Forecasting Stock Exchange Index Using Particle Swarm Optimization Comparing to Traditional Models

Document Type : Research Paper



The stock market is one of the most attractive investment choice from which a large amount of profit can be earned. This study presents a PSO-based methodology to deal with Stock market index prediction. The study showed superiority in applicability of the proposed approach by using Tehran Stock Exchange Index (TSEI) and comparing the outcomes with conventional method such as Simple Exponential Smoothing (SES), Hoelt-Winters Exponential Smoothing (HWES), Auto Regressive (AR), Moving Average (MA), Auto Regressive Integrated Moving Average (ARIMA). Experimental results clearly showed that PSO approach meaningfully outperforms all of the conventional method in terms of MAD, MSE, RMSE and MAPE. Additionally, evaluation statistics of the proposed approach significantly decrees variance of the errors compared to the conventional method.


- آذر، عادل و مومنی، منصور. (1377). آمار و کاربرد آن در مدیریت. تهران: انتشارات سمت، ج دوم، ص. 365-322.
- افسر، امیر. (1384). الگو‌سازی پیش‌بینی شاخص قیمت سهام با استفاده از شبکه‌های عصبی فازی و روش ترکیبی. تهران:. پایان نامه کارشناسی ارشد، دانشگاه تربیت مدرس.
- بت‌شکن،محمود. (1380). یش‌بینی قیمت سهام با استفاده از شبکه عصبی - فازی و مقایسه آن با الگوهای خطی پیش‌بینی، تهران: پایان نامه کارشناسی ارشد، دانشگاه تهران.
- طلوعی، عباس و حق‌دوست، شادی. (1386). الگو‌سازی پیش‌بینی قیمت سهام بااستفاده از شبکه عصبی و مقایسه آن با روش‌های پیش‌بینی ریاضی، پژوهشنامه اقتصادی، ص. 252-237.
- گجراتی، دامودار. (1385). مبانی اقتصاد سنجی، ترجمه:ابریشمی، تهران. انتشارات دانشگاه تهران، چ چهارم، ص. 963-907.
- مشیری، سعید و مروت، حبیب. (1384). پیش‌بینی شاخص کل بازدهی سهام تهران با استفاده از الگو‌های خطی و غیرخطی.
- وطن‌خواه، رامین. (1388). کنترل و بهینه‌سازی حرکت دسته‌ای یک توده ربایندهکی به وسیله روش‌های الهام گرفته از طبیعت، تهران: پایان نامه کارشناسی ارشد، دانشگاه صنعتی شریف.
- Armano, G., marchesi, A., and Murru, A. (2005). A hybrid genetic-neural architecture for stock indexes forecasting. Information sciences, pp. 3-33.
- Chang, T., Meade, N., Beasley, J., and Sharaiha, Y. (2000). Huristics For Cardinality Constrained Portfolio Optimisation. Comput Operation Research, pp. 1271-1302.
- Chiam, S., Tan, K., and Mamun, A. (2009). A memetic model of evolutionary PSO for computational finance applications. Expert Systems with Applications, Vol. 36, pp. 3695–3711.
- Dallagnol, V., Vandenberg, F., and Mous, L. (2009). Portfolio Management Using Value at Risk:A comparsion between Genetic Algorithm and Particle Swarm Optimization. International ofIntelligent System , Vol. 24, pp. 729-766.
- Egeli, B., Ozturan, M., and Badur, B. (2003). Stock Market Prediction Using Artificial Neural Networks. Bogazici University.
- Fourie, P. C., & Groenwold, A. A. (2002). The particle swarm optimization algorithm in size and shape optimization. Struct. Multidisc.OPT , Vol. 23, pp. 259-267.
- Haupt, R., & Haupt, S. E. (1998). Practical Genetic Algorithm. John Wiley & Sons
- Hernández, A., Muñoz, A., Villa, E., & Botello, S. (2007). COPSO: Constrained Optimization via PSO Algorithm. Centro de Investigación en Matemáticas, Guanajuato, Technical Report of the Computer Sciences Department, México.
- Hirotugu, A. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Contro, Vol. 19, pp. 716–723.
- Kennedy, J. (1999). Small Worlds and Mega-Minds: Effects of Neighborhood Topology on. Proceedings of the 1999 Congress on EvolutionaryComputation, pp. 1931–1938.
- Kennedy, j., and Eberhart, R. (1995). Particle Swarm Optimization. IEEE: International Conference on Neural Network , pp. 1942-1948.
- Kennedy, J., and Mendes, R. (2002). Population Structure and Particle Swarm Performance. Proceedings of the 2002 Congress on Evolutionary Computation, CEC02, pp. 1671-1676.
- Kiink, T., Vesterstroem, J. S., and Riget, J. (2002). Particle Swam Optimization with Spatial Particle Extension. Proceedings of the lEEE Congress on Evolutionary Computation, pp. 1474-1479.
- Koshino, M., Murata, H., and Kimura, H. (2007). Improved Particle Swarm Optimization and Application To Portfolio Selection. Electronics and Communications in Japan , Vol. 90.
- L., B. (1992). The little bootstrap and other methods for dimensionality selection in regression: X-fixed prediction error. Journal of the American Statistical Association , Vol. 87, pp 738-754.
- Leung, M. T., Chen, A. S., and Daouk, H. (2001). application of neural networks to an emerging financial market:forecasting and trading the taiwan stock index.
- Martinez, S., Cortes, j., and Bullo, F. (2007). Motion coordination with distributed information. IEEE Control Systems Magazine, Vol. 27, pp. 75–88.
- Pankratz, A. ( 1983). Forecasting with univariate Box–Jenkins models: concepts and cases. John Wiley & Sons .
- White, h. (1988). Economic prediction using neural network:The case of IBM daily stock returns. IEEE International conference on Neural Networks, San Deigo, Vol. 2, pp. 451-458.