Experiments in Economic Sciences1 Charting The Market: Fundamental and Chartist Strategies in a Participatory Stock Market Experiment László Gulyás ( ) MTA SZTAKI & AITIA, Inc., Hungary Balázs Adamcsek ( ) AITIA, Inc & Loránd Eötvös University, Hungary

Experiments in Economic Sciences 2 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

Experiments in Economic Sciences 3 The Problem

Experiments in Economic Sciences 4 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?

Experiments in Economic Sciences 5 The Method

Experiments in Economic Sciences 6 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.

Experiments in Economic Sciences 7 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.

Experiments in Economic Sciences 8 The Tools

Experiments in Economic Sciences 9 Tools for Agent-Based and Participatory Simulation ABM Tools:  Swarm, RePast, MASON ABM tools for participatory simulation  RePast+ GPPAR  The MASS (with MAC)

Experiments in Economic Sciences 10 The Model

Experiments in Economic Sciences 11 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.

Experiments in Economic Sciences 12 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.

Experiments in Economic Sciences 13 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.

Experiments in Economic Sciences 14 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?

Experiments in Economic Sciences 15 The Results

Experiments in Economic Sciences 16 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.

Experiments in Economic Sciences 17 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.

Experiments in Economic Sciences 18 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.

Experiments in Economic Sciences 19 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. !

Experiments in Economic Sciences 20 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. !

Experiments in Economic Sciences 21 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.

Experiments in Economic Sciences 22 Summary and Outlook

Experiments in Economic Sciences 23 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

Experiments in Economic Sciences 24 … 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)

Experiments in Economic Sciences25 Thank you! &