ISSN (Print) 0473-453X

Discussion Paper No. 1094

ISSN (Online) 2435-0982

A QUANTITATIVE EASING EXPERIMENT
Adrian Penalver
Nobuyuki Hanaki
Eizo Akiyama
Yukihiko Funaki

July 2020

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, JapanA quantitative easing experiment⇤
Adrian Penalver†Nobuyuki Hanaki‡

Eizo Akiyama§

Yukihiko Funaki¶

Ryuichiro Ishikawak
July 4, 2020

Abstract
We experimentally investigate the e↵ect of a central bank buying bonds for cash in a quantitative easing (QE) operation. In our experiment, the bonds are perfect substitutes for cash
and have a constant fundamental value which is not a↵ected by QE in the rational expectations
equilibrium. We find that QE raises bond prices above those in the benchmark treatment without QE. Subjects in the benchmark treatment learned to trade the bonds at their fundamental
value but those in treatments with QE became more convinced after repeated exposure to thesame treatment that QE boosts bond prices. This suggests the possibility of a behaviouralchannel for the observed e↵ects of actual QE operations on bond yields.
Keywords: Quantitative easing, experimental asset market, expectation dynamics
JEL Code: C90, D84⇤ This is a major revision, with a completely new set of experiments, of our Banque de France Working Paper
No. 651 with the same title. This research was partly funded by JSPS Kakenhi (18K19954), ANR ORA-Plus project“BEAM” (Behavioral and Experimental Analyses in Macro-Finance, ANR-15-ORAR-0004) as well as by the Frenchgovernment-managed l’Agence Nationale de la Recherche under Investissements d’Avenir U CAJEDI (ANR-15-IDEX01). In particular, we thank the UCAinACTION project. We thank International Joint Research Promotion Program(Osaka University) for their support. This paper has benefited from comments received from the associate editor
and reviewers, at the SEF conference in Nice, the CEF conference in New York, the Stony Brook Experimental
Macroeconomics workshop, ASSA 2019 in Atlanta, seminars at the Banque de France, UC Irvine, Cambridge, Paris
School of Economics, and CREST. We are particularly grateful for the discussion comments of Marianne Andries,
John Du↵y, Cars Hommes, and Paul-Pezanis Christou. We also thank Dr Amanda Fitzgibbons of Edanz Group(www.edanzediting.com/ac) for editing a draft of this manuscript. The experiments reported in this paper have been
approved by the Ethics Review Committee on Research with Human Subjects of Waseda University (No. 2015-334).
The views in this paper reflect those of the authors and do not necessarily reflect the views of the Banque de France
or the Eurosystem.
† Banque de France. E-mail: Adrian.PENALVER@banque-france.fr‡ Institute of Social and Economic Research, Osaka University. E-mail: nobuyuki.hanaki@iser.osaka-u.ac.jp§ Faculty of Engineering, Information and Systems, University of Tsukuba. E-mail: eizo@sk.tsukuba.ac.jp¶ School of Political Science and Economics, Waseda University. E-mail: funaki@waseda.jp
k School of International Liberal Studies, Waseda University. E-mail: r.ishikawa@waseda.jp

1

1

Introduction

Quantitative easing (QE) has been the biggest monetary policy experiment in modern times. Since
2008, seven major central banks have implemented large scale asset purchase programmes, buying
on average assets equivalent to 15% of nominal GDP.1 They have bought a wide range of assets,
including corporate and covered bonds, and asset-backed securities but the core interventions havebeen in government bond markets. In this most basic form, QE is the purchase of government bonds
in exchange for central bank reserves with the intention to retain them for a significant length oftime. In an era in which interest is paid on reserves, this amounts to the exchange of one interestbearing liability of the state for another. In textbook models with frictionless and complete markets
and fully rational and infinitely long-living agents, such a transaction can have no temporary or
permanent e↵ects on any macroeconomic variables in equilibrium (Eggertsson and Woodford, 2003).
In particular, short-term and long-term interest rates will be unchanged and there will be no e↵ect
on output and inflation.2 In these circumstances, QE would just be an irrelevant shortening of theaverage maturity of net public debt.
There is, however, strong evidence that QE programmes have moved bond prices and yields,
although the scale and duration of such e↵ects is still being debated (Krishnamurthy and VissingJorgensen, 2011; Joyce et al., 2011; Arrata and Nguyen, 2017). The literature has focused on two
departures from the textbook model to explain these e↵ects. One theory is that central bankmoney and government bonds are not perfect substitutes (Tobin, 1958) perhaps because markets
are segmented due to investors’ ‘preferred habitat’ (Vayanos and Vila, 2009) or investors do not likeholding the interest rate risk associated with long-term bonds. If long-term government bonds andcentral bank reserves are not perfect substitutes, a fall in the volume of long-term bonds in private
hands can raise the price and lower the yield relative to short-term rates. The alternative explanationis that QE is a means by which central banks can give credibility to forward guidance commitments todeviate from established monetary policy behaviour, such as a Taylor rule (Eggertsson and Woodford,2003). QE reinforces the signal that the short-term rate will remain low for longer than a timeconsistent policy rule would suggest. Lowering the expected path of short-term rates drags down
long-term rates through the expectations hypothesis of the term structure.
1 See the report by the Committee on the Global Financial System (2019). These programmes have been extended
in many countries in response to the crisis following the outbreak of covid-19.
2 Older irrelevance propositions for open market operations were described in Wallace (1981) and Sargent andSmith (1987).

2

This paper considers a di↵erent explanation. As currently implemented, QE is a commitment tobuy relatively quickly a fixed value of bonds at any price. Indeed, since the intermediate objectiveof the policy is to lower bond yields, the greater the rise in bond prices is, the more “successful”
the instrument is. The central bank is thus an unusual participant in the bond market because it
is not deterred from buying by a higher market price (at least up to some point). If there were
only one seller of government bonds, he or she could o↵er to sell at a price at which the central
bank was just indi↵erent between buying and reneging on its commitment at a reputational cost.
In other words, once the central bank has committed to buy, there is an exploitable opportunity forsellers collectively. However, in a completely competitive market with fully rational agents, common
knowledge, no segmentation and no ability to buy enough of the market to become a monopoly
seller, such an e↵ect should not exist.
But what if some of these assumptions do not hold, for example, when people’s behaviors are
better characterized by a level-k type thinking (Nagel, 1995; Camerer et al., 2004)? In such framework, if k = 0 players have an instinctive response that the presence of a large external buyer
should raise the price, then this will influence the views of k = 1 players which together a↵ect the
expectations of k = 2 players and so on. As long as it is believed that there are enough low k
players who believe prices will rise, even a very sophisticated player might be induced to make ano↵er above the fundamental price. This structure is similar to the guessing game of Ledoux madefamous by Nagel (1995).3 Level-k models can account for many non-equilibrium behaviors observedin the laboratory experiments, including, various types of auctions (see, among others, Crawford
and Iriberri, 2007; Crawford et al., 2013),4 Bertrand price competition (Dufwenberg and Gneezy,
2000; Baye and Morgan, 2004), and the travelers’ dilemma game (Capra et al., 1999).5 Recently,
researchers have started to incorporate level-k thinking in analyses of monetary policies. Lovino andSergeyev (2018) construct a model of level-k thinking applied to QE, and Farhi and Werning (2019)use it, together with incomplete credit market, in explaining the “forward guidance puzzle.” We
therefore anticipate that many participants will have an instinctive response that the presence of a3 See, Buhren et al. (2012) for historical details.
4 It is well known in the experimental literature on single unit demand auctions that subjects tend to overbid
compared with the risk-neutral theoretical predictions in both private and common value auctions of various formats.
See, among others, Kagel (1995) and Kagel and Levin (2017) for surveys. The literature suggests several explanations
for such observed deviations, including nonstandard preferences, such as ‘spite’ and the ‘joy of winning’ (see, forexample Cooper and Fang, 2008) or inconsistent beliefs, such as the cursed equilibrium (Eyster and Rabin, 2005) orlevel-k reasoning (Crawford and Iriberri, 2007). We also note that there are experimental studies pointing out thatthe level-k model, which relies on the best reply on the wrongly specified belief, cannot account for observed behavior
in a private value auction (Kirchkamp and Rei , 2011) or in a common value auction (Ivanov et al., 2010).
5 We thank an anonymous referee for pointing this out.

3

large external buyer should raise the price.
The main purposes of the experiment presented in this paper are (a) to investigate whether
interventions indeed raise the price of bonds, and (b) to investigate whether the observed e↵ect of
the interventions disappears with repeated exposure to the same treatment.
The environment which we consider is very simple, and is set out in more detail in Section 2.
The experiment is designed to remove as many extraneous sources of uncertainty (such as stochastic
dividend payments) and confusion (such as an inability to understand a declining fundamental
value) as possible. At the start of the experiment, participants are given bonds and cash, which
are essentially identical ways of transferring this endowment to the end of the experiment in therational expectations equilibrium (REE). The REE – or fundamental – price is constant throughout
the experiment. In the Benchmark treatment, participants can trade the bonds for cash among
themselves over 11 periods. There is no reason to trade one asset for the other at this fundamental
price and the volume of trade should be indeterminate.
In our first QE treatment (called Buy&Hold treatment), the central bank will buy a third of
the bonds available in the market before periods four and five, using a discriminatory price auction.
This will be described as a QE operation. The central bank holds these bonds until the end of theexperiment. However, as this operation changes neither the value of the bonds at maturity nor theequilibrium cash flows, the auctions are competitive and nobody can become a monopoly seller, themarket price for bonds in the REE does not change.
In our second QE treatment (called Buy&Sell treatment), the central bank again buys a thirdof the bonds available in the market before periods four and five, but sells them back again in areverse discriminatory auction before periods eight and nine. Thus, the participants are back where
they started and again there is no change in the fundamental price.
Despite its simplicity, we find statistically significant evidence of mispricing in the Benchmark
treatment and in the two QE treatments when participants are first exposed to the experiment.
And the degree of mispricing is higher in the two QE treatments in the periods after the firstintervention takes place.6 The participants then repeat the same treatment to which they wereoriginally exposed twice more. By the third round, as shown in the literature following Smithet al. (1988), participants in the Benchmark treatment have clearly realized that prices should notdeviate from the fundamental price and the median price is essentially flat across the 11 periods
6 We also found this in our first set of experiment reported online supplementary appendix I.

4

of trading at this level (Palan, 2013, Obs.1).7 By contrast, we find that prices remain statisticallysignificantly above the fundamental price in both the QE treatments. However, there is relatively
little to distinguish between the two QE treatment e↵ects except for the last few periods following
the reverse intervention.
Throughout the experiment, we collect forecasts of future market prices from the current period
onwards from each participant. Thus, we have a rich data set on the beliefs of the participants. We
report a very tight link between median forecasts and market prices and between past prices andcurrent forecasts. Forecast dispersion across participants in each group that trades together shrinks
very rapidly and although the participants do not know this (others’ price forecasts are not revealed
to participants), these common priors condition the evolution of future prices.
An interesting feature of both QE treatments is that participants begin to anticipate higher prices
from the start, particularly by the third round. Thus, it is not an inability to think forward thatprevents the equilibrium price from emerging. Instead, everyone comes to expect that the bonds
will be priced high in the auctions.
Our experiment is most similar in motivation to Haruvy et al. (2014). That paper consideredshare buybacks and share issues in a setup in which the fundamental price of an individual shareshould be independent of the quantity of outstanding shares. Thus, in REE, the market price should
not be a↵ected by these treatment operations. Nevertheless, the authors found that share prices
rise after a buyback and fall after an issuance of shares, as we do in our experiment. Haruvy et al.
(2014) proposes two possible reasons for this result: (i) the downward sloping nature of the demandschedule for the bonds, and (ii) the change in the traders’ beliefs about the intrinsic value of sharesowing to the change in the quantity of shares. In our experiment, the market demand schedule isdownward sloping by construction, thus the first reason certainly applies. For the traders’ beliefs,
although we cannot analyze their beliefs about “intrinsic value” of bonds, we do show that thehigher the average price paid during the buy operation in Round 1, the higher the period 1 priceexpectation in Round 2. Thus, the interventions do a↵ect participants expectations about market
prices. The latter analysis was made possible through our multiple rounds experiment with forecast
elicitation.8
7 See, Palan (2013), Powell and Shestakova (2016), and Nuzzo and Morone (2017) for recent surveys of the literature.
The mispricing can reappear, however, when the common experience is broken by a change in the market environment(Hussam et al., 2008) or by inflows of new and inexperienced traders (Dufwenberg et al., 2005; Akiyama et al., 2014;
Kirchler et al., 2015).
8 We thank an anonymous reviewer for pointing this out.

5

Furthermore, we depart from the approach of Haruvy et al. (2014) in several other ways. Our
bonds, unlike their shares, have no intrinsic uncertainty in their payo↵s so risk aversion plays norole in determining prices. In addition, we consider a setup in which the fundamental price of thebond should be constant and equal to its maturity value, so there is no risk in our experiment thatparticipants are confused by the idea of an asset that should fall in value, as noted by Kirchler
et al. (2012) and Stöckl et al. (2015). Furthermore, in our experiment, market transactions areorganized according to a call market, instead of a continuous double auction and, importantly,
market interventions take place outside of the usual trading periods, according to an auction rule,with subjects being clearly informed about the exact manner in which interventions will take place.
Our experiment setting also focuses on the direct e↵ect of the asset purchases on prices and priceforecasts and their interrelationship. The similar e↵ects of interventions observed both in Haruvy
et al. (2014) and in our study, despite of all these di↵erences in the experimental setups, suggestthat the initial e↵ects (i.e., before the repetition) of interventions are robust to these di↵erences.
The remainder of the paper is organized as follows. Section 2 describes the experiment in moredetail, section 3 presents the main results, and section 4 concludes.

2

Experiment

We first describe the aspects of the experiment that are common to all treatments. Then, we
describe each of our three treatments. See online supplementary material (OSM) VI for an English
translation of the instructions.

2.1

Basic experimental setup: Market structure

We set up an experimental bond market very similar to Bostian and Holt (2009). A market consists ofN traders who hold a portfolio of riskless bonds and cash on deposit. At the start of the experiment,each trader is endowed with eight bonds and 800 ECU of cash. Each round of the experiment lasts
for T = 11 periods. Each bond pays a dividend of 6 ECU per period and matures at the end of
period 11 for 120 ECUs. Cash on deposit receives a 5% interest rate per period. In this setting,the fundamental value (F Vt ) of bond at the beginning of period t is 120 ECU for all t = 1, ..., T in
REE.9
9 Consider the beginning of period T . If a trader buys a unit of bond at price P , then he or she or she will receiveT
6+120 ECU at the end of period T . If the trader kept the same amount cash until the end, then it will become6In each period, the participants have the opportunity (but are not obliged) to trade bonds andcash with each other in a call market, as in van Boening et al. (1993), Haruvy et al. (2007), andAkiyama et al. (2014, 2017). In a call market, traders submit an order by specifying a price–quantity
pair. In period t, for example, if trader i wishes to submit a buy order, i must specify the maximum
price at which i is willing to buy a unit of bond (bid, bit ) and how many units of bonds trader iwishes to purchase (dit ). If trader i wishes to submit a sell order in period t, i must specify theminimum price at which i is willing to sell a unit of bond (ask, ait ) and how many units of bonds iwishes to sell (sit ). In each period, each trader can submit both a buy order and a sell order, justone of them, or no order. Neither short-selling nor borrowing of cash is allowed. Thus, in the casewhere i submits a sell order in period t, i must have at least sit units of bonds in his or her portfolio.
Similarly, if i submits a buy order in period t, his or her cash holding has to be no less than bit dit .
Finally, in the case where i submits both buy and sell orders, we require that bit  ait .10 Once all
the traders in the market submit their orders, the orders are aggregated and a market-clearing priceis computed. Following the existing studies (Haruvy et al., 2007; Akiyama et al., 2014, 2017), when
there are multiple market-clearing prices, we choose the minimum among them.
We have employed a call market structure instead of a continuous double auction to facilitate theforecasting task performed by participants (which will be described in the next subsection). In thecall market, because there is only one market-clearing price per period, the prices that participants
need to forecast are clearly defined. In any case, the existing experimental results show that callmarkets and continuous double auctions generate similar price dynamics (Palan, 2013, Obs. 27),
although trading volumes can be di↵erent.

2.2

Basic experimental setup: Forecasting

By eliciting traders’ expectations regarding the current and future period prices at the beginningof each period, we aim to better understand the link between price expectations and market prices
and how and why price expectations evolve over time.
At the beginning of each period, before traders submit their orders, we ask subjects to submit
1.05PT after the interest payment. As these two have to be the same in the equilibrium, we have 1.05F VT = 126,
i.e., F VT = 120. Now, consider the beginning of period T 1. If a trader buys a unit of bond at PT 1 , he or she will
have 6 + F VT = 126 in fundamental terms at the end of the period. If the same amount of cash is held until the endof period, it will become 1.05PT 1 . As these two have to be equivalent, 1.05F VT 1 = 126, i.e., F VT 1 = 120. One
can do the same thing for all the remaining periods to obtain F Vt = 120 for all t = 1, . . . , T .
10 There was an additional technical constraint in that we only allowed ai and bi to take integer values between 1tt
and 2,000. This was purely due to the way the experimental software was programmed.

