Effect of Trading Halt System on Market
Functioning: Simulation Analysis of Market
Behavior with Artificial Shutdown*
Jun Muranaga
Bank of Japan

Tokiko Shimizu
Bank of Japan

Abstract
This paper explores how we determine when an observed plunge in stock prices is‘critical,’ and considers what trading halt system design would guarantee more
benefits than costs. We use an artificial market model demonstrated in Muranaga
and Shimizu [1999a] and analyze the market crash generating mechanism and theeffects of the trading halt system on market behavior. We find that a market crash isobserved in cases which include a feedback mechanism to a trader’s expected value.
The probability of a crash increases when there is a relatively limited transmission
range of exogenous shock or a high ratio of momentum traders, who trade by only
considering market price changes. We also find that in order to have a stable market
after trading resumes, it is important to resume trading after the declining speed ofthe market participants’ expected values has become small enough.

*The views expressed here are those of the authors and do not reflect those of the Bank of Japan.
(E-mail: jun.muranaga@boj.or.jp, tokiko.shimizu@boj.or.jp)

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Jun Muranaga and Tokiko Shimizu

Purpose of the study

The trading halt system, an institutional mechanism, that artificially stops tradingin order to prevent further price falls during a market crash, was proposed as oneof the four recommendations by the Presidential Task Force on Market Mechanism [1988] following the 1987 market crash, and has actually been introduced invarious stock exchanges.1 Market participants and theorists have presented theirevaluations of such a trading halt system, although few analyses have proved thesystem to be effective by showing that the system actually prevented or restricted
great market movement. The main reason for this is that Rule 80B, the trading
halt system in the New York Stock Exchange (NYSE), had never actually beenused prior to last autumn, while several theoretical problems in its institutional
design have been pointed out. Greenwald and Stein [1991] argued that a trading
halt increases trade execution risk (uncertainty of trade execution) and thus
induces market instability. Brennan [1986] pointed out that a trading halt leads toa decline in price information. In addition, when the circuit breakers were activated during the rapid stock price fall last autumn, market participants felt “thetrading halt increased investor anxiety and thus spurred the turmoil.”
As Brennan [1986] pointed out, a trading halt system imposes costs on market
participants by depriving them of profitable trading opportunities. Therefore,
based on the belief that such a system should not be activated unless there are
obvious benefits versus the costs, in February 1998, the NYSE decided to reconsider use of its circuit breakers.2 The new rules set a “standard value,”3 andtrading is halted according to the daily “rate of decline”4 of the Dow Jones Industrial Average (DJIA) from the standard value. When the DJIA declines 10% and
20% from the standard value, all stock trading is halted for a certain period oftime,5 and when the decline reaches 30%, all trading is halted for the day. Given
the current level of stock prices, the new rules indicate significant relaxation, andcan be regarded as responsive to the claims of market participants who favorminimal activation of such a system. Once trading is resumed, if the DJIA declines by an additional 20%, trading will be halted again for two hours if the1
As circuit breakers, which impose limits on trading activity, there are trading halt, price limit,
collar, transaction cost, margin requirement, and position limit. This paper focuses on themechanism in which all trading is halted.
2
Another trading halt system is activated when important information significantly affecting
investor judgement prevails in the market. Since April 1998, the Tokyo Stock Exchange (TSE)shortened the trading halt period from the whole day to 90 minutes after the relevant information has
been publicly released.
3
The standard value is regularly reviewed four times a year.
4
The current circuit breakers halt trading according to the ‘magnitude of decline.’ When the
Dow Jones Industrial Average declines 350 points from the previous day’s close, all stock trading ishalted for 30 minutes, and when the decline totals 550 points, all trading is halted for one hour.
5
In the case of a 10% decline, trading will be halted for one hour if the decline takes place by 2
p.m. Trading will be halted for 30 minutes if the decline takes place by 2:30 p.m. Trading will not be
halted if the decline occurs after 2:30 p.m.

Effect of trading halt system on market functioning

335

decline takes place by 1 p.m., for one hour if it takes place by 2 p.m., and for the
day if it takes place after 2 p.m.
A review of the NYSE circuit breakers highlighted several important issues
inherent in the trading halt system. The first issue relates to the timing of trading
halts. Even in the case of a ‘stock price plunge,’ if such a phenomenon is due torational behavior of market participants, and represents faster-than-normal trade
execution and a continuous decline in prices, there may be no need to artificiallyhalt trading (Greenwald and Stein [1988]). We should examine what kind of
market phenomenon deserves an artificial trading halt. The second issue is toconsider a system design which guarantees a smooth resumption of trading after
the halt without inducing a further crash in the market. We should develop an
effective system design which supports the stabilization effects of the trading halt
system and improves market conditions.
This paper tries to provide answers to these issues. Specifically, we will focus
on the following two questions:
1) How do we determine when an observed plunge in stock prices is not a
price adjustment phase resulting from rational trading behavior, but is instead, a ‘critical’ situation which requires trading to be artificially halted?
2) Provided that we can judge whether the market is in a ‘critical’ situation,
what system design would guarantee more benefits than costs for a trading
halt? We will examine institutional design, which enables trading to resume smoothly after the halt, without delaying the possibility of a market
crash or accumulation of downward pressure in the future.
The composition of this paper is as follows. Section 2 presents the frameworkof our analysis. We consider the market crash generating mechanism in Section 3.
In Section 4 we analyze the effects of the trading halt system on market behavior
in the case that a market crash occurs. In Section 5 conclusions and future tasks
are shown.

2

Framework of the analysis

In order to consider the merits and drawbacks of the trading halt system, we use
the Monte Carlo simulation based on an artificial market model. This is an analytical method demonstrated in Muranaga and Shimizu [1999a]. This model iscomposed of two stages: a micro stage, in which each trader’s decision-making
and ordering are described; and a macro stage, in which trade execution throughthe trade execution model and subsequent information distribution are described.
The underlying flow of simulation and the trader’s decision-making model are
shown in Appendix 1. In the following, we first look at a mechanism which
generates a market crash, and execution of the trading halt system.

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While traders make trades based on their own expected values and confidence
in their forecasts, they seem to, in fact, feed back available market information in
their decision-making process in various ways. Given the variety and complexityof such mechanisms, this paper focuses on the two simplest mechanisms, as wasdone in Muranaga and Shimizu [1999a,b]: feedback to trader’s expected value,and feedback to confidence in trader’s forecast, and analyze the effects of such
feedback mechanisms on market behavior. The details of these two feedback
mechanisms are shown in Appendix 2.
A market crash is not necessarily triggered by exogenous shocks. However,since we are dealing with post-crash measures, we will give an explicit exogenous
shock to our model and analyze issues such as whether a crash will be generated
and what role trading halt measures will play during a market crash. We add
exogenous shocks to the artificial market model used in Muranaga and Shimizu[1999a], and focus our analysis on the subsequent market behavior. If a shock is
added to an efficient market in which all information is perfectly reflected in theprices, such shock will also be instantaneously reflected in the prices, and thus
paths of market prices derived from the Monte Carlo simulation are those shownin Figure 1. In the actual market, however, since various market microstructures
do affect price formation in the market, price paths can also be those shown asLine A in Figure 2 (the shock is reflected in market prices over time), Line B(market prices continue to fall), or Line C (trade not executed for a while).
By conducting simulations according to various types of exogenous shock andsettings of market condition, we explore the relationship between various settingsand likelihood of a market crash. We consider four types of shock impact in our
analysis, which reduce value traders’6 expected values by 2.5%, 5.0%, 7.5%, and10.0%, respectively. As for the range in which such shocks will be transmitted,
we have considered the portion of traders who directly receive such shock as 20%,
40%, 60%, 80%, and 100% of the whole value traders. By adding these 20 typesof shock (Table 1) to the market, we will observe the subsequent market behavior.

6
We define ‘value traders’ as traders who place limit orders based on their expected values. See
Appendix 1 for detailed information.

