost
Table D.1: Median w
epre
iiMedian w
epre
and w
epost
in Round 1
B
QE
BS
QES
i
Preintervention (wepre )
4358.03 4202.81 3880.03 4168.39
i4277.37 4326.12 4719.25 4869.29
Postintervention (wepost )
p-values∗ (H0 : Pre = Post) 0.734
0.004
0.004
0.008

p-values+ for treatment comparisons in Round 1
Preintervention periods
Postintervention periods
B
QE
QES
B
QE
QES
QE
0.462
QE
0.969
QES 0.022 0.216
QES 0.556 1.000
0.003 0.113 1.000
BS
0.034 0.011 0.091 BSiiMedian w
epre
and w
epost
in Round 2
B
QE
BS
QES
i
Preintervention (wepre)
4413.53 4402.24 4278.49 4347.75
i)4371.66 4461.73 4733.41 4756.16
Postintervention (wepost∗
p-values (H0 : Pre = Post) 0.652
0.359
0.004
0.008

p-values+ for treatment comparisons in Round 2
Preintervention periods
Postintervention periods
B
QE
QES
B
QE
QES
QE
1.000
QE
1.000
QES 1.000 1.000
QES <0.001 0.022
BS
0.064 0.302 1.000 BS<0.001 0.024 1.000∗: p − values from the Wilcoxon signed-rank (WSR) test are reported.
+: p−values from the pairwise Mann–Whitney U (MW) test are reported. Bonferroni method
is used for correcting p-values for multiple comparisons.

44Online Supplementary Material

An English translation of the instructions
An English translation of the instruction for QES treatment is provided. The
upper half of the page (in box) shows the slide, and the bottom half of the page
show the transcript of the audio content.
l

The instruction of B treatment consists of materials from Section I to IV.

l

The instruction for QE treatment consists of materials for B and that in
Section VI.

l

The instruction of BS treatment consists of materials for B and that issection V.

Asset market experiment.
Instruction.
This experiment is an experiment on decision-making regarding market
transactions.
The amount of earnings you receive is determined by the decisions of you
and other participants and random factors.
Please make sure you understand the description of this experiment andmake appropriate decisions.
The payment for this experiment will be paid in cash after the end of thisexperiment.

1Section 1. The flow of the entire experiment.
In this experiment, you trade virtual bonds with seven other participants ina group using the experimental currency, franc.
The members of the other seven participants are the same until the end of
the experiment.
In the following, we call one experiment one round.
One round consists of multiple periods.
In today's experiment, the length of one round is randomly determined, as
will be explained later.
Therefore, it is unclear at this time how many rounds will be during theexperiment time.

2

At the beginning of each round, you will receive as many francs and bonds
as listed on the "Private Information Sheet" shown on the screen.
Further, during each round, at the beginning of each period, besides the
francs listed on the "Personal Information Sheet," you will additionallyreceive a dividend of 2 francs for each bond you hold.

3

In each period, you can use the franc to buy bonds from seven otherparticipants or sell your bonds to other participants in the same group.
More details on how to trade the bond will be introduced later in Section
four.

4

At the end of each period, your franc balance will be converted to Japanese
Yen based on your "Payoff Table."
The final payment you will receive will be determined by this Japanese Yen
earnings.
If the round continues, the bonds you own will be carried over to the next
period; otherwise, they will vanish and lose their value.
The detail of the conditions for continuing the round will be introduced
later.
So the benefit you hold a bond is:Firstly,
to get the dividend at the beginning of each period; andSecondly,to earn the franc by selling bonds until the round end.

5

Please look at the "Payoff Table" on the screen.
This table shows the exchange rate of francs on hand to the Japanese yenat the end of each period.
Further, the "Payoff Chart" illustrates this relationship graphically.
Please note the following three points.
Firstly,Your Japanese Yen earning will be negative if you hold very few francs atthe end of each period.
Secondly,
The more francs you have at the end of each period, the more Japanese
Yen you will earn.
Thirdly,
However, the more francs you have, the less Japanese Yen you can earn
for each additional franc.
For example, the Japanese Yen payoff difference between 56 and 57 francs
is larger than the difference between 93 and 94 francs.
Attention.

6

The bond dividend is equal for all participants, paying 2 francs per unit foreach period.

6Now, please answer the comprehension quizzes to see if you understandthe contents so far correctly.
If you have any questions, please ask the experimenter.

7Section 2. "Round", "Block", and "Period."
As mentioned earlier, today's experiment consists of one or more rounds.
Each round consists of one or more blocks, with six periods as one block.
The time of trading for each period is 2 minutes.

8

The length of each round is determined as follows:
At the end of each period, the computer rolls dice of six sides.
After the end of one block, that is, the six periods, the number of eyes on
the dice for each period in that block will be displayed on the computer
screen as follows.
If the numbers of eyes on the dice for all periods are between 1 and 5, theround continues, and a new block begins.
However, if at least one period's number of eyes on the dice is 6, the roundends when the block ends.
That is, the number of eyes on the dice for each period is displayed at theend of each block, and the round continues unless there exists a period
whose number of eyes is six.

9At each block, the Japanese Yen payoffs you earned in the first period
whose number of eyes on the dice is six will be the Japanese Yen payoff
you earned in this round.
Please note that payoffs of other periods are not eligible for final payment.
At the end of each block, the screen will show you the amount of Japanese
Yen payoffs for each period, as well as the number of eyes period of thatblock and whether the round will continue.

10

At the end of the round, if less than 35 minutes have passed since the startof the first round, a new round will begin.
Otherwise, if the elapsed time from the start of the first round exceeds 35
minutes, the experiment will end.

