nach oben

Journal of Economic Interaction and Coordination

Erschienen in:

01.04.2013 | Regular Article

Information dissemination in an experimentally based agent-based stock market

verfasst von: Jakob Grazzini

Erschienen in: Journal of Economic Interaction and Coordination | Ausgabe 1/2013

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

This paper builds an agent-based model to reproduce the results of an experimental stock market that studies how the market aggregates private information. The aim is to use experiments and agent-based modeling to analyze the trading behavior in experimental stock markets. Using the experimental environment and results, it is possible to formulate a hypothesis about the subjects’ behavior and thereby formalize (algorithmically) the trading behavior in an agent-based model. This may lead to a better understanding of how the market converges to an equilibrium and of the mechanism that allows dissemination of private information in the market.

Vorheriger Artikel Modeling the International-Trade Network: a gravity approach

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

A detailed description of the algorithms can be found in Cliff and Bruten (1997). The (Python) code is available on request.

In some experimental sessions the traders could trade more than one asset, but always a given number of assets. This procedure is useful as it simplifies the traders’ decision making and provides a definition of the demand and supply schedule.

$R_i$ is distributed as a uniform random variable $U(1,1.05)$ and $e_i$ is distributed as a uniform random variable U(0,0.05).

$R_i$ is distributed as a uniform random variable $U(0.95,1)$ and $e_i$ is distributed as a uniform random variable U($-$0.05,0).

The value of the parameters are the following: initial value for $\mu \sim U(0.30,0.45)$ for sellers and $\mu \sim U(-0.45,-0.30)$ for buyers; $\beta \sim U(0.1,0.5),\,\gamma \sim U(0.2,0.8)$. During each simulated period each agent will issue an order—if she is active—on average every 20 (simulated) seconds. Each trader has therefore the same given probability to issue an order in each time step. One period lasts 500 s.

Also the Augmented Dickey Fuller test rejects the null-hypothesis of non-stationarity of the price time series.

The author thanks Barner, Feri and Plott for providing the experimental data.

Francs are the experimental currency which will be converted into $ at a fixed rate (1,000 francs = 1$) at the end of the experiment.

The market consists of 22 traders. The code is available on request.

Resulting $\alpha $ from the first to the fifth minute: 14.6, 18.1, 12.8, 1,414,213,552.47, 1,055,280.66.

All orders below 200 and above 1,000. Of 1,221 orders, only 74 are eliminated. For example there is a sell order of 100 billion francs (this is probably an error) and many buy orders of almost 0.

Period 1: 32.14; period 2: 20.77; period 3: 9.63.

The traders’ private value is distributed as a Normal with expected value equal to the theoretical expected value and variance equal to 5. Note that the variance cannot be computed from the data since the private values are not observable. Different values of the variance influences the volatility of the price around the equilibrium price. The code is available on request.

The code is available on request.

Barner et al. (2005) call it ITRRH: Informed Traders Rat Race Hypothesis.

Note that Fig. 11 shows the only wrong non-compatible order placed by an informed trader in the first minute of a bubble. It comes after two transactions in the wrong direction and just before a non-informed trader accepts an (informed) sell proposal at a price of 575, which actually triggers the bubble.

The condition resulting from the definition of the behavioral hypothesis on the learning mechanism are $\omega ^{up} > \omega ^{down},\,\omega ^{up}<1$ and $\omega ^{down}>0$. Given these conditions, different values of the parameters have very little effect on the learning mechanism.

Non-compatibility has a subjective definition since it derives from the comparison between the private value and the price of the order.

In both simulations the learning parameters are $\omega ^{up} = 0.4$ and $\omega ^{down}=0.1$. The code is available on request.

Anufriev M, Hommes C (2012) Evolutionary selection of individual expectations and aggregate outcomes in asset pricing experiments. Am Econ J Microecon 4(4):35–64CrossRef

Anufriev M, Arifovic J, Ledyard J, Panchenko V (2011) Efficiency of continuous double auctions under individual evolutionary learning with full or limited information. J Evol Econ 1–35. doi:10.1007/s00191-011-0230-8

Axtell R (2000) Why agents? On the varied motivations for agent computing in the social sciences. Center on Social and Economic Dynamics Working Paper (17)

Barner M, Feri F, Plott CR (2005) On the microstructure of price determination and information aggregation with sequential and asymmetric information arrival in an experimental asset market. Ann Financ 1:73–107CrossRef

Boero R, Bravo G, Castellani M, Squazzoni F (2010a) Why bother with what others tell you? An experimental data-driven agent-based model. J Artif Soc Soc Simul 13(3). http://jasss.soc.surrey.ac.uk/13/3/6.html

Bravo G, Boero R, Squazzoni F (2012) Trust and partner selection in social networks: an experimentally grounded model. Soc Netw 34(4):481–492CrossRef

Camerer C, Weigelt K (1991) Information mirages in experimental asset markets. J Bus 64(4):463–493CrossRef

Chan NT, LeBaron B, Lo AW, Poggio T (2001) Agent-based models of financial markets: a comparison with experimental markets. MIT Sloan Working Paper (4195–01)