7

their forecasts of the bond price in the current period as well as in all the remaining periods. Thatis, at the beginning of period t, subject i submits his or her forecasts for bond prices in periods t,
t + 1, ..., T . This elicitation method allows us to observe the dynamics of subjects’ short-run andlong-run forecasts, and it has been employed, for example, by Haruvy et al. (2007) and Akiyama
et al. (2014, 2017).
Subjects receive a bonus of 0.5% of their final cash holding (after their bonds have matured)for each forecast that contained the realized price plus or minus 10%. Thus, if all 66 forecasts i
submitted during 11 periods satisfy this condition, they receive a bonus payment of 33% of their
final cash holding.11

2.3

Three treatments

We consider three treatments: (T1) Benchmark, (T2) Buy&Hold, and (T3) Buy&Sell. The Benchmark treatment has no central bank intervention and is conducted under the basic setting describedabove. In both the Buy&Hold and Buy&Sell treatments, participants are told before the experiment
begins that the central bank will try to buy a third of the outstanding bonds (by volume) througha discriminatory auction before the beginning of periods 4 and 5 (we call this the buy operation).
In the Buy&Sell treatment, it is pre-announced that, before the beginning of periods 8 and 9, thecentral bank will sell the bonds it has to the market (we call this the sell operation), again using a
discriminatory auction.12
Many of these features were chosen to mimic the way QE has been implemented. As mentioned
in the Introduction, central banks have bought very significant shares of the outstanding volume
of bonds. The ECB has set a purchase limit of 1/3 of the outstanding stock of any specific bondand purchases in some jurisdictions have approached this limit. Central banks have also announced
their intentions to purchase bonds well before they actually started buying. These announcements
have tended to have more e↵ect on bond prices than the actual purchases. To see whether this e↵ect
11 Akiyama et al. (2014, 2017) introduced this incentive scheme for the forecasting performance to minimize thepossibility of subjects attempting to improve their forecasting performance by engaging in unprofitable trading strategies simply to make the market prices closer to their forecasts. Recently, however, Hanaki et al. (2018) found thatrewarding subjects for their forecasting performance this way can enlarge mispricing compared with the experiments
where subjects only trade and no forecast is elicited. Although the setup considered by Hanaki et al. (2018) is slightlydi↵erent from ours, it is possible that similar e↵ects operate in the current experiment. However, it should be noted
that, because subjects are rewarded for their forecasting performance in an identical way across all the treatmentsthat we consider in this paper, any such e↵ects will not influence our analyses based on treatment comparisons.
12 The experiment was framed in a neutral language. Subjects were told that “the computer will buy (or sell) thebond” instead of “the central bank will buy (or sell).”

8

would be replicated in the lab, we had three periods of market trading before the central bank beganpurchasing. We also spread the purchases and sales (where applicable) over two periods to have aninterim market price. One di↵erence with the way QE was actually implement is that our central
bank commits to buy a quantity of bonds rather than a target value of bonds but we do not think
this di↵erence is important.
Part of the motivation for choosing a discriminatory auction for the market intervention is theway that the Bank of England implemented its asset purchase scheme.13
During the buy operations in T2 and T3, the central bank attempts to buy half of the stock ofbonds planned (i.e., a sixth of the outstanding bonds) in both periods 4 and 5. If it fails to buy thebonds planned in period 4, the residual amount is added to its operation in period 5. If it fails to
buy its revised target in period 5, the shortfall is ignored.14
During each of these two buy operations, each trader can submit a sell order that specifies aprice-quantity pair. Once all the orders are submitted, the central bank sorts the submitted o↵ers
in ascending order and buys up to the targeted amount, paying the specified price in each case.15
In the Buy&Hold treatment, once the central bank completes its buy operation, it will hold thebonds it bought until the end of period T. In other words, the central bank permanently removes
those bonds from the market. In the Buy&Sell treatment, the central bank sells the bonds it has tothe market participants during its sell operation before periods 8 and 9.
The sell operation before periods 8 and 9 in the Buy&Sell treatment is conducted in a similarmanner to the buy operation. The central bank attempts to sell back half of the targeted amountin period 8 and whatever amount remains in period 9. If it fails to sell back all the units of bonds
during these two sell operations, it will simply keep them until the end of period T. During these sell
operations, each trader can submit a buy order that specifies a price-quantity pair and the central
bank sorts the bids in descending order, and sells up to the targeted amount, in each case receiving
the specified price.
Since a primary motivation for our experiment is to see whether QE can work through an entirely
13 See The Bank of England’s Asset Purchase Facility (2017) for an explanation of how the Bank of Englandconducted QE. It should be noted that not all governments use discriminatory auctions in auctioning o↵ their securities,
with many using uniform price auctions. As summarized by Marszalec (2017), the literature, both theoretical andempirical, is inconclusive regarding which method of the two commonly used auction formats is superior in terms of
the revenue-raising ability.
14 Except for two cases to be reported in footnote 21, the central bank always bought or sold its planned amount ineach period.
15 In cases with multiple o↵ers at the same marginal o↵er price, purchases are randomly allocated across thoseparticipants.

9behavioural channel, it is important that there is no REE in which the central bank purchases could
actually a↵ect the price. Under our set-up there is not. As already mentioned, the central bank’spurchases do not in any way a↵ect the maturity value of the bonds, their interim coupon paymentsor the rate of return on holding deposits. Any bonds not sold to the central bank have a fundamental
value of 120 ECU as noted in footnote 9. The only possible cause of price increase is that if traders
could expect to make a profit from selling to the central bank. Two elements prevent this. At thefundamental price and given the interest rate, the maximum number of bonds any one participant
can accumulate by the end of period 4 is 16. But the central bank will only buy 16 of the 48 bonds
in circulation. Thus, it is not possible at the fundamental price for any participant to be certain of
selling to the central bank. The possibility of profit thus depends on the behaviour of other traders.
The second element is that the auctions are competitive and so any price above the fundamental
price cannot be a REE. Even if several participants were to corner the market, each would havean incentive to o↵er at ✏ less than the price of the other sellers until any alternative equilibrium
price would unravel.16 Furthermore, we can rule out the existence of mixed strategy equilibria a la
Kaplan and Wettstein (2000) because there is an upper limit for the order price one can submit in
our experiment.

2.4

Other aspects of the experiments

In all three cases, the same group of participants repeats the treatment that they were originallyassigned three times. We call each sequence of 11 periods a round. We are interested in how subjects
learn and adjust their forecasts and trading behavior based on their experience in playing the same
treatment. At the end of the final round of the game, one of the three rounds is chosen randomly
for payment. Subjects are paid based on their final cash holding and the bonus for their forecast
performance of this chosen round, in addition to their participation fee of 500 JPY. The exchange
rate between ECU and JPY was 1 ECU = 1 JPY.
Computerized experiments (with z-Tree, Fischbacher, 2007) were conducted at Waseda University
and the University of Tsukuba between January and July 2017. We conducted the whole exercise
with two market sizes, N = 6 and N = 12. This was intended to test whether our earlier results
16 In our experiment, because prices must be an integer, in addition to everyone bidding FV, there is anotherequilibrium in which everyone bids 1 ECU above the fundamental value. This is because when everyone bids FV+1,
each can expect to obtain 1/3 of ECU per bond, while bidding FV will result in zero profit. Thus, there is no incentive
to unilaterally deviate and bid lower. This issue of multiple equilibria due to an integer constraint is also known in
p-beauty contest games (López, 2001).

10

Table 1: Summary of experimental sessions
Treatment
Baseline N = 6
Baseline N = 12
Buy&Hold N = 6
Buy&Hold N = 12
Buy&Sell N = 6
Buy&Sell N = 12

Number ofParticipants
54
96
48
96
48
96

Number ofMarkets
9
8
8
8
8
8

Number of Sessions(Participants / sessions)
3 (24, 18, 12)
4 (all 24)
2 (all 24)
4 (all 24)
2 (all 24)
4 (all 24)

based on N = 6 (Penalver et al., 2017) were robust to the degree of competition in each market.
Because the results were not statistically significantly di↵erent between two market sizes, as reported
in OSM IV, we combined the two datasets and report pooled results. A total of 438 students wererecruited from a variety of disciplines and none had ever participated in similar experiments before.
Table 1 provides a summary of the experimental sessions and the number of participants in eachtreatment. The experiment lasted about 3 hours including the instruction and payment, and subjects
earned, on average, 4,350 JPY (⇡ 34 EUR during the period when experiments were conducted)
including the participation fee.

3

Results

3.1

Price dynamics

Figure 1 illustrates the dynamics of the median observed price in our experiments for the threerounds.17 The Benchmark is shown in thin blue, the Buy&Hold is in thick red, and the Buy&Sell
is in dashed black. The median price path for the Benchmark treatment is slightly above 120
throughout the first round. But in the second and particularly the third rounds, the median priceis scarcely di↵erent from the fundamental price.
In the Buy&Hold treatment, the median price drifts up from period 4 on (after the first intervention) and it stays high until period 8. The median price starts to decline from period 9 on and as
in the Benchmark treatment, it converges back to 120 by period 11. In Round 2 of the Buy&Hold
treatment, the action occurs earlier. The median price starts above 120 in period 1, and stays highagain until period 8. The median price begins to decline towards the fundamental price in period
17 The dynamics of observed prices in each market are shown in the OSM II.

11

Round 1

Round 2Round 3

 -

 -

 -







       

 

-

               

-

 

-

Figure 1: Dynamics of median price deviations from the fundamental value in T1: Benchmark (thinblue), T2: Buy&Hold (thick red), and T3: Buy&Sell (dashed black) over three rounds.
9. By Round 3, in the Buy&Hold treatment, the median price again starts above the fundamental
price but it peaks in period 3. The median price converges to the FV by period 9.
The Buy&Sell treatment shows a very similar pattern to the Buy&Hold treatment in Round 1
except for a noticeable drop in the median price for the Buy&Sell treatment when the central bankstarts to sell in period 8. The action again begins earlier in Round 2 and the median peaks inperiod 4. By Round 3, the median price is high initially and then declines after period 3.

3.2

Mispricing

Now, we formally compare the magnitude of mispricing observed in the three treatments over threerounds. Following Powell (2016), we measure the degree of mispricing using the volume-adjusted geometric absolute deviation (vGAD) and the volume-adjusted geometric deviation (vGD).18 vGADm
and vGDm for market m are defined as follows:

vGAD

m

vGD

m

⌘ exp
⌘ exp

PT

T
X

1

m
k=1 vk p=1

PT

T
X

1

m
k=1 vk p=1

vpm

ln

vpm ln

✓

✓

Ppm
F Vp

Ppm
F Vp

◆!

◆!

1

1

(1)

(2)

where vtm and Ptm are the volume and the realized price observed in period t of market m. F Vt = 120
for all t in our experiment, so if Ptm = F Vt for all t and all m, then vGADm =vGDm = 0 for all
m. Whereas vGAD measures the absolute magnitude of mispricing, vGD takes into account thedirection of mispricing.
18 The results are robust even if we use other measures of mispricing frequently used in the literature. See OSM III
for these additional analyses.

12

vGAD
Round 2Round 1Round 3

CDF
1.0

CDF
1.0

CDF
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.2

0.0

0.2

0.4

0.6

0.8

1.0

vGAD

0.0

0.2

0.6

0.8

1.0

vGAD

0.0

0.2

0.4

0.6

0.8

1.0

p=0.125 (KW)
T2
T3
T1 0.056 0.183
T2
0.386

p=0.007 (KW)
T2
T3
T1 0.003 0.061
T2
0.132

p<0.001 (KW)
T2
T3
T1 0.001 0.001
T2
0.327

Round 1vGD
Round 2Round 3

CDF
1.0

CDF
1.0

CDF
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2
-0.4

0.4

-0.2

0.0

0.2
0.2

0.4

p=0.749 (KW)
MW
T2
T3
T1
0.517 0.517
T2
0.910

vGD

-0.4

-0.2

0.0

0.2
0.2

0.4

vGD

p=0.005 (KW)
MW
T2
T3
T1
0.002 0.031
T2
0.214

-0.4

-0.2

0.0

0.2

0.4

p=0.001 (KW)
MW
T2
T3
T1
0.004 0.001
T2
0.451

Figure 2: ECD of vGAD (top) and vGD (bottom) for three rounds. T1: Benchmark (blue), T2:
Buy&Hold (red), and T3: Buy&Sell (dashed black). P-values from the KW test for multiple comparisons as well as from the MW tests for pairwise comparisons are reported.
Given variations in the behavior of each market within each treatment and each round, there isa distribution for each measure of mispricing and the respective empirical cumulative distributions(ECDs) are illustrated in Figure 2 (vGADs (top) and vGDs (bottom)). The Benchmark is shownin the thin blue line, Buy&Hold is shown in the thick red line, and Buy&Sell is shown in the thickdashed line. Below each panel, p-values from the Kruskal-Wallis (KW) test of a multiple comparisonand Mann-Whitney (MW) tests for pairwise comparisons are reported.
Visually, it is clear that there is very little di↵erence between the cumulative distributions inRound 1 and this is confirmed using the KW test. Pairwise, the degree of mispricing in the benchmark is less than in the Buy&Hold treatment, but this di↵erence is only marginally significant underthe vGAD measure and not significant under the vGD measure. The picture changes dramatically in
13

vGAD

vGD

Rounds 2 and 3. According to both measures, the level of mispricing in the Benchmark treatment islower than in both the Buy&Hold and the Buy&Sell treatments with a high statistical significance,
although there is no significant di↵erence between the latter two treatments over the whole period.
Looking at mis-pricing over the full 11 periods, however, hides some important di↵erences acrossthe three treatments between di↵erent sub-periods. The first three periods of trading rounds (periods
1–3) take place before the first buy operation (in Buy&Hold as well as Buy&Sell treatments). So,
if participants anticipate some e↵ect of the buy operation, this should show up in these periods inthese treatments compared to the benchmark. Trading in periods 5–7 occurs between the buy andthe sell operation (in Buy&Sell treatment), thus, if anticipation of the sell operation is to have anye↵ects, they should be visible in these periods in this treatment relative to the Buy&Hold treatment.
Finally, trading in the last three periods (periods 9-11) takes place after the sell operation, when
traders are only anticipating the maturity of the bonds. Analyzing these three sub-periods separately
give us insights into the e↵ects of these interventions.
Figure 3 shows the vGAD for each of the three subperiods, denoted as vGAD1 (periods 1–3,
left), vGAD2 (periods 5–7, middle), and vGAD3 (periods 9–11, right) in each round.19
Whereas there was no apparent di↵erence in Round 1 when viewed as a whole, there is now a
noticeable, albeit only marginally significant, di↵erence between the distributions during the second
subperiod after the central bank purchases but before the sales in the Buy&Sell treatment. The
degree of mispricing is markedly higher under the Buy&Hold and the Buy&Sell treatments thanunder the Benchmark treatment with the di↵erence between the Buy&Sell and the Benchmark being
particularly pronounced. The di↵erences become even starker in the third subperiod, although here
the main outlier is the Buy&Hold treatment, which is highly statistically significantly di↵erent fromthe other two. The other two are now statistically indistinguishable. To complete the analysis ofRound 1, there is no di↵erence between the distributions in the first subperiod. Thus, it is clearthat it is the buy operation in the Buy&Hold and the Buy&Sell treatments that drives mispricing
relative to the Benchmark in the second subperiod of Round 1, which is largely reversed by the sell
operation in the Buy&Sell treatment.
These qualitative di↵erences become more quantitatively significant in Rounds 2 and 3. The
19 See Appendix A for vGD in these three subperiods, The results are essentially the same as the results that wereport based on vGAD in the main text. The Appendix A also reports the summary statistics of vGADs and vGDs
and the p-values from various tests. In addition we provide results of analyses where, we include period 4 in the second
subperiod and period 8 to the third subperiod, which are in the middle of the operation. The results are qualitatively
the same.

14

Round 1
vGAD2

vGAD1

vGAD3

CDF
1.0

CDF
1.0

CDF
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2
0.0

0.2
0.2

0.4

0.6

0.8

1.0

vGAD1

0.0

0.2
0.2

0.4

0.6

0.8

1.0

p=0.611 (KW)
MW
T2
T3
T1
0.368 0.428
T2
0.910

p=0.052 (KW)
MW
T2
T3
T1
0.062 0.026
T2
0.722

vGAD1

Round 2
vGAD2

vGAD2

0.0

CDF
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.2

0.4

0.6

0.8

1.0

vGAD1

0.0

0.2

0.4

0.6

0.8

1.0

p=0.179 (KW)
MW
T2
T3
T1
0.072 0.207
T2
0.624

p=0.001 (KW)
MW
T2
T3
T1
0.001 0.007
T2
0.122

vGAD1

Round 3
vGAD2

vGAD2

0.0

CDF
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2
0.2

0.4

0.6

0.8

1.0

p=0.001 (KW)
MW
T2
T3
T1
0.001 0.002
T2
0.598

0.0

1.0

vGAD3

0.2

0.4

0.6

0.8

1.0

vGAD3

vGAD3

CDF
1.0

0.0

0.8

p=0.011 (KW)
MW
T2
T3
T1
0.009 0.534
T2
0.012

CDF
1.0

vGAD1

0.6

vGAD3

CDF
1.0

0.2

0.4

p=0.015 (KW)
MW
T2
T3
T1
0.032 0.326
T2
0.008

CDF
1.0

0.0

0.2

0.2
0.2

0.4

0.6

0.8

1.0

p=0.002 (KW)
MW
T2
T3
T1
0.003 0.002
T2
0.880

vGAD2

0.0

0.2

0.4

0.6

0.8

1.0

p=0.022 (KW)
MW
T2
T3
T1
0.016 0.018
T2
0.797

Figure 3: ECD of vGAD in three subperiods. vGAD1 (periods 1–3). vGAD2 (period 5–7). vGAD3(period 9-11) for three rounds. T1: Benchmark (blue), T2: Buy&Hold (red), and T3: Buy&Sell(dashed black). P-values from the KW test for multiple comparison as well as MW tests for pairwise
comparisons are reported.

