Financial Bubbles, Common Knowledge and Alternative
Accounting Regimes: An Experimental Analysis ofArtificial Spot Security Markets*
Accounting for or from the Market?
LARRY BENSIMHON

Laboratoire Interdisciplinaire de Rechrche
en Sciences de l’Action (LIRSA),CONSERVATOIRE NATIONAL DES ARTS ET MÉTIERSYURI BIONDI†

CENTRE NATIONAL DE LA RECHERCHE SCIENTIFIQUE
and ESCP EUROPE

ABSTRACT

We experimentally explore how common knowledge provided by accounting systems
affects investors’ decision and shapes the formation of security prices over time. We
design alternative accounting structures and run experiments in artificial security
markets framed by these structures. In sessions where investors receive exogenous
accounting information about ultimate earnings, prices converge to the fundamental
levels derived from those earnings through backward induction. Accounting plays a role
for the market. In sessions where investors receive endogenous accounting information
about earnings that are linked to the ongoing clearing price of the security, market pricelevels and paths become indeterminate and lose earnings anchor; investors tend to form
their expectations of future prices by forward, not backward, induction. Accounting
plays its role from the market, and loses its relevance in financial decision-making.
These laboratory results suggest that accounting information and its overarching
structure are important to prevent market exuberance, excess volatility and theformation of financial bubbles. They further have relevant effects on market allocative
efficiency, and revenue and wealth distribution among investors.
JEL Classification: G12; C9; D4; M41; M48; G1
Key Words: Share Price Bubbles; Market Exuberance; Excess Volatility;
Short-term Investors; Backward Induction; Forward Induction;
Experimental Financial Markets; Fair Value; Mark-to-Market;
Accounting Regime* Acknowledgments: We thank Shyam Sunder (Yale University) for his insightful suggestions and longstanding
encouragement. We also thank Maxim Frolov (LEEP of Paris) for his committed programming expertise andtechnical support. Usual disclaimer applies.
Disclosures: The project was funded by a grant of a French public institute, the Maison des Sciences de l’Homme- Paris Nord (MSH-PN). The experiment was programmed and conducted with the software z-Tree (Fischbacher
2007).
†
Corresponding Author: Address ESCP Europe, 79, Avenue de la République 75011 Paris, FRANCE. Telephone +33(0) 1 49 23 20 00 E-mail yuri.biondi@gmail.com website http://yuri.biondi.free.fr/
Received March 14, 2013; available online May 16, 2013 (Advance publication by J-STAGE)
DOI: 10.11640/tjar.3.2013.02
Copyright©2013 Research Institute for Economics & Business Administration - Kobe University.

22

The Japanese Accounting Review, 3 (2013), 21-59

1. Introduction
What is a financial bubble? Popular press and wisdom do associate bubbling with abruptchanges in security market prices over time. A bubble occurs when market prices rise intensely
and rapidly and drop thereafter, rapidly and intensely as well. Academic literature has provided
several definitions of financial bubbling, and insightful analyses of their formation over time. For
example, Smith et al. (1988) observed that artificial market prices consistently differed from thefundamental value of the security, and labeled those price patterns as bubbles or crashes. King etal. (1993) defined artificial market bubbles as “trade at high volumes at prices that areconsiderably at variance from intrinsic values”. From this perspective, bubbling is then related tomarket volatility and market exuberance.1
In fact, econometric tests of bubbling are confronted with difficulties in defining andestimating the fundamental value of reference, and other theoretical and statistical issues.
Fundamental values are somehow defined on the basis of the series of future dividends linked toholding securities when dividends are paid. In particular, tests are forced to assess all alternativemodels on the only market price series that has actually occurred over time. However, thealternative series of market prices that would have happened under a different set of conditions
can be imagined, but not empirically tested and comparatively assessed. From this viewpoint,
experimental analysis of artificial markets appears to be well suited to assess the formation of
bubbles and their evolution over time (see Ball and Holt (1998) and Davis and Holt (1993),
chapters 1, 3, and 7 for an introduction). Experimental design allows controlling for conditions,
including the series of fundamental values, while several market price patterns can be generated
under alternative experimental treatments. Experimental economic literature has investigatedbubbling under various sets of conditions. Generally speaking, experimental literature points out
limited information about present or future investors’ expectations; the existence of irrationalpresent or future investors; or the rational inclusion of the bubble term not linked to future
dividends at all. Plott (1989), Holt (1995), and Sunder (1995) provide literature surveys on
artificial financial markets.
In particular, Smith et al. (1988) paved the way to analyze bubbling under conditions ofcommon knowledge. They designed a security market with three adjustment dynamics: theprocess that describes changes in the security’s dividend value; the evolution of agents’ priceexpectations; and the security’s price adjustments. The dividend structure and actual dividend
draws were the same for every trader in the market, and this information was common
knowledge. Experimental results showed that a common dividend, and common knowledge
thereof were insufficient to align market prices with fundamental values. These authors and Lei,
Noussair and Plott (2001) provided alternative explanations of such bubbling based on theformation of agents’ expectations in those markets. The former conjectured that investors may
conduct transient speculative trades aiming at selling the security to others at higher prices. The
latter rejected this conjecture by showing that bubbles arise when investors cannot engage in
speculative trades, attributing bubbling to errors in traders’ decision-making.
Smith et al. (2000) designed a setting where a single dividend was paid out at the end of thetime horizon. The dividend was random but its distribution was common knowledge. This1

See Brunnermeier (2001), Camerer (1989) and Stiglitz (1990) for broad overviews.