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Table 1: Types of exogenous shocks

Transmission
range of
shock

2.5 %

Shock impact*
5.0 %
7.5 %

10.0 %

20 %Case 1Case 2

Case 3

Case 440 %Case 5

Case 6

Case 7

Case 860 %Case 9Case 10

Case 11

Case 1280 %Case 13

Case 14

Case 15

Case 16100 %Case 17

Case 18

Case 19

Case 20*Ratios by which traders reduce their own expected values.

3

Analysis of market crashes

3.1

Market consisting solely of value traders(1) Feedback to expected valueFirst, we assume the market follows a mechanism which allows traders to modify
their expected values in response to large changes in market prices. We assume
that the feedback is triggered when changes in market prices exceed the 90 percentile of the traders’ expected value distribution of the market (that is, trigger
level=10%).7 All 50 market participants are value traders. 20 types of shockshown in Table 1 are added to this market, and subsequent market behavior will
be monitored.
Figures 3 and 4 show movements of market prices and traders’ expectedvalues of asset. Each black line indicates movements of one trader’s expectedvalue. The black bold line indicates movements of the mean of all traders’ expected values. The gray bold line shows movements of market price. In both
figures, exogenous shocks are added to the market at the 26th period. In Figure 3
market price reaches a new equilibrium, while traders modify their expectations
one after another, and the market falls into a crash in Figure 4.
Figure 5 shows the resulting market prices (mean of bid and ask prices) of thesimulation conducted 20 times using 20 different types of shock impact andtransmission range. We can see that a crash is more likely due to a large shock(7.5% or more) compared with the price variance during normal times (about 5%)
when it is transmitted to 40-60% of the market participants (Figures 5-7, 5-8, 5-11,
5-12). When a shock is transmitted to relatively few (20%) participants, even alarge shock (10%) will be mostly absorbed. In contrast, when a shock has been
7
“Trigger level=10%” means that traders will change their expected values if they observe
market prices lower/higher than the price at the 90 percentile of their expected value distribution.

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transmitted quite broadly to the market (80% or more of the participants), the shock
will be properly reflected in prices regardless of its impact (Figures 5-13 to 5-20).
Figure 6 depicts the mean of traders’ expected values. We can see that, except
when a shock has been transmitted to all participants, traders adjust their expectedvalues after the shock by reacting to changes in market price which are generated
endogenously.
Figure 7 shows the divergence between the development in market price andthat in the mean of traders’ expected values, that is, an overshoot ratio.8 The
positive spike of the overshoot ratio in the 26th period represents the situation inwhich, upon receiving an exogenous shock, traders have reduced their expectedvalues while market prices cannot quite follow such reduction, with their levels
remaining somewhat higher than those of traders’ expected values. When theshock transmission range is between 80-100%, the market price seems to reach a
new equilibrium within 10 terms, in which all traders have traded at least once
(Figures 7-13 to 7-20). When the transmission range is between 40-60%, there isa price overshoot when a strong shock (7.5% or more) has been added (Figures 77, 7-8, 7-11, 7-12).
Figure 8 summarizes the development in limit order book accumulated in themarket. Buying order volume is plotted on the positive side of the vertical axis,
while selling order volume is plotted on the negative side. As added shock increases, order imbalance (difference between buying and selling order volume)
increases. In addition, when shock impact is 7.5% or more and transmission range
is between 40-60%, post-crash supply-demand imbalance and its duration increase (Figures 8-7, 8-8, 8-11, 8-12).
(2) Feedback to confidence
Second, we analyze cases where traders modify their confidence in response tolarge changes in market price. In such cases, traders will not adjust their expectedvalues, but they will modify their confidence in their forecasts and adjust theirestimates for risk. When traders’ risk amount increases, expected return on risk
will decrease more rapidly than the extent of risk aversion, and thus the traders
will not place orders. We have shown the developments in market prices, mean of
expected values, overshoot ratio, and order imbalance in Figures 9, 10, 11, and 11,
respectively. From Figure 9, we can see that crashes are not generated regardlessof the size and transmission range of the exogenous shock. In addition, priceovershoots rarely occur as shown in Figure 11. We can see that market liquidity
8

We define overshoot ratio (Ot) as follows:
Ot =

Pt

-1

N

å

Vi,t

N

i =1

where Pt denotes the mean price in term t, V i,t denotes the expected value of trader i in term t, and N
denotes the number of value traders.

Effect of trading halt system on market functioning

339

plays a role as a built-in stabilizer behind the phenomena in which crashes are notgenerated in the cases of feedback to confidence (Muranaga and Shimizu [1999b]).

3.2

Market consisting of value traders and momentum traders

In the previous part, we analyzed a market composed solely of value traders whomake arbitrage transactions by comparing market prices to their expected values.
Here, we add momentum traders, who are indifferent to the level of market prices
and place market orders taking into account only the trends and volatility ofmarket price changes. Momentum traders buy when market prices rise and sellwhen prices fall, therefore they make market prices more volatile.
(1) Feedback to expected value
As we have done earlier, we first look at a market where traders modify their
expected values in response to large changes in market price. In this setting, when
we compare developments in market prices, expected values, overshoot ratio, andorder imbalance in the market with 10 additional momentum traders (Figures 1316) with those of the market without momentum traders (Figures 5-8), we can see
the following two features:a) When shock impact is 7.5% or more and shock transmission range is
between 40-60%, market crashes are generated in both cases and probability is larger in the market with momentum traders (compare Figures 13-7,
13-8, 13-11, 13-12 with Figures 5-7, 5-8, 5-11, 5-12).
b) There are some shock patterns which induce market crashes only in markets with momentum traders. Specifically, market crashes are generated
under conditions such as: 10% shock impact and 20% transmission range(Figure 13-4), 5.0% shock impact and 60% transmission range (Figure 1310), and 10.0% shock impact and 80% transmission range (Figure 13-16).
(2) Feedback to confidence
Next, we turn to the market where traders make changes in market prices feedback to their confidence. In this setting, we compare developments in market
price, mean of expected values, overshoot ratio, and order imbalance in the market with 10 additional momentum traders with the same categories of the market
without momentum traders (Figures 9-12). Price volatility increases in accordance with the number of momentum traders, because variance of traders’ expected values increases.
We can summarize our above findings as follows.
a) As for feedback to expected values, a market crash or price overshoot isobserved when the exogenous shock is larger than the variance of market
participants’ expected values and the transmission range of the shock is

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relatively limited. As the share of momentum traders rises, market prices
are more likely to overshoot.
b) As for feedback to confidence in expectation, neither market crash nor
price overshoot may occur regardless of the number of momentum traders.
This result does not necessarily mean that the feedback mechanism to confidence is more favorable for price discovery function than feedback to expected value. Feedback to confidence has another problem that market liquidity evaporates and price discovery function halts spontaneously.9

4

Activation of the trading halt system

As described in Section 1, since the overshooting of market prices only leads toincome transfer among market participants, we should note that artificial haltingof the price overshooting may not be a desirable system design. However, we
regard such market price overshooting, or a market crash, as a destabilizing factorof the economy. Next, we will examine the effectiveness of trading halt systems
as a crash-preventing mechanism. Specifically, as an example, we examine thecase with feedback to expected value, which is likely to lead to a market crash.
We conduct our simulations by changing the price level which triggers the trading
halt system and halt period, and analyze the relationship between such setting andthe market behavior after resumption of trading.
First, we conduct our analysis based on the setting used in Figure 13-8, where
there were most crashes among the cases which include the feedback mechanism
to expected values. Specifically, we construct a market composed of 50 value
traders, who revise their expected values at the 10% trigger level, and 10 momentum traders. We then add to the market a 10.0% exogenous downward shockwith a 40% transmission range. The trigger price levels of the trading halt system
are set at -10% level (900 yen) and -20% level (800 yen) of the initial price (1,000yen), and the trading halt period set at 5, 10, 15, and 20. Figures 17-20 illustratedevelopments in market price, mean of expected values, price overshoot ratio, andorder imbalance.
The simulation results show us that market prices after resumption of trading
are relatively stable when the trading halt period exceeds the length of the trader’smemory (10 periods). In contrast, when the trading halt period is shorter than thelength of the trader’s memory (that is, in the cases of 5 and 10 trading halt periods), a crash cannot be avoided. When we examine each simulation path, we cansee how the declining speed of the mean of traders’ expected values at the pointof trading resumption has a substantial effect on subsequent market behavior.
Figure 21 shows the relationship between the declining speed of expected values
at the point trading resumes and market behavior after resumption of trading. In
9

See Muranaga and Shimizu [1999b].

