ISSN: 2225-8329
Open access
Conventional statistical techniques for forecasting are constrained by the underlying seasonality, non-stationary and other factors. Increasingly over the past decade, Artificial intelligence (AI) methods including Artificial Neural network (ANN), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) etc. have been used successfully to perform predictions in financial markets and other areas. This study presents a hybrid inertia factor and constriction coefficient PSO-based methodology to deal with the Stock market index problem. We will demonstrate the superiority an applicability of the proposed approach by using Tehran Stock Exchange Index (TSEI) data and comparing the outcomes with other PSO methods such as: standard PSO, Inertia Factor PSO and Constriction Coefficient PSO. Experimental results clearly show that a hybrid PSO approach meaningfully outperforms all of the other PSO methods in terms of MAD, MSE, RMSE and MAPE Evaluation statistics also, the proposed approach can be considered as a suitable AI model for stock market index forecasting problem.
Araujo, R. D. (2010). Swarm-based translation-invariant morphological prediction method for financial time series forecasting. Information Sciences. 180, 4784–4805.
Araujo, R. D., Madeiro, F., Sousa, R. D., Pessoa, L., Ferreira, T. (2006). An evolutionary morphological approach for financial time series forecasting. Proceedings of the IEEE Congress on Evolutionary Computation, 2467–2474.
George, A. S., Kimon, V. P. (2009). Forecasting stock market short-term trends using a neuro-fuzzy based methodology. Expert System with Application. 36, 10696–10707.
Bozdogan, H. (2000). Akaike's information criterion and recent developments in information complexity. Journal of mathematical psychology. 44 (1), 62-91.
Chan, F. T., Tiwari, K. M. (2007). Swarm Intelligence Focus on Ant and Particle Swarm Optimization. Vienna, Austria: I-TECH Education and Publishing.
Chaouachi, A., Kamel, R. M., Ichikawa, R., Hayashi, H., Nagasaka, K. (2009). Neural Network Ensemble-based Solar Power Generation Short-Term Forecasting. World Academy of Science, Engineering and Technology, 54, 54-59.
Chen, H., Yuan, S., Jiang, K. (2006). Selective neural network ensemble based on clustering. Lecture Notes in Computer Science,Springer Verlag, 3971, 545-550.
Chen, Y., Dong, X., Zhao, Y. (2005). Stock Index Modeling using EDA based Local Linear Wavelet Neural Network. International Conference on Neural Networks and Brain (ICNN&B), 1646-1650.
Chen, Y., Yang, B., Abraham, A. (2007). Flexible neural trees ensemble for stock index modeling. Neurocomputing. 70, 697–703.
Clerc, M., Kennedy, J. (2002). The particle swarm: explosion, stability, and convergence in a multi-dimensional complex space. IEEE Transactions on Evolutionary Computation, 658-73.
Clerc, M. (1999). The Swarm and the Queen: Towards a Deterministic and Adaptive Particle Swarm Optimization. Proc. of ICEC, Washington D.C., 1951-1957.
Eberhart, R., Shi, Y. (2000). Comparing Inertia Weights and Constriction Factors in Particle Swarm Optimization. Congress on Evolutionary Computing. 1, 84-88.
Emad, E., Tarek, H., Donald, G. (2005). Comparison among five evolutionary-based optimization algorithm. Advanced Engineering Informatics. 19, 43–53.
Farzi, S. (2008). A New Approach to Polynomial Neural Networks based on Genetic Algorithm. International Journal of Electrical and Computer Engineering, 3-7.
Gaing, Z. L. (2004). A particle swarm optimization approach for optimum design of PID controller in AVR system. IEEE Transactions on Energy Conversion, 384–391.
Hirotugu, A. (1974). A new look at the statistical model identification. IEEE Transactions on Automatic Control, 716–723.
Kass, R. E., Wasserman, L. (1995). A reference Bayesian test for nested hypotheses and its relationship to the Schwarz criterion. Journal of the American Statistical Association, 90, 377–395.
Kim, K., Lee, W. (2004). Stock Market Prediction using ANN with Optimal Feature Transformation. Neural Computing & Applications. 13 (3), 225-260.
Naka, S., Genji, T., Fukuyama, Y. (2001). Practical Distribution State Estimation Using Hybrid Particle Swarm Optimization. Proc. of IEEE Power Engineering Society Winter Meeting. Columbus, Ohio, 2, 815-820.
Oh, K., Kim, K. (2002). Analyzing stock market tick data using piecewise non linear model. Expert System with Applications. 22, 249-255.
Panda, S., Padhy, N. P. (2007). Comparison of particle swarm optimization and genetic algorithm for FACTS-based controller design. Applied Soft Computing, ASOC-417, 1–10.
Kennedy, J., Eberhart, R. C. (1995). Particle Swarm Optimization. IEEE: International Conference on Neural Network, 1942-1948.
Schutte, J. F., Groenwold, A. A. (2005). The Particle Swarm Optimization Algorithm. 31(1), 93-108.
Schwarz, G. E. (1978). Estimating the dimension of a model. Annals of Statistics. 6 (2), 461–464.
Shi, Y., Eberhart, R. (1998a). A modified particle swarm optimizer. Proceedings of the IEEE Congress on Evolutionary Computation, Anchorage, AK, 69–73.
Shi, Y., Eberhart, R. C. (1998b). Parameter Selection in Particle Swarm Optimization. Annual Conf. on Evolutionary Programming, San Diego, 7, 591-600.
Sivanagaraju, S., Viswanatha, J. (2008). Capacitor Placement in Balanced and Unbalanced Radial Distribution System by Discrete Particle SwarmOptimization. ICGST-ACSE, 8 (1), 7-15.
Takens, F. (1980). Detecting strange attractor in turbulence, in: A. Dold, B. Eckmann (Eds.), Dynamical Systems and Turbulence. Springer-Verlag, New York.
Trelea, I. C. (2003). The Particle Swarm Optimization Algorithm: Convergence Analysis and Parameter Selection. Information Processing Letters, 85, 317-325.
Khan, U. A., Bandopadhyaya, T. K., Sharma, S. (2008). Genetic Algorithm Based Backpropagation Neural Network Performs better than Backpropagation Neural Network in Stock Rates Prediction. International Journal of Computer Science and Network Security, 8 (7), 162-166.
Vandenbergh, F., Engelbrecht, A. (2004). A cooperative approach to particle swarm optimization. IEEE Transactions on Evolutionary Computation, 3, 225–239.
Wang, Y. (2003). Mining stock prices using fuzzy rough set system. Expert System with Applications, 24, 13-23.
Wang, Y. (2002). Predicting stock price using fuzzy grey prediction system. Expert System with Applications, 22, 33-39.
Zhang, X., Chen, Y., Yang, J. Y. (2007). Stock Index Forecasting Using PSO Bases Selective Neural Network Ensemble. International Conference on Artificial Intelligence (ICAI07), 260-264.
In-Text Citation: (Ashhar & Motamed, 2014)
To Cite this Article: Ashhar, M., & Motamed, A. R. (2014). Stock Market Index Prediction via Hybrid Inertia Factor PSO and Constriction Coefficient PSO. International Journal of Academic Research in Accounting Finance and Management Sciences, 4(3), 196–210.
Copyright: © 2014 The Author(s)
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