Cat Swarm Optimization Algorithm Tuned Multilayer Perceptron for Stock Price Prediction

  • S. Kumar Chandar School of Business & Management, CHRIST
  • Hitesh Punjabi K. J. Somaiya Institute of Management & Research

Abstract

Due to the nonlinear and dynamic nature of stock data, prediction is one of the most challenging tasks in the financial market. Nowadays, soft and bio-inspired computing algorithms are used to forecast the stock price. This article assesses the efficiency of the hybrid stock prediction model using the multilayer perceptron (MLP) and cat swarm optimization (CSO) algorithm. The CSO algorithm is a bio-inspired algorithm inspired by the behavior traits of cats. CSO is employed to find the appropriate value of MLP parameters. Technical indicators calculated from historical data are used as input variables for the proposed model. The model’s performance is validated using historical data not used for training. The model’s prediction efficiency is evaluated in terms of MSE, MAPE, RMSE and MAE. The model’s results are compared with other models optimized by various bio-inspired algorithms presented in the literature to prove its efficiency. The empirical findings confirm that the proposed CSO-MLP prediction model provides the best performance compared to other models taken for analysis.

Keywords

bio-inspired algorithm, particle swarm optimization, cat swarm optimization, MAE, MAPE, multilayer perceptron and stock prediction,

