ACE Research Area:
Agent-Based Financial Economics

Last Updated: 16 January 2023

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Table of Contents: Key Issues Rethinking Economic Methodology in the Wake of the Subprime Financial Crisis Agent-Based Financial Economics: Introductory Readings Other Readings Software, Toolkits, and Demos Resource Sites, Groups, and Some Early Individual Researchers

Key Issues

• What makes financial assets special relative to physical assets?
• How should individual and systemic risk be defined and measured for financial markets?
• How should the market mechanisms governing financial asset trading be modelled?
• How should the learning processes of financial traders be represented?
• What drives the co-evolution of financial strategies over time? What causes bubbles and crashes?
• 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?

Rethinking Economic Methodology in the Wake of the Subprime Financial Crisis

Note: Cookie acceptance and journal subscriptions are needed to access some of the journal articles posted in this section.

• Andrew G. Haldane, The Dappled World (pdf,2.3MB), GLS Shackle Biennial Memorial Lecture, Bank of England, 10 November 2016. An animated version of this lecture is also available.

  Abstract: Although (the recent) crisis in economics is a threat for some, for others it is an opportunity -- an opportunity to make a great leap forward, as Keynes did in the 1930s. For the students in this room, there is the chance to rethink economics with as clean a sheet of paper as you are ever likely to find. That is perhaps why the numbers of students applying to study economics has shot up over recent years. This is one of the silver linings of the crisis. No discipline could ask for a better endowment. But seizing this opportunity requires a re-examination of the contours of economics and an exploration of some new pathways. That is what I wish to do in this lecture..."

• Building a Science of Economics for the Real World, Committee on Science and Technology, U.S. House of Representatives, July 20, 2010, Congressional Testimony given by Brad Miller, Robert Solow, Sidney Winter, Scott Page, David Colander, and V.V. Chari

• Richard Bookstaber, "Using Agent-Based Models for Analyzing Threats to Financial Stability" (pdf,4MB), Office of Financial Research, U.S. Department of the Treasury, Working Paper No. 0003, December 21, 2012.

• Richard Bookstaber, The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction (Chapter 1, pdf, 909KB), Princeton University Press, Princeton:NJ, 2017

• Mark Buchanan's The Physics of Finance is a blog launched in May 2011 that looks at finance through the lens of physics in the wake of the subprime financial crisis. The new perspective advocated by Buchanan "emphasizes the myriad positive feed backs which often drive markets away from equilibrium and cause tumultuous crashes and other crises."

• Jean-Philippe Bouchaud, "Economics Needs a Scientific Revolution" (publisher site), Nature, Vol. 455, 2009, p. 1181.

• Jean-Philippe Bouchaud, "The (Unfortunate) Complexity of the Economy" (pdf,100KB), Physics World, April 2009.

• Mark Buchanan, "Meltdown Modeling: Could Agent-Based Computer Models Prevent Another Financial Crisis?" (pdf,1.2KB), Nature, Vol. 460, August 6, 2009, 680-682.

• William Buiter, "The Unfortunate Uselessness of Most `State of the Art' Academic Monetary Economics" (pdf,154KB), Financial Times, March 3, 2009.

• Ricardo J. Caballero, "Macroeconomics After the Crisis: Time to Deal with the Pretense-of-Knowledge Syndrome" (pdf,563KB), Journal of Economic Perspectives, Vol. 24, No. 4, Fall 2010, 85-102.

• David Colander, Hans Föllmer, Armin Haas, Michael Goldberg, Katarina Juselius, Alan Kirman, Thomas Lux, and Brigitte Sloth, "The Financial Crisis and the Systemic Failure of Academic Economics (Dahlem Report)" (pdf,163KB), Kiel Working Paper No. 1489, February 2009.

• David Colander, Michael Goldberg, Armin Haas, Katarina Juselius, Alan Kirman, Thomas Lux and Brigitte Sloth, The Financial Crisis and the Systemic Failure of the Economics Profession (pdf,107KB), Critical Review, Volume 21, Issues 2-3, 2009, 249-267.

