External constraints of neural cognition for CIMB stock closing price prediction

This paper investigates the accuracy of Feedforward Neural Network (FFNN) with different external parameters in predicting the closing price of a particular stock. Specifically, the feedforward neural network was trained using Levenberg-Marquardt backpropagation algorithm to forecast the CIMB stock’s closing price in the Kuala Lumpur Stock exchange (KLSE). The results indicate that the use of external parameters can improve the accuracy of the stock’s closing price.

[1]  Chin Kim On,et al.  Single- versus Multiobjective Optimization for Evolution of Neural Controllers in Ms. Pac-Man , 2013, Int. J. Comput. Games Technol..

[2]  E. Fama.  EFFICIENT CAPITAL MARKETS: A REVIEW OF THEORY AND EMPIRICAL WORK* , 1970 .

[3]  Sazali Yaacob,et al.  Formants Analysis with Zero Crossing Extraction in Automatic Speech Recognition using Neural Network , 2006 .

[4]  Chin Kim On,et al.  Mel-frequency cepstral coefficient analysis in speech recognition , 2006, 2006 International Conference on Computing & Informatics.

[5]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[6]  Thomas G. Dietterich.  What is machine learning? , 2020, Archives of Disease in Childhood.

[7]  Wichian Premchaiswadi,et al.  Stock Exchange of Thailand Index Prediction Using Back Propagation Neural Networks , 2010, 2010 Second International Conference on Computer and Network Technology.

[8]  Suraiya Jabin,et al.  Stock Market Prediction using Feed-forward Artificial Neural Network , 2014 .

[9]  Spyros Makridakis,et al.  ARMA Models and the Box–Jenkins Methodology , 1997 .

[10]  M Paulraj.  Introduction to Artificial Neural Networks , 2003 .

[11]  Martin J. Pring,et al.  Technical Analysis Explained: The Successful Investor's Guide to Spotting Investment Trends and Turning Points , 1980 .

[12]  D. Marquardt.  An Algorithm for Least-Squares Estimation of Nonlinear Parameters , 1963 .

[13]  Mohammad Bagher Menhaj,et al.  Training feedforward networks with the Marquardt algorithm , 1994, IEEE Trans. Neural Networks.

[14]  Salvador Torra,et al.  STAR and ANN models: forecasting performance on the Spanish “Ibex-35” stock index , 2005 .

[15]  C. D. Tilakaratne,et al.  Predicting Stock Market Index Trading Signals Using Neural Networks , 2007 .

[16]  B. Engel,et al.  Pork carcass composition derived from a neural network model of electromagnetic scans. , 1998, Journal of animal science.

[17]  A. K. Rigler,et al.  Accelerating the convergence of the back-propagation method , 1988, Biological Cybernetics.

[18]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[19]  Ajith Abraham,et al.  Hybrid Intelligent Systems for Stock Market Analysis , 2001, International Conference on Computational Science.

[20]  G. Lewicki,et al.  Approximation by Superpositions of a Sigmoidal Function , 2003 .

[21]  Kenneth Levenberg.  A METHOD FOR THE SOLUTION OF CERTAIN NON – LINEAR PROBLEMS IN LEAST SQUARES , 1944 .

[22]  Chang Kee Tong,et al.  Evolving Neural Controllers Using GA for Warcraft 3-Real Time Strategy Game , 2011, 2011 Sixth International Conference on Bio-Inspired Computing: Theories and Applications.

[23]  Cihan H. Dagli,et al.  Stock market prediction using different neural network classification architectures , 1996, IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr).

[24]  Kamaladdin Fataliyev,et al.  One-step and multi-step ahead stock prediction using backpropagation neural networks , 2013, 2013 9th International Conference on Information, Communications & Signal Processing.

[25]  Chin Kim On,et al.  Multi-objective artificial evolution of RF-localization behavior and neural structures in mobile robots , 2008, 2008 IEEE Congress on Evolutionary Computation (IEEE World Congress on Computational Intelligence).

[26]  Burton G. Malkiel,et al.  A random walk down Wall Street : including a life-cycle guide to personal investing , 1999 .

[27]  D. Ashok Kumar,et al.  Performance Analysis of MLPFF Neural Network Back Propagation Training Algorithms for Time Series Data , 2014, 2014 World Congress on Computing and Communication Technologies.