In this paper, first a precise mathematical model is obtained for four competing or cooperating companies' stock prices and then the optimal buy/sell signals are ascertained for five different agents which are trading in a virtual market and are trying to maximize their wealth over 1 trading year period. The model is so that gives a good prediction of the next 30th day stock prices. The companies used in this modeling are all chosen from Boston Stock Market. Genetic Programming (GP) is used to produce the predictive mathematical model. The interaction among companies and the effect imposed by each of five agents on future stock prices are also considered in our modeling. Namely, we have chosen eight companies in order that there is some kind of interrelation among them. Comparison of the GP models with Artificial Neural Networks (ANN) and Neuro-Fuzzy Networks (trained by the LoLiMoT algorithm) shows the superior potential of GP in prediction. Using these models; five players, each with a specific strategy and all with one common goal (wealth maximization), start to trade in a virtual market. We have also relaxed the short-sales constraint in our work. Each of the agents has a different objective function and all are going to maximize themselves. We have used Particle Swarm Optimization (PSO) as an evolutionary optimization method for wealth maximization.
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References
R. Thalheimer and M. M. Ali, Time series analysis and portfolio selection: An application to mutual savings banks, Southern Economic Journal, 45(3), 821–837 (1979).
M. Pojarliev and W. Polasek, Applying multivariate time series forecasts for active portfolio management, Financial Markets and Portfolio Management, 15, 201–211 (2001).
D. S. Poskitt and A. R. Tremayne, Determining a portfolio of linear time series models, Biometrika, 74(1), 125–137 (1987).
T. Basar and G. J. Olsder, Dynamic Noncooperative Game Theory. (San Diego, CA: Academic, 1995).
J. W. Weibull, Evolutionary Game Theory. (London: MIT Press, 1995).
D. Fudenberg and J. Tirole, Game Theory. (Cambridge, MA: MIT Press, 1991).
S. Lakshminarayanan, An Integrated stock market forecasting model using neural network, M. Sc. dissertation, College of Engineering and Technology of Ohio University (2005).
K. J. Kim and W. B. Lee, Stock market prediction using artificial neural networks with optimal feature transformation, Journal of Neural Computing & Applications, Springer, 13(3), 255–260 (2004).
K. Schierholt and C. H. Dagli, Stock market prediction using different neural network classification architectures, Computational Intelligence for Financial Engineering, Proceedings of the IEEEIAFE Conference, pp. 72–78 (1996).
K. Nygren, Stock Prediction — A Neural Network Approach, M.Sc. dissertation, Royal Institute of Technology, KTH (2004).
E. P. K. Tsang, J. Li, and J. M. Butler, EDDIE beats the bookies, International Journal of Software, Practice and Experience, 28(10), 1033–1043 (1998).
D. S. Barr and G. Mani, Using neural nets to manage investments, AI EXPERT, 34(3), 16–22 (1994).
R. N. Mantegna and H. E. Stanley, An Introduction to Econophysics: Correlation and Complexity in Finance (Cambridge University Press, Cambridge, MA, 2000).
J. P. Bouchaud and M. Potters, Theory of Financial Risks: From Statistical Physics to Risk management (Cambridge University Press, Cambridge, MA, 2000).
Y. K. Kwon, S. S. Choi, and B. R. Moon, Stock Prediction Based on Financial Correlation, In Proceedings of GECCO'2005, pp. 2061–2066 (2005).
Y. K. Kwon and B. R. Moon, Daily stock prediction using neuro-genetic hybrids, Proceedings of the Genetic and Evolutionary Computation Conference, pp. 2203–2214 (2003).
Y. K. Kwon and B. R. Moon, Evolutionary ensemble for stock prediction, Proceedings of the Genetic and Evolutionary Computation Conference, pp. 1102–1113 (2004).
F. Parisi and A. Vasquez, Simple technical trading rules of stock returns: evidence from 1987 to 1998 in Chile, Emerging Market Review, 1, 152–164 (2000).
J. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Evolution. (Cambridge, MA: MIT Press, 1992).
J. Madar, J. Abonyi, and F. Szeifert, Genetic Programming for the identification of nonlinear input-output models, Industrial Engineering Chemistry Research, 44, 3178–3186 (2005).
M. A. Kaboudan, Genetic Programming prediction of stock prices, Computational Economics, 16(3), 207–236 (2000).
S. Sette and L. Boullart, Genetic Programming: principles and applications, Engineering Applications of Artificial Intelligence, 14, 727–736 (2001).
Boston Stock Group web page (August 1, 2007); http://boston.stockgroup.com
J. Kennedy and R. Eberhart, Particle Swarm Optimization, Proceedings of the IEEE International Conference on Neural Networks, Perth, Australia, pp. 1942–1945 (1995).
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Rajabioun, R., Rahimi-Kian, A. (2009). A Dynamic Modeling of Stock Prices and Optimal Decision Making Using MVP Theory. In: Ao, SI., Gelman, L. (eds) Advances in Electrical Engineering and Computational Science. Lecture Notes in Electrical Engineering, vol 39. Springer, Dordrecht. https://doi.org/10.1007/978-90-481-2311-7_45
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