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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References
Aldous, D., Vazirani, U.: Go with the Winners Algorithms. In: Proc. 35th Symp. Found. Comp. Sci., pp. 492–501 (1994)
Allen, F., Karjalaine, R.: Using Genetic Algorithms to Find Technical Trading Rules. Journal of Financial Economics, 245–271 (1999)
Campbell, J.Y., Lo, A.W., MacKinlay, A.C.: The Econometrics of Financial Markets. Princeton Univ. Press, Princeton (1997)
Clearwater, S.H., Huberman, B.A., Hogg, T.: Cooperative Solution of Constraint Satisfaction Problems. Science 254, 1181–1183 (1991)
Cover, T.M.: Universal Portfolios. Math. Finance 1(1), 1–29 (1991)
De Paoli, F., Vizzari, G.: Context dependent management of field diffusion: an experimental framework. In: Proceedings of the Workshop from Object to Agents, WOA, Piagora Editrice, Cagliari, Italy (2003)
Decker, K., Sycara, K., Zeng, D.: Designing a multi-agent portfolio management system. In: Proceedings of the AAAI Workshop on Internet Information Systems (1996)
Dempster, M.: Computational Learning Techniques for Intraday fx Trading Using Popular Technical Indicators. IEEE Transaction on Neural Networks, 744–754 (2001)
Fibanc Mediulanum Banking Group, http://www.fibanc.es
Gestel, T., Suykens, J.: Financial Times Series Prediction Using Least Squares Support Vector Machines Within the Evidence Framework. IEEE Transactions on Neural Networks, 809–820 (2001)
Gmytrasiewicz, P.J., Durfee, E.H.: A Rigorous, Operational Formalization of Recursive Modeling. In: Proc. 1st Int. Conf. on Multi-Agent Systems (ICMAS 1995), pp. 125–132. AAAI Press, CA (1995)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice Hall, Englewood Cliffs (1999)
Helmbold, D.P., Schapire, R.E., Singer, Y., Warmuth, M.K.: On-Line Portfolio Selection Using Multiplicative Updates. Math. Finance 8(4), 155–177 (1998)
Hoog, T., Williams, C.P.: Solving the Really Hard Problems With Cooperative Search. In: Proc. 11th Nat. Conf. on Artificial Intelligence (AAAI 1993), pp. 231–236 (1993)
Irani, S., Karlin, A.R.: Online Computation. In: Hochbaum, D.S. (ed.) Approximation Algorithms for NP-Hard Problems, ch. 13, pp. 521–564. PWS Publishing (1997)
Knight, K.: Are Many Reactive Agents Better Than a Few Deliberative Ones? In: Proc. 13th Int. Joint. Conf. on Artificial Intelligence. IJCAI 1993, pp. 432–437. AAAI Press, CA (1993)
Kodogiannis, V., Lolis, A.: Forecasting Financial Times Series Using Neural Network and Fuzzy System-Based Techniques Neural Computing & Applications, pp. 90–102 (2002)
López, V.F., Alonso, L., Moreno, M.N., Segrera, S., Belloso, A.: A System for Efficient Portfolio Management. In: Yin, H., Tino, P., Corchado, E., Byrne, W., Yao, X. (eds.) IDEAL 2007. LNCS, vol. 4881, pp. 980–989. Springer, Heidelberg (2007)
Mamei, M., Zambonelli, F.: Field-Based Coordination for Pervasive Multiagent Systems, pp. 241–248. Springer, Heidelberg (2006)
Markowitz, H.: Portfolio Selection. Journal of Finance 7 (1952)
Markowitz, H.M.: Portfolio Selection: Efficient diversification of investments. Blackwell Publishers, Malden (1991)
Parkes, D., Huberman, B.: Multiagent Cooperative Search for Portfolio Selection. Games and Economic Behavior 35, 124–165 (2001)
Plikynas, P.: Portfolio design and optimization using neural network based multiagent system of investing agents. In: Sakalauskas, L., Weber, G.W., Zavadskas, E.K. (eds.) International Conference 20th EURO Mini Conference Continuous Optimization and Knowledge-Based Technologies (EurOPT 2008), EUROPT 2008 Selected papers, Neringa, LITHUANIA, Vilnius, pp. 137–142 (2008)
Tino, P., Schittenkopf, C.: Financial Volatility Trading Using Recurrent Neural Networks. IEEE Transactions on Neural Networks, 865–874 (2001)
Vidal, J.M., Durfee, E.H.: Learning Nested Agent Models in an Information Economy. J. Exp. Theoretical Artificial Intelligence 10, 291–308 (1998)
Vilariño, A.: Tubulencias Financieras y Riesgos de Mercado. Prentice Hall, Madrid (2001)
Wellman, M.P., Hu, J.: Conjectural Equilibrium in Multiagent Learning. Machine Learning 33, 179–200 (1998)
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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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DOI: https://doi.org/10.1007/978-3-642-12433-4_7
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