• Paul De Grauwe, "Top-Down Versus Bottom-Up Macroeconomics" (pdf,371KB), CESifo Conference, Munich, November 6-7, 2009.

• J. Doyne Farmer and John Geanakoplos, "The Virtues and Vices of Equilibrium and the Future of Financial Economics" (publisher site), Complexity, Vol. 14, 2009, 11-38.

• J. Doyne Farmer and Duncan Foley, "The Economy Needs Agent-Based Modelling" (pdf,922KB), Nature, Vol. 460, August 6, 2009, 685-686.

• Jean-Pierre Fouque and Joseph A. Langsam (eds.), Handbook on Systemic Risk (Google Books site), Cambridge University Press, 2013.

  Roman Frydman and Michael D. Goldberg, "An Economics of Magical Thinking" (pdf,51KB), Financial Times, September 23, 2009.

• Mauro Gallegati and Matteo G. Richiardi, "Agent Based Models in Economics and Complexity" (publisher site), pp. 30-53 in Robert A. Myers (Ed.), Complex Systems in Finance and Econometrics, Springer, 2011.

• Michal Gradzewicz, Krzysztof Makarski, and Joanna Tyrowicz, "Do we Really Need to Start from Scratch?" (pdf,163KB), Working Paper, Narodowy Bank Polski, 2010.

• Andrew G. Haldane and Robert M. May, "Systemic Risk in Banking Ecosystems" (publisher site), Nature, Vol. 469, 2011, 351-355.

• Andrew G. Haldane, "Rethinking the Financial Network" (pdf,499KB), Executive Director, Bank of England, Speech delivered at the Financial Student Association, Amsterdam, April 2009.

• Paul Jenkins, "Into the Unknown: Reflections on Risk, Uncertainty, and Monetary Policy Decision-Making" (pdf,566KB), C.D. Howe Institute, Commentary No. 549, July 2019.

• Neil Johnson and Thomas Lux, "Financial Systems: Ecology and Economics" (publisher site), Nature, Vol. 469, 2011, 302-303.

• Alan Kirman, "The Economic Crisis is a Crisis for Economic Theory" (pdf,149KB), CESifo Economic Studies, Vol. 56, No. 5, 2010, 498-535.

• Narayana Kocherlakota, "Some Thoughts on the State of Macro" (pdf,23KB), University of Minnesota and the Federal Reserve Bank of Minneapolis, 2009.

• Paul Krugman, "How Did Economists Get It So Wrong?" (publisher site), The New York Times Magazine, September 2, 2009. See also Robert Lucas, Jr., "In Defense of the Dismal Science: Reply to Krugman" (pdf,41KB), Economist, August 6, 2009.

• Andrew Lo, Adaptive Markets: Financial Evolution at the Speed of Thought, Princeton University Press, NJ, 2017.

  Abstract: "(Andrew) Lo's new paradigm explains how financial evolution shapes behavior and markets at the speed of thought--a fact revealed by swings between stability and crisis, profit and loss, and innovation and regulation. A fascinating intellectual journey filled with compelling stories, Adaptive Markets starts with the origins of market efficiency and its failures, turns to the foundations of investor behavior, and concludes with practical implications--including how hedge funds have become the Galápagos Islands of finance, what really happened in the 2008 meltdown, and how we might avoid future crises."

• Thomas Lux and Frank Westerhoff, "Economics Crisis" (publisher site), Nature Physics, Vol. 5, Nos. 2-3, 2009.

• Robert M. May, Simon A. Levin and George Sugihara, "Complex Systems: Ecology for Bankers" (publisher site), Nature, Vol. 451, 2008, 893-895.

• Exercepts from Section 3 of a speech by Jean-Claude Trichet, President of the European Central Bank, on November 18, 2010, titled "Reflections on the Nature of Monetary Policy Non-Standard Measures and Finance Theory." Available online here.

  "When the crisis came, the serious limitation of existing economic and financial models immediately became apparent. Arbitrage broke down ... markets froze ... market participants were gripped by panic. Macro models failed to predict the crisis and ... [to explain] what was happening ..."

