Department of Policy and Planning Sciences
Discussion Paper Series

No.1346

Bubbles and Information in Continuous Double Auction
and Call Market: An Experiment

byHiromine SAKURAI and Eizo AKIYAMAMarch 2017

UNIVERSITY OF TSUKUBA
Tsukuba, Ibaraki 305-8573
JAPAN

Bubbles and Information in Continuous Double
Auction and Call Market: An Experiment
Hiromine Sakurai a, 1 , Eizo Akiyama ba Department of policy and planning sciences, Graduate school of systems and informationengineering, University of Tsukuba, Tennodai 1-1-1, Tsukuba, Ibaraki 305-8573, Japanb Faculty of Engineering, Information and Systems, University of Tsukuba, Tennodai 1-1-1,

Tsukuba, Ibaraki 305-8573, Japan

Abstract
To assess the eﬀects of informational asymmetry among traders andof type of market institution on the emergence of price bubbles in asset
markets, we investigate price dynamics in experimental markets not only
under continuous double auctions (CDAs) but also under call markets(CMs). The results suggest that the eﬀects of informational asymmetry
on bubbles diﬀer between CDAs and CMs. Specifically, informational
asymmetry can contribute to the abatement of bubbles under CDAs as in
Sutter et al. (2012), while it can contribute to promoting bubbles underCMs.
JEL classification: C91, G11
Key words: experiment, bubbles, trading institutions, asymmetric information

1

Introduction

Informational asymmetry among traders in asset markets is a non-negligible feature, and the trading behavior of insiders, who have informational advantages,
aﬀects the price dynamics, especially the emergence of bubbles and crashes. 2
Tartaroglu’s (2014) empirical study investigates the trading behavior of insiders
during the technology bubble of the late 1990s and reports that the trading activity of insiders influenced the price dynamics during this period. 3 Allen et al.’s(1993) theoretical study presents a finite period general equilibrium model ofan exchange economy with asymmetric information. In their model, it is anecessary condition for a “strong bubble” that “each agent must have private
information in the period and state in which the bubble occurs”. 4
The question then arises, how does informational asymmetry that exists
between insiders and outsiders aﬀect bubbles, which are persistent deviationsin price from the underlying asset’s fundamental value (FV)?
The attempts to answer this question from the analyses of real markets
involve two diﬃculties. First, it is diﬃcult to accurately assess whether traders
1 Corresponding Author.

Email addresses: hiromine.sakurai@gmail.com (Hiromine Sakurai),eizo@sk.tsukuba.ac.jp (Eizo Akiyama)
2 Camerer (1989) discusses that “When traders have diﬀerent information or diﬀerent models of their economic world, it is possible for prices to deviate from intrinsic value (based on
their pooled information)”
3 Tartaroglu (2014) defines insiders as “the top management of firms (CEO, Chair, President, Oﬃcers and Board of Directors).”
4 In Allen et al.’s (1993) model, some agents’ private information is not always known toother agents. Thus, information is asymmetrically distributed among the agents.

1

have private information and what kind of information they have. Second,
it is diﬃcult to figure out what’s the FV of an asset in real markets. An
eﬀective approach to solving these problems is experimental methods, 5 whereby
experimenters can assess and even manipulate the traders (subjects)’ knowledge
and the FV.
Smith et al. (1988) reports the emergence of large bubbles and crashes inlaboratory asset markets, where all traders know the FV. Subjects act as tradersin multi-period asset markets in which holding an asset generates stochastic
dividend streams. The FV is defined as the discounted present value of all
future expected dividend payments under the assumption of risk neutrality.
Although the dividend structure is common knowledge among the traders, thetrading prices, on average, persistently deviate from the FV and then collapse
toward the last period, i.e., the existence of bubbles and crashes is observed in
the experimental markets. 6 Numerous studies following Smith et al. (1988)
report the robustness of bubbles and crashes in experimental markets. 7
Sutter et al. (2012) is one of several representative studies investigating theimpact of informational asymmetry on bubbles based on the experimental designof Smith et al. (1988). 8 Interestingly, in Sutter et al.’s experiment, asymmetric
distribution of information among traders reduces mispricing. In Sutter et al.’s
design, informed (uninformed) traders have (do not have) private information
about an asset’s future dividends, and the trading institution is the “open-bookcontinuous double auction” or “(open-book) CDA”.
A key feature of the experimental design of Sutter et al. (2012) we would
like to notice is that information about the realized prices and the limit ordersof the other traders within the current period is available to all traders throughthe order book. 9 Therefore, uninformed traders can partially infer private
information by referring to the order book (which might contains information
posted by informed traders, who have private information about the asset’s

5 Harrison and List (2004) discusses fundamental methodologies of the experimental approaches.
6 Another famous observation in Smith et al. (1988) and numerous subsequent studies isthe decrease in the deviation in trading prices from the FV as traders repeatedly participate
in the same types of markets with the same participants. Regarding the eﬀect of these traders’experiences on the emergence of bubbles, Dufwenberg et al. (2005) conducted further studies
in experimental markets following Smith et al. (1988) and showed that the trading prices
are close to the FV in markets where only some of the traders are experienced as well as inmarkets where all the traders are experienced.
7 Porter and Smith (1994), Palan (2013), and Powell and Shestakova (2016) provide comprehensive reviews of the experimental studies following Smith et al. (1988).
8 The eﬀects of informational asymmetry on bubbles were investigated, based on the experimental design of Smith et al. (1988), in several studies other than Sutter et al. (2012).
Similar to Sutter et al. (2012), Markstädter et al. (2014) show that the informational asymmetry drives market prices toward the FV. On the other hand, several experimental studies findthat the presence of private information does not reduce bubbles. (e.g. King, 1991; Oechssler
et al., 2011). Unlike Smith et al. (1988), many experimental studies have investigated the relationship between informational asymmetry among traders and market performance. Thesestudies are comprehensively surveyed in Markstädter et al. (2014) and Morone and Nuzzo(2016).
9 In the trading interface used by Sutter et al. (2012), “traders were informed about theirendowment in cash and stocks, the future dividends (if applicable), the current period’s trades,
and the open order books for bids and asks. The prices of trades were also shown in a graphicalchart”. However, traders did not know which of the uninformed or informed traders had posted
each order and traded each asset.

2

future dividends) within the current period. 10 In addition, informed traders
may directly exploit their informational advantage by accepting limit orders
against which they possibly arbitrage. These informed and uninformed traders’strategic actions through the order book would make them more aware of the
FV, and might lead to price convergence toward the FV and reduce mispricing,as discussed in Sutter et al. (2012). 11 Hereafter, we refer to this decrease inmispricing, which is caused by the traders’ strategic actions through the orderbook in a market with informational asymmetry, as “Abatement in the senseof Sutter et al. (2012)” or “Abatement”. Based on the above, we conjecturethat Abatement is likely (unlikely) to happen under open-book (closed-book)conditions.
Conversely, is there a possibility that informational asymmetry increases
mispricing?
Here, we consider the possibility of increased mispricing caused by the “strategic uncertainty” that exists in markets with asymmetric information. Strategic uncertainty is uncertainty about other traders’ behavior. In markets withinformational asymmetry, it is diﬃcult for uninformed traders to predict the
behavior of informed traders, whose actions are based on private information.
Furthermore, it might be diﬃcult for informed traders to predict the behaviorof uninformed traders because of the “curse of knowledge”: it would be diﬃcult
for informed traders to ignore their private information to simulate the condition of uninformed traders. 12 This increase in strategic uncertainty caused
by informational asymmetry among traders can occur not only in experimental
markets, but also in real markets.
Akiyama et al. (2013) reports that strategic uncertainty induces mispricing in experimental markets employing the structure proposed by Smith et al.
(1988). They report that initial forecast deviations from the FV are caused bystrategic uncertainty, as well as individual bounded rationality. 13 However, in
their experimental design, strategic uncertainty is not induced by informational
asymmetry, but by the inflow of new traders. It has not been shown whether
informational asymmetry enhances strategic uncertainty.
If informational asymmetry among traders increase strategic uncertainty,
it would promote bubbles, according to Akiyama et al. (2013). This eﬀect of
informational asymmetry on price dynamics is diﬀerent from and in the opposite
direction to Abatement. In the open-book markets of Sutter et al. (2012),
the order book would complement the information about the other traders’behaviors, and thus reduce strategic uncertainty.
The increase in strategic uncertainty caused by informational asymmetry
might enhance bubbles in closed-book markets, where Abatement is expected
to be less likely to occur. The main purpose of our paper is to test the following
10 Uninformed traders might infer from the seemingly high (low) limit prices or trading

prices of the other traders that the asset’s future dividends, which only the informed tradersknow, are high (low). However, such an inference by uninformed traders would not always becorrect.
11 Sutter et al. (2012) concludes that such strategic actions emerge based on the number oflimit and market orders posted and the intraperiod convergence of the trading price from FV.
12 Regarding the “curse of knowledge”, Camerer et al. (1989) notes that “Better-informed
agents are unable to ignore private information even when it is in their interest to do so; more
information is not always better.”
13 Akiyama et al. (2013) elicits subjects’ expectations about future prices as in Haruvy et al.
(2007).