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cases where the market moves towards a crash after resumption of trading, thedeclining speed of the fundamental values at the point of trading resumption islarge. On the other hand, in cases where the market shows stable movement after
resumption of trading, the rate of change in expected values approaches zero
during the trading halt period. The length of time needed for the declining speedof expected values to reach zero will depend on how much market participants
incorporate past market price changes (memory horizon) in adjusting their individual fundamental values. Therefore, the effectiveness of the trading halt system
may be improved by setting the trading halt period long enough relative to thelength of the memory horizon. In order to apply this finding in the actual market
design, we need to observe the declining speed of the market participants’ expected values. Such speed cannot be directly observed, although there may be asource which would enable us to determine the optimal timing in which to resume
trading in a stable way.10 With regard to the trigger price level of the trading halt,
it does not affect market behavior after resumption of trading. We cannot find asignificant difference between the case with the trigger price level at 800 yen andthe case with the level at 900 yen in Figures 17-20.
In our simulation, there was a case in which market prices rallied and diffused
upward with 15 trading halt periods. Upon examining this case in detail, we foundthat market price rallied because trading resumed at the point when market participants’ expected values also rallied. Although we may not be able to see such aphenomenon in the actual market, this finding may imply that the market can be
distorted according to the timing of the resumption of trading.

5

Conclusions and future tasks

By using the Monte Carlo simulation based on an artificial market model, an
analytical method used in Muranaga and Shimizu [1999a], we have analyzed themarket crash generating mechanism and the trading halt system. With respect tothe mechanism which allows traders to feedback market information to theirtrading behavior, this paper focused on the following two: feedback to expectedvalue, and feedback to confidence in forecast. As for the effects of exogenous
shocks, we added to our model 20 types of shocks with different impact andtransmission range, and analyzed the subsequent market behavior.
As a result, we found that a market crash (substantial fall in market prices) isobserved in cases which include the feedback mechanism to expected value. In
addition, the probability of a crash increases when there is a limited transmission
range of exogenous shock or a high ratio of momentum traders, who trade by only
considering market price changes.
10
We have tried to estimate the optimal timing for resumption of trading by using thedevelopment of traders’ limit order book as a proxy for change rates of their expected values, but theresults were not relevant.

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We then examined the market behavior when the trading halt system is activated. Specifically, we observed how market behavior differs when the price level
which triggers the trading halt system and halt period are changed. The results
implied that, in order to have a stable market after resumption of trading, it isimportant to resume trading after the declining speed of the market participants’
expected values has become small enough. Because the declining speed of traders’ expected values cannot be directly observed, we need to find a certain proxybased on available market price or order flow information.
The above simulation results are only valid in the simplified hypotheticalmarket model, and this model should be verified through the use of actual market
data. However, given the difficulty in collecting data related to market crashes,
we believe that our approach to understanding the market mechanism by aggregating micro-level behavioral mechanisms through simulation can be deemed
reasonable.
In order to explore the mechanisms operating in the actual market, we need tomake our model more realistic. For this purpose, the following four points deserve attention.
a) Inclusion of trader’s position and its market value
Our model does not consider the trader’s position and its market value
when he/she trades. In the actual market, however, traders seem to change
their trading behaviors based on their position and profit/loss situation (e.g.
loss cutting rules). Taking into account traders’ behavior based on theirpositions and profit/loss, we could explore traders’ decision making more
realistically as a consequence of inter-temporal utility maximization.
b) Consideration of traders’ memory effects
As stated in Section 1, it has become generally recognized that the role ofthe circuit breaker system including the trading halt system is to give thehuman mind the chance to catch up with market developments (Brady[1998]). We have also confirmed in our analysis that, when the market islikely to overshoot because of exogenous shocks, activation of the trading
halt system prevents market prices from overshooting. Such phenomenon
tells us that market participants’ behavior is affected by price changes ofthe past. We have used traders who trade according to the average andvariance of price changes over the past 10 periods. By deepening our understanding of traders’ “memory effects,” we may be able to obtain insight
into market dynamics, particularly the market’s recursive process.
c) Activation level of the trading halt system
While we have observed the effects of the length of trading halt period, theeffects of the activation price level of the trading halt system have not beenclearly identified and require further research. As for the effects of the activation level, we can conceive a situation where the existence of the
trading halt system induces a market crash where trading is halted prema-Effect of trading halt system on market functioning343

turely despite a substantial decline in market participants’ expected values.
d) Activation of the trading halt system based on price change ratio
Because momentum traders’ behavior is literally determined by momentum (historical trend of market price change) in the market where momentum traders are influential, we need to examine other factors such as declining speed of price in addition to price levels as activation standards ofthe trading halt system.

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Figure 1: Image of developments in market prices: Complete markets
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Asset
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Figure 3: A sample simulation result: New equilibrium
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Figure 5: Developments in market prices (Feedback to expected value)
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Figure 5, continued
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Figure 6: Developments in mean of traders’ expected values(Feedback to expected value)
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Figure 6, continued
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Figure 6-4 (Impact:-10.0%, Range:20%)

Figure 6-19 (Impact:-7.5%, Range:100%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 6-20 (Impact:-10.0%, Range:100%)800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

350

Jun Muranaga and Tokiko Shimizu

Figure 7: Developments in price overshoot ratio(Feedback to expected value)
Figure 7-1 (Impact:-2.5%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

Figure 7-2 (Impact:-5.0%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100

0Figure 7-5 (Impact:-2.5%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100
Figure 7-6 (Impact:-5.0%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 7-9 (Impact:-2.5%, Range:60%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

Figure 7-10 (Impact:-5.0%, Range:60%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100

0Figure 7-13 (Impact:-2.5%, Range:80%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 7-14 (Impact:-5.0%, Range:80%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 7-17 (Impact:-2.5%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100

010 20 30 40 50 60 70 80 90 100
Figure 7-18 (Impact:-5.0%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100

continued on next pageEffect of trading halt system on market functioning

351

Figure 7, continued
Figure 7-3 (Impact:-7.5%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 7-7 (Impact:-7.5%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

0

0

0

Figure 7-15 (Impact:-7.5%, Range:80%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

Figure 7-19 (Impact:-7.5%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 7-16 (Impact:-10.0%, Range:80%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 7-12 (Impact:-10.0%, Range:60%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 7-8 (Impact:-10.0%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100
Figure 7-11 (Impact:-7.5%, Range:60%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

Figure 7-4 (Impact:-10.0%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100
Figure 7-20 (Impact:-10.0%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100

352

Jun Muranaga and Tokiko Shimizu

Figure 8: Developments in order imbalance(Feedback to expected value)*50

Figure 8-1 (Impact:-2.5%, Range:20%)

0

0

-50

-50

-100

-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100

50

Figure 8-5 (Impact:-2.5%, Range:40%)

0

0

0
-50

-100

-100
-150

-150

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100
Figure 8-9 (Impact:-2.5%, Range:60%)

0

-50

-50

-100

-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100
Figure 8-13 (Impact:-2.5%, Range:80%)

0

0

0
-50

-100

-100

-150

10 20 30 40 50 60 70 80 90 100
Figure 8-14 (Impact:-5.0%, Range:80%)

