By D. J. A. Somefun, E. H. Gerding, S. Bohte, J. A. La Poutré (auth.), Peyman Faratin, David C. Parkes, Juan A. Rodríguez-Aguilar, William E. Walsh (eds.)
This booklet constitutes the completely refereed post-proceedings of the fifth overseas Workshop on Agent-Mediated digital trade, AMEC 2003, held in Melbourne, Australia in July 2003 as a part of AAMAS 2003.
The nine revised complete papers provided have been conscientiously chosen from 22 submissions in the course of rounds of reviewing and revision. The papers are prepared in topical sections on computerized negotiation, structures and mechanism layout, and multi-agent markets.
Read Online or Download Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems: AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15, 2003, Revised Selected Papers PDF
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Extra info for Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems: AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15, 2003, Revised Selected Papers
In principle, n is a good candidate for automatic tuning using machine learning techniques. We leave that for future work. Here we use a value of n = 1 for the competition and controlled experiments discussed in later sections. 0001; placeOrder(SELL, sellReferencePrice, volume); Table 2. 2 Performance Analysis When using the market making strategy, we expect that our agent will accumulate proﬁt when the stock price has a lot of ﬂuctuation over the trading day. If all trades match, our proﬁts are the average diﬀerences between prices of the order pairs times the number of the simulation ticks.
The price control, volume control and combined control methods for the market making strategy were also tested during the same period. There are small diﬀerences between the implementations of the control parameters and Two Stock-Trading Agents: Market Making and Technical Analysis 33 DATE Market Making Reverse Mar21, 2003 25557 7730 Mar24, 2003 -30845 -21602 Mar25, 2003 -4742 -4504 Mar26, 2003 28453 -5525 Mar27, 2003 5856 -21932 Mar28, 2003 13174 -7146 Mar31, 2003 8520 -3489 Apr01, 2003 31144 6500 Apr02, 2003 -34444 6087 Apr03, 2003 -31938 30929 Table 14.
9 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 Fig. 4. MSFT Stock Price: Dec. 2 Performance Analysis For any ﬁxed strategy (other than the degenerate do-nothing strategy), there are some market conditions in which it will make money and some under which it will lose money. We analyze the performance of the basic and reverse strategy based on what we think are realistic, though simpliﬁed, assumptions about the price trajectories. We assume that the price of MSFT oscillates consistently in a Two Stock-Trading Agents: Market Making and Technical Analysis 27 wave pattern around a constant price p.
Agent-Mediated Electronic Commerce V. Designing Mechanisms and Systems: AAMAS 2003 Workshop, AMEC 2003, Melbourne, Australia, July 15, 2003, Revised Selected Papers by D. J. A. Somefun, E. H. Gerding, S. Bohte, J. A. La Poutré (auth.), Peyman Faratin, David C. Parkes, Juan A. Rodríguez-Aguilar, William E. Walsh (eds.)