LSSVM-ABC Algorithm for Stock Price prediction

International Journal of ComputerTrends and Technology (IJCTT)          
© 2014 by IJCTT Journal
Volume-7 Number-2                          
Year of Publication : 2014
Authors : Osman Hegazy , Omar S. Soliman and Mustafa Abdul Salam
DOI :  10.14445/22312803/IJCTT-V7P121


      Osman Hegazy , Omar S. Soliman and Mustafa Abdul Salam. Article: LSSVM-ABC Algorithm for Stock Price prediction. International Journal of Computer Trends and Technology (IJCTT) 7(2):81-92, January 2014. Published by Seventh Sense Research Group.

      In this paper, Artificial Bee Colony (ABC) algorithm which inspired from the behavior of honey bees swarm is presented. ABC is a stochastic population-based evolutionary algorithm for problem solving. ABC algorithm, which is considered one of the most recently swarm intelligent techniques, is proposed to optimize least square support vector machine (LSSVM) to predict the daily stock prices. The proposed model is based on the study of stocks historical data, technical indicators and optimizing LSSVM with ABC algorithm. ABC selects best free parameters combination for LSSVM to avoid over-fitting and local minima problems and improve prediction accuracy. LSSVM optimized by Particle swarm optimization (PSO) algorithm, LSSVM, and ANN techniques are used for comparison with proposed model. Proposed model tested with twenty datasets representing different sectors in S&P 500 stock market. Results presented in this paper show that the proposed model has fast convergence speed, and it also achieves better accuracy than compared techniques in most cases.