50

-50

-150
0 10 20 30 40 50 60 70 80 90 100

50

Figure 8-10 (Impact:-5.0%, Range:60%)

50

0

50

10 20 30 40 50 60 70 80 90 100
Figure 8-6 (Impact:-5.0%, Range:40%)

50

-50

50

Figure 8-2 (Impact:-5.0%, Range:20%)

50

Figure 8-17 (Impact:-2.5%, Range:100%)

0

0

-50

-50

-100

-100

-150

Figure 8-18 (Impact:-5.0%, Range:100%)50

0

10 20 30 40 50 60 70 80 90 100-150
0 10 20 30 40 50 60 70 80 90 100

010 20 30 40 50 60 70 80 90 100

Buyingorder volume
is plottedside illustratesof the vertical
axis,
while
sellingorder*: Whatis plottedon the positive
sideonofthe
thepositive
vertical axis
buyingorder
volume,while
volumeorderis plotted
onisthenegativeside.
selling
volumeplottedon thenegative side.

continued on next pageEffect of trading halt system on market functioning

353

Figure 8, continued
Figure 8-3 (Impact:-7.5%, Range:20%)

50

Figure 8-4 (Impact:-10.0%, Range:20%)

50

0

0

-50

-50

-100

-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100

50

Figure 8-7 (Impact:-7.5%, Range:40%)

00 10 20 30 40 50 60 70 80 90 100
50
0

-50

-50

-100

-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100

50

Figure 8-11 (Impact:-7.5%, Range:60%)

0 10 20 30 40 50 60 70 80 90 100

50

0

0

-50

-50

-100

-100

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100
Figure 8-15 (Impact:-7.5%, Range:80%)

050

Figure 8-16 (Impact:-10.0%, Range:80%)

0

-50

-50

-100

-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100

50

Figure 8-12 (Impact:-10.0%, Range:60%)

-150

-150

50

Figure 8-8 (Impact:-10.0%, Range:40%)

Figure 8-19 (Impact:-7.5%, Range:100%)

0 10 20 30 40 50 60 70 80 90 100
50

0

0

-50

-50

-100

-100

-150

Figure 8-20 (Impact:-10.0%, Range:100%)-150
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100*: What is plotted on the positive side of the vertical axis illustrates buying order volume, while
selling order volume is plotted on the negative side.

354

Jun Muranaga and Tokiko Shimizu

Figure 9: Developments in market price (Feedback to confidence)
Figure 9-1 (Impact:-2.5%, Range:20%)

1100

1100

1000

1000

900

900

800

Figure 9-2 (Impact:-5.0%, Range:20%)

800
0 10 20 30 40 50 60 70 80 90 100
Figure 9-5 (Impact:-2.5%, Range:40%)

1100

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 9-6 (Impact:-5.0%, Range:40%)

800
0 10 20 30 40 50 60 70 80 90 100
Figure 9-9 (Impact:-2.5%, Range:60%)

1100

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 9-10 (Impact:-5.0%, Range:60%)

800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Figure 9-13 (Impact:-2.5%, Range:80%)

1100

1100

1000

1000

900

900

Figure 9-14 (Impact:-5.0%, Range:80%)

800

800

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100
Figure 9-17 (Impact:-2.5%, Range:100%)

1100

1100

1000

1000

900

900

Figure 9-18 (Impact:-5.0%, Range:100%)

800

800
0

10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

continued on next pageEffect of trading halt system on market functioning

355

Figure 9, continued
1100

Figure 9-3 (Impact:-7.5%, Range:20%)

1000

1000

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900

800

800

0 10 20 30 40 50 60 70 80 90 100

1100

Figure 9-7 (Impact:-7.5%, Range:40%)

0

1100

1000

1000

900

900

800

Figure 9-11 (Impact:-7.5%, Range:60%)

1100
1000

900

900

800

Figure 9-12 (Impact:-10.0%, Range:60%)

800
0 10 20 30 40 50 60 70 80 90 100
Figure 9-15 (Impact:-7.5%, Range:80%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 9-16 (Impact:-10.0%, Range:80%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 9-8 (Impact:-10.0%, Range:40%)

0 10 20 30 40 50 60 70 80 90 100

1000

1100

10 20 30 40 50 60 70 80 90 100

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 9-4 (Impact:-10.0%, Range:20%)

1100

Figure 9-19 (Impact:-7.5%, Range:100%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 9-20 (Impact:-10.0%, Range:100%)800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

356

Jun Muranaga and Tokiko Shimizu

Figure 10: Developments in mean of traders’ expected values(Feedback to confidence)
1100

Figure 10-1 (Impact:-2.5%, Range:20%)

1100

1000

1000

900

900

800

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 10-5 (Impact:-2.5%, Range:40%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 10-9 (Impact:-2.5%, Range:60%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

0 10 20 30 40 50 60 70 80 90 100

Figure 10-13 (Impact:-2.5%, Range:80%)

Figure 10-14 (Impact:-5.0%, Range:80%)

1100

1000

1000

900

900

800

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 10-10 (Impact:-5.0%, Range:60%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 10-6 (Impact:-5.0%, Range:40%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 10-2 (Impact:-5.0%, Range:20%)

Figure 10-17 (Impact:-2.5%, Range:100%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 10-18 (Impact:-5.0%, Range:100%)800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

continued on next pageEffect of trading halt system on market functioning

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Figure 10, continued
1100

Figure 10-3 (Impact:-7.5%, Range:20%)

1100

1000

1000

900

900

800

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 10-7 (Impact:-7.5%, Range:40%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 10-11 (Impact:-7.5%, Range:60%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 10-15 (Impact:-7.5%, Range:80%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 10-16 (Impact:-10.0%, Range:80%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 10-12 (Impact:-10.0%, Range:60%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 10-8 (Impact:-10.0%, Range:40%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 10-4 (Impact:-10.0%, Range:20%)

Figure 10-19 (Impact:-7.5%, Range:100%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 10-20 (Impact:-10.0%, Range:100%)800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

358

Jun Muranaga and Tokiko Shimizu

Figure 11: Developments in price overshoot ratio(Feedback to confidence)
Figure 11-1 (Impact:-2.5%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 11-5 (Impact:-2.5%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

Figure 11-13 (Impact:-2.5%, Range:80%)

Figure 11-17 (Impact:-2.5%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 11-10 (Impact:-5.0%, Range:60%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 11-14 (Impact:-5.0%, Range:80%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 11-6 (Impact:-5.0%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

10 20 30 40 50 60 70 80 90 100
Figure 11-9 (Impact:-2.5%, Range:60%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

Figure 11-2 (Impact:-5.0%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

0

10 20 30 40 50 60 70 80 90 100
Figure 11-18 (Impact:-5.0%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100

continued on next pageEffect of trading halt system on market functioning

359

Figure 11, continued
Figure 11-3 (Impact:-7.5%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 11-7 (Impact:-7.5%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

0

Figure 11-19 (Impact:-7.5%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 11-12 (Impact:-10.0%, Range:60%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 11-16 (Impact:-10.0%, Range:80%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 11-8 (Impact:-10.0%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100
Figure 11-15 (Impact:-7.5%, Range:80%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

0

10 20 30 40 50 60 70 80 90 100
Figure 11-11 (Impact:-7.5%, Range:60%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

Figure 11-4 (Impact:-10.0%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

0

10 20 30 40 50 60 70 80 90 100
Figure 11-20 (Impact:-10.0%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100

360

Jun Muranaga and Tokiko Shimizu

Figure 12: Developments in order imbalance (Feedback to confidence)*
Figure 12-1 (Impact:-2.5%, Range:20%)

50

50

0

0

-50

-50

-100

-100

-150

Figure 12-2 (Impact:-5.0%, Range:20%)-150
0

10 20 30 40 50 60 70 80 90 100
Figure 12-5 (Impact:-2.5%, Range:40%)