3

conjecture: the eﬀects of informational asymmetry on bubbles diﬀers dependingon the type of market. For this purpose, we basically use the same experimental
structure used by Sutter et al. (2012), with the exception of trading institutions.
We use not only a CDA, which is the trading institution employed in Sutter et al.
(2012), but also a “closed-book call market” or “(closed-book) CM”. A CM is a
trading institution operating under closed-book conditions and has often beenused in experimental studies following Smith et al. (1988). 14 Van Boening et al.
(1993) reports that in the framework of Smith et al. (1988), price bubbles areobserved in the experimental markets under CMs, as well as under CDAs. 15
In our experiments, it is expected that markets operating under a CDA (CM)discourage (promote) bubbles through Abatement (the eﬀect of the increase instrategic uncertainty) if the traders’ information is asymmetrically distributed.
Thus, we propose the following hypotheses.
HYPOTHESIS 1. Informational asymmetry contributes to reducing bubbles
in experimental markets employing an open-book continuous double auction.
HYPOTHESIS 2. Informational asymmetry contributes to promoting bubbles in experimental markets employing a closed-book call market.
To confirm that our experimental settings are able to replicate Abatement,
we examine Hypothesis 1. Hypothesis 2 states that the increase in strategic
uncertainty caused by informational asymmetry enhances bubbles, as expected.
We test these hypotheses by comparing price dynamics between the market withasymmetric distribution of information among traders and the market with symmetric distribution of information among traders for each trading institution. 16

2

Experimental Design

In our experimental design, we basically used the same parameterizations andtreatments as Sutter et al. (2012), but we employed not only an open-bookcontinuous double auction, the trading institution used in Sutter et al. (2012),
but also a closed-book call market.

2.1

Basic Design of Our Experimental Markets

In our experiments, subjects participated in experimental asset markets as
traders, and were able to buy and/or sell the assets of a fictional company.
Each market operated for four rounds, with each round consisting of 10 trading periods. Asset trading was organized under either a (open-book) CDA ora (closed-book) CM. No trader was allowed to purchase on credit or to sell
14 For example, Van Boening et al. (1993), Haruvy et al. (2007), Lugovskyy et al. (2011),
Akiyama et al. (2013), Akiyama et al. (2014), and Baghestanian et al. (2015) use a CM.
15 In addition to Van Boening et al. (1993), Lugovskyy et al. (2011) studies experimental
asset markets using the framework of Smith et al. (1988) under both CDAs and CMs andreports that “In a setting employing double auctions and call markets as trading institutions,bubbles and crashes are a quite robust phenomenon.”
16 Furthermore, we investigated the eﬀect of informational asymmetry on individuals’ expectations in relation to future prices. We elicited traders’ predictions regarding future pricetrajectories using the basically same method as that used by Haruvy et al. (2007). We confirmed that this elicitation process did not essentially change our results because the pricedynamics in our experimental markets were similar to those in the previous studies (Smithet al., 1988; Van Boening et al., 1993; Sutter et al., 2012). We present the methods and theresults of the elicitation of traders’ predictions in Appendix, Section A.

4

short. Cash and asset holdings were carried over to the next period within around. When a new round started, cash and asset holdings were initialized tothe original starting levels.
Each unit of the asset paid a stochastic dividend at the end of each period.
This dividend was either 0 or 20 experimental currency units (MARKs), eachwith 0.5 probability. The realized dividend was the same for all assets in agiven period. After traders received their dividend at the end of 10th periodof a round, the asset provided no further cash in a round, meaning that the“buyout” figure was zero. Thus, at the end of each round, the value of all assets
was zero.
The expected dividend in every period was 10 MARKs (= 0 MARKs ×
0.5 + 20 MARKs × 0.5). The asset’s fundamental value (FV) in any period t
was the expected dividend (10 MARKs) multiplied by the number of periods
remaining (11 − t). This value decreased linearly from 100 MARKs in period 1
to 10 MARKs in period 10.
Each market consisted of six traders, and these groupings remained unchanged during the four rounds. At the beginning of each round, three tradersin each market were endowed with 10 assets and 3000 MARKs, while the otherthree traders were endowed with 30 assets and 1000 MARKs.

2.2

Information Levels and Treatments

In our experiment, each trader was allocated to one of two information levels:“0” or “1”. The diﬀerence between the two types of traders was their knowledge
about the dividend at the end of the current period.
Traders with information level 0 did not know about the future dividend.
At the beginning of period t, the asset’s dividend for period t was unknown tothem. Only after trading in period t were they informed of and then provided
the dividend for period t.
On the other hand, traders with information level 1 knew about the asset’s
dividend for the current period in advance. At the beginning of period t, they
were informed of the asset’s dividend for period t, which they received at theend of period t.
The FV, which was determined by the sum of all expected future dividend
payments, diﬀered for traders with diﬀerent information levels. Specifically, theFV in period t of round r in market m for traders with information level 0, who
had no information about the future dividends, was defined as:
0
F Vt,r,m= 10 × (11 − t),(1)which was the same as the previous definition of FV, and only depended on the
period in question. On the other hand, the FV for traders with informationlevel 1, who knew the dividend for the current period in advance, was defined
as:
1
(2)
F Vt,r,m= 10 × (10 − t) + divt,r,m ,where divt,r,m was the dividend per asset in period t of round r in market m.
Based on the distribution of traders with information level 0 or 1 in the market,
we used the diﬀerent values of FV (F V 0 and F V 1 ) in our analyses.
The distributions of the traders with these information levels (0 or 1) determined our experimental treatments. Our experimental design consisted of three5treatments: “CONTROL”, “SYMM,” and “ASYMM”. All traders in CONTROL markets had information level 0. All traders in SYMM markets had
information level 1, whereby information about future dividends was symmetrically distributed among traders. Each ASYMM market consisted of three
traders with information level 0 and three traders with information level 1. 17
Each trader’s information level was randomly determined at the beginning of
the experiment and did not change during the four rounds.
There was the informational asymmetry in ASYMM, 18 but not in CONTROL or SYMM. In ASYMM, at the beginning of each period, the three traderswith information level 1 were informed in advance about the payment of a dividend for the current period, while the three traders with information level0 were not given this information. Thus, information about future dividends
was asymmetrically distributed among traders during each trading period in
ASYMM.
All traders in every treatment knew the distribution of information levels in
the market in which they participated and their own information level. 19

2.3

Trading Institutions

Each of the three treatments described above was applied to both types of trading institution: (open-book) CDA and (closed-book) CM. We basically followed
previous representative experimental studies in implementing each of the trading systems (CDAs and CMs). 20 The details of each trading system we applied
are shown in Appendix, Section B (experimental instructions).
In markets employing CDAs, the arrays of bids and asks (limit orders) thatwere posted in the current period were publicly displayed through the orderbook. A market order that accepted a limit order submitted by another trader
made a contract. The order book contained the arrays of bids and asks sorted
by price, and the transactions in time order, and was automatically updated assoon as new limit orders were posted or new market orders were posted (i.e., newcontracts were made). Traders were not told which other traders had submitted
particular limit and market orders. 21 Thus, in ASYMM markets, which had
a mixture of uninformed and informed traders, under a CDA, any trader was
not able to know the information level of the trader who had posted a limit ormarket order.
In CMs, all transactions in a period occurred at a single calculated pricerather than the multiple prices resulting from traders posting bids or asks. Allbids to buy (asks to sell) were limit orders submitted by traders nominating thehighest (lowest) price at which they were willing to buy (sell) and the quantity.
In each period, traders were only able to post one bid and ask at the same time.
17 The ASYMM treatment is called “ASYMM1” by Sutter et al. (2012).
18 Sutter et al. (2012) use two other treatments with the informational asymmetry among

traders apart from ASYMM, although “there is no significant diﬀerence . . . across the threetreatments with asymmetric information” (Sutter et al., 2012).
19 The term “information level” was not used in the instructions for CONTROL and SYMM
markets. Traders were only told that all the information displayed on their screen was publicly
known.
20 We implemented the CDAs’ systems following those in Sutter et al. (2012). Also, theCMs’ systems we used followed those in Haruvy et al. (2007).
21 The trader’s own bids/asks were displayed in blue and those of the other traders weredisplayed in black. In addition, the trader’s own transactions were shown in a separate areaof the screen to the order book.

