Abstract
In this work we present a multiagent system to draw up an optimum portfolio. By using a distributed architecture, the agents are trained to follow different investing strategies in order to optimize their portfolios to automate the one year forecast of a portfolio’s payoff and risk. The system allows to adopt a strategy that ensures a high rate of return at a minimum risk. The use of neural networks provides an interesting alternative decisions to the statistical classifier. With a modular agent composed by a few trained neural networks, the system makes investment decisions according to the assigned investment strategy and the behavior of the prices in a one-year period. The agent can take a decision on the purchase or sale of a given asset.
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López, V.F., Alonso, N., Alonso, L., Moreno, M.N. (2010). A Multiagent System for Efficient Portfolio Management. In: Demazeau, Y., et al. Trends in Practical Applications of Agents and Multiagent Systems. Advances in Intelligent and Soft Computing, vol 71. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12433-4_7
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