How should the market mechanisms governing financial asset trading be
modelled?
How should the learning processes of financial traders be represented?
How should the "fitness" of financial traders be measured? What drives
the co-evolution of these fitnesses over time?
Do sophisticated (informed) traders necessarily drive "noise traders"
from financial markets?
What are the basic "empirical stylized facts" for financial markets? How
well are these stylized facts captured by standard financial models?
By agent-based financial models?
W. Brian Arthur, "Complexity in Economic and Financial
Markets,"Complexity, Volume 1, Number 1, 1995, pp. 20-25.
Provides, among other things, a brief summary of the Santa
Fe Artificial Stock Market model by Arthur et al. (1997), below.
W. M. van den Bergh, K. Boer, A. de Bruin, U. Kaymak, and J. Spronk,
"On Intelligent Agent-Based Analysis of Financial Markets"(pdf,341K),
Working Paper, Erasmus University, Rotterdam, 2002.
J. Doyne Farmer and Andrew W. Lo,
"Frontiers of Finance: Evolution and Efficient Markets"(pdf,7pp,105K),
SFI Working Paper, April 1999.
Cars Hommes,
"Heterogeneous Agent Models in Economics and Finance",
in Leigh Tesfatsion and Kenneth L. Judd (editors),
Handbook of Computational Economics, Vol. 2: Agent-Based Computational
Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam,
Spring 2006.
Abstract:
This chapter surveys work on dynamic heterogeneous agent models
(HAMs) in economics and finance. Emphasis is given to simple models
that, at least to some extent, are tractable by analytic methods in
combination with computational tools. Most of these models are
behavioral models with boundedly rational agents using different
heuristics or rule of thumb strategies that may not be perfect, but
perform reasonably well. Typically these models are highly
nonlinear, e.g. due to evolutionary switching between strategies,
and exhibit a wide range of dynamical behavior ranging from a unique
stable steady state to complex, chaotic dynamics. Aggregation of
simple interactions at the micro level may generate sophisticated
structure at the macro level. Simple HAMs can explain important
observed stylized facts in financial time series, such as excess
volatility, high trading volume, temporary bubbles and trend
following, sudden crashes and mean reversion, clustered volatility
and fat tails in the returns distribution.
Blake LeBaron,
"Agent-Based Computational Finance",
in Leigh Tesfatsion and Kenneth L. Judd (editors),
Handbook of Computational Economics, Vol. 2: Agent-Based Computational
Economics, Handbooks in Economics Series, North-Holland/Elsevier, Amsterdam,
Spring 2006.
Abstract:
This chapter surveys research on agent-based models used in
finance. It concentrates on models where the use of computational
tools is critical for the process of crafting models that give
insights into the importance and dynamics of investor heterogeneity
in many financial settings.
Blake LeBaron, "Agent-Based Computational Finance: Suggested Readings
and Early Research"(pdf,150K),
Journal of Economic Dynamics and Control 24:5-7 (2000), 679-702.
Published article available at
Science Direct.
T. Lux and M. Marchesi (guest editors), Special Issue on
"Heterogeneous Interacting Agents in Financial Markets,"Journal of
Economic Behavior and Organization 49, No. 1, in press.
Article available at
Science Direct.
Leigh Tesfatsion,
"Introduction to Financial Markets"(html).
Leigh Tesfatsion,
"Information, Bubbles, and the Efficient Markets Hypothesis"(html).
Edward P. K. Tsang and Serafin Martinez-Jaramillo, Computational
Finance(pdf,310K,6pp),
Feature Article, IEEE Computational Intelligence Society, August 2004.
Abstract: This paper briefly outlines the scope and agenda of computational
finance research.
W. Brian Arthur, John H. Holland, Blake LeBaron, Richard Palmer, and Paul
Tayler,
"Asset Pricing under Endogenous Expectations in an Artificial Stock
Market"(pdf,120K),
December 1996 preprint. Final version published as pages 15-44 in
W. Brian Arthur, Steven N. Durlauf, and David A. Lane, The Economy as an
Evolving Complex System II, Santa Fe Institute Studies in the Sciences of
Complexity, Vol. XXVII, Addison-Wesley, 1997.
Abstract: This paper develops the Santa Fe Artificial Stock Market Model.
David F. Batten, "Coevolving Markets" (Chapter 7, pages
209-245), in Discovering Artificial Economics, Westview Press, 2000.
S.-H. Chen and C.-H. Yeh, "Evolving Traders and the Business School
with Genetic Programming: A New Architecture of the Agent-Based Stock
Market,"Journal of Economic Dynamics and Control 25 (3-4), March
2001, pages 363-393.
Article available at
Science Direct.
S.-H. Chen, T. Lux, and M. Marchesi, "Testing for Nonlinear
Structure in an Artificial Financial Market,"Journal of Economic
Behavior and Organization 46 (2001), 327-342.
Article available at
Science Direct.