[1] G. E. P. Box, G. M. Jenkins, and G. C. Reinsel, Time Series Analysis, Forecasting and Control (Englewood Cliffs, NJ: Prentice- Hall, 1994).
[2] X. Wang and M. Meng, A Hybrid Neural Network and ARIMA Model for Energy Consumption Forecasting, Journal of computers, 7(5), 1184-1190, May 2012.
[3] L. Hentschel, Nesting symmetric and asymmetric GARCH models, Journal of Financial Economics 39(1), 1995, 71-104.
[4] X. Leng, and H. Miller, Input dimension reduction for load forecasting based on support vector machines, IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies (DRPT2004), 2004, 510 - 514.
[5] V. Vapnik, The nature of statistical learning (Springer, 1999).
[6] V. Cherkassky, and Y. Ma, Practical Selection of SVM Parameters and Noise Estimation for SVM regression, Neural Networks, 17(1), 2004, 113-126.
[7] J. Suykens, V. Gestel, and J. Brabanter, Least squares support vector machines (World Scientific, 2002).
[8] M. ANDRÉS, D. CARLOS, and C. GERMÁN, Parameter Selection in Least Squares-Support Vector Machines Regression Oriented, Using Generalized Cross-Validation, Dyna journal, 79(171), 2012, 23-30.
[9] A. Carlos ?, B. Gary ?, and A. David, Evolutionary Algorithms for Solving Multi-Objective Problems (Springer, 2007).
[10] B. Basturk, and D. Karaboga, An Artificial Bee Colony (ABC) Algorithm for Numeric function Optimization, IEEE Swarm Intelligence Symposium, Indianapolis, Indiana, USA, 2006.
[11] D. Karaboga, and B. Basturk, A Powerful And Efficient Algorithm For Numerical Function Optimization: Artificial Bee Colony (ABC) Algorithm, Journal of Global Optimization, 39(3), 2007, 459–471.
[12] D. Karaboga, and B. Basturk, On The Performance Of Artificial Bee Colony (ABC) Algorithm, Applied SoftComputing journal, 8(1), 2008, 687–697.
[13] D. Karaboga, and B. Basturk, Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems, Advances in SoftComputing: Foundations of Fuzzy Logic and Soft Computing, 4529, 2007, 789–798.
[14] D. Karaboga, and B. Basturk, Artificial Bee Colony Algorithm on Training Artificial Neural Networks, 15th IEEE Signal Processing and Communications Applications, 2007, 1–4.
[15] D. Karaboga, B. Basturk, and C. Ozturk, Artificial Bee Colony (ABC) Optimization Algorithm for Training Feed-Forward Neural Networks, LNCS: Modeling Decisions for Artificial Intelligence, 4617, 2007, 318–319.
[16] A. Hadidi, and S. Kazemzadeh, Structural optimization using artificial bee colony algorithm, 2nd International Conference on Engineering Optimization, 2010, September 6 – 9, Lisbon, Portugal.
[17] Y. Zhang and L. Wu, Optimal multi-level Thresholding based on Maximum Tsallis Entropy via an Artificial Bee Colony Approach, Entropy, 13(4), 2011, 841-859.
[18] Y. Zhang, L. Wu, and S. Wang, Magnetic Resonance Brain Image Classification by an Improved Artificial Bee Colony Algorithm, Progress in Electromagnetics Research, 116, 2011, 65-79.
[19] Y. Zhang, L. Wu, S. Wang, and Y. Huo, Chaotic Artificial Bee Colony used for Cluster Analysis, Communications in Computer and Information Science, 134(1), 2011, 205-211.
[20] Y. Zhang, and L. Wu, Face Pose Estimation by Chaotic Artificial Bee Colony, International Journal of Digital Content Technology and its Applications, 5(2), 2011, 55-63.
[21] Y. Zhang and L. Wu, Artificial Bee Colony for Two Dimensional Protein Folding, Advances in Electrical Engineering Systems, 1(1), 2012, 19-23.
[22] A.R. Yildiz, A new hybrid artificial bee colony algorithm for robust optimal design and manufacturing, Applied Soft Computing, 13(5), 2013, 2906–2912.
[23] A.R. Yildiz, Optimization of cutting parameters in multi-pass turning using artificial bee colony-based approach, Information Sciences: an International Journal, 220, 2013, 399-407.
[24] F. Barani and M. Abadi, Intrusion Detection in AODV-based MANETs Using Artificial Bee Colony and Negative Selection Algorithms, the isc international journal of information security,4(1), 2012, 25-39.
[25] B. Akay, A Study on Particle Swarm Optimization and Artificial Bee Colony Algorithms for Multilevel Thresholding, Applied Soft Computing, 13(6), 2013, 3066–3091.
[26] T. Cavdar, M. Mohammad, and R.A. Milani, A New Heuristic Approach for Inverse Kinematics of Robot Arms, Advanced Science Letters 19(1), 2013, 329-333.
[27] D. Karaboga, and B. Gorkemli, A Combinatorial Artificial Bee Colony Algorithm for Traveling Salesman Problem, International Symposium on Innovations in Intelligent Systems and Applications, Istanbul, Turkey ,2011,50-53.
[28] I.M.S. Oliveira and R. Schirru, Identifying Nuclear Power Plant Transients Using The Discrete Binary Artificial Bee Colony (Dbabc) Algorithm, 2011 International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (M&C 2011), Rio de Janeiro, Brazil, 2011.
[29] F. Barani and M. Abadi, An ABC-AIS Hybrid Approach to Dynamic Anomaly Detection in AODV-based MANETs, International IEEE TrustCom, China , 2011.
[30] A. Khorsandia, S.H. Hosseiniana, and A. Ghazanfarib, Modified artificial bee colony algorithm based on fuzzy multi-objective technique for optimal power flow problem, Electric Power Systems Research, Elsevier, 95, 2013, 206–213.
[31] M. Basu, Artificial bee colony optimization for multi-area economic dispatch, International Journal of Electrical Power & Energy Systems, (49), 2013, 181–187.
[32] T. Suka, A. Chatterjee, MMSE design of nonlinear Volterra equalizers using artificial bee colony algorithm, Measurement, Elsevier, 46(1), 2013, 210–219.
[33] M. Tuba, I. Brajevic, and R. Jovanovic, Hybrid seeker optimization algorithm for global optimization, An International Journal of Applied Mathematics & Information Sciences, 7(3), 2013, 867-875.
[34] J. Suykens, J. Vandewalle, Least squares support vector machine classifiers, Neural Processing Letters, 9 (3), 1999, 293-300.
[35] D. Karaboga, An Idea Based On Honey Bee Swarm for Numerical Optimization, Technical Report-TR06, Erciyes University, Engineering Faculty, Computer Engineering Department, 2005.
[36] P. Mansouri, B. Asady, and N. Gupta, A Novel Iteration Method for solve Hard Problems (Nonlinear Equations) with Artificial Bee Colony Algorithm, World Academy of Science, Engineering and Technology, 5(11), 2011, 389 - 392. .

Keywords- Least square support vector machine, Artificial Bee Colony, technical indicators, and stock price prediction