  "[In] the face of crisis, we felt abandoned by conventional tools. ... The key lesson ... is the danger of relying on a single tool, methodology or paradigm. The atomistic, optimising agents underlying existing models do not capture behavior during a crisis period. Agent-based modelling ... allows for more complex interactions between agents. ... we need to better integrate the crucial role played by the financial system into our macroscopic models."

  "I would very much welcome inspiration from other disciplines: physics, engineering, psychology, biology. Bringing experts from these fields together with economists and central bankers is potentially very ... valuable."

  "A large number of aspects of the observed behaviour of financial markets is hard to reconcile with the efficient market hypothesis... But a determinedly empirical approach -- which places a premium on inductive reasoning based on the data, rather than deductive reasoning grounded in abstract premises or assumptions -- lies at the heart of these methods ... simulations will play a helpful role."

Agent-Based Financial Economics: Introductory Readings

• W. Brian Arthur, "Complexity in Economic and Financial Markets," Complexity, Vol. 1, No. 1, 1995, pp. 20-25.

  Abstract: This paper provides, among other things, a brief summary of the Santa Fe Artificial Stock Market (SF-ASMB) model developed by Arthur et al. (1997) -- see "Other Readings," 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,341KB), Working Paper, Erasmus University, Rotterdam, 2002.

• Mark Buchanan, Forecast: What Physics, Meteorology, and the Natural Sciences Can Teach Us About Economics (Book Site), Bloomsbury Publishing, London, 2013.

  Abstract: "In this thoroughly researched and piercingly intelligent book, physicist Mark Buchanan shows how a simple feedback loop can lead to major consequences, the kind predictable by mathematical models but hard for most people to anticipate. From his unique perspective, Buchanan argues that our basic assumptions about economic markets - that they are for the most part stable, with occasional interruptions - are simply wrong. Markets really act more like weather: a brief heat wave can become a massive storm in a matter of a few days, or even hours. Forecast re-imagines the basics of the financial world, with consequences that affect everyone."

• J. Doyne Farmer and Andrew W. Lo, "Frontiers of Finance: Evolution and Efficient Markets" (pdf,105KB), 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.

• Catherine Kyrtsou and Didier Sornette (guest editors), "New Facets of Economic Complexity in Modern Financial Markets" (html), The European Journal of Finance, Routledge, November 2012, 1-7. DOI: 10.1080/1351847X.2012.723282

  Abstract: "The present volume is clearly motivated by the disruptions introduced by the financial crisis and the many attempts that have followed to propose new ideas and remedies. It assembles contributions by authors from a variety of backgrounds, illustrating the potentials resulting from the marriage of financial economics, complexity theory and an out-of-equilibrium view of the economic world."

• 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,150KB), 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(2), 2002. Issue articles available at Science Direct.

• Leigh Tesfatsion, "Introduction to Financial Markets" (html,45KB).

• Leigh Tesfatsion, "Information, Bubbles, and the Efficient Markets Hypothesis" (html,35KB).

• Edward P. K. Tsang and Serafin Martinez-Jaramillo, Computational Finance (pdf,310KB), Feature Article, IEEE Computational Intelligence Society, August 2004.

  Abstract: This paper briefly outlines the scope and agenda of computational finance research.

• Michael P. Wellman and Elaine Way, "Strategic Agent-Based Modeling of Financial Markets" (pdf,1.1MB), The Russell Sage Foundation Journal of the Social Sciences 3(1), 2017, 104–119.

  Abstract: Understanding the implications of algorithmic trading calls for modeling financial markets at a level of fidelity that often precludes analytic solution. We describe how agent-based simulation modeling can be combined with game-theoretic reasoning to examine the effects of market variables on outcomes of interest. The approach is illustrated in a basic model where investors trade a single security through a continuous double auction mechanism. Our results demonstrate the feasibility of the approach, and raise questions about the use of spreads as a proxy for trading cost and welfare.