6

Traders were not informed of the other traders’ limit orders under CMs, unlike
under CDAs.
Under CMs, all bids and asks submitted by traders in each period were
aggregated into demand and supply curves. All trades had occurred at thetrading price where the market was cleared. 22 The bids (asks) whose prices onorder were equal to or higher (lower) than that price were accepted.
Under CDAs, traders traded assets during the current period through theorder book, which provided information regarding limit orders and transactions
executed by traders. On the other hand, under CMs, all transactions occurredat the price calculated by the computer after all traders had posted their buy
and sell orders in the period. As noted in the introduction, the information
about the orders and the realized trades that was available only CDAs was
expected to promote Abatement (in the sense of Sutter et al., 2012) and reduce
strategic uncertainty in markets with the informational asymmetry.

2.4

Experimental Procedures

We conducted six markets for each of six kinds of settings (= three treatments
× two trading institutions). Thus, we ran 36 markets.
Before the experiments commenced, we predetermined the dividend following the procedure used in Sutter et al. (2012). (The manner of this procedureand the values of all the dividends used in our experimental markets are shownin Appendix, Section C.) In this way, in each period of each round, the meanvalue of the realized dividend (0 or 20) in the six markets of each kind of setting(three treatments, two trading institutions) was identical to the expected value
1of the dividend (10). Thus, the mean of F Vt,r,m(= 10 × (10 − t) + divt,r,m ,where divt,r,m was the realized dividend in period t of round r in market m)over the six markets for each setting (three treatments, two trading institutions)0(= 10 × (11 − t)).
was identical to the (mean) F Vt,r,m
Our experimental markets were computerized using z-Tree (Fischbacher,2007). As outlined above, we implemented 36 markets in which 215 subjects
participated. 23 The subjects were undergraduate or graduate students at the
University of Tsukuba and Ibaraki Prefectural Tsukuba School of Nursing who
had no experience in experimental asset markets. We conducted our experiments between November 2015 and September 2016.
Subjects read the instructions (See Appendix, Section B) and practiced using
the trading interfaces before the experiment started. Each experimental session
took, on average, approximately three and a half hours. After the experiments
were finished, earnings were paid to each subject in accordance with the sum of
his cash holdings (MARKs) at the end of each round. Each MARK was converted to 0.2 yen. On average, subjects received 4000 yen (about 33.4 US dollars
at the time we conducted the experiments) including a 500 yen participation
fee. 24
22 This process determined the price that maximizes the trading volume. When there wasa price range where the trading volume was maximized, the lowest price in this price range
was chosen as the trading price.
23 Six subjects participated in each of 35 experimental markets we conducted. The remaining
market consisted of five subjects because one person canceled at the last minute.
24 We elicited traders’ predictions regarding future price trajectories before the trades ofassets in each period started, and traders were awarded a prediction bonus (MARKs) at
the end of each round. The average duration and earnings in our experiment contained the7

3

Results

The average trading price dynamics under open-book continuous double auction
for each treatment are shown in Figure 1, with period on the horizontal axis
and price on the vertical axis. 25 The single-transaction price dynamics underclosed-book call market for each treatment are shown in Figure 2. In Figs. 1
and 2, the dashed gray line, solid gray line, and dashed black line represent
the mean value of the market prices in the CONTROL, SYMM, and ASYMM
treatments, respectively. The market price under a (open-book) CDA (Fig. 1)
is the average trading price in each period, while that under a (closed-book)
CM (Fig. 2) is the single-transaction price in each period. The solid black line
represents the asset’s fundamental value (FV). 26
Figure 1: Mean price dynamics in open-book continuous double auctions foreach treatment

Figure 3 shows the price dynamics in each of six individual markets in round
one of each treatment (CONTROL, SYMM, and ASYMM) under each trading
institution (CDA and CM). In each panel, the gray lines show the price trajectories in each of the six individual markets, while the black line represents theFV. 27
Under CDA (Fig. 1), the deviations in prices from FV in CONTROL markets seem to decrease as traders gain experience across rounds. This is a typical
pattern in the experiments of Smith et al. (1988) and numerous subsequentstudies. The price dynamics in ASYMM is closer to FV than that in CONeliciting period and the prediction bonus respectively.
25 Each market had four rounds, each round consisting of 10 trading periods. Thus, eachmarket consisted of 40 periods.
26 The solid black lines in both Figs. 1 and 2 represent the FV not only for traders withinformation level 0 (F V 0 ), but also for traders with information level 1 (F V 1 ). This is because,
as in Section 2.4, the mean of F V 1 over the six markets for each setting (three treatments,
two trading institutions) was identical to the (mean) F V 0 .
27 Similar to Figs. 1 and 2, the FV (black line) in each panel in Fig. 3 is identical to not only
the mean FV for traders with information level 0, but also to that for traders with informationlevel 1.

8

Figure 2: Mean price dynamics in closed-book call markets for each treatment

TROL or SYMM, which suggests that informational asymmetry abates bubbles
in markets under CDAs. This diﬀerence in price dynamics among the treatments supports the results of Sutter et al. (2012). The observation in Fig. 1
supports Hypothesis 1: informational asymmetry contributes to abating bubbles
in experimental markets employing a CDA.
Under CMs (Fig. 2), the deviations in mean prices from FV in CONTROL
markets are very small. On the other hand, in the price chart for round 1
in each CONTROL market under a CM (lower left chart in Fig. 3), bubbles
can emerge, as observed in Van Boening et al. (1993). Furthermore, bubbles
occur less frequently in the CONTROL markets under CMs than in those under
CDAs, 28 which is consistent with that discussed in Sunder et al. (1992). It
points out that “While call markets may discover the equilibrium price more
precisely, continuous double auctions may have the advantage of faster (albeit
less precise) discovery of price during the inter-call periods when the call market
leaves the price undefined.”
In CMs, the deviations in transaction prices from FV in ASYMM appear to
be larger than those in CONTROL or SYMM. This is in contrast to the caseof markets under CDAs, suggesting that informational asymmetry promotes
bubbles in CMs. Thus, Fig. 2 supports Hypothesis 2: informational asymmetry
contributes to promoting bubbles in experimental markets employing a CM.
We measured and compared the bubbles in terms of the asset’s mispricing
and overvaluation, following Stöckl et al. (2010), who defined the relative absolute deviation (RAD) as the index of the asset’s mispricing and the relative
deviation (RD) as the index of the asset’s overvaluation. 29 RAD and RD were
28 Numerous experimental studies have compared the performance of the markets under
CDAs with that under CMs. These studies are comprehensively surveyed in Sunder et al.
(1992), Powell and Shestakova (2016) and Morone and Nuzzo (2016). In experimental asset
markets with asymmetric information, the performance between CDAs and CMs has been
compared in several experimental studies, where the focus is on aspects other than the eﬀect
of informational asymmetry on the emergence of bubbles (Liu, 1996; Schnitzlein, 1996; Morone
and Nuzzo, 2015).
29 According to Stöckl et al. (2010), RAD and RD fulfill the three features that it “seems

9Figure 3: Trading price dynamics in round one of each treatment under eachtrading institution

determined as follows:
RADr,m =10∑|Pt,r,m − F Vt,r,m ||F V r,m |t=1

RDr,m =

10∑
(Pt,r,m − F Vt,r,m )|F V r,m |

t=1

,

(3)

(4)

where Pt,r,m was the average price (single-transaction price) in period t of roundr in market m under a CDA (CM), F Vt,r,m was the FV in period t of roundr in market m, and F V r,m was the average FV over all periods in round r inmarket m. For F Vt,r,m , we used two diﬀerent values of FV (F V 0 and F V 1 ) in
the treatments (CONTROL, SYMM, and ASYMM). The FV for traders with0
information level 0 (F Vt,r,m) was used in CONTROL, and that for traders with1
information level 1 (F Vt,r,m) was used in SYMM and ASYMM. 30 Likewise,
for F Vt,r,m , F V r,m in CONTROL (SYMM and ASYMM) was calculated using
F V 0 (F V 1 ). The increase (decrease) in the value of both RAD and RD means
sensible to require” for bubble-related measures: the measures should “(i) relate the fundamental value and price, (ii) be monotone in the diﬀerence between fundamental value andprice, and (iii) be independent of the total number of periods and the absolute level of fundamental value.” Thus, a number of studies have conducted analyses where RAD and/or RD
are calculated to measure bubbles (see, for example, Kirchler et al., 2012; Sutter et al., 2012;
Cheung et al., 2014; Akiyama et al., 2013; Markstädter et al., 2014; Baghestanian et al., 2015).
30 In addition to the analyses where RAD and RD based on F V 1 were used in ASYMM,
we conducted analyses where RAD and RD based on F V 0 were used in ASYMM. We obtained similar results in both analyses regardless of the diﬀerence between F V 0 and F V 1 (SeeAppendix, Section D).