50

0 10 20 30 40 50 60 70 80 90 100
50

0

0

-50

-50

-100

-100

-150

Figure 12-6 (Impact:-5.0%, Range:40%)-150
0

10 20 30 40 50 60 70 80 90 100
Figure 12-9 (Impact:-2.5%, Range:60%)

50
00 10 20 30 40 50 60 70 80 90 100
50

Figure 12-10 (Impact:-5.0%, Range:60%)

0

-50

-50

-100

-100

-150

-150
0

10 20 30 40 50 60 70 80 90 100
Figure 12-13 (Impact:-2.5%, Range:80%)

50

0 10 20 30 40 50 60 70 80 90 100
50

0

0

-50

-50

-100

-100

-150

Figure 12-14 (Impact:-5.0%, Range:80%)-150
0

10 20 30 40 50 60 70 80 90 100
Figure 12-17 (Impact:-2.5%, Range:100%)50

0 10 20 30 40 50 60 70 80 90 100
50

0

0

-50

-50

-100

-100

-150

Figure 12-18 (Impact:-5.0%, Range:100%)-150
0

10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Buyingorder volume
is plotted
onofthethe vertical
axis,
while
sellingorder*: Whatis plottedon the positiveside
thepositive
vertical sideaxis ofillustrates
buyingorder
volume,while
volumeorderis plottedon isthenegativeside.
selling
volumeplottedon thenegative side.

continued on next pageEffect of trading halt system on market functioning

361

Figure 12, continued
Figure 12-3 (Impact:-7.5%, Range:20%)

50

50

0

0

-50

-50

-100

-100

-150

Figure 12-4 (Impact:-10.0%, Range:20%)-150
0

10 20 30 40 50 60 70 80 90 100
Figure 12-7 (Impact:-7.5%, Range:40%)

50

0 10 20 30 40 50 60 70 80 90 100
50

0

0

-50

-50

-100

-100

-150

Figure 12-8 (Impact:-10.0%, Range:40%)-150
0

10 20 30 40 50 60 70 80 90 100
Figure 12-11 (Impact:-7.5%, Range:60%)

50
00 10 20 30 40 50 60 70 80 90 100
50

Figure 12-12 (Impact:-10.0%, Range:60%)

0

-50

-50

-100

-100

-150

-150
0

10 20 30 40 50 60 70 80 90 100
Figure 12-15 (Impact:-7.5%, Range:80%)

50
00 10 20 30 40 50 60 70 80 90 100
50

Figure 12-16 (Impact:-10.0%, Range:80%)

0

-50

-50

-100

-100

-150

-150
0

10 20 30 40 50 60 70 80 90 100
Figure 12-19 (Impact:-7.5%, Range:100%)50

0 10 20 30 40 50 60 70 80 90 100
50

0

0

-50

-50

-100

-100

-150

Figure 12-20 (Impact:-10.0%, Range:100%)-150
0

10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100*: What is plotted on the positive side of the vertical axis illustrates buying order volume, while
selling order volume is plotted on the negative side.

362

Jun Muranaga and Tokiko Shimizu

Figure 13: Developments in market price(Feedback to expected value; with momentum traders)
Figure 13-1 (Impact:-2.5%, Range:20%)

1100

1100

1000

1000

900

900

800

800

0 10 20 30 40 50 60 70 80 90 100
1100

Figure 13-5 (Impact:-2.5%, Range:40%)

0

1100

1000

1000

900

900

800

Figure 13-6 (Impact:-5.0%, Range:40%)

0 10 20 30 40 50 60 70 80 90 100

Figure 13-9 (Impact:-2.5%, Range:60%)

Figure 13-10 (Impact:-5.0%, Range:60%)

1100

1100

1000

1000

900

900

800

800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Figure 13-13 (Impact:-2.5%, Range:80%)

Figure 13-14 (Impact:-5.0%, Range:80%)

1100

1000

1000

900

900

800

800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100
1100

10 20 30 40 50 60 70 80 90 100

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 13-2 (Impact:-5.0%, Range:20%)

Figure 13-17 (Impact:-2.5%, Range:100%)

1100

1000

1000

900

900

800

Figure 13-18 (Impact:-5.0%, Range:100%)800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

continued on next pageEffect of trading halt system on market functioning

363

Figure 13, continued
Figure 13-3 (Impact:-7.5%, Range:20%)

1100

1100

1000

1000

900

900

800

800
0 10 20 30 40 50 60 70 80 90 100
Figure 13-7 (Impact:-7.5%, Range:40%)

1100

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

0

0 10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 13-11 (Impact:-7.5%, Range:60%)

1100

1000

1000

900

900

800

Figure 13-15 (Impact:-7.5%, Range:80%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 13-16 (Impact:-10.0%, Range:80%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 13-12 (Impact:-10.0%, Range:60%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 13-8 (Impact:-10.0%, Range:40%)

800

800

1100

Figure 13-4 (Impact:-10.0%, Range:20%)

Figure 13-19 (Impact:-7.5%, Range:100%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 13-20 (Impact:-10.0%, Range:100%)800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

364

Jun Muranaga and Tokiko Shimizu

Figure 14: Developments in mean of traders’ expected values(Feedback to expected value; with momentum traders)
1100

Figure 14-1 (Impact:-2.5%, Range:20%)

1100

1000

1000

900

900

800

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 14-5 (Impact:-2.5%, Range:40%)

0 10 20 30 40 50 60 70 80 90 100
1100

1000

1000

900

900

800

Figure 14-9 (Impact:-2.5%, Range:60%)

0 10 20 30 40 50 60 70 80 90 100
1100

1000

1000

900

900

800

Figure 14-13 (Impact:-2.5%, Range:80%)

0 10 20 30 40 50 60 70 80 90 100
1100

1000

1000

900

900

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Figure 14-14 (Impact:-5.0%, Range:80%)

800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100
1100

Figure 14-10 (Impact:-5.0%, Range:60%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 14-6 (Impact:-5.0%, Range:40%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 14-2 (Impact:-5.0%, Range:20%)

Figure 14-17 (Impact:-2.5%, Range:100%)

1100

1000

1000

900

900

800

Figure 14-18 (Impact:-5.0%, Range:100%)800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

continued on next pageEffect of trading halt system on market functioning

365

Figure 14, continued
1100

Figure 14-3 (Impact:-7.5%, Range:20%)

1000

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800
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1100

Figure 14-7 (Impact:-7.5%, Range:40%)

0 10 20 30 40 50 60 70 80 90 100

1000

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Figure 14-11 (Impact:-7.5%, Range:60%)

0 10 20 30 40 50 60 70 80 90 100

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800

0 10 20 30 40 50 60 70 80 90 100
Figure 14-15 (Impact:-7.5%, Range:80%)

0

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1000

900

900

800

10 20 30 40 50 60 70 80 90 100
Figure 14-16 (Impact:-10.0%, Range:80%)

800
0 10 20 30 40 50 60 70 80 90 100

1100

Figure 14-12 (Impact:-10.0%, Range:60%)

1100

1000

1100

Figure 14-8 (Impact:-10.0%, Range:40%)

1100

1000

1100

Figure 14-4 (Impact:-10.0%, Range:20%)

1100

Figure 14-19 (Impact:-7.5%, Range:100%)

0 10 20 30 40 50 60 70 80 90 100

1100

1000

1000

900

900

800

Figure 14-20 (Impact:-10.0%, Range:100%)800
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

366

Jun Muranaga and Tokiko Shimizu

Figure 15: Developments in price overshoot ratio(Feedback to expected value; with momentum traders)
Figure 15-1 (Impact:-2.5%, Range:20%)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 15-5 (Impact:-2.5%, Range:40%)

0.3
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0

0

Figure 15-17 (Impact:-2.5%, Range:100%)

0.3
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0

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 15-10 (Impact:-5.0%, Range:60%)

0.3
0.2
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0

10 20 30 40 50 60 70 80 90 100
Figure 15-14 (Impact:-5.0%, Range:80%)