15

vGAD3

pairwise di↵erences between the mispricing in the Benchmark and the two treatments fall below the
5% significance level. Furthermore, in Round 3, the vGAD in the first subperiod is significantly
higher in the Buy&Hold and the Buy&Sell treatments than in the Benchmark.

3.3

Market interventions

In this subsection, to better understand the dynamics of prices across periods and rounds in thethree treatments, we first summarize the results of the market interventions. Table 2 reports theaverage prices during the buy operations (pbo , panel (a)) as well as their di↵erences from the average
market prices in the preceding three periods (pbop1 3 , panel (b) ) and sell operations (pso , panel(c)) and their di↵erences from the average prices in the preceding three periods (psop6 8 , panel(d)). It also reports the di↵erence between the average transaction prices between the buy and thesell operations (panel (e)).
We observe that, on average, the central bank paid prices substantially above the fundamental
value during its buy operations, which may be natural given that the market prices are also higher
than the fundamental value. The prices the central bank paid are, however, even higher than the
average market prices in the three preceding periods although the di↵erence becomes smaller inlater rounds. The prices paid during the buy operations of both the Buy&Hold and Buy&Selltreatments are very similar and do not display any significant di↵erences across three rounds.20 The
sell operation only occurs in the Buy&Sell treatment. Panel (d) of Table 2 reports that the central
bank sells for a price below the average market prices in the three periods before the operation,although the di↵erence becomes slightly smaller, in magnitude, in later rounds.
The observed high prices during the buy operation of our experiment, which result from subjects
not competing aggressively enough, is very similar to price dispersion observed in Bertrand pricecompetition experiments (called the “Bertrand Paradox”, see, Dufwenberg and Gneezy, 2000; Bayeand Morgan, 2004) as well as deviation from Nash equilibrium in experiments for the travelers’
dilemma game (Capra et al., 1999). What is rather surprising in our data is the absence of significant
di↵erences across the three rounds. In Dufwenberg and Gneezy (2000), for example, when numberof players is 3 or more, participants quickly learned to compete more aggressively in Bertrand
competition experiment. This is not just for the average prices the central bank paid, but even at
20 The estimated coefficients of the Rounds 2 and 3 dummies on the group fixed e↵ect regressions that regress pboon a constant and these two dummies are not statistically significantly di↵erent from zero for the two treatments.

16

Table 2: Summary of Market Interventions

T2. R1T2. R2T2. R3T3. R1T3. R2T3. R3(a) Average transaction pricein buy operations: pbo
Mean
SD
Min
Max
149.61a18.93
122.06
191.75162.80a
34.01
125.00263.13151.53a21.24
119.94186.41148.62a20.37
121.09180.50154.91a17.19
124.69179.75145.66a16.83
122.19174.00(b) Di↵erences from the average market
prices in preceding periods: pbo p1 3
Mean
SD
Min
Max
25.13b
25.94
21.27
77.10
15.93d32.86
21.67
130.79
5.84d
12.82
6.76
37.02
26.16b
22.14
5.57
69.88
10.89b
11.98
5.5
32.08
1.13
9.90
20.35
25.23

T3. R1T3. R2T3. R3(c) Average transaction pricein sell operations: pso
Mean
SD
Min
Max
140.19a24.58
116.81212.81
126.96a5.53
115.75137.38119.73
11.02
82.69
130.78(d) Di↵erences from the average market
prices in preceding periods: pso p6 8
Mean
SD
Min
Max
10.82c
15.79
38.52
26.03
8.37b
6.07
18.58
0.49
6.82b
4.33
15.31
1.53

T3. R1T3. R2T3. R3(e) Di↵erence between sell andbuy operations: pbo pso
Mean
SD
Min
Max
8.43
31.67
78.94
59.69
27.94b
14.06
0.00
48.38
25.94b
20.38
3.72
77.25

a

statistically significantly di↵erent from 120 at the 1% level (t-test, two tailed).
statistically significantly di↵erent from zero at the 1% level (t-test, two tailed).
cstatistically significantly di↵erent from zero at the 5% level (t-test, two tailed).
dstatistically significantly di↵erent from zero at the 10% level (t-test, two tailed).
b

individual level.
Figures V.4 to V.7 in OSM V show realized supply schedules during the buy operation for eachgroup in each round. These supply schedules are constructed based on the orders submitted during
the operations. In many of the groups, supply schedules become flatter in Rounds 2 and 3 comparedto Round 1, suggesting that there are fewer variations in the ask prices submitted by participants
during the buy operation, which is consistent with the lower coefficient of variations of the ask
prices in later rounds reported in the Table IV.2 in the OSM IV.2. However, it is only in a few
groups (within each experimental setting) that we observe convergence toward equilibrium bidding(for example, groups 2 and 3 shown in Figure V.4 demonstrate convergence toward equilibrium
bidding). In most of the groups, ask prices decline only slightly from one round to the next. Thereeven exists a case (group 6 of Buy&Hold with 6 traders) where ask prices increased in later rounds.
17

This absence of convergence toward the equilibrium bidding behavior is in sharp contrast with theconvergence to the equilibrium widely observed for the bidding behavior during the sell operations(see, Figures V.8 and V.9 in OSM V).
In a repeated multi-unit auction of Abbink et al. (2006), it takes as many as 25 repetitions forthe outcomes (revenue measured relative to the value of the item being auctioned o↵) to stabilize.
Although the slow learning observed in Abbink et al. (2006) may partly be a result of the value ofthe items being auctioned varying from one auction to another, it is possible that if the experiment
had been repeated many more times, participants in our experiment would have learned to bid morecompetitively during the buy operation. However, the current experiment is not sufficient to study
this question.
Panel (e), for convenience, displays the di↵erence in the prices paid and received in the Buy&Selltreatment. The di↵erence is not statistically significantly di↵erent from zero in Round 1 but becomesstrongly statistically significant in Rounds 2 and 3. As a result, the central bank makes a very large
loss and a substantial amount of cash (on average, 20% of the fundamental value of the bonds) wastransferred from the central bank to the market participants through these two operations.
Such a large transfer of cash from the central bank to market participants substantially increases
the cash-to-asset ratio. Existing studies (see, e.g., Kirchler et al., 2012; Deck et al., 2014) havefound that prices can rise when the ratio of cash to assets increases. So one reason why the two buy
operations might increase prices is simply the resulting higher cash to assets ratios. To check if thehigher cash-to-asset ratio is indeed the main driver of higher mispricing, we conduct the following
regression:
pm = b1 + b2 pbo m + µm

(3)where pbo m is the average transaction price per unit of bonds during the buy operation for marketm,pm ⌘ p 5 7 m

p1 3 m is the di↵erence in the average prices for the three periods immediately

before and after the buy operation for market m (with pl k m being the average price observed
between period l and k for market m).
Regression (3) tests whether the di↵erences in prices in the market before and after the buy
operation are related to the average prices paid by the central banks. If the cash-to-asset ratio was
an important driver for the price increase after the buy operation, there should be a statistically18

Table 3: Dependent Var:
Const
pbo
R2
N of Obs.
⇤⇤

Round 150.50
(43.88)-0.153(0.292)
0.009
32

Round 2-0.234(16.27)-0.003(0.101)0.000
32pRound 349.39⇤⇤(21.14)-0.404⇤⇤⇤(0.141)
0.214
32

statistically significant at 5% levelstatistically significant at 1% level⇤⇤⇤significant, positive relationship in regression (3).
The result is reported in Table 3. We do not observe any statistically significant, positive relationship between the change in the market prices before and after the buy operation and the average
price paid by the central bank during its buy operation. In Round 3, it is even negative rather thanpositive.21 Thus, the cash-in-the-market e↵ect does not seem to be the main driver of our results.
More importantly, the cash-in-the-market e↵ect cannot explain the higher mispricing observed in
the three periods before the buy operations in the two treatments with intervention compared with
the Benchmark in Rounds 2 and 3 that we have seen above (in Figure 3). This is because, in thesefirst three periods, the cash-to-asset ratios are the same in all the treatments.
Indeed, regressing vGD1 (vGD for periods 1–3)22 on treatment dummies and the outcome of thebuy operations in the previous round (reported in Table 4) shows that the mispricing in the first
three periods of the current round is positively and significantly related to the average price thatthe central bank paid during its buy operation in the previous round.
This suggests that the results of market interventions in a round influenced subjects’ price expectations, which in turn influenced the market prices, in the following round. In the next subsection,
we will investigate this channel through analyzing the dynamics of subjects’ forecasts.
21 There were two cases in Round 1 (for the market with six traders) where the central bank failed to buy thetargeted amount of bonds (instead of 16, it bought 14 or 15). For these two instances, the pbo is not a precise measureof the change in the cash-to-asset ratio caused by the buy operation. We ran the regression (shown in eq. (3)) droppingthese two instances, but the result is qualitatively the same. In addition, we ran the regressions separately for the
Buy&Hold and Buy&Sell treatments. The results are qualitatively the same as the those presented in the right panelof Table 3 for both treatments.
22 We use vGD , instead of vGAD here to take into account the direction of mis-pricing.
1
1

19

Table 4: Mispricing and the previous round buy operation
Dependent Var: vGD1Round 2T2 (Buy&Hold)
0.094⇤(0.047)
T3 (Buy&Sell)
0.074(0.046)
Previous round pbo /F V 1 0.511⇤⇤⇤(0.141)
adf R20.752
N of Obs.
32
H0 : T 2 == T 3 (p-value)
0.668

Round 3-0.022(0.072)0.063(0.064)0.584⇤⇤⇤(0.152)
0.620
32
0.215

⇤

statistically significant at the 10% levelstatistically significant at the 5% level⇤⇤⇤statistically significant at the 1% level⇤⇤

3.4

Forecasts

To set the scene, we first provide a comprehensive description of the paths of forecasts and prices
across the three rounds for one group. Figure 4 shows, for each period of Round 1 (top panel) andRound 2 (middle panel), as well as the first four periods of Round 3 (bottom panel), the complete
path of individual forecasts (thin lines), their medians (thick lines), and realized prices (dots) for
one group of six subjects in the Buy&Hold treatment. There are many interesting features that we
observe in this figure that illustrate results that we demonstrate more formally later.
First, the very first forecasts elicited in period 1 of Round 1, before the participants have any
experience of trading, vary widely.23 However, they vary less at the end than they do at the beginning, suggesting that most participants realized that prices should converge towards the maturity
value by period 11.24 The first realized price, represented by the dot in the first period, is close
to the median first-period forecast and very close to the fundamental price. Forecast paths narrow
dramatically in period 2 of Round 1 as participants update their beliefs in light of the first period’s
realized price. The distribution of price forecasts across horizons is now much flatter.
What the central bank pays in periods 4 and 5 (illustrated by the red diamond) is well above theprevious market prices and above the subsequent market prices in those periods. There is no obvious
23 As we show in Appendix B,

the initial set of forecasts do not vary across the three treatments. Thus, ourinexperienced student subjects initially did not systematically anticipate that di↵erent price paths would result fromthe pre-announced market interventions. This is also reflected in the absence of significant di↵erences in the mispricing
observed in the first three periods of round 1 in the three treatments.
24 One participant with extremely high initial forecasts is excluded to maintain a common scale throughout.

20T2, N=6, Group 5, Round 1
Period 2
Period 3

Period 1

Period 4

P
250

P
250

P
250

P
250

200

200

200

200

150

150

150

150

100

100

100

100

50
0

50
1

2

3

4

5

6

7

8

9 10 11

t

0

50
1

2

Period 5

5

6

7

8

9 10 11

t

200

50
4

5

6

7

8

9 10 11

t

0

2

3

4

5

6

7

8

9 10 11

t

3

4

5

6

7

8

9 10 11

t

0

0

0

2

3

4

5

6

7

8

9 10 11

t

0

2

3

4

5

6

7

8

9 10 11

t

0

1

2

3

4

5

6

7

8

9 10 11

200

200

150

150

100

100

100

100

50

50

50

9 10 11

t

0

1

2

Period 5

3

4

5

6

7

8

9 10 11

t

200

200

100
50

4

5

6

7

8

9 10 11

t

0

1

2

Period 9

4

5

6

7

8

9 10 11

t

3

4

5

6

7

8

9 10 11

t

0

5

6

7

8

9 10 11

t

0

0

2

3

4

5

6

7

8

9 10 11

t

0

2

3

4

5

6

7

8

9 10 11

t

0

1

2

3

4

5

6

7

8

9 10 11

1

2

200

150

150

100

100

100

100

50
9 10 11

t

0

3

4

5

6

7

9 10 118

9 10 11t

Period 4

200

8

8

t

Ï

Ï

T2, N=6, Group 5, Round 3
Period 2
Period 3
150

7

7

t

200

6

6

50
1

150

5

5

Ï

Ï

200

4

4

50
1

P
250

3

3

150

P
250

2

2

Ï

P
250

1

1

Period 8P250

0

9 10 11Ï

100

Period 1

50

8

t

100

150

50
2

7

200

100

1

6

200

Ï

Ï

150

50

5

Period 11

200

100

4

P
250

Ï

Ï

150

0

3

P
250

200

4

Ï

Period 10

P
250

3

P
250

150

50
3

3

Ï

Ï

150
100

2

2

200

Ï

50
1

9 10 11

Ï

50
1

P
250

100

0

2

Period 7

P
250
Ï

0

Period 6

P
250

8

t

Period 4

150

150

1

T2, N=6, Group 5, Round 2
Period 2
Period 38

9 10 11

t

200

7

8

50
1

150

6

7

Ï

Ï

150

200

5

6

50
1

P
250

4

5

100

P
250

3

4

Ï

150

P
250

2

3

Period 8

P
250

1

2

100

Period 1

0

1

200

50
2

t

Period 11100

1

9 10 11

200

Ï

Ï

150

50

8

P
250

200

100

7

Ï

Ï

Period 10

Ï

Ï

150

6

50
1

P
250

200

5

100

Period 9
P
250

4

P
250

150

50
3

3

200

100

2

2

Ï

Ï

150

100

1

50
1

P
250

Ï

Ï

150

0

Period 7

P
250

200

0

4

Period 6

P
250

0

3

Ï

50
1

2

3

4

5

6

7

8

9 10 11

t

0

Ï

50
1

2

3

4

5

6

7

8

9 10 11

t

0

1

2

3

4

5

6

7

Figure 4: Dynamics of forecasts and price observed in benchmark treatment with six traders permarket, Group 1. Top: Round 1. Middle: Round 2. Bottom: the first four periods of Round 3.
Thin lines: individual traders. Thick line: Medians. Dots: realized market prices. Red diamonds:
average price paid by the central bank.