10that the assets are more likely to be overvalued (undervalued) and traded atprices distant from (close to) FV: thus, bubbles become larger (smaller).
We conducted regression analyses using the ordinary least squares method
where the dependent variables were, in turn, RAD and RD. The independent
variables in both of these regression analyses were a dummy for treatment
ASYMM, a dummy for trading institution CDA, an interaction dummy between ASYMM and CDA, and a variable for the round. Table 1 presents asummary of these analyses.
Table 1: The eﬀects of informational asymmetry on bubbles under each trading
institution
Model1Model2
Dependent value
RAD
RD
Constant
0.33∗∗∗
0.16∗∗
ASYMM dummy(0.05)(0.07)∗∗

0.14∗(0.06)(0.07)∗∗∗

0.23∗∗∗(0.05)(0.06)

0.12

CDA dummy

0.14

∗∗∗

−0.06

Round

(0.02)

ASYMM & CDA dummy(0.02)−0.21−0.36∗∗∗

0.12
144

0.17
144

∗∗

(0.08)

Adjusted R2
N−0.08∗∗∗(0.10)

Notes. This table shows a summary of the regression analyses where the dependent variables
are RAD and RD for model 1 and model 2, respectively. The independent variables in both ofthese regressions are a dummy for treatment ASYMM, a dummy for trading institution CDA,
an interaction dummy between ASYMM and CDA, and a variable for the round. The valuesof the intercepts in both of these models represent the eﬀects of the baseline condition, where
the informational asymmetry among traders does not exist (CONTROL and SYMM) and the
trading institution is a CM. The sample size for each regression is 144 (= 2 trading institutions
× 3 treatments × 6 markets × 4 rounds). These regression analyses use the ordinary leastsquares method. The standard error of each coeﬃcient is included in parenthesis. *, **, and*** indicate significance at the 10%, 5%, and 1% levels, respectively.

In Table 1, the coeﬃcient of the ASYMM dummy in the regression of RAD(model 1) is significantly positive (two-sided t test, p = 0.030). Also, thatin the regression of RD (model 2) is weakly significantly positive (two-sided
t test, p = 0.054). These observations suggest that bubbles are promoted byinformational asymmetry, which, as noted in the introduction, could be a sourceof strategic uncertainty: uncertainty about other traders’ behavior.
The coeﬃcients of the interaction dummy between ASYMM and CDA inboth model 1 and model 2 are significantly negative (two-sided t test, p =0.010 and 0.000, respectively), which means that under CDA, the informational
asymmetry contributes to the abatement of bubbles, as found in Sutter et al.
(2012). These results suggest that Hypothesis 1 is supported.
We conjecture that in the ASYMM markets under CDA, there are two kindsof negative eﬀects of the information available in the order book on bubbles: (i)11the order book’s information about traders’ behavior can suppress the strategic
uncertainty caused by the informational asymmetry; and (ii) Abatement (inthe sense of Sutter et al., 2012) occurs, that is, the traders’ strategic actions
through the order book in the market with the informational asymmetry makethe traders more conscious of FV, and lead to price convergence toward FV. 31
On the other hand, in CMs, traders do not refer to the information in theorder book. Thus, we conjecture that, under CMs, neither eﬀect (i) nor eﬀect (ii)outlined above is likely to occur, and thus the increase in strategic uncertainty
caused by the informational asymmetry contributes promoting bubbles. The(weakly) significantly positive coeﬃcients of the ASYMM dummy in model 1
and model 2, respectively, suggests that Hypothesis 2 is (weakly) supported.
From the above, it is clear that the eﬀects of the informational asymmetry
on price dynamics are significantly diﬀerent between markets under CDAs andCMs.

4Discussion

To test how the diﬀerence in trading institutions aﬀects the eﬀects of informational asymmetry on the emergence of bubbles, we investigated experimental
markets using both an open-book continuous double auction, which is the trading institution employed by Sutter et al. (2012), and a closed-book call market.
Comparing the price trajectories between the markets with asymmetric distribution of information among traders and those with symmetric distributionof information among traders, under (open-book) CDAs we found that bubbles
are abated when the informational asymmetry exists. This result is consistent
with the findings of Sutter et al. (2012). Conversely, bubbles are facilitatedin the markets under (closed-book) CMs with the informational asymmetry.
We conducted statistical analyses and found that the informational asymmetry
itself, which could be a source of strategic uncertainty as discussed in the introduction, contributes to promoting bubbles. In CMs, traders do not refer to theinformation in the order book during a period, and “Abatement” in the sense of
Sutter et al. (2012) is unlikely to occur. We conjecture that this is why the increase in strategic uncertainty attributed to the informational asymmetry only
contributes to promoting bubbles in CMs. On the other hand, in CDAs, theinformational asymmetry contributes to reducing bubbles, which implies that
Abatement occurs. It is clear that the eﬀects of the informational asymmetry
on bubbles are significantly diﬀerent between markets under CDAs and CMs.
As discussed in Sutter et al. (2012), it is likely that Abatement occurs inreal markets with asymmetric distribution of information among traders. On
the other hand, as shown in our study, high levels of strategic uncertainty caused
by informational asymmetry can promote mispricing. This conjecture has aninteresting connection with the underpricing of initial public oﬀerings (IPOs), 32
31 As noted in the introduction, the actions of uninformed and informed traders are as follows: uninformed traders can partially infer the private information by referring to the orderbook (which might contains information posted by informed traders, who have private information about the asset’s future dividends) within the current period. In addition, informed
traders can exploit their informational advantage regarding FV by accepting limit orders
against which they can possibly arbitrage.
32 Ritter and Welch (2002) comprehensively review a number of studies of underpricing in
IPOs.

12

where the oﬀer price falls below the initial price. The results of our study imply
that strategic uncertainty attributed to informational asymmetry among traders
could be one factor that leads traders to purchase shares in IPOs at a price higher
than the oﬀer price. 33

Acknowledgement
Makoto Soga, Tomohiro Nishikawa and Junpei Yamazaki provided invaluable
help in organizing the experiments. Comments and suggestions on our manuscriptfrom Morimitsu Kurino, Ryoji Sawa and Ryuichiro Ishikawa, and bibliographic
information given by Michael Kirchler, Matthias Sutter, Jürgen Huber are acknowledged. This project is partly supported by the experiments reported inthis paper have been approved by Research Ethics Board of Faculty of Engineering, Information and Systems, University of Tsukuba (No. 2012R25-2). This
work was supported by JSPS KAKENHI [Grant Numbers 26350415, 26285043,
26245026, 26289170].

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33 IPO markets are characterized by informational asymmetry among traders.

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15

A

Appendix: Analyses of Elicitations of Traders’Predictions

In addition to testing these hypotheses, we investigate the eﬀect of informational
asymmetry on individuals’ expectations of prices by eliciting traders’ predictions
regarding future price trajectories. 34 For this purpose, we elicit traders’ beliefs
about prices as in Haruvy et al. (2007), whose elicitation method has been usedin studies attempting to investigate traders’ expectations (e.g., Akiyama et al.,2013, 2014; Markstädter et al., 2014; Baghestanian et al., 2015).

A.1

Method of Elicitation of Subjects’ Expectations

Each trader in the variously treated markets (CONTROL, SYMM, or ASYMM)
under each of the two trading institutions (open-book continuous double auction or closed-book call market) was asked to forecast the market price in future
periods of the current round before the trades of assets in each period started.
For example, before trades in period 6 started, each trader submitted five predictions of future market prices (for period 6, period 7, period 8, period 9, andperiod 10). In the (closed-book) CMs, the trading price calculated by the computer for each future period was the predictive target. On the other hand, inthe markets under a (open-book) CDA, traders predicted the average trading
price within a given period for each future period: the predictive target was theaverage trading price in a given period.
When forecasting the future price trajectory before the trades in each period
started, each trader was provided with information about the history of market
prices for all previous periods and rounds, the assets and cash holdings in thecurrent period, and future dividends (if his information level was 1).
Traders were awarded a prediction bonus (MARKs) at the end of each roundusing the same incentive scheme as that used by Akiyama et al. (2013). 35 Eachtrader’s prediction bonus was determined as follows: prediction bonus for rounds (MARKs) = 0.5% × (the number of predicted prices that were within 10%of the actual market price in round s) × (trader’s cash holding at the end ofrounds). 36 Each trader submitted 55 predictions in each round, i.e. the sum of
all predictions in each period, from 10 in period 1 to one in period 10. Thus, themaximum prediction bonus was 27.5% (0.5% × 55), which occurred when all 55
predictions in a given round were within 10% of the actual market price. We
used this incentive scheme to “reduce subjects’ incentive to influence the prices
to move closer to their forecasts by making losses” (Akiyama et al., 2013).