References

1. E. Hadavandi, A. Ghanbari, S. Abbasian-Naghneh, Developing an evolutionary neural network model for stock index forecasting, [in:] Communications in Computer and Information Science, Vol. 93, pp. 407–415, ICIC, Springer-Verlag, Berlin, Heidelberg, 2010.
2. H. Niu, J. Wang, Financial time series prediction by a random data-time effective RBF neural network, Soft Computing, 18(3): 497–508, 2014, doi: 10.1007/s00500-013-1070-2.
3. H. Chung, K.S. Shin, Genetic algorithm-optimized long short term memory network for stock market prediction. Sustainability, 10(10), Article ID: 3765, 2018, doi: 10.3390/su10103765.
4. G.S. Atsalakis, K.P. Valavanis, Surveying stock market forecasting techniques – Part II: Soft computing methods, Expert Systems with Applications, 36: 5932–5941, 2009, doi: 10.1016/j.eswa.2008.07.006.
5. S. Kumar Chander, Soft computing and bio inspired computing techniques for stock market prediction – A comprehensive survey, International Journal of Engineering and Technology, 7(3): 1836–1845, 2018, doi: 10.14419/ijet.v7i3.14716.
6. M. Pakdaman Naeini, H. Taremian, H. Baradaran Hashemi, Stock market value prediction using neural networks, [in:] International Conference on Computer Information Systems and Industrial Management Applications (CISIM), 2010, pp. 132–136, doi: 10.1109/CISIM.2010.5643675.
7. F. Andrade de Oliveira, L. Enrique Zárate, M. de Azevedo Reis, C. Neri Nobre, The use of artificial neural networks in the analysis and prediction of stock prices, [in:] 2011 IEEE International Conference on Systems, Man, and Cybernetics, 2011, pp. 2151–2155, doi: 10.1109/ICSMC.2011.6083990.
8. R. Minakhi, B. Majhi, R. Majhi, G. Panda, Forecasting of currency exchange rate using an adaptive ARMA model with differential evolution based training, Journal of King Saud University – Computer and Information Sciences, 26(1): 7–18, 2014, doi: 10.1016/j.jksuci.2013.01.00.
9. O. Hegazy, O.S. Soliman, M.A. Salam, Comparative study between FPA, BA, MCS, ABC and PSO algorithms in training and optimizing of LS-SVM for SMP, International Journal of Advanced Computer Research, 5(18): 35–45, 2015.
10. Z. Mustaffa, Y. Yusof, S.S. Kamaruddin, Gasoline price forecasting: An application of LSSVM with improved ABC, Procedia – Social and Behavioral Science, 129: 601–609, 2014, doi: 10.1016/j.sbspro.2014.03.718.
11. J.C. Hung, Adaptive fuzzy-GARCH model applied to forecasting the volatility of stock markets using particle swarm optimization, Information Sciences, 181(20): 4673–4683, 2011, doi: 10.1016/j.ins.2011.02.027.
12. R. Majhi, G. Panda, B. Majhi, G. Sahoo, Efficient prediction of stock market indices using adaptive bacterial foraging optimization (ABFO) and BFO based techniques, Expert Systems with Applications, 36(6): 10097–10104, 2009, doi: 10.1016/j.eswa.2009.01.012.
13. B. Majhi, M. Rout, V. Baghel, On the development and performance evaluation of a multiobjective GA-based RBF adaptive model for the prediction of stock indices, Journal of King Saud University – Computer and Information Sciences, 26(3): 319–331, 2014, doi: 10.1016/j.jksuci.2013.12.005.
14. A.K. Rout, P.K. Dash, R. Dash, R. Bosoi, Forecasting financial time series using a low complexity recurrent neural network and evolutionary learning approach, Journal of King Saud University – Computer and Information Sciences, 29(4): 536–552, 2017, doi: 10.1016/j.jksuci.2015.06.002.
15. Z. Zhang, Y. Shen, G. Zhang, Y. Song, Y. Zhu, Short-term prediction for opening price of stock market based on self-adapting variant PSO-Elman neural network, [in:] 8th IEEE International Conference on Soft Computing and Service Sciences, 2017, pp. 225–228, doi: 10.1109/ICSESS.2017.8342901.
16. K.V. Prema, N.M. Agarwal, M. Krishna, V. Agarwal, Stock market prediction using neuro-genetic model, Indian Journal of Science and Technology, 8(35): 1–9, 2015, doi: 10.17485/ijst/2015/v8i35/71306.
17. A.R. Garakani, Stock price prediction using multilayer perceptron neural network by monitoring frog leaping algorithm, Journal of Intelligent Computing, 9(1): 15–23, 2018.
18. M. Karazmodeh, S. Nasiri, S. Majid Hashemi, Stock price forecasting using support vector machines and improved particle swarm optimization, International Journal of Electrical Energy, 1(2): 173–176, 2013, doi: 10.12720/joace.1.2.173-176.
19. M. Hiransha, E.A. Gopalakrishanan, V.K. Menon, K.P. Soman, NSE stock market prediction using deep learning models, Procedia Computer Science, 132: 1351–1362, 2018, doi: 10.1016/j.procs.2018.05.050.
20. A.A. Ayodele, K.A. Charles, O.A. Marion, O.O. Sunday, Stock price prediction using neural network with hybridized market indicators, Journal of Emerging Trends in Computing and Information Sciences, 3(1): 1–9, 2012.
21. S.C. Chu, P.W. Tsai, Computational intelligence based on the behavior of cats, International Journal of Innovative Computing, Information and Control, 3(1): 163–173, 2007.
22. P.M. Pradhan, G. Panda, Solving multiobjective problems using cat swarm optimization, Expert Systems with Applications, 39(3): 2956–2964, 2012, doi: 10.1016/j.eswa.2011.08.157.
23. S.R. Das, D. Mishra, M. Rout, A survey on impact of bio inspired computation on stock market prediction, Journal of Engineering Science and Technology Review, 10(3): 104–114, 2017, doi: 10.25103/jestr.103.15.
24. S. Kumar Chandar, M. Sumathi, S.N. Sivanandam, Neural network based forecasting of foreign currency exchange rates, International Journal on Computer Science and Engineering, 6(6): 202–206, 2014.
Published
Apr 13, 2022
How to Cite
KUMAR CHANDAR, S.; PUNJABI, Hitesh. Cat Swarm Optimization Algorithm Tuned Multilayer Perceptron for Stock Price Prediction. Computer Assisted Methods in Engineering and Science, [S.l.], v. 29, n. 1–2, p. 145–160, apr. 2022. ISSN 2299-3649. Available at: <https://cames.ippt.pan.pl/index.php/cames/article/view/394>. Date accessed: 28 may 2022. doi: http://dx.doi.org/10.24423/cames.394.