21tRound 1

Round 2Round 3

Median s.d. ft,t60

Median s.d. ft,t60

Median s.d. ft,t
60

50

50

50

40

40

40

30

30

30

20

20

20

10

10
1 2 3 4 5 6 7 8 9 10 11t

10
1 2 3 4 5 6 7 8 9 10 11t

1 2 3 4 5 6 7 8 9 10 11t

Figure 5: Dynamics of the median within-market standard deviation of the short-term forecasts inthree rounds. T1: Benchmark (blue), T2: Buy&Hold (red), and T3: Buy&Sell (dashed black).
immediate impact on price forecasts, although the median is gradually rising in line with realized
prices. For this group, prices continue to rise until period 8 and then decline. It is interesting tonote that, in periods 8 and 9, two distinct forecast patterns emerge: some who think that prices willcontinue to rise and a majority who realize that prices ought to be close to the fundamental value
by the end.
When the participants start again in period 1 of Round 2, initial forecasts are again relatively
dispersed particularly for early periods but converge towards (although they are slightly above)the maturity price in period 11. Importantly, however, the distribution of forecasts has shifted
up. Short-term forecasts again narrow dramatically in period 2, centered on the realized price of
period 1, which is well above the fundamental value but very close to what the central bank paid in
Round 1. What the central bank pays in Round 2 is now squarely in line with previous and future
prices. Realized market prices in Round 2 fall fairly monotonically thereafter but are still slightly
above the maturity value in period 11.
By the time the participants start again in period 1 of Round 3, initial forecasts are virtuallyindistinguishable. There is a near universal belief that prices will initially jump to the price expected
to prevail in periods 4 and 5 (for which there is complete consensus that prices will be equal to what
the central bank paid in Round 2). Then, prices almost exactly track the initial median path, whichis scarcely updated over the first four rounds. (The remaining periods of Round 3 are uninteresting.)
Below, we analyze formally the suggestive evidence of Figure 4. We first show a close relationshipbetween the median short-term forecasts (forecasts for the current period price) and realized prices.
We then link how median short-term forecasts in early periods are influenced by market prices andthe central bank interventions from the previous round.
We focus on the median short-term forecasts because the short-term forecasts of participants in22

each market quickly converge around the median, as demonstrated in Figure 4 and more vividly
in Figure 5. Figure 5 shows the dynamics of the median within-market standard deviation of theshort-term forecasts in the three rounds in each of the three treatments. As one can clearly observe,
the variations of short-term forecasts within each market become very small after a few periods inRound 1, and remain small in Rounds 2 and 3, except for a small increase in the first period in
Round 2. Thus, we focus on the median instead of individual forecasts, as little insight will be lost.
Furthermore, Carle et al. (2019) who re-investigated the data of Haruvy et al. (2007), reported that
although short-term forecasts are related to subjects’ trading behavior in a statistically significant
manner, their long-term forecasts (i.e., forecasts for prices in all the remaining periods of a market)
are not. Thus, focusing on short-term forecasts will allow us to understand more clearly the link
between the outcomes across rounds.
3.4.1

Median forecasts and prices

The first link in the chain is to demonstrate statistically that market prices are highly correlatedwith median price forecasts within each market.
Table 5 reports the results of group random e↵ect regressions regressing the current period price
Pt on the median short-term forecasts, Medft,t , for the three treatments and three rounds. The
three treatments are considered jointly in a single regression by including two treatment dummies(for T2 and T3) and their interactions with Medft,t . The regressions show that actual prices follow
closely, although not perfectly, the median short-term forecasts from Round 1, and increasingly
more so in later rounds. In fact, the estimated coefficients of the Medft,t in the two treatmentswith the central interventions (T2 and T3) are not statistically significantly di↵erent from 1.0 in
Round 3. The estimated coefficients of the treatment dummies and the interaction terms themselves
are not statistically significantly di↵erent across three treatments. Such a close link between median
forecasts and realized market prices is also reported by Carle et al. (2019).
3.4.2

Initial median forecasts and previous round outcomes

Equipped with this result about a tight link between the realized prices and median short-term
forecasts, we now proceed to analyze how median short-term forecasts made in the first period in
Rounds 2 and 3 are influenced by outcomes in previous rounds for the same market. We regressRthe initial median forecast (Medf1,1) of Round R on the realized first-period price in the previous

23

Table 5: Current period prices and median short-term forecasts. Dependent variable Pt .
(1)
Round 1
13.14
(7.457)(2)
Round 26.766
(7.760)(3)
Round 3
-10.34(9.465)

T3 (Buy&Sell)

14.46⇤⇤(7.105)0.514(8.863)-6.071(11.47)

Med ft,t0.764⇤⇤⇤(0.0358)

0.803⇤⇤⇤(0.0426)0.855⇤⇤⇤(0.0517)

T2 ⇥ Med ft,t

-0.0823(0.0541)-0.0382(0.0557)

0.0774(0.0693)T3 ⇥ Med ft,t

-0.101(0.0523)-0.0197(0.0664)0.0319(0.0887)

Constant

31.51⇤⇤⇤(4.949)539

26.48⇤⇤⇤(5.861)539

17.89⇤⇤(6.960)539<0.001<0.001<0.0010.08
0.119<0.001<0.001<0.001
0.622
0.790

0.005
0.143
0.118
0.550
0.531

T2 (Buy&Hold)N
P-values for various hypothesis testings;
H0 : Medft,t = 1
H0 : Medft,t (1 + T 2) = 1
H0 : Medft,t (1 + T 3) = 1
H0 : T2=T3=0
H0 : T2⇥ Med ft,t =T3⇥ Med ft,t =0Standard errors in parentheses⇤⇤ p < 0.05, ⇤⇤⇤ p < 0.01

1
round (P1R 1 ), the median price paid by the central bank during the buy operation (MedpRbo ), anda constant.
Table 6 reports the results of separate ordinary least squares (OLS) regressions for each treatment.
It is worth noting immediately that the adjusted R2 s for these regressions are very high, particularly
for Round 3, implying that these three independent variables explain almost all the variation inmedian initial price forecasts across the various markets. For all the treatments, the realized pricefor the same (first) period in the previous round has a large and highly statistically significant e↵ect
on the median price forecast for the current round.25
Let us turn our attention to those treatments with the interventions. For Round 2, in both
25 We have checked for potential multicollinearity by computing variance inflation factor (VIF) for T2 and T3
regressions. For both Rounds 2 and 3, the maximum VIF was 1.17 suggesting that the high R2 is not due to the1correlation between P1R 1 and MedpR
bo .

24

Table 6: Median short-term first-period forecasts and outcomes in previous round.
Round 2T2
0.534⇤⇤⇤(0.055)T3
0.445⇤⇤⇤(0.098)

1
MedpR
bo

0.214⇤⇤(0.098)0.473⇤⇤(0.194)55.80⇤⇤⇤(13.12)Adjusted R2
0.663
Observations
17
P-values from the hypothesis testing
H0 : P1R 1 = 1<0.001
1
H0 : P1R 1 + MedpR=
1bo36.07⇤⇤(15.26)
0.872
16

9.597
(27.35)0.722
16<0.001
0.034<0.001
0.659

P1R 1

T1
0.600⇤⇤⇤(0.105)Constant

Round 3
T2
0.899⇤⇤⇤(0.069)T3
0.872⇤⇤⇤(0.049)0.069(0.050)0.187⇤⇤⇤(0.052)34.62⇤⇤⇤(7.89)
0.907
17

4.66
(10.17)
0.933
16

-9.64
(8.40)
0.967
16<0.001

0.169
0.658

0.021
0.320

T1
0.732⇤⇤⇤(0.0584)

Standard errors in parentheses⇤ p < 0.10, ⇤⇤ p < 0.05, ⇤⇤⇤ p < 0.01

the T2 (Buy&Hold) and T3 (Buy&Sell) treatments, the median prices paid by the central bankduring the buy operations in Round 1 are positively and significantly related to the initial period
price forecast.26 This is closely linked to the result we have seen Table 4 regarding the positive andsignificant relationship between the average price during the buy operation in the previous roundand the vGD of the first subperiod of the current round. For Round 3, the importance of the resultof the intervention in Round 2 diminishes and becomes insignificant in the Buy&Hold treatment.
However, the importance of the initial realized price in Round 2 markedly increases.
Drawing these strands together, realized market prices are highly correlated with median expectations, so the forecast formation process is critical. Price forecasts depend strongly on previous
experience, but in two distinct steps. In Round 2, price forecasts are strongly influenced by what
the central bank paid in Round 1, as well as by what was realized in the same period of Round 1.
By Round 3, each market seems to settle on an historical price that coordinates both market prices
and the price paid by the central bank. This strong coordination of expectations in the case of themarket interventions seems to dominate any possibility that the participants learn that this is not
the REE, which they do manage to achieve in the Benchmark case without intervention.
26 We prefer to use the median price at which central bank bought, instead of the average price, because the latter issensitive to some extremely low or high prices at which the central bank bought in some sessions. Using the average,
however, does not change the qualitative result for T3. For T2, the estimated coefficient for the average price thatthe central bank paid is not statistically significant in Round 2.

25

4

Summary and conclusion

In this paper, we have asked whether a large-scale asset purchases by a central bank (a quantitativeeasing operation) which should be neutral under a rational expectations equilibrium has an impact,
and whether such an impact, if exists, survives after participants gaining experience by repeatedly
experiencing the same experiment.
It is clear from the repeated Benchmark treatment that participants in our experiment can learn,in line with the results of the literature, that prices should not deviate from the fundamental pricein the absence of QE after a couple of repetitions.
In the Buy&Hold treatment, in which the QE operations permanently remove some bonds fromthe market, prices rise statistically significantly well above the fundamental price and stay there, evenafter the central bank has stopped buying. In most markets, repeated exposure only strengthens thebelief that prices should rise. However, in a minority of cases, the intervention has a limited e↵ect.
We find that the central bank considerably overpays relative to the fundamental price and the mostrecent market price in Round 1. Rather than learning to compete this e↵ect away, participants cometo expect it. Indeed, by Round 3, the price path in the earlier rounds significantly conditions theirprice expectations. In addition, it was notable that the peak price e↵ect occurs earlier in the laterrounds, as participants start to anticipate higher prices from the beginning.
Price dynamics in the Buy&Sell treatment are remarkably similar to those in the Buy&Hold
treatment, particularly over periods 1 to 7. The main di↵erence occurs thereafter, as prices tend todrop to the fundamental price as the central bank sells. Overall, the evidence from this paper seems
to suggest that QE could work through a purely behavioural mechanism.
In order to accommodate repeating the experiment three times, we have opted for rather shortmarket horizon of 11 periods. In order to better separate the e↵ect of anticipation of the maturityof bonds and those of the sell interventions, an experiment involving a longer time horizon and thus
put sell operation much earlier than bond maturity would be desirable. Indeed, there are papers(for example, Lahav, 2011; Hoshihata et al., 2017) that demonstrate quite di↵erent price dynamics
in much longer horizon market (200 periods in the former and 100 periods in the latter) compared
to short horizon market (of 15 or less periods). We leave this as an interesting avenue for future
research.
Of course, the analysis in this paper involves caveats. Abbink et al. (2006) suggested that it

26

may take several repetitions, certainly more than three, for subjects to learn to bid aggressively ina discriminatory multi-unit auction.27 Thus, it is possible that if we repeat our experiment many
more times, subjects will eventually learn to compete more aggressively during the interventions
and thus, learn to trade the bond at its fundamental value from the beginning of the market round.
Abbink et al. (2006) also showed that subjects learn to bid aggressively much more quickly undera uniform price multi-unit auction than under discriminatory one. Thus, it is possible that we may
have observed di↵erent results if the intervention had been implemented with a uniform instead ofa discriminatory price auction. However, we leave such comparisons to future research.
The experimental paradigm on which we have based our experiment is the one where no trade
theorem applies just like many other asset market experiments that follow the paradigm of Smithet al. (1988). It has been recently pointed out, however, that the observed mis-pricing tends to belarger in such an environment compared to those in which participants have intrinsic motives totrade, e.g., to smooth consumption across periods (Asparouhova et al., 2016; Crockett et al., 2019)because of, e.g., participants’ desire to do something during the experiment (“active participation
hypothesis,” Lei et al., 2001) in the absence of alternative activities during the experiment. Thus,
it is possible that the e↵ect of market intervention we report in the current paper would be quitedi↵erent in the environment similar to the one considered by Asparouhova et al. (2016) and Crockett
et al. (2019). We believe future research investigating this issue will be of high importance.28
Furthermore, professional bond traders are likely to be more strategically sophisticated thanundergraduate students exposed to bond trading for the first time and thus, the results reported inthis paper may not apply to real bond markets. However, recent research shows that this may not be
a serious concern. For example, Weitzel et al. (2020) demonstrated that experimental asset markets
consisting of financial professionals result in mispricing, despite its magnitude being smaller than in
the case of markets consisting of students. Of course, it would be fruitful to conduct experimentswith professionals to check if the mis-pricing caused by a neutral intervention persists as well.

27 See Kwasnica and Sherstyuk (2013), among others, for a survey of multi-unit auction experiments.
28 We thank an anonymous reviewer for pointing this out.

27

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33

A

Subperiods analyses

Figure A shows the ECD of three subperiods, vGD1 (left), vGD2 (right), and vGD3 (right) acrossthe three rounds. The three treatments are represented by di↵erent colors: T1: Benchmark (blue),T2: Buy&Hold (red), and T3: Buy&Sell (dashed black). Below the ECD, p-values from KW test
for multiple comparison, as well as MW tests for pairwise comparisons are reported.
Round 1
vGD2

vGD1

-0.4

-0.2

CDF
1.0

CDF
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.0

0.2

0.4

vGD1

-0.4

p=0.858 (KW)
MW
T1
T2

T2
0.517

-0.2

-0.2

MW
T1
T2

-0.4

T2
0.062

-0.2

0.0

MW
T1
T2

T2
0.018

CDF
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2

0.0

0.2

0.4

-0.4

T2
0.066

-0.2

0.0

MW
T1
T2

0.2

0.4

-0.4

T2
0.001

-0.2

0.0

MW
T1
T2

CDF
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2
0.4

p=0.005 (KW)
T2
0.016

T3
0.002
0.851

-0.4

-0.2

0.2

0.4

p=0.004 (KW)

T3
0.985
0.012

T2
0.012

T3
0.001
0.970

vGD2

-0.4

-0.2

0.0

0.2

0.4

p=0.013 (KW)
MW
T1
T2

T2
0.007

T3
0.868
0.018

Figure A.1: ECD of vGD in three subperiods. vGD1 (periods 1–3). vGD2 (periods 5–7). vGD3
(periods 9–11) for three rounds. T1: Benchmark (blue), T2: Buy&Hold (red), and T3: Buy&Sell(dashed black). P-values from KW test for multiple comparison, as well as MW tests for pairwise
comparisons are reported.

34

vGD3

0.2

0.0

MW
T1
T2

0.4

vGD3

CDF
1.0

0.2

0.2

T2
0.006

CDF
1.0

0.0

T3
0.070
0.001

p=0.010 (KW)

T3
0.007
0.122

Round 3
vGD2

vGD1

vGD3

0.2
vGD2

p=0.001 (KW)

T3
0.084
0.651

0.4

vGD3

CDF
1.0

vGD1

0.2

p=0.001 (KW)

T3
0.028
0.752

Round 2
vGD2

vGD1

MW
T1
T2

0.4

p=0.056 (KW)

T3
0.914
0.821

p=0.109 (KW)

-0.2

0.2

vGD2

CDF
1.0

MW
T1
T2

-0.4

0.2

0.0

vGD1

-0.4

vGD3

CDF
1.0

vGD3

Table A.1 reports the mean and standard deviations of the vGAD, vGD, and TO of the threesubperiods, as well as p-values from KW and MW tests. It also reports, for a robustness check,the outcome when we include period 4 in the second subperiods and period 8 included in the third
subperiods (shown with ⇤). As one can see, the results are qualitatively the same.
Table A.1: Mean (standard deviation) of vGAD, vGD, TO of three subperiods
Benchmark

Buy&Hold

vGAD1
vGAD2
vGAD3
vGAD2 *vGAD3 *
vGD1
vGD2
vGD3
vGD2 *
vGD3 *
TO1
TO2
TO3
TO2 *
TO3 *

0.192 (0.197)
0.166 (0.165)
0.051 (0.045)
0.170 (0.168)
0.062 (0.055)
0.084 (0.237)
0.157 (0.174)
0.046 (0.050)
0.158 (0.179)
0.056 (0.060)
0.240 (0.078)
0.156 (0.066)
0.167 (0.146)
0.230 (0.089)
0.215 (0.161)

0.238 (0.226)
0.246 (0.124)
0.105 (0.073)
0.236 (0.119)
0.134 (0.074)
0.016 (0.236)
0.246 (0.124)
0.104 (0.072)
0.236 (0.119)
0.134 (0.073)
0.232 (0.055)
0.210 (0.098)
0.197 (0.110)
0.290 (0.125)
0.237 (0.120)

vGAD1
vGAD2
vGAD3
vGAD2 *vGAD3 *
GD1
GD2
GD3
GD2 *
GD3 *
TO1
TO2
TO3
TO2 *
TO3 *

0.141 (0.138)
0.085 (0.100)
0.024 (0.026)
0.093 (0.105)
0.025 (0.031)
0.122 (0.155)
0.085 (0.100)
0.006 (0.029)
0.093 (0.105)
0.010 (0.033)
0.292 (0.122)
0.167 (0.127)
0.188 (0.141)
0.218 (0.138)
0.235 (0.165)

0.224 (0.149)
0.239 (0.138)
0.060 (0.041)
0.243 (0.139)
0.076 (0.053)
0.220 (0.156)
0.239 (0.138)
0.048 (0.055)
0.243 (0.139)
0.063 (0.067)
0.248 (0.070)
0.210 (0.117)
0.201 (0.149)
0.250 (0.111)
0.243 (0.138)

vGAD1
vGAD2
vGAD3
vGAD2 *vGAD3 *
vGD1
vGD2
vGD3
vGD2 *
vGD3 *
TO1
TO2
TO3
TO2 *
TO3 *

0.092 (0.130)
0.041 (0.063)
0.008 (0.009)
0.050 (0.073)
0.009 (0.011)
0.082 (0.137)
0.032 (0.069)-0.001 (0.011)
0.038 (0.080)
0.001 (0.011)
0.260 (0.097)
0.124 (0.067)
0.181 (0.187)
0.178 (0.072)
0.218 (0.205)

0.397 (0.589)
0.151 (0.119)
0.040 (0.051)
0.161 (0.115)
0.045 (0.056)
0.186 (0.291)
0.142 (0.129)
0.032 (0.057)
0.155 (0.119)
0.036 (0.063)
0.242 (0.070)
0.192 (0.102)
0.229 (0.214)
0.229 (0.114)
0.257 (0.213)

