A CIMB Stock Price Prediction Case Study with Feedforward Neural Network and Recurrent Neural Network

G. Kim Soon, C. Kim On, A. Rayner, A. Patricia, J. Teo


Artificial Neural Network (ANN) is one of the popular techniques used in stock market price prediction. ANN is able to learn from data pattern and continuously improves the result without prior information about the model. The two popular variants of ANN architecture widely used are Feedforward Neural Network (FFNN) and Recurrent Neural Network (RNN). The literature shows that the performance of these two ANN variants is studied dependent. Hence, this paper aims to compare the performance of FFNN and RNN in predicting the closing price of CIMB stock which is traded on the Kuala Lumpur Stock Exchange (KLSE). This paper describes the design of FFNN and RNN and discusses the performances of both ANNs.


Artificial Neural Network; Feedforward Neural Network; Recurrent Neural Network; Stock Prediction;

Full Text:



O. Suliman, “Efficient market hypothesis,” in The American Middle Class: An Economic Encyclopedia of Progress and Poverty [2 volumes], 2017, pp. 126-129.

M. Sewell, “History of the efficient market hypothesis,” Research Note /11(04), London: University College of London Department of Computer Science, 2011.

S. F. LeRoy, “Efficient market hypothesis,” in Encyclopedia of Quantitative Finance, New York: Wiley, 2010.

L. Snopek, “Random walk theory,” in The Complete Guide to Portfolio Construction and Management, UK: Wiley, 2012, pp. 171-171.

G. F. Lawler and V. Limic, Random Walk: A Modern Introduction, vol. 123, Cambridge University Press, 2010.

M. C. Thomsett, Getting Started in Fundamental Analysis. Hoboken: Wiley, 2006.

J. Elleuch, and L. Trabelsi, “Fundamental analysis strategy and the prediction of stock returns,” International Research Journal of Finance and Economics, vol. 30, no. 1, pp. 95-107, 2009.

M. C. Thomsett, Annual Reports 101, AMACOM Div American Management Association, 2007.

M. C. Thomsett, “The role of fundamental and technical analysis,” in The Mathematics of Options, Palgrave Macmillan, 2017, pp. 31-53.

M. C. Thomsett, Technical Analysis of Stock Trends Explained. An Easy-to-Understand System for Trading Successfully. Ethan Hathaway Co LTD, 2012.

R. D. Edwards, W. H. C. Bassetti, and J. Magee, Technical Analysis of Stock Trends 10th edition. CRC press, 2012.

D. Asteriou, and S. G. Hall, Applied Econometrics. Palgrave Macmillan, 2015.

E. Stellwagen, and L. Tashman, “ARIMA: The models of Box and Jenkins,” Foresight: The International Journal of Applied Forecasting, vol. 30, pp. 28-33, 2013.

T. Terasvirta, D. Tjostheim, and C. W. Granger, Modelling Nonlinear Economic Time Series. Oxford: OUP, 2010.

R. S. Michalski, J. G. Carbonell, and T. M. Mitchell, (Eds.), Machine Learning: An Artificial Intelligence Approach. CA: Tioga Press, 1983.

E. Alpaydin, Introduction to Machine Learning. MA: MIT press, 2014.

S. Marsland, Machine Learning: An Algorithmic Perspective. CRC press, 2015.

C. Kim On, J. Teo, and A. Saudi, “Multi-objective artificial evolution of RF-localization behavior and neural structures in mobile robots,” in IEEE World Congress on Computational Intelligence Evolutionary Computation CEC 2008, pp. 350-356.

C. Kee Tong, C. Kim On, J. Teo, and A. M. J. Kiring, “Evolving neural controllers using GA for Warcraft 3-real time strategy game,” in Sixth International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA), IEEE, 2011, pp. 15-20.

C. Kim On, T. Kein Yau, R. Alfred, J. Teo, P. Anthony, and W. Cheng, “Backpropagation neural ensemble for localizing and recognizing nonstandardized Malaysia’s car plates,” in International Journal on Advanced Science, Engineering and Information Technology, vol. 6, no. 6, pp. 1112-1119, 2016.

S. Haykin, Neural networks: A comprehensive foundation, 2nd edition. Upper Saddle River: Prentice Hall, 2004.

D. Graupe, Principles of Artificial Neural Networks, 3rd edition. MA: World Scientific, 2013.

H. B. Demuth, M. H. Beale, O. De Jess, and M. T. Hagan, Neural Network Design. Martin Hagan, 2014.

A. J. Maren, C. T. Harston, and R. M. Pap, Handbook of Neural Computing Applications. Academic Press, 2014.

P. Sutheebanjard, and W. Premchaiswadi, “Stock exchange of Thailand index prediction using back propagation neural networks,” in Second International Conference on Computer and Network Technology (ICCNT), IEEE, 2010, pp. 377-380.