Other Readings

• George Akerlof and Robert J. Shiller, Animal Spirits: How Human Psychology Drives the Economy, and Why It Matters for Global Capitalism, Princeton University Press, 2009.

  Note: A short summary of the theme of this book, which provides the essence of George Akerlof's 2009 T. W. Schultz Memorial Address at the 2009 ASSA meeting in San Francisco in January 2009, can be obtained here (pdf,21KB).

• Luca Arciero, Claudia Biancotti, Leandro D'Aurizio and Claudio Impenna, Exploring Agent-Based Methods for the Analysis of Payment Systems: A Crisis Model for StarLogo TNG (html), Journal of Artificial Societies and Social Simulation 12(1)2, 2009.

  Abstract: "This paper presents an exploratory agent-based model of a real time gross settlement (RTGS) payment system. Banks are represented as agents who exchange payment requests, which are then settled according to a set of simple rules. The model features the main elements of a real-life system, including a central bank acting as liquidity provider, and a simplified money market. A simulation exercise using synthetic data of BI-REL (the Italian RTGS) predicts the macroscopic impact of a disruptive event on the flow of interbank payments. In our reduced-scale system, three hypothetical distinct phases emerge after the disruptive event: 1) a liquidity sink effect is generated and the participants' liquidity expectations turn out to be excessive; 2) an illusory thickening of the money market follows, along with increased payment delays; and, finally 3) defaulted obligations dramatically rise. The banks cannot staunch the losses accruing on defaults, even after they become fully aware of the critical event, and a scenario emerges in which it might be necessary for the central bank to step in as liquidity provider."

• W. Brian Arthur, John H. Holland, Blake LeBaron, Richard Palmer, and Paul Tayler, "Asset Pricing under Endogenous Expectations in an Artificial Stock Market" (pdf,120KB), 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.

• Rafa Baptista, J. Doyne Farmer, Marc Hinterschweiger, Katie Low, Daniel Tang, and Arz Uluc, (pdf,978KB), Staff Working Paper No. 619, Bank of England, October 2016.

  Abstract: "This paper develops an agent-based model of the UK housing market to study the impact of macroprudential policies on key housing market indicators. This approach enables us to tackle the heterogeneity in this market by modelling the individual behaviour and interactions of first-time buyers, home owners, buy-to-let investors, and renters from the bottom up, and observe the resulting aggregate dynamics in the property and credit markets. The model is calibrated using a large selection of micro-data, mostly from household surveys and housing market data sources. We perform a series of comparative statics exercises to investigate the impact of the size of the rental/buy-to-let sector and different types of buy-to-let investors on housing booms and busts. The results suggest that an increase in the size of the buy-to-let sector may amplify house price cycles and increase house price volatility."

• Karen Braun-Munzinger, Zijun Liu, and Arthur Turrell, An Agent-Based Model of Dynamics in Corporate Bond Trading (pdf,801KB), Staff Working Paper No. 592, Bank of England, April 2016.

  Abstract: "We construct a heterogeneous agent-based model of the corporate bond market capturing the interaction of market maker behaviour, fund trading strategies, and cash allocation by investors in funds to study feedback effects and the impact of market changes. The model parameters are calibrated against empirical data on US corporate bond trading. Where available, inputs are taken from market data. Others are calibrated through matching statistical features of market returns such as auto-correlations, volatility and fat tails. We use the model to explore the impact of shocks. We find that the sensitivity of the market maker to demand and the degree to which momentum traders are active strongly influence the over and undershooting of yields in response to a shock. This suggests that correlation in funds’ trading strategies can exacerbate extreme price movements and contribute to the procyclicality of financial markets."

• Anirban Chakraborti, Ioane Muni Toke, Marco Patriarca, and Frederic Abergel, "Econophysics: Empirical Facts and Agent-Based Models", (pdf, 3.2MB), Eprint, Arxiv.0909.1974, 2009.

