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Presented by: Alexander Sverdov

Review of: An Agent Oriented Business Model for E-Commerse based on the NYSE Specialist system, paper by Kenneth Griggs. Presented by: Alexander Sverdov. The Paper Outline. Description of different auctions. Description of NYSE Specialist role.

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Presented by: Alexander Sverdov

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  1. Review of: An Agent Oriented Business Model for E-Commerse based on the NYSE Specialist system, paper by Kenneth Griggs Presented by: Alexander Sverdov

  2. The Paper Outline • Description of different auctions. • Description of NYSE Specialist role. • Proposal of an agent architecture that serves the same purpose as NYSE Specialists.

  3. English Auction • Reserve Price (or bid floor) below which no bids are accepted. • Auctioneer guides the bidding process upwards until a final highest bid is accepted.

  4. Dutch Auction • Reverse of an English Auction. • Auctioneer states a high starting price. • The Auctioneer proceeds to reduce the price by a fixed amount until a bid is made. • The first bid is taken.

  5. Continuous Double Auction • Multiple buyers and sellers compete in the market. • Bids and Asks are cleared continuously.

  6. Specialist in CDA at NYSE • Specialists are employees of independent specialist firms. • Manage trading of a certain stock. • Governed by stock exchange rules.

  7. Illustration

  8. Specialists • Play the role of Market Makers. • Always willing to buy and sell at a “fair” price (even if they don’t own shares). • Ensure orderly market; control the rising/lowering of stock prices---by possibly buying/selling against the market trends (by using their own capital to cushion sharp price changes). • Can act as regular traders.

  9. Roles of Specialists • Agents: buy/sell shares on customer’s request (act as floor broker). • Auctioneer: Provide a market for a security. Always be ready to buy/sell. • Catalyst: The specialist keeps track of all known interest in the stock, and alerts interested parties. • Principal: Buy and sell stock for their own account (with some rules).

  10. Specialist Book • Maintain all transactions. • All market and limit orders. • Provides a unique view of the market.

  11. Intelligent Software Agents • Autonomy: agents can operate without direct intervention by humans or others. • Social ability: agents can interact with other agents and/or humans. • Reactivity: agents perceive their environment and respond in a timely fashion to changes that occur in it.

  12. Intelligent Software Agents, Cont. • Pro-activeness: agents can exhibit goal-directed behavior by taking the initiative. • Mobility: agents can move to other environments. • Temporal continuity: agents are continuously running processes.

  13. Proposed Architecture

  14. Architecture Details

  15. More Detailed Details

  16. Four Agent Types • Trading Agent • Principal Agent • Notification Agent • Representation Agent

  17. Trading Agent • Invokes trading rules • Matches orders • Maintains Bid/Ask spread • Records transactions to the tape • Updates Inventory • Notifies other agents when required • Records direct buyer-seller trades

  18. Principal Agent • Performs an analysis using data from trade repository. • Invokes principal behavior rules. • Requests trades from the trading agent.

  19. Notification Agent • Catalyst function. • Notifies possible buyers and sellers of market conditions • Maintains and updates the notification DB and the tape. • Requests trades from the trading agent. • Notifies the representation agent when applicable.

  20. Representation Agent • Interacts with represented buyers and sellers. • Requests trades from trading agent. • Negotiates commissions • Updates trade repository

  21. Database, Knowledge, etc. • The system maintains information about all participants, and all the relevant stock data. • Trading rules are handled using an expert system. • Principal behavior is determined by neural network or some heuristic system.

  22. Things to be addressed • Consistent agent & market semantics. • A deeper understanding of specialist knowledge and functions (process model) • Agent development tools. • A high level agent scripting language. • An analysis of knowledge representation techniques to be used by the agents (rule-based, Expert System shell, neural net, genetic algorithm, etc.)

  23. Conclusion • The paper provides a first attempt at modeling the NYSE specialist role using an agent based system. • The paper does not describe an implementation, but rather a possible design for such a system.

  24. The End.

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