1. 2004-12-16Experiments in Economic Sciences1 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

2. 2004-12-16 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

3. 2004-12-16 Experiments in Economic Sciences 3 The Problem

4. 2004-12-16 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?

5. 2004-12-16 Experiments in Economic Sciences 5 The Method

6. 2004-12-16 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.

7. 2004-12-16 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.

8. 2004-12-16 Experiments in Economic Sciences 8 The Tools

9. 2004-12-16 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)

10. 2004-12-16 Experiments in Economic Sciences 10 The Model

11. 2004-12-16 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.

12. 2004-12-16 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.

13. 2004-12-16 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.

14. 2004-12-16 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?

15. 2004-12-16 Experiments in Economic Sciences 15 The Results

16. 2004-12-16 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.

17. 2004-12-16 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.

18. 2004-12-16 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.

19. 2004-12-16 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. !

20. 2004-12-16 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. !

21. 2004-12-16 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.

22. 2004-12-16 Experiments in Economic Sciences 22 Summary and Outlook

23. 2004-12-16 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

24. 2004-12-16 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) www.vbroker.hu

25. 2004-12-16Experiments in Economic Sciences25 Thank you! gulyas@sztaki.hugulyas@sztaki.hu & gulya@aitia.aigulya@aitia.ai