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

23

setting consistently helped to align prices and the expected value of the single dividend draw.
Noussair, Robin and Ruffieux (2001) further refined this experimental analysis by distinguishing
between single dividend final payout and constant fundamental value as market stabilizing
devices, deciding in favor of the former. The dividend process, the number of periods, and theexistence and amount of the final buyout were common knowledge to all subjects. Moreover,
Kirchler (2009) implemented experimental asset markets with fluctuating fundamental values
following a stochastic process that had a positive drift and a large standard deviation.
Starting from Smith et al. (1988) and Lei et al. (2001), Hirota and Sunder (2007) designed
two alternative sets of artificial markets that are distinguished by featuring final payouts: fromone hand, investors exit the market by receiving an exogenously specified dividend; from another
hand, they exit the market by receiving the prices that are endogenously generated throughout
the session. Bubbles did not occur in the former treatment, while they did in the latter, where
prices lost dividend anchors and price levels and paths became indeterminate. They conjectured
about investors’ horizon to explain this result. Those horizons would be long-term in the former,
and short-term in the latter treatment, justifying whether bubbles occur or not. In fact, theirdesign does not denote explicitly investors’ horizons, which are not under direct experimental
control, but it does denote the payout structure and the common knowledge of it. A collective
device is then introduced having two alternative regimes: under the former regime, the device
paid out a stable value of reference; under the latter regime, the device paid out a shifting valuethat fluctuates along with the market price over time.
This regime-based setting is path-breaking in putting forth the idea that bubbles can occur
because of lack of “knowledge, beliefs and coordination devices necessary for prices to coincidewith the fundamental values” (Hirota and Sunder 2007, p. 1877, italics added). Theoreticallyspeaking, this understanding of the formation of share prices over time stresses the share
Exchange as a coordinating mechanism among investors. Tirole (1982) showed that coordinationlack between investors’ expectations and plans can justify the occurrence of bubbles. Both Abreu
and Brunnermeier (2003), and Azariadis (1981) stressed coordination failures to explain
bubbling. In particular, the need of coordination devices points to the implications of theinstitutional architecture that frames and enables the working of the market over time (Frydman
1982).2 Together with investors’ knowledge and beliefs (related to their expectations andhorizons), this architecture contributes to determine the evolution of the market price series andits connection with fundamental values of reference over time. In all treatments, investors
received two distinctive collective signals from this architecture: one from the collective device
fixing the security final payout, and another one from the market price system that generates
current security prices over time through ongoing bids and asks. Therefore, investors wereconfronted to two distinctive ways of earning money: designed final payouts and capital gains(losses) from trading. Furthermore, their shared information set, i.e., their common knowledge
was jointly composed by market-driven (the history of market prices and returns) and firmspecific information (the history of enterprise payouts and returns), as early claimed by Fama andFrench (1992).
The working of that collective device fixing final payout relates to the role plaid by theaccounting system of the business firm in the share market pricing process. Like this collective
2This may be seen as an application of incentive regulation to the financial market microstructure and working(Laffont and Tirole 1993).

24

The Japanese Accounting Review, 3 (2013), 21-59

device, the accounting system provides signals of fundamental information about financial
performance and position of the business firm that has issued the traded shares (Shubik 1993).
This information is somehow related to the fundamental value of those securities (Lintner 1956)
and is common knowledge among all investors (Sunder 2002). In particular, the accounting
system design is an integral part of the institutional architecture that defines share payouts: Fromthe monetary viewpoint, accounting identifies the financial performance accrued and eventuallydistributed to shareholders (which are investors who hold shares at the moment of dividend
distribution); from the cognitive viewpoint, it declares a collective signal of fundamental
performance of the business firm.
Our paper aims at investigating security market bubbling under alternative accounting
regimes. We experimentally explore how common knowledge provided by accounting systems
affects investors’ decisions and shapes the ongoing formation of security prices. Previous
experimental evidence proved that common fundamental value is not sufficient to prevent
bubbling, and that the design of the collective device labeled “accounting system” hereafter
contributes to investors’ decision-making and shapes the security market pricing over time. On
this basis, we design alternative accounting structures and run experiments in artificial security
markets framed by these structures.
Generally speaking, two distinctive families of accounting models exist, one based on fairvalue accounting, another one on historical cost accounting (Anthony 2004, p. 25). They
respectively perform:• The collective provision of firm-specific information that follows the information provided
by the market (so-called fair value accounting model);3• The collective provision of firm-specific accounting information that constitutes anautonomous source of information (so-called historical cost accounting model).
Under the first regime, the accounting model is expected to replicate the market signal overtime. This corresponds to a pure fair value accounting that assumes a perfect alignment ofaccounting information with current market price. The accounting system adopts then a perfect
stock method providing fundamental information that relies on the most updated market priceavailable (Penman 2011).
Under the second regime, the accounting model is expected to provide an exogenous timeseries that is independent from the market. This corresponds to a pure historical cost accountingthat determines the accrued performance over the period independently from market conditions.
The accounting system adopts then a perfect flow method providing fundamental information
jointly composed by exogenous positive and negative flows (Biondi 2011).
We run two series of comparable market experiments under both accounting regimes, inorder to test whether and under which accounting regime security prices better align with
fundamental values of reference; whether accounting regimes reduce the likelihood and intensityof bubbles; whether accounting regimes improve on the allocative efficiency of the market; andwhether and how accounting regimes shape the distribution of incomes generated by market
3

We consider “mark-to-market” accounting and “fair value” accounting synonymously. While the first approach
implies the use of observable market prices to measure the value of every asset and liability, the second approach
includes the recourse to observable and unobservable inputs to reproduce that value.

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

25

trading.
According to our experimental results, in historical cost accounting sessions where investors
received exogenous accounting information about ultimate earnings, prices converge to thefundamental levels derived from those earnings through backward induction. Accounting plays
a role for the market. In fair value accounting sessions where investors receive endogenous
accounting information about earnings that are linked to the ongoing clearing price of the share,
price levels and paths become indeterminate and lose earnings anchor; investors tend to form
their expectations of future prices by forward, not backward, induction. Accounting plays its rolefrom the market, and loses any stabilizing property and its relevance in financial decision-making.
These laboratory results suggest that accounting information and its overarching regime areimportant to prevent market exuberance, excess volatility and the formation of bubbles.
Historical cost accounting structures further appear to improve on market allocative efficiency,
and have positive effects in reducing revenue and wealth dispersion among investors.
The rest of the paper is organized as follows. We describe experimental design and thefeaturing conditions for determinants of the market price process to be investigated: individual
investors, security Exchange microstructure, and alternative accounting regimes. On this basis,
we provide economic analysis of our experimental results, summarized on five different results
concerned with the quality of market pricing process by technical and fundamental approach, theformation of individual expectations and bidding/asking strategies, the allocative efficiency andthe income and wealth distribution generated by that process. Each result is accompanied by aspecific appendix providing graphical and numerical analysis thereof. A brief summary concludes.