T1=T2=T3a
Round 10.231 (0.229)
p=0.6110.285 (0.177)
p=0.052
0.078 (0.181)
p=0.015
0.263 (0.157)
p=0.0880.090 (0.147)
p=0.0080.011 (0.225)
p=0.858
0.282 (0.179)
p=0.056-0.000 (0.128)
p=0.001
0.259 (0.161)
p=0.073
0.036 (0.138)
p=0.0020.189 (0.059)
p=0.100
0.247 (0.210)
p=0.1510.119 (0.124)
p=0.0500.315 (0.237)
p=0.295
0.154 (0.129)
p=0.089
Round 20.196 (0.143)
p=0.179
0.179 (0.082)
p=0.001
0.025 (0.024)
p=0.0110.193 (0.091)
p=0.003
0.036 (0.024)
p=0.0020.196 (0.143)
p=0.1090.179 (0.082)
p=0.001
0.005 (0.033)
p=0.0100.193 (0.091)
p=0.003
0.021 (0.032)
p=0.0090.207 (0.091)
p=0.055
0.215 (0.093)
p=0.2220.122 (0.091)
p=0.236
0.272 (0.125)
p=0.278
0.161 (0.108)
p=0.201
Round 30.232 (0.132)
p=0.001
0.131 (0.083)
p=0.002
0.033 (0.047)
p=0.0210.146 (0.095)
p=0.001
0.039 (0.045)
p=0.0080.232 (0.132)
p=0.0050.130 (0.084)
p=0.004-0.016 (0.048)
p=0.0130.145 (0.096)
p=0.001-0.003(0.054)
p=0.0500.210 (0.057)
p=0.2480.178 (0.103)
p=0.1160.137 (0.126)
p=0.2380.226 (0.104)
p=0.3770.171 (0.137)
p=0.316
Buy&Sella Based on the KW testb Based on the MW test

35

T1=T2b

T1=T3b

T2=T3bp=0.368
p=0.062p=0.032
p=0.052p=0.008
p=0.517p=0.062p=0.018
p=0.048p=0.006
p=0.842p=0.069p=0.207p=0.121p=0.304

p=0.428p=0.026p=0.326p=0.066
p=0.777p=0.914p=0.028p=0.070p=0.052p=0.400
p=0.099
p=0.144p=0.198
p=0.296p=0.234

p=0.910p=0.722p=0.008
p=0.851p=0.008
p=0.821p=0.752p=0.001
p=1.000
p=0.001p=0.041p=0.734
p=0.017p=0.734
p=0.029p=0.072
p=0.001p=0.009
p=0.003
p=0.003
p=0.066
p=0.001p=0.006
p=0.003
p=0.006
p=0.437p=0.248p=0.971p=0.256
p=0.829p=0.207p=0.007p=0.534
p=0.008
p=0.048p=0.084
p=0.007p=0.985p=0.008
p=0.280p=0.032
p=0.077p=0.139p=0.135p=0.171

p=0.624
p=0.122p=0.012p=0.327p=0.013p=0.651p=0.122p=0.012p=0.327p=0.022p=0.061
p=0.850
p=0.145p=0.611p=0.090p=0.001p=0.003
p=0.016
p=0.003
p=0.009
p=0.016
p=0.012p=0.007p=0.002
p=0.011p=0.745p=0.052p=0.312p=0.207p=0.437p=0.002
p=0.002
p=0.018
p=0.001p=0.006
p=0.002
p=0.001p=0.868
p=0.001p=0.859
p=0.129p=0.125p=0.448p=0.256
p=0.428p=0.598
p=0.880p=0.797
p=0.880p=0.985p=0.851p=0.970p=0.018
p=0.970p=0.109
p=0.185p=0.650
p=0.100
p=0.940p=0.136

Mean forecast deviations

Median forecast deviations

f1,p - FV
40

f1,p - FV
40

20

20
1 2 3 4 5 6 7 8 9 10 11p

1 2 3 4 5 6 7 8 9 10 11

-20

-20

-40
T1: H0 : f1,t = 120
T2: H0 : f1,t = 120
T3: H0 : f1,t = 120
H0 : T 1 = T 2 = T 3

p

-40
t=1
0.0002
0.996
0.647
0.084

t=2
0.538
0.723
0.381
0.567

t=3
0.058
0.376
0.418
0.125

p-values
t=4
t=5
0.425
0.441
0.122
0.019
0.196
0.078
0.103
0.333

t=6
0.592
0.120
0.142
0.277

t=7
0.097
0.022
0.060
0.446

t=8
0.005
0.030
0.090
0.525

t=9
0.002
0.06
0.091
0.828

t=10
0.008
0.087
0.096
0.938

t=11
0.001
0.115
0.981
0.814

Figure B.1: The average (left) and the median (right) initial forecasts deviations from FV for 11
periods in three treatments. T1: Benchmark (thin blue), T2: Buy&Hold (thick red), and T3:
Buy&Sell (black dashed)

B

Initial forecasts

Here we analyze the initial set of forecasts submitted by the subjects to investigate whether theannounced large-scale intervention had any impact on the future price expectations of inexperienced
subjects. The deviations of the mean and median paths from FV are presented in Figure B.1 (T1:
Benchmark (thin blue), T2: Buy&Hold (thick red), and T3: Buy&Sell (black dashed)). Both paths
show a tendency to increase, although this is much more evident for the mean than for the median.
The median paths are very similar across the three treatments and quite close to the FV. Indeed it
is interesting to note that the median forecast for the final period (when bonds mature at FV) is
exactly equal to the FV for the three treatments. The means are all above the median, suggestingthat there is an upward skew in the initial distribution across the participants.
Figure B.1 shows the p-values for testing whether, for each treatment, the forecast for the periodp price is di↵erent from the FV, as well as whether the forecasts for the period p price are di↵erent
across the three treatments. Those periods in which the test rejects the null hypothesis at the 5%
significance level are shown in bold. These tests are conducted based on running the following OLS
regression (for each period) and testing the equality of the estimated coefficients.
if1,p= ↵1 DTi 1 + ↵2 DTi 2 + ↵3 DTi 3 + ✏ip

36

(B.1)

RAFD1

RFD1

CDF
1.0

CDF
1.0

0.8

0.8

0.6

0.6

0.4

0.4

0.2

0.2

0.0

0.5
1.0
1.5
2.0
Mean (Std. Dev.)
T1
T2
T3
0.503
0.546
0.543
(0.676) (0.908) (0.838)
p=0.807

RAFD

-2

-1

0

1

2

RFD

Mean (Std. Dev).
T1
T2
T3
0.062
0.155
0.127
(0.821) (1.03) (0.983)
p=0.538

Figure B.2: Empirical CDF of RAF D1 and RF D1 . T1: Benchmark (thin blue), T2: Buy&Hold(thick red), and T3: Buy&Sell (black dashed)iwhere f1,pis subject i’s forecast for the period p price elicited at the beginning of period 1 and Dxi s

are dummy variables that take a value of one if i has participated in treatment x 2 {T 1, T 2, T 3}.
The standard errors are corrected for a potential within-group clustering e↵ect. We have chosen thistest to control for possible correlation among subjects within a group.29 Overall, we do not observea significant e↵ect of the announced di↵erences in treatments on the initial forecasts.
To further check that the policy announcement did not influence subjects’ initial forecasts, wemeasured the deviation of price forecasts from FVs using two measures proposed by Akiyama et al.
(2014, 2017), the relative absolute forecast deviations (RAFD) and the relative forecast deviation(RFD). For the set of forecasts submitted by subject i at the beginning of period t, RAF Dti andRF Dti are defined as:
RAFDit =

T
i
X
|ft,p
120|
1
11 t + 1 p=t
120

RFDit =

T
i
X
ft,p
120
1
11 t + 1 p=t
120

(B.2)

(B.3)

i
where ft,pis the forecast asset price in period p submitted by subject i at the beginning of period t.

Figure B.2 shows the empirical cumulative distribution (ECD) of RAF D1i (left) and RF D1i
29 However, note that because this is the first set of forecasts submitted by the subjects without observing the pastrealized prices, the e↵ect of such a correlation should be very limited. Not correcting for the clustering e↵ect does notchange the result qualitatively.

37

(right) for the three treatments: T1: Benchmark (thin blue), T2: Buy&Hold (thick red), and T3:
Buy&Sell (black dashed). It also reports the mean, standard error, and the p-values for treatmentcomparison.30
The absence of treatment e↵ects in RAF D1 and RF D1 confirms the earlier finding. The announcement of a large intervention does not influence the subjects’ initial expectations.31

30 Just as we have done for the forecasts for the period p price, the test of equality among the three treatments is done
by running OLS regressions (and correcting for the clustering e↵ect) of the form Y i = a+b1 DT 1 +b2 DT 2 +b3 DT 3 +µi(where Y i is either RAF D1i or RF D1i . Dx is a dummy variable that takes a value of one if the treatment is x.) andtesting whether the estimated coefficients of the treatment dummies are equal, that is if H0 : b1 = b2 = b3 , to control
for the possible correlation among subjects within a group.
31 Furthermore, the nonparametric KW test provides the same conclusion: p=0.867 for RAFD and p=0.836 for
RDF.

38

Online Supplementary Material
The supplementary material is organized as follows:
I. Summary of results of the experiment reported in Panelvar et al. (2007)
II. Price dynamics in Individual Markets
III. Other measures of market outcomes
IV. Comparison between outcomes of two market sizes
V. Supply and demand schedules during central bank operations
VI. Instructions

I

Summary of results in Panelvar et al. (2007)
Benchmark

Buy&Hold

Buy&Sell

P
300

P
300

P
300

250

250

250

200

200

200

150

150

150

100

100

100

50

50
1 2 3 4 5 6 7 8 9 10 11t50
1 2 3 4 5 6 7 8 9 10 11t

1 2 3 4 5 6 7 8 9 10 11tNote: Thin lines are the realization of each of the 8 markets. The thick line is the volume weighted
average price across the 8 markets.

Figure I.1: Price dynamics in three treatments
Here we report the main results of our first set of experiments reported in earlier version of
our paper (Penalver et al., 2017) for completeness. The three treatments (Benchmark, Buy&Hold,
Buy&Sell) are the same as those described in the main text. However, unlike the experiment reported
in the main text where participants repeated the same treatment three times, here in each session,
participants participated in all the three treatments, but according to one of the 6 possible orders.
Here, we report the outcome of the first treatment participants are exposed to.
Computerized experiments were carried out between June and July 2016 at Waseda University
in Japan. 144 students were recruited from across the campus and each student participated inonly one experimental session. Each experimental session consisted of 24 subjects. Each session
was further divided into 4 groups of 6 students who played the game together. Thus 8 groups were
exposed to the benchmark treatment first, 8 groups were exposed to the Buy&Hold treatment first,
and 8 groups were exposed to the Buy&Sell treatment first.
Figure I.1 illustrates the price paths over the 11 periods in three treatments: Benchmark (left),
Buy&Hold (middle), and Buy&Sell (left). In each panel. the price paths for each of the 8 markets
alongside the volume-weighted average price are shown.
The dynamics of prices observed in Benchmark treatment confirm, especially for individual markets, previous results in the literature that prices can deviate quite substantially from the fundamental price (see, for example, Smith et al., 1988; Bostian and Holt, 2009). However, in our experiment,
unlike the previous finding in the literature, the average price path exhibits no obvious pattern andis not statistically significantly di↵erent from the fundamental price as one can see from the resultof statistical tests reported in the top panel of Table I.1. There is initially some variance in prices

1Table I.1: Dynamics of average prices (unweighted and weighted) in three treatments
Benchmark treatment
Unweighted
P-value

1
124
0.78

2
123
0.77

3
134
0.28

4
117
0.87

5
128
0.43

6
124
0.65

7
127
0.41

8
125
0.56

9
116
0.45

10
119
0.65

11
120
0.84

Weighted
P-value

118
0.85

119
0.96

138
0.14

108
0.52

112
0.21

123
0.75

129
0.16

131
0.12

118
0.67

122
0.55

118
0.13

Unweighted
P-value

1
125
0.70

2
135
0.26

3
136
0.12

4
145
0.01***

5150
0.00***

6
153
0.00***

7
157
0.01***

8155
0.03**

9
155
0.06*

10
148
0.11

11
111
0.63

Weighted
P-value

113
0.48

130
0.31

136
0.03**

142
0.00***

152
0.00***

162
0.00***

164
0.00***

167
0.01**

171
0.02**

144
0.15

129
0.21

Unweighted
P-value

1
127
0.49

2
131
0.18

3
134
0.12

4
146
0.01***

5
143
0.09*

6
148
0.12

7
146
0.11

8
129
0.05**

9
121
0.83

10
116
0.25

11
115
0.25

Weighted
P-value

126
0.48

130
0.24

133
0.11

142
0.01**

147
0.04**

148
0.05**

144
0.08*

140
0.00***

118
0.61

106
0.00***119
0.15

Buy&Hold treatment

Buy&Sell treatment

Note: P-values are the probability that the sample is drawn from a distribution with a mean equal to the fundamental
value of 120 using a two-sided Student’s t-test with 7 degrees of freedom. *** 0.01, ** 0.05, * 0.1.

but these converge very close to the fundamental price by period 11 in all 8 markets.
The middle panel of Figure I.1 illustrates the results of the Buy&Hold treatment. This showsa quite di↵erent profile to the Benchmark and if nothing else indicates that central bank actions
can disturb the price determination process. In one market, the market price scarcely deviatedfrom the fundamental price and in two others, prices dropped initially and then rose dramatically
before collapsing in a path resembling a bubble. Prices remained at or above the fundamental pricein all 8 markets until round 10.1 Nevertheless, prices in almost all markets converged towards thefundamental price by period 11.2 Average prices are statistically significantly above the fundamental
price from periods 4 to 9 (reported in the middle panel of Table I.1).
The top panel of Table I.2 presents simple comparison of means tests which confirm the visual
impression that average prices are statistically significantly higher over periods 5 to 9 when the
1 With the exception of 1 price in one period at 119.
2 No trading takes place at the extremely low price in period 11 in one of the games.

2Table I.2: Comparison of means
Benchmark vs Buy&Hold
Unweighted
Buy&Hold
BenchmarkP-value
Weighted
Buy&Hold
BenchmarkP-value

1

2

3

4

5

6

7

8

9

10

11

125
124
0.92

135
123
0.50

136
134
0.89

145
117
0.20

150
128
0.07*

153
124
0.04**

157
127
0.06*

155
125
0.08*

155
116
0.05*

148
119
0.10

111
120
0.62

113
118
0.71

130
119
0.46

136
138
0.88

142
108
0.11

152
112
0.00***

162
123
0.01***

164
129
0.02**

167
131
0.05**

171
118
0.02**

144
122
0.19

129
118
0.14

1

2

3

4

5

6

7

8

9

10

11

125
127
0.95

135
131
0.79

136
134
0.89

145
146
0.95

150
143
0.62

153
148
0.78

157
146
0.56

155
129
0.09*

155
121
0.07*

148
116
0.08*

111
115
0.83

113
126
0.33

130
130
1.00

136
133
0.69

142
142
0.93

152
147
0.68

162
148
0.33

164
144
0.24

167
140
0.09*

171
118
0.02**

144
106
0.04**

129
119
0.17

Buy&Hold vs Buy&Sell
Unweighted
Buy&Hold
Buy&SellP-value
Weighted
Buy&Hold
Buy&SellP-value

Note: P-values using a two-sided Student’s t-test with 7 degrees of freedom. *** 0.01, ** 0.05, * 0.1.

central bank undertakes QE than when it doesn’t.
The left panel of Figure I.1 illustrates the results of the Buy&Sell treatment. As reported in
the bottom panel of Table I.1, average prices in this treatment were statistically significantly above
fundamentals in periods 4 to 8 in the weighted-average case and periods 4, 5 and 8 in the unweighted
case.3 Prices were then statistically significantly below fundamentals in the weighted-average case inperiod 10. Despite these fluctuating dynamics, prices converged to fundamentals in the final period.
The bottom panel of Table I.2 reports a comparison of means between Buy&Hold and Buy&Selltreatments, and it is evident that behaviour before, during and immediately after the common buy
operation are indistinguishable. But prices fall in the case where the central bank subsequent sells
and are statistically significantly below the Buy&Hold in periods 8, 9 and 10.
It is clear, therefore, that in these experiments QE does make a significant di↵erence for bondprices and that the two QE treatments have starkly di↵erent e↵ects.

3 Periods 6 and 7 were quite close to the 10% significance threshold.

3

II

Price dynamics in Individual Markets

Figure II.1 illustrates the observed price dynamics in our experiments. The columns indicate, respectively, the Benchmark treatment, the Buy&Hold treatment, and the Buy&Sell treatment. The rows
show the first, second and third rounds for each treatment. In each panel, a thin line represents theobservations from one market, whereas a thick line represents their median.4 We observe substantial
variations across markets within each treatment.5
T1: Benchmark
pt - FV

pt - FV

5050
1 2 3 4 5 6 7 8 9 10 11t

1 2 3 4 5 6 7 8 9 10 11-50

pt - FV

pt - FV

5050t

1 2 3 4 5 6 7 8 9 10 11t

pt - FV
50-50

pt - FV

pt - FV

50

50

-50

t

Round 21 2 3 4 5 6 7 8 9 10 11t

pt - FV-50-50

1 2 3 4 5 6 7 8 9 10 11

T3: Buy&Sell

50

-50

1 2 3 4 5 6 7 8 9 10 11

T2: Buy&Hold
Round 1t

1 2 3 4 5 6 7 8 9 10 11t-50

Round 3pt - FV50

1 2 3 4 5 6 7 8 9 10 11-50t

1 2 3 4 5 6 7 8 9 10 11-50

Figure II.1: Dynamics of price deviations in T1: Benchmark (left), T2: Buy&Hold (center), and T3:
Buy&Sell (right) over three rounds. The medians across markets are shown using thick lines.

