2004-12-16 Experiments in Economic Sciences 1
Charting The Market:Fundamental and Chartist Strategies in a Participatory
Stock Market Experiment László Gulyás ([email protected])MTA SZTAKI & AITIA, Inc., Hungary
Balázs Adamcsek ([email protected])AITIA, Inc & Loránd Eötvös University, Hungary
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Overview
The Problem Artifactual System: Stock Market Emergent Coordination: Fundamental versus Technical Trading
The Method The Social Sciences and the Scientific Method Agent-Based and Participatory Simulation Co-Creative Decision Making: Humans and Bounded Rational Agents
The Tools RePast and GPPAR The Multi-Agent Simulation Suite (MASS)
The Model The Participatory Santa Fe Institute Artificial Stock Market
The Results From Technical to Fundamental Trading? And vice versa…
Summary and Outlook
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The Problem
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Coordination in Stock Markets Stock Market: most famous Artifactual System
Distributed decision-making and emergent coordination. Co-Creation: Humans and Programmed entities. Bounded rational actors (humans & programs).
Dichotomy: Theory versus Practice Fundamental versus Technical Trading
Evolution of Automated Rules (in Agents) Do we also need ‘fundamental’ information?
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The Method
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Social Sciences and the Scientific Method “No proof, but arguments.” “The social sciences are the hard sciences.”
(Herbert Simon, Nobel laurate)
Need for Controlled experiments, and replication.
Methodological answer Experimental Economics, and Computational Methods – i.e., Simulation.
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Agent-Based and Participatory Simulation Agent-Based Simulation
Bottom-up approach Emergence.
Models the individual with its idiosyncrasies, and The agents’ cognitive limitations
Bounded rationality, information access. Explicit representation of the interaction networks.
Where the information comes from and where it goes.
Participatory Simulation Co-creative decision making. Human subjects control a number of agents. Artificial and human agents are indistinguishable.
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The Tools
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Tools for Agent-Based and Participatory Simulation ABM Tools:
Swarm, RePast, MASON
ABM tools for participatory simulation RePast + GPPAR The MASS (with MAC)
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The Model
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The Santa Fe Institute Artificial Stock Market (1/3) “Asset Pricing Under Endogenous Expectations
in an Artificial Stock Market” (Arthur-Holland-LeBaron-Palmer-Tayler, in The Economy as an Evolving Complex System II, Addison-Wesley, 1997)
A minimalist model of two assets: “Money”: fixed, risk-free, infinite supply, fixed interest. “Stock”: unknown, risky behavior, finite supply, varying dividend.
Artificial traders Developing trading strategies. In an attempt to maximize their wealth.
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The Santa Fe Institute Artificial Stock Market (2/3) Trading rules of the agents
Actions (buy, sell, hold) based on market indicators: Fundamental and Technical Indicators
Price > Fundamental Value, or Price < 100-period Moving Average, etc.
Reinforced if their ‘advice’ would have yielded profit. A classifier system.
A Genetic algorithm Activated in random intervals (individually for each agent). Replaces 10-20% of weakest the rules.
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The Santa Fe Institute Artificial Stock Market (3/3) Two behavioral regimes (depending on learning speed).
One (Fundamental Trading) – Theory Consistent with Rational Expectations Equilibrium. Price follows fundamental value of stock. Trading volume is low.
Two (Technical/Chartist Trading) – Practice “Chaotic” market behavior. “Bubbles” and “crashes”: price oscillates around FV. Trading volume shows wild oscillations. “In accordance” with actual market behavior.
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The Participatory SFI-ASM
“An Early Agent-Based Stock Market: Replication and Participation“ (Gulyás-Adamcsek-Kiss, in Rendiconti Per Gli Studi Economici Quantitativi, 2004)
“Experimental Economics Meets Agent-Based Finance: A Participatory Artificial Stock Market” (Gulyás-Adamcsek-Kiss, in Proceedings of 34th Annual Conference of International Simulation and Gaming Association, 2003)
Questions: Can agents adapt to external trading strategies, just as
well as they did to those developed by fellow agents?
Will computational agents outperform humans, particularly in a fast game?
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The Results
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Humans Increase Market Volatility The presence of human traders increased
market volatility. The higher percentage of the population was
human, the higher the difference was w.r.t. the performance of the fully computational population.
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Participants Learn Fundamental Trading First set of Experiments:
Humans initially applied technical trading, but gradually discovered fundamental strategies.
The winning human’s strategy was: Buy if price < FV, sell otherwise.
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Artificial Chartist Agents
Second set of Experiments:
We introduced artificial chartist (technical) agents.
Base experiments show: Chartist agents normally increase market volatility.
That is, humans are subjected to extreme bubbles and crashes.
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Participants Learn Technical Trading Subjects received a bias towards
fundamental indicators.
Still, they reported gradually switching for technical strategies after confronting with the ‘chartist’ market.
!
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Participants Moderate Market Deviations However, chartist human subjects actually
modulated the market’s volatility. The market actually show REE-like behavior.
The absolute winner’s strategy in this case was a pure technical rule.
!
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Hypothesis about the Role of Human Adaptation Rate and Impatience The learning rate again.
The participants may have adapted quicker.
The effect of human ‘impatience’. Cf. NY Stock Market crash
due to programmed trading. An apparent lesson:
learning agents may do no better.
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Summary and Outlook
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Summary…
Co-creative emergent coordination in the artifactual system of stock markets:
Learning rate’s implications with regard to market volatility.
A novel method that joins the strengths of Theoretical computer modeling, Bounded rationality and Experimental economics.
Dedicated tools for participatory ABM: RePast & GPPAR The MASS
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… and Outlook
A mass-user online experiment/game. Co-creative decision making. Simulated virtual market with human and
artificial traders. Bounded rational traders (specialists) ensure the
liquidity of the market.
Further Development Cooperative Simulation Laboratory
(AITIA & ELTE)
www.vbroker.hu