0.3
0.2
0.1
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10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 15-6 (Impact:-5.0%, Range:40%)

0.3
0.2
0.1
0
-0.1
-0.2
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10 20 30 40 50 60 70 80 90 100
Figure 15-13 (Impact:-2.5%, Range:80%)

0.3
0.2
0.1
0
-0.1
-0.2
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0

10 20 30 40 50 60 70 80 90 100
Figure 15-9 (Impact:-2.5%, Range:60%)

0.3
0.2
0.1
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-0.1
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-0.3

Figure 15-2 (Impact:-5.0%, Range:20%)

0.3
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0

10 20 30 40 50 60 70 80 90 100
Figure 15-18 (Impact:-5.0%, Range:100%)

0.3
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10 20 30 40 50 60 70 80 90 100

continued on next pageEffect of trading halt system on market functioning

367

Figure 15, continued
Figure 15-3 (Impact:-7.5%, Range:20%)

0.3
0.2
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-0.1
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10 20 30 40 50 60 70 80 90 100
Figure 15-7 (Impact:-7.5%, Range:40%)

0.3
0.2
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0
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0

0

Figure 15-19 (Impact:-7.5%, Range:100%)

0.3
0.2
0.1
0
-0.1
-0.2
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0

10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 15-12 (Impact:-10.0%, Range:60%)

0.3
0.2
0.1
0
-0.1
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10 20 30 40 50 60 70 80 90 100
Figure 15-16 (Impact:-10.0%, Range:80%)

0.3
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-0.1
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10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 15-8 (Impact:-10.0%, Range:40%)

0.3
0.2
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10 20 30 40 50 60 70 80 90 100
Figure 15-15 (Impact:-7.5%, Range:80%)

0.3
0.2
0.1
0
-0.1
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0

10 20 30 40 50 60 70 80 90 100
Figure 15-11 (Impact:-7.5%, Range:60%)

0.3
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-0.1
-0.2
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Figure 15-4 (Impact:-10.0%, Range:20%)

0.3
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0
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0

10 20 30 40 50 60 70 80 90 100
Figure 15-20 (Impact:-10.0%, Range:100%)

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10 20 30 40 50 60 70 80 90 100

368

Jun Muranaga and Tokiko Shimizu

Figure 16: Developments in order imbalance*(Feedback to expected value; with momentum traders)50

Figure 16-1 (Impact:-2.5%, Range:20%)

Figure 16-2 (Impact:-5.0%, Range:20%)

50

0

0

-50

-50

-100

-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100

0Figure 16-5 (Impact:-2.5%, Range:40%)

Figure 16-6 (Impact:-5.0%, Range:40%)

50

50

0

0

-50

-50

-100

-100

-150

-150
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0Figure 16-9 (Impact:-2.5%, Range:60%)

50

Figure 16-10 (Impact:-5.0%, Range:60%)

0

-50

-50

-100

-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100
Figure 16-13 (Impact:-2.5%, Range:80%)

00

10 20 30 40 50 60 70 80 90 100
Figure 16-14 (Impact:-5.0%, Range:80%)

50
0

-50

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10 20 30 40 50 60 70 80 90 100

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10 20 30 40 50 60 70 80 90 100

Figure 16-17 (Impact:-2.5%, Range:100%)

0

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Figure 16-18 (Impact:-5.0%, Range:100%)50

0

10 20 30 40 50 60 70 80 90 100-150
0 10 20 30 40 50 60 70 80 90 100

010 20 30 40 50 60 70 80 90 100

is plottedon the positiveside
thepositive
vertical sideaxis illustrates
buyingorder
volume,while*: What
Buyingorder volume
is plotted
onoftheof the vertical
axis,
while
sellingordervolumeis plottedon isthenegativeside.
selling ordervolumeplottedon thenegative side.

continued on next pageEffect of trading halt system on market functioning

369

Figure 16, continued
50

Figure 16-3 (Impact:-7.5%, Range:20%)

50

0

0

-50

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-150
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50

Figure 16-7 (Impact:-7.5%, Range:40%)

00 10 20 30 40 50 60 70 80 90 100
50

-50

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-150
0 10 20 30 40 50 60 70 80 90 100
Figure 16-11 (Impact:-7.5%, Range:60%)

0 10 20 30 40 50 60 70 80 90 100
50

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Figure 16-15 (Impact:-7.5%, Range:80%)

0 10 20 30 40 50 60 70 80 90 100
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-150

Figure 16-16 (Impact:-10.0%, Range:80%)-150
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50

Figure 16-12 (Impact:-10.0%, Range:60%)-150
0 10 20 30 40 50 60 70 80 90 100

50

Figure 16-8 (Impact:-10.0%, Range:40%)

0

-100

50

Figure 16-4 (Impact:-10.0%, Range:20%)

Figure 16-19 (Impact:-7.5%, Range:100%)

0 10 20 30 40 50 60 70 80 90 100
50

0

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-150

Figure 16-20 (Impact:-10.0%, Range:100%)-150
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100*: What is plotted on the positive side of the vertical axis illustrates buying order volume, while
selling order volume is plotted on the negative side.

370

Jun Muranaga and Tokiko Shimizu

Figure 17: Developments in market price(Feedback to expected value; with momentum traders & a trading halt)
1100

Figure 17-1 (without trading halt)

1000
900
800
700
600
0 10 20 30 40 50 60 70 80 90 100

1100
1000
900
800
700
600

1100
1000

Figure 17-2
(Level: 900 yen, 5 periods of halt)

1100
1000
900
800
700
600

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Figure 17-4
(Level: 900 yen, 15 periods of halt)

Figure 17-5
(Level: 900 yen, 20 periods of halt)

1100
1000

900
800

900
800

700
600

700
600
0 10 20 30 40 50 60 70 80 90 100

1100
1000
900
800
700
600

1100
1000
900
800
700
600

Figure 17-3
(Level: 900 yen, 10 periods of halt)

Figure 17-6
(Level: 800 yen, 5 periods of halt)

0 10 20 30 40 50 60 70 80 90 100

1100
1000
900
800
700
600

Figure 17-7
(Level: 800 yen, 10 periods of halt)

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Figure 17-8
(Level: 800 yen, 15 periods of halt)

Figure 17-9
(Level: 800 yen, 20 periods of halt)

0 10 20 30 40 50 60 70 80 90 100

1100
1000
900
800
700
600

0 10 20 30 40 50 60 70 80 90 100

Effect of trading halt system on market functioning

371

Figure 18: Developments in mean of traders’ expected values(Feedback to expected value; with momentum traders & a trading halt)
Figure 18-1 (without trading halt)
1100
1000
900
800
700
600
0 10 20 30 40 50 60 70 80 90 100

1100
1000
900
800
700
600

Figure 18-2
(Level: 900 yen, 5 periods of halt)
1100
1000
900
800
700
600

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

1100

Figure 18-4
(Level: 900 yen, 15 periods of halt)

1000

1100

800

800
700

700
600

600
0 10 20 30 40 50 60 70 80 90 100

1100
1000
900
800
700
600

Figure 18-5
(Level: 900 yen, 20 periods of halt)

1000
900

900

1100
1000
900
800
700
600

Figure 18-3
(Level: 900 yen, 10 periods of halt)

0 10 20 30 40 50 60 70 80 90 100

Figure 18-6
(Level: 800 yen, 5 periods of halt)

Figure 18-7
(Level: 800 yen, 10 periods of halt)
1100
1000
900
800
700
600

0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Figure 18-8
(Level: 800 yen, 15 periods of halt)

Figure 18-9
(Level: 800 yen, 20 periods of halt)

1100
1000
900
800
700
600
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

372

Jun Muranaga and Tokiko Shimizu

Figure 19: Developments in price overshoot ratio(Feedback to expected value; with momentum traders & a trading halt)
Figure 19-1 (without trading halt)
0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100