  Abstract: "This article aims at reviewing recent empirical and theoretical developments usually grouped under the term Econophysics. Since its name was coined in 1995 by merging the words “Economics” and “Physics”, this new interdisciplinary field has grown in various directions: theoretical macroeconomics (wealth distributions), microstructure of financial markets (order book modeling), econometrics of financial bubbles and crashes. We give a brief introduction in the first part and begin with discussing interactions between Physics, Mathematics, Economics and Finance that led to the emergence of Econophysics in the second part. Then the third part is dedicated to empirical studies revealing statistical properties of financial time series. ... The fourth part of our review deals with models in Econophysics through the point of view of agent-based modeling. Using previous work originally presented in the fields of behavioural finance and market microstructure theory, econophysicists have developed agent-based models of orderdriven markets that are extensively reviewed here. We then turn to models of wealth distribution where an agent-based approach also prevails: kinetic theory models, and continue with game theory models and review the now classic minority games. We end this review by providing an outlook on possible directions of research."

• 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.

• Germán Creamer, "Model Calibration and Automated Trading Agent for Euro Futures" (pdf,328KB), Quantitative Finance 12(4), 2012, 531-545.

  Abstract: "We explored the application of a machine learning method, Logitboost, to automatically calibrate a trading model using different versions of the same technical analysis indicators. ...We tested this approach with high frequency data of the Dow Jones EURO STOXX 50 Index Futures (FESX) and the DAX Futures (FDAX) for March 2009. Our method was implemented with different learning algorithms and outperformed a combination of the same group of technical analysis indicators using the parameters typically recommended by practitioners. We incorporated this method of model calibration in a trading agent that relies on a layered structure consisting of the machine learning algorithm described above, an online learning utility, a trading strategy, and a risk management overlay. ... The trading agent algorithm generated positive returns for the two major European index futures (FESX and FDAX) and outperformed a buy and hold strategy."

• Vincent Darley and Alexander V. Outkin, Nasdaq Market Simulation: Insights on a Major Market From the Science of Complex Adaptive Systems, New York: World Scientific Publishing, 2007.

• 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.

• Norman Ehrentreich, Agent-Based Modeling: The Santa Fe Institute Artificial Stock Market Model Revisited, Springer-Verlag: Berlin 2008. JASSS review by Blake LeBaron available here.

  Abstract (from LeBaron review): "This book is much more than just an analysis of the SFI market. It is a useful primer for anyone interested in getting started in the area of agent-based finance. The early chapters give the reader a background on agent-based modeling, and learning models in economics. There is also an excellent chapter on some of the important empirical puzzles that are well known from the financial econometrics world. Many of these empirical features have influenced research in agent-based financial modeling, so this chapter is a much needed part of the book."

• 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.

• Prasanna Gai, Andrew Haldane, and Sujit Kapadia, "Complexity, Concentration, and Contagion" (pdf,621KB), Journal of Monetary Economics, Vol. 58, 2011, 453-470.

  Abstract: "This paper develops a network model of interbank lending in which unsecured claims, repo activity and shocks to the haircuts applied to collateral assume centre stage. We show how systemic liquidity crises of the kind associated with the interbank market collapse of 2007-2008 can arise within such a framework, with funding contagion spreading widely through the web of interlinkages. Our model illustrates how greater complexity and concentration in the financial network may amplify this fragility. The analysis suggests how a range of policy measures - including tougher liquidity regulation, macro-prudential policy, and surcharges for systemically important financial institutions - could make the financial system more resilient."

• Matheus R. Grasselli and Omneia R. H. Ismail, "An Agent-Based Computational Model for Bank Formation and Interbank Networks" (pdf,478KB ), Working Paper, Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario L8S 4K1, Canada, October 2, 2011

  Abstract: The authors introduce a simple framework where banks emerge as a response to a natural need in a society of individuals with heterogeneous liquidity preferences. Theye examine bank failures and the conditions for an interbank market is to be established.

• Jens Grossklags, Carsten Schmidt, and Jonathan Siegel, "Dumb Software Agents on an Experimental Asset Market" (pdf,266KB), Working Paper, School of Information and Management Systems, UC Berkeley.

• Dirk Helbing, "Globally Networked Risks and How to Respond", Nature, Vol. 497, 51-59. Note: For more details about this work, see the summary review; or the publisher's homepage.

