L ászló Gulyás ( gulyas @sztaki.hu ) MTA SZTAKI & AITIA, Inc., Hungary

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 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. www.vbroker.hu … 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 Sciences

25. Thank you! gulyas@sztaki.hu & gulya@aitia.ai Experiments in Economic Sciences