John Duffy, "Learning to Speculate: Experiments with Artificial and
Real Agents,"Journal of Economic Dynamics and Control 25(3/4),
March 2001, pages 295-319. Article available at
Science Direct.
Abstract: This paper employs parallel experiments with real and computational
agents to explore issues originally raised by Kiyotaki and Wright in their
well-known search model of money (JPE, 1989). The primary issue of interest
is how individuals might come to accept or learn to adopt a convention in
which the particular commodity functioning as "money" is dominated in rate of
return by other assets, in the sense that it has a higher storage cost. The
key offsetting factor is anticipations ("speculation") concerning the ease
with which the "money" good can be turned over in trade for other goods that
agents have a higher desire to consume. The author shows how each type of
experiment can contribute to the experimental design and interpretation of
results for the other.
J. Doyne Farmer and John Geanakoplos (eds.), Beyond Equilibrium and
Efficiency, 352pp., Oxford University Press, 2005. ISBN: 0-195-15094-5
Abstract: From the publisher: "This book presents recent thought on market
efficiency, using a complex systems approach to move past equilibrium models
and quantify the actual efficiency of markets. The older view that markets
are perfectly efficient has come under attack from several different
directions, including studies of market anomalies, human psychology, bounded
rationality, agent-based modeling, and evolutionary game theory. This volume
brings together some of the best economists, physicists, and biologists
working on quantitative models of complex self-organized behavior relevant to
measuring market efficiency, to stimulate new approaches to understanding
financial markets."
J. Doyne Farmer is McKinsey Professor at the Santa Fe Institute, and
John Geanakoplos is Professor of Economics at Yale University.
Jens Grossklags, Carsten Schmidt, and Jonathan Siegel, "Dumb Software
Agents on an Experimental Asset Market"
(pdf,22pp.),
Working Paper, School of Information and Management Systems, UC Berkeley.
Arvid O. I. Hoffmann, Wander Jager, and J. Henk Von Eije, Social Simulation of Stock Markets: Taking It to the Next Level(html),
Journal of Artificial Societies and Social Simulation, Vol. 10, No. 2,7, March 31, 2007.
Abstract: This paper studies the use of social simulation in linking micro level investor behaviour and macro level stock market dynamics. Empirical data from a survey on individual investors' decision-making and social interaction were used to formalize the trading and interaction rules of the agents of an artificial stock market called SimStock Exchange (SSE). Multiple simulation runs were performed with this artificial stock market, which generated macro level results such as stock market prices and returns over time. These outcomes were subsequently compared to empirical macro level data from real stock markets. Partial qualitative and quantitative agreement between the simulated asset returns distributions and the asset returns distributions of the real stock markets were found.
Two versions of the SSE are available for downloading from the above site, together with a manual: (1) a stand-alone executable version for Windows operating systems; and (2) a Java archive for MacOS and Linux operating systems.
Paul Johnson, "What I Learned from the Artificial Stock Market"(pdf,20pp,107K),
Working Paper, Department of Political Science, University of Kansas,
November 5, 2001.
Abstract: This essay describes some of the changes that
were incorporated in the ASM-2.2 revision of the code for the Santa Fe
Artificial Stock Market model.
It also presents some important lessons for agent-based modelers that can be
illustrated with the code.
Deddy P. Koesrindartoto, "Treasury Auctions, Uniform or
Discriminatory?: An Agent-Based Approach"(html),
Economics Working Paper No. 04013, Department of Economics, Iowa State
University, July 2004.
Abstract: This study develops an agent-based computational
economics (ACE) framework to explore experimentally how a Treasury should
auction its securities. Buyers are modeled as profit seekers capable of
submitting strategic bids via reinforcement learning. Two distinct auction
pricing rules are considered, uniform and discriminatory. The author shows
that these two rules result in systematically different auction outcomes
under different treatment conditions for relative capacity and for price
volatility in a secondary security market. In particular, which auction
pricing rule generates greater Treasury revenues varies systematically with
these treatment factor specifications. These findings help to explain why
previous Treasury auction studies attempting to determine "the" best Treasury
auction pricing rule have reached contradictory conclusions.
Blake LeBaron,
"A Builder's Guide to Agent-Based Financial Markets"(pdf,207K),
Quantitative Finance 1 (2001), 254-261.
Blake LeBaron, "Building the Santa Fe Artificial Stock
Market"(pdf,123K),
Working Paper, Brandeis University, June 2002.
Abstract: LeBaron provides an insider's look at the construction of the Santa
Fe Artificial Stock Market model. He considers the many design questions
that went into building the model from the perspective of a decade of
experience with agent-based financial markets. He also provides an
assessment of the model's overall strengths and weaknesses.
Blake LeBaron,
"Calibrating an Agent-Based Financial Market"(pdf,44pp),
Graduate School of International Economics and Finance, Working Paper,
Brandeis University, Revised March 2003.