  Abstract: In this Perspective, the author argues that systemic failures and extreme events are consequences of the highly interconnected systems and networked risks humans have created. When networks are interdependent, this makes them even more vulnerable to abrupt failures. Such interdependencies in our hyper-connected world establish what the author refers to as "hyper-risks."

• Dirk Helbing and Alan Kirman, "Rethinking Economics Using Complexity Theory" (pdf,59KB), Real-World Economic Review, 2013.

  Abstract: "In this paper we argue that if we want to find a more satisfactory approach to tackling the major socio-economic problems we are facing, we need to thoroughly rethink the basic assumptions of macroeconomics and financial theory. Making minor modifications to the standard models to remove “imperfections” is not enough, the whole framework needs to be revisited.

• 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.

• Cars Hommes, "Behavioral Rationality and Heterogeneous Expectations in Complex Economic Systems", Cambridge University Press, January 2013. Introductory materials (including Chapter 1) can be viewed (here,6MB).

  Abstract: "Recognising that the economy is a complex system with boundedly rational interacting agents, the book presents a theory of behavioral rationality and heterogeneous expectations in complex economic systems and confronts the nonlinear dynamic models with empirical stylized facts and laboratory experiments. ... The complexity tools - bifurcations, chaos, multiple equilibria - discussed in this book will help students, researchers and policy makers to build more realistic behavioral models with heterogeneous expectations to describe financial market movements and macro-economic fluctuations, in order to better manage crises in a complex global economy."

• Cars Hommes, The Heterogeneous Expectations Hypothesis: Some Evidence from the Lab, Journal of Economic Dynamics and Control, Volume 35, Issue 1, January 2011, 1-24.

• Paul Johnson, "What I Learned from the Artificial Stock Market" (pdf,107KB), 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" (pdf,506KB), 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.

• Catherine Kyrtsou and Anastasios G. Malliaris, "The Impact of Information Signals on Market Prices When Agents Have Non-Linear Trading Rules" (pdf,1.4MB), Economic Modelling 26 (2009), 167-176.

  Abstract: "Several methods have been developed for filtering seasonal influences and extreme returns in financial and economic time series. The theoretical support for these approaches is rather questionable since it focuses on the effects of shocks on prices and not on their sources. Removing such effects modifies the true generating system of market dynamics because of the non-proportional character of non-linearity. Thus, taking into account that the underlying process of economic time series is highly non-linear we cannot be certain a priori what the impact of new information will be on the dynamic structure of a system. The main contribution of this paper is to demonstrate using the methodology of simulations the eventual distortions in time series data arising from the arrival of news when agents follow non-linear trading strategies. We argue that if news can really modify the dynamical behaviour of a system, then the methodology of filtering exogenous distortions needs to be re-examined."

• Blake LeBaron, "Heterogeneous Gain Learning and the Dynamics of Asset Prices" (pdf,753KB), Working Paper, Brandeis University, June 2010, Revised Dec 2011, forthcoming in the Journal of Economic Behavior and Organization.

  Abstract: "This paper presents a new agent-based financial market. It is designed to be both simple enough to gain insights into the nature and structure of what is going on at both the agent and macro levels, but remain rich enough to allow for many interesting evolutionary experiments. The model is driven by heterogeneous agents who put varying weights on past information as they design portfolio strategies. It faithfully generates many of the common stylized features of asset markets. It also yields some insights into the dynamics of agent strategies and how they yield market instabilities."

• Blake LeBaron, "A Builder's Guide to Agent-Based Financial Markets" (pdf,207KB), Quantitative Finance 1 (2001), 254-261.

• Blake LeBaron, "Building the Santa Fe Artificial Stock Market" (pdf,123KB), 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,710KB), 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,324KB), 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.

• Scott C. Linn and Nicholas S. P. Tay, "Complexity and the Character of Stock Returns: Empirical Evidence and a Model of Asset Prices Based on Complex Investor Learning" (pdf,207KB), Management Science, Vol. 53, No. 7, July 2007, pp. 1165-1180.

