1. On Markets Structures and some thoughts about Information … Working Index. Alessandro Cappellini February 24 th 2006 cappellini@econ.unito.it

2. Index Introduction Humans Experiments and Policy Rules MAD Investor confidence Survey Information Extracting useful information from a simulated limit-order book MIB: Memetic Information Behaviour

3. Introduction The main aim of this work, is to line the experiences collected from the master thesis, a stock market experiment, during the Ph.D. program. The knowledge obtained is large, starting from practice in simulation and coding, enlarging literature familiarity, cooperation with other discipline with a general enrichment and awareness. The thesis is basically a collection of heterogeneous and heterodox works on financial markets.

4. Thesis Framework Interdisciplinary. Different Sciences related by Topics (Finance) Enrich economics/financial simulation with real behaviours and check statistical properties of data. PsychologyPhysics Economics

5. Thesis Framework Increase Realism PsychologyPhysics Economics Investigate Traders Behaviours Examine data statistical properties Inspect Rules and Regulations

6. Thesis Framework Knowledge PsychologyPhysics Economics Social information processing, Cognitivism Mechanical Statistics Rules and regulations, “Classical” Finance

7. Thesis Framework Analysis & Tools: PsychologyPhysics Economics Surveys, experiments “Data crunching” Simulation

8. Thesis Framework Relevant issues: PsychologyPhysics Economics Prospect, Believes, Confidence… Waiting time, High Frequency Data, Book imbalance… Price, Volumes, OHLC…

9. Humans Experiments and Policy Rules The chapter is mainly based on the paper presented in ESA conference held in Alessandria, about market manipulation. There will be a description of regulations against manipulation (EU directive) and a description of some behaviours in SumWEB experiment. Taking idea from these facts, I, Enrico Scalas (University of Eastern Piedmont), Michael Kirchler and Juergen Huber (University of Innsbruck) started in regulation analysis/comparisons on different stock markets. Early results could be ready in spring 2006. This idea became:

10. MAD - Market Microstructures and Architectures Database Stock markets are complex systems. Many autonomous agents interact through a mechanism know as "continuous double auction". They are sensitive to exogenous as well as endogenous influences, that can cause domino effects or avalanches, known as bubbles and crashes. Not only two-agent interactions are relevant, but also many-agent interactions cannot be neglected. Agents playing in these markets are intelligent and try to manipulate the outcome. Recent results on asymmetric information, behavioural and experimental economics clearly show that the neoclassical paradigm of walsarian markets is not sufficient to describe every feature of real-world stock exchanges. With this Database, our aim is to provide scholars an updated set of information on market architectures and rules. This should be helpful both for further studies on agent-based computer simulations (Boer-Sorban, de Bruin and Kaymak, 2005) and on analytical models (Hommes 2005). http://www.complexity-research.org/mad/

11. Investigate traders behaviours: surveys and experiments During the months of September-December 2005 two sets of questionnaires will be filled by a group of skilled people and by students. During January-March a survey will be published on the internet. Results will be collected and analysed with Enrico Rubaltelli (University of Modena and Reggio Emilia) and A.I.Fin.C. (Italian Association of Behavioural Finance). In late spring an experiment…

12. Investor confidence Survey Two paper and pencil surveys (150 student + ~80 professionals) One Internet survey (three month survey, actually: 101 complete answers on 814 contacts, quasi halving function) Main Features: Bias Affect Prospect Semantic Values Survey on methods/knowledge of Traders Confidence (comparison with Shiller’s) Risk Propensity

13. Information External –publicly broadcast News (media) Data providers (semi-public?) Analysis –privately spread Gossip Word-of-mouth Insider trading Internal –Technical (collected from the market) –Fundamental (balance sheets) …

14. Two Papers Both will be presented at: Complexity 2006 Workshop May 17 th -21 th 2006, Aix-en-Provence, France Special session on Information in Financial Markets

15. Extracting useful information from a simulated limit-order book Co-Author: Gilles Daniel “book imbalance can be exploited to forecast future price changes and outperform the simple buy-and-hold strategy” Statistical analysis (MatLab) “Smart” Agent introduction in NatLab (Lev Muchnick’s platform) (and in SUM?)

16. MIB: Memetic Information Behaviour “We would like to describe a social information process based on both spreading and learning algorithm in a stock market. We propose an alternative decision making process mainly based on communication, and opinion building without any consideration on financial stylized facts. Those opinions will be treated according to the memetic paradigm. The information ows among agents is establish on a fixed social network. The memetic agents, by “informal” chatting into the group, increase their knowledge, and after pondering, raise believes used both acting in the market and spreading to others. “To think”, agents exploit services supplied by an original focused algorithm. Our simulation model is the well know SUM-SumWEB project, an artificial stock market with a continuous double auction mechanism inspired by Milan stock exchange regulation.”

17. Schema M M M M M M M R A R R R Memetic AgentsOther Agents Social Network Book

18. Memes: examples Merry Lunch Analist: Tim Hooker BUY Gialli 15,5 € SELL Blu Reference: 4 Trust: 2

19. Implementing Co-Author: Gianluigi Ferraris Adapt a GA Develop a MA GA against MA: literature review TOTA: Trader Oriented by Talked Actions