2. Experimental design
We organize artificial double auction markets for trading units of securities on a computer
network in a laboratory. All experiments were run in the Laboratory of Experimental Economics
of Paris (LEEP), between 2 and 5 August 2011. A fresh set of subjects were recruited for eachof the nine sessions, and none had participated in any previous research experiments on security
markets. Recruiting mailing list included, but was not limited to, undergraduate and graduate
students, which were 67 (69%) on 97 participants. Subjects’ age ranged between 18 and 72 years
old; mean (median) age was 28 (24) years old. Male subjects were 36 (37%). Participants to theexperiments were randomly assigned to different treatments retained for the procedure. To be
recruited, subjects were informed that the experiment consisted to make investment decisions on
artificial financial markets.
Every session comprises a series of 15 periods of 3 minutes each, followed by 1 or 2 minutesof paperwork (the next trading period did not start up unless all investors agree with by clicking
on their button). Before each session, participants read the instructions by themselves (seeAppendix.Game). Thereafter, instructors review again and explained these instructions to theparticipants, which were further trained for three periods of 3 minutes each, followed by open
questions and answers with instructors. Participants were strongly encouraged to ask forquestions and clarifications. They were then inexperienced but well-trained traders on the simple
procedures of our artificial markets. All investors can trade freely through these periods. Investors
were informed that the total euro amount to be paid to them is proportional to the number ofpoints/ECU earned in the session (following Hirota and Sunder 2007, p. 1886 and footnote n.

26

The Japanese Accounting Review, 3 (2013), 21-59

8). The subjects were announced that there will be up to 30 periods, but that the session can andwill be likely to end before that, at the end of whatever period. Every whole session lasted for twohours in average.
We retain two experimental treatments. The experimental design aims at studying the effectof two alternative accounting information regimes on the market pricing process. We differentiate
the fair value accounting treatment, in which the payout index is linked to current market price,from the historical cost accounting treatment, in which the payout index is linked to exogenous
fundamental information. In both treatments, the security pays out only one single liquidation
value to its holders at the end of the last period (final payout). During the training phase, before
each session, all investors were informed about the final payment linked to holding shares, andthe ongoing payout index that continuously provides fundamental information about thatpayment on their screens. They were informed that payout index was expected to change overtime during every period. No further explanation was provided about the mode of payout index
change. The theoretical connection with accounting was not declared, to avoid any bias fromprevious knowledge and belief concerning the index.
In addition to final payments linked to holding securities, investors are allowed to exchange
their securities against points, acquiring capital gains (or incurring capital losses) by trading withother investors. These additional profits (or losses) derive from ongoing payments between
investors, and are different from final payments by holding securities that depend on alternative
accounting regimes.

3. Determinants of the market pricing process
Drawing upon Smith et al (1988) and Hirota and Sunder (2007), the market pricing process
is characterized here by three adjustment dynamics: the process that describes changes in the
security’s liquidation value; the evolution of investors’ endowments and trading behavior; and the
security’s clearing price adjustments over time.

3.1 Individual investors

Each investor is endowed with 100 securities having an initial liquidation value of 100
Experimental Currency Unit (ECU or point thereafter), and 10 000 points at the beginning of
period 1. This implies a balanced portfolio between cash and securities worth. Huge number ofshares per individual portfolio and their low face value relative to cash were intended to enhancemarket liquidity and encourage trading. Short selling being prohibited, investors can both buy
securities if they have cash to pay for them, and/or sell any shares they have. Securities and cash
are carried over from one trading period to the next. The investors’ portfolio dynamics is thenendogenous to the market pricing process.
During each trading period, every investor has been informed about the evolution of themarket clearing price and the final payout by numerical and graphical representations. Figure in
Appendix.Game captures participant’s screen during the period. Together with the ongoingseries of market prices and liquidation values, this screen shows the best bid and the best ask
price currently available for matching; the current period number; remaining time to the end of
the period; and cases (to be filled in with integer numbers) and buttons (to be pressed by mouseclick) to accept or improve on outstanding proposals. Investors are also informed of theirBensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

27

endowment of shares number and cash that are updated after each of their own transaction.
Between two periods, investors have been informed about the over-the-period adjustments ofcurrent market price, liquidation value, their portfolio composition, and their potential income(as if the session stopped). Figure in Appendix.Game captures participant’s screen during thepaperwork pause. The screen shows the number of points and the number of securities (in the“endowment” window area), the market prices and the liquidation values (in the “market”
window area), and the individual incomes (in the “income” area), all at the beginning and at theend of the elapsed period. In addition, Investors were provided with pens and papers to do
calculations and notes, if they wish to do it.

3.2 Share Exchange microstructure

The trading procedure consists of continuous double auction market, implemented throughthe z-Tree software (Fischbacher 2007). Investors are free to bid (proposals to sell shares) and/or
ask (proposals to buy shares) for one security unit each time, by entering the wished pricethrough their mouse and keyboard during trading periods. For sake of simplicity, they cannot
enter but integer prices. They are free to accept any other trader’s ask or bid, or improve on themby heightening the current ask price and lowering the current bid price by one, two, three andso on ECU/points. Agents are likewise free either to buy and/or sell whatever number of shares
during the session, provided they have means to do it (no short sale or naked transaction), or to
wait and do nothing.
Following Smith et al. (1988) and Hirota and Sunder (2007), price quotes must progress so
as to reduce the bid-ask spread. The basic rule is that only the highest bid to buy and the lowest offer
to sell are displayed to the entire market and are open to acceptance. According to Smith andWilliams (1983), this structure of the double auction market tends to better allocative efficiency
by generating a competitive clearing price. Furthermore, previous price quotes that violate thisrule are removed from the market book order. Agents are prevented by the price system tosubmit new quotes that violate this rule. The price system also checks if the agent has sufficient
cash to pay for the purchase, or the share being to be sold. Once accepted by the price system,
submitted quotes cannot be removed from the book order. When two quotes match, a newtransaction is passed and the price system displays an updated clearing price at the transacted
price. If bid and ask cross (bid is above ask), transaction is executed at the price equal to the bid
or ask that came first.