Figure 19-2
(Level: 900 yen, 5 periods of halt)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

10 20 30 40 50 60 70 80 90 100
Figure 19-4
(Level: 900 yen, 15 periods of halt)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3
0

0

0

0

Figure 19-8
(Level: 800 yen, 15 periods of halt)
0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100
Figure 19-7
(Level: 800 yen, 10 periods of halt)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

0 10 20 30 40 50 60 70 80 90 100

10 20 30 40 50 60 70 80 90 100
Figure 19-5
(Level: 900 yen, 20 periods of halt)

0.3
0.2
0.1
0
-0.1
-0.2
-0.3

10 20 30 40 50 60 70 80 90 100
Figure 19-6
(Level: 800 yen, 5 periods of halt)

0.3
0.2
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0
-0.1
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-0.3

Figure 19-3
(Level: 900 yen, 10 periods of halt)

0.3
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0
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-0.3

10 20 30 40 50 60 70 80 90 100
Figure 19-9
(Level: 800 yen, 20 periods of halt)

0.3
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0

10 20 30 40 50 60 70 80 90 100

010 20 30 40 50 60 70 80 90 100

Effect of trading halt system on market functioning

373

Figure 20: Developments in order imbalance*(Feedback to expected value; with momentum traders & a trading halt)
Figure 20-1 (without trading halt)*: BuyingvolumeWhat is orderplottedon theisplottedtheofpositiveside
positiveonside
the verticaloftheillustrates
vertical axis,
while
axis
buyingorder
selling
volume,orderwhilevolume
sellingisorderplottedthe negative
volumeonis plottedon theside.
negative side.

50
0
-50
-100
-150
0 10 20 30 40 50 60 70 80 90 100

50

Figure 20-2
(Level: 900 yen, 5 periods of halt)

0

50
0

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-50

-100

-100

-150

50

Figure 20-3
(Level: 900 yen, 10 periods of halt)-150
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Figure 20-4
(Level: 900 yen, 15 periods of halt)

Figure 20-5
(Level: 900 yen, 20 periods of halt)

50

0

0

-50

-50

-100

-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Figure 20-6
(Level: 800 yen, 5 periods of halt)

Figure 20-7
(Level: 800 yen, 10 periods of halt)

50

50

0

0

-50

-50

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-100

-150

-150
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

Figure 20-8
(Level: 800 yen, 15 periods of halt)

Figure 20-9
(Level: 800 yen, 20 periods of halt)

50

50

0

0

-50

-50

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-100

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-150
0 10 20 30 40 50 60 70 80 90 100

0 10 20 30 40 50 60 70 80 90 100

374

Jun Muranaga and Tokiko Shimizu

Figure 21: Change ratio of mean of traders’ expected values upon resumption of trading and market behavior after the resumption

market price
priceMarketat the end of simulation_
simulation

100000000
10000000
1000000
100000
10000
1000
100
10
1
-0.06 -0.04 -0.02

0

0.02

0.04

0.06

0.08

change ratio
Changeratio ofof meanmeanofoftraders'
traders’expected
expectedvalues
valuesresumption ofof trading
trading
at the time of resumption

market price
priceMarketat
at thethe endend ofof simulation
simulation_

1500

1000

500
normal condition

0-0.04 -0.03 -0.02 -0.01

crash

0

0.01

0.02

0.03

change ratio of mean
mean ofof traders’traders' expected values
Changeatatthethetimetimeofof resumption
resumption ofof trading
trading

0.04

Effect of trading halt system on market functioning

375

Appendix 1
Simulation flows and traders’ decision-making mechanism
In order to verify the decision-making mechanism and to explore the possibilityof quantitative analysis, we use a Monte Carlo simulation in our analysis. The
reason for using this procedure is that when an analytical approach is adopted, it
often results in solutions only being obtained in a relatively simple setting compared with that of actual market conditions. Since market microstructure theory,on which our analysis is based, formulates an individual trader’s trading behavior
at the micro level and aggregates the behavior of many such traders to analyze
market behavior, a simulation approach becomes quite useful. By using the Monte
Carlo simulation, which has developed rapidly in the field of finance theory, we
can incorporate in our models traders who have complex decision-making functions or conditions and analyze the market behavior patterns.
Our model consists of two major parts. The first part models an individual
trader’s decision-making and ordering (micro stage), and the second part models
order flow aggregation in the trade execution system (macro stage). Parameters
incorporated in the model are those that are unique to each market participant,such as expected value, confidence in their forecasts, the extent of risk aversion,and sensitivity to feedback information obtained from the macro stage. Based onthese parameters, a trader compares the benefit and cost (risk) of each trade,selects a trade which maximizes the net benefit, and places an order if the net
benefit is consistent with the extent of risk aversion. Of the factors which affecttrading behavior, we assume explicit trading costs to be zero. We employ a continuous auction system which allows market orders and limit orders (duringcontinuous sessions only) based on the trade execution model of the Tokyo Stock
Exchange (TSE).11

A1.1. Simulation flow
The structure of our model is as follows: in a TSE-type market composed of N
traders, we will conduct a simulation for M periods. In period t (t =1, 2,..., M), all
N traders can place an order once. Ordering rotation is randomly decided in thestarting period, and each trader’s order will be executed based on the first-in rule,
as in the TSE. The flow of the simulation is summarized in Figure A1-1.

11
In modeling a trade execution system, there are various methods, such as modeling a market
maker system or modeling a market with a plural execution system for one product. Since this paper
focuses on the micro stage, i.e. how the market microstructure affects an individual trader’s
decision-making, we do not go as far as attempting various models of the execution system.

376

Jun Muranaga and Tokiko Shimizu

Figure A1-1: Simulation flow

t=t+1

decide ordering turn oftraders in period t

historical dataset- market data
- trader data
i= i + 1

decide order of
trader iorder handling/trade execution

i=N?

No

compile market data andtrader data of period ti

Yescompile market data andtrader data of period t

Figure A1-2 shows a rather detailed flow of the trader’s order decision process. Based on historical data such as price, order-book, and indication, a trader
will forecast future market price and market liquidity (depth), take into accounthis/her own portfolio composition and risk preference, and make decisions about
the order (order/not order, limit order/market order, order volume, and order pricein the case of a limit order). Except for adding external shocks,12 traders are
uninformed of security prices, and thus all market behavior can be regarded as
endogenous.

12
As a method of adding an external shock to the simulation, we plan to change the initialconditions in the middle of the simulation. For example, we are thinking of placing signals of futurefinancial conditions (price, depth, price change ratio, etc.) which traders normally forecast by using
their own unique forecasting models.

Effect of trading halt system on market functioning

377

Figure A1-2: Order decision of trader

historical dataset

priceexpectation

depth
expectation
trader data
- holding portfolio- risk preference- trading strategy

order decision- limit order (price & volume)- market order (volume)- pass up

The format for accumulating market data is outlined in Figure A1-3. In periodt, trader i places an order, the order is executed in the trade execution system, andmarket data will be produced as output: market data forms a matrix of N rows and
8 columns. In columns 1-4, the number of the trader whose limit/market order has
been executed in the corresponding period (= period ti), the trade execution price,
the trade volume, and the timing of the order will be entered, while in columns
5-8, the trader number whose limit order is remaining on the order-book unexecuted, the buy/sell quote, the order volume, and the timing of the order will beentered according to price (from high to low) and time (first-in basis).

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Jun Muranaga and Tokiko Shimizu

Figure A1-3: Market data for period ti (order book information)col. 1col. 2col. 3col. 4col. 5

trade
trade timing trader
execution
volume of order No.
pricecol. 6col. 7col. 8sellingselling timing
limit
volume of order
order

row 1

trader
No.

row 2

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

trader
No.

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

·

buyingbuying timing
limit
volume of order
order

The input (initial conditions) and the output (results) of the simulation aresummarized as in Figure A1-4.