11

Section 3. the final payment in this experiment.
After the experiment ends, the computer will randomly choose one round.
The final payment you receive will be of an amount of the sum of theJapanese Yen payoff you earned in that round plus the experimental
participation fee of 1000 Yen. (rounded up to in units of 10 Yen)
Please note that if the earned Japanese Yen payoff is negative, it will be
deducted from the participation fee.

12Now, please answer the comprehension quizzes to see if you understandthe contents so far correctly.
If you have any questions, please ask the experimenter.

13Section 4. bond transactions.
During each two-minute trading period, you can buy or sell bonds.
Transactions are made on the screen shown below.
The upper left side shows the current round and period number, and theupper right shows the remaining trading time.
Further, the box just below shows your franc balance and your bonds.
You can only buy and sell one bond at a time, but you can repeat thetransaction as many times as you like before you run out of time.

14

If you buy a bond: type in the amount of francs you are willing to pay for aunit of the bond in the “Buying price" box on the left. Then click on the“Post Buying Price" button on the bottom left.
The computer will tell you if you don't have enough francs to place a buy
order.
All submitted Buying orders are displayed in the "Buying Pricing" box in thecenter-left of the computer screen.
In the "Buying Pricing" box, the buying prices are displayed in descendingorder.
If your buying price is greater than or equal to the current lowest selling
price, that is, the lowest selling price displayed in the "Selling Price" box in
the center-right, the transaction is completed.
In this case, the transaction price is the lowest selling price displayed on
the screen.

15

If you sell a bond: type in the amount of francs you are willing to accept fora unit of the bond in the “Selling price" box on the right.
Then click on the “Post Selling Price" button on the bottom right.
If you do not hold the bonds, you will not be able to place a sell order.
All submitted Selling orders are displayed in the "Selling Pricing" box in thecenter-right of the computer screen.
In the "Selling Pricing" box, the Selling prices are displayed in ascendingorder.
If your selling price is lower than or equal to the current highest buying
price, that is, the highest buying price displayed in the "Buying Price" boxin the center-left, the transaction is completed.
In this case, the transaction price is the highest buying price displayed on
the screen.

16

The entire history of transaction prices will remain in the middle column forthe duration of each trading period.
As long as trading remains open, you can post new buy and sell prices andagree to make transactions following the same rules given above.

17

At the end of each period, the screen will show the following information.
The left half of the screen shows your buying and selling price in thesituation of the transaction complete and the average transaction price inthe current period.
On the right half of the screen, the upper side half shows the total numberof bonds traded and the average transaction price of the entire groupduring the current period. The lower right half shows your franc balance,
the number of bonds held, and your Japanese Yen payoff for the current
period.

18

When you trade the bond, please note the following two points.
First.
If there are multiple orders at the outstanding lowest selling price or theoutstanding highest buying price, the computer will execute thetransaction based on the earliest submitted order among them.
Second.
You cannot trade with yourself.
For example, when both the highest outstanding buying price and thelowest outstanding selling prices are submitted by you, no transaction will
occur even if the selling price you submitted is higher than the outstanding
highest buying price.

19

Now, let's practice trading by actually using the software. If you have any
questions, please ask the experimenter.

20Section 5. savings.
After the transaction of each period, just before the transaction result
screen appears, you can decide how much experimental currency (francs)
you want to save in the balance of francs on the following screen.
The saved francs will not be converted into Japanese yen payoff andbrought into the next period.
In addition, at the beginning of the next period, you will earn 20% interest
on your savings.
Note that the interest is truncated after the decimal point.
For example, if you save 9 francs, the interest from the 20% rate is 1.8
francs, but the actual interest you get is 1 franc.
If you save 10 francs, the actual interest you will get will be 2 francs.

21

At the beginning of the second period in each round, your available francs
consist of income (according to the “Private Information Sheet”), thedividend (from the bonds), the saving (from the immediately preceding
period), and the interest.
However, Please note that your savings will vanish at the end of the round,so you cannot take them over when a new round begins.
If you do not save, please put "0" in the "Saving" box.

22

Now, please answer the comprehension quizzes to see if you understandthe contents so far correctly.
If you have any questions, please ask the experimenter.

23Section 6. purchase of bonds by the computer.
In this experiment, the computer will purchase the bonds from participantsat the beginning of period four of each round before trading among
participants.
Specifically, the computer will solicit sell orders from participants, thenbuys from these sell orders in ascending order of price.
The upper limit of the bonds that the computer can purchase is six units.
Then the experiment will continue to start trading among participants even
if the upper limit is not reached.

24

The screen of the computer purchase is as follows.
If you wish to sell bonds to the computer, please enter the price you arewilling to accept and the desired number of units to sell, respectively.
If you do not wish to sell, please enter 0 in the "Sell Price" and "Number ofbonds you want to sell" boxes.

25After all the participants post the order, the computer will buy up to 6-unit
bonds in ascending price order at the same transaction price.
The transaction price is the highest selling price within the orderspurchased by the computer.
When there are multiple orders with the same selling price that satisfy theconditions of the purchase, and the total number of the bonds of theseorders exceeds the 6-unit purchase limit, the computer will randomly
select from these orders and buy.

26

To get an accurate picture of the rules for purchasing bonds by computer,please look at the example shown in the table on this screen.
Let's assume that participant A sells 2-unit bonds at a price of a francs,
and Participants B and C sell 3-unit bonds at b francs.
In this case, the minimum selling price is a francs, so the computer firstly
buys two units from participant A.
Then, since the computer maximum buys up to 6 bonds from participants,
the computer will buy three units from a participant randomly chosen
between Participant B and Participant C and finally buy the last one unit
from the remaining one participant.
Since the highest selling price for the purchased bonds is b francs, thecomputer will buy all the bonds at b francs.

27

Now, please answer the comprehension quizzes to see if you understandthe contents so far correctly.
If you have any questions, please ask the experimenter.

28