  Abstract: "We propose a model of complex, self-referential learning and reasoning amongst economic agents that jointly produces security returns consistent with...general observed facts and which are supported here by empirical results presented for a benchmark sample of 50 stocks traded on the New York Stock Exchange. The market we postulate is populated by traders who reason inductively while compressing information into a few fuzzy notions that they can in turn process and analyze with fuzzy logic. We analyze the implications of such behavior for the returns on risky securities within the context of an artificial stock market model. Dynamic simulation experiments of the market are conducted, from which market-clearing prices emerge, allowing us to then compute realized returns. We test the effects of varying values of the parameters of the model on the character of the simulated returns. The results indicate that the model proposed in this paper can jointly account for the presence of a power-law characterization of the positive tail of the survivor function of returns with exponent on the order of three, for autoregressive conditional heteroskedasticity, for long memory in volatility, and for general nonlinear dependencies in returns."

• Sheri Markose, Simone Giansante, Mateusz Gatkowski, and Ali Rais Shaghaghi, "Too Interconnected To Fail: Financial Contagion Systemic Risk in a Network Model of CDS and Other Credit Enhancement Obligations of US Banks" (pdf,1.2MB), Discussion Paper Series No. 683, Centre for Computational Finance and Economic Agents (CCFEA), Economics Department, University of Essex, UK, February 2010.

  Abstract (paraphrased from the authors' executive summary): The authors briefly review the Agent-based Computational Economics (ACE) approach to financial network modeling for systemic risk assessment. They then develop an empirically-based ACE modeling of the US credit default swap (CDS) network based on FDIC Q4 2008 data. This model is used to conduct a series of stress tests to investigate the implications of the fact that 92% of 2008 US CDS trading activity was concentrated among the top five US banks. The authors make use of the May-Wigner network stability condition to understand the lack of robustness of this US CDS trade network structure. They also provide a Systemic Risk Ratio and an implementation of concentration risk in CDS settlement for major US banks in terms of the loss of aggregate core capital. The authors compare their stress test results with those provided by the Supervisory Capital Assessment Program (SCAP). Based on their stress-test findings, the authors recommend the discontinuance of the Basel II provision permitting banks to reduce their capital requirements for assets with CDS guarantees. For related work by the authors, visit here.

• Sheri M. Markose, "Systemic Risk from Global Financial Derivatives: A Network Analysis of Contagion and Its Mitigation with Super-Spreader Tax" (pdf,2.4MB), IMF Working Paper No. 12/282, November 2012.

  Abstract: "Financial network analysis is used to provide firm level bottom-up holistic visualizations of interconnections of financial obligations in global OTC derivatives markets. This helps to identify Systemically Important Financial Intermediaries (SIFIs), analyse the nature of contagion propagation, and also monitor and design ways of increasing robustness in the network."

• 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,69KB)

• Leigh Tesfatsion, "Detailed Notes on the Santa Fe Artificial Stock Market Model" (html,47KB).

• Stefan Thurner, J. Doyne Farmer, and John Geanakoplos, "Leverage Causes Fat Tails and Clustered Volatility" (pdf,1.3MB), Santa Fe Institute Working Paper #09-08-031, August, 2009.

  Abstract: "We study a very simple model of leveraged asset purchases with margin calls. Investment funds use what is perhaps the most basic nancial strategy, called `value investing,' i.e. systematically attempting to buy underpriced assets. When funds do not borrow, the price uctuations of the asset are normally distributed and uncorrelated across time. All this changes when the funds are allowed to leverage, i.e. borrow from a bank, to purchase more assets than their wealth would otherwise permit. When funds use leverage, price fluctuations become heavy tailed and display clustered volatility, similar to what is observed in real markets. Previous explanations of fat tails and clustered volatility depended on `irrational behavior', such as trend following. We show that the immediate cause of the increase in extreme risks in our model is the risk control policy of the banks."

• 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.

• Li Xihao, "Auction Market System in Electronic Security Trading Platform" (pdf,595KB), Discussion Paper, Bielefeld Graduate School of Economics and Management, May 2010.