Figure A1-4: Input/output of the simulation< Input >market structure- number of traders- composition of traders by type
trader data
- initial portfolio- risk preference- initial trading strategy(forecasting model
model,etc.)
etc.)
external shocks- shocks to the whole market- shocks to part of the market< Simulation >path-dependent process
Traders’ behaviors
endogeneously
affecteffect each other.

< Output >market behavior
in normal /shock time
- trade frequency- trade volume- bid-ask spread- price volatility- market impact- market resiliency
each trader’strading behavior

Effect of trading halt system on market functioning

379

A1.2. Trader’s decision-making model
For our trade execution model we employ a continuous auction system patterned
after that of the Tokyo Stock Exchange (TSE). There is no designated liquidity
provider and market liquidity is only provided through trade orders from market
participants. There are two traders in the market: ‘value traders’ who place limit
orders based on their expected values, and ‘momentum traders’ who make market
orders following short-term market trends.13 The former maintain expected values
about asset prices based on information other than market data, and place limit
orders according to differences between market prices and their expected values.
The latter make market orders following short-term trends in market prices, and,as a result, they buy when market prices are rising and sell when market prices are
falling.14 In the following, we first look at the short-term price forecasting model,
which both trader types use, and explain the trading behavior patterns of both
value traders and momentum traders.

A1.2.1. Short-term price forecasting of the trader
In determining a trading order, a trader will make a short-term forecast of future
market price based on historical market data.15 The simplest pattern is shown in
Figure A1-5. At period t, a trader monitors mean price movements during theperiod immediately before ( b ), measures the trend (mh) and volatility (sh) of
mean price, and based on this data, forecasts the market price’s expected value
E(Pt+f) and variance Var(Pt+f) of the next trading opportunity, which is a period of f
ahead.

13
The traders in this paper are of the simplest type. Other than these two types, there is a varietyof traders in the actual market, such as ‘gamma traders’ who make delta hedges on their option
positions, and ‘noise traders’ who have trading incentives which are irrelevant to market movements.
It should be noted that we are not at all suggesting that actual traders can be neatly classified into
the two types that we are using. Rather, it is reasonable to believe that the attributes of value tradersand momentum traders coexist within a single trader.
14
In the simulation, the size of trade orders is constant.
15‘Short-term’ refers to the interval between the point in time when the trader accesses themarket to decide on a trading order and the next trading opportunity.

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Jun Muranaga and Tokiko Shimizu

Figure A1-5: Price forecast of the trader(m h ,s h )E (P t + f )Var (P t + f )Price ( P )

P(t)

b

f

Periodt

A1.2.2. Trading behavior of value traders
In addition to forecasting future short-term prices, value traders also maintain
subjective information V about the fundamental value of an asset. We assume thatthe expected values of all traders in the market will, when aggregated, follow a
lognormal distribution with a standard deviation of sV. Each trader individuallyforms his expected value; its initial value is exogenously provided in our simulation, although this initial value does not represent an asset’s true value. The value
trader will determine his/her order based on his/her expected value V and expectation distribution of the short-term price Pt+f . In placing limit orders, the orderprice (selling: Pask, buying: Pbid) will be established to allow for maximized expected return E(R | Pask) or E(R | Pbid), which can be obtained by multiplying thereturn of the order and the probability of being executed within the interval (t tot+f) prior to the next ordering opportunity.
Selling limit order

Maximum E ( R Pask ) = Pr( Pt + f ³ Pask ) × ( Pask - V )Buying limit order

Maximum E ( R Pbid ) = Pr( Pt + f £ Pbid ) × (V - Pbid )Value traders will recognize their lack of confidence in their forecasts as therisk in submitting orders. When the expected return is substantially larger than therisk, the trader will place either a selling or buying order, which has a larger
expected return. In our model, this lack of confidence is given as an expectedEffect of trading halt system on market functioning381

dispersion (standard deviation) g of the aggregate of each value trader’s expectedvalues.16

E ( R Pask ) < E ( R Pbid ) and E ( R Pask )E ( R Pask ) < E ( R Pbid ) andƒÁ> A

E ( R Pbid )g®

Selling limit order (Pask)> A ® Buying limit order (Pbid)

where A is a positive constant reflecting a trader’s risk aversion.

A1.2.3. Trading behavior of momentum traders
Momentum traders are indifferent to the level of market prices and their trading isbased on the trends and volatility of market prices. Namely, based on the shortterm forecast by taking into account historical market data, they compare theexpected earnings ratio with risks until the next trading opportunity. If the extent
of their risk aversion allows, they make selling market orders (when market prices
are falling), or buying market orders (when market prices are rising). The order ofthe momentum trader is:

mh / s h < - B®

Selling market ordermh /s h > B®

Buying market orderwhere mh and sh are trend and volatility of mean price, respectively, and B is apositive constant which reflects a trader’s risk aversion.

16

g represents each trader’s forecast of the actual dispersion sV .

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Jun Muranaga and Tokiko Shimizu

Appendix 2
Feedback effects on traders’ expectations
As shown in Figure A2-1, the feedback mechanism to expected value is when atrader revises his/her own expected value based on market information he/she
receives. Specifically, when trader i, who holds initial expected value Vi,0 , observes that the market price just before his/her order is below a certain confidence
level of his/her expected distribution of V, he/she forecasts the market equilibrium
price based on the market trend and revises Vi,t, accordingly. Hereafter, we call thecertain confidence level which causes such a revision, ‘trigger.’

Figure A2-1: Feedback to expected value
trader i 's
expectation

g i,0

V i,0

pt

g i,0

V i, tactual distributionof expectations

periodAs shown in Figure A2-2, the feedback mechanism to confidence is when atrader revises his/her forecast on confidence, that is, variance of market participants’ expected values based on market information he/she receives. Specifically,
when trader i, who holds an initial expected standard deviation of gi,0, observes
that the market price just before his/her order is below the ‘trigger,’ he/she revises
g at time t to gi,t, so that the observed market price will fall within the range gi,t
above the trigger.

Effect of trading halt system on market functioning

383

Figure A2-2: Feedback to confidence
trader i 's
expectation

V i,0

g i,t

g i,0

pt

actual distributionof expectations

PeriodReferences
Brady, N. F., 1998, “The Crash, the Problems Exposed, and the Remedies,” in
R. E. Litan and A. M. Santomero, eds., Brookings-Wharton Papers on Financial
Services. Brookings Institution Press.
Brennan, M. J., 1986, “Theory of Price Limits in Futures Markets,” Journal of
Financial Economics 16.
Gerety, M. S., and J. H. Mulherin, 1992, “Trading Halts and Market Activity: An
Analysis of Volume at the Open and the Close,” Journal of Finance Vol.
XLVII, No. 5.
Greenwald, B., and J. Stein, 1988, “The Task Force Report: The Reasoning
Behind the Recommendations,” Journal of Economic Perspectives Vol. 2, No.
3.
Greenwald, B., and J. Stein, 1991, “Transaction Risk, Market Crashes, and theRole of Circuit Breakers,” Journal of Business Vol. 64, No. 4.
Lauterbach, B., and U. Ben-Zion, 1993, “Stock Market Crashes and the
Performance of Circuit Breakers: Empirical Evidence,” Journal of FinanceVol. XLVIII, No. 5.
Muranaga, J., and T. Shimizu, 1999a, “Market Microstructure and Market Liquidity,”

384

Jun Muranaga and Tokiko Shimizu

mimeo.
Muranaga, J., and T. Shimizu, 1999b, “Expectations and Market Microstructure
when Liquidity is Lost,” mimeo.
Presidential Task Force on Market Mechanism: Report Submitted January 1988.
Roll, R., 1988, “The International Crash of October 1987,” Financial Analysts
Journal, September.
Subrahmanyam, A., 1994, “Circuit Breakers and Market Volatility: A Theoretical
Perspective,” Journal of Finance Vol. XLIX, No. 1.