G. Dong, K. Fataliyev, and L. Wang, “One-step and multi-step ahead stock prediction using backpropagation neural networks,” in 9th International Conference on Information, Communications and Signal Processing (ICICS), IEEE, 2013, pp. 1-5.

S. Jabin, “Stock market prediction using feed-forward artificial neural network”, International Journal Computer Application (IJCA), vol. 99, no. 9, pp. 4-8, 2014.

G. Dematos, M. S. Boyd, B. Kermanshahi, N. Kohzadi, and I. Kaastra, “Feedforward versus recurrent neural networks for forecasting monthly japanese yen exchange rates,” Financial Engineering and the Japanese Markets, vol. 3, no. 1, pp. 59-75, 1996.

A. Agarwal, S. Dubey, M. A. Khan, R. Gangopadhyay, and S. Debnath, “Learning based primary user activity prediction in cognitive radio networks for efficient dynamic spectrum access,” in International Conference on Signal Processing and Communications (SPCOM), IEEE, 2016, pp. 1-5.

R. Singh, and S. Kansal, “Artificial neural network based spectrum recognition in cognitive radio,” in IEEE Students' Conference on Electrical, Electronics and Computer Science (SCEECS), 2016, pp. 1- 6.

K. Imran, W. Adnan, W. Shaukat, and K. Saima, “Comparative analysis of ANN techniques for predicting channel frequencies in cognitive radio,” International Journal of Advanced Computer Science and Applications (IJACSA), vol. 8, no. 12, pp. 298-303, 2017.

Z. Iqbal, R. Ilyas, W. Shahzad, Z. Mahmood, and J. Anjum, “Efficient Machine Learning Techniques for Stock Market Prediction,” International Journal of Engineering Research and Applications, vol. 3, no. 6, pp. 855-867, 2013.

H. Grigoryan, “Stock market prediction using artificial neural networks - case study of TAL1T, Nasdaq OMX Baltic stock,” Database Systems Journal, vol. 6, no. 2, pp. 14-23, 2015.

C. Wang, “Time series neural network systems in stock index forecasting,” Journal Computer Modelling & New Technologies, vol. 19, no 1B, pp. 57-61, 2015.

C. A. Mitrea, C. K. M. Lee, and Z. Wu. “A comparison between neural networks and traditional forecasting methods: A case study,” International Journal of Engineering Business Management, vol. 1, no. 2, pp. 19-24, 2009.

N. K. Singh, A. K. Singh, A. and M. Tripathy, “Short-term load/price forecasting in deregulated electric environment using ELMAN neural network,” in International Conference on Energy Economics and Environment (ICEEE), IEEE, 2015, pp. 1-6.

N. Mittal, and A. Saxena, “Layer recurrent neural network based power system load forecasting,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 16, no 3, pp. 423-430, 2015.

M. Sivanandam, and M. Paulraj, Introduction to Artificial Neural Networks. Vikas publishing House PVT LTD, 2009.

K. Hornik, M. Stinchcombe, and H. White, “Multilayer feedforward networks are universal approximators,” Neural Networks, vol. 2, no. 5, pp. 359-366, 1989.

G. Cybenko, “Approximation by superpositions of a sigmoidal function,” Mathematics of Control, Signals and Systems (MCSS), vol. 2, no. 4, pp. 303-314, 1989.

E. P. Berg, B. A. Engel, and J. C. Forrest, “Pork carcass composition derived from a neural network model of electromagnetic scans,” Journal of Animal Science, vol. 76, no, 1, pp. 18-22, 1998.

A. K. Sharma, and R. K. Sharma, “Effectiveness of heuristic rules for model selection in connectionist models to predict milk yield in dairy cattle,” TECHNIA–International Journal Computing Sciences Communication Technology, vol. 2, no. 1, pp. 384-386, 2009.

K. Levenberg, “A method for the solution of certain problems in leastsquares,” Quart. Appl. Math., vol 2, pp. 164-168, 1944.

D. W. Marquardt, “An algorithm for least-squares estimation of nonlinear parameters,” Journal of the Society for Industrial and Applied Mathematics, vol. 11, no. 2, pp. 431-441, 1963.

C. Sim Vui, C. Kim On, G. Kim Soon, A. Rayner and A. Patricia, “External constraints of neural cognition for cimb stock closing price prediction,” Pertanika Journal of Science and Technology, to be published.

G. Kim Soon, C. Sim Vui, C. Kim On and A. Patricia, “BioGenTool: a generic bio-inspired neural tool,” Advanced Science Letter, vol. 24, no. 2, pp. 1532–1537, 2018.


  • There are currently no refbacks.

Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.

ISSN: 2180-1843

eISSN: 2289-8131