3.3 Alternative accounting regimes: the final payout structure

We consider a security which pays only one ultimate payout FT at a future period T.
Assuming no discount or interest rates, and risk-neutral assessment, the fundamental value of the
security is Vt＝Et (FT ) where Et is the homogeneous expectation at time t 頴 [1; T ]. The payout
index is continuously announced during each period, and changes every time that a new market
clearing price is transacted.4 In historical cost accounting sessions, the payout index depends onan exogenous amount Ft＝F 一± where F has a constant value of 100 ECU and ± is a white noise
4

A further experiment may analyze the market price formation when the respective timings of payout index andmarket price differ. Biondi, Giannoccolo and Galam (2012) provide an analytical approach taking into accounting
the respective timings of individual and inter-individual decision-making, fundamental information, and the market
pricing.

28

The Japanese Accounting Review, 3 (2013), 21-59

of zero mean and unitary standard deviation. In fair value accounting sessions, the payout index
depends on an endogenous amount linked to the current market price Ft＝pt 一± where pt is thelast market clearing price and ± is a white noise of zero mean and unitary standard deviation. In
both regimes, Ft can be understood as a measure of accounting earnings, which have been
accrued and shall be eventually distributed to shareholders. Both white noises capture thenmeasurement errors and other factors affecting the working of accounting systems. Underhistorical cost accounting, that noise may also capture the intrinsic volatility of the security
generated by the ongoing economy of the business firm. For sake of comparability, both white
noises were randomly and identically generated for both kinds of treatment.
Investors were informed that every security would pay only one payout equal to the lastannounced value of the payout index at the end of the whole session, when t ＝T. They were notinformed about the actual number of periods (15) in the session until the latter was actuallyterminated, but they were informed that the number can be equal or inferior than 30, and they
could have estimated that the length of time for which they had been recruited into thelaboratory would end well before Period 30. This makes the final payout linked to holding shares
relevant for investing and trading decision-making within every period of the whole session, orat least during the final time of each period, not only at the end of any announced last period.
The payoff index becomes then the liquidation value of reference for holding shares.5
One may argue that, under fair value accounting regime, this setting is sensitive to market
manipulation at the end of the period by individual strategies. One investor could purposefully
offer to buy (sell) securities at increasingly higher (lower) prices at the end of the period, in orderto drive the market clearing price up (down), and the strategy is self-fulfilling if other tradersagree to settle those orders. Those strategies are nevertheless submitted to increasingly higher
risk if some traders reverse the trend by transacting a lower (higher) price. As a matter of fact,
neither clearing market series nor price orders series corroborate the existence of such strategies
in any of our sessions.

4. Economic analysis of results
Because of the aleatory structure of the payout index, and random changes in participants,
every market pricing process is somewhat unique. For sake of robustness, we have then replicatedeach treatment several times and assessed every result on all the periods and all the sessions bytreatment (accounting design). In four sessions (numbered FV1, FV2, FV3, FV4), investors
traded under the fair value accounting regime. In five sessions (numbered HC1, HC2, HC3,
HC4, HC5), investors traded under the historical cost regime.6
Figures in Appendix.DescriptiveStatistics summarize the market price series generated by all
the sessions. Each figure shows the evolution of the market series and its divergence from the last
These liquidation values cannot be considered as the fundamental values of reference in its usual meaning. Nevertheless,they provide collective signals about the payoffs linked to holding shares, and those signals are common knowledge
for all the investors.
6
All results were calculated by excluding price orders and clearing prices that result from order-of-magnitude
typographical errors (69 (0.28%) orders in total, on 25 034 (100%) orders throughout all sessions; 10 (0.14%)
clearing prices in total, on 7304 (100%) prices throughout all sessions).

5

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

29

available liquidation value, time after time, within each period and session. Tables inAppendix.DescriptiveStatistics summarize descriptive statistics of the market price series by
period and session for both treatments (accounting designs). Generally speaking, independently
from accounting regime, every session shows high volumes of proposals (one every second on
average) and transacted prices (one every three seconds on average). Every investor passed 300
orders and settled 70 transactions, on average.
Concerning historical cost accounting, sessions HC3 and HC4 show a particularly stable
market that keeps in line with the liquidation value of reference over time. Sessions HC1 andHC2 are relatively stable markets that remain substantially in line with liquidation values but
show bubble formation both in early and late periods. Session HC5 shows a particularly stable
market that peculiarly remains at a mean (median) price level of 95.45 (95), below the liquidation
value of reference over time. Other sessions remarkably trade at median prices of 100 on all
periods, while the mean price is above 100 in three cases (HC1: 100.34; HC2: 100.38; HC3:
100.20) and below 100 in one case (HC4: 99.84).
Concerning fair value accounting, all sessions show instable markets trading with ample
fluctuations of both the market price series and its divergence from last announced liquidation
values over time. Market price sequences with relative stability appear to switch toward sequencesof higher volatility and divergence. In particular, session FV1 appears to trade around a central
value situated around 82 points; sessions FV2 shows a remarkable downward trend over time,from 100 to 31 points; while session FV3 and FV4 largely fluctuate in both directions. For all
sessions, market prices remain below the initial liquidation value of 100 points for most of thetime.
We shall infer five main results from analyzing the data. These results concern the market
clearing price process by technical and fundamental analysis, the formation of individual
expectations, allocative efficiency, and revenue and wealth dispersion under alternative accounting
regimes.

4.1 Market clearing price process under technical analysis
Result 1: Under historical cost accounting regime, market price series shows less pricevolatility and amplitude than under fair value accounting regime.
According to LeRoy (1989), economic analyses of bubbling can be distinguished between
technical and fundamental analysis. Technical analysis retains measures of and controls forbubbling related to the market price series alone. By assuming “what if” the market price always
equals the fundamental value of reference, they test the implied characteristics of the market
price series.
We retain here some classic measures of bubbling from technical analysis, computed period
by period. In particular, volatility is defined as the ratio of price standard deviation on mean
price. Turnover is defined as the number of trades transacted during a period on the total share
units (TSU) available on that period (9000 or 10000 according to the number of participants),or Óq t /TSU. Normalised volatility is then the volatility for the turnover. We further introduce
two measures of Price Trend Duration in both directions (downward and upward), i.e., thenumber of consecutive market prices that follow the same upward (downward) pattern, and acomposite measure of price trend duration obtained by adding the previous two measures

30

The Japanese Accounting Review, 3 (2013), 21-59

normalized for the number of trades of the period (Normalized Trend Duration).
Test results are summarized by Table in Appendix.Result1. They confirm that pricebubbling measures are materially and significantly reduced (increased) by historical cost (fairvalue) accounting regime. In particular Mean Volatility is significantly and materially increased
under fair value accounting (0.072461) than under historical cost accounting (0.0118676).
Normalized Volatility is also increased (709.1495 against 116.24439). Concerning bubble
duration, upward trends appear more durable under fair value accounting (duration max 1.98 andmean 0.37211, against 1.53 and 0.2489 respectively), while downward trend differences do notappear especially significant.
The influence of accounting design on bubbling is confirmed under technical analysis.
Another literature strand further tests for divergences between market price and fundamental
value of reference.