Cliff D, Bruten J (1997) Minimal-intelligence agents for bargaining behaviors in market based environments. HP Laboratories Bristol (HPL-97-91)

Contini B, Leombruni R, Richiardi M (2007) Exploring a new expace the complementarities between experimental economics and agent-based computational economics. J Soc Complex 3(1):13–22

Copeland TE, Friedman D (1986) The effects of sequential information arrival on asset prices: an experimental study. J Financ 42(3):763–797CrossRef

Copeland TE, Friedman D (1991) Partial revelation of information in experimental asset markets. J Financ 46(1):265–295CrossRef

Duffy J (2006) Agent-based models and human subject experiments. In: Handbook of computational economics, vol 2. Elsevier, Amsterdam, pp 949–1011

Duffy J, Ünver MU (2006) Asset price bubbles and crashes with near-zero-intelligence traders. Econ Theory 27:537–563CrossRef

Epstein JM, Axtell R (1996) Growing artificial societies: social science from the bottom up. The MIT Press, Cambridge

Fano S, LiCalzi M, Pellizzari P (2011) Convergence of outcomes and evolution of strategic behavior in double auctions. J Evol Econ. doi:10.1007/s00191-011-0226-4

Forsythe R, Lundholm R (1990) Information aggregation in an experimental market. Econometrica 58(2):309–347CrossRef

Gjerstad S, Dickhaut J (1998) Price formation in double auctions. Games Econ Behav 22(1):1–29CrossRef

Gjerstad S, Sachat JM (2007) Individual rationality and market efficiency. Institute for Research in the Behavioral, Economic and, Management Science (1204)

Gneezy U (1996) Probability judgments in multi-stage problems: experimental evidence of systematic biases. Acta Psychol 93:59–68CrossRef

Gode DK, Sunder S (1993) Allocative efficiency of markets with zero-intelligence traders: market as a partial substitute for individual rationality. J Polit Econ 101(1):119–137CrossRef

Grazzini J (2012) Analysis of the emergent properties: stationarity and ergodicity. J Artif Soc Soc Simul 15(2)

Holden CW, Subrahmanyam A (1992) Long-lived private information and imperfect competition. J Financ 47(1):247–270CrossRef

Hommes C (2011) The heterogeneous expectations hypothesis: some evidence from the lab. J Econ Dyn Control 35:1–24CrossRef

Hommes C, Sonnemans J, Tunistra J, van de Velde H (2005) Coordination of expectations in asset pricing experiments. Rev Financ Stud 18(3):955–980CrossRef

King RR, Smith VL, Williams A, van Boening M (2001) The robustness of bubbles and crashes in experimental stock markets. Econometrica 69(4):831–859CrossRef

Kyle AS (1985) Continuous auction and insider trading. Econometrica 53(6):1315–1335CrossRef

Lei V, Noussair CN, Plott CR (2001) Nonspeculative bubbles in experimental asset markets: lack of common knowledge of rationality vs actual irrationality. Econometrica 69(4):831–859CrossRef

Mitchell TM (1997) Machine learning. McGraw-Hill, New York

Noussair C, Robin S, Ruffleux B (2001) Price bubbles in laboratory asset markets with constant fundamental values. Exp Econ 4:87–105

Plott CR (2000) Market as information gathering tools. South Econ J 67(1):1–15CrossRef

Plott CR, Sunder S (1982) Efficiency of experimental security markets with insider information: an application of rational-expectations models. J Polit Econ 90(4):663–698CrossRef

Plott CR, Sunder S (1988) Rational expectations and the aggregation of diverse information in laboratory security markets. Econometrica 56(5):1085–1118CrossRef

Smith VL (1962) An experimental study of competitive market behavior. J Polit Econ 70(2):111–137CrossRef

Smith VL (1982) Microeconomic systems as an experimental science. Am Econ Rev 72:923–955

Smith VL, Suchanek GL, Williams AW (1988) Bubbles, crashes, and endogenous expectations in experimental spot asset markets. Econometrica 56(5):1119–1151CrossRef

Smith VL, van Boening M, Wellford CP (2000) Dividend timing and behavior in laboratory asset markets. Econ Theory 16(3):567–583

Tversky A, Kahneman D (1974) Judgment under uncertainty: heuristics and biases. Science 185(4157): 1124–1131

Zhang T, Brorsen BW (2011) Oligopoly firms with quantity-price strategic decisions. J Econ Inter Coord 6:157–170CrossRef

Titel: Information dissemination in an experimentally based agent-based stock market
verfasst von: Jakob Grazzini
Publikationsdatum: 01.04.2013
Verlag: Springer-Verlag
Erschienen in: Journal of Economic Interaction and Coordination / Ausgabe 1/2013
Print ISSN: 1860-711X
Elektronische ISSN: 1860-7128
DOI: https://doi.org/10.1007/s11403-013-0109-x

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Wirtschaft"

Weitere Artikel der Ausgabe 1/2013

Agent-based financial markets and New Keynesian macroeconomics: a synthesis

Do price limits hurt the market?

Diffusion of competing innovations in influence networks

Global networks of trade and bits

Modeling the International-Trade Network: a gravity approach

On the distributional properties of size, profit and growth of Icelandic firms