  Abstract: "Under the background of the electronic security trading platform Xetra operated by Frankfurt Stock Exchange, we consider the Xetra auction market system (XAMS) where the interactions among heterogenous traders and Xetra auction market mechanism generate the non market-clearing price dynamics. First we develop an integrative framework which serves as general guidance to analyze the economic system from `bottom-up' and to construct the corresponding agentbased model. Then we apply this integrative framework to construct the agent-based model of XAMS. By conducting market experiments with the computer implementation of the agent-based model of XAMS, we investigate the role of the price setter who assumes its trading behavior can manipulate the market price. The main finding of the market experiment is that the introduction of the price setter in the setting of XAMS improves market efficiency while does not significantly influence price volatility of the market."

Software, Toolkits, and Demos

• SimStockExchange: Multi-Agent Simulation Stock Market Model (Java, Microsoft Windows Only) (homepage)

• Santa Fe Stock Market Demonstration Software (homepage)

  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).

• Annotated list of pointers to ACE/CAS General Software and Toolkits

• Annotated list of pointers to ACE/CAS Computational Laboratories and Demonstration Software

• 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.

• The Adaptive Modeler, developed by Jim Witkam (Altreva, Inc.), creates agent-based market simulation models for price forecasting of real world stocks, currencies or other market traded securities. The agent-based model simulates a financial market consisting of thousands of agents whose (technical) trading rules evolve through a special adaptive form of genetic programming. The evolution of trading rules combined with market pricing dynamics drives the agent population to learn to recognize and anticipate recurring price patterns while adapting to changing market behavior. Forecasts can be based on either the behavior of all agents or on a dynamic group of the best performing agents. For ACE researchers this application may be of interest to study the behavior and emergent predictive abilities of an agent-based market model that includes information from a real-world market. Several model initialization options are included such as a user configurable genetic programming engine for the creation of trading rules. Simulation of zero intelligence trading is also supported. Various population statistics and other data can be visualized in charts, distribution histograms and scatter plots, all in real-time. Data can be exported to CSV files for further analysis in other applications. A free (non-expiring) evaluation version with extensive documentation can be downloaded from the Adaptive Modeler homepage, above. Adaptive Modeler is targeted for Windows platforms and requires an installation of Microsoft .Net 2.0 or higher.

Resource Sites, Groups, and Some Early Individual Researchers

• Financial Markets and Institutions: Web Resources

• General Resources for Macroeconomics and Financial Economics

• The European Union is supporting a new project that will involve the development of two coupled agent-based models of the European economy, one for the financial system and one for the macroeconomy. As project activities proceed, information about these activities will be posted at the following project homepage: Complexity Research Initiative for Systemic Instabilities.

• Intensive Course in Financial Instability and Crises, The Ninth Trento Summer School, sponsored by the CEEL Program in Adaptive Economic Dynamics, June/July 2008.

• The CCFEA (Centre for Computational Finance and Economic Agents, University of Essex, UKB) 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. For more information, visit here.

• In 2009 the Centre for Computational Finance and Economic Agents (CCFEA) at the University of Essex, UK, launched a website titled Agent-based Computational Economics and Financial Modeling (ACEFINMOD) The ACEFINMOD website showcases work by CCFEA researchers and their collaborators. This work reflects a complex adaptive systems perspective. The methodolgy of Agent-based Computational Economics (ACE) is used to build large-scale empirically grounded financial and economic models for purposes of market and policy design.

• Shu-Heng Chen (Economics, National Chengchi University, Taipei, Taiwan): Agent-based computational economic modeling and policy design; Computational and experimental economics; Agent-based artificial markets; Computational intelligence; Genetic programming models of learning; Financial market modeling; Agent-based econometrics.

• 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, OKB): 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 Bamberg, Germany): Financial market dynamics; Technical and fundamental trading rules; Nonlinear dynamics and chaos; Bounded rationality and behavioral finance; Regulation/design of financial markets.

Copyright © Leigh Tesfatsion. All Rights Reserved.