4.2 Market clearing price process under fundamental analysis
Result 2: Under historical cost accounting regime, market price shows better alignment withthe liquidation value of reference than under fair value accounting regime
According to LeRoy (1989), another strand of economic analysis of bubbling relates tofundamental analysis. This literature retains measures of and controls for bubbling related to thealignment between the market clearing price series and the fundamental value series of reference.
By assuming that the market price evolves over time around the fundamental value of reference,
they test the implied connection between them.
In our design, fundamental values are represented by liquidation values, assuming no
discount or interest rates, and investors’ risk-neutrality. We retain here some classic measures of
bubbling from fundamental analysis based upon the liquidation value series (F). In particular,
Divergence consists of the difference between the market price and the last liquidation value(Pt芋Ft-1), computed time after time. Cumulated absolute divergence is the sum of the absolutevalues of Divergence Ó角Pt芋Ft-1角, computed period by period. Price Amplitude is the difference
between the maximum and the minimum of the divergence normalized by the liquidation valueof reference, time after time, or max ((P t芋Ft-1)/Ft-1) - min ((P t芋Ft-1)/Ft-1). Normalized Deviationis the turnover for the cumulated absolute divergence, or (Óq t /TSU)･ Ó角Pt芋Ft-1角, period by
period. Average Bias is the sum of timely divergence normalized by the liquidation value ofreference, period by period, or Ó((Pt芋Ft-1)/Ft-1). Absolute Average Bias is the sum of absolute
divergence normalized by the liquidation value of reference, period by period, orÓ(角Pt芋Ft-1角/Ft-1).
Tables in Appendix.Result2 report all these intrinsic bubbling measures, with and without
the session HC.5 that is peculiar. Our tests confirm that all intrinsic bubbling measures arematerially and significantly reduced (increased) by historical cost (fair value) accounting regime.
In particular, the mean divergence and the mean average bias is generally less than one point
under historical cost accounting, provided that the minimum trade unit allowed to traders had
been one point, while they are respectively 2.1046 and 9.310 under fair value accounting. The
cumulated absolute divergence represents the market cumulative over- and underestimation
relative to the ongoing fundamental value of reference, and is more than twice under fair value
accounting; price amplitude is almost seven times bigger; the normalized deviation is at least theBensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

31

double; the average bias is nine times bigger; and the absolute average bias is four time bigger.
As a matter of fact, under historical cost accounting, only one session shows significant but
not material divergence between market price and fundamental value of reference. This is why
we have analyzed all measures with and without that session. In fact, the disconnection may be
explained by a sort of behavioral financial safety margin that became consensual among investors
trading on that market. Even though investors collectively renounced to potential capital gains
as computed on ongoing liquidation values, they successfully prevented potential capital losses bytrading just under the frontier generated by the minimum threshold level of the fundamental
value series. This result is also in line with discounted fundamental value as computed by riskadverse investors, according to Smith et al. (1988, p. 1144): the present value of future payouts
less a constant. In our case, this implies a discounted fundamental value of (97.083±0.058)＝
100－2.917, including a 2.9% discount.7 This value remains then within the range of experienced
minimum liquidation values of reference, which, by construction, add a white noise with nullmean and unitary standard deviation.
The influence of accounting design on bubbling is confirmed under fundamental analysis.
Features of the market pricing process further relate to the formation of individual expectations
and bidding/asking strategies.

4.3 Individual expectations and bidding/asking strategies
Result 3: Under historical cost accounting regime, individual price expectations are consistent
with backward induction; under fair value accounting regime, they are consistent with forward
induction.
Artificial security markets enable to control the whole market environment, and the analysis
of individual expectations is then greatly facilitated. In fact, our analysis focalizes on coordination
issue and the response to this issue that is provided by collective devices generating payouts andinformation thereof, i.e., the accounting systems. Nevertheless, we run simple linear regression
analysis to assess the impact of featuring factors that are assumed to shape the formation of themarket clearing price over time.
In particular, we analyze the market price formation process using two backward induction
factors and two forward induction factors. From the backward induction side, the heuristic
model assumes that investors collectively generate the current price (Pt) by inducting from theongoing liquidation values of reference (Ft-1). The divergence of the price from the past liquidation
value (Pt芋Ft-1) and the intrinsic variation of the latter (Ft芋Ft-1) are then expected to become
relevant to investing and trading decision-making. From the forward induction side, the heuristic
model assumes that investors collectively generate the current price (Pt) by extrapolating from themarket price trend (Pt芋Pt-1) and “revealed excess demand arising from capital gains expectations”(Bt-1芋At-1), the latter factor being defined as the difference between the best bid price and thebest ask price before the trade (comp. Smith et al. 1988, p. 1141). The combined linear
regression model is defined as follows, time after time:
7

This coefficient was estimated by the combined linear regression model discussed below. Its t test was 1663,
significant at ＜1%.