4 We have eliminated all prices in the periods in which no trading took place, which is why there are some gaps

in the series. No transaction is possible when the maximum submitted bid is below the minimum submitted ask orwhen the quantity demanded or supplied or both are zero.
5 The literature suggests that these variations can be due to variations in the characteristics of market participants,
such as their cognitive ability (Bosch-Rosa et al., 2018; Breaban and Noussair, 2015), their risk preferences (Breaban
and Noussair, 2015), and their tendency to speculate (Janssen et al., 2019). We have not gathered data for these
individual characteristics, and thus we are not able to further investigate the reason behind the variations withintreatment.

4

t

III

Other measures of market outcomes
Table III.1: Definitions of the measures of mispricing

Relative absolute deviation (RAD)Relative deviation (RD)
Geometric absolute deviation (GAD)
Geometric deviation (GD)
Volatility (Vol)
Turnover (TO)
Volume-adjusted relative absolute deviation (vRAD)
Volume-adjusted relative deviation (vRD)
Volume-adjusted geometric absolute deviation (vGAD)
volume-adjusted geometric deviation (vGD)

PT |Pp F Vp |1
p=1
TPT Pp|FFVV|p1
T⇣p=1P |F V | ⇣⌘⌘P
T
exp T1 p=1 ln F Vpp
1
⇣ P
⇣
⌘⌘
Pp
T
1
exp T p=1 ln F Vp
1
PT
1
Vol =
|(Pp F Vp ) (Pp 1
PT T 1 p=2
p=1 vp /OSpPT
|Pp F Vp |PT 1
p=1 vp|F V |
p=1 vpPT
Pp F Vp
PT 1v
p|F V | ⇣p=1⇣ vp p=1
⌘⌘
PT
Pp
1
P
exp
v
ln
1
p
p=1
v
F
V
p⌘⌘
⇣ k kP⇣PT
p
1
exp P vk p=1 vp ln F Vp
1k

In addition to the vGAD and vGD presented in the main text, we have computed various
measures proposed in the literature to characterize market outcomes, as summarized in Table III.1.
Furthermore, we consider two measures of the concentration of market activities to a few traders.
One measure considers the concentration of transactions, and the other concerns the concentrationof a few traders being marginal price setters, which captures the degree to which market prices are
determined by a few traders.
To measure the degree of concentration of transactions, we compute, for each market, the normalized Herfindahl index (HIST) based on the share of transactions. We use the normalized index toenable comparisons between markets with two di↵erent sizes. Namely, for each subject i in market
m, we compute his or her share of transactions sim as:
sim = PPTi
t=1 |qt |PTjj2m
t=1 |qt |(III.1)

where |qti | is the quantity subject i has transacted in period t and j 2 m represents subject j inmarket m. Then, the normalized Herfindahl index for the share of transactions in market m is:

HISTm =Pi 2
i2m (sm )

1

5

1/N

1/N

(III.2)

F Vp 1 )|where N is number of traders in m.
To measure the degree of concentration of subjects being a marginal price setter, we compute,for each market, the normalized Herfindahl index for the share of frequencies for subjects being amarginal price setter. We refer to this as HIF P Sm for market m. We identify the marginal pricesetter as the buyer (or buyers) who has (have) submitted the buy order with a bid equal to, or theclosest bid above, the market-clearing price, and the seller (or sellers) who has (have) submitted
the sell order with the ask equal to, or the closest ask below, the market price. In the case where
there is no transaction, given the way our price determination algorithm operates, it is the buyer
who has submitted the buy order with a bid just below the price, as well as the seller who has
submitted the sell order with an ask just above the market price. Note that because, in general, themarginal price-setting buyer and the marginal price-setting seller are di↵erent, we have more than
one marginal price setter in each period.
Table III.2 reports the means and standard deviations of these measures for each treatment
separately for three rounds. It also reports the p-values for treatment comparisons. The treatmentcomparisons reported in Table III.2 confirm that the di↵erences in the magnitude of mispricing
across the three treatments that we have reported in the main text are robust even if we use othermispricing measures. At the same time, Table III.2 shows that the three measures that are notrelated to non-mispricing – turnover (TO), HIST, and HIFPS – are not significantly di↵erent acrossthe three treatments. This suggests that di↵erences in the magnitude of mispricing across the threetreatments are not caused by di↵erences in the volume or degree of market concentrations acrossthe treatments.

6

7

0.080 (0.079)
0.072 (0.084)
0.078 (0.076)
0.069 (0.082)
0.745 (0.352)
4.041 (3.248)
0.092 (0.082)
0.077 (0.092)
0.089 (0.079)
0.073 (0.089)
0.072 (0.066)
0.061 (0.034)
0.051 (0.059)
0.029 (0.072)
0.076 (0.134)
0.012 (0.112)
0.656 (0.253)
3.659 (5.564)
0.054 (0.072)
0.043 (0.078)
0.052 (0.069)
0.012 (0.074)
0.068 (0.058)
0.051 (0.017)

RAD
RD
GAD
GD
TO
VOL
vRAD
vRD
vGAD
vGD
HIST
HIFPS

RAD
RD
GAD
GD
TO
VOL
vRAD
vRD
vGAD
vGD
HIST
HIFPS

a Based on the KW testb Based on the MW test

0.137 (0.115)
0.108 (0.120)
0.133 (0.107)
0.099 (0.110)
0.684 (0.275)
6.600 (4.733)
0.148 (0.133)
0.113 (0.143)
0.146 (0.126)
0.104 (0.135)
0.048 (0.032)
0.036 (0.021)

RAD
RD
GAD
GD
TO
VOL
vRAD
vRD
vGAD
vGD
HIST
HIFPS

Benchmark0.152 (0.085)
0.133 (0.107)
0.158 (0.088)
0.119 (0.122)
0.727 (0.323)
6.838 (5.585)
0.178 (0.100)
0.143 (0.143)
0.210 (0.198)
0.123 (0.182)
0.054 (0.037)
0.050 (0.024)

0.187 (0.091)
0.171 (0.093)
0.215 (0.186)
0.144 (0.135)
0.741 (0.211)
7.769 (7.328)
0.196 (0.092)
0.191 (0.094)
0.190 (0.088)
0.184 (0.089)
0.061 (0.042)
0.054 (0.020)

0.178 (0.073)
0.139 (0.086)
0.180 (0.075)
0.125 (0.089)
0.759 (0.218)
10.08 (5.966)
0.197 (0.090)
0.120 (0.115)
0.214 (0.125)
0.101 (0.133)
0.067 (0.051)
0.060 (0.042)

Buy&Hold

Buy&Sell
T1=T2=T3a
Round 10.182 (0.111)
p=0.201
0.121 (0.096)
p=0.498
0.193 (0.135)
p=0.159
0.098 (0.094)
p=0.5620.658 (0.351)
p=0.160
10.92 (7.244)
p=0.069
0.180 (0.115)
p=0.157
0.120 (0.114)
p=0.6640.189 (0.132)
p=0.1250.100 (0.104)
p=0.7490.044 (0.018)
p=0.734
0.053 (0.024)
p=0.134
Round 20.138 (0.073)
p=0.004
0.132 (0.072)
p=0.008
0.133 (0.069)
p=0.0030.127 (0.069)
p=0.0160.640 (0.273)
p=0.4125.775 (3.166)
p=0.0940.149 (0.080)
p=0.008
0.145 (0.078)
p=0.006
0.144 (0.077)
p=0.0070.140 (0.075)
p=0.005
0.057 (0.026)
p=0.869
0.053 (0.025)
p=0.808
Round 30.131 (0.091)
p<0.001
0.101 (0.066)
p=0.0010.169 (0.236)
p=0.001
0.075 (0.117)
p=0.001
0.607 (0.223)
p=0.5495.731 (6.687)
p=0.018
0.148 (0.089)
p<0.001
0.132 (0.086)
p=0.0020.145 (0.088)
p<0.0010.126 (0.085)
p=0.001
0.063 (0.037)
p=0.6650.047 (0.025)
p=0.668
p<0.001p=0.002
p=0.001p=0.001p=0.540p=0.004p<0.001p=0.004p<0.001p=0.004
p=0.666
p=0.957

p=0.002
p=0.005
p=0.002
p=0.013p=0.601p=0.042p=0.003
p=0.002
p=0.003
p=0.002p=0.885
p=1.000

p=0.105
p=0.235p=0.098
p=0.313p=0.207p=0.048p=0.084
p=0.471
p=0.056
p=0.517
p=0.494
p=0.098

T1=T2bp=0.002
p=0.002
p=0.004
p=0.006
p=0.577p=0.135p=0.001p=0.001p=0.001p=0.001
p=0.692
p=0.313p=0.031p=0.022p=0.026p=0.021p=0.540p=0.109
p=0.056
p=0.037p=0.061p=0.031p=0.564
p=0.505

p=0.150
p=0.471p=0.105
p=0.719
p=0.678
p=0.048p=0.171
p=0.407p=0.183
p=0.517
p=0.692
p=0.078

T1=T3bTable III.2: Mean (standard deviation) of various measures of market outcomes

p=0.366
p=0.274
p=0.228p=0.228p=0.291p=0.235p=0.327p=0.429p=0.327p=0.451p=0.346p=0.624

p=0.147p=0.250
p=0.142p=0.327p=0.152p=0.611p=0.152p=0.200
p=0.132
p=0.214p=0.792
p=0.651p=0.880p=0.720
p=0.821p=0.451p=0.052p=0.970p=0.407p=0.940p=0.386p=0.910p=0.572
p=0.792

T2=T3b

IV

Comparison between outcomes of two market sizes

We compare the outcomes for markets with six and 12 players, based on various measures of market
outcomes (subsection IV.1), outcomes of the interventions (subsection IV.2), and forecast deviations(subsection IV.3)

IV.1

Market characteristics

Table IV.1 reports, for each round and each treatment, means and the standard deviations ofvarious measures of market outcomes presented in Appendix III separately for markets with six
traders/market experiment and 12 traders/market experiment. The p-values from two-sample permutation tests (two-tailed) are also reported.
For the Benchmark and Buy&Hold treatments, except for HIST or HIFPS in Rounds 1 and 2, we
do not observe any statistically significant di↵erence in all the measures that we consider between
the sessions with six traders and 12 traders in any of the three rounds. In the Buy&Sell treatment,
we observe many measures of mispricing to be marginally significantly di↵erent between the twomarket sizes in Rounds 1 and 2. Among them, however, only vGAD in Round 1, and RD and GD
in Round 2 are significantly di↵erent at the 5% level.

IV.2

Outcomes of Buy&Sell operations

We compare the outcomes in the buy as well as the sell operations between two market sizes. We
analyse (a) the share of traders participating in, i.e., submitting bids or asks, these operations (sAskt
and sBidt for buy and sell operations in period t, respectively), (b) the coefficient of variation in thebids or asks submitted (cvAskt and cvBidt for buy and sell operations in period t, respectively), (c)the degree of concentration of the transactions during the operation measured by the normalized
Herfindahl index (HISTBt and HISTSt for buy and sell operations in period t, and HISTB and
HISTS for buy operations and sell operations over two periods), (d) the average price paid for aunit of bonds by the central bank in their buy operations (avePBt for period t, and avePB overtwo periods), and (d) the average price received by the central bank for a unit of bonds in their sell
operation (avePSt for period t, and avePS over two periods).
Table IV.2 shows the results. We observe statistically significant di↵erence in the two market
sizes in the degree of concentrations of transactions in buy operation (HISTB ) in Rounds 1 and 2 of8

9p-value
0.064
0.244
0.054
0.481
0.462
0.207
0.054
0.127
0.025
0.188
0.028
0.903

Buy&Sell Round 1
6 traders
12 traders0.232 (0.105) 0.132 (0.097)
0.149 (0.106) 0.093 (0.081)
0.254 (0.144) 0.132 (0.099)
0.115 (0.114) 0.081 (0.082)
0.726 (0.498) 0.591 (0.075)
13.313 (7.239) 8.525 (6.853)
0.240 (0.116) 0.121 (0.081)
0.164 (0.133) 0.076 (0.074)
0.257 (0.141) 0.122 (0.084)
0.135 (0.126) 0.065 (0.066)
0.054 (0.021) 0.034 (0.008)
0.052 (0.033) 0.054 (0.015)

RAD
RD
GAD
GD
TO
VOL
vRAD
vRD
vGAD
vGD
HIST
HIFPS

p-value
0.060
0.048
0.059
0.046
0.750
0.260
0.096
0.074
0.104
0.076
0.045
0.057

Means (standard deviations) as well as p-values from two-sample permutation tests are reported.

Buy&Sell Round 2
6 traders
12 traders0.172 (0.057) 0.104 (0.074)
0.168 (0.060) 0.097 (0.069)
0.165 (0.054) 0.101 (0.071)
0.160 (0.058) 0.093 (0.064)
0.614 (0.374) 0.666 (0.135)
6.700 (2.934) 4.850 (3.305)
0.182 (0.061) 0.115 (0.086)
0.180 (0.062) 0.110 (0.081)
0.175 (0.059) 0.113 (0.083)
0.173 (0.060) 0.106 (0.077)
0.070 (0.027) 0.045 (0.018)
0.064 (0.030) 0.041 (0.011)

Buy&Sell Round 3
6 traders
12 traders0.159 (0.103) 0.103 (0.073)
0.106 (0.063) 0.096 (0.073)
0.238 (0.322) 0.100 (0.070)
0.057 (0.155) 0.092 (0.069)
0.603 (0.281) 0.612 (0.165)
8.350 (8.610) 3.113 (2.450)
0.173 (0.082) 0.123 (0.095)
0.147 (0.081) 0.118 (0.094)
0.170 (0.082) 0.120 (0.092)
0.138 (0.082) 0.114 (0.091)
0.078 (0.044) 0.047 (0.022)
0.053 (0.034) 0.042 (0.014)

p-value
0.234
0.782
0.223
0.684
0.936
0.053
0.273
0.522
0.264
0.587
0.100
0.385

p-value
0.438
0.960
0.217
0.854
0.972
0.346
0.392
0.806
0.252
0.713
0.617
0.385

RAD
RD
GAD
GD
TO
VOL
vRAD
vRD
vGAD
vGD
HIST
HIFPS

Buy&Hold Round 3
6 traders
12 traders0.169 (0.076) 0.136 (0.095)
0.134 (0.123) 0.131 (0.096)
0.186 (0.083) 0.130 (0.090)
0.112 (0.153) 0.126 (0.092)
0.723 (0.433) 0.732 (0.192)
8.338 (7.051) 5.338 (3.462)
0.200 (0.097) 0.156 (0.106)
0.134 (0.181) 0.153 (0.104)
0.270 (0.257) 0.151 (0.100)
0.099 (0.245) 0.148 (0.099)
0.060 (0.044) 0.048 (0.030)
0.044 (0.023) 0.055 (0.025)

p-value
0.187
0.550
0.182
0.676
0.865
0.738
0.412
0.875
0.556
0.979
0.006
0.036

Buy&Hold Round 1
6 traders
12 traders0.154 (0.077) 0.203 (0.065)
0.126 (0.094) 0.152 (0.081)
0.154 (0.072) 0.205 (0.073)
0.115 (0.096) 0.135 (0.088)
0.749 (0.265) 0.768 (0.178)
10.625 (6.233) 9.525 (6.059)
0.178 (0.102) 0.216 (0.078)
0.115 (0.124) 0.124 (0.114)
0.195 (0.135) 0.233 (0.121)
0.100 (0.142) 0.102 (0.133)
0.099 (0.053) 0.035 (0.020)
0.082 (0.050) 0.038 (0.015)

RAD
RD
GAD
GD
TO
VOL
vRAD
vRD
vGAD
vGD
HIST
HIFPS
Buy&Hold Round 2
6 traders
12 tradersp-value
0.185 (0.100) 0.188 (0.088)
0.949
0.155 (0.100) 0.188 (0.088)
0.494
0.249 (0.255) 0.182 (0.083)
0.650
0.108 (0.171) 0.181 (0.083)
0.318
0.752 (0.248) 0.730 (0.184)
0.851
9.012 (9.670) 6.525 (4.247)
0.649
0.199 (0.109) 0.201 (0.079)
0.832
0.181 (0.111) 0.201 (0.079)
0.684
0.186 (0.104) 0.194 (0.074)
0.848
0.174 (0.107) 0.194 (0.074)
0.657
0.076 (0.051) 0.045 (0.025)
0.145
0.064 (0.023) 0.043 (0.008)
0.022

Table IV.1: Comparison between six traders/market sessions and 12 traders/market sessions
Benchmark Round 1
Benchmark Round 2
Benchmark Round 3
6 traders
12 tradersp-value
6 traders
12 tradersp-value
6 traders
12 tradersp-value
0.161 (0.148) 0.110 (0.062)
0.384
0.074 (0.084) 0.087 (0.078)
0.747
0.061 (0.069) 0.040 (0.048)
0.481
0.117 (0.160) 0.098 (0.060)
0.769
0.060 (0.092) 0.085 (0.078)
0.541
0.024 (0.089) 0.036 (0.051)
0.737
0.155 (0.136) 0.108 (0.061)
0.390
0.072 (0.081) 0.084 (0.075)
0.762
0.110 (0.177) 0.038 (0.046)
0.333
0.102 (0.145) 0.095 (0.060)
0.901
0.057 (0.089) 0.082 (0.076)
0.531-0.008 (0.149) 0.034 (0.049)
0.532
0.604 (0.210) 0.775 (0.323)
0.221
0.711 (0.358) 0.784 (0.366)
0.681
0.648 (0.313) 0.665 (0.184)
0.894
8.011 (5.821) 5.013 (2.655)
0.202
3.989 (3.295) 4.050 (3.421)
0.940
4.756 (7.458) 2.425 (1.972)
0.581
0.179 (0.175) 0.114 (0.054)
0.349
0.084 (0.088) 0.100 (0.080)
0.687
0.056 (0.082) 0.051 (0.065)
0.975
0.123 (0.196) 0.103 (0.053)
0.777
0.058 (0.103) 0.098 (0.080)
0.384
0.041 (0.090) 0.047 (0.068)
0.887
0.176 (0.164) 0.112 (0.054)
0.327
0.082 (0.084) 0.097 (0.077)
0.691
0.054 (0.077) 0.049 (0.062)
0.979
0.109 (0.184) 0.099 (0.053)
0.885
0.054 (0.098) 0.095 (0.077)
0.359
0.038 (0.085) 0.045 (0.065)
0.872
0.062 (0.036) 0.033 (0.019)
0.050
0.094 (0.083) 0.047 (0.028)
0.160
0.083 (0.072) 0.050 (0.032)
0.246
0.042 (0.025) 0.030 (0.014)
0.223
0.063 (0.039) 0.058 (0.031)
0.768
0.055 (0.020) 0.047 (0.014)
0.370

both the Buy&Hold and the Buy&Sell treatments, as well as in the sell operations (HISTS ) in Round
2. In the Buy&Sell treatment, we also observe statistically significant di↵erence in the coefficient ofvariation of asks in Round 1 as well as average price the central bank bought and sold in Round 2.
Apart from these, there are no statistically significant di↵erences between the two market sizes in
the measures we have considered.

