2004-12-16 Experiments in Economic Sciences 1

Charting The Market:Fundamental and Chartist Strategies in a Participatory

Stock Market Experiment László Gulyás (gulyas@sztaki.hu)MTA SZTAKI & AITIA, Inc., Hungary

Balázs Adamcsek (abalazs@aitia.ai)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

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Thank you!

gulyas@sztaki.hu & gulya@aitia.ai