32

The Japanese Accounting Review, 3 (2013), 21-59

Pt 厩 c茨a1(Pt芋Pt-1)茨a2(Bt-1芋At-1)茨a3(Ft芋Ft-1)茨a4(Pt芋Ft-1)
Where Pi is the market clearing price at time i; Fi is the liquidation value of reference at timei; Bi is the best bid price at time i; and A i is the best ask price at time i. The combined modelallows for the possibility that investors use some combinations of the four simple factors to form
their expectations. It also helps to detect any biases due to the omitted variables problem. We
estimate regressions on two samples, one had pooled data from all the four fair value accounting
sessions, and the other has pooled the data from all the five historical cost accounting sessions.
Furthermore, we repeat regressions on each session by distinguishing between fair value andhistorical cost treatment. Our null hypothesis is that expectations are static, Et(Pt+1) 厩 Pt茨±,
implying that all the four parameters a are expected to be zero.
Tables in Appendix.Result3 show results from pooled regressions (session regressions areomitted).8 Under fair value accounting regime, both forward induction factors are significantly
correlated to the clearing market price. Market price trend has a positive coefficient as expected,
and so does bid-ask spread before trade that appears to have a negative coefficient.9 Our
estimations indicate that the market price trend factor has a considerable effect on market
clearing price: its rise of 1 point raising the market price by 0.7 points, according to its linear
regression non-standardized coefficient. On the contrary, no backward induction factors are
significant, neither the liquidation value trend or the market price divergence. In thisenvironment, market price levels and paths become erratic and lose earnings anchor; investors
tend to form their expectations of future prices by forward, not backward, induction. Accounting
plays its role from the market, and loses its relevance in financial decision-making. Underhistorical cost accounting regime, both backward induction factors are significantly correlated tothe market clearing price. Interestingly, the liquidation value trend has a negative coefficient,while the divergence has a positive one.10, 11 Furthermore, even one forward induction factor, themarket price trend, has a small and significantly positive coefficient. These results suggest thatthe liquidation value not only determines transaction prices under historical cost accounting, butalso critically affects the price expectations. Whenever market prices tend to increase when the
Under fair value accounting, linear regression coefficients by session were significant and positive for Market Price
Trend in FV1 and FV3; significant and positive in FV1, while significant and negative in FV4 for Bid-Ask Spread
Before Trade; never significant for Liquidation Value Trend and Divergence or elsewhere. Under historical costaccounting, linear regression coefficients by session were significant and positive for Market Price Trend in all
sessions; significant and negative in HC1 and HC3, significant and positive in HC2 and HC5, and not significant
in HC4 for Bid-Ask Spread Before Trade; significant and negative for Liquidation Value Trend in all sessions;significant and positive for Divergence in all sessions.
9
According to Smith et al. (1988, p. 1141), “excess demand is positively correlated to excess bids (numbers ofbids entered minus number of offers) in those markets which spontaneously self-generate an expectation of capitalgains (losses).” Here, we utilize the bid-ask spread that reverses the correlation (bid meaning “offer” while ask
meaning “bid”).
10
Hirota and Sunder (2007, p. 1899) also estimated a significantly and materially positive coefficient for Dmax - Pt ,in long-horizon sessions, corresponding here to the reverse of the divergence factor (Pt芋Ft-1) in historical costaccounting sessions. Liquidation value trend did not apply to their design.
11
This correlation is made even more remarkable by noticing that trade proposals where limited at integer prices,while the liquidation values evolved on a continuous range. The fine tuning was then reduced by trading constraints.
Under historical cost accounting, 46.8% of liquidation values were actually at less than one point of distance fromthe central value of 100.
8

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

33

divergence increases, their limited price trend behavior is overcompensated by the evolution of
the liquidation value that plays a decisive role as an accounting lighthouse. When the market
price goes too far from fundamentals, it eventually reverses on them, generating the significantly
and materially negative correlation with the liquidation value trend. In this environment,
speculative trades by fundamentalist strategies (which trade on the basis of fundamentals) leadthe market to keep in line with fundamentals by dominating speculative trades by chartist
strategies (which trade on the basis of market price trend and other market conditions) thatwould tend to divert it from them. These findings are also consistent with Hommes et al. (2005)
showing that individual forecasting may coordinate on trend following strategies, causing security
prices to fluctuate and deviate from their fundamentals.
On this basis, to refine our regression analysis, we add a further factor to the previous model
to take into account the forecasting error correction ERRt defined by Biondi, Giannoccolo andGalam (2012) as follows:
Nn

ERRt 厩 郁 (芋â) (Pt -n芋Pt -n+1茨m(Pt -n芋Pt -n-1))茨
n=1

N

n

郁 (芋â) (ã ･Ft -n)n=1

Where, for sake of simplicity, â ＝0.5, m＝0.5, ã ＝1, and N＝5, assuming a uniform
population with the same weights for forecasting error correction, fundamentalist and chartist
strategies over time and individuals, as well as a truncated past memory of 5 periods. Table inAppendix.Result3 summarizes the further estimations. They confirm all the correlations andtheir signs already estimated, and enhance previous results by making the market price trend
insignificant under historical cost accounting regime. Further specifications of the model
including lagged factors and non-linear relationships may further investigate these insights,
including dynamic estimation fitting of the forecasting error correction factor.
Moreover, the working of the market pricing process has important consequences for its
allocative efficiency, that is, its capacity to generate a “fair” price from the viewpoint of resource
allocation over time and among investors.

4.4 Allocative efficiency
Result 4: Market exchange appears to show better allocative efficiency under historical costaccounting design when all transactions are taken into account. This effect disappears when onlytransactions traded at a large distance from liquidation values are considered.
According to Hirota and Sunder (2007, p. 1900, footnote 19), when all investors had thesame dividend, allocative efficiency is undefined. We try here to measure it on the basis of thequota of securities transferred near to their “fundamental values” of reference, which are ongoingliquidation values in our setting. When securities are traded far from these values, investors
increasingly confront with transacted prices that diverge from the expected liquidation value ofthat security, i.e., from its intrinsic value at that point of time. Since every trader would
eventually get the same liquidation payout from holding the security, the market is expected toexert pressure to make the allocation of resources more efficient by reducing the distance between
price and that ultimate payout (comp. Hirota and Sunder 2007, p. 1909).
We identify all the shares transferred at 5% and 10% above the intrinsic value of reference

34

The Japanese Accounting Review, 3 (2013), 21-59

at the time of the transaction. This implies that the absolute value of Price Amplitude defined as(P t+1芋Ft)/Ft is more than 0.05 (5%) and 0.1 (10%) for that transaction. For all thesetransactions, we compute the cumulated number and cumulated relative value defined as
Ó角Pt芋Ft-1/Ft-1角, period by period. Table in Appendix.Result4 reports descriptive statistics andtests for these measures. Results do not corroborate that allocative efficiency is improved(worsened) by historical cost (fair value) accounting regime. Most significance tests failed, andthe differences are not material. However, these results might depend on the structure ofliquidation values that, under fair value accounting, follow the market prices time after time.
Furthermore, these results might depend on the peculiar measures of market inefficiency defined
above for the test. Instead, we can have recourse to some measures already utilized to assess
fundamental bubbling. In particular, the Absolute Average Bias is defined as Ó(角Pt+1芋Ft角/Ft) for
all transactions within a period: its mean (median) was 19.477 (12.262) under fair value
accounting, while it was 4.8648 (3.27515) under historical cost accounting, that means a materialand significant inefficiency when all transactions are taken into account. The same is true for
Cumulative Absolute Divergence defined as Ó角Pt+1芋Ft角for all transactions within a period: its
mean (median) is 1201.09986 (935.837) under fair value accounting, while it is 486.7235(326.54388) under historical cost accounting. The latter result is even more important provided
that the market price levels remained systematically lower in fair value sessions than in historicalcost sessions. In fact, further specific tests are required to comfort our preliminary results on
allocative efficiency and accounting design.
Moreover, bubbles may also have significant distributive effects in the form of increasing thedispersion of revenue and wealth among investors.