IV.3

Forecasts deviations

Figures IV.1, IV.2, and IV.3 show the dynamics of the average RAF Dt and RF Dt for six traders/session(solid) and 12 traders/ session (dashed) in the benchmark, the Buy&Hold, and the Buy&Sell treatment, respectively. It also shows the p-values of the market size e↵ect. Note that we are simply
taking the average across subjects for the plot, but for the statistical test, we correct for within-groupcorrelation. Namely, these p-values are obtained by running linear regressions, which correct for theclustering e↵ect (at group level), of the form Y = ↵ + D12 , where Y is either RAF D or RF D andD12 takes a value of one for 12 traders/session and zero for six traders/session, and testing whether
is significantly di↵erent from zero.
RAF Dt and RF Dt are not statistically significantly di↵erent (at the 10% level) between thetwo market sizes in any of the periods in the benchmark treatment. Similarly, for the Buy&Hold
treatment, except for the RAF Dt in period 10 of Round 1, they are not statistically significantlydi↵erent at the 10% level between two market sizes. For Buy&Sell treatments, there are more periods
where RAF Dt and RF Dt are statistically significantly di↵erent at the 10% level between the twomarket sizes. Nevertheless, for most of the periods, they are not statistically significantly di↵erent.

10

11

Buy&Sell Round 3
6 traders
12 traders0.917 (0.089) 0.938 (0.059)
0.875 (0.148) 0.885 (0.062)
0.066 (0.051) 0.422 (0.682)
0.061 (0.057) 0.237 (0.519)
0.419 (0.189) 0.375 (0.170)
0.611 (0.335) 0.379 (0.278)
153.8 (13.80) 143.1 (20.27)
148.8 (14.57) 137.9 (17.20)
0.658 (0.207) 0.459 (0.262)
150.8 (14.07) 140.5 (18.67)
0.896 (0.124) 0.865 (0.133)
0.854 (0.107) 0.854 (0.118)
0.203 (0.197) 0.097 (0.099)
0.261 (0.373) 0.101 (0.113)
0.376 (0.200) 0.210 (0.096)
0.637 (0.362) 0.400 (0.310)
124.3 (12.05)
124.7 (5.45)
109.7 (28.46)
121.7 (3.62)
0.756 (0.345) 0.485 (0.162)
116.3 (14.66) 123.2 ( 4.24)

Means (standard deviations) as well as p-values from two-sample permutation tests (two-sided) are reported.

p-value
0.898
0.546
0.568
0.349
0.009
0.345
0.045
0.018
0.023
0.026
1.000
0.595
0.463
0.512
0.068
0.254
0.171
0.128
0.001
0.084

p-value
0.423
0.779
0.222
0.616
0.624
0.139
0.274
0.193
0.118
0.232
0.742
0.911
0.199
0.328
0.050
0.174
0.931
0.179
0.060
0.215

sAsk4
sAsk5
cvAsk4
cvAsk5HISTS4HISTS5
avePB4
avePB5
HISTB
avePB
sBid8
sBid9
cvBid8cvBid9HISTS8HISTS9avePS8avePS9HISTS
avePS

Buy&Sell Round 2
6 traders
12 traders0.896 (0.177) 0.875 (0.109)
0.792 (0.214) 0.844 (0.094)
0.251 (0.470) 0.333 (0.594)
0.067 (0.069) 0.229 (0.424)
0.545 (0.390) 0.169 (0.066)
0.475 (0.293) 0.354 (0.197)
165.5 (14.72) 146.0 (19.83)
163.0 (10.42) 145.1 (15.25)
0.646 (0.357) 0.336 (0.104)
164.3 (12.00) 145.6 (17.02)
0.917 (0.126) 0.917 (0.077)
0.875 (0.173) 0.823 (0.129)
0.163 (0.177) 0.101 (0.078)
0.141 (0.152) 0.097 (0.119)
0.503 (0.278) 0.256 (0.210)
0.409 (0.158) 0.281 (0.252)
131.3 (9.57)
126.1 (3.53)
127.5 (6.90)
123.1 (3.27)
0.787 (0.206) 0.369 (0.129)
129.4 ( 6.72) 124.6 ( 2.70)

Buy&Sell Round 1
6 traders
12 traders0.917 (0.126) 0.896 (0.124)
0.771 (0.177) 0.865 (0.147)
0.397 (0.444) 1.062 (0.617)
0.108 (0.078) 0.382 (0.576)
0.484 (0.377) 0.308 (0.223)
0.258 (0.193) 0.205 (0.132)
148.8 (27.36) 136.7 (19.80)
162.8 (17.00) 146.4 (17.71)
0.531 (0.164) 0.377 (0.139)
155.7 (20.95) 141.5 (18.32)
0.875 (0.118) 0.927 (0.083)
0.854 (0.107) 0.885 (0.109)
0.220 (0.128) 0.340 (0.188)
0.218 (0.142) 0.203 (0.137)
0.269 (0.127) 0.221 (0.137)
0.489 (0.276) 0.250 (0.208)
147.6 (37.21) 143.7 (19.56)
136.8 (29.19) 132.6 (16.03)
0.604 (0.257) 0.391 (0.234)
142.2 (31.48) 138.1 (17.15)

sAsk4
sAsk5
cvAsk4
cvAsk5HISTB4HISTB5
avePB4
avePB5HISTB
aveP Bp-value
0.873
0.217
0.028
0.083
0.273
0.528
0.317
0.080
0.064
0.165
0.243
0.559
0.156
0.836
0.482
0.068
0.801
0.773
0.109
0.796

Table IV.2: Market interventions. Comparison between six traders/market sessions and 12 traders/market sessions
Buy&Hold Round 1
Buy&Hold Round 2
Buy&Hold Round 3
6 traders
12 tradersp-value
6 traders
12 tradersp-value
6 traders
12 tradersp-value
0.875 (0.077) 0.792 (0.100)
0.138
0.854 (0.165) 0.927 (0.053)
0.189
0.854 (0.165) 0.885 (0.088)
0.750
0.833 (0.089) 0.802 (0.099)
0.655
0.854 (0.107) 0.802 (0.109)
0.452
0.854 (0.188) 0.792 (0.118)
0.515
0.724 (0.473) 0.623 (0.381)
0.635
0.328 (0.418) 0.315 (0.475)
0.956
0.168 (0.328) 0.262 (0.527)
0.546
0.276 (0.408) 0.378 (0.319)
0.594
0.341 (0.490) 0.268 (0.520)
0.565
0.226 (0.376) 0.067 (0.042)
0.201
0.564 (0.311) 0.257 (0.100)
0.014
0.663 (0.366) 0.251 (0.989)
0.01
0.545 (0.304) 0.369 (0.135)
0.174
0.452 (0.297) 0.249 (0.150)
0.107
0.531 (0.321) 0.373 (0.293)
0.315
0.508 (0.299) 0.498 (0.273)
0.947
140.9 (20.63) 146.3 (16.91)
0.571
180.0 (65.09) 154.6 (16.29)
0.358
155.4 (25.05) 152.5 (17.98)
0.792
161.7 (40.57)
149.5 (9.91)
0.460
160.0 (30.10) 156.7 (20.28)
0.797
151.2 (25.04) 147.1 (20.94)
0.722
0.338 (0.180) 0.156 (0.087)
0.020
0.256 (0.114) 0.123 (0.065)
0.015
0.223 (0.120) 0.202 (0.047)
0.664
151.3 (24.70) 147.9 (12.29)
0.731
170.0 (45.79) 155.4 (16.28)
0.464
153.3 (24.20) 149.8 (19.34)
0.748

Round 1

Dynamics of the average RAFDt in the Benchmark
Round 2Round 3

RAFD
1.0

RAFD
1.0

RAFD
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2
1

2

Round 1Round 2Round 3

3

4

5

t=1
0.974
0.546
0.966

6

7

8

9 10 11

t=2
0.186
0.440
0.764

Round 1

t

1

2

3

4

5

6

7

8

9 10 11

1 2 3 4 5 6 7 8 9 10 11

Round 1Round 2Round 3

2

Dynamics of the average RFDt in the Benchmark
Round 2
RFD
1.0
0.8
0.6
0.4
0.2

t=1
0.621
0.772
0.743

1

P-values for the treatment di↵erence
t=3
t=4
t=5
t=6
t=7
t=8
0.152 0.146 0.174 0.396 0.796 0.153
0.172 0.193 0.306 0.539 0.458 0.536
0.725 0.985 0.823 0.569 0.942 0.998

RFD
1.0
0.8
0.6
0.4
0.2
-0.2
-0.4

0.2
t

t=2
0.401
0.312
0.671

t
-0.2
-0.4

3

4

5

t=9
0.577
0.831
0.929

6

7

8

9 10 11

t=10
0.450
0.860
0.570

t

t=11
0.293
0.697
0.293

Round 3

RFD
1.0
0.8
0.6
0.4
0.2
1 2 3 4 5 6 7 8 9 10 11

t
-0.2
-0.4

1 2 3 4 5 6 7 8 9 10 11

P-values for the treatment di↵erence
t=3
t=4
t=5
t=6
t=7
t=8
0.242 0.256 0.264 0.444 0.862 0.087
0.135 0.185 0.302 0.507 0.481 0.357
0.721 0.936 0.823 0.545 0.731 0.534

t=9
0.355
0.863
0.641

t=10
0.267
0.927
0.067

t

t=11
0.276
0.371
0.146

Figure IV.1: Dynamics of the average RAFDt (top) and the average RFDt (bottom) over 10 periodsin three rounds of the Benchmark treatment for sessions with six traders (solid) and 12 traders(dashed).

12

Dynamics of the average RAFDt in Buy&Hold
Round 2Round 1
RAFD
1.0

RAFD
1.0

RAFD
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2
1

2

Round 1Round 2Round 3

3

4

5

t=1
0.580
0.224
0.716

6

7

8

9 10 11

t=2
0.625
0.840
0.975

Round 1

t

0.2
1

2

3

4

5

6

7

8

9 10 11t

1 2 3 4 5 6 7 8 9 10 11

Round 1Round 2Round 3

t=2
0.798
0.840
0.938

2

Dynamics of the average RFDt in Buy&Hold
Round 2
RFD
1.0
0.8
0.6
0.4
0.2

t=1
0.220
0.490
0.801

1

P-values for the treatment di↵erence
t=3
t=4
t=5
t=6
t=7
t=8
0.629 0.886 0.447 0.529 0.358 0.452
0.610 0.671 0.586 0.362 0.406 0.433
0.631 0.922 0.756 0.770 0.232 0.892

RFD
1.0
0.8
0.6
0.4
0.2
-0.2
-0.4

Round 3

t
-0.2
-0.4

3

4

5

t=9
0.161
0.439
0.485

6

7

8

9 10 11

t=10
0.275
0.573
0.667

t

t=11
0.057
0.912
0.599

Round 3

RFD
1.0
0.8
0.6
0.4
0.2
1 2 3 4 5 6 7 8 9 10 11

t
-0.2
-0.4

1 2 3 4 5 6 7 8 9 10 11

P-values for the treatment di↵erence
t=3
t=4
t=5
t=6
t=7
t=8
0.307 0.516 0.566 0.399 0.343 0.445
0.628 0.688 0.625 0.410 0.417 0.519
0.685 0.981 0.835 0.785 0.190 0.625

t=9
0.145
0.394
0.955

t=10
0.143
0.997
0.780

t

t=11
0.335
0.950
0.748

Figure IV.2: Dynamics of the average RAFDt (top) and the average RFDt (bottom) over 10 periodsin three rounds of the Buy&Hold treatment for sessions with six traders (solid) and 12 traders(dashed).

13

Dynamics of the average RAFDt in Buy&Sell
Round 2Round 1
RAFD
1.0

RAFD
1.0

RAFD
1.0

0.8

0.8

0.8

0.6

0.6

0.6

0.4

0.4

0.4

0.2

0.2
1

2

Round 1Round 2Round 3

3

4

5

t=1
0.624
0.052
0.062

6

7

8

9 10 11

t=2
0.534
0.174
0.063

Round 1

t

0.2
1

2

3

4

5

6

7

8

9 10 11t

1 2 3 4 5 6 7 8 9 10 11

Round 1Round 2Round 3

t=2
0.999
0.197
0.063

2

3

Dynamics of the average RFDt in Buy&Sell
Round 2
RFD
1.0
0.8
0.6
0.4
0.2

t=1
0.595
0.092
0.056

1

4

P-values for the treatment di↵erence
t=3
t=4
t=5
t=6
t=7
t=8
0.406 0.070 0.556
0.545
0.416 0.455
0.392 0.252 0.040 0.019 0.099 0.283
0.271 0.265 0.243
0.496 0.059 0.306

RFD
1.0
0.8
0.6
0.4
0.2
-0.2
-0.4

Round 3

t
-0.2
-0.4

5

6

7

t=9
0.438
0.199
0.282

8

9 10 11

t

t=10
0.111
0.486
0.308

t=11
0.037
0.723
0.275

Round 3

RFD
1.0
0.8
0.6
0.4
0.2
1 2 3 4 5 6 7 8 9 10 11

t
-0.2
-0.4

1 2 3 4 5 6 7 8 9 10 11

P-values for the treatment di↵erence
t=3
t=4
t=5
t=6
t=7
t=8
0.379 0.057 0.409
0.540 0.344 0.410
0.390 0.219 0.039 0.016 0.144 0.736
0.270 0.245 0.239
0.501 0.121 0.997

t=9
0.449
0.396
0.343

t=10
0.690
0.461
0.299

t

t=11
0.572
0.716
0.313

Figure IV.3: Dynamics of the average RAFDt (top) and the average RFDt (bottom) over 10 periodsin three rounds of the Buy&Sell treatment for sessions with six traders (solid) and 12 traders(dashed).

14

V

Supply and demand schedules during central bank operations

Figures V.4 to V.7 show the supply schedules constructed from orders submitted during the buy
operations for each group and treatment. Red line is for the operation between periods 3 and 4, andblue dashed line is for the one between periods 4 and 5. The equilibrium supply schedule is shownin gray solid line while the quantity to be purchased is shown in gray dashed vertical line. For eachgroup, the outcomes from three rounds are displayed.
Figures V.8 and V.9 show the demand schedules constructed from orders submitted during thesell operation for each group and treatment. Just as for the supply schedules shown in Figures V.4 to
V.7, red line is for the operation between periods 7 and 8, and blue dashed line is for the one between
periods 8 and 9. The equilibrium demand schedule is shown in gray solid line while the quantity
to be sold is shown in gray dashed vertical line. For each group, the outcomes from three rounds
are displayed. Unlike the supply schedules for the buy operations, convergence to the equilibrium isobserved by Round 3 in many of the groups shown in Figures V.8 and V.9.

15

Group 1, Round 1

Group 1, Round 2

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Figure V.4: Supply schedule during Buy operation. The one between periods 3 and 4 is shown inred, and the one between periods 4 and 5 is shown in blue dashed. Buy & Hold. N=616

Group 1, Round 1

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Figure V.5: Supply schedule during Buy operation. The one between periods 3 and 4 is shown inred, and the one between periods 4 and 5 is shown in blue dashed. Buy & Sell. N=617

Group 1, Round 1

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Figure V.6: Supply schedule during Buy operation. The one between periods 3 and 4 is shown inred, and the one between periods 4 and 5 is shown in blue dashed. Buy & Hold. N=1218

Group 1, Round 1

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Figure V.7: Supply schedule during Buy operation. The one between periods 3 and 4 is shown inred, and the one between periods 4 and 5 is shown in blue dashed. Buy & Sell. N=1219

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Figure V.8: Demand schedule during Sell operation. The one between periods 7 and 8 is shown inred, and the one between periods 8 and 9 is shown in blue dashed. Buy & Sell. N=620

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Figure V.9: Demand schedule during Sell operation. The one between periods 7 and 8 is shown inred, and the one between periods 8 and 9 is shown in blue dashed. Buy & Sell. N=12

21

VI

English translation of the instruction

The instructions for the three treatments begin with information common to all threetreatments. The common part of the instructions is for the baseline treatment. After apractice round, the instructions for each treatment are presented as follows. For the buy
and hold treatment, an explanation of the buying operation is added after the common
information. For the buy and sell treatment, an explanation of the selling operation isadded after the instructions for the buy and hold treatment. Below is the instruction formarket with six traders. For those with 12 traders, numbers are replaced accordingly.
The handout of the instruction below is distributed to the participants, and the
instruction is explained by the movie with the sound that a computer reads out thesentences. Both the movie and sentences to be read out are identical to the instruction
below.