A.2

Results of Traders’ Belief Dynamics

In our experiment, the informational asymmetry contributes to the abatementof bubbles in markets under a CDA. Conversely, the informational asymmetry
34 Sutter et al. (2012) did not investigate traders’ predictions regarding future prices.
Markstädter et al. (2014) examined the correlation between predicted prices and realized
prices in experimental markets with asymmetric information.
35 The experimental currency MARK was converted into real money.
36 In our experiment under CDAs, the market price (predictive target) is defined as theaverage trading price. Also, under CMs, the market price is the single trading price calculatedby the computer. Under both trading institutions, if no transactions occur, the market priceis defined as the highest bid price in a given period.

16

contributes to promoting bubbles in a CM. In the following analyses, we examine how traders develop their expectations of future price trajectories (beliefs)
during trading.
We measured the deviations from the asset’s fundamental value (FV) in the
traders’ predictions of future prices using two indexes: belief’s relative absolutedeviation (BRAD) and belief’s relative deviation (BRD). 37 BRAD and BRD
are defined as follows:
10i∑− F Vp,r,m ||fe,p,r,m1
10 − e + 1 p=e
F V e,r,m

(5)

10
i
∑
(fe,p,r,m− F Vp,r,m )1,
10 − e + 1 p=e
F V e,r,m(6)iBRADe,r,m=i
BRDe,r,m=iwhere fe,p,r,m
is the forecast of the asset price in period p elicited by i in period
e of round r in market m, F Vp,r,m is the FV in period p of round r in market m,
and F V e,r,m is the average FV over the remaining periods when elicited (fromperiod e to 10) in round r in market m. In calculating BRAD/BRD for eachtrader, F V and F V were based on the trader’s information level. Specifically,
the F Vp,r,m (F V e,r,m ) used in the calculation of both BRAD and BRD fortraders with information level 0 was set to (calculated based on) the FV for
traders with information level 0. Similarly, the F Vp,r,m (F V e,r,m ) used in thecalculation of both BRAD and BRD for traders with information level 1 was
set to (calculated based on) the FV for traders with information level 1.
These two measures are the revised versions of RAD and RD, which are thebubble measures defined by Stöckl et al. (2010) for the evaluation of traders’predictions. Akiyama et al. (2013) defined bubble measures RAFD and RFD
similar to BRAD and BRD to evaluate traders’ beliefs. If BRAD, 38 which isa positive value, is close to 0, the trader forecasts the future price trajectory
tracking the FV. A positive (negative) value of BRD means that the trader
predicts future prices higher (lower) than FV on average.
We conducted regression analyses using the ordinary least squares method
where the dependent variables were, in turn, BRAD and BRD. The regressionsof BRAD/BRD for traders with information level 0 and those with informationlevel 1 were carried out separately. The independent variables in both regressions were a dummy for treatment ASYMM, a dummy for trading institution
CDA, an interaction dummy between ASYMM and CDA, a variable for the

37 In Section E, we illustrate the traders’ belief dynamics in each treatment (CONTROL,SYMM, and ASYMM) under each trading institution (CDA and CM) using three-dimensionalbar charts following Haruvy et al. (2007).
38 The only diﬀerence between the definitions of RFD (RAFD) and BRD (BRAD) is themanner of calculation of F V e,r,m, whereby the average (absolute) deviation of forecast prices
from FV was used in the definitions of these indexes. The F V e,r,m used in the definition of
RAFD/RFD is the average FV over all periods (i.e., from period 1 to period 10). On theother hand, that used in the definition of BRAD/BRD is the average FV over all remaining
periods (i.e., from period e to period 10). In the calculation of BRDe,p,r,m (BRADe,p,r,m ),the average of the (absolute) deviations of the forecasts from FV in the remaining periods(from period e to period 10) at period e is normalized by the average FV in the remaining
periods (from period e to period 10) at period e, which is expected to clarify the changes inthe traders’ beliefs across periods.

17

round, and a variable for the period. 39 The sample size for the regressions
for traders with information level 0 was 3852, 40 and that for traders withinformation level 1 was 3888. Table 2 presents a summary of the analyses of
BRAD/BRD for traders with information level 0 and those with informationlevel 1.
It can be seen from Table 2 that the coeﬃcients of the ASYMM dummy
in both the regressions of BRAD (model 3 and model 5) and those of BRD(model 4 and model 6) are significantly positive. This means that both traderswith information level 0 and those with information level 1 are likely to forecast future price trajectories higher than FV in CMs with the informational
asymmetry, where bubbles seem to be expanded. In addition, the coeﬃcientsof the interaction dummy between ASYMM and CDA in both the regressionsof BRAD (model 3 and model 5) and those of BRD (model 4 and model 6) aresignificantly negative. This implies that both traders with information level 0
and those with information level 1 are likely to forecast future price trajectories
tracking FV in markets with the informational asymmetry under CDAs, wherebubbles seem to be abated. Under both CDAs and CMs, the eﬀect of information asymmetry on traders’ forecast price dynamics is in the same direction
as that on the realized price dynamics. These correlations between traders’ beliefs and the realized price dynamics support the observations of Haruvy et al.
(2007) and Markstädter et al. (2014). The coeﬃcient of the CDA dummy inboth regressions of BRAD (model 3 and model 5) and those of BRD (model
4 and model 6) is significantly positive. This is in the same direction as therealized price dynamics, where market prices are more likely to deviate from
FV in markets under CDA than in CMs.
The coeﬃcients of the round in both regressions of BRAD (model 3 andmodel 5) and those of BRD (model 4 and model 6) are significantly negative.
This implies the convergence of the predictions toward FV for traders withexperience across rounds, which is consistent with the findings of Haruvy et al.
(2007). Conversely, the coeﬃcients of the period in both regressions of BRAD
(model 3 and model 5) and those of BRD (model 4 and model 6) are significantly
positive. The forecast future prices tend to get higher than FV with periods ina round. This might be caused by confusion among traders about the dynamics
of FV in the experimental markets following the design of Smith et al. (1988). 41
The eﬀects of this confusion were pointed out and discussed in Kirchler et al.

39 The variable of the period is 1, 2, . . . , 9. We excluded the forecast data submitted inperiod 10 from these analyses. This is because in the calculation of BRAD/BRD based on
FV for the traders with information level 1 (F V 1 ), F V e,r,m can be 0 in period 10 when thedividend in period 10 is 0, and because BRAD/BRD calculated based on F V 1 in period 10
cannot be defined when the dividend in period 10 is 0.
40 The planned number of forecasts submitted by traders was 7776 (= 2 trading institutions
× 3 treatments × 6 markets × 4 rounds × 9 periods × 6 traders). These 7776 forecasts weredivided into two equal groups and used in the regressions concerning traders with informationlevel 0 and those with information level 1. However The number of forecasts used in the
regressions concerning traders with information level 0 was actually 36 less than planned (=4 rounds × 9 periods) because one market in CONTROL under CM was one subject shortdue to cancellation at the last minute.
41 The FV in the experimental markets, following the design of Smith et al. (1988), declines
over a round. Regarding this characteristic of the FV, Kirchler et al. (2012) reports that “thedeclining fundamental value process confuses subjects, as they expect the fundamental value
to stay constant.”

18

Table A.2: The eﬀects of informational asymmetry on the dynamics of forecast
prices under each trading institution
Use prediction dataelicited by only traderswith information level
Model3Model4Model5Model6
Dependent value
RAD
RD
RAD
RD
Constant
0.53∗∗∗
0.37∗∗∗
0.74∗∗∗
0.63∗∗∗

0

ASYMM dummy
CDA dummyRound(12.13)(7.66)(14.95)(11.66)

∗∗∗

∗∗∗

∗∗∗

0.40∗∗∗

0.38

0.46

ASYMM &
CDA dummy(10.72)(7.29)(8.47)

∗∗∗

∗∗∗

∗∗∗

0.68∗∗∗(17.53)

0.55

0.58

0.57

(17.44)(16.70)(15.98)

∗∗∗

−0.23

−0.24

∗∗∗

(−18.86)

∗∗∗

∗∗∗

∗∗∗

−0.30

(−22.94)−0.33∗∗∗(−23.48)∗∗∗0.10∗∗∗

0.06

0.06

0.11

(12.85)(10.54)(19.60)(16.88)−0.73∗∗∗−0.84∗∗∗−0.86∗∗∗−1.05∗∗∗

0.19
3,852

0.17
3,852

0.24
3,888

0.24
3,888

(−13.37)

Adjusted R2
N

0.32(9.98)(−20.01)

Period1(13.91)(−13.90)(−15.65)

Notes. This table shows a summary of each of four regression analyses where the dependent
variable is either BRAD or BRD. In model 3 (model 4), a regression of BRAD (BRD) using
forecast data elicited from traders with information level 0 is conducted. In model 5 (model
6), a regression of BRAD (BRD) using forecast data elicited from traders with informationlevel 1 is conducted. The independent variables, which are common to all four regressions,are a dummy for treatment ASYMM, a dummy for trading institution CDA, an interaction
dummy between ASYMM and CDA, a variable for the round, and a variable for the period.
The values of the intercepts in both model 3 and model 4 (model 5 and model 6) represent
the eﬀects of the baseline condition, where the informational asymmetry among traders does
not exist (CONTROL and SYMM) and the trading institution is a CM. The sample size inboth model 3 and model 4 is 3852, which was 36 less than planned because one market in
CONTROL under CM was one subject short due to cancelation at the last minute. The
sample size in both model 5 and model 6 is 3888. All of these regression analyses employ theordinary least squares method. The t-value of each coeﬃcient is included in parentheses. *,**, and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

19

(2012) and other studies. 42

A.3

Discussion on Traders’ Belief

In real-world markets, it is hard to clarify the relationship between the trajectories of trading prices and the traders’ predictions about them because weusually do not have data about how traders in real-world markets forecast future price dynamics. Conversely, this relationship can be directly investigatedby eliciting subjects’ forecasts in experimental markets. Positive correlations
between the dynamics of realized prices and those of forecast prices have beenreported by Haruvy et al. (2007), Markstädter et al. (2014), and by numerous
other experimental studies. 43 It is noteworthy that in our study, the positive(negative) eﬀects of the informational asymmetry on the deviation from FV oftraders’ forecasts were synchronized with the expansion (reduction) of bubbles
in the realized prices attributed to the informational asymmetry.