4.5 Revenue and wealth dispersion
Result 5: Historical cost accounting regime reduces inter-individual dispersion of investor
revenue and wealth relative to fair value accounting regime
We analyze two classic measures of revenue and wealth dispersion, the Gini and Teil
Indexes, by period and by session. For every period, end-of-period individual profits arecomputed by adding held cash to the current liquidation value of all held shares.
The Gini Index (Gt) at the end of each period is computed on the non-decreasing ranking
of individual profits ( y i 浦 y i+1) as follows:n

Gt 厩

2 郁 i･yii =1nn 郁 yii =1

芋n茨1
where n is the number of individuals (9 or 10 in ours samples)nMore the wealth distribution is uniform, more the Gini Index approaches to zero. More thewealth distribution is concentrated in few hands, more the Gini Index approaches 0,889 for asample of 9 participants, or 0.9 for a sample of 10 participants.
The Theil index is computed at the end of each period on the basis of relative profits ofindividual investors. Relative profits consist of the end-of-period trader’s profit in points (x i)Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

35

芋normalized by the average profit on all traders (x)in the same period.
nxixi
郁 ( 芋 ･ ln 芋 )
i =1
x
x
Tt 厩
where n is the number of individuals
n ･ ln (n)More relative profits are equal, more the Theil index is near to zero. More relative profits
are concentrated, more the Theil index approaches to one.
Tables and Figures in Appendix.Result5 show significant and material differences between
alternative accounting regimes. The mean (median) Gini Index under fair value accounting is0.0288222 (0.0119574), while it is 0.0029952 (0.0021996) under historical cost accounting. The
mean (median) Theil Index under fair value accounting is 0.019956755 (0.0000247), while it is0.0010712 (0.0000068) under historical cost accounting. Both indexes corroborate that interindividual dispersion of revenue/wealth is less under historical cost accounting than under fairvalue accounting regime, with several statistical difference tests significant at ＜0.2% or less.

5. Conclusion
In nine artificial spot security markets, we observe that, when investors trade under fair value
accounting regime, the market price tends to deviate from fundamentals to form bubbles. In
these circumstances, prices lose their anchors on earnings and are more volatile. The allocationof resources becomes less efficient and wealth distribution more skewed. On the contrary, underhistorical cost accounting regime, the market price tends to align with fundamentals and theoccurrence, duration and materiality of bubbling is reduced. Market prices remain connected toearnings and are less volatile. The allocation of resources is more efficient and wealth distribution
less skewed. These results are robust to several replications of both alternative experimental
treatments under analogous conditions with different subjects.
These findings show how the institutional architecture enabling trade actively frames andshapes the formation of market clearing prices over time, particularly the accounting regime thatprovides collective signals of accrued earnings (and eventual dividend payouts) to shareholders.
Together with individual expectations and the market microstructure (the price system), theaccounting system contributes then to the working of the share Exchange. In particular,
accounting information and its overarching structure are important to prevent market
exuberance, excess volatility and the formation of financial bubbles. They further have relevant
effects on market allocative efficiency, and revenue and wealth distribution among investors.
REFERENCES
ABREU, D., AND M. K. BRUNNERMEIER. 2003. Bubbles and crashes. Econometrica 71 (1): 173-204.
ANTHONY, R. N. 2004. Rethinking the rules of financial accounting. Examining the rules for proper
reporting. New York: McGraw-Hill.
AZARIADIS, C. 1981. Self-fulfilling prophecies. Journal of Economic Theory 25 (3): 380-396.
BALL, S. B., AND C. A. HOLT. 1998. Speculation and bubbles in an asset market. Journal of Economic