Abstract: This paper develops an agent-based computational stock market with
market participants who adapt and evolve their behaviors over time. The
market model is calibrated to match the variability and growth of dividend
payments in U.S. data. The market model generates some features that are
remarkably similar to those from actual U.S. data, including the volatility
of the dividend process, the persistence in volatility and volume, and
fat-tailed return distributions.
Blake LeBaron, W. Brian Arthur, and Richard Palmer,
"Time Series Properties of an
Artificial Stock Market Model"(pdf,324K),
Journal of Economic Dynamics and Control 23 (1999), 1487-1516.
Abstract: This paper privides a rigorous technical discussion of the Santa Fe Artificial
Stock Market Model, including implementation details. Anyone interested
in the actual implementation of this model should consult this paper in
addition to Arthur et al. (1997) cited above; see also the overview and detailed notes
on this model by Tesfatsion below.
T. H. Noe, M. J. Rebello, and J. Wang, "Corporate Financing: An
Agent-Based Analysis,"Journal of Finance, Vol. 58, 943-973, June
2003.
Nicholas S. P. Tay and Scott C. Linn, "Fuzzy Inductive Reasoning,
Expectation Formation, and the Behavior of Security Prices,"Journal
of Economic Dynamics and Control 25, March 2001, pages 321-361.
Article available at
Science Direct.
Leigh Tesfatsion, The Santa Fe Artificial Stock Market: Overview(pdf,69K)
Leigh Tesfatsion,
"Detailed Notes on the Santa Fe Artificial Stock Market Model"(html).
Frank Westerhoff, "Speculative Markets and the Effectiveness of Price
Limits", Journal of Economic Dynamics and Control 28 (2003),
493-508.
Article available at
Science Direct.
Santa Fe Stock Market Demonstration Software(html,46K)
The Santa Fe Institute (SFI) has open-sourced the Santa Fe Artificial
Stock Market (ASM) simulation model, originally developed by a number of SFI
researchers in Objective C using the Swarm toolkit. The
latest ASM-Swarm file releases can be downloaded from the above
ASM sourceforge page and repository maintained by Paul E. Johnson (Political Science, University of Kansas, Lawrence).
Blake LeBaron (Brandeis University) maintains list of pointers to
Interactive Finance Sites
that permit users to enter their own information, test hypotheses, watch
actual data move across the screen, and perform many other interactive
functions.
Resource Sites, Groups, and Individual Researchers
The
CCFEA (Centre for Computational Finance and Economic Agents, University of Essex, UK)
is an interdisciplinary research centre. Fields of research include computational finance, evolutionary methods for finance and economics, simulations of artificially intelligent agents in markets, market and policy design. CCFEA offers programmes leading to MSc and PhD in Computational Finance, Computational Economics and in Financial Software Engineering. Selected course materials can be found
here.
Shu-Heng Chen
(Economics, National Chengchi University, Taipei, Taiwan): Incorporating
learning into economic models; Genetic programming models of learning;
Financial market modelling; Auctions.
J. Doyne Farmer
(Santa Fe Institute, New Mexico): Evolution and the efficiency of financial
markets; Finance.
Christophre Georges
(Economics, Hamilton College, Clinton, NY): Learning and agent interactions in
macroeconomics and finance; Disequilibrium dynamics.
Cars Hommes
(Center for Nonlinear Dynamics in Economics and Finance - CeNDEF, University
of Amsterdam, The Netherlands): Complex adaptive systems; Multi-agent
systems; Evolutionary dynamics; Expectations and learning; Bounded
rationality; Bifurcations and chaos.
Blake LeBaron
(Graduate School of International Economics and Finance, Brandeis University,
Waltham, Massachusetts): Quantitative dynamics of interacting systems of
adaptive agents, and how these systems replicate real world phenomena;
Behavior of traders in financial markets; Nonlinear behavior of financial and
macroeconomic time series. LeBaron maintains a Web site on
Agent-Based Computational Finance (ACF).
ACF is an application of agent-based computational methods to finance and
financial markets. The Web site is designed to give researchers interested
in this area a starting point in terms of finding relevant online materials.
Resources incorporated to date include pointers and paper lists. LeBaron
also maintains a list of pointers to
Interactive Finance Sites
that permit users to enter their own information, test hypotheses, watch
actual data move across the screen, and perform many other interactive
functions.
Scott C. Linn
(Division of Finance, University of Oklahoma, Norman, OK): Corporate finance;
Corporate governance; Behaviors of security prices and energy markets;
Artificial stock market modeling with fuzzy inductive reasoning.
Thomas Lux
(Department of Economics, University of Kiel, Germany): Agent-based
computational finance; Stock market dynamics; Speculative bubbles.
Frank H. Westerhoff
(Department of Economics, University of Osnabrueck, Germany):
Financial market dynamics; Technical and fundamental trading rules; Nonlinear
dynamics and chaos; Bounded rationality and behavioral finance;
Regulation/design of financial markets.