42 For example, Huber and Kirchler (2012), Akiyama et al. (2013) and Cheung et al. (2014).
43 Palan (2013) comprehensively surveyed the positive correlation between the realized prices
and the forecast prices in studies that implemented experimental markets based on the designof Smith et al. (1988). Unlike Smith et al. (1988), Hey (1994) and Hommes et al. (2005)
reported a positive correlation in the experimental markets.

20

B

Appendix: Experimental instructions (in English)

We conducted the experiments in Japanese. The following section presents our
experimental instructions translated from Japanese into English. Because ourstudy’s primary goal was to replicate the experimental results of Haruvy et al.
(2007) and Sutter et al. (2012), we created experimental instructions that wereas close as possible to theirs. 44
Note that the footnotes of following instructions were not viewed by subjects.

B.1

Instructions for ASYMM markets under CDA

In the instructions for the CONTROL (SYMM) markets, Chapter 2. Information Levels is eliminated (shortened) and the explanations of the screeninterfaces are modified accordingly.
B.1.1

General information

This experiment simulates a stock market. The experimental session includes
four rounds, each consisting of ten trading periods. During this process, you
can sell and buy the stocks of a virtual enterprise.
Your objective in the experiment is to maximize your profits. We use a currency called MARKs. One MARK is equivalent to 0.2 yen. At the end of theexperiment, your profits will be converted into yen and paid to you.
There are three ways of making a profit:• You can realize a profit margin through buying and selling stocks• You can earn dividends on your stock holdings• You can accurately predict the future prices of the stocks
The first and second ways of making a profit are described in more detail in
Chapter 1. The third way is described in more detail in Chapter 3.
B.1.2

Chapter 1. Market Description

The market in which you will trade consists of six participants. The otherfive participants are randomly determined and do not change during the four
rounds. Three of the six traders receive an initial endowment of 10 assets andworking capital of 3000 MARKs, while the other three are endowed with 30
assets and working capital of 1000 MARKs. You can sell and/or buy assets inevery period, and your inventory (assets and money) at the end of each period
is carried over to the next trading period.

44 We

accessed the experimental instructions used by Haruvy et al. (2007) at
https://www.aeaweb.org/atypon.php?doi=10.1257/aer.97.5.1901, and that used in Sutter
et al. (2012) at https://papers.ssrn.com/sol3/papers.cfm?abstract id=1268742.

21

1.1 How to realize a profit margin through buying and selling stocks
Each trader can be a buyer and a seller at the same time. Thus, buying low
and selling high allows you to realize a profit margin.
ExampleIf you can buy the stock for 100 MARKs and sell it for 120 MARKs, youearn 20 MARKs. On the other hand, if you buy the stock for 100 MARKs
and sell it for 80 MARKs, you lose 20 MARKs.
The figure below shows the screen used for trading. You can submit buy/sellorders at prices ranging from 0 MARKs to a maximum of 300 MARKs. For
every bid/ask you make, you must enter the price of the assets you intend to
trade. To submit a bid, you must enter the price at which you would like to buy
an asset in the “Purchase Bids” box and click on the “Buying Order” button.
To submit an ask, you must enter the price at which you would like to sell an
asset in the “Sales Asks” box and click on the “Selling Order” button. No trade
can take place until another participant accepts your bid (ask) by clicking on
the “Sell” (“Buy”) button. 45 Note that your inventory of cash and assets cannot
be less than zero.

1.2 How to earn dividends on your stock holdings
At the end of each trading period, every asset pays a dividend. The dividendfor one asset is either 0 or 20 MARKs, with equal probability. Thus, an asset’s
average dividend amounts to 10 MARKs in every period. After a trading period
is complete, dividends for assets you hold in your inventory are added to yourcash holding. Assets have a lifespan of 10 trading periods, i.e., after the final
dividends are paid out at the end of period 10 in each round, the assets areworthless.
You can use the table below to help you make your decisions. The first column,labelled “Ending Period”, indicates the last trading period of the round. The
second column, labelled “Current Period”, indicates the period during which theaverage holding value is being calculated. The third column shows the number
45 In Sutter et al. (2012), traders were able to post limit/market orders for multiple sharesof the assets. On the other hand, in our experiments under CDAs, the maximum share of theasset for each limit/market order was one. To simplify the order book’s information about
limit orders and transactions, the number of shares of the asset for each limit or market order
was set at a maximum of one in our experiment. We confirmed that this modification did notessentially change our results because the price dynamics in our experimental markets using
a CDA was similar to that in Sutter et al. (2012).

22

of holding periods remaining from the period listed in the second column until
the end of the market. The fourth column, labelled “Average Dividend ValuePer Period”, shows the average dividend in each period for each unit held inyour inventory. The fifth column, labelled “Average Holding Value Per Unit
of Inventory”, shows the expected total dividend earnings (per asset) for theremainder of the round. That is, for each asset you hold in your inventory forthe remainder of the round, you expect to receive the amount listed in column
5. The number in column 5 is calculated by multiplying the numbers in columns
3 and 4.
Ending
Period
10
10
10
10
10
10
10
10
10
10

Current
Period
1
2
3
4
5
6
7
8
9
10

Number ofHolding
Periods
10
9
8
7
6
5
4
3
2
1

×

Average
Dividend ValuePer Period
10
10
10
10
10
10
10
10
10
10

=

Average Holding
Value Per Unit
of Inventory
100
90
80
70
60
50
40
30
20
10

ExampleSuppose there are four periods remaining in a round. Since the dividend
on a unit of asset has a 50% chance of being 0 MARKs and a 50% chanceof being 20 MARKs, the expected dividend is, on average, expected to be
10 MARKs per period for each asset. If you hold one asset for four periods,
the total dividend paid on the unit over the four periods is expected to be
4 × 10 = 40 MARKs.

1.3 After first market
After the round is complete (i.e., after 10 trading periods), the round is replicated three more times. Note that once again, the actual dividends will berandomly chosen by the computer (with an equal probability of either 0 or 20
MARKs). Regardless of the profit you earned in the first round, you will once
again be endowed with an inventory of 10 (30) assets and 3000 (1000) MARKs
23

at the beginning of rounds two, three, and four. Your total profit will be calculated by adding up your earnings and prediction bonuses from all four rounds
at the end of the experiment. Prediction bonuses are explained in Chapter 3.
B.1.3

Chapter 2. Information Levels

The present market is characterized by diﬀerent information levels (0, 1) oftraders. At the beginning of the experiment, you will be randomly assigned to
one of these levels.
Three of the six participants (with information level 0) receive no information
about dividend realization, i.e., they have no information about the actual dividend payment in the current period. They just know that the dividend in every
period will amount to either 0 or 20 MARKs. At the end of a period, all participants (including those with information level 0) are told about the actual
dividend payment in the just-terminated trading period.
The other three participants in the market are classified as traders with information level 1, and have a prediction horizon of one period in advance, i.e., they
possess information about the actual dividend payment (0 or 20 MARKs) forthe current period before trading commences.
ExampleIf the actual dividend payment for period 3 is 20 MARKs, the traderswith information level 1 know that the dividend will be 20 MARKs before
trading in period 3 commences, while the traders with information level 0
only learn this at the end of period 3.
The following figure is the screenshot showing your information level. This isshown at the beginning of each market.

B.1.4

Chapter 3. How to make a profit by accurately predicting thefuture prices of stocks

You can make a profit not only by achieving a margin between buying and selling
prices and through dividends, but also by accurately predicting the future pricesof stocks (prediction bonus). Before each period begins, you will be given two
minutes to predict the average trading prices in all the remaining periods. 46
46 In “Price History” area (lower left area), the highest bid price in a given period wasrecorded if no transactions occurred.

