Computer Science – Multiagent Systems
Scientific paper
2007-05-12
Computer Science
Multiagent Systems
6 pages, In Proceedings of the 2001 International Symposia on Soft Computing and Intelligent Systems for Industry, Scotland
Scientific paper
A population of committees of agents that learn by using neural networks is implemented to simulate the stock market. Each committee of agents, which is regarded as a player in a game, is optimised by continually adapting the architecture of the agents using genetic algorithms. The committees of agents buy and sell stocks by following this procedure: (1) obtain the current price of stocks; (2) predict the future price of stocks; (3) and for a given price trade until all the players are mutually satisfied. The trading of stocks is conducted by following these rules: (1) if a player expects an increase in price then it tries to buy the stock; (2) else if it expects a drop in the price, it sells the stock; (3)and the order in which a player participates in the game is random. The proposed procedure is implemented to simulate trading of three stocks, namely, the Dow Jones, the Nasdaq and the S&P 500. A linear relationship between the number of players and agents versus the computational time to run the complete simulation is observed. It is also found that no player has a monopolistic advantage.
Abramov Victor V.
Correia Luís
Goossenaerts J.
Mariano P.
Marwala** Tshilidzi
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