Abstract
One of the important problems in financial markets is making the profitable stocks trading rules using historical stocks market data. This paper implemented Particle Swarm Optimization (PSO) which is a new robust stochastic evolutionary computation Algorithm based on the movement and intelligence of swarms, and compared it to a Genetic Algorithm (GA) for generating trading rules. The results showed that PSO shares the ability of genetic algorithm to handle arbitrary nonlinear functions, but with a much simpler implementation clearly demonstrates good possibilities for use in Finance.
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References
Kennedy, J., Eberhart, R.: Particle Swarm Optimization. IEEE, Los Alamitos (1995)
Boeringer, D.W., Werner, D.H.: Particle Swarm Optimization Versus Genetic Algorithms for Phased Array Synthesis. IEEE Transaction on Antennas and Propagation 52(3) (2004)
Eberhart, R.C., Shi, Y.: Evolving artificial neural networks. In: Proc. Int. Conf. Neural Networks and Brain, P.R.C, Beijing (1998)
Salman, A., Ahmad, I., Al-Madani, S.: Particle swarm optimization for task assignment problem. Microprocessors and Microsystems 26, 363–371 (2002)
Robinson, J., Rahmat-Samii, Y.: Particle Swarm Optimization in Electormagnetics. IEEE Transactions on Antennas and Propagation 52(2) (2004)
Fourie, P.C., Groenwold, A.A.: The particle swarm optimization algorithm in size and shape optimization. Struct Multidisc Optim. 23, 259–267 (2002)
Abido, M.A.: Optimal Design of Power–System Stabilizers Using Particle Swarm Optimization. IEEE Transactions on Energy Conversion 17(3) (2002)
Ourique, C.O., Biscaia Jr, E.C., Pinto, J.C., Pinto, J.C.: The use of particle swarm optimization for dynamical analysis in chemical processes. Computers and Chemical Engineering 26, 1783–1793 (2002)
Allen, F., Karjalainen, R.: Using genetic algorithms to find technical trading rules. Journal of Financial Economics 51, 245–271 (1999)
Grace, B.K.: Black&Scholes option pricing via genetic algorithms. Applied Economics Letters, 129–132 (2000)
Dempster, M.A.H., Jones, C.M.: A real-time adaptive trading system using genetic programming. Quantitative Finance 1, 397–413 (2001)
Potvina, J.-Y., Sorianoa, P., Vall, M.: Generating trading rules on the stock markets with genetic programming. Computers & Operations Research 31, 1033–1047 (2004)
Alexander, S.S.: Price movements in speculative markets: trends or random walks. In: Cootner, P. (ed.) The Random Character of Stock Market Prices, vol. 2, pp. 338–372. MIT Press, Cambridge (1964)
Fama, E.F., Blume, M.E.: Filter rules and stock market trading. Security prices: a supplement. Journal of Business 39, 226–241 (1966)
Fama, E.F.: Efficient capital markets: a review of theory and empirical work. Journal of Finance 25, 383–417 (1970)
Brock, W., Lakonishok, J., LeBaron, B.: Simple technical trading rules and the stochastic properties of stock returns. Journal of Finance 47, 1731–1764 (1992)
Holland, J.H.: adaptation in Natural and Artificial Systems. University of Michigan Press (1975)
Kennedy, J., Eberhart, R.C.: A discrete binary version of the particle swarm algorithm. IEEE, Los Alamitos (1997)
Eberhart, R.C., Shi, Y.: Particle Swarm Optimization: Development, Applications and Resources. IEEE, Los Alamitos (2002)
Whitley, D.: The GENITOR Algorithm and Selection Pressure: Why Rank-based Allocation of Reproductive Trials is Best. In: Schaffer, D.J. (ed.) Proceddings of the Third International Conference on Genetic Algorithms, pp. 116–121. Morgan Kaufmann, San Mateo (1989)
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Lee, Js., Lee, S., Chang, S., Ahn, BH. (2005). A Comparison of GA and PSO for Excess Return Evaluation in Stock Markets. In: Mira, J., Álvarez, J.R. (eds) Artificial Intelligence and Knowledge Engineering Applications: A Bioinspired Approach. IWINAC 2005. Lecture Notes in Computer Science, vol 3562. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11499305_23
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DOI: https://doi.org/10.1007/11499305_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26319-7
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