24

The computer will keep a record of the number of accurate predictions (that is,your predicted prices that were between 90% and 110% of the realized average
trading price for each period). At the end of each round, you will be oﬀered a
prediction bonus based on the number of accurate predictions you made according to the following formula: (your final cash balance) × 0.5% × (the numberof accurate predictions). The maximum bonus payable is 0.5% × 55 = 27.5%.
Please be aware that because your final cash balance depends on both profits from trading and dividends, the prediction bonus decreases as profits fromtrading and dividends decrease.
B.1.5

Chapter 4. Calculating Your Earnings

Your earnings for a period are given by the actual dividends received at theend of the period plus revenue from assets sold minus expenditure on assets
purchased.
YOUR EARNINGS FOR A PERIOD =DIVIDEND PER UNIT × NUMBER OF UNITS IN INVENTORY (AT THE
END OF THE PERIOD) + REVENUES - EXPENDITURES (ACCRUING IN
THE COURSE OF TRADING).
If you buy assets, your cash holding is diminished by the associated expenditure (price × volume). Conversely, if you sell assets, your cash holding will be
increased by the associated revenue (price × volume). Your total profit in themarket results from the initial cash endowment (1000 or 3000 MU), plus thesum of earnings acquired in all 10 trading periods, plus Prediction Bonus in themarket.
YOUR TOTAL EARNINGS IN THE MARKET =INITIAL CASH ENDOWMENT +25

EARNINGS FOR PERIOD 1 + EARNINGS FOR PERIOD 2 +EARNINGS FOR PERIOD 3 + EARNINGS FOR PERIOD 4 +EARNINGS FOR PERIOD 5 + EARNINGS FOR PERIOD 6 +EARNINGS FOR PERIOD 7 + EARNINGS FOR PERIOD 8 +EARNINGS FOR PERIOD 9 + EARNINGS FOR PERIOD 10 + Prediction
Bonus in the market
At the end of each round, assets have a value of zero. Therefore, your total
profit for the experiment is the sum of your profits from all four (independent)
rounds. This profit is converted into yen at the exchange rate of 100 MARKs= 20 yen. You will also receive a participation fee of 500 yen.
YOUR TOTAL EARNINGS IN THE EXPERIMENT [yen] =TOTAL EARNINGS FOR MARKET 1 [MARKs] +TOTAL EARNINGS FOR MARKET 2 [MARKs] +TOTAL EARNINGS FOR MARKET 3 [MARKs] +TOTAL EARNINGS FOR MARKET 4 [MARKs] × 20/100 + 500 [yen]B.1.6

Chapter 5. Trading Screen

The following figure illustrates the trading procedure. Each trading period
automatically terminates after two minutes. You can submit orders and tradestocks in the * area. Descriptions of how to submit orders and trade stocks inthe * area are provided on the next page.

26

How to submit orders and trade stocks in the * area
• (1) Buying Order: you click on this button to enter a bid price. A tradedoes not take place until another participant accepts your oﬀer.
• (2)Selling Order: you click on this button to enter an ask price. A tradedoes not take place until another participant accepts your oﬀer.
• (3)Buy Order: this shows buying orders from all traders, with your own
bids shown in blue and those of the other traders in black. The oﬀer witha blue background is always the best, i.e., it yields the highest revenue forthe seller.
• (4)Selling Order: this shows selling orders from all traders, with your own
asks shown in blue and those of the other traders shown in black. Theoﬀer with a blue background is always the best, i.e., it is the cheapest
option for the buyer.
• (5)Buy: You click on this button to buy a stock at the price shown against
a blue background.
• (6) Sell: You click on this button to sell a stock at the price shown against
a blue background.
• (7)Traded: this shows all transactions in the current period in chronological order.
27

B.1.7

Chapter 6. History Screen

After every trading period, this screen provides you with vital information for
30 seconds. In particular, the box containing the # symbol shows important
information about your predictions concerning the current period. The average
actual trading price in the current period is shown on the first line. The secondline shows the number of predicted prices that were in the range between 90%
and 110% of the average actual trading price.

28

B.2

Instructions for ASYMM markets under CM

In the instructions for the CONTROL (SYMM) markets, Chapter 2. Information Levels is eliminated (shortened) and the explanations of the screeninterfaces are modified accordingly.
B.2.1

General information

Analogue to the instructions in CDA ( B.1).
B.2.2

Chapter 1. Market Description

Analogue to the instructions in CDA ( B.1).
1.1 How to realize a profit margin through buying and selling stocks
Each trader can be a buyer and a seller at the same time. Thus, buying low
and selling high allows you to realize a profit margin.
ExampleIf you can buy the stock for 100 MARKs and sell it for 120 MARKs, youearn 20 MARKs. On the other hand, if you buy the stock for 100 MARKs
and sell it for 80 MARKs, you lose 20 MARKs.
The following figure shows part of the screen used for trading. At the beginningof each trading period, if you wish to purchase assets, you can place a buy order.
Your buy order indicates the number of assets you would like to buy and thehighest price that you are willing to pay. Similarly, if you wish to sell assets,
you can place a sell order. Your sell order indicates the number of assets you
are oﬀering to sell and the lowest price that you are willing to accept. The priceat which you oﬀer to buy must be less than or equal to the price at which you
oﬀer to sell. The price you specify in your order is a per-unit price. Note thatyour cash and asset inventory cannot be less than zero.

The computer program will organize the buy and sell orders and use them todetermine the market price at which units are bought and sold. All transactions
in a given period occur at the same market price. This will generally be the priceat which the number of assets with sell order prices at or below it is equal to
the number of assets with buy order prices at or above it. Traders who submit
buy orders at prices above or equal to the market price make purchases, andthose who submit sell orders at prices below or equal to the market price make29

sales. 47 The following examples show how the market price is determined.
Consider the following buy/sell orders placed by four traders:• Trader A: One sell order, which can be executed at 10 MARKs or higher• Trader B: Two sell orders, which can be executed at 40 MARKs or higher• Trader C: One buy order, which can be executed at 60 MARKs or lower• Trader D: One buy order, which can be executed at 20 MARKs or lower
A graph summarizing these orders is shown below.

A seller is willing to sell at either the price requested or a higher price. A buyer
is willing to buy at either the price oﬀered or a lower price. As shown above,
there is only one stock oﬀered at 10 MARKs or higher. If the price rises to 40
MARKs, the number of stocks oﬀered increases to three. Conversely, only one
stock is demanded at 60 MARKs or lower. If the price falls to 20 MARKs, thenumber of stocks 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 to the minimum price in this range, i.e., 21 MARKs.
Consider the following buy/sell orders placed by five traders:• Trader A: One sell order, which can be executed at 10 MARKs or higher• Trader B: One sell order, which can be executed at 30 MARKs or higher• Trader C: One sell order, which can be executed at 30 MARKs or higher• Trader D: One buy order, which can be executed at 60 MARKs or lower• Trader E: One buy order, which can be executed at 30 MARKs or lower
A graph summarizing these orders is shown below.

47 When there were excess bids (asks) at a particular price, the computer excluded theexcess bids (asks) in ascending (descending) order of prices until demand equaled supply. If
the highest bid price was lower than the lowest ask price (i.e., the demand curve was below
the supply curve), no transactions occurred and the traders were informed of the highest bid
price.

30

As shown above, only one stock is oﬀered at 10 MARKs or higher, as in theprevious example. If the price rises to 30 MARKs, the number of stocks thatare oﬀered increases to three. However, there is only one stock demanded at
60 MARKs or lower. If the price falls to 30 MARKs, the quantity demanded
increases to two. As a result, two transactions can be completed at 30 MARKs.
In this case, the market price is set to 30 MARKs. The orders that will be
fulfilled are determined as follows. Priority is given to Trader A, because he/sherequested a price less than the market price. In addition, the order of either
Trader B or Trader C will be fulfilled, the choice being determined randomly
by the computer.
1.2 How to earn dividends on your stock holdings
Analogue to the instructions in CDA ( B.1).
1.3 After first market
Analogue to the instructions in CDA ( B.1).

B.2.3

Chapter 2. Information Levels

Analogue to the instructions in CDA ( B.1).

B.2.4

Chapter 3. How to make a profit by accurately predicting thefuture prices of stocks

Analogue to the instructions in CDA ( B.1).

B.2.5

Chapter 4. Calculating Your Earnings

Analogue to the instructions in CDA ( B.1).