Instructions for Today’s Experiment
Let’s start today’s experiment. The experiment is explained in the handout in front of you.

Please turn to the next page.
We first explain the instructions for today’s game. There is a practice period for the game so that you
may familiarize yourself with the computer interface before the real experiment. The experimentconsists of three games, each of which has 11 periods. After completing the games, we will ask you to
respond to a questionnaire and take some quizzes. Your earnings will be paid in cash at the conclusionof the experiment.
Your earnings will consist of a participation fee of 500 yen, and an amount that depends on the resultsof the games. The questionnaire and quizzes will not impact your earnings. The three games are
independent of each other, so that the result of one game does not affect the other games. You will
have a short bathroom break before the game begins.
[Today’s experiment]
You will participate in a bond trading game in which you trade national bonds in an artificial market.
Please listen to the instructions carefully. If you do not understand any part of an instruction, ask forclarification by raising your hand. Moreover, if you have any questions during the experiment, raise
your hand and an instructor will come to you and answer your question privately.
Throughout the experiment, please respect the following rules.

22

1. Do not talk to the other participants during the experiment or the breaks.
ü This may affect the results of the experiment.
2. Use your mouse or keyboard only when instructed to do so by the instructor; otherwise, it may
cause a problem.
ü If any malfunction occurs, all participants will have to restart the game.

Please turn to the next page.
[Outline of bond trading game]You will be divided into several groups. You will not know the identities of the members of each group.
Each group will consist of six subjects. You will play the bond trading game with the other five people
in the group to which you belong.
[Objectives of the game]Your objective in this game is to make as much profit as you can. There are two ways of making a
profit:•First, you can realize a profit margin through buying and selling bonds, from dividends on yourbond holdings, and from interest on your cash holdings.

•

Second, you can make a profit by accurately predicting the future prices of the bonds.

We use Marks as the currency for the experiment. At the end of the experiment, your profit will be
converted into Yen (1 Mark = 1 Yen) and paid out to you.

Please turn to the next page.
[Earning a profit margin]
You will be given eight bonds and 800 Marks at the beginning of the game. To earn a profit margin bytrading, you need to buy bonds at a low price and sell them at a higher price. For example, supposethat you buy a bond for 100 Marks, and that the price of the bond then increases to 120 Marks. If you
sell the bond, you earn 120 (selling price) - 100 (purchase price) = 20 Marks profit. In contrast, supposethat you buy a bond for 100 Marks, and that the price of the bond then decreases to 80 Marks. If you
sell the bond, you will make 80 (selling price) - 100 (purchase price) = 20 Marks loss. We explain later
how the prices are determined.
We now explain how to use the program interface.
[Order entry screen]
The following screen is used to enter your orders for each period.

23

①
⑥
②

③

④

⑤

⑦
⑧

⑨

①

This shows the time remaining for entering your orders. The time limit for entering your orders
is 60 seconds. When the time has elapsed, a red warning message will flash in the top-right cornerof your screen. A period ends once everyone has pressed “OK”; note that this could be within the
60-second time limit.

② This indicates your cash balance or the amount of money at your disposal; you may buy bonds
up to this amount.
③ This shows the number of bonds you have. You may sell a maximum of this number of bonds.
④ This is where you enter the maximum price you are willing to pay to buy a bond in this period.
You must enter a whole number between 1 and 2000.
⑤ This is where you enter the maximum number of bonds that you want to buy in this period. If you
do not want to purchase any bonds, enter 0. The product of ④ and ➄ must be no greater thanyour cash balance shown in ➁. An error message will appear if (the number of bonds you wish
to buy) × (the maximum price you are prepared to pay for these) exceeds your cash balance.
In practice, the price you actually pay for a bond may not be the same as the maximum price you arewilling to pay. This is because the market price depends on all the orders placed by the market
participants. If the market price is greater than the maximum you are willing to pay, then your order
will not be processed. This will be further clarified at a later stage.

Please turn to the next page.
⑥

This is where you enter the minimum price at which you are willing to sell your bonds in thisperiod. You must enter a whole number between 1 and 2000. The price you enter here should not
be greater than that given in ④.

24

⑦This is where you enter the number of bonds that you want to sell in this period. If you do notwant to sell any bonds, enter 0. The maximum number of bonds you can sell is the number ofbonds you hold, as shown in ③. If the number of bonds you want to sell exceeds the number ofbonds you hold, an error message will appear.

In practice, the price at which you sell a bond may not be the same as the minimum price at which you
are willing to sell. This is because the market price depends on all the orders placed by the market
participants. If the market price is lower than your minimum price, then your order will not beprocessed. This will be further clarified at a later stage.
⑧

After entering appropriate values in ④~⑦, press the “OK” button. Once all market participants
have pressed this button, the current period ends.

⑨

This table gives a history of the market prices. Thus the cells corresponding to future periods areblank.

The most important points for buying and selling bonds are summarized below.
•

You can simultaneously place buy and sell orders, or you can place only a buy order or only asell order. It is also possible to not submit any order.

•

If you do not want to submit a buy order, please enter 0 as the quantity to buy. If you do not wantto submit a sell order, please enter 0 as the quantity to sell.

•

The screen displays an error message if any of the following conditions are violated.
1.

The maximum quantity to sell must be less than or equal to the number of units you hold.

2.

The maximum purchase price multiplied by the quantity to buy must be less than or equal
to the cash you have available.

3.

If you simultaneously place buy and sell orders, the maximum purchase price must be lessthan or equal to the minimum selling price.

Please turn to the next page.
[End of each period screen]①

Market prices

The price is set according to the order book within your market. There is a single price for all bondsbought and sold in each period. The price is set to equate the number of buy orders and the number ofsell orders.
We explain how the market prices are set by using the following two examples.
[Example 1: how the market price is determined]25

Consider the following buy/sell orders placed by four traders.
－Trader 1: One sell order, which can be executed at 10 Marks or higher－Trader 2: Two sell orders, which can be executed at 40 Marks or higher－Trader 3: One buy order, which can be executed at 60 Marks or lower－Trader 4: One buy order, which can be executed at 20 Marks or lower
A graph summarizing these orders is shown below.

Quantity3

Quantity demanded = Quantity supplied at
21 Marks <= p <=39 Marks

2

The market price will be set to theminimum price of this interval: 21 marks



1

0

10 20 30 40 50 60

Price

A seller is willing to sell at the price requested or higher. A buyer is willing to buy at the price specified
or lower. As shown above, there is only one bond supplied at 10 Marks. If the price rises to 40 Marks,
the number of bonds supplied increases to three. However, only one bond is demanded at 60 Marks.
If the price falls to 20 Marks, the quantity demanded increases to two. Therefore, the quantity
demanded is equal to the quantity supplied at prices between 21 Marks and 39 Marks. The market
price is set at the minimum price in this interval; that is, 21 Marks.
Next we consider the second example.

Please turn to the next page.
[Example 2: how the market price is determined]
Consider the following buy/sell orders placed by five traders.
－Trader 1: One sell order, which can be executed at 10 Marks or higher－Trader 2: One sell order, which can be executed at 30 Marks or higher－Trader 3: One sell order, which can be executed at 30 Marks or higher－Trader 4: One buy order, which can be executed at 60 Marks or lower－Trader 5: One buy order, which can be executed at 30 Marks or lower
A graph summarizing these orders is shown below.

26Quantity3Market price = 30
Quantity traded =2
One share of trader 1 and one
share of either trader 2 and 3
will be traded.

2




1

0

10 20 30 40 50 60

PriceAs shown above, only one bond is supplied at 10 Marks as in the previous example. If the price rises
to 30 Marks, the number of bonds that are supplied increases to three. However, there is only one bonddemanded at 60 Marks. If the price falls to 30 Marks, the quantity demanded increases to two. As aresult, two transactions can be completed at 30 Marks. In this case, the market price is set at 30 Marks.
The orders that are fulfilled are determined as follows.
Priority is given to Trader 1, because he/she requested a price less than the market price. In addition
to the order of Trader 1, the order of either Trader 2 or Trader 3 is fulfilled. The choice between
Trader 2 and Trader 3 is determined randomly by a computer.
[End of each period screen]
At the end of each period, the following screen is displayed with the information described below.

⑨
①
②
④
⑤

③
⑥
⑦
⑧

27

The details in the green sentences will be explained later.
①

This shows the market price as explained previously.

②

A positive value indicates the number of bonds you purchased in the current period, while anegative value indicates the number of bonds you sold in the current period.

③

This shows the interest payments for the current period.

④

This shows the dividend payments for the current period.

⑤

This shows your cash holdings after the transactions, the interest payments and the dividend
payments have been processed for the current period.

⑥

This is the number of bonds you currently hold.

⑦

This is the number of market prices that you have predicted correctly.

⑧

By clicking the “Continue” button, you move to the next period.

⑨

The remaining time (maximum of 20 seconds) that this screen will be visible is displayed here.
After observing the information on the screen, press the “Continue” button ⑧. Once all of theparticipants have pressed this button, the computer will display the next screen.

Please turn to the next page.
[Earning interest from cash holdings]
In each game, there are 11 periods in which you can submit buy/sell orders and trade with other traders
in your market. You will be paid interest of 5% on the amount of cash you hold at the end of eachperiod.
The interest earned is rounded up to a whole number. For instance, suppose that at the end of the 5th
period your cash holdings are 90 Marks and you have two bonds. You are paid interest of 5% on the
cash holdings of 90 Marks. The interest of 4.5 Marks is rounded to 5 Marks, so your cash holdings are
95 Marks after adding the interest.
Likewise, dividends from bonds are added to your cash holdings when you hold bonds. This is
explained below.
[Earning dividends from bonds]
You will be paid a dividend of 6 Marks per bond for the bonds you hold at the end of each period. The
dividend income at the end of each period is calculated as: 6 Marks × (number of bonds you hold).
In the example above, 12 Marks (= 6 Marks * 2 bonds) is added to your cash holdings of 95 Marks
after adding the interests. Thus you start the 6th period with 107 Marks and two bonds.
If you hold bonds at the end of the game (after the 11th period), the bonds you hold are bought for 120

28

Marks each after any dividend payments.

Please turn to the next page.
[Earning a profit by predicting future prices correctly]
Before each period begins, you will be asked to predict the market prices in the remaining periods.
The following screen will appear.
The time limit for predicting the market prices is (number of periods remaining) x (20 seconds). Before
the beginning of the first period, the time limit is 220 seconds. After that, the time limit decreases by
20 seconds each period. The time limit before the 11th period is 20 seconds.
When the time limit is reached, a warning message to complete your prediction will flash in red in theupper right corner of your screen. The next period begins when everyone has finished entering theirprice predictions and has pressed “OK”.

[Prediction of future prices]
You will be asked to predict the prices for all the remaining periods before each period begins.
That is:–before the beginning of period 1, there are 11 periods remaining so you must predict 11
prices;–before the beginning of period 2, there are 10 periods remaining so you must predict 10
prices;
・・・

29

–

before the beginning of period 11, only one period remains so you must predict one price.

Thus, you will make a total of 66 predictions of market prices.

Please turn to the next page.
[Earning a profit by predicting future prices]
The computer keeps a record of the number of accurate predictions (that is, when the market pricerealized is between 90% and 110% of your predicted price for the corresponding period).
At the end of each game, you will be paid a bonus based on the number of accurate predictions
according to the following formula: (your final cash balance) x 0.5% x (the number of accurate
predictions). The maximum bonus percentage is 0.5% x 66 = 33%. Please be aware that your final
cash balance depends on earnings made from profit margins, interest and dividends, so the size of yourbonus decreases as your earnings from profit margins, interest and dividends decrease.
[Summary of ways to make a profit]
There are two ways of making a profit: (1) earning a profit margin, earning returns from dividends andearning interest on cash holdings, and (2) predicting market prices of bonds.
The computer randomly chooses one of three games. At the end of the experiment, your final cash
holdings from the game will be converted at a rate of 1 Mark to 1 Yen and paid out to you. In addition
to the aforementioned rewards, you will be offered 500 yen as payment for participating in theexperiment.

After the instructions, we will announce the practice round.
[Practice]
We start with a practice round so that you can familiarize yourself with the software. In particular, youwill learn how to enter the required information. The first screen displayed is for predicting future
prices. Press the “OK” button after you have entered all your price forecasts. The computer will display
the order entry screen once everyone has pressed “OK”.
The practice round ends when everyone has entered their orders and pressed the “OK” button. The
results of the practice round will not be displayed. Rewards do NOT take the practice round into
consideration.
Let us start the practice round.
[Finish the practice round]30

Before starting the game, we will announce the following:
Let’s start the game.
•

There are six people in the market.

•

All the people in the market are in this room.

•

You will be given eight bonds and 800 Marks at the beginning of the game.
(The instructions for the baseline treatment finish here.)[Instructions for buying operation]
During this game, the computer will buy bonds you hold. The buying operation will be conducted
before the beginning of the 4th and 5th periods. During these two operations, you will be asked tosubmit a sell order. The computer will buy bonds in ascending order of the prices submitted by themarket participants.
The target quantity for the first buying operation is eight bonds, and a total of 16 bonds are to bebought in the two operations. If fewer than eight bonds are purchased in the first buying operation,then the shortfall will be added to the second buying operation at the beginning of the 5th period. For
instance, suppose that the computer purchases only six bonds in the first buying operation, which is
two units short of the target. Then the target quantity for purchasing in the second buying operation
is 10 bonds.
Even if fewer than 16 bonds are purchased during the two buying operations, there is no additional
buying operation.
For the buying operations, the following screen is displayed.
If you want to sell the bonds you hold, you need to enter a selling price and the maximum number ofbonds to sell in ① and ②, respectively. Otherwise, please leave ① blank, and enter 0 in ②.

①

Please turn to the next page.

31②

[How the computer purchases bonds during the buying operation]
The sales prices submitted by the market participants are ordered from lowest to highest. The
computer will purchase bonds in ascending order of the specified price until the target quantity isreached. If there are orders with identical selling prices, and meeting all of them will exceed thetarget quantity, then the computer will randomly choose which orders to meet (some orders may bepartially met).
After the buying operations, neither interest nor dividends will be offered for cash and bondholdings.
(The instructions for the buying operation finish here.)[Instructions for selling operation]
In this game, the computer will also sell the bonds. The selling operations will be conducted before
the beginning of the 8th and 9th periods. You will be asked to submit a buy order prior to these selling
operations. The computer will sell the bonds in descending order of the prices submitted by themarket participants.
The target quantity for the first selling operation is eight bonds, and a total of 16 bonds are to be soldin the two operations. If fewer than eight bonds are sold in the first selling operation, then the unsold
bonds are added to the second selling operation before the beginning of the 9th period. For instance,
suppose that the computer sells only five bonds in the first selling operation, so that there are three
unsold bonds. Then there are 11 bonds available for the second selling operation.
Even if fewer than 16 bonds are sold during the two selling operations, there is no additional selling
operation.

Please turn to the next page.
[How to buy the bonds]
During the selling operations, the following screen is displayed.
When you want to buy the bonds, you need to enter a buying price and the maximum number ofbonds that you want to buy in ① and ②, respectively. Otherwise, please leave ① blank, and enter
0 in ②.

32

① ②[How the computer sells the bonds]
The buying prices submitted by the market participants are ordered from highest to lowest.
The computer will sell the bonds in descending order of the specified prices until the target quantity
is reached. If there are orders with identical buying prices, and meeting all of them will exceed thetarget quantity, then the computer randomly chooses which orders to meet (some orders may bepartially met).
After the selling operations, neither interest nor dividends will be offered for cash and bondholdings.
(The instructions for the buying & selling operations finish here.)

Additional Information for readers:After the round with the first buying operation, the following screen is displayed. The left part of thescreen shows the following.
①

How many bonds the computer has bought during the buying operation.

②

Explanation of the information displayed in the center of the screen.

③

How many bonds you sold to the computer, and the payment received (Marks) from the trade.

④

The number of bonds and the amount of cash (Marks) you hold after the buying operation.

The center of the screen shows the prices, in ascending order, at which the bonds were bought by thecomputer. After the second buying operation, the list of prices appears on the right of the screen
while the center disappears.

33

①
②

③
④

The screen displayed after the selling operations is similar.

⑤
⑥
⑦
⑧

The left part of the screen shows the following.
⑤

How many bonds the computer sold during the selling operation.

⑥

Explanation of the information displayed in the center of the screen.

34

⑦

How many bonds you bought from the computer, and the amount paid (Marks) for the trade.

⑧

The number of bonds and the amount of cash (Marks) you hold after the selling operation.

The center of the screen shows the prices, in descending order, at which the bonds were sold by thecomputer. After the second selling operation, the list of prices appears on the right of the screen
while the center disappears.

35