36

The Japanese Accounting Review, 3 (2013), 21-59

Perspectives 12 (1): 207-218.
BIONDI, Y. 2011. The pure logic of accounting: A critique of the Fair Value Revolution. Accounting,Economics, and Law: A Convivium 1 (1).
BIONDI, Y., P. GIANNOCCOLO, AND S. GALAM. 2012. The formation of share market prices underheterogeneous beliefs and common knowledge. Physica A 391 (22): 5532-5545.
BRUNNERMEIER, M. K. 2001. Asset pricing under asymmetric information: Bubbles, crashes, technical analysis
and herding. Oxford: Oxford University Press.
CAMERER, C. 1989. Bubbles and fads in asset prices. Journal of Economic Surveys 3 (1): 3-41.
DAVIS, D. D., AND C. A. HOLT. 1993. Experimental Economics. Princeton: Princeton University Press.
FISCHBACHER, U. 2007. z-Tree: Zurich Toolbox for ready-made economic experiments. Experimental
Economics 10 (2): 171-178.
FRYDMAN, R. 1982. Towards an understanding of market processes: Individual expectations, learning, andconvergence to rational expectations equilibrium. American Economic Review 72 (4): 652-668.
HOLT, C. A. 1995. Industrial organization: A survey of laboratory research. KAGEL, J. H., AND A. E. ROTH
eds. Handbook of Experimental Economics. Princeton, New Jersey: Princeton University Press: 349443.
LAFFONT, J. -J, AND J. TIROLE. 1993. A Theory of Incentives in Regulation and Procurement. Cambridge,
MA: MIT Press.
HOMMES, C. H., J. SONNEMANS, J. TUINSTRA, AND H. VAN DE VELDEN. 2005. Coordination of expectationsin asset pricing experiments. Review of Financial Studies 18 (3): 955-980.
KIRCHLER, M. 2009. Underreaction to fundamental information and asymmetry in mispricing between
bullish and bearish markets. An experimental study, Journal of Economic Dynamics and Control 33 (2):491-506.
LEROY, S. 1989. Efficient capital markets and martingales. Journal of Economic Literature 27 (4): 15831621.
LINTNER, J. 1956. Distribution of incomes of corporations among dividends, retained earnings and taxes.
American Economic Review, Papers and Proceedings 46 (2): 97-113.
MALKIEL, B. 2003. The efficient market hypothesis and its critics. Journal of Economic Perspectives 17 (1):59-82.
PENMAN, S. 2011. Accounting for Value. New York: Columbia Business School Publishing.
PLOTT, C. 1989. An updated review of industrial organization: Applications of experimental methods.
SCHMALENSEE, R., AND R. WILLIG eds. Handbook of Industrial Organization. North-Holland,
Amsterdam.
SHUBIK, M. 1993. Accounting and its relationship to general equilibrium theory. Reprinted. KIRAT, T., Y.
BIONDI, AND A. CANZIANI eds. 2007. The Firm as an Entity: Implications for Economics, Accounting,and Law. New York and London: Routledge: 73-81.
SMITH, V. L., AND A. W. WILLIAMS. 1983. An experimental comparison of alternative rules forcompetitive market exchange. ENGELBRECHT-WIGGANS, R., M. SHUBIK, AND R. STARR eds. Auctions,
Bidding and Contracting: Uses and Theory. New York: New York University Press.
SMITH, V. L., G. L. SUCHANEK, AND A. W. WILLIAMS. 1988. Bubbles, crashes, and endogenous
expectations in experimental spot asset markets. Econometrica 56 (5): 1119-1151.
SMITH, V. L., M. VAN BOENING, AND C. P. WELLFORD. 2000. Dividend timing and behavior in laboratory
asset markets. Economic Theory 16 (3): 567-583.
STIGLITZ, J. E. 1990. Symposium on bubbles. Journal of Economic Perspectives. 4 (2): 13-18.
SUNDER, S. 1995. Experimental asset markets: A survey. ROTH, A. AND J. KAGEL eds. Handbook ofExperimental Economics. Princeton: Princeton University Press: 445-500.
SUNDER, S. 2002. Knowing what others know: common knowledge, accounting, and capital markets.
Accounting Horizons 16 (4): 305-318.

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

37

APPENDIX.DESCRIPTIVE STATISTICS.TABLE: MARKET PRICE SERIES BY SESSION38

The Japanese Accounting Review, 3 (2013), 21-59

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

39

40

The Japanese Accounting Review, 3 (2013), 21-59

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

41

42

The Japanese Accounting Review, 3 (2013), 21-59

APPENDIX.DESCRIPTIVE STATISTICS.FIGURES

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

43

44

The Japanese Accounting Review, 3 (2013), 21-59

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

45

46

The Japanese Accounting Review, 3 (2013), 21-59

APPENDIX.DESCRIPTIVE STATISTICS.TABLE: DESIGN SUMMARY OF ALL SESSIONS

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

47

The Japanese Accounting Review, 3 (2013), 21-59

APPENDIX.RESULT1 TABLES

48

APPENDIX.RESULT2.TABLE: INCLUDING SESSION HC.5

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

49

50

APPENDIX.RESULT2.TABLE: EXCLUDING SESSION HC.5

The Japanese Accounting Review, 3 (2013), 21-59

APPENDIX.RESULT3.TABLE: INDIVIDUAL EXPECTATIONS AND BIDDING-ASKING STRATEGIES

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

51

52

The Japanese Accounting Review, 3 (2013), 21-59

APPENDIX.RESULT4.TABLE: ALLOCATIVE EFFICIENCY

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

53

54

The Japanese Accounting Review, 3 (2013), 21-59

APPENDIX.RESULT5.FIGURESAPPENDIX.RESULT5.TABLES

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

55

56

APPENDIX.GAME.FIGURES: SCREEN CAPTURESThe Japanese Accounting Review, 3 (2013), 21-59

Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

57

58

The Japanese Accounting Review, 3 (2013), 21-59

APPENDIX.GAME: INSTRUCTIONS SHEET FOR ALL PARTICIPANTS
This experience concerns investment decision-making. These instructions are simple and more
you take good decisions, more you will gain points. Your purpose is then to accumulate amaximum amount of points before the end of the session. The total of your points will be thenconverted in euro and paid cash: your earnings may range between 0 and 150 euro. More points
you earn, more euros you will receive at the end of the session.
You cannot talk to other participants and you should follow the instructions given byexperimenters. Any breach of this rule may involve a reduction of your points.
The session consists of a financial market to trade securities. You can trade securities. As aninvestor, you can buy and/or sell these securities at current proposed prices.
At the beginning of the session, you have:- An initial endowment of 100 securities. You can sell these securities or keep them indefinitely
at will, until the end of the session.
- An initial endowment of 10 000 points. You can utilize these points to buy securities or keepthem indefinitely at will, until the end of the session.

Security Trading
The session consists of several exchange periods of three minutes each, with a short interruption
between each period. Buying a security implies its payment in points at its offered price. Afterevery purchase, your endowment of points will be reduced by the price paid for the security.
Furthermore, you can also sell the securities you hold. Every sale will increase your endowmentof points by the price exchanged for the security.

The conversion amount of the securities
The number of periods may be equal or less than thirty (30), but it is likely to be less than its
maximum duration. The experience may stop at the end of whatever period, and your securities
will be then converted in points. The initial conversion amount is 100 point for each security; it
may vary over time and will have been continuously showed on the screen of all the participants
during the whole duration of the session.

Your final income
Therefore, your final income (= number of points accumulated) depends on two sources:- Profits and losses realized through your trading activity,- The conversion of your titles at the conversion amount.
On this basis, your income at the end of the session will be determined as follows:Bensimhon and Biondi: Financial Bubbles, Common Knowledge and Alternative Accounting Regimes:
An Experimental Analysis of Artificial Spot Security Markets

59

Final income ＝ (conversion amount * number of held securities) ＋ final endowment of points－ initial reserve of points (that is, 10 000 points)
The conversion amount for computing the final income is the last amount announced on all
screens.
(Literal translation from French)