31

B.2.6

Chapter 5. Trading Screen

The following figure illustrates the trading procedure. 48 At the beginning ofeach trading period, you have 60 seconds to submit a buy/sell order. 49

The most important points in relation to the buying and selling of stocks are
summarized below.
• You can simultaneously place buy and sell orders, or you can place only
a buy or a sell order. You can also decide not to place any orders at all.
• If you do not want to submit a buy order, enter 0 as the quantity to buy.
If you do not want to submit a sell order, enter 0 as the quantity to sell.
• The screen displays an error message if any of the following conditions are
violated:
1. The quantity you wish to sell must be less than or equal to the numberof units you hold.
2. The maximum purchase price multiplied by the quantity you wish to
buy must be less than or equal to your cash holding at the time.
48 The middle and lower areas of this screen show the information commonly used in both
CDAs and CMs. Therefore, we implemented these areas as similar to those of CDAs’ screens
as possible.
49 To equalize the total amount of time spent on experiments employing CMs and those
employing CDAs, the time limit for the CM trading screen is set to 60 seconds (120 seconds
under CDAs). This is because the calculations in the experiments employing CMs take longer
than those in the experiments employing CDAs. Note that, in CMs, even if the time has
expired in a period, none of the traders leave the history screen until all the traders havesubmitted an order. In many cases, all the traders submit their orders within 60 seconds.

32

3. If you simultaneously place buy and sell orders, the maximum priceat which you are prepared to buy must be less than or equal to theminimum selling price you are prepared to accept. 50
B.2.7

Chapter 6. History Screen

After every trading period, this screen provides you with vital information for
30 seconds. In particular, the box containing the # symbol shows important
information concerning the current period. The first line shows the market
price, as explained previously. The second and third lines show the number ofstocks you have purchased and sold, respectively, in the current period. The
fourth line shows the actual dividend payment (0 or 20 MARKs) for the current
period. The fifth and sixth lines show your current cash and stock holdings,
respectively. The seventh line shows the number of predicted prices that werein the range between 90% and 110% of the market price.

50 This constraint is typical of experimental studies employing CMs. Many experimental
studies using CMs impose a similar constraint on orders (Boening et al. 1993, Haruvy et al.
2007, Akiyama et al. 2013, Akiyama et al. 2014). This is because buying and selling at prices
where the purchase price is higher than the selling price results in the loss of cash, and thus
is nonsense.

33

C

Appendix: Predetermining Dividends and Realized Values of Those

C.1

Predetermining Dividends

We predetermined the dividend (0 or 20 MARKs) following the procedure usedin Sutter et al. (2012).
Before the experiments commenced, we created 40 dividends (10 periods ×
4 rounds), which we named the first array. The “mirroring” process converted
the first array into the second array: when the n-th element of the first array
was 0 (20) MARKs, the n-th element of the second array was 20 (0) MARKs.
In this way, we created six arrays (three pairs of first array and second array).
There are six kinds of settings (= three treatments × two trading institutions)in our study. We conducted six markets for each kind of setting. The dividend
stream of the six markets in each of six kinds of settings followed the above six
array.
This procedure was designed “to not confound possible diﬀerences acrosstreatments” and trading institutions “by diﬀerent realizations of the dividends”(Sutter et al., 2012) using three treatments and two trading institutions.

C.2

Dividends Used in This Study

The following Tables C.1 - C.4 shows all values in the six arrays of 40 realized
dividends (10 periods × 4 rounds) that were applied in our experimental markets. In Tables C.1 - C.4, Array 1, 3 and 5 are classified as first array. Array 2,
4 and 6 are the second array corresponding to Array 1, 3 and 5 respectively.
Table C.1: Actual dividends across 10 periods of round 1 in the 6 markets ofeach treatment
Period Array 1 Array 2 Array 3 Array 4 Array 5 Array 6
1
20
0
20
0
0
20
2
20
0
0
20
20
0
3
20
0
20
0
20
0
4
0
20
20
0
20
0
5
0
20
20
0
0
20
6
0
20
0
20
20
0
7
0
20
20
0
20
0
8
20
0
0
20
0
20
9
20
0
20
0
0
20
10
0
20
0
20
20
0

34

Table C.2: Actual dividends across 10 periods of round 2 in the 6 markets ofeach treatment
Period Array 1 Array 2 Array 3 Array 4 Array 5 Array 6
11
0
20
0
20
20
0
12
20
0
0
20
0
20
13
20
0
0
20
20
0
14
20
0
0
20
0
20
15
0
20
0
20
0
20
16
0
20
20
0
0
20
17
20
0
20
0
0
20
18
20
0
20
0
20
0
19
0
20
0
20
20
0
20
20
0
0
20
20
0

Table C.3: Actual dividends across 10 periods of round 3 in the 6 markets ofeach treatment
Period Array 1 Array 2 Array 3 Array 4 Array 5 Array 6
21
20
0
20
0
0
20
22
20
0
20
0
20
0
23
20
0
0
20
20
0
24
20
0
20
0
0
20
25
20
0
0
20
0
20
26
20
0
20
0
0
20
27
0
20
20
0
20
0
28
20
0
0
20
20
0
29
0
20
20
0
20
0
30
0
20
0
20
20
0

Table C.4: Actual dividends across 10 periods of round 4 in the 6 markets ofeach treatment
Period Array 1 Array 2 Array 3 Array 4 Array 5 Array 6
31
20
0
0
20
0
20
32
0
20
0
20
20
0
33
0
20
0
20
20
0
34
20
0
20
0
20
0
35
0
20
20
0
20
0
36
20
0
20
0
20
0
37
0
20
0
20
0
20
38
20
0
0
20
0
20
39
0
20
20
0
0
20
40
20
0
0
20
0
20

35

D

Appendix: Realized Price Analyses where RAD
and RD based on F V 0 were used in ASYMM

We conducted regression analyses where the dependent variables were RAD(model 7) and RD (model 8), in addition to the analyses reported in Table 1,
where RAD and RD in the ASYMM markets were calculated based on the asset’s
fundamental value (FV) for the traders with information level 1 (F V 1 ). In theseanalyses, the RAD and RD in the ASYMM markets were calculated based on the
FV for traders with information level 0 (F V 0 ), and were used as the dependent
variables. The independent variables and sample size for these analyses are
identical to those in the analyses reported in Table 1. These regression analyses
also employed the ordinary least squares method. All coeﬃcients displayed
the qualitatively same values as those reported in Table 1. Thus, the results
reported in Table 1 are robust to the diﬀerence between values of RAD/RD in
ASYMM markets based on F V 0 and those based on F V 1 .
Table D.1: The eﬀects of informational asymmetry on bubbles under each of
trading institutions (using RAD/RD of the ASYMM markets based on F V 0 )Model 7
Model 8
Dependent value
RAD
RD
Constant
0.33∗∗∗
0.16∗∗
ASYMM dummy(6.21)(2.27)∗

0.14∗(1.69)(2.00)∗∗∗

0.23∗∗∗(3.09)(3.92)

0.10

CDA dummy

0.14

∗∗∗

−0.06

Round

(−3.62)ASYMM & CDA dummy
Adjusted RN(−3.88)−0.18−0.36∗∗∗

0.12
144

0.17
144

∗∗

(−2.19)2−0.08∗∗∗(−3.53)

Notes. This table shows a summary of regression analyses where the dependent variables are
RAD (model 7) and RD (model 8). In these analyses, RAD and RD in the ASYMM markets
are based on FV0 (the FV for traders with information level 0). The independent variables inboth of these regressions are a dummy for treatment ASYMM, a dummy for trading institution
CDA, an interaction dummy between ASYMM and CDA, and a variable for the round. The
values of the intercepts in both of these models represent the eﬀects of the baseline condition,
where the informational asymmetry among traders does not exist (CONTROL and SYMM)
and the trading institution is a CM. The sample size for the regressions of RAD and RD is 144(= 2 trading institutions × 3 treatments × 6 markets × 4 rounds). These regression analyses
employ the ordinary least squares method. The t-values are included in parentheses. *, **,and *** indicate significance at the 10%, 5%, and 1% levels, respectively.

36

E

Appendix: Traders’ belief dynamics using 3D
bar charts in the same way as Haruvy et al.
(2007)

Figures E.1 - E.8 show the dynamics of the average forecast prices submittedby traders with each information level (0 or 1) in the markets under various
treatments (CONTROL, SYMM, or ASYMM) under each trading institution(open-book continuous double auction or closed-book call market). In eachfigure, the dynamics of the forecast prices in rounds 1 to 4 are shown separately
using a three-dimensional bar chart, as in Haruvy et al. (2007). In each chart,
the abscissa axis represents the period for which the subjects predicted themarket price, the ordinate axis represents the period during which their forecasts
were elicited, and the applicate axis represents the average of the forecast prices.
Figure E.1: The CONTROL markets under CDA

37

Figure E.2: The SYMM market under CDA

Figure E.3: The ASYMM market under CDA (information level 0)38

Figure E.4: The ASYMM market under CDA (information level 1)

Figure E.5: The CONTROL market under CM

39

Figure E.6: The SYMM market under CM

Figure E.7: The ASYMM market under CM (information level 0)40

Figure E.8: The ASYMM markets under CM (information level 1)41